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

Abstract

A server comprising a transmission means configured to acquire intermediate information from a network node of a mobile communication network in use of an analysis service provided by the network node, and transmit, to the network node, propriety information indicating whether it is possible to use a mode for acquiring an analysis result by using the intermediate information as an input of a learning model.

Description

Network node, server and computer-readable storage medium for mobile communication network

[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. In the following description, a network function (NF) that provides a service is referred to as a producer NF, and an NF that uses (subscribes to) a service provided by the producer NF is referred to as 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) in 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 by subscribing the server (NF) operated by the service provider to a data analysis service.

[0005] The data analysis service may be provided in a first mode shown in FIG. 1A or a second mode shown in FIG. 1B. In the first mode, only the NWDAF uses the learning model. The NWDAF obtains analysis results by inputting information held by the mobile communication network (hereinafter, NW information) into the learning model, and transmits the obtained analysis results to the NF. In the second mode, the NWDAF uses the first learning model, and the NF uses the second learning model. The first learning model held by the NWDAF and the second learning model held by the NF may be generated from the learning model held by the NWDAF in the first mode. In the second mode, the NWDAF obtains intermediate information by inputting NW information into the first learning model, and transmits the obtained intermediate information to the NF. Then, the NF obtains analysis results by inputting the intermediate information into the second learning model.

[0006] 3GPP TS23.288, V18.3.0, September 2023

[0007] In the second mode, the NF can fine-tune the second learning model, thereby improving the accuracy of analysis. On the other hand, in the second mode, the NF needs to execute processing using the second learning model, which increases the computer resources required for the NF. For example, if the NF does not have sufficient computer resources, the second mode may not be available in the first place.

[0008] According to one aspect of the present disclosure, the server includes a transmitting means configured to, when using an analysis service provided by a network node of a mobile communication network, obtain intermediate information from the network node and transmit to the network node availability information indicating whether it is possible to use a mode in which an analysis result is obtained by using the intermediate information as input to a learning model.

[0009] 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.

[0010] An explanatory diagram of a mode of providing an analysis service. An explanatory diagram of a mode of providing an analysis service. A system configuration diagram. A diagram showing an example of a sequence. A flowchart of a process for determining a mode of provision. A diagram showing an example of the configuration of a network node. A diagram showing an example of the configuration of a server.

[0011] 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.

[0012] 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. In the following description, the network node 1 is also referred to as an NWDAF. The core network 100 also includes various network functions of different types from the NWDAF.

[0013] The NWDAF provides an analysis service. To provide the analysis service, the NWDAF stores one or more first learning models and one or more second learning models corresponding to the first learning models, or is configured to have access to a storage device that stores them. Each learning model is identified (specified) by an identifier. The identifier may be configured to identify a pair of a first learning model and a second learning model to be used with the first learning model. In the first mode, the NWDAF generates analysis results to be sent to the NF by using the first learning model and a second learning model corresponding to the first learning model. In the second mode, the NWDAF generates intermediate information to be sent to the NF by using the first learning model. Then, the NF generates analysis results by using the second learning model that is a pair of the first learning model used by the NWDAF.

[0014] 2, the server 3 is operated by, for example, a service provider that provides services such as video distribution services to wireless devices (WD) in a mobile communication network, and is located on an external data network (DN) connected to the core network 100, such as the Internet. The server 3 implements an NF (consumer NF) that uses the analysis service provided by the NWDAF. In the following description, the server 3 is also referred to as an NF.

[0015] FIG. 3 is a sequence diagram illustrating a process for starting to use an analysis service provided by an NWDAF. In S10, the NF transmits an analysis request message to the NWDAF. The analysis request message includes information necessary to identify the learning model to be used for the analysis, such as specific information indicating the desired analysis results (analysis content). Furthermore, the analysis request message may include availability information indicating whether the second mode can be used for the NF. This availability information may be set in advance for the NF by, for example, a service provider that operates the NF. If the availability information indicates that the second mode is applicable, the analysis request message further includes resource information indicating the amount of computer resources of the NF that can be used for inference processing using the second learning model.

[0016] In S11, the NWDAF determines whether to apply the first mode or the second mode to the NF based on information received from the NF together with the analysis request message. If it determines to apply the second mode, the NWDAF transmits to the NF a second learning model that outputs the analysis result requested by the NF in S12. Thereafter, the NWDAF generates intermediate information based on the first learning model, which is a pair of the second learning model distributed to the NF, and transmits it to the NF. The NF obtains the analysis result based on the intermediate information from the NWDAF.

[0017] If it is determined that the first mode is to be applied, the process of S12 is not performed. If it is determined that the first mode is to be applied, the NWDAF transmits the analysis result requested by the NF to the NF by using a pair of the first learning model and the second learning model that output the analysis result requested by the NF.

[0018] FIG. 4 is a flowchart of the determination process executed by the NWDAF in S11 of FIG. 3. In S20, the NWDAF determines whether the availability information received from the NF indicates that the second mode is applicable. If the second mode is not applicable, the NWDAF determines in S23 to apply the first mode. If the second mode is applicable, the NWDAF selects a second learning model that outputs the analysis results requested by the NF from the second learning models stored in the NWDAF or a storage device. The analysis results requested by the NF are indicated by specific information. Then, in S21, the NWDAF determines whether any of the selected second learning models can be executed by the NF. A second learning model that can be executed by the NF is a second learning model whose amount of computer resources required for execution is less than or equal to the amount of computer resources available to the NF. The amount of computer resources available to the NF is indicated in the resource information.

[0019] If there is no second learning model executable by the NF, the NWDAF determines to apply the first mode in S23. On the other hand, if there is a second learning model executable by the NF and the optional S24 is not executed, the NWDAF determines to apply the second mode in S22. If it is determined to apply the second mode, the NWDAF transmits the second learning model executable by the NF to the NF in S12 of Fig. 3. Note that if there are multiple second learning models executable by the NF, the NWDAF selects one second learning model from the multiple second learning models executable by the NF using an arbitrary method and transmits it to the NF.

[0020] With the above configuration, the first mode is applied to NFs that do not want to use the second mode or that cannot use the second mode due to limitations on the NF's computer resources, and the second mode is applied to NFs that allow use of the second mode and can use the second mode. Therefore, the form in which the analysis service is provided can be appropriately controlled.

[0021] Next, a case where optional S24 is executed will be described. In this case, in S24, NWDAF determines whether the amount of information in the intermediate information is equal to or less than the amount of information in the analysis result for each of one or more second learning models executable by NF. Here, if there is a second learning model whose amount of information in the intermediate information is equal to or less than the amount of information in the analysis result, NWDAF determines to apply the second mode. In this case, NWDAF transmits to NF a second learning model whose amount of information in the intermediate information is equal to or less than the amount of information in the analysis result. On the other hand, if the amount of information in the intermediate information is greater than the amount of information in the analysis result for all of one or more second learning models executable by NF, NWDAF determines to apply the first mode in S23.

[0022] By executing the optional step S24, it is possible to suppress an increase in the amount of information transmitted from the NWDAF to the NF.

[0023] Furthermore, if the NF already has a second learning model, the NF may indicate applicability in the availability information of the analysis request message and include an identifier of the second learning model held by the NF in the analysis request message. If the NF has the second learning model, the NWDAF may determine to apply the second mode. In this case, the NWDAF does not need to transmit the second learning model in S12 of FIG. 3. Furthermore, the NWDAF generates intermediate information using the first learning model corresponding to the second learning model notified by the NF.

[0024] In addition, if there are multiple other NWDAFs in the mobile communication network or if the NF has acquired the second learning model from another mobile communication network, the NWDAF may not be able to acquire the first learning model corresponding to the second learning model notified from the NF. Therefore, if the NWDAF cannot acquire the first learning model corresponding to the second learning model possessed by the NF, the NWDAF determines to apply the first mode.

[0025] <Device Configuration> FIG. 5 is a configuration diagram of a network node 1 according to some embodiments. 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. 5 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 capable of communicating with each other. Note that FIG. 5 shows only parts necessary for understanding the embodiments, and the network node 1 may include other functional blocks not shown.

[0026] The processing unit 11 performs processing necessary to provide analysis services such as collecting NW information, making inferences using the NW information, and generating intermediate information. The transmitting unit 14 and the receiving unit 13 perform transmission and reception processing with other NFs. For example, the receiving unit 13 is configured to receive availability information indicating whether the second mode is available from the server 3 that requests the use of the analysis service.

[0027] The determination unit 12 determines whether to apply the first mode or the second mode to the server 3. For example, when the determination unit 12 receives availability information from the server 3 indicating that the second mode cannot be used, the determination unit 12 determines to apply the first mode, and when the determination unit 12 receives availability information indicating that the second mode can be used, the determination unit 12 may determine whether to apply the first mode or the second mode based on the amount of computer resources available to the server 3 for processing using the learning model. Specifically, the determination unit 12 may determine to apply the second mode when the amount of computer resources available to the server 3 is equal to or greater than the amount of computer resources required for processing using the second learning model, and may otherwise determine to apply the first mode.

[0028] The determination unit 12 can further be used to determine a mode to apply to the server 3 based on the amount of information in the intermediate information and the amount of information in the analysis result. For example, the determination unit 12 can determine that the second mode is to be applied when the amount of computer resources available in the server 3 is equal to or greater than the amount of computer resources required for processing using the second learning model and the amount of information in the intermediate information is equal to or less than the amount of information in the analysis result, and that the first mode is to be applied in other cases.

[0029] When the determination unit 12 receives from the server 3 an identifier of the second learning model together with availability information indicating that the second mode can be used, the determination unit 12 determines whether the processing unit 11 can generate intermediate information to be input to the second learning model identified by the identifier and transmit it to the server 3. That is, the determination unit 12 determines whether the processing unit 11 can use the first learning model corresponding to the second learning model identified by the identifier. Then, if the processing unit 11 cannot generate intermediate information to be input to the second learning model identified by the identifier and transmit it to the server 3, the determination unit 12 may determine to apply the first mode, and otherwise determine to apply the second mode.

[0030] FIG. 6 is a configuration diagram of a server 3 according to some embodiments. The server 3 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. 6 may be realized by one or more processors executing a computer program stored in the one or more memory devices. The server 3 may also be realized by a single device. Alternatively, the server 3 may be realized by multiple devices capable of communicating with each other. Note that FIG. 6 shows only parts necessary for understanding the embodiments, and the server 3 may include other functional blocks not shown.

[0031] The processing unit 31 performs processing necessary for using the analysis service provided by the network node 1. The transmitting unit 33 and the receiving unit 32 perform transmission processing and reception processing with the network node 1. For example, the transmitting unit 33 transmits availability information indicating whether the second mode can be used when using the analysis service to the network node 1. Furthermore, when transmitting availability information indicating that the second mode can be used, the transmitting unit 33 transmits resource information indicating the amount of computer resources available in the server 3 for processing using the second learning model to the network node 1. Furthermore, when the processing unit 31 has the second learning model, the transmitting unit 33 transmits an identifier of the second learning model held by the processing unit 32 to the network node 1 together with availability information indicating that the second mode can be used.

[0032] The present disclosure provides a computer program, which, when executed by one or more processors of an apparatus having one or more processors, causes the apparatus to function as the network node 1 or the server 3, and a computer-readable storage medium storing the computer program. Furthermore, the present disclosure provides a method executed by the network node 1 or the server 3 with respect to the processes shown in Figures 3 and 4, a computer program causing the network node 1 / server 3 to execute the method shown in Figures 3 and 4, and a computer-readable storage medium storing the computer program.

[0033] 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.

[0034] This application claims priority based on Japanese Patent Application No. 2024-031587, filed March 1, 2024, the entire contents of which are incorporated herein by reference.

Claims

1. A server comprising a transmitting means configured to, when using an analysis service provided by a network node of a mobile communication network, acquire intermediate information from the network node and transmit to the network node availability information indicating whether a mode for acquiring an analysis result by using the intermediate information as input to a learning model can be used.

2. A server as described in claim 1, wherein when transmitting the availability information indicating that the mode can be used, the transmitting means is configured to transmit resource information to the network node indicating the amount of computer resources available to the server for processing using the learning model.

3. A server as described in claim 1 or 2, wherein, when the server has the learning model, the transmitting means is configured to transmit to the network node the availability information indicating that the mode can be used and an identifier of the learning model held by the server.

4. A server according to any one of claims 1 to 3, wherein the network node is a node implementing a Network Data Analysis Function (NWDAF).

5. 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 server according to any one of claims 1 to 4.

6. A network node of a mobile communication network that provides an analysis service, comprising: a receiving means configured to receive, from a server requesting the use of the analysis service, availability information indicating whether a second mode for obtaining analysis results in the server can be used by transmitting intermediate information to the server and using the intermediate information as input for a learning model; and a determining means configured to, when receiving the availability information indicating that the second mode cannot be used, determine that the network node should apply to the server a first mode for transmitting analysis results to the server, and, when receiving the availability information indicating that the second mode can be used, determine whether to apply the first mode or the second mode to the server based on the amount of computer resources available to the server for processing using the learning model.

7. A network node as described in claim 6, wherein the determination means is configured to determine that the second mode is to be applied to the server if the amount of computer resources available to the server is equal to or greater than the amount of computer resources required for processing using the learning model, and to determine that the first mode is to be applied to the server otherwise.

8. The network node described in claim 6, wherein the determination means is configured to determine that the second mode is to be applied to the server if the amount of computer resources available to the server is equal to or greater than the amount of computer resources required for processing using the learning model and the amount of information in the intermediate information is equal to or less than the amount of information in the analysis result, and to determine that the first mode is to be applied to the server otherwise.

9. A network node as described in any one of claims 6 to 8, wherein when the determination means receives from the server an identifier of the learning model together with the availability information indicating that the second mode can be used, the determination means determines to apply the second mode to the server if the intermediate information to be used as input to the learning model identified by the identifier can be sent to the server, and determines to apply the first mode to the server if the intermediate information to be used as input to the learning model identified by the identifier cannot be sent to the server.

10. A network node according to any one of claims 6 to 9, wherein the network node is a node implementing a Network Data Analysis Function (NWDAF).

11. 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 6 to 10.

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