Network node, server, and program for mobile communication network
By allowing NWDAF to dynamically select between first and second modes based on NF resource availability, the mechanism optimizes resource usage in mobile communication networks, ensuring efficient analysis service delivery.
Patent Information
- Application Number
- JP2024031587
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- Filing Date
- 2024-03-01
- Publication Date
- 2025-09-11
AI Technical Summary
In mobile communication networks, the second mode of using a learning model by network functions (NFs) requires significant computer resources, which can be a barrier for NFs lacking sufficient resources, limiting the availability of this mode.
A mechanism is introduced where a network data analysis function (NWDAF) determines whether to use the first or second mode based on the NF's availability and resource information, selectively transmitting learning models and intermediate information to optimize resource usage.
This approach allows for efficient resource allocation, enabling NFs to utilize the second mode when feasible and reducing unnecessary processing, thereby enhancing the provision of analysis services.
Smart Images
Figure 2025133563000001_ABST
Abstract
Description
[Technical Field]
[0001] The present disclosure relates to a technology for utilizing learning models in a mobile communication system. [Background technology]
[0002] Non-Patent Document 1 discloses a configuration for data analytics services 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 a 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 data analysis services is provided in the 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, such as a server located on the Internet. The server is operated, for example, by a service provider that provides a specific service, such as 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. 1(A) or a second mode shown in FIG. 1(B). 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. [Prior art documents] [Non-patent literature]
[0006] [Non-Patent Document 1] 3GPP TS23.288,V18.3.0,September 2023 Summary of the Invention [Problem to be solved by the invention]
[0007] In the second mode, the NF can fine-tune the second learning model, improving analysis accuracy. However, in the second mode, the NF must execute processing using the second learning model, which increases the computer resources required by 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] The present disclosure provides a mechanism for appropriately controlling the manner in which analysis services are provided. [Means for solving the problem]
[0009] According to one aspect of the present disclosure, the server includes a transmitting means for transmitting, to the network node, availability information indicating whether it is possible to use a mode in which, when using an analysis service provided by a network node of a mobile communication network, intermediate information is obtained from the network node and the intermediate information is used as input to a learning model to obtain an analysis result. [Effects of the Invention]
[0010] According to the present disclosure, a mechanism for appropriately controlling the manner in which analysis services are provided is provided. [Brief explanation of the drawings]
[0011] [Figure 1] FIG. 1 is an explanatory diagram of a form of providing an analysis service. [Figure 2] system configuration diagram. [Figure 3] FIG. [Figure 4] 10 is a flowchart of a process for determining a provision format. [Figure 5] FIG. 1 is a diagram showing an example of the configuration of a network node. [Figure 6] FIG. 2 is a diagram showing an example of the configuration of a server. DETAILED DESCRIPTION OF THE INVENTION
[0012] 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.
[0013] 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. Note that the core network 100 also includes various network functions of types different from the NWDAF.
[0014] The NWDAF provides an analysis service. To provide the analysis service, the NWDAF stores one or more first learning models and one or more corresponding second learning models, or is configured to have access to a storage device that stores them. Each learning model is identified by an identifier. The identifier may be configured to identify a pair of the 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. The NF then generates analysis results by using a second learning model that is a pair of the first learning model used by the NWDAF.
[0015] 2, the server 3 is operated by, for example, a service provider that provides services such as video distribution services to wireless devices (WDs) 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.
[0016] FIG. 3 is a sequence diagram for starting to use the analysis service provided by the 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 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. The 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.
[0017] 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 along with the analysis request message. If it determines to apply the second mode, the NWDAF transmits to the NF a second learned model that outputs the analysis results requested by the NF in S12. Thereafter, the NWDAF generates intermediate information based on the first learned model, which is a pair of the second learned model distributed to the NF, and transmits it to the NF, and the NF obtains the analysis results based on the intermediate information from the NWDAF.
[0018] 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.
[0019] 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 S20, 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 equal to or less than the amount of computer resources available to the NF. The amount of computer resources available to the NF is indicated in the resource information.
[0020] If there is no second learned model executable by the NF, the NWDAF determines to apply the first mode in S23. On the other hand, if there is a second learned model executable by the NF and the optional S21 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 learned model executable by the NF to the NF in S12 of FIG. 3. Note that if there are multiple second learned models executable by the NF, the NWDAF selects one second learned model from the multiple second learned models executable by the NF using an arbitrary method and transmits it to the NF.
[0021] With the above configuration, the first mode is applied to NFs that do not want to use the second mode or to NFs 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 the use of the second mode and can use the second mode. Therefore, it is possible to appropriately control the form in which analysis services are provided.
[0022] Next, the case where optional S21 is executed will be described. In this case, in S21, the 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 the 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, the NWDAF determines to apply the second mode. In this case, the NWDAF transmits to the NF the 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 the NF, the NWDAF determines to apply the first mode in S23.
[0023] By performing optional S21, the amount of information transmitted from the NWDAF to the NF can be reduced.
[0024] Furthermore, if the NF already has the 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.
[0025] In addition, if there are multiple other NWDAFs in the mobile communication network or if the NF has acquired the second learned model from another mobile communication network, the NWDAF may not be able to acquire the first learned model corresponding to the second learned model notified from the NF. Therefore, if the NWDAF cannot acquire the first learned model corresponding to the second learned model possessed by the NF, the NWDAF determines to apply the first mode.
[0026] <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.
[0027] 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 receiving unit 13 perform transmission processing 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 requesting the use of the analysis service.
[0028] 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 may determine 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.
[0029] 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 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 applied in other cases.
[0030] When the determination unit 12 receives from the server 3 the 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.
[0031] 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. The functional blocks shown in FIG. 6 may be realized by one or more processors executing computer programs 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.
[0032] The processing unit 31 performs processing required to use 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.
[0033] According to the present disclosure, there is provided 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 a network node 1 or a server 3, and a computer-readable storage medium storing the computer program. Furthermore, according to the present disclosure, there is provided a method executed by the network node 1 or a method executed by the server 3 regarding the processes shown in Figures 3 and 4, a computer program that causes 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.
[0034] This provides a mechanism for appropriately controlling the delivery of analytical services, making it possible to contribute to Goal 9 of the United Nations' Sustainable Development Goals (SDGs), which is to "Build resilient infrastructure, promote sustainable industrialization and foster innovation."
[0035] 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. [Explanation of symbols]
[0036] 31: Processing unit, 32: Receiving unit, 33: Transmitting unit
Claims
1. a server, A server comprising a transmitting means for transmitting to the network node whether or not it is possible to use a mode in which, when using an analysis service provided by a network node of a mobile communication network, intermediate information is acquired from the network node and the mode in which analysis results are acquired by using the intermediate information as input to a learning model.
2. The server described in claim 1, wherein when transmitting the availability information indicating that the mode can be used, the transmitting means transmits resource information to the network node indicating the amount of computer resources available to the server for processing using the learning model.
3. The server of claim 1, wherein, when the server has the learning model, the transmitting means transmits 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. The server of claim 1 , wherein the network node is a node implementing a Network Data Analysis Function (NWDAF).
5. A program that, when executed by one or more processors of a device having one or more processors, causes the device to function as the 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 for transmitting intermediate information from a server requesting the use of the analysis service to the server and receiving availability information indicating whether a second mode for obtaining an analysis result in the server can be used by using the intermediate information as an input for a learning model; a determination means for determining to apply to the server a first mode in which the network node transmits an analysis result to the server when the availability information indicating that the second mode is not available is received, and for determining whether to apply to the server the first mode or the second mode based on the amount of computer resources available to the server for processing using the learning model when the availability information indicating that the second mode is available is received; A network node comprising:
7. The network node described in claim 6, wherein the determination means determines 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 otherwise determines that the first mode is to be applied to the server.
8. The network node described in claim 6, wherein the determination means determines that the second mode is to be applied to the server if the amount of computer resources available to the server is greater than or equal to the amount of computer resources required for processing using the learning model and the amount of information of the intermediate information is less than or equal to the amount of information of the analysis result, and otherwise determines that the first mode is to be applied to the server.
9. The network node described in claim 6, 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. The network node of claim 7 , wherein the network node is a node implementing a Network Data Analysis Function (NWDAF).
11. 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.