JP7412379B2 - Feature data generation device, wireless usage prediction system, feature data generation method, and computer program - Google Patents

Description

The present invention relates to a feature data generation device, a wireless usage prediction system, a feature data generation method, and a computer program.

In recent years, consideration has been given to allowing a plurality of different wireless communication systems to share the same frequency band. For example, consideration is being given to secondary use of a frequency band (shared frequency band) allocated to an existing wireless communication system (primary carrier system) by another wireless communication system (secondary carrier system). ing. In this frequency sharing between different systems, there are restrictions on the times and places at which the secondary carrier system can use the shared frequency band so as not to interfere with the operation of the primary carrier system. For this reason, it is desirable to be able to predict the future usage status of the shared frequency band of the primary carrier system from the perspective of making investment decisions and usage planning for the secondary carrier system.

Patent Document 1 describes a technique for predicting future time-series data of busy states and idle states of a wireless channel using a stochastic neural network from time-series data of busy states and idle states of a wireless channel. ing.

Japanese Patent Application Publication No. 2019-101792

In order to improve the prediction accuracy of future channel states (busy state and idle state) with respect to the technology described in Patent Document 1 mentioned above, in addition to time-series data of past channel states, we also use time-series data of channel states. It is conceivable to predict future channel conditions by adding feature quantities that become fluctuation factors. However, if feature quantities unrelated to channel state fluctuations are used for prediction, there is a problem in that prediction accuracy deteriorates on the contrary. For this reason, it is desirable to accurately generate feature amount time-series data indicating temporal changes in feature amounts used for prediction processing of future usage conditions of wireless communication (for example, channel conditions and received power).

The present invention has been made in consideration of these circumstances, and its purpose is to provide time-series feature data indicating temporal changes in feature values used in prediction processing of future usage status of wireless communications. The aim is to improve accuracy.

(1) One aspect of the present invention is a feature data generation device that generates feature time series data used for prediction processing of future usage status of wireless communication, the device comprising: a propagation path calculation unit that calculates a radio wave propagation path from a transmission point to a reception point; and a feature amount time series that indicates past temporal changes in feature amounts that affect the radio wave propagation for each area including the radio wave propagation path. A feature data generation device includes a feature time series data generation unit that generates data.
(2) One aspect of the present invention is the feature data generation according to (1) above, wherein the feature time series data is data indicating past temporal changes in the number of objects that influence the propagation of the radio waves. It is a device.
(3) One aspect of the present invention is that the feature time-series data generation unit weights the feature time-series data for each area including the radio wave propagation path according to the past usage state of the radio waves. The feature amount data generation device according to either (1) or (2) above.

(4) One aspect of the present invention provides a feature data generation device according to any one of (1) to (3) above, and a wireless usage prediction device that generates wireless usage prediction data indicating a prediction of a future usage state of wireless communication. a data generation device, the wireless usage prediction data generation device includes usage status time series data indicating temporal changes in past usage status of radio waves used for wireless communication, and the feature amount data generation device. The wireless usage prediction system generates the wireless usage prediction data based on the generated feature amount time series data.
(5) In one aspect of the present invention, the usage state time-series data is data indicating past temporal changes in received power of the radio wave at the reception point of the radio wave, and the wireless usage prediction data is The radio usage prediction system according to (4) above is a received power predicted value time series data indicating a prediction of a temporal change in the received power of the radio wave at a reception point.

(6) One aspect of the present invention is a feature data generation method executed by a feature data generation device that generates feature time series data used for prediction processing of future usage status of wireless communication, a propagation path calculation step in which the feature data generation device calculates a radio wave propagation path from the transmission point to the reception point of the radio waves used in the wireless communication; The feature data generation method includes a step of generating feature time series data that indicates past temporal changes in the feature values that affect the propagation of radio waves.

(7) One aspect of the present invention is that a computer of a feature data generation device that generates feature time series data used for prediction processing of future usage status of wireless communication is configured to transmit radio waves used for the wireless communication. a propagation path calculation step for calculating a radio wave propagation path from a transmission point to a reception point; and a feature amount time series indicating past temporal changes in feature amounts that affect the radio wave propagation for each area including the radio wave propagation path. This is a computer program for executing a step of generating feature amount time series data to generate data.

According to the present invention, it is possible to improve the accuracy of feature amount time-series data indicating temporal changes in feature amounts used for prediction processing of future usage status of wireless communication.

FIG. 1 is a block diagram illustrating a configuration example of a feature data generation device and a wireless usage prediction system according to an embodiment. FIG. 2 is a schematic explanatory diagram of a feature data generation method according to an embodiment. FIG. 2 is a detailed explanatory diagram of a feature data generation method according to an embodiment.

Embodiments of the present invention will be described below with reference to the drawings.
FIG. 1 is a block diagram illustrating a configuration example of a feature data generation device and a wireless usage prediction system according to an embodiment. In FIG. 1, a wireless usage prediction system 1 includes a feature data generation device 10 and a wireless usage prediction data generation device 30.

The wireless usage prediction system 1 is a system that predicts the future usage status of wireless communication. The usage status of wireless communication includes, for example, the channel status of radio waves used for wireless communication (busy state and idle state of the radio channel), the received power of radio waves used for wireless communication, and the like.

The feature data generation device 10 generates feature time series data 130, based on input data 110, 120, to be used in a process of predicting the future usage state of wireless communication. The feature amount time-series data 130 is time-series data that indicates past temporal changes in feature amounts that are a factor of variation in the state of use of wireless communication.

The wireless usage prediction data generation device 30 generates usage status time series data 140 indicating temporal changes in the past usage status of radio waves used for wireless communication, and feature amount time series data 130 generated by the feature amount data generation device 10. Based on this, wireless usage prediction data 150 indicating a prediction of the future usage status of wireless communication is generated. The wireless usage prediction data generation device 30 uses, for example, machine learning to predict the future usage state of wireless communication.

For example, the wireless usage prediction data generation device 30 generates channel state time-series data (usage state time-series data ) 140 and the feature amount time series data 130 generated by the feature amount data generation device 10, the channel state prediction time indicates a prediction of a temporal change in the future channel state (busy state and idle state of the wireless channel). Sequence data (wireless usage prediction data) 150 is generated.

For example, the wireless usage prediction data generation device 30 generates received power time-series data (usage state time-series data) 140 that indicates temporal changes in past received power of radio waves used for wireless communication at a receiving point of the radio waves, Based on the feature amount time series data 130 generated by the feature amount data generation device 10, received power predicted value time series data (wireless Usage prediction data) 150 is generated.

Each function of the feature data generation device 10 and the wireless usage prediction data generation device 30 is performed by the feature data generation device 10 and the wireless usage prediction data generation device 30 using a computer such as a CPU (Central Processing Unit) and memory. It is realized by having hardware and having a CPU execute a computer program stored in memory. Note that the feature amount data generation device 10 and the wireless usage prediction data generation device 30 may be configured using general-purpose computer devices, or may be configured as dedicated hardware devices. For example, the feature data generation device 10 and the wireless usage prediction data generation device 30 may be configured using a server computer connected to a communication network such as the Internet. Further, each function of the feature amount data generation device 10 and the wireless usage prediction data generation device 30 may be realized by cloud computing. Further, the feature amount data generation device 10 and the wireless usage prediction data generation device 30 may be realized by a single computer, or the functions of the feature amount data generation device 10 and the wireless usage prediction data generation device 30 may be realized by a single computer. It may also be realized by being distributed over multiple computers.

(Feature amount data generation device)
The feature data generation device 10 includes a propagation path calculation section 11 and a feature time series data generation section 12.

The propagation path calculation unit 11 calculates a radio wave propagation path from a transmission point to a reception point of radio waves used for wireless communication. In this embodiment, a ray tracing method is used as an example of a method for determining a radio wave propagation path. Based on the input data 110, the propagation path calculation unit 11 calculates a radio wave propagation path from a transmission point to a reception point of radio waves used for wireless communication using a ray tracing method. Input data 110 is data indicating information necessary for the ray tracing method. The input data 110 is data including transmitting station specification information and three-dimensional building data. The transmitting station specification information is information indicating the specifications of a transmitting station that transmits radio waves used for wireless communication. The transmitting station specification information is information including, for example, the position (latitude, longitude) of the transmitting station (transmission point), antenna height, directional characteristics of transmitted radio waves, frequency of transmitted radio waves, and the like. The three-dimensional building data is data indicating three-dimensional information regarding a building, such as the height, size, and shape of the building.

The feature time series data generation unit 12 generates feature quantities that affect the propagation of radio waves used for wireless communication based on the input data 120 for each area including the radio wave propagation path determined by the propagation path calculation unit 11. Feature amount time series data 130 indicating past temporal changes is generated. The input data 120 is time-series data showing past temporal changes in feature quantities that affect the propagation of radio waves used for wireless communication for each area for all the areas serviced by the wireless usage prediction system 1. be. The feature amount that affects the propagation of radio waves used in wireless communication is, for example, the number of objects that affect the propagation of the radio waves. Objects that affect the propagation of the radio waves are, for example, pedestrians and vehicles. Note that objects such as pedestrians and vehicles that affect the propagation of radio waves are sometimes referred to as blockers.

FIG. 2 is a schematic explanatory diagram of the feature data generation method according to the present embodiment. Although FIG. 2 shows the case where received power is predicted as an example of the prediction of the usage state of wireless communication, the same applies when predicting the channel state (busy state and idle state of the wireless channel). .

In FIG. 2, radio waves transmitted by a transmitting station 31 (transmission point) reach a radio wave sensor 32 (reception point) through various radio wave propagation paths 33. The radio wave sensor 32 receives radio waves transmitted by the transmitting station 31 (transmission point) and detects the reception strength of the received radio waves. As the reception strength, for example, received signal strength indicator (RSSI) is detected. The radio waves transmitted by the transmitting station 31 may directly reach the radio wave sensor 32, or may reach the radio wave sensor 32 after being reflected by the building 41. Pedestrians 43 and vehicles 42 exist in the area including the radio wave propagation path 33.

Pedestrians 43 and vehicles 42 existing in the area including the radio wave propagation path 33 are objects that affect the propagation of radio waves transmitted by the transmitting station 31. Therefore, the number of pedestrians 43 and vehicles 42 existing in the area including the radio wave propagation path 33 is a feature quantity that becomes a factor in the fluctuation of the usage state of the radio waves transmitted by the transmitting station 31. For example, the number of pedestrians 43 and vehicles 42 existing in the area including the radio wave propagation path 33 becomes a factor that changes the channel state of the radio waves transmitted by the transmitting station 31 (busy state and idle state of the radio channel). For example, the number of pedestrians 43 and vehicles 42 existing in the area including the radio wave propagation path 33 becomes a factor in the variation in the reception power of the radio waves transmitted by the transmitting station 31 at the radio wave sensor 32.

Therefore, of all the areas serviced by the wireless usage prediction system 1, the area including the radio wave propagation path 33 is the area from which the numbers of pedestrians 43 and vehicles 42 are extracted as feature quantities. In addition, not only the area around the radio wave sensor 32 (receiving point) but also the entire area from the transmitting station 31 (transmission point) to the radio wave sensor 32 (receiving point) should be used as the area from which feature quantities are extracted. is preferred. Note that instead of uniformly giving all the areas the same level of importance, each area may be weighted according to the degree of influence on fluctuations in the usage status of radio waves transmitted by the transmitting station 31. For example, each area including the radio wave propagation path 33 may be weighted according to the past usage status of radio waves transmitted by the transmitting station 31. For example, for each area including the radio wave propagation path 33, depending on the channel state (busy state and idle state of the wireless channel) of the radio waves transmitted by the transmitting station 31 in the past for each area, the more busy the area is, the more weight is given. Make it bigger. For example, for each area including the radio wave propagation path 33, depending on the past received power for each area of radio waves transmitted by the transmitting station 31, the weight is increased as the received power increases. For example, for each area including the radio wave propagation path 33, depending on the past received power for each area of radio waves transmitted by the transmitting station 31, the weight is increased for an area where the fluctuation range of received power is larger. Further, for each area including the radio wave propagation path 33, the weight may be increased as the area is closer to the radio wave sensor 32 (receiving point).

On the other hand, pedestrians 43 and vehicles 42 existing in areas that do not include the radio wave propagation path 33 are objects that do not affect the propagation of radio waves transmitted by the transmitting station 31, or even if they affect the propagation of the radio waves, An object whose degree of influence on fluctuations in the usage status of radio waves is tolerably small in terms of prediction accuracy of the usage status of the radio waves. Therefore, of all the areas serviced by the wireless usage prediction system 1, areas that do not include the radio wave propagation path 33 are not targeted for extracting the number of pedestrians 43 and vehicles 42 as feature quantities. As a result, the number of pedestrians 43 and vehicles 42, which are almost unrelated to fluctuations in the usage status (for example, channel status and received power) of radio waves transmitted by the transmitting station 31, are not extracted as feature quantities. Therefore, it is possible to efficiently extract the number of pedestrians 43 and vehicles 42, which are a factor of fluctuations in the usage status (for example, channel status and received power) of radio waves transmitted by the transmitting station 31, as a feature quantity.

As a result, temporal changes in feature quantities (for example, the number of blockers such as pedestrians 43 and vehicles 42) used for prediction processing of the future usage status of wireless communication (for example, channel status and received power) can be calculated. The accuracy of the feature amount time series data 130 shown can be improved, and this can contribute to improving the prediction accuracy of future usage conditions of wireless communication (for example, channel conditions and received power).

FIG. 3 is a detailed explanatory diagram of the feature data generation method according to this embodiment. Although FIG. 3 shows a case where reception power is predicted as an example of prediction of wireless communication usage status, the same applies to prediction of channel status (busy state and idle state of a wireless channel). .

The feature data generation method according to this embodiment will be described in detail with reference to FIG. 3. Note that here, the area may be referred to as a mesh.

Input data 110 and 120 are input to the feature data generation device 10 . The input data 110 is data including transmitting station specification information and three-dimensional building data. Input data 120 is blocker information. Blocker information (input data) 120 includes walking objects, which are objects that affect the propagation of radio waves used for wireless communication, for each area (mesh) for all areas (mesh) serviced by the wireless usage prediction system 1. This is time-series data showing past temporal changes in the number of blockers (features) such as persons 43 and vehicles 42. The blocker information (input data) 120 is composed of, for example, a set of time and blocker position (latitude, longitude). The range (latitude, longitude) of each mesh is set in advance. The blocker information (input data) 120 is generated using, for example, position information obtained by a mobile terminal such as a smartphone using a GPS (Global Positioning System) or the like. Further, the blocker information (input data) 120 may be generated using connected terminal number data indicating the number of mobile terminals such as smartphones connected to the base station (number of connected terminals).

Received power time-series data (usage state time-series data) 140 is input to the wireless usage prediction data generation device 30 . The received power time-series data (usage status time-series data) 140 is time-series data that shows temporal changes in past received power of radio waves used for wireless communication at a receiving point of the radio waves. The received power time series data (usage state time series data) 140 is composed of, for example, a set of time and received power strength (Received Signal Strength Indicator: RSSI). The received power time series data (usage status time series data) 140 is generated using, for example, the RSSI detected by the radio wave sensor 32 installed at the reception point. The reception point from which received power time series data (usage status time series data) 140 is acquired is a location where future received power of wireless communication is desired to be predicted.

(Step S1) Based on the input data 110, the propagation path calculation unit 11 of the feature data generation device 10 calculates a radio wave propagation path from the transmission point Tx to the reception point Rx of radio waves used for wireless communication using the ray tracing method. demand. This radio wave propagation path calculation result 201 is sent to the feature quantity time series data generation section 12. The radio wave propagation path calculation result 201 includes information indicating which mesh the radio wave propagation path passes through among all the meshes serviced by the wireless usage prediction system 1. In other words, the mesh through which the radio wave propagation path passes is a mesh that includes the radio wave propagation path.

(Step S2) Based on the radio wave propagation path calculation result 201, the feature time series data generation unit 12 extracts a mesh that includes the radio wave propagation path from among all the meshes that the wireless usage prediction system 1 serves as a service target. do. This mesh extraction result 202 is information indicating a mesh including a radio wave propagation path (propagation path mesh) among all the meshes serviced by the wireless usage prediction system 1.

(Step S3) The feature amount time series data generation unit 12 calculates the number of blockers for each time and for each extraction result mesh (propagation path mesh) based on the blocker information (input data) 120 and the mesh extraction result 202. Tally. As a result of this aggregation, blocker number time-series data (feature amount time-series data) 130 of each propagation path mesh is generated. Blocker number time series data (feature amount time series data) 130 is a time series showing past temporal changes in feature amounts (blocker number) that affect the propagation of radio waves used for wireless communication for each propagation path mesh. It is data. The feature data generation device 10 transmits blocker number time series data (feature time series data) 130 to the wireless usage prediction data generation device 30.

Note that the feature amount time series data generation unit 12 may weight each propagation path mesh according to the past received power. For example, for each propagation path mesh, based on the measured data of received power in each propagation path mesh, a propagation path mesh with a higher received power is given a larger weight. For example, for each propagation path mesh, based on the measured data of received power in each propagation path mesh, the weight is set to be larger for a propagation path mesh with a larger fluctuation range in received power. Further, for each propagation path mesh, the weight may be increased for a propagation path mesh that is closer to the reception point. The feature amount time series data generation unit 12 reflects the weight of each propagation path mesh on the total result of the number of blockers of each propagation path mesh. For example, for each propagation path mesh, the total result of the number of blockers is multiplied by a weighting coefficient that corresponds to the weight of the propagation path mesh. The feature amount time series data generation unit 12 generates blocker number time series data (feature amount time series data) 130 for each propagation path mesh based on the aggregation result of the number of blockers of each propagation path mesh in which the weight of each propagation path mesh is reflected. Make it.

(Step S4) The wireless usage prediction data generation device 30 generates received power time series data (usage state time series data) 140 and blocker number time series data (feature amount time series data) 130 received from the feature amount data generation device 10. Based on this, received power predicted value time series data (wireless usage predicted data) 150 indicating a prediction of a temporal change in future received power of the radio wave at the receiving point of the radio wave used for wireless communication is generated.

The wireless usage prediction data generation device 30 uses, for example, machine learning to predict future temporal changes in received power. In the learning stage, received power time series data (usage state time series data) 140 and blocker number time series data (feature value time series data) 130 in the learning stage are used as learning data to A machine learning model is trained to output a predicted value of future received power as time series data from the state time series data) 140 and the blocker number time series data (feature amount time series data) 130. The machine learning model is, for example, a neural network. In the prediction stage, the received power time series data (usage state time series data) 140 and blocker number time series data (feature value time series data) 130 in the prediction stage are input as input data to the trained machine learning model, and the relevant Time series data of predicted values of future received power output from the machine learning model as an input result is obtained. The wireless usage prediction data generation device 30 generates received power predicted value time series data (wireless usage prediction data) 150 using time series data of future predicted received power values output from the machine learning model.

According to the present embodiment, it is possible to accurately extract feature quantities that are factors of fluctuations in the usage status of wireless communication (for example, channel status and received power) from the viewpoint of radio wave propagation, so that the future usage status of wireless communication can be accurately extracted. The effect of improving the accuracy of feature amount time series data indicating temporal changes in feature amounts used in prediction processing can be obtained. This makes it possible to contribute to improving the accuracy of predicting the future usage status of wireless communications.

Furthermore, according to the present embodiment, the feature amount time series data is composed of input data suitable for the prediction process of the future usage state of wireless communication, which contributes to reducing the number of dimensions of the input data to the prediction process. can do. Thereby, the amount of processing of the prediction process is reduced, and the effect of shortening the processing time can be obtained.

Furthermore, according to the present embodiment, a ray tracing method can be used as a method for determining the radio wave propagation path. The ray tracing method can be implemented relatively easily because commercially available three-dimensional building data and ray tracing simulation software are available.

Furthermore, as this can improve the overall quality of service in, for example, frequency sharing between different systems, it is possible to achieve goal 9 of the Sustainable Development Goals (SDGs) led by the United Nations, ``Developing resilient infrastructure.'' It will be possible to contribute to "promoting sustainable industrialization and expanding innovation."

Although the embodiment of the present invention has been described above in detail with reference to the drawings, the specific configuration is not limited to this embodiment, and design changes and the like may be made without departing from the gist of the present invention.

Further, a computer program for realizing the functions of each device described above may be recorded on a computer-readable recording medium, and the program recorded on the recording medium may be read into a computer system and executed. Note that the "computer system" here may include hardware such as an OS and peripheral devices.
Furthermore, "computer-readable recording media" refers to flexible disks, magneto-optical disks, ROMs, writable non-volatile memories such as flash memory, portable media such as DVDs (Digital Versatile Discs), and media built into computer systems. A storage device such as a hard disk.

Furthermore, "computer-readable recording medium" refers to volatile memory (for example, DRAM (Dynamic It also includes those that retain programs for a certain period of time, such as Random Access Memory).
Further, the program may be transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by a transmission wave in a transmission medium. Here, the "transmission medium" that transmits the program refers to a medium that has a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line.
Moreover, the above-mentioned program may be for realizing a part of the above-mentioned functions. Furthermore, it may be a so-called difference file (difference program) that can realize the above-described functions in combination with a program already recorded in the computer system.

DESCRIPTION OF SYMBOLS 1...Radio usage prediction system, 10...Feature amount data generation device, 11...Propagation path calculation part, 12... Feature amount time series data generation part, 30... Radio usage prediction data generation device

Claims (7)

A feature data generation device that generates feature time series data used for prediction processing of future usage status of wireless communication,
a propagation path calculation unit that calculates a radio wave propagation path from a transmission point to a reception point of radio waves used in the wireless communication;
a feature time-series data generation unit that generates feature time-series data indicating past temporal changes in feature quantities that affect the propagation of radio waves for each area including the radio wave propagation path;
A feature data generation device comprising: The feature amount time series data is data indicating past temporal changes in the number of objects that influence the propagation of the radio waves.
The feature data generation device according to claim 1. The feature amount time series data generation unit weights the feature amount time series data for each area including the radio wave propagation path according to the past usage state of the radio waves.
The feature data generation device according to claim 1 or 2. The feature data generation device according to any one of claims 1 to 3;
A wireless usage prediction data generation device that generates wireless usage prediction data indicating a prediction of future usage status of wireless communication,
The wireless usage prediction data generation device generates usage status time-series data indicating temporal changes in past usage status of radio waves used for wireless communication, and feature amount time-series data generated by the feature amount data generation device. generating the wireless usage prediction data based on;
Wireless usage prediction system. The usage state time series data is data indicating past temporal changes in received power of the radio wave at the reception point of the radio wave,
The wireless usage prediction data is received power predicted value time series data indicating a prediction of a temporal change in the received power of the radio wave at the reception point of the radio wave.
The wireless usage prediction system according to claim 4. A feature data generation method executed by a feature data generation device that generates feature time series data used for prediction processing of future usage status of wireless communication, the method comprising:
a propagation path calculation step in which the feature data generation device calculates a radio wave propagation path from a transmission point to a reception point of radio waves used for the wireless communication;
Feature amount time series data generation, wherein the feature amount data generation device generates feature amount time series data indicating past temporal changes in feature amounts that affect the propagation of the radio waves for each area including the radio wave propagation path. step and
Feature data generation method including. A computer of a feature data generation device that generates feature time series data used for prediction processing of future usage status of wireless communication,
a propagation path calculation step of calculating a radio wave propagation path from a transmission point to a reception point of radio waves used in the wireless communication;
a step of generating feature time-series data for each area including the radio wave propagation path, generating feature time-series data indicating past temporal changes in feature values that affect the propagation of the radio waves;
A computer program for running.

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