CN110505688B

CN110505688B - Paging method, device, system, network equipment and storage medium

Info

Publication number: CN110505688B
Application number: CN201810483106.XA
Authority: CN
Inventors: 胡玉双
Original assignee: China Mobile Communications Group Co Ltd; China Mobile Communications Ltd Research Institute
Current assignee: China Mobile Communications Group Co Ltd; China Mobile Communications Ltd Research Institute
Priority date: 2018-05-18
Filing date: 2018-05-18
Publication date: 2022-04-29
Anticipated expiration: 2038-05-18
Also published as: CN110505688A

Abstract

The invention discloses a paging method, a paging device, a paging system, network equipment and a storage medium. The method comprises the following steps: determining a paging range using paging optimization information of a User Entity (UE); the paging optimization information is obtained by performing machine learning on the historical paging area information of the UE according to network slicing granularity by network data analysis (NWDA); the paging optimization information characterizes movement of the UE within a particular time period; and paging the UE according to the determined paging range.

Description

Paging method, device, system, network equipment and storage medium

Technical Field

The present invention relates to the field of wireless communications, and in particular, to a paging method, apparatus, system, network device, and storage medium.

Background

Currently, in a Long Term Evolution (LTE) network, the network may page idle and connected User Entities (UEs). When the network pages the UE, the current location of the UE is tracked through a Mobility Management Entity (MME).

However, in the above paging procedure, both the periodic Tracking Area Update (TAU) procedure and the procedure in which the UE is not paged through the tracking area list to expand the paging range waste base station resources.

Disclosure of Invention

In order to solve the existing technical problem, embodiments of the present invention provide a paging method, apparatus, system, network device, and storage medium.

The technical scheme of the embodiment of the invention is realized as follows:

the embodiment of the invention provides a paging method, which is applied to an access and mobility management function (AMF) and comprises the following steps:

determining a paging range by using paging optimization information of the UE; the paging optimization information is obtained by performing machine learning on the historical paging area information of the UE according to NetWork slice granularity by NetWork Data analysis (NWDA, NetWork Data Analytics); the paging optimization information characterizes movement of the UE within a particular time period;

and paging the UE according to the determined paging range.

In the above scheme, the method further comprises:

obtaining the paging optimization information from one of the following network elements:

NWDA；

user Data Management (UDM);

a User Data Repository (UDR);

policy Control Function (PCF).

In the above scheme, acquiring the paging optimization information from the UDM or the UDR includes:

receiving paging optimization information sent by the UDM or the UDR; and the received paging optimization information is sent after the UDM or the UDR determines that the paging optimization information exists in the UE by using the subscription data of the UE.

In the above solution, the obtaining the paging optimization information from the PCF includes:

sending a policy acquisition request to the PCF;

receiving paging optimization information sent by the PCF; the received paging optimization information is sent by the PCF after the PCF determines that the UE has the paging optimization information by utilizing the subscription data of the UE and acquires the paging optimization information from one of the following network elements:

UDM；

UDR；

NWDA。

in the above solution, the paging optimization information at least includes: allocating a UE id periodic registration timer and one of a cell list and a tracking area list corresponding to the UE in the specific time period;

the determining the paging range by using the paging optimization information of the UE includes:

determining an access network by using one of a cell list and a tracking area list corresponding to the UE in the specific time period; the cell list and the tracking area list corresponding to the UE in the specific time period are updated according to the UE id periodic registration timer allocation.

In the above scheme, the method further comprises:

and when the paging fails according to the determined paging range, paging the UE by using the tracking area list.

In the above scheme, the method further comprises:

and recording the paging event as an abnormal event, and storing the corresponding abnormal event for the NWDA to update the paging optimization information of the UE.

The embodiment of the invention also provides a paging method applied to the NWDA, which comprises the following steps:

acquiring a data sample of UE; the data sample is historical paging area information of the UE;

according to the network slice granularity, determining paging optimization information of the UE in a specific time period by using the data sample and combining a machine learning algorithm; the determined paging optimization information is used for AMF to determine a paging range and page the UE according to the paging range; the determined optimization information characterizes movement of the UE for a particular time period.

In the above scheme, the method further comprises:

when the number of abnormal events recorded by the AMF in a preset time length exceeds a threshold value, updating the data sample of the UE by using the recorded abnormal events; the abnormal event represents that the AMF fails to page the UE by using the determined paging optimization information;

and according to the network slice granularity, re-determining paging optimization information of the UE in a specific time period by using the updated data sample and combining a machine learning algorithm.

In the above scheme, the method further comprises:

and regularly pushing the determined paging information of the UE in a specific time period to at least one of the following network elements for storage:

UDM；

UDR；

AMF。

an embodiment of the present invention further provides a paging device, including:

a first determining unit, configured to determine a paging range by using paging optimization information of the UE; the paging optimization information is obtained by performing machine learning on the historical paging area information of the UE by the NWDA according to the network slice granularity; the paging optimization information characterizes movement of the UE within a particular time period;

and the paging unit is used for paging the UE according to the determined paging range.

In the foregoing solution, the paging unit is further configured to:

In the above scheme, the apparatus further comprises:

and the storage unit is used for recording the current paging event as an abnormal event and storing the corresponding abnormal event so that the NWDA can update the paging optimization information of the UE.

The embodiment of the invention also provides a paging device, which comprises:

an obtaining unit, configured to obtain a data sample of a UE; the data sample is historical paging area information of the UE;

a second determining unit, configured to determine, according to a network slicing granularity, paging optimization information of the UE in a specific time period by using the data sample and combining a machine learning algorithm; the determined paging optimization information is used for the AMF to determine a paging range and page the UE according to the paging range; the determined optimization information characterizes movement of the UE for a particular time period.

In the foregoing solution, the second determining unit is further configured to:

In the above scheme, the apparatus further comprises:

a pushing unit, configured to push the determined paging information of the UE in a specific time period to at least one of the following network elements for storage:

UDM；

UDR；

AMF。

an embodiment of the present invention further provides a network device, including:

a first communication interface;

a first processor for determining a paging range using paging optimization information of a UE; the paging optimization information is obtained by performing machine learning on the historical paging area information of the UE by the NWDA according to the network slice granularity; the paging optimization information characterizes movement of the UE within a particular time period; and paging the UE through the first communication interface according to the determined paging range.

In the foregoing solution, the first processor is further configured to:

and when the paging fails according to the determined paging range, paging the UE through the first communication interface by using a tracking area list.

In the above-mentioned scheme, the first step of the method,

the first processor is further configured to record the current paging event as an abnormal event, so that the NWDA updates paging optimization information of the UE.

a second communication interface;

a second processor for obtaining data samples of the UE through the second communication interface; the data sample is historical paging area information of the UE; according to the network slice granularity, determining paging optimization information of the UE in a specific time period by using the data sample and combining a machine learning algorithm; the determined paging optimization information is used for the AMF to determine a paging range and page the UE according to the paging range; the determined optimization information characterizes movement of the UE for a particular time period.

In the foregoing solution, the second processor is further configured to:

In the foregoing solution, the second communication interface is further configured to periodically push the determined paging information of the UE in a specific time period to at least one of the following network elements for storage:

UDM；

UDR；

AMF。

an embodiment of the present invention further provides a network device, including: a first processor and a first memory for storing a computer program capable of running on the processor,

wherein the first processor is configured to execute the steps of any of the methods of the AMF side when running the computer program.

An embodiment of the present invention further provides a network device, including: a second processor and a second memory for storing a computer program capable of running on the processor,

wherein the second processor is configured to execute the steps of any of the above-mentioned NWDA side methods when running the computer program.

An embodiment of the present invention further provides a paging system, including:

the NWDA is used for obtaining data samples of the UE; the data sample is historical paging area information of the UE; according to the network slice granularity, determining paging optimization information of the UE in a specific time period by using the data sample and combining a machine learning algorithm; the determined optimization information characterizes movement of the UE within a particular time period;

the AMF is used for determining a paging range by utilizing the paging optimization information of the UE determined by the NWDA; and paging the UE according to the determined paging range.

In the above solution, the system further includes: UDM, UDR, PCF;

the AMF is further configured to: obtaining the paging optimization information from one of the following network elements:

NWDA；

UDM；

UDR；

PCF。

in the foregoing solution, the UDM or UDM is further configured to notify the NWDA and/or AMF when it is determined that the UE has paging optimization information by using subscription data of the UE.

In the above solution, the AMF is configured to send a policy acquisition request to the PCF;

the PCF is configured to, after receiving the policy acquisition request, acquire paging optimization information of the UE from one of the following network elements when determining that the paging optimization information exists for the UE using the subscription data of the UE: UDM; UDR; NWDA; and sending to the AMF;

and the AMF is used for receiving the paging optimization information sent by the PCF.

In the above scheme, the AMF is further configured to page the UE by using a tracking area list when paging fails according to the determined paging range.

In the foregoing solution, the AMF is further configured to: recording the paging event as an abnormal event for the NWDA to update the paging optimization information of the UE;

the NWDA is further used for updating the data sample of the UE by using the recorded abnormal event when the number of the abnormal events recorded by the AMF in a preset time length exceeds a threshold value; and according to the network slice granularity, the paging optimization information of the UE in a specific time period is re-determined by using the updated data sample and combining a machine learning algorithm.

In the foregoing solution, the NWDA is further configured to periodically push the determined paging information of the UE in a specific time period to at least one of the following network elements for storage:

UDM；

UDR；

AMF。

an embodiment of the present invention further provides a storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of any one of the methods in the AMF side or implements the steps of any one of the methods in the NWDA side.

According to the paging method, the paging device, the paging system, the network equipment and the storage medium provided by the embodiment of the invention, the NWDA acquires a data sample of the UE; the data sample is historical paging area information of the UE, and paging optimization information of the UE in a specific time period is determined by using the data sample and combining a machine learning algorithm according to network slice granularity; the AMF determines a paging range by using the paging optimization information of the UE determined by the NWDA; and paging the UE according to the determined paging range, determining the paging range based on the paging optimization information predicted by the NWDA, namely predicting the paging area, and then paging the UE by using the predicted paging area.

Drawings

Fig. 1 is a schematic diagram of a 5G network architecture according to an embodiment of the present invention;

fig. 2 is a flowchart illustrating an AMF side paging method according to an embodiment of the present invention;

FIG. 3 is a flowchart illustrating an NWDA-side paging method according to an embodiment of the present invention;

FIG. 4 is a flowchart illustrating a paging method according to an embodiment of the present invention;

FIG. 5 is a flowchart illustrating a paging method according to an embodiment of the present invention;

FIG. 6 is a flowchart illustrating a paging method according to a second embodiment of the present invention;

FIG. 7 is a flowchart illustrating a third paging method according to an embodiment of the present invention;

fig. 8 is a schematic structural diagram of a paging device according to an embodiment of the present invention;

fig. 9 is a schematic structural diagram of another paging device according to an embodiment of the present invention;

fig. 10 is a schematic structural diagram of a network device according to an embodiment of the present invention;

fig. 11 is a schematic structural diagram of another network device according to an embodiment of the present invention;

fig. 12 is a schematic structural diagram of a paging system according to an embodiment of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples.

Currently, in an LTE network, the network may page UEs in idle and connected states to obtain the location of the UE; specifically, the network side tracks the current location of the UE through the MME.

When the UE registers with the network, the MME may allocate a set of Tracking Area lists (Tracking Area lists) to the UE, and the MME may page the UE using the allocated Tracking Area lists.

In the paging process, the situation of wasting the base station resources occurs, which is specifically represented as follows:

in the first case, when the UE is in an evolved packet system (ESP) mobility management-REGISTERED (EMM-REGISTERED) state, the UE performs a TAU after a period of TAU time expires, and the periodic TAU wastes base station resources.

In the second case, when the UE is in an ESP connectivity management-IDLE (ECM-IDLE) state, in which the MME knows the location of the UE through the tracking area list, then when paging (i.e., paging), all base stations of the tracking areas registered by the UE under the ECM-IDLE should be considered by the MME, and the UE may be registered in multiple tracking areas, and all tracking areas registered by the UE on the tracking area list are served by the same MME; moreover, when the UE cannot be paged by the MME through the tracking area list, the range is expanded to continue paging the UE. The process of paging a UE with a tracking area list can be very wasteful of base station resources.

On the other hand, in a 5G network, there will be different types of UEs, e.g., smart phones, internet of things devices, V2X devices, etc., which have different mobility characteristics. Also, the same type of UE may have different movement behaviors, for example, a smart terminal used by a working team and a smart terminal used by an employee have different movement trajectories. It is clear that 5G networks should not employ a unified mobility management scheme to serve them.

Meanwhile, UEs serving different communication scenarios may also have different services of different communication modes/models, e.g., the smart terminal user may talk/communicate less often during the midday period; small data transmission such as frequent use or frequent use of water meter equipment; as another example, V2X devices require reliable or low latency communication. To guarantee user experience and improve network resource utilization efficiency, a 5G network should be able to customize mobility management for different UEs of different communication modes/modes.

In the third aspect, in the 5G network, it is necessary to meet the requirements of massive connections and higher speed of users, so that the 5G network adopts a novel network architecture, and under the novel network architecture, as shown in fig. 1, the NWDA is an additional network element, and has the following characteristics:

(1) an NWDA is defined as an operator managed network analysis logic function.

(2) The function of the NWDA is to provide specific network data analysis at a slice granularity (which may be understood as per service) and can be provided to the PCF via the N23 interface.

(3) The NWDA may analyze the specific network information state of the subscribed user according to the slice granularity and send it to the PCF, and the PCF may also collect all the required user information directly from the NWDA; the PCF then makes policy decisions on the overall network based on these data.

The interface between the NWDA and other network elements is referred to as nwdaf, which is a service interface, and through this interface, the other network elements can obtain the following information:

network slice instance identification;

load information for the network slice instance.

Here, it should be noted that: since the interface between the NWDA and the PCF has been defined as N23, the interface between the NWDA and the PCF is named with a specific interface, that is, the interface between the NWDA and the PCF is the N23 interface.

The above analysis can yield: the mobility patterns of the UE may be tracked and managed using the functionality of the NWDA.

Based on this, in various embodiments of the invention: the NWDA predicts paging optimization information of the UE in a specific time period based on a machine learning algorithm according to the collected paging area information of the UE; and the network determines a paging range by using the predicted paging optimization information in the specific time period, and pages the UE.

According to the scheme provided by the embodiment of the invention, the paging range is determined based on the paging optimization information predicted by the NWDA, namely the paging area is predicted, and then the UE is paged by using the predicted paging area.

An embodiment of the present invention provides a paging method, which is applied to an AMF, and as shown in fig. 2, the method includes:

step 201: determining a paging range by using paging optimization information of the UE;

here, the paging optimization information is obtained by performing machine learning on the historical paging area information of the UE by NWDA according to network slice granularity; the paging optimization information characterizes movement of the UE for a particular time period.

That is, the paging optimization information is a paging area of the UE within a specific time period predicted by the NWDA.

The functions of the AMF are mainly: and the system is responsible for network access control, registration management, connection management, mobility management and the like of the user.

Wherein the specific time period can be set as needed.

In practical application, the AMF needs to obtain paging optimization information.

Based on this, in an embodiment, before performing step 101, the method further includes:

NWDA；

UDM；

UDR；

PCF。

in an embodiment, the obtaining the paging optimization information from the UDM or the UDR includes:

Here, in practical application, after the UE is registered, the UDM or UDR may determine whether paging optimization information exists in the UE by using subscription data of the UE, and when the UDM or UDR determines that the paging optimization information exists in the UE by using subscription data of the UE, the UDM or UDR may send the paging optimization information to the AMF; in addition, when there is an update of paging optimization information, the updated paging optimization information is transmitted to the AMF.

It should be noted that: in practical application, the UDM or UDR may be selected to store the subscription data of the UE as needed (the UDM or UDR may obtain the subscription data of the UE from the NWDA), and the selected network element may further determine whether the paging optimization information exists and send the paging optimization information to the AMF.

In an embodiment, the obtaining the paging optimization information from the PCF includes:

sending a policy acquisition request to the PCF;

UDM；

UDR；

NWDA。

here, in actual application, the PCF may query the UDM or UDR for a request; and after receiving the request, the UDM or the UDR judges whether the UE has paging optimization information according to the subscription data of the UE to obtain a corresponding query result, and returns the corresponding query result (whether the UE has the paging optimization information) to the PCF.

It should be noted that: in practical application, the UDM or UDR may be selected to store subscription data of the UE, and the PCF determines whether paging optimization information exists for the UE by sending an inquiry request to a selected network element.

In one embodiment, the paging optimization information includes at least: allocating a UE id periodic registration timer and one of a cell list and a tracking area list corresponding to the UE in the specific time period;

accordingly, the determining the paging range by using the paging optimization information of the UE includes:

That is to say, the predicted cell list and tracking area list corresponding to the UE in the specific time period have a life cycle, where the life cycle is the duration of the UE id periodic registration timer, in other words, after the NWDA predicts the cells in the cell list and tracking area list corresponding to the UE in the specific time period, the UE id periodic registration timer will start timing, and after the timer expires, it needs to predict the cells in the cell list and tracking area list corresponding to the UE again, that is, update the cells in the cell list and tracking area list corresponding to the UE, so that the paging range can be accurately determined when the UE is paged subsequently.

In addition, in practical applications, the duration of the UE id periodic registration timer may be updated according to the UE behavior (such as UE mobility). For example, when the UE moves particularly frequently, the duration of the UE id periodic registration timer needs to be shortened, and if the UE moves infrequently, the duration of the UE id periodic registration timer can be longer, i.e. the cells in the cell list and tracking area list can be updated once in a long time.

Step 202: and paging the UE according to the determined paging range.

Here, after the access network is determined, the UE is paged through the determined access network.

In practical application, when paging fails according to the determined paging range, the UE may be paged by using a tracking area list.

That is, when paging fails according to the determined paging range, the UE may continue to be paged using a general paging manner.

At this time, it is described that the paging area predicted by NWDA is inaccurate, and it is necessary to predict the paging area again based on the failure information.

Based on this, in an embodiment, the method may further include:

Here, in actual application, the AMF may store the exception event locally or to NWDA.

When the data is stored locally, when the number of abnormal events recorded in a preset time length exceeds a threshold value, the AMF uploads the recorded number of abnormal events and corresponding abnormal events to the NWDA; the reported information is used for the NWDA to update the paging optimization information of the UE.

When the abnormal event is stored in the NWDA, and the number of abnormal events (which may also be understood as events stored in the NWDA) recorded by the AMF exceeds a threshold value within a preset time period, the NWDA may update the paging optimization information of the UE using the recorded abnormal event.

Correspondingly, an embodiment of the present invention further provides a paging method, applied to NWDA, as shown in fig. 3, where the method includes:

step 301: acquiring a data sample of UE;

here, the data sample is historical paging area information of the UE.

Step 302: and according to the network slice granularity, determining paging optimization information of the UE in a specific time period by using the data samples and combining a machine learning algorithm.

Here, the determined paging optimization information is used for the AMF to determine a paging range and page the UE according to the paging range; the determined optimization information characterizes movement of the UE for a particular time period.

That is, the NWDA predicts paging optimization information of the UE within a specific time period based on a machine learning algorithm by using collected historical paging area information of the UE, including a paging range corresponding to the user at a certain time, i.e. accurately predicts a regular mobility pattern of the UE and a related UE tracking area.

The network slice granularity may also be referred to as network slicing, which is a relatively abstract concept. In the prior art, network element is taken as a basic unit to process all service types (service is not distinguished), and a network slicing concept is introduced into a network framework structure taking service as a guide, so that the function of the network element can be segmented according to the service. Each network element function is a server providing a specific function service, and the service corresponding to each specific network segmentation appears in the form of network element segmentation example. In brief, corresponding to target services in a 5G system, such as enhanced mobile broadband (eMBB) service, ultra-reliable low-latency communication (urlclc) service, mass machine type communication (mtc) service, etc., a segmentation identifier (which may also be referred to as a network slice instance identifier) may be used to inform a network of what specific type of service is provided, so that the network may be specifically provided with customized services according to different service characteristics.

In practical application, a machine learning algorithm can be selected to predict according to needs. For example, a machine learning algorithm may be selected, the data samples trained to obtain a linear regression model, and the obtained model used to predict paging optimization information (paging zones) over a specified time period.

The specific time period may be determined as needed.

In practical application, when the AMF fails to page the UE according to the paging range determined by the determined paging optimization information, the AMF records the abnormal event; and the NWDA needs to re-predict the paging zone at a particular time period.

Based on this, in an embodiment, the method may further include:

When the exception event recorded by the AMF can be stored locally or stored in the NWDA.

The abnormal event may be expressed by using the location of the UE and the corresponding time.

Specifically, at the time of update, the updated data samples need to be trained to update parameters in the model, and then the updated model is used to re-predict the paging zone in a specific time period.

The preset time period can be set according to needs, such as one month, two months, and the like.

The threshold value may be set as desired, such as 6 times or 7 times, etc.

In an embodiment, the NWDA may push the paging optimization information of the UE determined by itself to at least one of the following network elements for storage:

UDM；

UDR；

AMF。

therefore, on one hand, the storage space of the AMF can be saved, and on the other hand, the AMF can quickly obtain the paging optimization information of the UE, so that the UE can be paged quickly.

Of course, the NWDA may also store the paging optimization information of the UE locally.

An embodiment of the present invention further provides a paging method, as shown in fig. 4, the method includes:

step 401: the NWDA acquires a data sample of the UE;

here, the data sample is historical paging area information of the UE.

Step 402: the NWDA determines paging optimization information of the UE in a specific time period by using the data samples and combining a machine learning algorithm according to network slice granularity;

here, the determined optimization information characterizes a movement of the UE for a specific time period.

Step 403: the AMF determines a paging range by using the paging optimization information of the UE determined by the NWDA; and paging the UE according to the determined paging range.

Wherein the AMF obtains the paging optimization information from one of the following network elements:

NWDA；

UDM；

UDR；

PCF。

wherein, the method can also comprise:

and when the UE is determined to have paging optimization information by using subscription data of the UE, the UDM or the UDM informs the NWDA and/or the AMF.

When the AMF acquires paging optimization information of UE from PCF (namely the AMF acquires a mobility management strategy from PCF), the AMF sends a strategy acquisition request to the PCF; after receiving the strategy acquisition request, the PCF acquires the paging optimization information of the UE from one of the following network elements when determining that the paging optimization information exists in the UE by using the subscription data of the UE: UDM; UDR; NWDA; and sending to the AMF; and the AMF receives the paging optimization information sent by the PCF.

In an embodiment, the AMF pages the UE with a tracking area list when paging fails according to the determined paging range.

In addition, the AMF may record the current paging event as an abnormal event, so that the NWDA updates the paging optimization information of the UE;

when the number of abnormal events recorded by the AMF in a preset time length exceeds a threshold value, the NWDA updates the data sample of the UE by using the recorded abnormal events; and according to the network slice granularity, the paging optimization information of the UE in a specific time period is re-determined by using the updated data sample and combining a machine learning algorithm.

It should be noted that: the processing procedure of each network element has been described in detail above, and is not described in detail here.

In the paging method provided by the embodiment of the invention, an NWDA acquires a data sample of UE; the data sample is historical paging area information of the UE, and paging optimization information of the UE in a specific time period is determined by using the data sample and combining a machine learning algorithm according to network slice granularity; the AMF determines a paging range by using the paging optimization information of the UE determined by the NWDA; and paging the UE according to the determined paging range, determining the paging range based on the paging optimization information predicted by the NWDA, namely predicting the paging area, and then paging the UE by using the predicted paging area.

In addition, when the paging fails according to the determined paging range, the AMF may page the UE by using the tracking area list, that is, continue to page the UE by using a common method, so that it is ensured that the UE can be paged, and a guarantee is provided for performing a service.

In addition, the AMF records the current paging event as an abnormal event, and stores the corresponding abnormal event, when the number of times of the abnormal event (which may also be understood as an event stored in the NWDA) recorded by the AMF exceeds a threshold value within a preset time period, the NWDA updates paging optimization information of the UE by using the recorded abnormal event, and the paging optimization information is re-predicted according to the movement of the UE, so that a paging range can be further accurately determined, and waste of base station resources is greatly reduced.

The present invention will be described in further detail with reference to the following application examples.

Application embodiment 1

In the embodiment of the application, the network issues a mobility management policy (paging optimization policy) to the AMF, so as to implement paging of the UE.

The paging method in the present application embodiment, as shown in fig. 5, includes the following steps:

step 500: the NWDA pushes paging optimization information of the UE to the UDM/UDR at regular time; and UDM/UDR obtains subscription information (which may also be understood as subscription data) of the UE from the NWDA;

here, the UDM/UDR may be referred to as a network function service caller (NF network function), which takes user subscription information from the NWDA by calling nwdaf _ Analytics _ Info _ Request service (a program).

The NWDA periodically sends paging optimization information (UE id periodic registration timer allocation, corresponding cell list or tailist in a certain time period) of the UE to the UDM/UDR by calling nwdaf _ analysis _ Info _ Response service (a procedure).

Of course, the NWDA may also store the paging optimization information of the UE locally. And when the UDM/UDR finds that the registered UE has the paging optimization information, the paging optimization information of the registered UE is pushed to the AMF by the NWDA, and the NWDA informs the NWDA.

Step 501: when the UE registers (attaches) to the network, the UDM/UDR finds that the registered UE has paging optimization information from the user subscription information of the registered UE (by optimizing a paging indicator bit), and pushes the paging optimization information of the registered UE to the AMF;

here, UDM is pushed directly to AMF since UDM/UDR has no PCF id.

When there is paging optimization information, it indicates that the UE has a corresponding NWDA for which slice-granularity services are provided.

Step 502: the AMF determines a paging area range by using the paging optimization information, and pages the UE based on the paging area range;

here, in fact, since the role of the paging optimization information can be understood as: the behavior of the UE, which is counted in the past, can be used to know that the activity of the UE is regular at certain time. For example, during the day, the UE user is at work and goes home at night. Therefore, when the UE is paged in daytime, the UE can be paged directly through a base station near the working place of the user of the UE, and the UE is paged without searching the position of the last call of the UE through the traditional method, so that the AMF can directly position AN Access Network (AN), specifically a Radio Access Network (RAN), by using paging optimization information, and the UE is paged through the positioned AN.

Step 503: and if the paging is successful, transmitting the data.

Application example two

In the embodiment of the application, the AMF goes to PCF to acquire the mobility management strategy, thereby realizing the paging of the UE.

In the embodiment of the present application, paging is triggered by downlink data, and the tracking area list and the timer information are already stored in the AMF. (configured on AMF, universal for all networks). Specifically, after receiving the data packet, the UPF sends the data packet to the SMF, the SMF buffers the data packet, and then the SMF notifies the AMF of paging data to page the corresponding UE.

The method for paging in the present application embodiment, as shown in fig. 6, includes the following steps:

step 601: when UE registers to network, UPF finds down data, informs AMF to page data, AMF obtains mobile management strategy from PCF;

step 602: PCF sends request to UDM/UDR to judge whether there is paging optimization information in UE subscription data;

here, when there is paging optimization information, it indicates that the UE has a corresponding NWDA for which a slice granularity service is provided.

Step 603: when the UDM/UDR determines that the paging optimization information exists in the UE according to the subscription data of the UE, the paging optimization information of the UE is pushed to the PCF;

step 604: after receiving the paging optimization information, the PCF sends the paging optimization information (UE id periodic registration timer allocation, corresponding cell list or TAlist in a certain time period) to the AMF;

step 605: determining a paging area range by the AMF paging optimization information, and paging the UE based on the paging area range;

here, in fact, since the role of the paging optimization information can be understood as: the behavior of the UE, which is counted in the past, can be used to know that the activity of the UE is regular at certain time. For example, during the day, the UE user is at work and goes home at night. Therefore, when the UE is paged in daytime, the UE can be paged directly through a base station near the office place of the user of the UE, and the UE is paged without finding the position of the last call of the UE through the traditional method, so that the AMF can be directly positioned to the AN (access network) by using the paging optimization information, particularly the RAN, and the UE is paged through the positioned AN.

During the paging process, the downlink data is sent to the UE, that is, after the paging is successful, the UE receives the downlink data.

Step 606: and if the paging is successful, sending data.

Application example three

In the embodiment of the present application, a procedure of how the network side pages the UE when the UE cannot be paged by using the paging optimization information, that is, when the paging of the UE by using the paging optimization information fails, is described.

As shown in fig. 7, the paging procedure includes:

step 701: the AMF acquires paging optimization information;

here, the information may be acquired by applying the first embodiment, or may be acquired by applying the second embodiment. In particular, the amount of the solvent to be used,

step 701 a: the UDM/UDR actively issues paging optimization information to the AMF (the network side directly issues the paging optimization information);

step 701 b: the PCF sends the paging optimization information to the AMF (the AMF obtains the paging optimization information from the PCF).

Step 702: the AMF pages the UE in the paging area range determined by the paging optimization information;

step 703: when the UE is not paged, the AN replies AMF as the UE is not paged (replying paging ACK), and the paging fails;

step 704-705: and the AMF immediately switches the common paging mode to re-page the UE according to the TAlist.

Step 706-707: paging is successful;

step 708: the AMF records the event as an abnormal value, namely an abnormal event, and stores the abnormal event;

step 709: uploading data (including the counted abnormal value times and the local cached abnormal value) to the NWDA when the occurrence frequency of the abnormal event reaches a threshold value M within a specific time length so as to inform the NWDA of updating the paging optimization information;

here, the specific time period may be set as needed, such as one month, two months, etc.

Step 710: and the NWDA records corresponding events and updates the paging optimization information of the UE.

As can be seen from the above description, in the solution provided in the embodiment of the present invention, the AMF uses the paging optimization information (taist or Cell list) to page the UE, so that the AN can be directly located, thereby reducing the paging load (page load) of the base station (e.g., the gNB) and saving corresponding processing resources.

The network side (NWDA) may obtain a regular curve by using the historical paging area information, so as to predict the mobility of the UE according to the regular curve, i.e. predict and optimize the paging information. The AMF cannot page the UE by using the paging optimization information, which shows that the position information of the UE is an abnormal value deviating from the curve of the rule at the moment, and the AMF can remember the event. When the abnormal value reaches the threshold value, reporting to the NWDA. The NWDA records the location information of the UE at a specific time, and the NWDA will automatically change the whole paging strategy, i.e. update the paging optimization information, such as reassigning base stations, etc., when the next abnormal event occurs.

In practical application, when the UE is in IDLE mode, the network side automatically allocates a taist or a celllist and a corresponding timer, that is, a periodic registration timer of the UE, according to an algorithm learned from UE behavior. For example: according to past paging experience, the network gradually shrinks the list of cells allocated to the UE as long as the UE stays in the same list of cells for a fixed period of time. If the UE is monitored to be in a fixed location area for a certain period of time, the network will extend the time for triggering the TAU procedure next time.

In order to implement the method according to the embodiment of the present invention, an embodiment of the present invention further provides a paging device, which is disposed in the AMF, and as shown in fig. 8, the paging device includes:

a first determining unit 81, configured to determine a paging range by using paging optimization information of the UE; the paging optimization information is obtained by performing machine learning on the historical paging area information of the UE by the NWDA according to the network slice granularity; the paging optimization information characterizes movement of the UE within a particular time period;

and a paging unit 82, configured to page the UE according to the determined paging range.

Based on this, in an embodiment, as shown in fig. 8, the apparatus may further include:

an obtaining unit 83, configured to obtain the paging optimization information from one of the following network elements:

NWDA；

UDM；

UDR；

PCF。

Based on this, in an embodiment, the paging unit 82 is further configured to:

Based on this, in an embodiment, the apparatus may further include:

In practical applications, the first determining unit 81 and the storing unit may be implemented by a processor in the paging device, and the paging unit 82 and the obtaining unit 83 may be implemented by a processor in the paging device in combination with a communication interface.

It should be noted that: in the paging device provided in the above embodiment, only the division of the above program modules is used for illustration when paging is performed, and in practical applications, the above processing allocation may be completed by different program modules according to needs, that is, the internal structure of the device is divided into different program modules to complete all or part of the above described processing. In addition, the paging device and the paging method provided by the above embodiments belong to the same concept, and specific implementation processes thereof are described in the method embodiments and are not described herein again.

To implement the method according to the embodiment of the present invention, an embodiment of the present invention further provides a paging device, which is disposed in an NWDA, and as shown in fig. 9, the paging device includes:

an obtaining unit 91, configured to obtain a data sample of the UE; the data sample is historical paging area information of the UE;

a second determining unit 92, configured to determine, according to a network slicing granularity, paging optimization information of the UE in a specific time period by using the data sample and combining a machine learning algorithm; the determined paging optimization information is used for the AMF to determine a paging range and page the UE according to the paging range; the determined optimization information characterizes movement of the UE for a particular time period.

Based on this, in an embodiment, the second determining unit 92 is further configured to:

In an embodiment, the apparatus may further include: a pushing unit, configured to push the determined paging information of the UE in a specific time period to at least one of the following network elements for storage:

UDM；

UDR；

AMF。

In practical application, the acquiring unit 91 and the pushing unit may be implemented by a processor in the paging device in combination with a communication interface; the second determining unit 92 may be implemented by a processor in the paging device.

Based on the foregoing hardware implementation, to implement the method according to the embodiment of the present invention, an embodiment of the present invention further provides a network device, which is an AMF, and as shown in fig. 10, the network device 100 includes:

the first communication interface 101 can perform information interaction with other network equipment;

and the first processor 102 is connected with the first communication interface 101 to implement information interaction with other network devices, and is used for executing the method provided by one or more technical solutions of the AMF side when running a computer program. And the computer program is stored on the first memory 103.

Specifically, the first processor 102 is configured to determine a paging range by using paging optimization information of a UE; the paging optimization information is obtained by performing machine learning on the historical paging area information of the UE by the NWDA according to the network slice granularity; the paging optimization information characterizes movement of the UE within a particular time period; and paging the UE through the first communication interface 101 according to the determined paging range.

Based on this, in an embodiment, the first processor 102 is further configured to obtain the paging optimization information from one of the following network elements through the first communication interface 101:

NWDA；

UDM；

UDR；

PCF。

Based on this, in an embodiment, the first processor is further configured to:

paging the UE through the first communication 101 interface using a tracking area list when paging fails according to the determined paging range.

Based on this, in an embodiment, the first processor 101 is further configured to record the current paging event as an abnormal event, so that the NWDA updates the paging optimization information of the UE.

It should be noted that: the specific processing procedure of the first processor 102 is described in detail in the method embodiment, and is not described herein again.

Of course, in practice, the various components of the network device 100 are coupled together by the bus system 104. It is understood that the bus system 104 is used to enable communications among the components. The bus system 104 includes a power bus, a control bus, and a status signal bus in addition to a data bus. For clarity of illustration, however, the various buses are labeled as bus system 104 in fig. 10.

The first memory 103 in the embodiment of the present invention is used to store various types of data to support the operation of the network device 100. Examples of such data include: any computer program for operating on network device 100.

The method disclosed in the above embodiments of the present invention may be applied to the first processor 102, or implemented by the first processor 102. The first processor 102 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be implemented by integrated logic circuits of hardware or instructions in the form of software in the first processor 102. The first Processor 102 may be a general purpose Processor, a Digital Signal Processor (DSP), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc. The first processor 102 may implement or perform the methods, steps and logic blocks disclosed in the embodiments of the present invention. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed by the embodiment of the invention can be directly implemented by a hardware decoding processor, or can be implemented by combining hardware and software modules in the decoding processor. The software module may be located in a storage medium located in the first memory 103, and the first processor 102 reads the information in the first memory 103 and completes the steps of the foregoing method in combination with its hardware.

In an exemplary embodiment, the network Device 100 may be implemented by one or more Application Specific Integrated Circuits (ASICs), DSPs, Programmable Logic Devices (PLDs), Complex Programmable Logic Devices (CPLDs), Field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, Micro Controllers (MCUs), microprocessors (microprocessors), or other electronic components for performing the aforementioned methods.

Based on the hardware implementation of the foregoing apparatus, in order to implement the method according to the embodiment of the present invention, an embodiment of the present invention further provides a network device, as shown in fig. 11, where the network device 110 includes:

the second communication interface 111 can perform information interaction with other network equipment;

and the second processor 112 is connected to the second communication interface 111 to implement information interaction with other network devices, and is configured to execute a method provided by one or more technical solutions of the NWDA side when running a computer program. And the computer program is stored on the second memory 113.

Specifically, the second processor 112 is configured to obtain a data sample of the UE through the second communication interface 111; the data sample is historical paging area information of the UE; according to the network slice granularity, determining paging optimization information of the UE in a specific time period by using the data sample and combining a machine learning algorithm; the determined paging optimization information is used for the AMF to determine a paging range and page the UE according to the paging range; the determined optimization information characterizes movement of the UE for a particular time period.

Based on this, in an embodiment, the second processor 112 is further configured to:

In an embodiment, the second communication interface 111 is further configured to periodically push the determined paging information of the UE in a specific time period to at least one of the following network elements for storage:

UDM；

UDR；

AMF。

It should be noted that: the specific processing procedure of the second processor 112 is detailed in the method embodiment, and is not described here again.

Of course, in practice, the various components in network device 110 are coupled together by bus system 114. It will be appreciated that the bus system 114 is used to enable communications among the components. The bus system 114 includes a power bus, a control bus, and a status signal bus in addition to a data bus. For clarity of illustration, however, the various buses are labeled as bus system 114 in FIG. 11.

The second memory 113 in the embodiment of the present invention is used to store various types of data to support the operation of the network device 110. Examples of such data include: any computer program for operating on network device 110.

The method disclosed in the above embodiments of the present invention may be applied to the second processor 112, or implemented by the second processor 112. The second processor 112 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be implemented by integrated logic circuits of hardware or instructions in the form of software in the second processor 112. The second processor 112 described above may be a general purpose processor, a DSP, or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The second processor 112 may implement or perform the methods, steps and logic blocks disclosed in the embodiments of the present invention. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed by the embodiment of the invention can be directly implemented by a hardware decoding processor, or can be implemented by combining hardware and software modules in the decoding processor. The software module may be located in a storage medium located in the second memory 113, and the second processor 112 reads the information in the second memory 113 and, in conjunction with its hardware, performs the steps of the foregoing method.

In an exemplary embodiment, the network device 110 may be implemented by one or more ASICs, DSPs, PLDs, CPLDs, FPGAs, general-purpose processors, controllers, MCUs, microprocessors, or other electronic components for performing the foregoing methods.

It will be appreciated that the memories (first memory 103 and second memory 113) of embodiments of the present invention may be either volatile memory or non-volatile memory, and may include both volatile and non-volatile memory. Among them, the nonvolatile Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a magnetic random access Memory (FRAM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical disk, or a Compact Disc Read-Only Memory (CD-ROM); the magnetic surface storage may be disk storage or tape storage. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Enhanced Synchronous Dynamic Random Access Memory (Enhanced DRAM), Synchronous Dynamic Random Access Memory (SLDRAM), Direct Memory (DRmb Access), and Random Access Memory (DRAM). The described memory for embodiments of the present invention is intended to comprise, without being limited to, these and any other suitable types of memory.

An embodiment of the present invention further provides a paging system, as shown in fig. 12, the system includes:

an NWDA 121 for obtaining data samples of the UE; the data sample is historical paging area information of the UE; according to the network slice granularity, determining paging optimization information of the UE in a specific time period by using the data sample and combining a machine learning algorithm; the determined optimization information characterizes movement of the UE within a particular time period;

an AMF 122, configured to determine a paging range using the paging optimization information of the UE determined by the NWDA 121; and paging the UE according to the determined paging range.

In an embodiment, the system may further include: UDM, UDR, PCF;

the AMF 122 is further configured to: obtaining the paging optimization information from one of the following network elements:

NWDA 121；

UDM；

UDR；

PCF。

in an embodiment, the UDM or UDM is further configured to notify the NWDA 121 and/or AMF 122 when it is determined that paging optimization information exists for the UE using subscription data of the UE.

In an embodiment, the AMF 122 is configured to send a policy acquisition request to the PCF;

the PCF is configured to, after receiving the policy acquisition request, acquire paging optimization information of the UE from one of the following network elements when determining that the paging optimization information exists for the UE using the subscription data of the UE: UDM; UDR; NWDA 121; and sending to the AMF;

the AMF 122 is configured to receive paging optimization information sent by the PCF.

In an embodiment, the AMF 122 is further configured to page the UE with a tracking area list when paging fails according to the determined paging range.

In an embodiment, the AMF 122 is further configured to: recording the paging event as an abnormal event for the NWDA 121 to update the paging optimization information of the UE;

the NWDA 121 is further configured to update a data sample of the UE with the recorded abnormal event when the number of abnormal events recorded by the AMF within a preset time duration exceeds a threshold; and according to the network slice granularity, the paging optimization information of the UE in a specific time period is re-determined by using the updated data sample and combining a machine learning algorithm.

In an embodiment, the NWDA 121 is further configured to periodically push the determined paging information of the UE in a specific time period to at least one of the following network elements for storage:

UDM；

UDR；

AMF 122。

it should be noted that: the specific processing procedure of each network element is shown in the method embodiment, and is not described herein again.

In an exemplary embodiment, an embodiment of the present invention further provides a storage medium, specifically a computer-readable storage medium, for example, the storage medium includes a first memory 103 storing a computer program, and the computer program is executable by the first processor 102 of the network device 100 to perform the steps of the AMF side method. For example, the second memory 113 may store a computer program that may be executed by the second processor 112 of the network device 110 to perform the steps of the NWDA side method described above. The computer readable storage medium may be Memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface Memory, optical disk, or CD-ROM.

It should be noted that: the technical schemes described in the embodiments of the present invention can be combined arbitrarily without conflict.

The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.

Claims

1. A method for paging, comprising:

determining a paging range by using paging optimization information of a User Entity (UE); the paging optimization information is obtained by performing machine learning on the historical paging area information of the UE according to network slicing granularity by network data analysis (NWDA); the paging optimization information characterizes movement of the UE within a particular time period;

paging the UE according to the determined paging range;

the paging optimization information at least includes: allocating a UE id periodic registration timer and one of a cell list and a tracking area list corresponding to the UE in the specific time period;

2. The method of claim 1, further comprising:

NWDA；

a User Data Management (UDM);

a user data repository, UDR;

a policy control function PCF.

3. The method of claim 2, wherein obtaining the paging optimization information from the UDM or UDR comprises:

4. The method of claim 2, wherein obtaining the paging optimization information from the PCF comprises:

sending a policy acquisition request to the PCF;

UDM；

UDR；

NWDA。

5. the method of claim 1, further comprising:

6. The method of claim 5, further comprising:

7. A method for paging, comprising:

according to the network slice granularity, determining paging optimization information of the UE in a specific time period by using the data sample and combining a machine learning algorithm; the determined paging optimization information is used for an access and mobility management function (AMF) to determine a paging range and page the UE according to the paging range; the determined optimization information characterizes movement of the UE within a particular time period;

the determined paging optimization information is used for an access and mobility management function (AMF) to determine a paging range, and comprises the following steps:

8. The method of claim 7, further comprising:

9. The method according to claim 7 or 8, characterized in that the method further comprises:

UDM；

UDR；

AMF。

10. a paging device, comprising:

the paging unit is used for paging the UE according to the determined paging range;

11. The apparatus of claim 10, wherein the paging unit is further configured to:

12. The apparatus of claim 11, further comprising:

13. A paging device, comprising:

a second determining unit, configured to determine, according to a network slicing granularity, paging optimization information of the UE in a specific time period by using the data sample and combining a machine learning algorithm; the determined paging optimization information is used for the AMF to determine a paging range and page the UE according to the paging range; the determined optimization information characterizes movement of the UE within a particular time period;

14. The apparatus of claim 13, wherein the second determining unit is further configured to:

15. The apparatus of claim 13 or 14, further comprising:

UDM；

UDR；

AMF。

16. a network device, comprising:

a first communication interface;

a first processor for determining a paging range using paging optimization information of a UE; the paging optimization information is obtained by performing machine learning on the historical paging area information of the UE by the NWDA according to the network slice granularity; the paging optimization information characterizes movement of the UE within a particular time period; paging the UE through the first communication interface according to the determined paging range;

17. The device of claim 16, wherein the first processor is further configured to:

18. The apparatus of claim 17,

19. A network device, comprising:

a second communication interface;

a second processor for obtaining data samples of the UE through the second communication interface; the data sample is historical paging area information of the UE; according to the network slice granularity, determining paging optimization information of the UE in a specific time period by using the data sample and combining a machine learning algorithm; the determined paging optimization information is used for the AMF to determine a paging range and page the UE according to the paging range; the determined optimization information characterizes movement of the UE within a particular time period;

20. The apparatus of claim 19,

the second processor is further configured to:

21. The apparatus of claim 19 or 20, wherein the second communication interface is further configured to periodically push the determined paging information of the UE in a specific time period to at least one of the following network elements for storage:

UDM；

UDR；

AMF。

22. a network device, comprising: a first processor and a first memory for storing a computer program capable of running on the processor,

wherein the first processor is adapted to perform the steps of the method of any one of claims 1 to 6 when running the computer program.

23. A network device, comprising: a second processor and a second memory for storing a computer program capable of running on the processor,

wherein the second processor is adapted to perform the steps of the method of any of claims 7 to 9 when running the computer program.

24. A paging system, comprising:

the AMF is used for determining a paging range by utilizing the paging optimization information of the UE determined by the NWDA; paging the UE according to the determined paging range;

25. The system of claim 24, further comprising: UDM, UDR, PCF;

NWDA；

UDM；

UDR；

PCF。

26. the system of claim 25, wherein the UDM or UDM is further configured to notify the NWDA and/or AMF when it is determined that paging optimization information exists for the UE using subscription data of the UE.

27. The system of claim 25 wherein said AMF is configured to send a policy acquisition request to said PCF;

28. The system of claim 24, wherein the AMF is further configured to page the UE with a tracking area list when paging fails according to the determined paging range.

29. The system of claim 28, wherein the AMF is further configured to: recording the paging event as an abnormal event for the NWDA to update the paging optimization information of the UE;

30. The system according to any of claims 24 to 29, wherein said NWDA is further configured to periodically push and store the determined paging information of said UE in a specific time period to at least one of the following network elements:

UDM；

UDR；

AMF。

31. a storage medium having stored thereon a computer program for performing the steps of the method of any one of claims 1 to 6 or for performing the steps of the method of any one of claims 7 to 9 when executed by a processor.