[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

WO2022262687A1 - 一种数据处理方法及装置 - Google Patents

一种数据处理方法及装置 Download PDF

Info

Publication number
WO2022262687A1
WO2022262687A1 PCT/CN2022/098476 CN2022098476W WO2022262687A1 WO 2022262687 A1 WO2022262687 A1 WO 2022262687A1 CN 2022098476 W CN2022098476 W CN 2022098476W WO 2022262687 A1 WO2022262687 A1 WO 2022262687A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
correlation matrix
matrix information
channel
filtering
Prior art date
Application number
PCT/CN2022/098476
Other languages
English (en)
French (fr)
Inventor
周明月
王勃
秦一平
Original Assignee
华为技术有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 华为技术有限公司 filed Critical 华为技术有限公司
Publication of WO2022262687A1 publication Critical patent/WO2022262687A1/zh

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0242—Channel estimation channel estimation algorithms using matrix methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/025—Channel estimation channel estimation algorithms using least-mean-square [LMS] method
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0254—Channel estimation channel estimation algorithms using neural network algorithms

Definitions

  • the present application relates to the field of communication technologies, and in particular to a data processing method and device in a multi-antenna scenario.
  • the access network equipment When receiving in a single cell, the access network equipment mostly adopts a centralized architecture to process the data.
  • This centralized architecture needs to centralize all antenna data, and additional data aggregation processing is required in addition to normal data processing.
  • the centralized processing architecture not only rapidly increases the complexity of data processing exponentially, but also rapidly increases the bus bandwidth for summarizing data, which in turn increases the difficulty in the design of the access network device chip itself.
  • the access network equipment needs to support the application of various antenna scale scenarios, the dynamic link bandwidth requirements brought about by centralized processing make the access network equipment need to design different connection schemes according to different connection forms, and introduce additional connection costs. . Therefore, with the application of larger-scale antennas in the future, centralized processing may become a bottleneck.
  • the present application provides a data processing method and device, which are used to solve the problems that the data processing complexity becomes higher with the increase of antennas, the bandwidth of the transmission data bus becomes wider, and the data processing cost increases.
  • the first aspect of the embodiment of the present application provides a system, including: a first interface module and a first processing module; the first interface module is used to: obtain the first correlation matrix information and the first filtering data, the first correlation matrix The information is the correlation matrix information corresponding to the first channel experienced by the first data, and the first filtered data is the data obtained after the first data is filtered; the second data is obtained, wherein the second data and the first data contain information from data from the same device;
  • the first processing module is configured to: obtain second correlation matrix information corresponding to a second channel based on the second data and the first correlation matrix information, and the second channel is a channel experienced by the second data; based on the first filtering Data is filtered on the second data to obtain second filtered data; output data is obtained based on the second correlation matrix information and the second filtered data; the first interface module is also used to output the output data.
  • the first processing module is configured to obtain second correlation matrix information corresponding to the second channel based on the second data and the first correlation matrix information, including: the The first processing module is configured to: perform channel estimation based on the second data to obtain a channel estimation result of the second data; obtain the second correlation matrix information based on the channel estimation result of the second data and the first correlation matrix information; the The first processing module is configured to filter the second data based on the first filtered data to obtain second filtered data, including: the first processing module is configured to pair a channel estimation result based on the second data and the first filtered data The second data is filtered to obtain the second filtered data.
  • the first processing module is further configured to obtain an interference noise estimation result of the second data according to the second data and a channel estimation result of the second data; the The first processing module is used to obtain the second correlation matrix information corresponding to the second channel based on the second data and the first correlation matrix information, including: the first processing module is used to obtain the first correlation matrix based on the second data The information and the interference noise estimation result of the second data obtain the second correlation matrix information corresponding to the second channel.
  • the interference noise information may be taken into consideration when obtaining the second correlation matrix information, so that the second correlation moment information is closer to an ideal value.
  • the first processing module is configured to filter the second data based on the first filtering data to obtain second filtering data, including: the first processing module uses The second data is obtained by filtering the second data based on the interference noise estimation result of the first filtered data and the second data.
  • the information of the interference noise may be taken into consideration when obtaining the second filtered data, so that the second filtered data is more accurate.
  • the first processing module is configured to perform channel estimation based on the second data to obtain a channel estimation result of the second data, including: the first processing module is used to
  • the channel estimation is performed according to any of the following methods: least squares estimation, least mean square error estimation, compressed sensing estimation, or machine learning.
  • the first processing module is configured to filter the second data based on the first filtering data to obtain second filtering data, including: the first processing module uses The filtering is performed according to any of the following methods: matched filtering, zero-forcing algorithm, least squares method, least mean square error method, maximum likelihood method, or machine learning.
  • the first data comes from at least one first antenna
  • the second data comes from at least one second antenna
  • the second aspect of the embodiment of the present application provides a system, including: a second interface module and a second processing module; the second interface module is used to: obtain the first data; the second processing module is used to: based on the first The data obtains the first correlation matrix information corresponding to the first channel, and the first channel is the channel experienced by the first data; the first data is filtered to obtain the first filtered data; the second interface module is also used to output the first A correlation matrix information and the first filtering data.
  • the second processing module is configured to obtain first correlation matrix information corresponding to the first channel based on the first data, including: the second processing module is configured to: Perform channel estimation based on the first data to obtain a channel estimation result of the first data; obtain the first correlation matrix information based on the channel estimation result of the first data; the second processing module is configured to filter the first data to obtain The first filtered data includes: the second processing module is configured to filter the first data based on a channel estimation result of the first data to obtain first filtered data.
  • the second processing module is further configured to obtain an interference noise estimation result of the first data based on the first data and the channel estimation result of the first data; the The second processing module is used to obtain the first correlation matrix information corresponding to the first channel based on the first data, including: the second processing module is used to obtain the first correlation matrix information based on the first data and the interference noise estimation result of the first data.
  • the first correlation matrix information corresponding to the channel is further configured to obtain an interference noise estimation result of the first data based on the first data and the channel estimation result of the first data;
  • the second processing module is configured to filter the first data to obtain first filtered data, including: the second processing module is configured to filter the first data based on the first data filtering the first data with the interference noise estimation result of the first data to obtain the first filtered data.
  • the second processing module is configured to perform channel estimation based on the first data to obtain a channel estimation result of the first data, including: the second processing module is used to The channel estimation is performed according to any of the following methods: least squares estimation; least mean square error estimation; compressed sensing estimation; or machine learning.
  • the second processing module is configured to filter the first data to obtain the first filtered data, including: the second processing module is configured to obtain the first filtered data according to any of the following A method for performing the filtering: matched filtering; zero-forcing algorithm; least squares method; least mean square error method; maximum likelihood method; or machine learning.
  • the first data comes from at least one first antenna.
  • the third aspect of the embodiment of the present application provides a data processing method, including: acquiring first correlation matrix information and first filtered data, where the first correlation matrix information is the correlation matrix information corresponding to the first channel experienced by the first data, The first filtered data is data obtained after filtering the first data; obtaining second data, wherein the second data and the first data include data from the same device; based on the second data and the first correlation matrix Information to obtain the second correlation matrix information corresponding to the second channel, the second channel is the channel experienced by the second data; filter the second data based on the first filter data to obtain second filter data; based on the second correlation
  • the matrix information and the second filtered data result in output data; the output data is output.
  • obtaining second correlation matrix information corresponding to the second channel based on the second data and the first correlation matrix information includes: performing channel estimation based on the second data Obtain the channel estimation result of the second data, obtain the second correlation matrix information based on the channel estimation result of the second data and the first correlation matrix information; filter the second data based on the first filtering data to obtain the second
  • the filtering data includes: filtering the second data based on a channel estimation result of the second data to obtain second filtering data.
  • the method further includes: obtaining an interference noise estimation result of the second data according to the second data and the channel estimation result of the second data;
  • Obtaining the second correlation matrix information corresponding to the second channel with the first correlation matrix information includes: obtaining the second correlation matrix information corresponding to the second channel based on the second data, the first correlation matrix information and the interference noise estimation result of the second data Two correlation matrix information.
  • filtering the second data based on the first filtering data to obtain second filtering data includes: interference based on the first filtering data and the second data
  • the noise estimation result filters the second data to obtain second filtered data.
  • performing channel estimation based on the second data to obtain a channel estimation result of the second data includes: performing the channel estimation according to any of the following methods: least squares Estimation; Minimum Mean Square Error Estimation; Compressed Sensing Estimation; or Machine Learning.
  • filtering the second data based on the first filtering data to obtain the second filtering data includes: performing the filtering according to any of the following methods: matched filtering; Zero forcing; least squares; least mean square error; maximum likelihood; or machine learning.
  • the first data comes from at least one first antenna
  • the second data comes from at least one second antenna
  • the fourth aspect of the embodiment of the present application provides a data processing method, including: obtaining first data; obtaining first correlation matrix information corresponding to a first channel based on the first data, and the first channel is the first data experienced channel; filtering the first data to obtain first filtered data; outputting the first correlation matrix information and the first filtered data.
  • obtaining the first correlation matrix information corresponding to the first channel based on the first data includes: performing channel estimation based on the first data to obtain the channel of the first data The estimation result is to obtain the first correlation matrix information based on the channel estimation result of the first data; the filtering of the first data to obtain the first filtered data includes: the first data is obtained based on the channel estimation result of the first data Filtering is performed to obtain first filtered data.
  • the method further includes: obtaining an interference noise estimation result of the first data based on the first data and a channel estimation result of the first data;
  • Obtaining the first correlation matrix information corresponding to the first channel from the data includes: obtaining the first correlation matrix information corresponding to the first channel based on the first data and an interference noise estimation result of the first data.
  • the filtering the first data to obtain the first filtered data includes: obtaining the first filtered data based on the first data and an interference noise estimation result of the first data A data is filtered to obtain the first filtered data.
  • the fifth aspect of the embodiment of the present application provides a system, including: a third interface module and a third processing module; the third interface module is used to: obtain the first correlation matrix information and the first filtering data, the first correlation matrix The information is the correlation matrix information corresponding to the first channel experienced by the first data, and the first filtered data is the data obtained after the first data is filtered; the third data is obtained, wherein the third data is related to the first data and the The second data includes data from the same device; the third processing module is configured to: obtain third correlation matrix information corresponding to a third channel based on the third data and the first correlation matrix information, and the third channel is the third correlation matrix information The channels experienced by the data; filtering the third data based on the first filtering data to obtain third filtering data; the third interface module is also used to output the third correlation matrix information and the third filtering data.
  • the third interface module may output the third correlation matrix information and the third filtered data to the first interface module for the first processing module to perform data processing.
  • the third processing module is configured to obtain third correlation matrix information corresponding to a third channel based on the third data and the first correlation matrix information, including: the The third processing module is configured to: perform channel estimation based on the third data to obtain a channel estimation result of the third data; obtain the third correlation matrix information based on the channel estimation result of the third data and the first correlation matrix information; the The third processing module is configured to filter the third data based on the first filtered data to obtain third filtered data, including: the third processing module is configured to pair a channel estimation result based on the third data and the first filtered data The third data is filtered to obtain the third filtered data.
  • the third processing module is further configured to obtain an interference noise estimation result of the third data according to the third data and a channel estimation result of the third data; the The third processing module is used to obtain the third correlation matrix information corresponding to the third channel based on the third data and the first correlation matrix information, including: the third processing module is used to obtain the first correlation matrix based on the third data The information and the interference noise estimation result of the third data obtain the third correlation matrix information corresponding to the third channel.
  • the third processing module is configured to filter the third data based on the first filtering data to obtain third filtering data, including: the third processing module uses The third filtered data is obtained by filtering the third data based on the interference noise estimation result of the first filtered data and the third data.
  • the third processing module is configured to perform channel estimation based on the third data to obtain a channel estimation result of the third data, including: the third processing module is used to The channel estimation is performed according to any of the following methods: least squares estimation; least mean square error estimation; compressed sensing estimation; or machine learning.
  • the third processing module is configured to filter the third data based on the first filtering data to obtain third filtering data, including: the third processing module uses The filtering is performed according to any of the following methods: matched filtering; zero-forcing algorithm; least squares method; least mean square error method; maximum likelihood method; or machine learning.
  • the embodiment of the present application does not limit the number of systems provided in the fifth aspect, for example, there may be two or more systems between the system provided in the first aspect and the system provided in the second aspect
  • the fifth aspect provides a system.
  • the sixth aspect of the embodiment of the present application provides a data processing method, including: acquiring first correlation matrix information and first filtered data, where the first correlation matrix information is the correlation matrix information corresponding to the first channel experienced by the first data, The first filtered data is data obtained after the first data is filtered; third data is acquired, wherein the third data and the first data and the second data contain data from the same device; based on the third data and The first correlation matrix information obtains third correlation matrix information corresponding to a third channel, and the third channel is a channel experienced by the third data; filtering the third data based on the first filtering data to obtain third filtering data; Outputting the third correlation matrix information and the third filtered data.
  • obtaining the third correlation matrix information corresponding to the third channel based on the third data and the first correlation matrix information includes: performing channel estimation based on the third data obtaining a channel estimation result of the third data; obtaining the third correlation matrix information based on the channel estimation result of the third data and the first correlation matrix information; filtering the third data based on the first filtering data to obtain a third
  • the filtering data includes: filtering the third data based on the channel estimation result of the third data and the first filtering data to obtain the third filtering data.
  • the method further includes: obtaining an interference noise estimation result of the third data according to the third data and a channel estimation result of the third data; Obtaining the third correlation matrix information corresponding to the third channel from the data and the first correlation matrix information includes: obtaining the third channel corresponding to the third channel based on the third data, the first correlation matrix information and the interference noise estimation result of the third data The third correlation matrix information.
  • filtering the third data based on the first filtering data to obtain third filtering data includes: interference based on the first filtering data and the third data
  • the noise estimation result filters the third data to obtain third filtered data.
  • the method for performing channel estimation based on the third data to obtain the channel estimation result of the third data includes: performing the channel estimation according to any of the following methods: least squares estimation; least mean square error estimation; compressed sensing estimation; or machine learning.
  • filtering the third data based on the first filtering data to obtain third filtering data includes: performing the filtering according to any of the following methods: matched filtering; Zero forcing; least squares; least mean square error; maximum likelihood; or machine learning.
  • the seventh aspect of the embodiment of the present application provides a communication device, including a processor and an interface circuit, and the interface circuit is used to receive signals from devices other than the device and transmit them to the processor or transmit signals from the processor The signal is sent to other devices other than the device, and the processor implements the third aspect or the method described in the possible implementation manners of the third aspect through a logic circuit or by executing code instructions.
  • the eighth aspect of the embodiment of the present application provides a communication device, including a processor and an interface circuit, the interface circuit is used to receive signals from devices other than the device and transmit them to the processor or transmit signals from the processor The signal is sent to other devices other than the device, and the processor implements the fourth aspect or the method described in the possible implementation manners of the fourth aspect through a logic circuit or by executing code instructions.
  • the ninth aspect of the embodiment of the present application provides a communication device, including a processor and an interface circuit, the interface circuit is used to receive signals from other devices than the device and transmit them to the processor or transmit The signal is sent to other devices other than the device, and the processor implements the sixth aspect or the method described in the possible implementation manners of the sixth aspect through a logic circuit or by executing code instructions.
  • the tenth aspect of the embodiments of the present application provides a computer-readable storage medium, the computer-readable storage medium stores computer programs or instructions, and when the computer programs or instructions are executed by a computing device, the third aspect or the third aspect is realized.
  • the eleventh aspect of the embodiment of the present application provides a computer program product, the computer program product includes a computer program or instruction, when the computer program or instruction is executed by a communication device, the third aspect or the possible realization of the third aspect can be realized
  • the twelfth aspect of the embodiment of the present application provides a communication system, including the system described in the first aspect or a possible implementation of the first aspect and the system described in the second aspect or a possible implementation of the second aspect or include the system described in the first aspect or a possible implementation of the first aspect, the system described in the second aspect or a possible implementation of the second aspect and the fifth aspect or a possible implementation of the fifth aspect
  • FIG. 1 is a schematic diagram of a possible communication architecture provided by an embodiment of the present application.
  • FIG. 2 is a schematic block diagram of a data processing system provided by an embodiment of the present application.
  • FIG. 3a is a schematic flow chart of a data processing method provided by an embodiment of the present application.
  • FIG. 3b is another schematic block diagram of a data processing system provided by an embodiment of the present application.
  • FIG. 4 is another schematic block diagram of a data processing system provided by an embodiment of the present application.
  • Fig. 5a is another schematic flow chart of a data processing method provided by the embodiment of the present application.
  • Fig. 5b is another schematic block diagram of a data processing system provided by an embodiment of the present application.
  • FIG. 6 is a schematic block diagram of a communication device provided by an embodiment of the present application.
  • FIG. 7 is another schematic block diagram of a communication device provided by an embodiment of the present application.
  • the size of the serial numbers of the processes does not mean the order of execution, and the execution order of the processes should be determined by their functions and internal logic, and should not constitute the implementation process of the embodiments of the present application. Any restrictions.
  • first correlation matrix information and “Second correlation matrix information”, in which "first” and “second” are usually only used to distinguish these two groups of information, and should not limit the implementation process of the embodiment of the present application.
  • At least one means one or more, and “multiple” means two or more.
  • At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items.
  • at least one item (piece) of a, b, or c can represent: a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, c can be single or multiple .
  • FIG. 1 is a schematic structural diagram of a communication system 100 applied in an embodiment of the present application.
  • the communication system includes a radio access network 110 and a core network 120 , and optionally, the communication system 100 may also include the Internet 130 .
  • the radio access network 110 may include at least one radio access network device (such as 111a and 111b in FIG. 1 ), and may also include at least one terminal (such as 112a-112j in FIG. 1 ).
  • the terminal is connected to the wireless access network device in a wireless manner, and the wireless access network device is connected to the core network in a wireless or wired manner.
  • the core network equipment and the wireless access network equipment can be independent and different physical equipment, or the functions of the core network equipment and the logical functions of the wireless access network equipment can be integrated on the same physical equipment, or it can be a physical equipment It integrates some functions of core network equipment and some functions of wireless access network equipment. Terminals and wireless access network devices may be connected to each other in a wired or wireless manner.
  • FIG. 1 is only a schematic diagram.
  • the communication system may also include other network devices, such as wireless relay devices and wireless backhaul devices, which are not shown in FIG. 1 .
  • the radio access network equipment can be a base station (base station), an evolved base station (evolved NodeB, eNodeB), a transmission reception point (transmission reception point, TRP), and the next generation in the fifth generation (5th generation, 5G) mobile communication system
  • Base station (next generation NodeB, gNB), the next generation base station in the sixth generation (6th generation, 6G) mobile communication system, the base station in the future mobile communication system or the access node in the WiFi system, etc.; it can also complete the base station part
  • a functional module or unit for example, can be a centralized unit (central unit, CU) or a distributed unit (distributed unit, DU).
  • the wireless access network device may be a macro base station (as shown in 111a in Figure 1), a micro base station or an indoor station (as shown in Figure 111b), or a relay node or a donor node. It can be understood that all or part of the functions of the radio access network device in this application may also be realized by software functions running on hardware, or by virtualization functions instantiated on a platform (such as a cloud platform). The embodiment of the present application does not limit the specific technology and specific equipment form adopted by the radio access network equipment. For ease of description, a base station is used as an example of a radio access network device for description below.
  • a terminal may also be called terminal equipment, user equipment (user equipment, UE), mobile station, mobile terminal, and so on.
  • Terminals can be widely used in various scenarios, such as device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things ( internet of things, IOT), virtual reality, augmented reality, industrial control, autonomous driving, telemedicine, smart grid, smart furniture, smart office, smart wearables, smart transportation, smart city, etc.
  • Terminals can be mobile phones, tablet computers, computers with wireless transceiver functions, wearable devices, vehicles, drones, helicopters, airplanes, ships, robots, robotic arms, smart home devices, etc.
  • the embodiment of the present application does not limit the specific technology and specific device form adopted by the terminal.
  • Base stations and terminals can be fixed or mobile. Base stations and terminals can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; they can also be deployed on aircraft, balloons and artificial satellites in the air. The embodiments of the present application do not limit the application scenarios of the base station and the terminal.
  • the roles of the base station and the terminal can be relative.
  • the helicopter or drone 112i in FIG. base station for base station 111a, 112i is a terminal, that is, communication between 111a and 112i is performed through a wireless air interface protocol.
  • communication between 111a and 112i may also be performed through an interface protocol between base stations.
  • relative to 111a, 112i is also a base station. Therefore, both base stations and terminals can be collectively referred to as communication devices, 111a and 111b in FIG. 1 can be referred to as communication devices with base station functions, and 112a-112j in FIG. 1 can be referred to as communication devices with terminal functions.
  • the communication between the base station and the terminal, between the base station and the base station, and between the terminal and the terminal can be carried out through the licensed spectrum, the communication can also be carried out through the unlicensed spectrum, and the communication can also be carried out through the licensed spectrum and the unlicensed spectrum at the same time; Communications may be performed on frequency spectrums below megahertz (gigahertz, GHz), or communications may be performed on frequency spectrums above 6 GHz, or communications may be performed using both frequency spectrums below 6 GHz and frequency spectrums above 6 GHz.
  • the embodiments of the present application do not limit the frequency spectrum resources used for wireless communication.
  • the functions of the base station may also be performed by modules (such as chips) in the base station, or may be performed by a control subsystem including the functions of the base station.
  • the control subsystem including base station functions here may be the control center in the application scenarios of the above-mentioned terminals such as smart grid, industrial control, intelligent transportation, and smart city.
  • the functions of the terminal may also be performed by a module (such as a chip or a modem) in the terminal, or may be performed by a device including the terminal function.
  • the base station sends a downlink signal or downlink information to the terminal, and the downlink information is carried on the downlink channel;
  • the terminal sends an uplink signal or uplink information to the base station, and the uplink information is carried on the uplink channel.
  • the access network equipment architecture When receiving in a single cell, the access network equipment architecture mostly adopts a centralized architecture to process data. This architecture needs to centralize all antenna data, and additional data aggregation processing is required in addition to normal data processing.
  • the centralized processing architecture not only rapidly increases the complexity of data processing exponentially, but also rapidly increases the bus bandwidth for summarizing data, which in turn increases the difficulty in the design of the access network device chip itself.
  • the access network equipment needs to support the application of various antenna scale scenarios, the dynamic link bandwidth requirements brought about by centralized processing make the access network equipment need to design different connection schemes according to different connection forms, and introduce additional connection costs. . Therefore, with the application of larger-scale antennas in the future, centralized processing may become a bottleneck.
  • the distributed access network equipment processing scheme mainly divides the antennas into clusters, and then fuses or interacts with the antenna data of other clusters after processing the antenna data of each cluster. After the data of all clusters are fused or interacted, the final data is output.
  • the current distributed access network equipment processing scheme can alleviate the complexity of data processing, in order to ensure data processing performance, the current distributed access network equipment processing scheme will have loops and coupling, and there may also be data iteration processing, which introduces additional latency and caching as well as additional data processing complexity.
  • the present application provides a method and device for data processing, which are used to solve the problems of high data processing complexity, widened transmission data bus bandwidth, and increased data processing cost with the increase of antennas.
  • FIG. 2 shows a schematic block diagram of a data processing system 200 according to an embodiment of the present application.
  • a first system 210 and a second system 220 are involved, and the first system includes The first interface module 211 and the first processing module 212 , the second system 220 includes the second interface module 221 and the second processing module 222 .
  • the first system 210 can communicate with the second system 220 .
  • the first system 210 or the second system 220 may be the radio access network device or network element, the terminal or a part of the terminal, or a chip, or the first system 210 or the second system
  • the system 220 may also be a software system, and the first interface module 211 , the first processing module 212 , the second interface module 221 or the second processing module 222 may be software modules in the software system.
  • the first interface module 220 of the first system 210 can be used to obtain the first correlation matrix information and the first filtered data, the first correlation matrix information is the correlation matrix information corresponding to the first channel experienced by the first data, the first The filtered data is data obtained after filtering the first data; the first interface module 220 is also configured to obtain second data, and the second data and the first data include data from the same device.
  • the first processing module 212 of the first system 210 may be configured to obtain second correlation matrix information corresponding to a second channel based on the second data and the first correlation matrix information, the second channel being the channel experienced by the second data
  • the first processing module 212 can also be used for filtering the second data based on the first filtering data to obtain second filtering data; the first processing module 212 can also be used for filtering the second data based on the second correlation matrix information and
  • the second filtered data obtains output data; the first interface module 220 is also used to output the output data.
  • FIG. 3a shows a schematic flowchart of a data processing method 300a according to an embodiment of the present application.
  • a first system and a second system are involved, and the first system Communication is possible between a system and the second system, the first system and the second system respectively correspond to the first system 210 and the second system 220 in FIG. 2 , the first system includes a first interface module and a first processing module , the second system includes a second interface module and a second processing module.
  • the data processing method 300a includes but not limited to the following steps:
  • S301 Acquire first data, and obtain first correlation matrix information and first filtering data based on the first data.
  • the second interface module of the second system obtains the first data
  • the second processing module of the second system obtains the first correlation matrix information corresponding to the first channel based on the first data, and the first channel is the channel experienced by the first data
  • the second processing module filters the first data to obtain first filtered data.
  • the first data may be a signal received by an antenna of the radio access network device, and the first data may satisfy:
  • R 1 H 1 S+G 1 Z+n 1
  • R1 is the first data
  • H1 is the channel experienced by the first data
  • S is the frequency domain modulation symbol sent by the user
  • G1 is the equivalent channel of the interfering user
  • Z is the frequency domain modulation symbol sent by the interfering user
  • n 1 is additive white Gaussian noise (AWGN)
  • the distribution is N(0, N 0 ), that is, the distribution obeys a Gaussian distribution with a mean value of 0 and a variance of N 0 .
  • the second processing module of the second system obtains the first correlation matrix information corresponding to the first channel based on the first data, which may satisfy:
  • R 1 S * H 1 SS * +G 1 ZS * +n 1 S *
  • H 1 SS * R 1 S * -G 1 ZS * -n 1 S *
  • H 1 R 1 S * -G 1 ZS * -n 1 S *
  • S * is the conjugate of the frequency domain modulation symbol S sent by the user, is the conjugate transpose of the channel H 1 experienced by the first data
  • B 1 is the first correlation matrix information
  • R 1 , H 1 , S, G 1 , Z, n 1 please refer to the above description, and will not repeat them here, It should be understood that this is only an example, and other implementation methods are not excluded.
  • the second processing module filters the first data to obtain the first filtered data, which may satisfy:
  • Y 1 is the first filtering data
  • It is the conjugate transposition of the channel H 1 experienced by the first data
  • R 1 is the first data. It should be understood that this is only an example, and other implementation methods are not excluded.
  • the second system can also output the first correlation matrix information to the first system, the second system can output the first correlation matrix information through the second interface module, and the first system can obtain the first correlation matrix information through the first interface module Correlation matrix information.
  • the first correlation matrix information is used to indicate the first correlation matrix, for example, the first correlation matrix information may include the first correlation matrix data, the matrix obtained after compressing the first correlation matrix, or the first correlation matrix index etc.
  • the second system may also output first filtered data to the first system.
  • the second system can output the first filtering data through the second interface module, and the first system can obtain the first filtering data through the first interface module.
  • S302 may be executed first and then S303 may be executed, or S303 may be executed first and then S302 may be executed, or S302 and S303 may be executed simultaneously.
  • S304 acquires second data, and obtains second correlation matrix information and second filtering data based on the second data.
  • the first system may also obtain second data through the first interface module, wherein the second data and the first data include data from the same device.
  • the first system may use the first processing module to obtain second correlation matrix information corresponding to a second channel based on the second data and the first correlation matrix information, and the second channel is a channel experienced by the second data.
  • the first processing module of the first system may also filter the second data based on the first filter data to obtain second filter data.
  • the first interface module receiving the second data, the first interface module receiving the first correlation matrix, and the first interface module receiving the first filtered data may be implemented by the same interface module, or may be implemented by different interface modules .
  • the interface module receiving the second data and the interface module receiving the first correlation matrix are different interface modules, and the interface module receiving the first filtered data is another interface module, then the interface module receiving the second data, receiving the first
  • the interface module for the correlation matrix and the interface module for receiving the first filter data are three different interface modules; for another example, the interface module for receiving the second data and the interface module for receiving the first correlation matrix and the interface for receiving the first filter data
  • the modules are the same interface module, that is, the first interface module, which is not limited herein.
  • the second data may be a signal received by an antenna of the wireless access network device, and the second data may satisfy:
  • R 2 H 2 S+G 2 Z+n 2
  • R 2 is the second data
  • H 2 is the channel experienced by the second data
  • S is the frequency domain modulation symbol sent by the user
  • G 2 is the equivalent channel of the interfering user
  • Z is the frequency domain modulation symbol sent by the interfering user
  • n 2 is additive white Gaussian noise (AWGN)
  • the distribution is N(0, N 0 ), that is, the distribution obeys a Gaussian distribution with a mean value of 0 and a variance of N 0 .
  • the first system can use the first processing module to obtain the second correlation matrix information corresponding to the second channel based on the second data and the first correlation matrix information, which can satisfy:
  • H2 R2S * -G2ZS * -n2S *
  • S * is the conjugate of the frequency domain modulation symbol S sent by the user, is the conjugate transpose of the channel H 2 experienced by the second data
  • B 1 is the information of the first correlation matrix
  • B 2 is the information of the second correlation matrix
  • R 2 , H 2 , S, G 2 , Z, n 2 Please refer to the above description, which will not be repeated here. It should be understood that this is only an example, and other implementation methods are not excluded.
  • the first processing module of the first system may also filter the second data based on the first filtered data to obtain second filtered data, which may satisfy:
  • Y 2 is the second filtering data, is the conjugate transposition of the channel H 2 experienced by the second data, R 2 is the second data, and Y 1 is the first filtered data. It should be understood that this is only an example, and other implementation methods are not excluded.
  • the second data and the first data include data from the same device, the first data and the second data may be data sent from the same terminal, and the first data may be a radio access network device
  • the first receiving antenna cluster of the wireless access network device receives the data sent by the terminal, and the second data may be the data sent by the terminal received by the second receiving antenna cluster of the wireless access network device. It should be understood that the wireless access network device still exists Additional receive antenna clusters.
  • S305 acquires the output data, and outputs the output data.
  • the first processing module of the first system After obtaining the second correlation matrix information and the second filtering data, the first processing module of the first system obtains output data based on the second correlation matrix information and the second filtering data.
  • the first processing module obtains output data based on the second correlation matrix information and the second filtering data, which can satisfy:
  • the first interface module of the first system outputs the output data After the output data is output, it will be further processed, such as demodulation, decoding and other processing.
  • the data processing method and device provided by the embodiment of the present application can solve the problems of increasing data processing complexity, widening transmission data bus bandwidth, and increasing data processing cost with the increase of antennas through unidirectional operation of a chain structure.
  • the second processing module of the second system performs channel estimation based on the first data, and obtains a channel estimation result of the first data. Then, the second processing module obtains the first correlation matrix information based on the channel estimation result of the first data.
  • the correlation matrix information can satisfy:
  • R 1 S * H 1 SS * +G 1 ZS * +n 1 S *
  • H 1 SS * R 1 S * -G 1 ZS * -n 1 S *
  • H 1 R 1 S * -G 1 ZS * -n 1 S *
  • S * is the conjugate of the frequency domain modulation symbol S sent by the user, is the conjugate transpose of the channel H 1 experienced by the first data
  • B 1 is the first correlation matrix information
  • R 1 , H 1 , S, G 1 , Z, n 1 please refer to the above description, and will not repeat them here, It should be understood that this is only an example, and other implementation methods are not excluded.
  • the second processing module of the second system filters the first data based on a channel estimation result of the first data to obtain the first filtered data.
  • the first filtered data can satisfy:
  • Y 1 is the first filtering data
  • It is the conjugate transposition of the channel H 1 experienced by the first data
  • R 1 is the first data. It should be understood that this is only an example, and other implementation methods are not excluded.
  • the method shown in 300a further includes that the second processing module of the second system obtains an interference noise estimation result of the first data based on the first data and the channel estimation result of the first data, for example , the interference noise estimation result of the first data can satisfy:
  • U 1 (k) is the interference noise of the kth resource of the first data
  • R 1 (k) is the data of the kth resource of the first data
  • H 1 (k) is the kth resource of the first data
  • S is the frequency domain modulation symbol sent by the user on the kth resource
  • R U1U1 is the interference noise estimation result of the first data
  • N is the number of resources occupied by the first data
  • the N resources are The time-frequency resource occupied by the first data air interface transmission
  • k is the serial number of the resource
  • the second processing module of the second system obtains the first correlation matrix information corresponding to the first channel based on the first data and the interference noise estimation result of the first data, for example , the first correlation matrix information can satisfy:
  • B 1 is the first correlation matrix information, is the conjugate transpose of the channel estimation result H 1 of the first data, H 1 is the channel estimation result of the first data, It is the inverse of the interference noise estimation result R U1U1 of the first data. It should be understood that this is only an example, and other implementation methods are not excluded.
  • the interference noise information may be taken into consideration when obtaining the first correlation matrix information, so that the first correlation moment information is closer to an ideal value.
  • the second processing module of the second system filters the first data based on the first data and an interference noise estimation result of the first data to obtain the first filtered data,
  • the first filtered data can satisfy:
  • Y1 is the first filtering data
  • H1 of the first data is the conjugate transpose of the channel estimation result H1 of the first data
  • R U1U1 is the inverse of the interference noise estimation result
  • R 1 is the first data. It should be understood that this is only an example, and other implementation methods are not excluded.
  • the information of the interference noise may be taken into consideration when obtaining the first filtered data, so that the first filtered data is more accurate.
  • the second processing module of the second system performs channel estimation on the first data according to any of the following methods to obtain a channel estimation result of the first data: least squares estimation, Minimum mean square error estimation, compressed sensing estimation, or machine learning.
  • the least square estimation is a mathematical optimization method, which finds the best function matching of the data through the sum of the squares of the minimum error, and the unknown data can be easily obtained by using the least square method, and the obtained data can be compared with the actual The sum of squares of the errors between the data is minimized.
  • the minimum mean square error estimation is also a data optimization method, which minimizes the mean square error between the unknown quantity and the known quantity, and determines the required unknown quantity under this condition.
  • Compressive sensing estimation is a technique for finding sparse solutions of underdetermined linear systems, which can restore the original unknown quantity to be known from fewer measured values.
  • Machine learning is a science that studies how to use computers to simulate or realize human learning activities. Using machine learning, methods such as supervised learning, unsupervised learning, and reinforcement learning can be used to estimate unknown quantities.
  • the second processing module of the second system filters the first data according to any of the following methods to obtain the first filtered data: matched filtering, zero-forcing algorithm, least square method , minimum mean square error, maximum likelihood, or machine learning.
  • matched filtering refers to a filtering method in which the ratio of the instantaneous power of the output signal to the average power of the noise is the largest.
  • the maximum likelihood method is a class of phylogenetic tree reconstruction methods based entirely on statistics, which uses a probabilistic model, and its goal is to find a phylogenetic tree that can produce observed data with a high probability.
  • the first data comes from at least one first antenna.
  • the radio access network equipment receives data transmitted from the terminal
  • the antenna of the radio access network equipment includes at least one first antenna, at least one second antenna and other antennas, the first antenna and the second antenna are the
  • the first data comes from data received by at least one first antenna
  • the second data comes from data received by at least one second antenna.
  • the first processing module of the first system performs channel estimation based on the second data to obtain a channel estimation result of the second data. Then, the first processing module of the first system obtains the second correlation matrix information based on the channel estimation result of the second data and the first correlation matrix information.
  • the first processing module of the first system filters the second data based on the channel estimation result of the second data and the first filter data to obtain the second filter data.
  • the method shown in 300a further includes that the first processing module of the first system obtains an interference noise estimation result of the second data based on the second data and the channel estimation result of the second data, for example , the interference noise estimation result of the second data can satisfy:
  • U 2 (k) is the interference noise of the kth resource of the second data
  • R 2 (k) is the data of the kth resource of the second data
  • H 2 (k) is the kth resource of the second data
  • S is the frequency domain modulation symbol sent by the user on the kth resource
  • R U2U2 is the interference noise estimation result of the second data
  • N is the number of resources occupied by the second data
  • the N resources are The time-frequency resource occupied by the first data air interface transmission
  • k is the serial number of the resource
  • the first processing module of the first system obtains the corresponding The second correlation matrix information, for example, the second correlation matrix information may satisfy:
  • B 2 is the second correlation matrix information
  • B 1 is the first correlation matrix information
  • H2 is the channel estimation result of the second data
  • R U2U2 is the interference noise estimation result
  • the first processing module of the first system filters the second data based on the first filtered data and an interference noise estimation result of the second data to obtain the second filtered data , for example, the second filtered data can satisfy:
  • Y 2 is the second filtering data
  • Y 1 is the first filtering data
  • Y 1 is the first filtering data
  • R 2 is the second data. It should be understood that this is only an example, and other implementation methods are not excluded.
  • the first processing module of the first system performs channel estimation on the second data according to any of the following methods to obtain a channel estimation result of the second data: least squares estimation, Minimum mean square error estimation, compressed sensing estimation, or machine learning.
  • the first processing module of the first system filters the second data according to any of the following methods to obtain second filtered data: matched filtering, zero-forcing algorithm, least square method , minimum mean square error, maximum likelihood, or machine learning.
  • the first system obtains output data based on the second correlation matrix and the second filtering data, for example, the output data may satisfy:
  • B 2 is the second correlation matrix information
  • I is the identity matrix
  • B 2 +I) -1 is the inverse matrix of the sum of the second correlation matrix and the identity matrix
  • Y 2 is the second correlation matrix
  • the data processing method and device provided by the embodiment of the present application can solve the problems of increasing data processing complexity, widening transmission data bus bandwidth, and increasing data processing cost with the increase of antennas through unidirectional operation of a chain structure.
  • FIG. 3b shows a schematic block diagram of a data processing system 300b according to an embodiment of the present application.
  • a first chip corresponds to the first system in FIG. 3a
  • the second chip corresponds to the second system in FIG. 3a.
  • the first chip and the second chip can exchange information.
  • the second chip acquires the first data
  • the first data may be the terminal data received by the antenna sub-cluster 1
  • the first data may also be the data of the terminal received by the antenna sub-cluster 1 via the intermediate radio frequency processed data.
  • the radio frequency processing may include performing spectrum shifting and other processing on the received terminal data.
  • the antenna subcluster 1 may be an antenna subcluster composed of one or more antennas on one antenna panel in the cell, or may be an antenna subcluster composed of one or more antennas on different antenna panels in the cell. In this way, the data received by the antenna sub-cluster 1 can be processed first, thereby reducing the complexity of data processing.
  • the second chip performs baseband processing on the first data.
  • the second chip may obtain first correlation matrix information and first filtering data based on the first data.
  • first correlation matrix information and first filtering data For a specific implementation manner of obtaining the first correlation matrix information and the first filtered data, reference may be made to the description of the related embodiment in FIG. 3a above, which will not be repeated here.
  • the second chip sends the first correlation matrix information and the first filtering data to the first chip.
  • the first chip acquires second data
  • the second data may be the terminal data received by the antenna sub-cluster 2
  • the second data may also be the data of the terminal received by the antenna sub-cluster 2 after intermediate radio frequency processing.
  • the radio frequency processing may include performing spectrum shifting and other processing on the received terminal data.
  • the antenna subcluster 2 may be an antenna subcluster composed of one or more antennas on the antenna panel of the cell, or may be an antenna subcluster composed of one or more antennas on different antenna panels in the cell. In this way, the data received by the antenna sub-cluster 2 can be processed, thereby reducing the complexity of data processing.
  • the first chip performs baseband processing on the second data.
  • the first chip acquires the first correlation matrix information and the first filtering data sent by the second chip.
  • the first chip can obtain second correlation matrix information based on the correlation matrix information and the second data, and the first chip can filter the second data based on the first filtering data to obtain second filtering data.
  • the first chip obtains output data based on the second correlation matrix information and the second filtering data, and outputs the output data.
  • the data processing method provided by the embodiment of the present application can reduce the complexity of data processing and reduce the bandwidth of the transmission data bus by dividing the received terminal data into the first data and the second data, and performing a one-way operation in a chain structure. Reduce data processing costs.
  • FIG. 4 shows a schematic block diagram of a data processing system 400 according to an embodiment of the present application.
  • a first system 410, a second system 420, and a third system 430 are involved.
  • the first system 410 includes a first interface module 411 and a first processing module 412
  • the second system 420 includes a second interface module 421 and a second processing module 422
  • the third system 430 includes a third interface module 431 and a third interface module 432 .
  • the first system 410 and the third system 430 can exchange information
  • the third system 430 can exchange information with the first system 410 and the second system 420 respectively.
  • the first system 410 may also perform information interaction with the second system 420, which is not limited here.
  • FIG. 5a shows a schematic flowchart of a data processing method 500a according to an embodiment of the present application.
  • the first system, the second system and the third system are involved. system, the first system and the third system can perform information interaction, and the third system can perform information interaction with the first system and the second system respectively.
  • the first system, the second system and the third system respectively correspond to the first system 410, the second system 420 and the third system 430 in FIG. 4, the first system includes a first interface module and a first processing module, the The second system includes a second interface module and a second processing module, and the third system includes a third interface module and a third processing module.
  • the data processing method 500a includes but not limited to the following steps:
  • S501 Acquire first data, and obtain first correlation matrix information and first filtering data based on the first data.
  • the second interface module of the second system obtains the first data
  • the second processing module of the second system obtains the first correlation matrix information corresponding to the first channel based on the first data, and the first channel is the channel experienced by the first data
  • the second processing module filters the first data to obtain first filtered data.
  • the first data may be a signal received by an antenna of a wireless access network device, for example, the first data may satisfy:
  • R 1 H 1 S+G 1 Z+n 1
  • R1 is the first data
  • H1 is the channel experienced by the first data
  • S is the frequency domain modulation symbol sent by the user
  • G1 is the equivalent channel of the interfering user
  • Z is the frequency domain modulation symbol sent by the interfering user
  • n 1 is additive white Gaussian noise (AWGN)
  • the distribution is N(0, N 0 ), that is, the distribution obeys a Gaussian distribution with a mean value of 0 and a variance of N 0 .
  • the second processing module of the second system obtains the first correlation matrix information corresponding to the first channel based on the first data, which may satisfy:
  • R 1 S * H 1 SS * +G 1 ZS * +n 1 S *
  • H 1 SS * R 1 S * -G 1 ZS * -n 1 S *
  • H 1 R 1 S * -G 1 ZS * -n 1 S *
  • S * is the conjugate of the frequency domain modulation symbol S sent by the user, is the conjugate transpose of the channel H 1 experienced by the first data
  • B 1 is the first correlation matrix information
  • R 1 , H 1 , S, G 1 , Z, n 1 please refer to the above description, and will not repeat them here, It should be understood that this is only an example, and other implementation methods are not excluded.
  • the second processing module filters the first data to obtain the first filtered data, which may satisfy:
  • Y 1 is the first filtering data
  • It is the conjugate transposition of the channel H 1 experienced by the first data
  • R 1 is the first data. It should be understood that this is only an example, and other implementation methods are not excluded.
  • the second system can also output the first correlation matrix information to the third system, the second system can output the first correlation matrix information through the second interface module, and the third system can obtain the first correlation matrix information through the third interface module Correlation matrix information.
  • the first correlation matrix information is used to indicate the first correlation matrix, for example, the first correlation matrix information may include the first correlation matrix data, the matrix obtained after compressing the first correlation matrix, or the first correlation matrix index of.
  • the second system may also output first filtered data to the third system.
  • the second system can output the first filtering data through the second interface module, and the third system can obtain the first filtering data through the third interface module.
  • S302 may be executed first and then S303 may be executed, or S303 may be executed first and then S302 may be executed, or S302 and S303 may be executed simultaneously.
  • S504 Acquire third data, and obtain third correlation matrix information and third filtering data based on the third data.
  • the third system can also acquire third data through the third interface module, wherein the third data and the first data include data from the same device.
  • the third system may use a third processing module to obtain third correlation matrix information corresponding to a third channel based on the third data and the first correlation matrix information, and the third channel is a channel experienced by the third data.
  • the third processing module of the third system may also filter the third data based on the first filtered data to obtain third filtered data.
  • the third interface module receiving the third data, the third interface module receiving the first correlation matrix, and the third interface module receiving the first filtered data may be implemented by the same interface module, or may be implemented by different interface modules .
  • the interface module receiving the third data is a third interface module different from the interface module receiving the first correlation matrix
  • the interface module receiving the first filtered data is another third interface module, then the interface module receiving the third data 1.
  • the interface module receiving the first correlation matrix and the interface module receiving the first filter data are three different interface modules; for another example, the interface module receiving the third data and the interface module receiving the first correlation matrix and receiving the first
  • the interface module for filtering data is the same interface module, that is, the third interface module, which is not limited herein.
  • the third data may be a signal received by an antenna of the wireless access network device, for example, the third data may satisfy:
  • R 3 H 3 S+G 3 Z+n 3
  • R 3 is the third data
  • H 3 is the channel experienced by the third data
  • S is the frequency domain modulation symbol sent by the user
  • G 3 is the equivalent channel of the interfering user
  • Z is the frequency domain modulation symbol sent by the interfering user
  • n 3 is additive white gaussian noise (AWGN)
  • the distribution is N(0, N 0 ), that is, the distribution obeys a Gaussian distribution with a mean of 0 and a variance of N 0. It should be understood that this is only a example, other implementation methods are not excluded.
  • the third system can use the third processing module to obtain the third correlation matrix information corresponding to the third channel based on the third data and the first correlation matrix information, which can satisfy:
  • H 3 R 3 S * -G 3 ZS * -n 3 S *
  • S * is the conjugate of the frequency domain modulation symbol S sent by the user, is the conjugate transpose of the channel H 3 experienced by the third data
  • B 1 is the information of the first correlation matrix
  • B 3 is the information of the second correlation matrix
  • R 3 , H 3 , S, G 3 , Z, n 3 Please refer to the above description, which will not be repeated here. It should be understood that this is only an example, and other implementation methods are not excluded.
  • the third processing module of the third system can also filter the third data based on the first filtered data to obtain third filtered data, which can satisfy:
  • Y 3 is the third filtering data, is the conjugate transpose of the channel H 3 experienced by the third data, R 3 is the third data, and Y 1 is the first filtered data. It should be understood that this is only an example, and other implementation methods are not excluded.
  • the first data and the third data include data from the same device, the first data and the third data may be data sent from the same terminal, and the first data may be a wireless access network device
  • the first receiving antenna cluster of the wireless access network device receives the data sent by the terminal, and the third data may be the data sent by the terminal received by the third receiving antenna cluster of the wireless access network device. It should be understood that the wireless access network device still exists Additional receive antenna clusters.
  • the third system can also output the third correlation matrix information to the first system, the third system can output the third correlation matrix information through the third interface module, and the first system can obtain the third correlation matrix information through the first interface module Correlation matrix information.
  • the third correlation matrix information is used to indicate the third correlation matrix, for example, the third correlation matrix information may include third correlation matrix data, a matrix obtained after compressing the third correlation matrix, or a third correlation matrix index etc.
  • S506 outputs third filtered data.
  • the third system may also output third filtered data to the first system.
  • the third system can output the third filtering data through the third interface module, and the first system can obtain the third filtering data through the first interface module.
  • S505 may be executed first and then S506 may be executed, or S506 may be executed first and then S505 may be executed, or S505 and S506 may be executed simultaneously.
  • S507 Acquire second data, and obtain second correlation matrix information and second filtering data based on the second data.
  • the first system may also obtain second data through the first interface module, wherein the second data and the first data include data from the same device.
  • the first system may use the first processing module to obtain second correlation matrix information corresponding to a second channel based on the second data and the third correlation matrix information, and the second channel is a channel experienced by the second data.
  • the first processing module of the first system may also filter the second data based on the third filtered data to obtain second filtered data.
  • first interface module receiving the second data may be implemented by the same interface module, or may be implemented by different interface modules , which is not limited in this paper.
  • the second data may be a signal received by an antenna of the radio access network device, for example, the second data may satisfy:
  • R 2 H 2 S+G 2 Z+n 2
  • R 2 is the second data
  • H 2 is the channel experienced by the second data
  • S is the frequency domain modulation symbol sent by the user
  • G 2 is the equivalent channel of the interfering user
  • Z is the frequency domain modulation symbol sent by the interfering user
  • n 2 is additive white gaussian noise (AWGN)
  • the distribution is N(0, N 0 ), that is, the distribution obeys a Gaussian distribution with a mean of 0 and a variance of N 0. It should be understood that this is only a example, other implementation methods are not excluded.
  • the first system can use the first processing module to obtain the second correlation matrix information corresponding to the second channel based on the second data and the third correlation matrix information, which can satisfy:
  • H2 R2S * -G2ZS * -n2S *
  • S * is the conjugate of the frequency domain modulation symbol S sent by the user, is the conjugate transposition of the channel H 2 experienced by the second data
  • B 3 is the information of the third correlation matrix
  • B 2 is the information of the second correlation matrix
  • R 2 , H 2 , S, G 2 , Z, n 2 Please refer to the above description, which will not be repeated here. It should be understood that this is only an example, and other implementation methods are not excluded.
  • the first processing module of the first system can also filter the second data based on the third filtered data to obtain the second filtered data, which can satisfy:
  • Y 2 is the second filtering data
  • R 2 is the second data
  • Y 3 is the first filtered data
  • the first data, the second data and the third data include data from the same device, the first data, the second data and the third data may be data sent from the same terminal, the The first data may be data sent by the terminal received by the first receiving antenna cluster of the wireless access network device, and the second data may be data sent by the terminal received by the second receiving antenna cluster of the wireless access network device, The third data may be data sent by the terminal received by the third receiving antenna cluster of the wireless access network device, and it should be understood that there are other receiving antenna clusters in the wireless access network device.
  • the first processing module of the first system After obtaining the second correlation matrix information and the second filtering data, the first processing module of the first system obtains output data based on the second correlation matrix information and the second filtering data.
  • the first processing module obtains output data based on the second correlation matrix information and the second filtering data, which can satisfy:
  • the first interface module of the first system outputs the output data It should be understood that this is only an example, and other implementation methods are not excluded.
  • the data processing method and device provided by the embodiment of the present application can solve the problems of increasing data processing complexity, widening transmission data bus bandwidth, and increasing data processing cost with the increase of antennas through unidirectional operation of a chain structure.
  • the second processing module of the second system performs channel estimation based on the first data, and obtains a channel estimation result of the first data. Then, the second processing module obtains the first correlation matrix information based on the channel estimation result of the first data.
  • the second processing module of the second system filters the first data based on a channel estimation result of the first data to obtain the first filtered data.
  • the method shown in 500a further includes that the second processing module of the second system obtains an interference noise estimation result of the first data based on the first data and the channel estimation result of the first data, for example , the interference noise estimation result of the first data can satisfy:
  • U 1 (k) is the interference noise of the kth resource of the first data
  • R 1 (k) is the data of the kth resource of the first data
  • H 1 (k) is the kth resource of the first data
  • S is the frequency domain modulation symbol sent by the user on the kth resource
  • R U1U1 is the interference noise estimation result of the first data
  • N is the number of resources occupied by the first data
  • the N resources are The time-frequency resource occupied by the air interface transmission of the first data, where k is the serial number of the resource.
  • the second processing module of the second system obtains the first correlation matrix information corresponding to the first channel based on the first data and the interference noise estimation result of the first data, for example , the first correlation matrix information can satisfy:
  • B 1 is the first correlation matrix information, is the conjugate transpose of the channel estimation result H 1 of the first data, H 1 is the channel estimation result of the first data, It is the inverse of the interference noise estimation result R U1U1 of the first data. It should be understood that this is only an example, and other implementation methods are not excluded.
  • the second processing module of the second system filters the first data based on the first data and an interference noise estimation result of the first data to obtain the first filtered data,
  • the first filtered data can satisfy:
  • Y1 is the first filtering data
  • H1 of the first data is the conjugate transpose of the channel estimation result H1 of the first data
  • R U1U1 is the inverse of the interference noise estimation result
  • R 1 is the first data. It should be understood that this is only an example, and other implementation methods are not excluded.
  • the second processing module of the second system performs channel estimation on the first data according to any of the following methods to obtain a channel estimation result of the first data: least squares estimation, Minimum mean square error estimation, compressed sensing estimation, or machine learning.
  • the second processing module of the second system filters the first data according to any of the following methods to obtain the first filtered data: matched filtering, zero-forcing algorithm, least square method , minimum mean square error, maximum likelihood, or machine learning.
  • the first data comes from at least one first antenna.
  • the radio access network equipment receives data transmitted from the terminal
  • the antenna of the radio access network equipment includes at least one first antenna, at least one second antenna and other antennas, the first antenna and the second antenna are the
  • the first data comes from data received by at least one first antenna
  • the second data comes from data received by at least one second antenna.
  • the third processing module of the third system performs channel estimation based on the third data to obtain a channel estimation result of the third data. Then, the third processing module of the third system obtains the third correlation matrix information based on the channel estimation result of the third data and the first correlation matrix information.
  • the third processing module of the third system filters the third data based on the channel estimation result of the third data and the first filter data to obtain the second filter data.
  • the method shown in 500a further includes that the third processing module of the third system obtains an interference noise estimation result of the third data based on the third data and the channel estimation result of the third data, for example , the interference noise estimation result of the third data can satisfy:
  • U 3 (k) is the interference noise of the kth resource of the third data
  • R 3 (k) is the data of the kth resource of the third data
  • H 3 (k) is the kth resource of the third data
  • S is the frequency domain modulation symbol sent by the user on the kth resource
  • R U3U3 is the interference noise estimation result of the third data
  • N is the number of resources occupied by the third data
  • the N resources are The time-frequency resource occupied by the first data air interface transmission
  • k is the serial number of the resource
  • the third processing module of the third system obtains the corresponding The third correlation matrix information, for example, the third correlation matrix information may satisfy:
  • B 3 is the third correlation matrix information
  • B 1 is the first correlation matrix information
  • H3 is the channel estimation result of the third data
  • It is the inverse of the interference noise estimation result R U3U3 of the third data.
  • the third processing module of the third system filters the third data based on the first filtered data and an interference noise estimation result of the third data to obtain the third filtered data , for example, the third filtered data can satisfy:
  • Y 3 is the third filtering data
  • Y 1 is the first filtering data
  • Be the conjugate transposition of the channel estimation result H 3 of the third data is the inverse of the interference noise estimation result R U3U3 of the third data
  • R 3 is the third data. It should be understood that this is only an example, and other implementation methods are not excluded.
  • the third processing module of the third system performs channel estimation on the third data according to any of the following methods to obtain a channel estimation result of the third data: least squares estimation, Minimum mean square error estimation, compressed sensing estimation, or machine learning.
  • the third processing module of the third system filters the third data according to any of the following methods to obtain third filtered data: matched filtering, zero-forcing algorithm, least square method , minimum mean square error, maximum likelihood, or machine learning.
  • the third data comes from at least one third antenna.
  • a wireless access network device receives data transmitted from a terminal, and the antenna of the wireless access network device includes at least one first antenna, at least one second antenna, at least one third antenna, and other antennas.
  • the first antenna, the The second antenna and the third antenna are different antennas on the radio access network device, the first data comes from at least one data received by the first antenna, and the second data comes from at least one data received by the second antenna,
  • the third data is from at least one data received by the third antenna.
  • the first processing module of the first system performs channel estimation based on the second data to obtain a channel estimation result of the second data. Then, the first processing module of the first system obtains the second correlation matrix information based on the channel estimation result of the second data and the third correlation matrix information.
  • the first processing module of the first system filters the second data based on the channel estimation result of the second data and the third filter data to obtain the second filter data.
  • the method shown in 500a further includes that the first processing module of the first system obtains an interference noise estimation result of the second data based on the second data and the channel estimation result of the second data, for example , the interference noise estimation result of the second data can satisfy:
  • U 2 (k) is the interference noise of the kth resource of the second data
  • R 2 (k) is the data of the kth resource of the second data
  • H 2 (k) is the kth resource of the second data
  • S is the frequency domain modulation symbol sent by the user on the kth resource
  • R U2U2 is the interference noise estimation result of the second data
  • N is the number of resources occupied by the second data
  • the N resources are The time-frequency resource occupied by the first data air interface transmission
  • k is the serial number of the resource
  • the first processing module of the first system obtains the corresponding The second correlation matrix information, for example, the second correlation matrix information may satisfy:
  • B 2 is the second correlation matrix information
  • B 3 is the third correlation matrix information
  • H2 is the channel estimation result of the second data
  • It is the inverse of the interference noise estimation result R U2U2 of the second data.
  • the first processing module of the first system filters the second data based on the third filtered data and an interference noise estimation result of the second data to obtain the second filtered data , for example, the second filtered data can satisfy:
  • Y 2 is the second filtering data
  • Y 3 is the third filtering data
  • R 2 is the second data. It should be understood that this is only an example, and other implementation methods are not excluded.
  • the first processing module of the first system performs channel estimation on the second data according to any of the following methods to obtain a channel estimation result of the second data: least squares estimation, Minimum mean square error estimation, compressed sensing estimation, or machine learning.
  • the first processing module of the first system filters the second data according to any of the following methods to obtain second filtered data: matched filtering, zero-forcing algorithm, least square method , minimum mean square error, maximum likelihood, or machine learning.
  • the first system obtains output data based on the second correlation matrix and the second filtering data, for example, the output data may satisfy:
  • B 2 is the second correlation matrix information
  • I is the identity matrix
  • B 2 +I) -1 is the inverse matrix of the sum of the second correlation matrix and the identity matrix
  • Y 2 is the second correlation matrix
  • FIG. 4 only shows a schematic block diagram of a third system, and this embodiment of the present application does not limit the number of third systems.
  • this embodiment of the present application does not limit the number of third systems.
  • the number of the third system can be determined according to the complexity of data processing.
  • the data processing method and device provided by the embodiment of the present application operate in one direction through a chain structure, which can solve the problem of increasing data processing complexity with the increase of antennas.
  • the bandwidth of the data bus becomes wider, and the cost of data processing increases.
  • cell 1 is the above-mentioned first system and third system
  • cell 2 is the above-mentioned second system. After the data of cell 1 is processed by the first system and the third system, it is sent to the second system of cell 2 to continue data processing. .
  • Figure 5b shows a schematic block diagram of a data processing system 500b according to an embodiment of the present application
  • the data processing system shown in Figure 5b involves a first chip, a second chip, The third chip and the terminal.
  • the data processing system further includes an intermediate radio frequency processing module.
  • the first chip corresponds to the first system in FIG. 5a
  • the second chip corresponds to the second system in FIG. 5a
  • the third chip corresponds to the third system in FIG. 5a.
  • the first chip communicates with the third chip
  • the third chip communicates with the first chip and the second chip.
  • the second chip obtains the first data
  • the first data may be the terminal data received by the antenna sub-cluster 1
  • the first data may also be the data of the terminal received by the antenna sub-cluster 1 via the intermediate radio frequency processed data.
  • the radio frequency processing may include performing spectrum shifting and other processing on the received terminal data.
  • the antenna subcluster 1 may be an antenna subcluster composed of one or more antennas on the antenna panel of the cell, or may be an antenna subcluster composed of one or more antennas on different antenna panels in the cell. In this way, the data received by the antenna sub-cluster 1 can be processed first, thereby reducing the complexity of data processing.
  • the second chip performs baseband processing on the first data.
  • the second chip may obtain first correlation matrix information and first filtering data based on the first data.
  • first correlation matrix information and first filtering data For a specific implementation manner of obtaining the first correlation matrix information and the first filtered data, reference may be made to the description of the related embodiment in FIG. 5 a , and details are not repeated here.
  • the second chip sends the first correlation matrix information and the first filter data to the third chip.
  • the third chip acquires third data
  • the third data may be the terminal data received by the antenna sub-cluster 3
  • the second data may also be the data of the terminal received by the antenna sub-cluster 3 after intermediate radio frequency processing. It should be understood that the third data and the second data may originate from the same terminal.
  • the radio frequency processing may include performing spectrum shifting and other processing on the received terminal data.
  • the antenna subcluster 3 may be an antenna subcluster composed of one or more antennas on the antenna panel of the cell, or may be an antenna subcluster composed of one or more antennas on different antenna panels in the cell. In this way, the data received by the antenna sub-cluster 3 can be processed, thereby reducing the complexity of data processing.
  • the third chip performs baseband processing on the third data.
  • the third chip acquires the first correlation matrix information and the first filtering data sent by the second chip.
  • the third chip can obtain third correlation matrix information based on the correlation matrix information and the third data, and the third chip can filter the third data based on the first filtering data to obtain third filtering data.
  • the third chip sends the third correlation matrix information and the third filtering data to the first chip.
  • the first chip acquires second data
  • the second data may be the terminal data received by the antenna sub-cluster 2
  • the second data may also be the data of the terminal received by the antenna sub-cluster 2 after intermediate radio frequency processing.
  • the radio frequency processing may include performing spectrum shifting and other processing on the received terminal data.
  • the antenna subcluster 2 may be an antenna subcluster composed of one or more antennas on the antenna panel of the cell, or may be an antenna subcluster composed of one or more antennas on different antenna panels in the cell. In this way, the data received by the antenna sub-cluster 2 can be processed, thereby reducing the complexity of data processing.
  • the first chip performs baseband processing on the second data.
  • the first chip acquires the third correlation matrix information and the third filtering data sent by the third chip.
  • the first chip can obtain second correlation matrix information based on the three correlation matrix information and the second data, and the first chip can filter the second data based on the third filtering data to obtain second filtering data.
  • the first chip obtains output data based on the second correlation matrix information and the second filtering data, and outputs the output data.
  • the data processing method provided by the embodiment of the present application can reduce the complexity of data processing by dividing the received terminal data into the first data, the second data and the third data, and perform chain structure one-way operation. Transmission data bus bandwidth, reducing data processing costs.
  • FIG. 6 is a schematic block diagram of a communication device provided by an embodiment of the present application.
  • These communication devices can be used to realize the functions of the system in the foregoing method embodiments, and thus can also realize the beneficial effects possessed by the foregoing method embodiments.
  • the communication device may be an access network device, or a module (such as a chip) applied to the access network device, or software capable of realizing all or part of the functions of the access network device .
  • the communication device 600 includes a processing module 610 and an interface module 620 .
  • the communication device 600 is configured to implement the functions of the first system in the embodiment corresponding to FIG. 2 , FIG. 3 , FIG. 4 or FIG. 5 .
  • FIG. 2 When the communication device 600 is used to realize the function of the first system in the method embodiment shown in FIG. 2, FIG. 3a, FIG. 3b, FIG. 4, FIG. 5a or FIG. 5b, for example:
  • the interface module 620 is used to obtain the second data.
  • the interface module 620 is also used to output the output data.
  • the interface module 620 is also configured to receive first correlation matrix information.
  • the first correlation matrix information may be the first correlation matrix data, or compressed data of the first correlation matrix, etc.
  • the interface module 620 is also configured to receive the first filtering data.
  • the interface module 620 is also configured to receive third correlation matrix information.
  • the third correlation matrix information may be the data of the third correlation matrix, or the compressed data of the third correlation matrix, etc.
  • the interface module 620 is also configured to receive third filtering data.
  • the processing module 610 may be configured to obtain second correlation matrix information based on the second data.
  • the processing module 610 may also be configured to obtain second filtering data based on the second data.
  • the processing module 610 may also be configured to obtain an interference noise estimation result of the second data based on the second data.
  • the processing module 610 may also be configured to obtain output data based on the second correlation matrix information and the second filtered data.
  • the communication device 600 can also be used to realize the function of the second system in the method embodiment shown in FIG. 2, FIG. 3a, FIG. 3b, FIG. 4, FIG. 5a or FIG. 3a, FIG. 3b, FIG. 4, FIG. 5a or the second system function in the method embodiment shown in FIG. 5b, illustratively:
  • the interface module 620 is used to acquire the first data.
  • the interface module 620 is also configured to send the first correlation matrix information.
  • the first correlation matrix information may be the first correlation matrix data, or compressed data of the first correlation matrix, etc.
  • the interface module 620 is also configured to send the first filtering data.
  • the processing module 610 may be configured to obtain first correlation matrix information based on the first data.
  • the processing module 610 may also be configured to obtain first filtered data based on the first data.
  • the processing module 610 may also be configured to obtain an interference noise estimation result of the first data based on the first data.
  • FIG. 5a or FIG. 5b When the communication device 600 is used to realize the function of the third system in the method embodiment shown in FIG. 4, FIG. 5a or FIG. 5b, for example:
  • the interface module 620 is used to acquire the third data.
  • the interface module 620 is also configured to receive first correlation matrix information.
  • the first correlation matrix information may be the first correlation matrix data, or compressed data of the first correlation matrix, etc.
  • the interface module 620 is also configured to receive the first filtering data.
  • the interface module 620 is also configured to send third correlation matrix information.
  • the third correlation matrix information may be the data of the third correlation matrix, or the compressed data of the third correlation matrix, etc.
  • the interface module 620 is also configured to send third filtering data.
  • the processing module 610 may be configured to obtain third correlation matrix information based on the third data.
  • the processing module 610 may also be configured to obtain third filtering data based on the third data.
  • the processing module 610 may also be configured to obtain an interference noise estimation result of the third data based on the third data.
  • FIG. 7 is another schematic block diagram of a communication device provided by an embodiment of the present application. As shown in Figure 7.
  • the communication device 700 includes a processor 710 and an interface circuit 730 .
  • the processor 710 and the interface circuit 730 are coupled to each other. It can be understood that the interface circuit 730 can be a transceiver or an input-output interface.
  • the communication device 700 may further include a memory 720 for storing instructions executed by the processor 720 or storing input data required by the processor 710 to execute the instructions or storing data generated by the processor 710 after executing the instructions.
  • the processor 710 is configured to implement the functions of the processing module 610 described above
  • the interface circuit 730 is configured to implement the functions of the interface module 620 described above.
  • the communication device 700 further includes a bus 740 , and the processor 710 , the interface circuit 730 and the memory 720 can communicate through the bus 740 .
  • the embodiment of the present application also provides a system chip, the system chip includes input and output interfaces, at least one processor, at least one memory and a bus, the at least one memory is used to store instructions, and the at least one processor is used to call the at least one Instructions of the memory to perform the operations of the methods of the various aspects described above.
  • a processor may be an integrated circuit chip with signal processing capabilities.
  • each step of the above-mentioned method embodiments may be completed by an integrated logic circuit of hardware in a processor or instructions in the form of software.
  • the above-mentioned processor can be a general-purpose processor, a digital signal processor (digital signal processor, DSP), an application specific integrated circuit (application specific integrated circuit, ASIC), an off-the-shelf programmable gate array (field programmable gate array, FPGA) or other available Program logic devices, discrete gate or transistor logic devices, discrete hardware components.
  • a general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.
  • the steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor.
  • the software module can be located in a mature storage medium in the field such as random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, register.
  • the storage medium is located in the memory, and the processor reads the information in the memory, and completes the steps of the above method in combination with its hardware.
  • the memory in the embodiments of the present application may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories.
  • the non-volatile memory can be read-only memory (read-only memory, ROM), programmable read-only memory (programmable ROM, PROM), erasable programmable read-only memory (erasable PROM, EPROM), electrically programmable Erases programmable read-only memory (electrically EPROM, EEPROM) or flash memory.
  • Volatile memory can be random access memory (RAM), which acts as external cache memory.
  • RAM random access memory
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • DRAM synchronous dynamic random access memory
  • SDRAM double data rate synchronous dynamic random access memory
  • ESDRAM enhanced synchronous dynamic random access memory
  • SLDRAM direct memory bus random access memory
  • direct rambus RAM direct rambus RAM
  • all or part may be implemented by software, hardware, firmware or any combination thereof.
  • software When implemented using software, it may be implemented in whole or in part in the form of a computer program product.
  • the computer program product may comprise one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the processes or functions according to the embodiments of the present application will be generated in whole or in part.
  • the computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable devices.
  • the computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website, computer, server or data center Transmission to another website site, computer, server, or data center by wired (eg, coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (eg, infrared, wireless, microwave, etc.) means.
  • the computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device such as a server or a data center integrated with one or more available media.
  • the available medium may be a magnetic medium (such as a floppy disk, a hard disk, a magnetic disk), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)).
  • the disclosed systems, devices and methods may be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of the units is only a logical function division. In actual implementation, there may be other division methods.
  • multiple units or components can be combined or May be integrated into another system, or some features may be ignored, or not implemented.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.
  • the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit.
  • the functions described above are realized in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium.
  • the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application.
  • the aforementioned storage medium includes: various media capable of storing program codes such as U disk, mobile hard disk, read-only memory, random access memory, magnetic disk or optical disk.

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Radio Transmission System (AREA)

Abstract

本申请提供了一种数据处理方法及装置，其中，方法包括：获取第一相关矩阵信息和第一滤波数据，该第一相关矩阵信息为第一数据经历的第一信道对应的相关矩阵信息，该第一滤波数据为该第一数据被滤波后获得的数据，获取第二数据，其中该第二数据和该第一数据包含来自相同设备的数据，基于该第二数据和该第一相关矩阵信息得到第二信道对应的第二相关矩阵信息，该第二信道为该第二数据经历的信道，基于该第一滤波数据对该第二数据进行滤波得到第二滤波数据，基于该第二相关矩阵信息和该第二滤波数据得到输出数据，输出所述输出数据。该技术方案，可以将简化数据处理的复杂度，降低数据处理的成本。

Description

一种数据处理方法及装置
本申请要求于2021年06月15日提交中国专利局、申请号为202110661362.5、申请名称为“一种数据处理方法及装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及通信技术领域，尤其涉及一种多天线场景下的数据处理方法及装置。
背景技术
单小区接收时，接入网设备多采用集中式架构对数据进行处理。该集中式架构需要将所有的天线数据集中起来，除了正常的数据处理外还需要额外数据汇总处理。当天线不断增加时，集中式处理架构不仅使数据处理的复杂度呈指数倍地快速增加，还使汇总数据的总线带宽迅速增加，进而加大接入网设备芯片本身的设计困难。同时，由于接入网设备需要支撑多种不同天线规模场景的应用，集中式处理带来的动态链接带宽需求使接入网设备需要根据不同的连接形态设计不同的连接方案，引入额外的连接成本。因此，随着未来更大规模天线的应用，集中式处理可能会成为瓶颈。
因此如何设计一种可以解决随着天线增加数据处理复杂度变高，传输数据总线带宽变宽，数据处理成本增加的数据处理方法，成为亟待解决的问题。
发明内容
本申请提供了一种数据处理方法及装置，用以解决随着天线增加数据处理复杂度变高，传输数据总线带宽变宽，数据处理成本增加的问题。
本申请实施例第一方面提供了一种系统，包括：第一接口模块和第一处理模块；该第一接口模块用于：获取第一相关矩阵信息和第一滤波数据，该第一相关矩阵信息为第一数据经历的第一信道对应的相关矩阵信息，该第一滤波数据为该第一数据被滤波后获得的数据；获取第二数据，其中该第二数据和该第一数据包含来自相同设备的数据；
该第一处理模块用于：基于该第二数据和该第一相关矩阵信息得到第二信道对应的第二相关矩阵信息，该第二信道为该第二数据经历的信道；基于该第一滤波数据对该第二数据进行滤波得到第二滤波数据；基于该第二相关矩阵信息和该第二滤波数据得到输出数据；该第一接口模块还用于输出该输出数据。
基于该系统，可以解决随着天线增加，数据处理复杂度变高，传输数据总线带宽变宽，数据处理成本增加的问题。
结合第一方面，在第一方面的某些实施方式中，该第一处理模块用于基于该第二数据和该第一相关矩阵信息得到第二信道对应的第二相关矩阵信息，包括：该第一处理模块用于：基于该第二数据进行信道估计得到该第二数据的信道估计结果；基于该第二数据的信道估计结果和该第一相关矩阵信息获得该第二相关矩阵信息；该第一处理模块用于基于该第一滤波数据对该第二数据进行滤波得到第二滤波数据，包括：该第一处理模块用于基于该第二数据的信道估计结果和该第一滤波数据对该第二数据进行滤波得到该第二滤波数据。
结合第一方面，在第一方面的某些实施方式中，该第一处理模块还用于根据该第二数据和该第二数据的信道估计结果获得该第二数据的干扰噪声估计结果；该第一处理模块用于基于该第二数据和该第一相关矩阵信息得到第二信道对应的第二相关矩阵信息，包括：该第一处理模块用于基于该第二数据，该第一相关矩阵信息和该第二数据的干扰噪声估计结果得到该第二信道对应的第二相关矩阵信息。
基于该第二数据的干扰噪声估计结果，可以使得在获得该第二相关矩阵信息时考虑到干扰噪声的信息，使得该第二相关矩信息更加接近理想值。
结合第一方面，在第一方面的某些实施方式中，该第一处理模块用于基于该第一滤波数据对该第二数据进行滤波得到第二滤波数据，包括：该第一处理模块用于基于该第一滤波数据和该第二数据的干扰噪声估计结果对该第二数据进行滤波得到第二滤波数据。
基于该第二数据的干扰噪声估计结果，可以使得在获得该第二滤波数据时考虑到干扰噪声的信息，使得该第二滤波数据更加准确。
结合第一方面，在第一方面的某些实施方式中，该第一处理模块用于基于该第二数据进行信道估计得到该第二数据的信道估计结果，包括：该第一处理模块用于根据以下任一项方法进行该信道估计：最小二乘估计，最小均方误差估计，压缩感知估计，或机器学习。
结合第一方面，在第一方面的某些实施方式中，该第一处理模块用于基于该第一滤波数据对该第二数据进行滤波得到第二滤波数据，包括：该第一处理模块用于根据以下任一项方法进行该滤波：匹配滤波，迫零算法，最小二乘法，最小均方误差法，最大似然法，或机器学习。
结合第一方面，在第一方面的某些实施方式中，该第一数据来自至少一个第一天线，该第二数据来自至少一个第二天线。
本申请实施例第二方面提供了一种系统，包括：第二接口模块和第二处理模块；该第二接口模块用于：获取第一数据；该第二处理模块用于：基于该第一数据得到第一信道对应的第一相关矩阵信息，该第一信道为该第一数据经历的信道；对该第一数据进行滤波得到第一滤波数据；该第二接口模块还用于输出该第一相关矩阵信息和该第一滤波数据。
基于该系统，可以解决随着天线增加，数据处理复杂度变高，传输数据总线带宽变宽，数据处理成本增加的问题。
结合第二方面，在第二方面的某些实施方式中，该第二处理模块用于基于该第一数据得到第一信道对应的第一相关矩阵信息，包括：该第二处理模块用于：基于该第一数据进行信道估计得到该第一数据的信道估计结果；基于该第一数据的信道估计结果得到该第一相关矩阵信息；该第二处理模块用于对该第一数据进行滤波得到第一滤波数据，包括：该第二处理模块用于基于该第一数据的信道估计结果对该第一数据进行滤波得到第一滤波数据。
结合第二方面，在第二方面的某些实施方式中，该第二处理模块还用于基于该第一数据和该第一数据的信道估计结果得到该第一数据的干扰噪声估计结果；该第二处理模块用于基于该第一数据得到第一信道对应的第一相关矩阵信息，包括：该第二处理模块用于基于该第一数据和该第一数据的干扰噪声估计结果得到第一信道对应的第一相关矩阵信息。
结合第二方面，在第二方面的某些实施方式中，该第二处理模块用于对该第一数据进行滤波得到第一滤波数据，包括：该第二处理模块用于基于该第一数据和该第一数据的干扰噪声估计结果对该第一数据进行滤波得到该第一滤波数据。
结合第二方面，在第二方面的某些实施方式中，该第二处理模块用于基于该第一数据进 行信道估计得到该第一数据的信道估计结果，包括：该第二处理模块用于根据以下任一项方法进行该信道估计：最小二乘估计；最小均方误差估计；压缩感知估计；或机器学习。
结合第二方面，在第二方面的某些实施方式中，该第二处理模块用于对该第一数据进行滤波得到第一滤波数据，包括：该第二处理模块用于根据以下任一项方法进行该滤波：匹配滤波；迫零算法；最小二乘法；最小均方误差法；最大似然法；或机器学习。
结合第二方面，在第二方面的某些实施方式中，该第一数据来自至少一个第一天线。
本申请实施例第三方面提供了一种数据处理方法，包括：获取第一相关矩阵信息和第一滤波数据，该第一相关矩阵信息为第一数据经历的第一信道对应的相关矩阵信息，该第一滤波数据为该第一数据被滤波后获得的数据；获取第二数据，其中该第二数据和该第一数据包含来自相同设备的数据；基于该第二数据和该第一相关矩阵信息得到第二信道对应的第二相关矩阵信息，该第二信道为该第二数据经历的信道；基于该第一滤波数据对该第二数据进行滤波得到第二滤波数据；基于该第二相关矩阵信息和该第二滤波数据得到输出数据；输出该输出数据。
基于该系统，可以解决随着天线增加数据处理复杂度变高，传输数据总线带宽变宽，数据处理成本增加的问题。
结合第三方面，在第三方面的某些实施方式中，基于该第二数据和该第一相关矩阵信息得到第二信道对应的第二相关矩阵信息，包括：基于该第二数据进行信道估计得到该第二数据的信道估计结果，基于该第二数据的信道估计结果和该第一相关矩阵信息获得该第二相关矩阵信息；基于该第一滤波数据对该第二数据进行滤波得到第二滤波数据，包括：基于该第二数据的信道估计结果对该第二数据进行滤波得到第二滤波数据。
结合第三方面，在第三方面的某些实施方式中，该方法还包括：根据该第二数据和该第二数据信道估计结果获得该第二数据的干扰噪声估计结果；基于该第二数据和该第一相关矩阵信息得到第二信道对应的第二相关矩阵信息，包括：基于该第二数据，该第一相关矩阵信息和该第二数据的干扰噪声估计结果得到第二信道对应的第二相关矩阵信息。
基于该数据处理方法，可以解决随着天线增加，数据处理复杂度变高，传输数据总线带宽变宽，数据处理成本增加的问题。
结合第三方面，在第三方面的某些实施方式中，基于该第一滤波数据对该第二数据进行滤波得到第二滤波数据，包括：基于该第一滤波数据和该第二数据的干扰噪声估计结果对该第二数据进行滤波得到第二滤波数据。
结合第三方面，在第三方面的某些实施方式中，基于该第二数据进行信道估计得到该第二数据的信道估计结果，包括：根据以下任一项方法进行该信道估计：最小二乘估计；最小均方误差估计；压缩感知估计；或机器学习。
结合第三方面，在第三方面的某些实施方式中，基于该第一滤波数据对该第二数据进行滤波得到第二滤波数据，包括：根据以下任一项方法进行该滤波：匹配滤波；迫零算法；最小二乘法；最小均方误差法；最大似然法；或机器学习。
结合第三方面，在第三方面的某些实施方式中，该第一数据来自至少一个第一天线，该第二数据来自至少一个第二天线。
本申请实施例第四方面提供了一种数据处理方法，包括：获取第一数据；基于该第一数据得到第一信道对应的第一相关矩阵信息，该第一信道为该第一数据经历的信道；对该第一数据进行滤波得到第一滤波数据；输出该第一相关矩阵信息和该第一滤波数据。
基于该数据处理方法，可以解决随着天线增加，数据处理复杂度变高，传输数据总线带宽变宽，数据处理成本增加的问题。
结合第四方面，在第四方面的某些实施方式中，基于该第一数据得到第一信道对应的第一相关矩阵信息，包括：基于该第一数据进行信道估计得到该第一数据的信道估计结果，基于该第一数据的信道估计结果得到该第一相关矩阵信息；该对该第一数据进行滤波得到第一滤波数据，包括：基于该第一数据的信道估计结果对该第一数据进行滤波得到第一滤波数据。
结合第四方面，在第四方面的某些实施方式中，该方法还包括：基于该第一数据和该第一数据的信道估计结果得到该第一数据的干扰噪声估计结果；基于该第一数据得到第一信道对应的第一相关矩阵信息，包括：基于该第一数据和该第一数据的干扰噪声估计结果得到第一信道对应的第一相关矩阵信息。
结合第四方面，在第四方面的某些实施方式中，该对该第一数据进行滤波得到第一滤波数据，包括：基于该第一数据和该第一数据的干扰噪声估计结果对该第一数据进行滤波得到该第一滤波数据。
本申请实施例第五方面提供了一种系统，包括：第三接口模块和第三处理模块；该第三接口模块用于：获取第一相关矩阵信息和第一滤波数据，该第一相关矩阵信息为第一数据经历的第一信道对应的相关矩阵信息，该第一滤波数据为该第一数据被滤波后获得的数据；获取第三数据，其中该第三数据与该第一数据和该第二数据包含来自相同设备的数据；该第三处理模块用于：基于该第三数据和该第一相关矩阵信息得到第三信道对应的第三相关矩阵信息，该第三信道为该第三数据经历的信道；基于该第一滤波数据对该第三数据进行滤波得到第三滤波数据；该第三接口模块还用于输出该第三相关矩阵信息和该第三滤波数据。
应理解，该第三接口模块可以将该第三相关矩阵信息和该第三滤波数据输出给该第一接口模块，用于该第一处理模块进行数据处理。
基于该系统，可以解决随着天线增加，数据处理复杂度变高，传输数据总线带宽变宽，数据处理成本增加的问题。
结合第五方面，在第五方面的某些实施方式中，该第三处理模块用于基于该第三数据和该第一相关矩阵信息得到第三信道对应的第三相关矩阵信息，包括：该第三处理模块用于：基于该第三数据进行信道估计得到该第三数据的信道估计结果；基于该第三数据的信道估计结果和该第一相关矩阵信息获得该第三相关矩阵信息；该第三处理模块用于基于该第一滤波数据对该第三数据进行滤波得到第三滤波数据，包括：该第三处理模块用于基于该第三数据的信道估计结果和该第一滤波数据对该第三数据进行滤波得到该第三滤波数据。
结合第五方面，在第五方面的某些实施方式中，该第三处理模块还用于根据该第三数据和该第三数据的信道估计结果获得该第三数据的干扰噪声估计结果；该第三处理模块用于基于该第三数据和该第一相关矩阵信息得到第三信道对应的第三相关矩阵信息，包括：该第三处理模块用于基于该第三数据，该第一相关矩阵信息和该第三数据的干扰噪声估计结果得到该第三信道对应的第三相关矩阵信息。
结合第五方面，在第五方面的某些实施方式中，该第三处理模块用于基于该第一滤波数据对该第三数据进行滤波得到第三滤波数据，包括：该第三处理模块用于基于该第一滤波数据和该第三数据的干扰噪声估计结果对该第三数据进行滤波得到第三滤波数据。
结合第五方面，在第五方面的某些实施方式中，该第三处理模块用于基于该第三数据进行信道估计得到该第三数据的信道估计结果，包括：该第三处理模块用于根据以下任一项方 法进行该信道估计：最小二乘估计；最小均方误差估计；压缩感知估计；或机器学习。
结合第五方面，在第五方面的某些实施方式中，该第三处理模块用于基于该第一滤波数据对该第三数据进行滤波得到第三滤波数据，包括：该第三处理模块用于根据以下任一项方法进行该滤波：匹配滤波；迫零算法；最小二乘法；最小均方误差法；最大似然法；或机器学习。
应理解，本申请实施例并不限制第五方面提供的一种系统的数量，例如在第一方面提供的一种系统与第二方面提供的一种系统之间可以有两个或两个以上的该第五方面提供的一种系统。
本申请实施例第六方面提供了一种数据处理方法，包括：获取第一相关矩阵信息和第一滤波数据，该第一相关矩阵信息为第一数据经历的第一信道对应的相关矩阵信息，该第一滤波数据为该第一数据被滤波后获得的数据；获取第三数据，其中该第三数据与该第一数据和该第二数据包含来自相同设备的数据；基于该第三数据和该第一相关矩阵信息得到第三信道对应的第三相关矩阵信息，该第三信道为该第三数据经历的信道；基于该第一滤波数据对该第三数据进行滤波得到第三滤波数据；输出该第三相关矩阵信息和该第三滤波数据。
基于该数据处理方法，可以解决随着天线增加，数据处理复杂度变高，传输数据总线带宽变宽，数据处理成本增加的问题。
结合第六方面，在第六方面的某些实施方式中，基于该第三数据和该第一相关矩阵信息得到第三信道对应的第三相关矩阵信息，包括：基于该第三数据进行信道估计得到该第三数据的信道估计结果；基于该第三数据的信道估计结果和该第一相关矩阵信息获得该第三相关矩阵信息；基于该第一滤波数据对该第三数据进行滤波得到第三滤波数据，包括：基于该第三数据的信道估计结果和该第一滤波数据对该第三数据进行滤波得到该第三滤波数据。
结合第六方面，在第六方面的某些实施方式中，该方法还包括：根据该第三数据和该第三数据的信道估计结果获得该第三数据的干扰噪声估计结果；基于该第三数据和该第一相关矩阵信息得到第三信道对应的第三相关矩阵信息，包括：基于该第三数据，该第一相关矩阵信息和该第三数据的干扰噪声估计结果得到该第三信道对应的第三相关矩阵信息。
结合第六方面，在第六方面的某些实施方式中，基于该第一滤波数据对该第三数据进行滤波得到第三滤波数据，包括：基于该第一滤波数据和该第三数据的干扰噪声估计结果对该第三数据进行滤波得到第三滤波数据。
结合第六方面，在第六方面的某些实施方式中，该用于基于该第三数据进行信道估计得到该第三数据的信道估计结果，包括：根据以下任一项方法进行该信道估计：最小二乘估计；最小均方误差估计；压缩感知估计；或机器学习。
结合第六方面，在第六方面的某些实施方式中，基于该第一滤波数据对该第三数据进行滤波得到第三滤波数据，包括：根据以下任一项方法进行该滤波：匹配滤波；迫零算法；最小二乘法；最小均方误差法；最大似然法；或机器学习。
本申请实施例第七方面提供了一种通信装置，包括处理器和接口电路，该接口电路用于接收来自该装置之外的其它装置的信号并传输至该处理器或将来自该处理器的信号发送给该装置之外的其它装置，该处理器通过逻辑电路或执行代码指令用于实现第三方面或第三方面的可能的实现方式中所描述的方法。
本申请实施例第八方面提供了一种通信装置，包括处理器和接口电路，该接口电路用于接收来自该装置之外的其它装置的信号并传输至该处理器或将来自该处理器的信号发送给该 装置之外的其它装置，该处理器通过逻辑电路或执行代码指令用于实现第四方面或第四方面的可能的实现方式中所描述的方法。
本申请实施例第九方面提供了一种通信装置，包括处理器和接口电路，该接口电路用于接收来自该装置之外的其它装置的信号并传输至该处理器或将来自该处理器的信号发送给该装置之外的其它装置，该处理器通过逻辑电路或执行代码指令用于实现第六方面或第六方面的可能的实现方式中所描述的方法。
本申请实施例第十方面提供了一种计算机可读存储介质，该计算机可读存储介质中存储有计算机程序或指令，当该计算机程序或指令被计算设备执行时，实现第三方面或第三方面的可能的实现方式中所描述的方法，或实现第四方面或第四方面的可能的实现方式中所描述的方法，或实现第六方面或第六方面的可能的实现方式中所描述的方法。
本申请实施例第十一方面提供了一种计算机程序产品，该计算机程序产品包含计算机程序或指令，当该计算机程序或指令被通信装置执行时，实现第三方面或第三方面的可能的实现方式中所描述的方法，或实现第四方面或第四方面的可能的实现方式中所描述的方法，或实现第六方面或第六方面的可能的实现方式中所描述的方法。
本申请实施例第十二方面提供了一种通信系统，包括如第一方面或第一方面的可能的实现方式中描述的系统和第二方面或第二方面的可能的实现方式中描述的系统；或者包括如第一方面或第一方面的可能的实现方式中描述的系统，第二方面或第二方面的可能的实现方式中描述的系统和第五方面或第五方面的可能的实现方式中描述的系统；或者包括如第七方面和第八方面提供的通信装置；或者包括如第七方面，第八方面和第九方面提供的通信装置。
附图说明
图1为本申请实施例提供的一种可能的通信架构示意图；
图2为本申请实施例提供的一种数据处理系统的示意性框图；
图3a为本申请实施例提供的一种数据处理方法的示意性流程图；
图3b为本申请实施例提供的一种数据处理系统的再一示意性框图；
图4为本申请实施例提供的一种数据处理系统的再一示意性框图；
图5a为本申请实施例提供的一种数据处理方法的再一示意性流程图；
图5b为本申请实施例提供的一种数据处理系统的再一示意性框图；
图6为本申请实施例提供的一种通信装置的示意性框图；
图7为本申请实施例提供的一种通信装置的再一示意性框图。
具体实施方式
下面将结合附图，对本申请中的技术方案进行描述。
应理解，在本申请实施例中，各过程的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本申请实施例的实施过程构成任何限定。
应理解，在本申请的实施例中，对术语进行的编号一般是为了区分方便而进行的描述，编号并不意味着该术语存在顺序或者优先级的区别，比如“第一相关矩阵信息”和“第二相关矩阵信息”，其中的“第一”和“第二”，通常只用于区分这两组信息，而不应对本申请实施例的实施过程构成限定。
应理解，在本申请实施例中，“至少一个”是指一个或者多个，“多个”是指两个或两个以 上。“以下至少一项(个)”或其类似表达，是指的这些项中的任意组合，包括单项(个)或复数项(个)的任意组合。例如，a,b,或c中的至少一项(个)，可以表示：a,b,c,a-b,a-c,b-c,或a-b-c，其中a,b,c可以是单个，也可以是多个。
应理解，在本申请实施例中，术语“系统”和“网络”在本文中常被可互换使用。
应理解，在本申请实施例中，术语“和/或”，通常用于描述关联对象之间的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。应理解，在本申请实施例中出现的字符“/”，一般表示前后关联对象是一种“或”的关系。
本申请实施例提供的方法及装置可以应用于通信系统中。图1是本申请的实施例应用的通信系统100的架构示意图。如图1所示，该通信系统包括无线接入网110和核心网120，可选的，通信系统100还可以包括互联网130。其中，无线接入网110可以包括至少一个无线接入网设备(如图1中的111a和111b)，还可以包括至少一个终端(如图1中的112a-112j)。终端通过无线的方式与无线接入网设备相连，无线接入网设备通过无线或有线方式与核心网连接。核心网设备与无线接入网设备可以是独立的不同的物理设备，也可以是将核心网设备的功能与无线接入网设备的逻辑功能集成在同一个物理设备上，还可以是一个物理设备上集成了部分核心网设备的功能和部分的无线接入网设备的功能。终端和终端之间以及无线接入网设备和无线接入网设备之间可以通过有线或无线的方式相互连接。图1只是示意图，该通信系统中还可以包括其它网络设备，如还可以包括无线中继设备和无线回传设备，在图1中未画出。
无线接入网设备可以是基站(base station)、演进型基站(evolved NodeB，eNodeB)、发送接收点(transmission reception point，TRP)、第五代(5th generation，5G)移动通信系统中的下一代基站(next generation NodeB，gNB)、第六代(6th generation，6G)移动通信系统中的下一代基站、未来移动通信系统中的基站或WiFi系统中的接入节点等；也可以是完成基站部分功能的模块或单元，例如，可以是集中式单元(central unit，CU)，也可以是分布式单元(distributed unit，DU)。无线接入网设备可以是宏基站(如图1中的111a)，也可以是微基站或室内站(如图1中的111b)，还可以是中继节点或施主节点等。可以理解，本申请中的无线接入网设备的全部或部分功能也可以通过在硬件上运行的软件功能来实现，或者通过平台(例如云平台)上实例化的虚拟化功能来实现。本申请的实施例对无线接入网设备所采用的具体技术和具体设备形态不做限定。为了便于描述，下文以基站作为无线接入网设备的例子进行描述。
终端也可以称为终端设备、用户设备(user equipment，UE)、移动台、移动终端等。终端可以广泛应用于各种场景，例如，设备到设备(device-to-device，D2D)、车物(vehicle to everything，V2X)通信、机器类通信(machine-type communication，MTC)、物联网(internet of things，IOT)、虚拟现实、增强现实、工业控制、自动驾驶、远程医疗、智能电网、智能家具、智能办公、智能穿戴、智能交通、智慧城市等。终端可以是手机、平板电脑、带无线收发功能的电脑、可穿戴设备、车辆、无人机、直升机、飞机、轮船、机器人、机械臂、智能家居设备等。本申请的实施例对终端所采用的具体技术和具体设备形态不做限定。
基站和终端可以是固定位置的，也可以是可移动的。基站和终端可以部署在陆地上，包括室内或室外、手持或车载；也可以部署在水面上；还可以部署在空中的飞机、气球和人造卫星上。本申请的实施例对基站和终端的应用场景不做限定。
基站和终端的角色可以是相对的，例如，图1中的直升机或无人机112i可以被配置成移动基站，对于那些通过112i接入到无线接入网110的终端112j来说，终端112i是基站；但对于基站111a来说，112i是终端，即111a与112i之间是通过无线空口协议进行通信的。当然，111a与112i之间也可以是通过基站与基站之间的接口协议进行通信的，此时，相对于111a来说，112i也是基站。因此，基站和终端都可以统一称为通信装置，图1中的111a和111b可以称为具有基站功能的通信装置，图1中的112a-112j可以称为具有终端功能的通信装置。
基站和终端之间、基站和基站之间、终端和终端之间可以通过授权频谱进行通信，也可以通过免授权频谱进行通信，也可以同时通过授权频谱和免授权频谱进行通信；可以通过6千兆赫(gigahertz，GHz)以下的频谱进行通信，也可以通过6GHz以上的频谱进行通信，还可以同时使用6GHz以下的频谱和6GHz以上的频谱进行通信。本申请的实施例对无线通信所使用的频谱资源不做限定。
在本申请的实施例中，基站的功能也可以由基站中的模块(如芯片)来执行，也可以由包含有基站功能的控制子系统来执行。这里的包含有基站功能的控制子系统可以是智能电网、工业控制、智能交通、智慧城市等上述终端的应用场景中的控制中心。终端的功能也可以由终端中的模块(如芯片或调制解调器)来执行，也可以由包含有终端功能的装置来执行。
在本申请中，基站向终端发送下行信号或下行信息，下行信息承载在下行信道上；终端向基站发送上行信号或上行信息，上行信息承载在上行信道上。
可以理解，随着网络的演进，上述网元的名称可能发生变化，网元的功能也可能发生合并、分离、甚至改变，但这些变化并不意味着脱离了本申请方案的适用范围。
单小区接收时，接入网设备架构多采用集中式架构对数据进行处理。该架构需要将所有的天线数据集中起来，除了正常的数据处理外还需要额外数据汇总处理。当天线不断增加时，集中式处理架构不仅使数据处理的复杂度呈指数倍地快速增加，还使汇总数据的总线带宽迅速增加，进而加大接入网设备芯片本身的设计困难。同时，由于接入网设备需要支撑多种不同天线规模场景的应用，集中式处理带来的动态链接带宽需求使接入网设备需要根据不同的连接形态设计不同的连接方案，引入额外的连接成本。因此，随着未来更大规模天线的应用，集中式处理可能会成为瓶颈。
分布式接入网设备处理方案主要是将天线分簇，每簇天线数据处理后再与其他簇的天线数据进行融合或交互，待所有簇的数据都进行融合或者完成交互后，再输出最终数据结果，当前的分布式接入网设备处理方案虽然可以缓解数据处理的复杂度，但是为了保证数据处理性能，当前的分布式接入网设备处理方案会存在环路和耦合，也可能存在数据迭代处理，这样会引入额外的时延和缓存以及额外的数据处理复杂度。
因此如何设计一种可以解决随着天线增加数据处理复杂度变高，传输数据总线带宽变宽，数据处理成本增加等问题的数据处理方法，成为亟待解决的问题。
本申请提供了一种数据处理的方法及装置，用于解决随着天线增加数据处理复杂度高，传输数据总线带宽变宽，数据处理成本增加等问题。
请参见图2，图2示出了根据本申请实施例的数据处理系统200示意性框图，在图2所示的数据处理系统中涉及第一系统210和第二系统220，该第一系统包括第一接口模块211和第一处理模块212，该第二系统220包括第二接口模块221和第二处理模块222。该第一系统210与该第二系统220可进行通信。该第一系统210或该第二系统220可以是上述无线接入网设备或网络元件，也可以是上述终端或该终端的一部分，还可以是芯片，或者，该第一 系统210或该第二系统220还可以是软件系统，该第一接口模块211、第一处理模块212、第二接口模块221或第二处理模块222可以为软件系统中的软件模块。该第一系统210的第一接口模块220可以用于获取第一相关矩阵信息和第一滤波数据，该第一相关矩阵信息为第一数据经历的第一信道对应的相关矩阵信息，该第一滤波数据为所述第一数据被滤波后获得的数据；第一接口模块220还用于获取第二数据，该所述第二数据和该第一数据包含来自相同设备的数据。该第一系统210的第一处理模块212可以用于基于该第二数据和该第一相关矩阵信息得到第二信道对应的第二相关矩阵信息，该第二信道为该第二数据经历的信道；该第一处理模块212还可以用于基于所述第一滤波数据对所述第二数据进行滤波得到第二滤波数据；该第一处理模块212还可以用于基于该第二相关矩阵信息和该第二滤波数据得到输出数据；该第一接口模块220还用于输出该输出数据。
基于图2，请参见图3a，图3a示出了根据本申请实施例的数据处理方法300a示意性流程图，在图3a所示的数据处理方法中涉及第一系统和第二系统，该第一系统和该第二系统之间可以通信，该第一系统和该第二系统分别对应图2中第一系统210和第二系统220，该第一系统包括第一接口模块和第一处理模块，该第二系统包括第二接口模块和第二处理模块。该数据处理方法300a包括但不限于如下步骤：
S301获取第一数据，基于第一数据得到第一相关矩阵信息和第一滤波数据。
第二系统的第二接口模块获取第一数据，第二系统的第二处理模块基于该第一数据得到第一信道对应的第一相关矩阵信息，该第一信道为该第一数据经历的信道，该第二处理模块对该第一数据进行滤波得到第一滤波数据。
示例性地，该第一数据可以为无线接入网设备的天线接收信号，该第一数据可以满足：
R 1＝H 1S+G 1Z+n 1
其中，R 1为第一数据，H 1为第一数据经历的信道，S为用户发送的频域调制符号，G 1为干扰用户的等效信道，Z为干扰用户发送的频域调制符号，n 1是加性高斯白噪声(additive white gaussian noise，AWGN)，分布为N(0，N 0)，即分布服从均值为0，方差为N 0的高斯分布。
示例性地，该第二系统的第二处理模块基于该第一数据得到第一信道对应的第一相关矩阵信息可以满足：
R 1S *＝H 1SS *+G 1ZS *+n 1S *
H 1SS *＝R 1S *-G 1ZS *-n 1S *
H 1＝R 1S *-G 1ZS *-n 1S *
Figure PCTCN2022098476-appb-000001
其中，S *为用户发送的频域调制符号S的共轭，
Figure PCTCN2022098476-appb-000002
为第一数据经历的信道H 1的共轭转置，B 1为该第一相关矩阵信息，R 1，H 1，S，G 1，Z，n 1请参考上述描述，这里不再赘述，应理解，这里仅是一种示例，并不排除其他的实现方法。
示例性地，该第二处理模块对该第一数据进行滤波得到第一滤波数据，可以满足：
Figure PCTCN2022098476-appb-000003
其中，Y 1为该第一滤波数据，
Figure PCTCN2022098476-appb-000004
为第一数据经历的信道H 1的共轭转置，R 1为第一数据，应理解，这里仅是一种示例，并不排除其他的实现方法。
S302输出第一相关矩阵信息。
该第二系统还可以向该第一系统输出第一相关矩阵信息，该第二系统可以通过第二接口模块输出该第一相关矩阵信息，该第一系统可以通过第一接口模块获取该第一相关矩阵信息。 应理解，该第一相关矩阵信息用于指示第一相关矩阵，例如，该第一相关矩阵信息可以包括第一相关矩阵数据，对第一相关矩阵进行压缩后获取的矩阵，或者第一相关矩阵的索引等。
S303输出第一滤波数据。
该第二系统还可以向该第一系统输出第一滤波数据。该第二系统可以通过第二接口模块输出该第一滤波数据，该第一系统可以通过第一接口模块获取该第一滤波数据。
应理解，本申请对S302和S303的执行顺序不做限定。例如，可以先执行S302再执行S303，也可以先执行S303再执行S302，还可以同时执行S302和S303。
S304获取第二数据，基于第二数据得到第二相关矩阵信息和第二滤波数据。
该第一系统还可以通过该第一接口模块获取第二数据，其中该第二数据和该第一数据包含来自相同设备的数据。该第一系统可以通过第一处理模块基于该第二数据和该第一相关矩阵信息得到第二信道对应的第二相关矩阵信息，该第二信道为该第二数据经历的信道。该第一系统的该第一处理模块还可以基于该第一滤波数据对该第二数据进行滤波得到第二滤波数据。
应理解，接收第二数据的第一接口模块、接收第一相关矩阵的第一接口模块和接收第一滤波数据的第一接口模块可以由相同的接口模块实现，也可以由不同的接口模块实现。例如，接收第二数据的接口模块与接收第一相关矩阵的接口模块为不同的接口模块，接收第一滤波数据的接口模块为另外的接口模块，则接收第二数据的接口模块、接收第一相关矩阵的接口模块和接收第一滤波器数据的接口模块为三个不同的接口模块；再例如，接收第二数据的接口模块与接收第一相关矩阵的接口模块和接收第一滤波数据的接口模块为同一个接口模块，即第一接口模块，本文对此不做限制。
示例性地，该第二数据可以为无线接入网设备的天线接收信号，该第二数据可以满足：
R 2＝H 2S+G 2Z+n 2
其中，R 2为第二数据，H 2为第二数据经历的信道，S为用户发送的频域调制符号，G 2为干扰用户的等效信道，Z为干扰用户发送的频域调制符号，n 2是加性高斯白噪声(additive white gaussian noise，AWGN)，分布为N(0，N 0)，即分布服从均值为0，方差为N 0的高斯分布。
示例性地，该第一系统可以通过第一处理模块基于该第二数据和该第一相关矩阵信息得到第二信道对应的第二相关矩阵信息，可以满足：
H 2＝R 2S *-G 2ZS *-n 2S *
Figure PCTCN2022098476-appb-000005
其中，S *为用户发送的频域调制符号S的共轭，
Figure PCTCN2022098476-appb-000006
为第二数据经历的信道H 2的共轭转置，B 1为该第一相关矩阵信息，B 2为该第二相关矩阵信息，R 2，H 2，S，G 2，Z，n 2请参考上述描述，这里不再赘述，应理解，这里仅是一种示例，并不排除其他的实现方法。
示例性地，该第一系统的该第一处理模块还可以基于该第一滤波数据对该第二数据进行滤波得到第二滤波数据，可以满足：
Figure PCTCN2022098476-appb-000007
其中，Y 2为该第二滤波数据，
Figure PCTCN2022098476-appb-000008
为第二数据经历的信道H 2的共轭转置，R 2为第二数据，Y 1为该第一滤波数据，应理解，这里仅是一种示例，并不排除其他的实现方法。
示例性地，该第二数据和该第一数据包含来自相同设备的数据，该第一数据和该第二数据可以为来自同一个终端发送的数据，该第一数据可以为无线接入网设备的第一接收天线簇收到该终端发送的数据，该第二数据可以为该无线接入网设备的第二接收天线簇收到该终端 发送的数据，应理解该无线接入网设备还存在其他接收天线簇。
S305获取输出数据，输出该输出数据。
该第一系统的该第一处理模块在获得该第二相关矩阵信息和该第二滤波数据后，基于该第二相关矩阵信息和该第二滤波数据得到输出数据。
示例性地，该第一处理模块基于该第二相关矩阵信息和该第二滤波数据得到输出数据，可以满足：
Figure PCTCN2022098476-appb-000009
其中，
Figure PCTCN2022098476-appb-000010
为该输出数据，(B 2) -1为该第二相关矩阵信息的逆矩阵，Y 2为该第二滤波数据，应理解，这里仅是一种示例，并不排除其他的实现方法。
在该第一处理模块得到该输出数据
Figure PCTCN2022098476-appb-000011
后，该第一系统的该第一接口模块输出该输出数据
Figure PCTCN2022098476-appb-000012
该输出数据被输出后，会被继续处理，例如会做解调，解码等处理。
本申请实施例提供的数据处理方法及装置，通过链式结构单向操作，可以解决随着天线增加数据处理复杂度变高，传输数据总线带宽变宽，数据处理成本增加等问题。
在S301的一种可能的实施方式中，该第二系统的该第二处理模块基于该第一数据进行信道估计，得到该第一数据的信道估计结果。然后，该第二处理模块基于该第一数据的信道估计结果得到该第一相关矩阵信息。例如，该一相关矩阵信息可以满足：
R 1S *＝H 1SS *+G 1ZS *+n 1S *
H 1SS *＝R 1S *-G 1ZS *-n 1S *
H 1＝R 1S *-G 1ZS *-n 1S *
Figure PCTCN2022098476-appb-000013
其中，S *为用户发送的频域调制符号S的共轭，
Figure PCTCN2022098476-appb-000014
为第一数据经历的信道H 1的共轭转置，B 1为该第一相关矩阵信息，R 1，H 1，S，G 1，Z，n 1请参考上述描述，这里不再赘述，应理解，这里仅是一种示例，并不排除其他的实现方法。
在S301的一种可能的实施方式中，该第二系统的该第二处理模块基于所述第一数据的信道估计结果对该第一数据进行滤波得到该第一滤波数据。例如，该第一滤波数据可以满足：
Figure PCTCN2022098476-appb-000015
其中，Y 1为该第一滤波数据，
Figure PCTCN2022098476-appb-000016
为第一数据经历的信道H 1的共轭转置，R 1为第一数据，应理解，这里仅是一种示例，并不排除其他的实现方法。
可选地，在300a所示的方法中还包括，该第二系统的该第二处理模块基于该第一数据和该第一数据的信道估计结果得到该第一数据的干扰噪声估计结果，例如，该第一数据的干扰噪声估计结果可以满足：
U 1(k)＝R 1(k)-H 1(k)S 1(k)
Figure PCTCN2022098476-appb-000017
其中，U 1(k)为第一数据的第k个资源的干扰噪声，R 1(k)为第一数据的第k个资源的数据，H 1(k)为第一数据的第k个资源的数据经历的信道，S为用户在第k个资源上发送的频域调制符号，R U1U1为第一数据的干扰噪声估计结果，N为第一数据占用的资源数，该N个资源为该第一数据空口传输占用的时频资源，k为资源的序号，应理解，这里仅是一种示例，并不排除其他的实现方法。
在S301的一种可能的实施方式中，该第二系统的该第二处理模块基于该第一数据和该第一数据的干扰噪声估计结果得到该第一信道对应的第一相关矩阵信息，例如，该第一相关矩阵信息可以满足：
Figure PCTCN2022098476-appb-000018
其中，B 1为第一相关矩阵信息，
Figure PCTCN2022098476-appb-000019
为第一数据的信道估计结果H 1的共轭转置，H 1为第一数据的信道估计结果，
Figure PCTCN2022098476-appb-000020
为第一数据的干扰噪声估计结果R U1U1的逆，应理解，这里仅是一种示例，并不排除其他的实现方法。
基于该第一数据的干扰噪声估计结果，可以使得在获得该第一相关矩阵信息时考虑到干扰噪声的信息，使得该第一相关矩信息更加接近理想值。在S301的一种可能的实施方式中，该第二系统的该第二处理模块基于该第一数据和该第一数据的干扰噪声估计结果对该第一数据进行滤波得到该第一滤波数据，例如，该第一滤波数据可以满足：
Figure PCTCN2022098476-appb-000021
其中，Y 1为第一滤波数据，
Figure PCTCN2022098476-appb-000022
为该第一数据的信道估计结果H 1的共轭转置，
Figure PCTCN2022098476-appb-000023
为第一数据的干扰噪声估计结果R U1U1的逆，R 1为该第一数据，应理解，这里仅是一种示例，并不排除其他的实现方法。
基于该第一数据的干扰噪声估计结果，可以使得在获得该第一滤波数据时考虑到干扰噪声的信息，使得该第一滤波数据更加准确。
在S301的一种可能的实施方式中，该第二系统的该第二处理模块根据以下任一项方法对该第一数据进行信道估计得到该第一数据的信道估计结果：最小二乘估计、最小均方误差估计、压缩感知估计或机器学习。
其中，最小二乘估计是一种数学优化方法，它通过最小误差的平方和寻找数据的最佳函数匹配，利用最小二乘法可以简便地求得未知的数据，并使得这些求得的数据与实际数据之间的误差的平方和最小。
最小均方误差估计也是一种数据优化方法，它使未知量与已知量的均方误差达到最小化，在这种条件下来确定所需的未知量。
压缩感知估计是一种寻找欠定线性系统的稀疏解的技术，可以从较少的测量值还原原来整个欲得知的未知量。
机器学习是研究怎样使用计算机模拟或实现人类学习活动的科学，利用机器学习可以采用监督学习、无监督学习、强化学习等方法对未知量进行估计。
在S301的一种可能的实施方式中，该第二系统的该第二处理模块根据以下任一项方法对该第一数据进行滤波得到第一滤波数据：匹配滤波、迫零算法、最小二乘法、最小均方误差法、最大似然法或机器学习。
其中，匹配滤波是指输出的信号的瞬时功率与噪声平均功率的比值最大的滤波方法。
最大似然法是一类完全基于统计的系统发生树重建方法，它使用概率模型，其目标是寻找能够以较高概率产生观察数据的系统发生树。
在S301的一种可能的实施方式中，该第一数据来自至少一个第一天线。例如无线接入网设备接收来自终端发射的数据，无线接入网设备的天线包括至少一个第一天线、至少要一个第二天线以及其他天线，该第一天线和该第二天线为该无线接入网设备上不同的天线，该第一数据来自至少一个该第一天线接收的数据，该第二数据来自至少一个该第二天线接收的数据。
在S304的一种可能的实施方式中，该第一系统的该第一处理模块基于该第二数据进行信道估计得到该第二数据的信道估计结果。然后，该第一系统的该第一处理模块基于该第二数据的信道估计结果和该第一相关矩阵信息获得该第二相关矩阵信息。
在S304的一种可能的实施方式中，该第一系统的该第一处理模块基于该第二数据的信道估计结果和该第一滤波数据对该第二数据进行滤波得到该第二滤波数据。
可选地，在300a所示的方法中还包括，该第一系统的该第一处理模块基于该第二数据和该第二数据的信道估计结果获得该第二数据的干扰噪声估计结果，例如，该第二数据的干扰噪声估计结果可以满足：
U 2(k)＝R 2(k)-H 2(k)S 2(k)
Figure PCTCN2022098476-appb-000024
其中，U 2(k)为第二数据的第k个资源的干扰噪声，R 2(k)为第二数据的第k个资源的数据，H 2(k)为第二数据的第k个资源的数据经历的信道，S为用户在第k个资源上发送的频域调制符号，R U2U2为第二数据的干扰噪声估计结果，N为第二数据占用的资源数，该N个资源为该第一数据空口传输占用的时频资源，k为资源的序号，应理解，这里仅是一种示例，并不排除其他的实现方法。
在S304的一种可能的实施方式中，该第一系统的该第一处理模块基于该第二数据，该第一相关矩阵信息和该第二数据的干扰噪声估计结果得到该第二信道对应的第二相关矩阵信息，例如，该第二相关矩阵信息可以满足：
Figure PCTCN2022098476-appb-000025
其中，B 2为第二相关矩阵信息，B 1为第一相关矩阵信息，
Figure PCTCN2022098476-appb-000026
为第二数据的信道估计结果H 2的共轭转置，H 2为第二数据的信道估计结果，
Figure PCTCN2022098476-appb-000027
为第二数据的干扰噪声估计结果R U2U2的逆，应理解，这里仅是一种示例，并不排除其他的实现方法。
在S304的一种可能的实施方式中，该第一系统的该第一处理模块基于该第一滤波数据和该第二数据的干扰噪声估计结果对该第二数据进行滤波得到该第二滤波数据，例如，该第二滤波数据可以满足：
Figure PCTCN2022098476-appb-000028
其中，Y 2为该第二滤波数据，Y 1为该第一滤波数据，
Figure PCTCN2022098476-appb-000029
为该第二数据的信道估计结果H 2的共轭转置，
Figure PCTCN2022098476-appb-000030
为第二数据的干扰噪声估计结果R U2U2的逆，R 2为该第二数据，应理解，这里仅是一种示例，并不排除其他的实现方法。
在S304的一种可能的实施方式中，该第一系统的该第一处理模块根据以下任一项方法对该第二数据进行信道估计得到该第二数据的信道估计结果：最小二乘估计、最小均方误差估计、压缩感知估计或机器学习。
在S304的一种可能的实施方式中，该第一系统的该第一处理模块根据以下任一项方法对该第二数据进行滤波得到第二滤波数据：匹配滤波、迫零算法、最小二乘法、最小均方误差法、最大似然法或机器学习。
在S305的一种可能的实施方式中，该第一系统基于该第二相关矩阵和该第二滤波数据得到输出数据，例如，该输出数据可以满足：
Figure PCTCN2022098476-appb-000031
其中，
Figure PCTCN2022098476-appb-000032
为该输出数据，B 2为该第二相关矩阵信息，I为单位阵，(B 2+I) -1为该第二相 关矩阵和该单位阵之和的逆矩阵，Y 2为该第二滤波数据，应理解，这里仅是一种示例，并不排除其他的实现方法。
本申请实施例提供的数据处理方法及装置，通过链式结构单向操作，可以解决随着天线增加数据处理复杂度变高，传输数据总线带宽变宽，数据处理成本增加等问题。
基于图2和图3a，请参见图3b，图3b示出了根据本申请实施例的数据处理系统300b示意性框图，在图3b所示的数据处理系统中涉及第一芯片、第二芯片、终端，可选地，该数据处理系统还包括中射频处理模块。其中，该第一芯片对应图3a中的第一系统，该第二芯片对应图3a中的第二系统。该第一芯片与该第二芯片可进行信息的交互。
如图3b所示，第二芯片获取第一数据，该第一数据可以为天线子簇1接收到的终端数据，该第一数据也可以为天线子簇1接收到的终端的数据经过中射频处理后的数据。该中射频处理可以包括将该接收到的终端数据进行频谱搬移等处理。该天线子簇1可以为该小区一块天线面板上的一根或多根天线组成的天线子簇，也可以为该小区内不同的天线面板上的一根或多根天线组成的天线子簇。这样可以先对天线子簇1接收到的数据进行处理，从而降低数据处理的复杂度。
该第二芯片对该第一数据进行基带处理。示例性地，该第二芯片可以基于该第一数据获得第一相关矩阵信息和第一滤波数据。该第一相关矩阵信息、该第一滤波数据的具体获得的实现方式可以参见前述图3a相关实施例的描述，这里不再赘述。
该第二芯片将该第一相关矩阵信息和该第一滤波数据发送给该第一芯片。
该第一芯片获取第二数据，该第二数据可以为天线子簇2接收到的终端数据，该第二数据也可以为天线子簇2接收到的终端的数据经过中射频处理后的数据。应理解，该第一数据和该第二数据可以来源于同一个终端。该中射频处理可以包括将该接收到的终端数据进行频谱搬移等处理。该天线子簇2可以为该小区天线面板上的一根或多根天线组成的天线子簇，也可以为该小区内不同的天线面板上的一根或多根天线组成的天线子簇。这样可以对天线子簇2接收到的数据进行处理，从而降低数据处理的复杂度。
该第一芯片对该第二数据进行基带处理。示例性地，该第一芯片获取该第二芯片发送的第一相关矩阵信息和该第一滤波数据。该第一芯片可以基于该一相关矩阵信息和该第二数据得到第二相关矩阵信息，该第一芯片可以基于该第一滤波数据对该第二数据进行滤波得到第二滤波数据。该第一芯片基于该第二相关矩阵信息和该第二滤波数据得到输出数据，并输出该输出数据。该第二相关矩阵信息、该第二滤波数据和该输出数据的具体获得的实现方式可以参见前述图3a相关实施例的描述，这里不再赘述。
本申请实施例提供的数据处理方法，通过将接收的终端数据通过分为第一数据和第二数据，并进行链式结构单向操作，可以降低数据处理的复杂度，降低传输数据总线带宽，降低数据处理成本。
请参见图4，图4示出了根据本申请实施例的数据处理系统400示意性框图，在图4所示的数据处理系统中涉及第一系统410、第二系统420和第三系统430，该第一系统410包括第一接口模块411和第一处理模块412，该第二系统420包括第二接口模块421和第二处理模块422，该第三系统430包括第三接口模块431和第三接口模块432。该第一系统410与该第三系统430可进行信息的交互，该第三系统430可分别和该第一系统410和该第二系统420进行信息的交互。应理解，该第一系统410也可以与该第二系统420进行信息交互，这里对此不做限制。
基于图4，请参见图5a，图5a示出了根据本申请实施例的数据处理方法500a示意性流程图，在图5a所示的数据处理方法中涉及第一系统、第二系统和第三系统，该第一系统和该第三系统可进行信息交互，第三系统可分别和第一系统、第二系统进行信息交互。该第一系统、该第二系统和该第三系统分别对应图4中第一系统410、第二系统420和第三系统430，该第一系统包括第一接口模块和第一处理模块，该第二系统包括第二接口模块和第二处理模块，该第三系统包括第三接口模块和第三处理模块。该数据处理方法500a包括但不限于如下步骤：
S501获取第一数据，基于第一数据得到第一相关矩阵信息和第一滤波数据。
第二系统的第二接口模块获取第一数据，第二系统的第二处理模块基于该第一数据得到第一信道对应的第一相关矩阵信息，该第一信道为该第一数据经历的信道，该第二处理模块对该第一数据进行滤波得到第一滤波数据。
示例性地，该第一数据可以为无线接入网设备的天线接收信号，例如，该第一数据可以满足：
R 1＝H 1S+G 1Z+n 1
其中，R 1为第一数据，H 1为第一数据经历的信道，S为用户发送的频域调制符号，G 1为干扰用户的等效信道，Z为干扰用户发送的频域调制符号，n 1是加性高斯白噪声(additive white gaussian noise，AWGN)，分布为N(0，N 0)，即分布服从均值为0，方差为N 0的高斯分布。
示例性地，该第二系统的第二处理模块基于该第一数据得到第一信道对应的第一相关矩阵信息可以满足：
R 1S *＝H 1SS *+G 1ZS *+n 1S *
H 1SS *＝R 1S *-G 1ZS *-n 1S *
H 1＝R 1S *-G 1ZS *-n 1S *
Figure PCTCN2022098476-appb-000033
其中，S *为用户发送的频域调制符号S的共轭，
Figure PCTCN2022098476-appb-000034
为第一数据经历的信道H 1的共轭转置，B 1为该第一相关矩阵信息，R 1，H 1，S，G 1，Z，n 1请参考上述描述，这里不再赘述，应理解，这里仅是一种示例，并不排除其他的实现方法。
示例性地，该第二处理模块对该第一数据进行滤波得到第一滤波数据，可以满足：
Figure PCTCN2022098476-appb-000035
其中，Y 1为该第一滤波数据，
Figure PCTCN2022098476-appb-000036
为第一数据经历的信道H 1的共轭转置，R 1为第一数据，应理解，这里仅是一种示例，并不排除其他的实现方法。
S502输出第一相关矩阵信息。
该第二系统还可以向该第三系统输出第一相关矩阵信息，该第二系统可以通过第二接口模块输出该第一相关矩阵信息，该第三系统可以通过第三接口模块获取该第一相关矩阵信息。应理解，该第一相关矩阵信息用于指示第一相关矩阵，例如，该第一相关矩阵信息可以包括第一相关矩阵数据，对第一相关矩阵进行压缩后获取的矩阵，或者第一相关矩阵的索引。
S503输出第一滤波数据。
该第二系统还可以向该第三系统输出第一滤波数据。该第二系统可以通过第二接口模块输出该第一滤波数据，该第三系统可以通过第三接口模块获取该第一滤波数据。
应理解，本申请对S302和S303的执行顺序不做限定。例如，可以先执行S302再执行S303，也可以先执行S303再执行S302，还可以同时执行S302和S303。。
S504获取第三数据，基于第三数据得到第三相关矩阵信息和第三滤波数据。
该第三系统还可以通过该第三接口模块获取第三数据，其中该第三数据和该第一数据包含来自相同设备的数据。该第三系统可以通过第三处理模块基于该第三数据和该第一相关矩阵信息得到第三信道对应的第三相关矩阵信息，该第三信道为该第三数据经历的信道。该第三系统的该第三处理模块还可以基于该第一滤波数据对该第三数据进行滤波得到第三滤波数据。
应理解，接收第三数据的第三接口模块、接收第一相关矩阵的第三接口模块和接收第一滤波数据的第三接口模块可以由相同的接口模块实现，也可以由不同的接口模块实现。例如，接收第三数据的接口模块与接收第一相关矩阵的接口模块为不同的第三接口模块，接收第一滤波数据的接口模块为另外的第三接口模块，则接收第三数据的接口模块、接收第一相关矩阵的接口模块和接收第一滤波器数据的接口模块为三个不同的接口模块；再例如，接收第三数据的接口模块与接收第一相关矩阵的接口模块和接收第一滤波数据的接口模块为同一个接口模块，即第三接口模块，本文对此不做限制。
示例性地，该第三数据可以为无线接入网设备的天线接收信号，例如，该第三数据可以满足：
R 3＝H 3S+G 3Z+n 3
其中，R 3为第三数据，H 3为第三数据经历的信道，S为用户发送的频域调制符号，G 3为干扰用户的等效信道，Z为干扰用户发送的频域调制符号，n 3是加性高斯白噪声(additive white gaussian noise，AWGN)，分布为N(0，N 0)，即分布服从均值为0，方差为N 0的高斯分布，应理解，这里仅是一种示例，并不排除其他的实现方法。
示例性地，该第三系统可以通过该第三处理模块基于该第三数据和该第一相关矩阵信息得到第三信道对应的第三相关矩阵信息，可以满足：
H 3＝R 3S *-G 3ZS *-n 3S *
Figure PCTCN2022098476-appb-000037
其中，S *为用户发送的频域调制符号S的共轭，
Figure PCTCN2022098476-appb-000038
为第三数据经历的信道H 3的共轭转置，B 1为该第一相关矩阵信息，B 3为该第二相关矩阵信息，R 3，H 3，S，G 3，Z，n 3请参考上述描述，这里不再赘述，应理解，这里仅是一种示例，并不排除其他的实现方法。
示例性地，该第三系统的该第三处理模块还可以基于该第一滤波数据对该第三数据进行滤波得到第三滤波数据，可以满足：
Figure PCTCN2022098476-appb-000039
其中，Y 3为该第三滤波数据，
Figure PCTCN2022098476-appb-000040
为第三数据经历的信道H 3的共轭转置，R 3为第三数据，Y 1为该第一滤波数据，应理解，这里仅是一种示例，并不排除其他的实现方法。
示例性地，该第一数据和该第三数据包含来自相同设备的数据，该第一数据和该第三数据可以为来自同一个终端发送的数据，该第一数据可以为无线接入网设备的第一接收天线簇收到该终端发送的数据，该第三数据可以为该无线接入网设备的第三接收天线簇收到该终端发送的数据，应理解该无线接入网设备还存在其他接收天线簇。
S505输出第三相关矩阵信息。
该第三系统还可以向该第一系统输出第三相关矩阵信息，该第三系统可以通过第三接口 模块输出该第三相关矩阵信息，该第一系统可以通过第一接口模块获取该第三相关矩阵信息。应理解，该第三相关矩阵信息用于指示第三相关矩阵，例如，该第三相关矩阵信息可以包括第三相关矩阵数据，对第三相关矩阵进行压缩后获取的矩阵，或者第三相关矩阵的索引等。
S506输出第三滤波数据。
该第三系统还可以向该第一系统输出第三滤波数据。该第三系统可以通过第三接口模块输出该第三滤波数据，该第一系统可以通过第一接口模块获取该第三滤波数据。
应理解，本申请对S505和S506的执行顺序不做限定。例如，可以先执行S505再执行S506，也可以先执行S506再执行S505，还可以同时执行S505和S506。
S507获取第二数据，基于第二数据得到第二相关矩阵信息和第二滤波数据。
该第一系统还可以通过该第一接口模块获取第二数据，其中该第二数据和该第一数据包含来自相同设备的数据。该第一系统可以通过第一处理模块基于该第二数据和该第三相关矩阵信息得到第二信道对应的第二相关矩阵信息，该第二信道为该第二数据经历的信道。该第一系统的该第一处理模块还可以基于该第三滤波数据对该第二数据进行滤波得到第二滤波数据。
应理解，接收第二数据的第一接口模块、接收第三相关矩阵的第一接口模块和接收第三滤波数据的第一接口模块可以由相同的接口模块实现，也可以由不同的接口模块实现，本文对此不做限制。
示例性地，该第二数据可以为无线接入网设备的天线接收信号，例如，该第二数据可以满足：
R 2＝H 2S+G 2Z+n 2
其中，R 2为第二数据，H 2为第二数据经历的信道，S为用户发送的频域调制符号，G 2为干扰用户的等效信道，Z为干扰用户发送的频域调制符号，n 2是加性高斯白噪声(additive white gaussian noise，AWGN)，分布为N(0，N 0)，即分布服从均值为0，方差为N 0的高斯分布，应理解，这里仅是一种示例，并不排除其他的实现方法。
示例性地，该第一系统可以通过第一处理模块基于该第二数据和该第三相关矩阵信息得到第二信道对应的第二相关矩阵信息，可以满足：
H 2＝R 2S *-G 2ZS *-n 2S *
Figure PCTCN2022098476-appb-000041
其中，S *为用户发送的频域调制符号S的共轭，
Figure PCTCN2022098476-appb-000042
为第二数据经历的信道H 2的共轭转置，B 3为该第三相关矩阵信息，B 2为该第二相关矩阵信息，R 2，H 2，S，G 2，Z，n 2请参考上述描述，这里不再赘述，应理解，这里仅是一种示例，并不排除其他的实现方法。
示例性地，该第一系统的该第一处理模块还可以基于该第三滤波数据对该第二数据进行滤波得到第二滤波数据，可以满足：
Figure PCTCN2022098476-appb-000043
其中，Y 2为该第二滤波数据，
Figure PCTCN2022098476-appb-000044
为第二数据经历的信道H 2的共轭转置，R 2为第二数据，Y 3为该第一滤波数据，应理解，这里仅是一种示例，并不排除其他的实现方法。
示例性地，该第一数据、该第二数据和该第三数据包含来自相同设备的数据，该第一数据、该第二数据和该第三数据可以为来自同一个终端发送的数据，该第一数据可以为无线接入网设备的第一接收天线簇收到该终端发送的数据，该第二数据可以为该无线接入网设备的第二接收天线簇收到该终端发送的数据，该第三数据可以为该无线接入网设备的第三接收天 线簇收到该终端发送的数据，应理解该无线接入网设备还存在其他接收天线簇。
S508获取输出数据，输出该输出数据。
该第一系统的该第一处理模块在获得该第二相关矩阵信息和该第二滤波数据后，基于该第二相关矩阵信息和该第二滤波数据得到输出数据。
示例性地，该第一处理模块基于该第二相关矩阵信息和该第二滤波数据得到输出数据，可以满足：
Figure PCTCN2022098476-appb-000045
其中，
Figure PCTCN2022098476-appb-000046
为该输出数据，(B 2) -1为该第二相关矩阵信息的逆矩阵，Y 2为该第二滤波数据。
在该第一处理模块得到该输出数据
Figure PCTCN2022098476-appb-000047
后，该第一系统的该第一接口模块输出该输出数据
Figure PCTCN2022098476-appb-000048
应理解，这里仅是一种示例，并不排除其他的实现方法。
本申请实施例提供的数据处理方法及装置，通过链式结构单向操作，可以解决随着天线增加数据处理复杂度变高，传输数据总线带宽变宽，数据处理成本增加等问题。
在S501的一种可能的实施方式中，该第二系统的该第二处理模块基于该第一数据进行信道估计，得到该第一数据的信道估计结果。然后，该第二处理模块基于该第一数据的信道估计结果得到该第一相关矩阵信息。
在S501的一种可能的实施方式中，该第二系统的该第二处理模块基于所述第一数据的信道估计结果对该第一数据进行滤波得到该第一滤波数据。
可选地，在500a所示的方法中还包括，该第二系统的该第二处理模块基于该第一数据和该第一数据的信道估计结果得到该第一数据的干扰噪声估计结果，例如，该第一数据的干扰噪声估计结果可以满足：
U 1(k)＝R 1(k)-H 1(k)S 1(k)
Figure PCTCN2022098476-appb-000049
其中，U 1(k)为第一数据的第k个资源的干扰噪声，R 1(k)为第一数据的第k个资源的数据，H 1(k)为第一数据的第k个资源的数据经历的信道，S为用户在第k个资源上发送的频域调制符号，R U1U1为第一数据的干扰噪声估计结果，N为第一数据占用的资源数，该N个资源为该第一数据空口传输占用的时频资源，k为资源的序号。
在S501的一种可能的实施方式中，该第二系统的该第二处理模块基于该第一数据和该第一数据的干扰噪声估计结果得到该第一信道对应的第一相关矩阵信息，例如，该第一相关矩阵信息可以满足：
Figure PCTCN2022098476-appb-000050
其中，B 1为第一相关矩阵信息，
Figure PCTCN2022098476-appb-000051
为第一数据的信道估计结果H 1的共轭转置，H 1为第一数据的信道估计结果，
Figure PCTCN2022098476-appb-000052
为第一数据的干扰噪声估计结果R U1U1的逆，应理解，这里仅是一种示例，并不排除其他的实现方法。
在S501的一种可能的实施方式中，该第二系统的该第二处理模块基于该第一数据和该第一数据的干扰噪声估计结果对该第一数据进行滤波得到该第一滤波数据，例如，该第一滤波数据可以满足：
Figure PCTCN2022098476-appb-000053
其中，Y 1为第一滤波数据，
Figure PCTCN2022098476-appb-000054
为该第一数据的信道估计结果H 1的共轭转置，
Figure PCTCN2022098476-appb-000055
为第一数据的干扰噪声估计结果R U1U1的逆，R 1为该第一数据，应理解，这里仅是一种 示例，并不排除其他的实现方法。
在S501的一种可能的实施方式中，该第二系统的该第二处理模块根据以下任一项方法对该第一数据进行信道估计得到该第一数据的信道估计结果：最小二乘估计、最小均方误差估计、压缩感知估计或机器学习。
在S501的一种可能的实施方式中，该第二系统的该第二处理模块根据以下任一项方法对该第一数据进行滤波得到第一滤波数据：匹配滤波、迫零算法、最小二乘法、最小均方误差法、最大似然法或机器学习。
在S501的一种可能的实施方式中，该第一数据来自至少一个第一天线。例如无线接入网设备接收来自终端发射的数据，无线接入网设备的天线包括至少一个第一天线、至少要一个第二天线以及其他天线，该第一天线和该第二天线为该无线接入网设备上不同的天线，该第一数据来自至少一个该第一天线接收的数据，该第二数据来自至少一个该第二天线接收的数据。
在S504的一种可能的实施方式中，该第三系统的该第三处理模块基于该第三数据进行信道估计得到该第三数据的信道估计结果。然后，该第三系统的该第三处理模块基于该第三数据的信道估计结果和该第一相关矩阵信息获得该第三相关矩阵信息。
在S504的一种可能的实施方式中，该第三系统的该第三处理模块基于该第三数据的信道估计结果和该第一滤波数据对该第三数据进行滤波得到该第二滤波数据。
可选地，在500a所示的方法中还包括，该第三系统的该第三处理模块基于该第三数据和该第三数据的信道估计结果获得该第三数据的干扰噪声估计结果，例如，该第三数据的干扰噪声估计结果可以满足：
U 3(k)＝R 3(k)-H 3(k)S 3(k)
Figure PCTCN2022098476-appb-000056
其中，U 3(k)为第三数据的第k个资源的干扰噪声，R 3(k)为第三数据的第k个资源的数据，H 3(k)为第三数据的第k个资源的数据经历的信道，S为用户在第k个资源上发送的频域调制符号，R U3U3为第三数据的干扰噪声估计结果，N为第三数据占用的资源数，该N个资源为该第一数据空口传输占用的时频资源，k为资源的序号，应理解，这里仅是一种示例，并不排除其他的实现方法。
在S504的一种可能的实施方式中，该第三系统的该第三处理模块基于该第三数据，该第一相关矩阵信息和该第三数据的干扰噪声估计结果得到该第三信道对应的第三相关矩阵信息，例如，该第三相关矩阵信息可以满足：
Figure PCTCN2022098476-appb-000057
其中，B 3为第三相关矩阵信息，B 1为第一相关矩阵信息，
Figure PCTCN2022098476-appb-000058
为第三数据的信道估计结果H 3的共轭转置，H 3为第三数据的信道估计结果，
Figure PCTCN2022098476-appb-000059
为第三数据的干扰噪声估计结果R U3U3的逆，应理解，这里仅是一种示例，并不排除其他的实现方法。
在S504的一种可能的实施方式中，该第三系统的该第三处理模块基于该第一滤波数据和该第三数据的干扰噪声估计结果对该第三数据进行滤波得到该第三滤波数据，例如，该第三滤波数据可以满足：
Figure PCTCN2022098476-appb-000060
其中，Y 3为该第三滤波数据，Y 1为该第一滤波数据，
Figure PCTCN2022098476-appb-000061
为该第三数据的信道估计结果 H 3的共轭转置，
Figure PCTCN2022098476-appb-000062
为第三数据的干扰噪声估计结果R U3U3的逆，R 3为该第三数据，应理解，这里仅是一种示例，并不排除其他的实现方法。
在S504的一种可能的实施方式中，该第三系统的该第三处理模块根据以下任一项方法对该第三数据进行信道估计得到该第三数据的信道估计结果：最小二乘估计、最小均方误差估计、压缩感知估计或机器学习。
在S504的一种可能的实施方式中，该第三系统的该第三处理模块根据以下任一项方法对该第三数据进行滤波得到第三滤波数据：匹配滤波、迫零算法、最小二乘法、最小均方误差法、最大似然法或机器学习。
在S504的一种可能的实施方式中，该第三数据来自至少一个第三天线。例如无线接入网设备接收来自终端发射的数据，无线接入网设备的天线包括至少一个第一天线、至少要一个第二天线、至少要一个第三天线以及其他天线，该第一天线、该第二天线和该第三天线为该无线接入网设备上不同的天线，该第一数据来自至少一个该第一天线接收的数据，该第二数据来自至少一个该第二天线接收的数据，该第三数据来自至少一个该第三天线接收的数据。
在S507的一种可能的实施方式中，该第一系统的该第一处理模块基于该第二数据进行信道估计得到该第二数据的信道估计结果。然后，该第一系统的该第一处理模块基于该第二数据的信道估计结果和该第三相关矩阵信息获得该第二相关矩阵信息。
在S507的一种可能的实施方式中，该第一系统的该第一处理模块基于该第二数据的信道估计结果和该第三滤波数据对该第二数据进行滤波得到该第二滤波数据。
可选地，在500a所示的方法中还包括，该第一系统的该第一处理模块基于该第二数据和该第二数据的信道估计结果获得该第二数据的干扰噪声估计结果，例如，该第二数据的干扰噪声估计结果可以满足：
U 2(k)＝R 2(k)-H 2(k)S 2(k)
Figure PCTCN2022098476-appb-000063
其中，U 2(k)为第二数据的第k个资源的干扰噪声，R 2(k)为第二数据的第k个资源的数据，H 2(k)为第二数据的第k个资源的数据经历的信道，S为用户在第k个资源上发送的频域调制符号，R U2U2为第二数据的干扰噪声估计结果，N为第二数据占用的资源数，该N个资源为该第一数据空口传输占用的时频资源，k为资源的序号，应理解，这里仅是一种示例，并不排除其他的实现方法。
在S507的一种可能的实施方式中，该第一系统的该第一处理模块基于该第二数据，该第三相关矩阵信息和该第二数据的干扰噪声估计结果得到该第二信道对应的第二相关矩阵信息，例如，该第二相关矩阵信息可以满足：
Figure PCTCN2022098476-appb-000064
其中，B 2为第二相关矩阵信息，B 3为第三相关矩阵信息，
Figure PCTCN2022098476-appb-000065
为第二数据的信道估计结果H 2的共轭转置，H 2为第二数据的信道估计结果，
Figure PCTCN2022098476-appb-000066
为第二数据的干扰噪声估计结果R U2U2的逆，应理解，这里仅是一种示例，并不排除其他的实现方法。
在S507的一种可能的实施方式中，该第一系统的该第一处理模块基于该第三滤波数据和该第二数据的干扰噪声估计结果对该第二数据进行滤波得到该第二滤波数据，例如，该第二滤波数据可以满足：
Figure PCTCN2022098476-appb-000067
其中，Y 2为该第二滤波数据，Y 3为该第三滤波数据，
Figure PCTCN2022098476-appb-000068
为该第二数据的信道估计结果H 2的共轭转置，
Figure PCTCN2022098476-appb-000069
为第二数据的干扰噪声估计结果R U2U2的逆，R 2为该第二数据，应理解，这里仅是一种示例，并不排除其他的实现方法。
在S507的一种可能的实施方式中，该第一系统的该第一处理模块根据以下任一项方法对该第二数据进行信道估计得到该第二数据的信道估计结果：最小二乘估计、最小均方误差估计、压缩感知估计或机器学习。
在S507的一种可能的实施方式中，该第一系统的该第一处理模块根据以下任一项方法对该第二数据进行滤波得到第二滤波数据：匹配滤波、迫零算法、最小二乘法、最小均方误差法、最大似然法或机器学习。
在S508的一种可能的实施方式中，该第一系统基于该第二相关矩阵和该第二滤波数据得到输出数据，例如，该输出数据可以满足：
Figure PCTCN2022098476-appb-000070
其中，
Figure PCTCN2022098476-appb-000071
为该输出数据，B 2为该第二相关矩阵信息，I为单位阵，(B 2+I) -1为该第二相关矩阵和该单位阵之和的逆矩阵，Y 2为该第二滤波数据，应理解，这里仅是一种示例，并不排除其他的实现方法。
应理解，图4仅示意出一个第三系统示意框图，本申请实施例并不限制第三系统的数量。例如该第一系统和该第二系统之间可以有两个或两个以上第三系统，第三系统之间可以进行信息交互。可以根据数据处理的复杂度确定该第三系统的个数，这样本申请实施例提供的数据处理方法及装置通过链式结构单向操作，可以解决随着天线增加数据处理复杂度变高，传输数据总线带宽变宽，数据处理成本增加等问题。
应理解，本申请实施例还可以用于多个小区接收数据时，小区内的数据处理架构。例如小区1为上述的第一系统和第三系统，小区2为上述的第二系统，小区1的数据在经过第一系统和第三系统处理后发送给小区2的第二系统继续进行数据处理。
基于图4和图5a，请参见图5b，图5b示出了根据本申请实施例的数据处理系统500b示意性框图，在图5b所示的数据处理系统中涉及第一芯片、第二芯片、第三芯片和终端，可选地，该数据处理系统还包括中射频处理模块。其中，该第一芯片对应图5a中的第一系统，该第二芯片对应图5a中的第二系统，该第三芯片对应图5a中的第三系统。该第一芯片与该第三芯片通信，该第三芯片与第一芯片和第二芯片通信。
如图5b所示，第二芯片获取第一数据，该第一数据可以为天线子簇1接收到的终端数据，该第一数据也可以为天线子簇1接收到的终端的数据经过中射频处理后的数据。该中射频处理可以包括将该接收到的终端数据进行频谱搬移等处理。该天线子簇1可以为该小区的天线面板上的一根或多根天线组成的天线子簇，也可以为该小区内不同的天线面板上的一根或多根天线组成的天线子簇。这样可以先对天线子簇1接收到的数据进行处理，从而降低数据处理的复杂度。
该第二芯片对该第一数据进行基带处理。示例性地，该第二芯片可以基于该第一数据获得第一相关矩阵信息和第一滤波数据。该第一相关矩阵信息和该第一滤波数据的具体获得的实现方式可以参见前述图5a相关实施例的描述，这里不再赘述。
该第二芯片将该第一相关矩阵信息和该第一滤波数据发送给该第三芯片。
该第三芯片获取第三数据，该第三数据可以为天线子簇3接收到的终端数据，该第二数据也可以为天线子簇3接收到的终端的数据经过中射频处理后的数据。应理解，该第三数据 和该第二数据可以来源于同一个终端。该中射频处理可以包括将该接收到的终端数据进行频谱搬移等处理。该天线子簇3可以为该小区天线面板上的一根或多根天线组成的天线子簇，也可以为该小区内不同的天线面板上的一根或多根天线组成的天线子簇。这样可以对天线子簇3接收到的数据进行处理，从而降低数据处理的复杂度。
该第三芯片对该第三数据进行基带处理。示例性地，该第三芯片获取该第二芯片发送的第一相关矩阵信息和该第一滤波数据。该第三芯片可以基于该一相关矩阵信息和该第三数据得到第三相关矩阵信息，该第三芯片可以基于该第一滤波数据对该第三数据进行滤波得到第三滤波数据。该第三芯片将该第三相关矩阵信息和该第三滤波数据发送给第一芯片。该第二相关矩阵信息和该第二滤波数据的的具体获得的实现方式可以参见前述图5a的相关实施例的描述，这里不再赘述。
该第一芯片获取第二数据，该第二数据可以为天线子簇2接收到的终端数据，该第二数据也可以为天线子簇2接收到的终端的数据经过中射频处理后的数据。应理解，该第一数据、该第二数据和该第三数据可以来源于同一个终端。该中射频处理可以包括将该接收到的终端数据进行频谱搬移等处理。该天线子簇2可以为该小区天线面板上的一根或多根天线组成的天线子簇，也可以为该小区内不同的天线面板上的一根或多根天线组成的天线子簇。这样可以对天线子簇2接收到的数据进行处理，从而降低数据处理的复杂度。
该第一芯片对该第二数据进行基带处理。示例性地，该第一芯片获取该第三芯片发送的第三相关矩阵信息和该第三滤波数据。该第一芯片可以基于该三相关矩阵信息和该第二数据得到第二相关矩阵信息，该第一芯片可以基于该第三滤波数据对该第二数据进行滤波得到第二滤波数据。该第一芯片基于该第二相关矩阵信息和该第二滤波数据得到输出数据，并输出该输出数据。该第二相关矩阵信息、该第二滤波数据和该输出数据的具体获得的实现方式可以参见前述图5a的相关实施例的描述，这里不再赘述。
本申请实施例提供的数据处理方法，通过将接收的终端数据通过分为第一数据、第二数据和第三数据，并进行链式结构单向操作，从而可以降低数据处理的复杂度，降低传输数据总线带宽，降低数据处理成本。
图6为本申请实施例提供的一种通信装置的示意性框图。这些通信装置可以用于实现上述方法实施例中系统的功能，因此也能实现上述方法实施例所具备的有益效果。在本申请的实施例中，该通信装置可以是接入网设备，也可以是应用于接入网设备中的模块(如芯片)，还可以是能够实现接入网设备全部或部分功能的软件。
如图6所示，通信装置600包括处理模块610和接口模块620。通信装置600用于实现上述图2、图3、图4或图5中所对应的实施例中第一系统的功能。
当通信装置600用于实现图2、图3a、图3b、图4、图5a或图5b中所示的方法实施例中第一系统的功能时，示例性地：
接口模块620用于获取第二数据。
接口模块620还用于输出该输出数据。
可选地，接口模块620还用于接收第一相关矩阵信息。该第一相关矩阵信息可以是该第一相关矩阵数据，还可以是该第一相关矩阵的压缩数据等。
可选地，接口模块620还用于接收第一滤波数据。
可选地，接口模块620还用于接收第三相关矩阵信息。第三相关矩阵信息可以是该第三相关矩阵数据，还可以是该第三相关矩阵的压缩数据等。
可选地，接口模块620还用于接收第三滤波数据。
处理模块610可以用于基于该第二数据得到第二相关矩阵信息。
处理模块610还可以用于基于该第二数据得到第二滤波数据。
处理模块610还可以用于基于该第二数据得到第二数据的干扰噪声估计结果。
处理模块610还可以用于基于该第二相关矩阵信息和该第二滤波数据得到输出数据。
以上仅为当通信装置600用于实现图2、图3a、图3b、图4、图5a或图5b中所示的方法实施例中第一系统的功能时的部分举例，通信装置600中处理模块610和接口模块620的功能，可参考图2、图3a、图3b、图4、图5a或图5b中所示的方法实施例中第一系统的操作。
通信装置600还可以用于实现图2、图3a、图3b、图4、图5a或图5b中所示的方法实施例中第二系统的功能，当通信装置600用于实现图2、图3a、图3b、图4、图5a或图5b中所示的方法实施例中第二系统功能时，示例性地：
接口模块620用于获取第一数据。
可选地，接口模块620还用于发送第一相关矩阵信息。该第一相关矩阵信息可以是该第一相关矩阵数据，还可以是该第一相关矩阵的压缩数据等。可选地，接口模块620还用于发送第一滤波数据。
处理模块610可以用于基于该第一数据得到第一相关矩阵信息。
处理模块610还可以用于基于该第一数据得到第一滤波数据。
处理模块610还可以用于基于该第一数据得到第一数据的干扰噪声估计结果。
以上仅为当通信装置600用于实现图2、图3a、图3b、图4、图5a或图5b中所示的方法实施例中第二系统的功能时的部分举例，通信装置600中处理模块610和接口模块620的功能，可参考图2、图3a、图3b、图4、图5a或图5b中所示的方法实施例中第二系统的操作。
当通信装置600用于实现图4、图5a或图5b中所示的方法实施例中第三系统的功能时，示例性地：
接口模块620用于获取第三数据。
可选地，接口模块620还用于接收第一相关矩阵信息。该第一相关矩阵信息可以是该第一相关矩阵数据，还可以是该第一相关矩阵的压缩数据等。可选地，接口模块620还用于接收第一滤波数据。
可选地，接口模块620还用于发送第三相关矩阵信息。第三相关矩阵信息可以是该第三相关矩阵数据，还可以是该第三相关矩阵的压缩数据等。
可选地，接口模块620还用于发送第三滤波数据。
处理模块610可以用于基于该第三数据得到第三相关矩阵信息。
处理模块610还可以用于基于该第三数据得到第三滤波数据。
处理模块610还可以用于基于该第三数据得到第三数据的干扰噪声估计结果。
以上仅为当通信装置600用于实现图4、图5a或图5b中所示的方法实施例中第三系统的功能时的部分举例，通信装置600中处理模块610和接口模块620的功能，可参考图4、图5a或图5b中所示的方法实施例中第三系统的操作。
图7为本申请实施例提供的一种通信装置的再一示意性框图。如图7所示。通信装置700包括处理器710和接口电路730。处理器710和接口电路730之间相互耦合。可以理解的 是，接口电路730可以为收发器或输入输出接口。
可选的，通信装置700还可以包括存储器720，用于存储处理器720执行的指令或存储处理器710运行指令所需要的输入数据或存储处理器710运行指令后产生的数据。
当通信装置700用于实现图2、图3a、图3b、图4、图5a或图5b所示的第一系统、第二系统或图4、图5a或图5b的第三系统的功能时，处理器710用于实现上述处理模块610的功能，接口电路730用于实现上述接口模块620的功能。
可选地，通信装置700还包括总线740，该处理器710、该接口电路730和该存储器720可以通过总线740进行通信。
本申请实施例还提供了一种系统芯片，该系统芯片包括输入输出接口、至少一个处理器、至少一个存储器和总线，该至少一个存储器用于存储指令，该至少一个处理器用于调用该至少一个存储器的指令，以进行上述各个方面的方法的操作。
在本申请实施例中，应注意，本申请实施例上述的方法实施例可以应用于处理器中，或者由处理器实现。处理器可能是一种集成电路芯片，具有信号的处理能力。在实现过程中，上述方法实施例的各步骤可以通过处理器中的硬件的集成逻辑电路或者软件形式的指令完成。上述的处理器可以是通用处理器、数字信号处理器(digital signal processor，DSP)、专用集成电路(application specific integrated circuit，ASIC)、现成可编程门阵列(field programmable gate array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。可以实现或者执行本申请实施例中的公开的各方法、步骤及逻辑框图。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。结合本申请实施例所公开的方法的步骤可以直接体现为硬件译码处理器执行完成，或者用译码处理器中的硬件及软件模块组合执行完成。软件模块可以位于随机存储器，闪存、只读存储器，可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于存储器，处理器读取存储器中的信息，结合其硬件完成上述方法的步骤。
可以理解，本申请实施例中的存储器可以是易失性存储器或非易失性存储器，或可包括易失性和非易失性存储器两者。其中，非易失性存储器可以是只读存储器(read-only memory，ROM)、可编程只读存储器(programmable ROM，PROM)、可擦除可编程只读存储器(erasable PROM，EPROM)、电可擦除可编程只读存储器(electrically EPROM，EEPROM)或闪存。易失性存储器可以是随机存取存储器(random access memory，RAM)，其用作外部高速缓存。通过示例性但不是限制性说明，许多形式的RAM可用，例如静态随机存取存储器(static RAM，SRAM)、动态随机存取存储器(dynamic RAM，DRAM)、同步动态随机存取存储器(synchronous DRAM，SDRAM)、双倍数据速率同步动态随机存取存储器(double data rate SDRAM，DDR SDRAM)、增强型同步动态随机存取存储器(enhanced SDRAM，ESDRAM)、同步连接动态随机存取存储器(synchlink DRAM，SLDRAM)和直接内存总线随机存取存储器(direct rambus RAM，DR RAM)。应注意，本文描述的系统和方法的存储器旨在包括但不限于这些和任意其它适合类型的存储器。
上述实施例中，可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件实现时，可以全部或部分地以计算机程序产品的形式实现。所述计算机程序产品可以包括一个或多个计算机指令。在计算机上加载和执行所述计算机程序指令时，全部或部分地产生按照本申请实施例所述的流程或功能。所述计算机可以是通用计算机、专用计算机、计算机网络或者其他可编程装置。所述计算机指令可以存储在计算机可读存储介质中，或者从 一个计算机可读存储介质向另一个计算机可读存储介质传输，例如，所述计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、数字用户(DSL))或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。所述计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可用介质集成的服务器、数据中心等数据存储设备。所述可用介质可以是磁性介质(例如，软盘、硬盘、磁盘)、光介质(例如，DVD)、或者半导体介质(例如固态硬盘solid state disk(SSD))等。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本申请的范围。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统、装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本申请所提供的几个实施例中，应该理解到，所揭露的系统、装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。
所述功能如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器、随机存取存储器、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应所述以权利要求的保护范围为准。

Claims (30)

  1. 一种系统，其特征在于，包括：第一接口模块和第一处理模块；
    所述第一接口模块用于：
    获取第一相关矩阵信息和第一滤波数据，所述第一相关矩阵信息为第一数据经历的第一信道对应的相关矩阵信息，所述第一滤波数据为所述第一数据被滤波后获得的数据；
    获取第二数据，其中所述第二数据和所述第一数据包含来自相同设备的数据；
    所述第一处理模块用于：
    基于所述第二数据和所述第一相关矩阵信息得到第二信道对应的第二相关矩阵信息，所述第二信道为所述第二数据经历的信道；
    基于所述第一滤波数据对所述第二数据进行滤波得到第二滤波数据；
    基于所述第二相关矩阵信息和所述第二滤波数据得到输出数据；
    所述第一接口模块还用于输出所述输出数据。
  2. 根据权利要求1所述的系统，其特征在于，所述第一处理模块用于基于所述第二数据和所述第一相关矩阵信息得到第二信道对应的第二相关矩阵信息，包括：
    所述第一处理模块用于：
    基于所述第二数据进行信道估计得到所述第二数据的信道估计结果；
    基于所述第二数据的信道估计结果和所述第一相关矩阵信息获得所述第二相关矩阵信息；
    所述第一处理模块用于基于所述第一滤波数据对所述第二数据进行滤波得到第二滤波数据，包括：
    所述第一处理模块用于基于所述第二数据的信道估计结果和所述第一滤波数据对所述第二数据进行滤波得到所述第二滤波数据。
  3. 根据权利要求2所述的系统，其特征在于，
    所述第一处理模块还用于根据所述第二数据和所述第二数据的信道估计结果获得所述第二数据的干扰噪声估计结果；
    所述第一处理模块用于基于所述第二数据和所述第一相关矩阵信息得到第二信道对应的第二相关矩阵信息，包括：
    所述第一处理模块用于基于所述第二数据，所述第一相关矩阵信息和所述第二数据的干扰噪声估计结果得到所述第二信道对应的第二相关矩阵信息。
  4. 根据权利要求3所述的系统，其特征在于，所述第一处理模块用于基于所述第一滤波数据对所述第二数据进行滤波得到第二滤波数据，包括：
    所述第一处理模块用于基于所述第一滤波数据和所述第二数据的干扰噪声估计结果对所述第二数据进行滤波得到第二滤波数据。
  5. 根据权利要求2-4中任一项所述的系统，其特征在于，所述第一处理模块用于基于所述第二数据进行信道估计得到所述第二数据的信道估计结果，包括：所述第一处理模块用于根据以下任一项方法进行所述信道估计：
    最小二乘估计；
    最小均方误差估计；
    压缩感知估计；或
    机器学习。
  6. 根据权利要求1-5中任一项所述的系统，其特征在于，所述第一处理模块用于基于所述第一滤波数据对所述第二数据进行滤波得到第二滤波数据，包括：所述第一处理模块用于 根据以下任一项方法进行所述滤波：
    匹配滤波；
    迫零算法；
    最小二乘法；
    最小均方误差法；
    最大似然法；或
    机器学习。
  7. 根据权利要求1-6中任一项所述的系统，其特征在于，所述第一数据来自至少一个第一天线，所述第二数据来自至少一个第二天线。
  8. 一种系统，其特征在于，包括：第二接口模块和第二处理模块；
    所述第二接口模块用于：获取第一数据；
    所述第二处理模块用于：
    基于所述第一数据得到第一信道对应的第一相关矩阵信息，所述第一信道为所述第一数据经历的信道；
    对所述第一数据进行滤波得到第一滤波数据；
    所述第二接口模块还用于输出所述第一相关矩阵信息和所述第一滤波数据。
  9. 根据权利要求8所述的系统，其特征在于，所述第二处理模块用于基于所述第一数据得到第一信道对应的第一相关矩阵信息，包括：
    所述第二处理模块用于：
    基于所述第一数据进行信道估计得到所述第一数据的信道估计结果；
    基于所述第一数据的信道估计结果得到所述第一相关矩阵信息；
    所述第二处理模块用于对所述第一数据进行滤波得到第一滤波数据，包括：
    所述第二处理模块用于基于所述第一数据的信道估计结果对所述第一数据进行滤波得到第一滤波数据。
  10. 根据权利要求9所述的系统，其特征在于，所述第二处理模块还用于基于所述第一数据和所述第一数据的信道估计结果得到所述第一数据的干扰噪声估计结果；
    所述第二处理模块用于基于所述第一数据得到第一信道对应的第一相关矩阵信息，包括：
    所述第二处理模块用于基于所述第一数据和所述第一数据的干扰噪声估计结果得到第一信道对应的第一相关矩阵信息。
  11. 根据权利要求10所述的系统，其特征在于，所述第二处理模块用于对所述第一数据进行滤波得到第一滤波数据，包括：
    所述第二处理模块用于基于所述第一数据和所述第一数据的干扰噪声估计结果对所述第一数据进行滤波得到所述第一滤波数据。
  12. 根据权利要求9-11中任一项所述的系统，其特征在于，所述第二处理模块用于基于所述第一数据进行信道估计得到所述第一数据的信道估计结果，包括：所述第二处理模块用于根据以下任一项方法进行所述信道估计：
    最小二乘估计；
    最小均方误差估计；
    压缩感知估计；或
    机器学习。
  13. 根据权利要求8-12中任一项所述的系统，其特征在于，所述第二处理模块用于对所 述第一数据进行滤波得到第一滤波数据，包括：所述第二处理模块用于根据以下任一项方法进行所述滤波：
    匹配滤波；
    迫零算法；
    最小二乘法；
    最小均方误差法；
    最大似然法；或
    机器学习。
  14. 根据权利要求8-13中任一项所述的系统，其特征在于，所述第一数据来自至少一个第一天线。
  15. 一种数据处理方法，其特征在于，包括：
    获取第一相关矩阵信息和第一滤波数据，所述第一相关矩阵信息为第一数据经历的第一信道对应的相关矩阵信息，所述第一滤波数据为所述第一数据被滤波后获得的数据；
    获取第二数据，其中所述第二数据和所述第一数据包含来自相同设备的数据；
    基于所述第二数据和所述第一相关矩阵信息得到第二信道对应的第二相关矩阵信息，所述第二信道为所述第二数据经历的信道；
    基于所述第一滤波数据对所述第二数据进行滤波得到第二滤波数据；
    基于所述第二相关矩阵信息和所述第二滤波数据得到输出数据；
    输出所述输出数据。
  16. 根据权利要求15所述的方法，其特征在于，所述基于所述第二数据和所述第一相关矩阵信息得到第二信道对应的第二相关矩阵信息，包括：
    基于所述第二数据进行信道估计得到所述第二数据的信道估计结果，基于所述第二数据的信道估计结果和所述第一相关矩阵信息获得所述第二相关矩阵信息；
    基于所述第一滤波数据对所述第二数据进行滤波得到第二滤波数据，包括：
    基于所述第二数据的信道估计结果对所述第二数据进行滤波得到第二滤波数据。
  17. 根据权利要求16所述的方法，其特征在于，所述方法还包括：
    根据所述第二数据和所述第二数据信道估计结果获得所述第二数据的干扰噪声估计结果；
    所述基于所述第二数据和所述第一相关矩阵信息得到第二信道对应的第二相关矩阵信息，包括：
    基于所述第二数据，所述第一相关矩阵信息和所述第二数据的干扰噪声估计结果得到第二信道对应的第二相关矩阵信息。
  18. 根据权利要求17所述的系统，其特征在于，所述基于所述第一滤波数据对所述第二数据进行滤波得到第二滤波数据，包括：
    基于所述第一滤波数据和所述第二数据的干扰噪声估计结果对所述第二数据进行滤波得到第二滤波数据。
  19. 根据权利要求16-18中任一项所述的方法，其特征在于，所述基于所述第二数据进行信道估计得到所述第二数据的信道估计结果，包括：根据以下任一项方法进行所述信道估计：
    最小二乘估计；
    最小均方误差估计；
    压缩感知估计；或
    机器学习。
  20. 根据权利要求15-19中任一所述的系统，其特征在于，所述基于所述第一滤波数据对所述第二数据进行滤波得到第二滤波数据，包括：根据以下任一项方法进行所述滤波：
    匹配滤波；
    迫零算法；
    最小二乘法；
    最小均方误差法；
    最大似然法；或
    机器学习。
  21. 根据权利要求15-20中任一项所述的方法，其特征在于，所述第一数据来自至少一个第一天线，所述第二数据来自至少一个第二天线。
  22. 一种数据处理方法，其特征在于，包括：
    获取第一数据；
    基于所述第一数据得到第一信道对应的第一相关矩阵信息，所述第一信道为所述第一数据经历的信道；
    对所述第一数据进行滤波得到第一滤波数据；
    输出所述第一相关矩阵信息和所述第一滤波数据。
  23. 根据权利要求22所述的方法，其特征在于，所述基于所述第一数据得到第一信道对应的第一相关矩阵信息，包括：
    基于所述第一数据进行信道估计得到所述第一数据的信道估计结果，基于所述第一数据的信道估计结果得到所述第一相关矩阵信息；
    所述对所述第一数据进行滤波得到第一滤波数据，包括：
    基于所述第一数据的信道估计结果对所述第一数据进行滤波得到第一滤波数据。
  24. 根据权利要求23所述的方法，其特征在于，所述方法还包括：
    基于所述第一数据和所述第一数据的信道估计结果得到所述第一数据的干扰噪声估计结果；
    所述基于所述第一数据得到第一信道对应的第一相关矩阵信息，包括：
    基于所述第一数据和所述第一数据的干扰噪声估计结果得到第一信道对应的第一相关矩阵信息。
  25. 根据权利要求24所述的方法，其特征在于，所述对所述第一数据进行滤波得到第一滤波数据，包括：
    基于所述第一数据和所述第一数据的干扰噪声估计结果对所述第一数据进行滤波得到所述第一滤波数据。
  26. 一种通信装置，其特征在于，包括处理器和接口电路，所述接口电路用于接收来自所述装置之外的其它装置的信号并传输至所述处理器或将来自所述处理器的信号发送给所述装置之外的其它装置，所述处理器通过逻辑电路或执行代码指令用于实现如权利要求15-21中任一项所述的方法。
  27. 一种通信装置，其特征在于，包括处理器和接口电路，所述接口电路用于接收来自所述装置之外的其它装置的信号并传输至所述处理器或将来自所述处理器的信号发送给所述装置之外的其它装置，所述处理器通过逻辑电路或执行代码指令用于实现如权利要求22-25中任一项所述的方法。
  28. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质中存储有计算机程序或指令，当所述计算机程序或指令被通信装置执行时，实现权利要求15-21任一项所述的方法，或实现权利要求22-25任一项所述的方法。
  29. 一种计算机程序产品，其特征在于，当所述计算机程序产品被通信装置执行时，实现权利要求15-21任一项所述的方法，或实现权利要求22-25任一项所述的方法。
  30. 一种通信系统，包括如权利要求1-7任一种所述系统和8-14任一种所述系统，或者包括如权利要求26和27所述的通信装置。
PCT/CN2022/098476 2021-06-15 2022-06-13 一种数据处理方法及装置 WO2022262687A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202110661362.5 2021-06-15
CN202110661362.5A CN115484132A (zh) 2021-06-15 2021-06-15 一种数据处理方法及装置

Publications (1)

Publication Number Publication Date
WO2022262687A1 true WO2022262687A1 (zh) 2022-12-22

Family

ID=84419784

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2022/098476 WO2022262687A1 (zh) 2021-06-15 2022-06-13 一种数据处理方法及装置

Country Status (2)

Country Link
CN (1) CN115484132A (zh)
WO (1) WO2022262687A1 (zh)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1604512A (zh) * 2003-09-29 2005-04-06 富士通株式会社 联合检测方法及装置
CN1909525A (zh) * 2005-08-05 2007-02-07 松下电器产业株式会社 多输入多输出系统的信道估计和检测方法
CN101138150A (zh) * 2005-02-09 2008-03-05 哈里公司 基于先前、当前和/或未来的自相关矩阵估计执行块均衡的无线通信装置及相关方法
CN111641572A (zh) * 2020-05-22 2020-09-08 Oppo广东移动通信有限公司 一种噪声功率评估方法及装置、存储介质
WO2021001408A1 (fr) * 2019-07-03 2021-01-07 Airbus Defence And Space Sas Procédé et dispositif de calcul de fonctions de visibilité pour un radiomètre à synthèse d'ouverture interférométrique

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1604512A (zh) * 2003-09-29 2005-04-06 富士通株式会社 联合检测方法及装置
CN101138150A (zh) * 2005-02-09 2008-03-05 哈里公司 基于先前、当前和/或未来的自相关矩阵估计执行块均衡的无线通信装置及相关方法
CN1909525A (zh) * 2005-08-05 2007-02-07 松下电器产业株式会社 多输入多输出系统的信道估计和检测方法
WO2021001408A1 (fr) * 2019-07-03 2021-01-07 Airbus Defence And Space Sas Procédé et dispositif de calcul de fonctions de visibilité pour un radiomètre à synthèse d'ouverture interférométrique
CN111641572A (zh) * 2020-05-22 2020-09-08 Oppo广东移动通信有限公司 一种噪声功率评估方法及装置、存储介质

Also Published As

Publication number Publication date
CN115484132A (zh) 2022-12-16

Similar Documents

Publication Publication Date Title
US20220247469A1 (en) Method and device for transmitting channel state information
CN110431851B (zh) 包含用于基带单元及远程无线电头的配置模式的实例的无线装置及系统
WO2023036268A1 (zh) 一种通信方法及装置
WO2022267633A1 (zh) 信息传输的方法和装置
WO2022262687A1 (zh) 一种数据处理方法及装置
WO2023185776A1 (zh) 数据处理方法、装置及系统
WO2023125985A1 (zh) 模型的数据处理方法及装置
WO2023123062A1 (zh) 虚拟信道样本的质量评估方法和设备
WO2024036631A1 (zh) 一种信息反馈方法、装置、设备及存储介质
WO2023231934A1 (zh) 一种通信方法及装置
CN113839736A (zh) 编码方法及装置
WO2023185890A1 (zh) 一种数据处理方法及相关装置
CN113452490A (zh) 载波配置方法、装置及系统
WO2024113249A1 (zh) 一种数据处理方法及装置
WO2023274101A1 (zh) 一种获得预编码矩阵的方法及装置
WO2023216920A1 (zh) 一种通信方法及装置
WO2024027424A1 (zh) 一种数据质量度量方法及装置
WO2023030236A1 (zh) 一种数据传输方法、数据接收方法和通信装置
WO2024216636A1 (zh) 一种通信方法及通信装置
WO2024208316A1 (zh) 一种压缩信息的反馈方法及装置
US20230259742A1 (en) Communication method, apparatus, and system
WO2024208033A1 (zh) 一种通信方法及装置
WO2023231881A1 (zh) 一种模型应用方法及装置
WO2022206613A1 (zh) 一种相位校正方法和通信装置
WO2023179458A1 (zh) 通信方法和通信装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 22824167

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 22824167

Country of ref document: EP

Kind code of ref document: A1