Network node of mobile communication network and computer-readable storage medium

Abstract

This network node of a mobile communication network comprises: a first reception means configured to receive, from a first server, first intermediate information generated by the first server using a first trained model; a first transmission means configured to transmit, to a second server using a second trained model, second intermediate information based on the first intermediate information; a second reception means configured to receive, from the second server, second error information indicating an error of the second intermediate information; and a second transmission means configured to transmit, to the first server, first error information indicating an error of the first intermediate information derived from the second intermediate information.

Description

Network node of mobile communication network and computer-readable storage medium

[0001] The present disclosure relates to a technology for utilizing learning models in a mobile communication system.

[0002] Non-Patent Document 1 discloses a configuration for a data analysis service in a mobile communication network. A network function (NF) that provides a service is also called a producer NF, and an NF that uses (subscribes to) a service provided by the producer NF is also called a consumer NF. Note that a producer NF can also operate as a consumer NF at the same time.

[0003] According to Non-Patent Document 1, a network data analysis function (NWDAF) that is a producer NF that provides a data analysis service is provided in a core network of a mobile communication network. A consumer NF obtains analysis results by subscribing to the data analysis service provided by the NWDAF.

[0004] The consumer NF is implemented, for example, in an external data network (DN) connected to the mobile communication network, for example, in a server located on the Internet. The server is operated, for example, by a service provider that provides a specific service, for example, a video distribution service, to wireless devices (WDs) on the mobile communication network. The service provider can obtain analysis results, such as the quality of experience (QoE), of users of WDs that use the service provider's services from the mobile communication network by subscribing the server (NF) operated by the service provider to a data analysis service.

[0005] In order to obtain the analysis results of the quality of experience, etc., information held by the mobile communication network (hereinafter referred to as NW information) and information held by the service provider, such as information on the application for using the service (hereinafter referred to as APP information), can be used. When using both NW information and APP information for analysis, the NWDAF performs inference using the NW information collected within the mobile communication network and the APP information notified from the NF as inputs to a learning model, and notifies the NF of the result (analysis result).

[0006] However, since the APP information may include privacy information that the service provider cannot submit to the outside, there may be cases where the APP information cannot be transmitted to the NWDAF. Therefore, it is also possible to consider a configuration in which the NWDAF distributes a learning model to the NF, and the NF performs inference using the learning model. However, since the NW information may also include privacy information that the mobile communication network operator cannot submit to the outside, there may be cases where the NW information cannot be transmitted to the NF.

[0007] In such a case, the configuration of vertical federated learning (VFL) disclosed in Non-Patent Document 2 can be applied. Specifically, as shown in FIG. 1 , the NWDAF generates intermediate information using network information as an input to learning model A and transmits the intermediate information to the NF. The NF performs inference using the intermediate information acquired from the NWDAF and APP information collected by the NF as input to learning model B. By applying the VFL configuration, the information transmitted from the NWDAF to the NF can be treated as intermediate information different from the network information, and the NF can perform inference to obtain analysis results.

[0008] 3GPP TS23.288, V18.3.0, September 2023 3GPP S2-2306424

[0009] When the VFL configuration is applied to the analysis service provided by NWDAF, NF determines the error in the output of learning model B and updates learning model B, and also calculates the error in the intermediate information and feeds this error in the intermediate information back to NWDAF, which then updates learning model A based on the error in the intermediate information.

[0010] However, when the NF (hereinafter referred to as the first NF) that has the APP information for obtaining the analysis results is different from the NF (hereinafter referred to as the second NF) that acquires the analysis results, no procedure is defined for the first NF to provide the analysis results to the second NF without sending the APP information to the second NF.

[0011] According to one aspect of the present disclosure, a network node of a mobile communication network comprises a first receiving means configured to receive first intermediate information generated by a first server using a first learning model from the first server, a first transmitting means configured to transmit second intermediate information based on the first intermediate information to a second server using a second learning model, a second receiving means configured to receive second error information indicating an error in the second intermediate information from the second server, and a second transmitting means configured to transmit first error information indicating an error in the first intermediate information derived from the second intermediate information to the first server.

[0012] Other features and advantages of the present invention will become apparent from the following description taken in conjunction with the accompanying drawings, in which the same or similar elements are designated by the same reference numerals.

[0013] An explanatory diagram of VFL. A system configuration diagram. A diagram showing an example of a sequence. A diagram showing an example of a learning configuration. A diagram showing an example of a learning configuration. A diagram showing an example of a network node configuration.

[0014] Hereinafter, the embodiments will be described in detail with reference to the accompanying drawings. Note that the following embodiments do not limit the scope of the invention as claimed, and not all combinations of features described in the embodiments are necessarily essential to the invention. Two or more of the features described in the embodiments may be arbitrarily combined. Furthermore, the same reference numerals are used for the same or similar components, and redundant explanations will be omitted.

[0015] Fig. 2 is a configuration diagram of a communication system according to this embodiment. According to Fig. 2, a core network 100 of a mobile communication network includes a network node 1 that implements an NWDAF and a network node 2 that implements a network repository function (NRF). Note that the network node 1 and the network node 2 may be the same network node. In the following description, the network node 1 is also referred to as an NWDAF, and the network node 2 is also referred to as an NRF.

[0016] The NWDAF can operate as a consumer NF within the mobile communication network or as a producer NF that provides analysis services to consumer NFs outside the mobile communication network. The NRF has a function of intermediating between producer NFs in the mobile communication network and consumer NFs that want to use the services provided by the producer NF. Note that the core network 100 also includes various network functions of types different from the NWDAF and NRF.

[0017] The server 4 in Figure 2 is located in a DN connected to the core network 100, for example, the Internet, and is operated by a service provider that provides services such as video distribution services to WDs in a mobile communication network. The server 4 implements a consumer NF that uses an analysis service provided by the NWDAF. The server 3 is located in a DN connected to the core network 100 and has the function of collecting APP information necessary for the service provider operating the server 4 to obtain analysis results, such as information on the location of WDs that use the services of the service provider operating the server 4. In the following description, the NF implemented in the server 3 will also be referred to as a first NF, and the NF implemented in the server 4 will also be referred to as a second NF.

[0018] The first NF can also operate as the second NF, and the second NF can also operate as the first NF. That is, a certain NF can operate to obtain necessary analysis results using APP information held by other NFs, and can also operate to provide the APP information held by the certain NF so that the other NFs can obtain analysis results.

[0019] 3 is a sequence diagram of a process for generating a learning model for providing the second NF with the analysis results requested by the second NF. Note that multiple servers, including servers 3 and 4, that can operate as consumer NFs of the NWDAF are assumed to have registered in advance in the NRF at least one of collectable APP information, desired analysis results, and information required to obtain the analysis results.

[0020] In S10, the NWDAF searches for information registered in the NRF to determine the NF requesting the analysis result and the NF that can collect the APP information required to obtain the analysis result, thereby determining the learning configuration. Here, it is assumed that the first NF has determined to provide the analysis result to the second NF based on the APP information that it can collect.

[0021] In S11, the NWDAF performs processing necessary to start learning with the first NF and the second NF. For example, the NWDAF may notify the first NF of APP information to be used and notify the second NF of the analysis results to be provided. In S12, the NWDAF distributes a first learning model to be used by the first NF to the first NF, and in S13, the NWDAF distributes a second learning model to be used by the second NF to start learning.

[0022] 4 shows a learning configuration in one embodiment. The first NF generates first intermediate information by using specified APP information (hereinafter, first APP information) as input to a first learning model. The first NF transmits the first intermediate information to the NWDAF in S14. The NWDAF generates second intermediate information by using the first intermediate information and NW information collected by the NWDAF from within the network as input to a third learning model. The NWDAF transmits the second intermediate information to the second NF in S15. The second NF obtains analysis results by using the second intermediate information and the APP information collected by the second NF (hereinafter, second APP information) as input to a second learning model.

[0023] Next, the second NF updates the second learning model based on the error of the analysis result, and calculates the error of the second intermediate information (hereinafter, the second error), and in S16, transmits second error information indicating the second error to the NWDAF. The NWDAF updates the third learning model based on the second error, and calculates the error of the first intermediate information (hereinafter, the first error), and in S17, transmits first error information indicating the first error to the first NF. The first NF updates the first learning model based on the first error. Thereafter, S14 to S17 are repeated.

[0024] With the above configuration, the first NF can provide the analysis results to the second NF without transmitting the information collected by the first NF to the second NF.

[0025] If the analysis result requested by the second NF does not require the second APP information, the second NF acquires the analysis result by inputting only the second intermediate information into the second learning model. If the analysis result requested by the second NF does not require NW information, the NWDAF does not use the third learning model and transmits the first intermediate information received from the first NF in S14 as the second intermediate information in S15. In this case, the NWDAF transmits the second error information received from the second NF in S16 as the first error information to the first NF in S17.

[0026] The learning configuration is not limited to that shown in FIG. 4 and can be configured as shown in FIG. 5, for example. In FIG. 5, the NWDAF uses a third learning model and a fourth learning model. The NWDAF generates third intermediate information by inputting the NW information to the third learning model, and generates second intermediate information by inputting the first intermediate information and the third intermediate information to the fourth learning model. The NWDAF also updates the fourth learning model based on the second error, calculates the error of the third intermediate information and the first error of the first intermediate information, and updates the third learning model based on the error of the third intermediate information.

[0027] Furthermore, the learning configuration may be as shown in FIG. 6 . In FIG. 6 , the NWDAF generates third interim information by inputting the NW information into a third learning model, and transmits the first interim information received from the first NF and the third interim information generated by the NWDAF to the second NF. In this case, the second interim information in S15 of FIG. 3 includes the first interim information and the third interim information. The second NF outputs the analysis result by inputting the first interim information and the third interim information included in the second interim information and the second APP information into the second learning model. Furthermore, the second NF updates the second learning model based on the error of the analysis result, calculates the first error of the first interim information and the third error of the third interim information, and transmits second error information including third error information indicating the first error information and the third error to the NWDAF in S16. The NWDAF updates the third learning model based on the third error, and transmits the first error information to the first NF.

[0028] <Device Configuration> Fig. 7 is a diagram showing an example configuration of a network node 1 that implements NWDAF. The network node 1 includes, for example, one or more processors and one or more memory devices. The one or more memory devices may include volatile memory devices and non-volatile memory devices. Each functional block shown in Fig. 7 may be realized by one or more processors executing a computer program stored in the one or more memory devices. The network node 1 may also be realized by a single device. Alternatively, the network node 1 may be realized by multiple devices that can communicate with each other. Note that Fig. 7 shows only parts necessary for understanding the embodiment, and the network node 1 may include other functional blocks not shown.

[0029] The first transmitting unit 15 and the second receiving unit 18 perform transmission and reception processing with the server 4 implementing the second NF, and the second transmitting unit 16 and the first receiving unit 17 perform transmission and reception processing with the server 3 implementing the first NF. Note that the first transmitting unit 15 and the first transmitting unit 16 can transmit signals via the same interface. Also, the first receiving unit 17 and the second receiving unit 18 can receive signals via the same interface.

[0030] The first receiving unit 17 may receive first interim information from the server 3. The first interim information is generated by the server 3 using the first APP information as input to the first learning model. The first transmitting unit 15 may transmit second interim information based on the first interim information to the server 4. The second receiving unit 18 may receive second error information indicating an error in the second interim information from the server 4. The server 4 may obtain an analysis result by using the second interim information as input to the second learning model, and may determine an error in the second interim information based on the error in the analysis result. The second transmitting unit 16 may transmit first error information indicating an error in the first interim information derived from the second interim information to the server 3.

[0031] In one embodiment, the second intermediate information may be the same as the first intermediate information, and the first error information may be the same as the second error information.

[0032] In one embodiment, the generator 11 may generate the second intermediate information by inputting the NW information and the first intermediate information collected by the network node 1 to the third learning model. In this case, the updater 12 updates the third learning model based on the second error information and derives the first error information.

[0033] In one embodiment, the generation unit 11 may generate third intermediate information by inputting the NW information collected by the network node 1 to a third learning model, and may generate second intermediate information by inputting the third intermediate information and the first intermediate information to a fourth learning model. In this case, the update unit 12 updates the fourth learning model based on the second error information, derives third error information indicating the error between the first error information and the third intermediate information, and updates the third learning model based on the third error information.

[0034] In one embodiment, the generator 11 generates third interim information by using the NW information collected by the network node 1 as input to the third learning model, and the first transmitter 15 may transmit information including the first interim information and the third interim information as second interim information to the server 4. In this case, the second receiver 18 receives information including first error information indicating an error in the first interim information and third error information indicating an error in the third interim information as second error information.

[0035] In one embodiment, the acquisition unit 14 may acquire the first learning model or the second learning model from a learning model stored in the network node 1 or a learning model stored in a storage device of the mobile communication network. Then, the first transmission unit 15 may transmit the second learning model to the server 4, and the second transmission unit 16 may transmit the first learning model to the server 3.

[0036] In one embodiment, the selection unit 13 can select server 3 and server 4 from multiple servers based on information that can be collected at the servers and information indicating the analysis results requested by the servers, which multiple servers including server 3 and server 4 have registered with the network node 2.

[0037] The present disclosure also provides a computer program, which, when executed by one or more processors in an apparatus having one or more processors, causes the apparatus to function as network nodes 1 and 2 or servers 3 and 4, and a computer-readable storage medium storing the computer program. The present disclosure also provides a method executed by network nodes 1 and 2 or a method executed by servers 3 and 4 with respect to the processing shown in Figure 3, a computer program that causes network nodes 1 and 2 or servers 3 and 4 to execute the method shown in Figure 3, and a computer-readable storage medium storing the computer program.

[0038] The invention is not limited to the above-described embodiment, and various modifications and variations are possible within the scope of the gist of the invention.

[0039] This application claims priority based on Japanese Patent Application No. 2024-031586, filed March 1, 2024, the entire contents of which are incorporated herein by reference.

Claims

1. A network node of a mobile communication network, comprising: a first receiving means configured to receive from a first server first intermediate information generated by the first server using a first learning model; a first transmitting means configured to transmit second intermediate information based on the first intermediate information to a second server using a second learning model; a second receiving means configured to receive from the second server second error information indicating an error in the second intermediate information; and a second transmitting means configured to transmit to the first server first error information indicating an error in the first intermediate information derived from the second intermediate information.

2. The network node according to claim 1, wherein the second intermediate information is the same as the first intermediate information, and the first error information is the same as the second error information.

3. The network node of claim 1, further comprising: a generation means configured to generate the second intermediate information by inputting the first information and the first intermediate information collected by the network node into a third learning model; and an update means configured to update the third learning model based on the second error information and derive the first error information.

4. The network node of claim 1, further comprising: a generating means configured to generate third intermediate information by inputting the first information collected by the network node into a third learning model, and to generate the second intermediate information by inputting the third intermediate information and the first intermediate information into a fourth learning model; and an updating means configured to update the fourth learning model based on the second error information, derive third error information indicating the error between the first error information and the third intermediate information, and update the third learning model based on the third error information.

5. The network node of claim 1, further comprising a generating means configured to generate third intermediate information by using the first information collected by the network node as input to a third learning model, wherein the second intermediate information includes the first intermediate information and the third intermediate information, and the second error information includes third error information indicating an error between the first error information and the third intermediate information.

6. A network node described in any one of claims 1 to 5, wherein the second learning model is transmitted by the first transmitting means to the second server, and the first learning model is transmitted by the second transmitting means to the first server.

7. A network node as described in any one of claims 1 to 6, wherein the first server generates the first intermediate information by using the second information as input to the first learning model, and the second server obtains analysis results by using the second intermediate information as input to the second learning model, and further comprises a selection means configured to select the first server and the second server from the plurality of servers based on information indicating information that can be collected at the server, which is registered by the plurality of servers in other network nodes of the mobile communications network, and information indicating analysis results requested by the server.

8. The network node of claim 7, wherein the other network node is a node implementing a Network Repository Function (NRF).

9. A network node according to any one of claims 1 to 8, wherein the network node is a node implementing a Network Data Analysis Function (NWDAF).

10. A computer-readable storage medium storing a program that, when executed by one or more processors of a device having one or more processors, causes the device to function as a network node according to any one of claims 1 to 9.

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