Information processing device, information processing method, and communication system

Abstract

The present invention enables suitable network control based on prediction of quality of service experience (QoE). With respect to communication of interest, the present invention acquires, from at least one network node, at least one of first information indicating the result of observation of QoE relating to the communication of interest or second information indicating the result of observation of communication performance relating to the communication; and generating a learning model for predicting a change in QoE assuming a specific network control parameter is applied, using at least one of the first information or the second information.

Description

Information processing device, information processing method, and communication system

[0001] The present disclosure relates to an information processing device, an information processing method, and a communication system.

[0002] There is a wireless communication system that reduces congestion in a cellular network by allocating the connection destinations of a group of terminals from a cellular base station to a nearby wireless LAN access point (see, for example, Patent Document 1). There is also a wireless communication device that transmits channel selection order information stored in a storage device as information indicating the priority according to the communication quality for each of a plurality of channels used for wireless communication to one or more partner devices (see, for example, Patent Document 2).

[0003] JP 2014-551800 A JP 2018-201238 A

[0004] An object of the present disclosure is to provide an information processing device and an information processing method that enable suitable network control based on prediction of quality of experience (QoE).

[0005] One aspect of the present disclosure is an information processing device that includes a control unit that performs the following operations: acquiring, from at least one network node, at least one of first information indicating observed results of quality of service experience (QoE) related to a target communication and second information indicating observed results of communication performance related to the communication; and using at least one of the first information and the second information, generating a learning model that predicts changes in QoE when a specific network control parameter is applied.

[0006] One aspect of the present disclosure is an information processing device that includes a control unit that transmits one or more network control parameter sets related to a target communication to a network node, receives a response to the one or more network control parameter sets from the network node, and selects a network control parameter set to be used for controlling the communication from the one or more network control parameter sets based on the response.

[0007] Other aspects include a communication system, an information processing method corresponding to the above-mentioned information processing device, a program for causing a computer to execute the information processing method, or a computer-readable storage medium non-temporarily storing the program.

[0008] According to the present disclosure, it is possible to perform suitable network control based on prediction of service quality of experience (QoE).

[0009] 1(A) and 1(B) are explanatory diagrams of a fifth generation mobile communication system (5G) network. FIG. 2 is a diagram showing a configuration example of an information processing device. FIG. 3 is a sequence diagram showing a first operation example of the communication system. FIG. 4 is a sequence diagram showing a second operation example of the communication system. FIG. 5 is a sequence diagram showing a third operation example of the communication system. FIG. 6 is a sequence diagram showing a fourth operation example of the communication system.

[0010] A communication system and a communication control method according to an embodiment will be described below with reference to the drawings. The configurations of the embodiments are examples, and the present disclosure is not limited to the configurations of the embodiments.

[0011] <Configuration of communication system> Figure 1 (A) shows the components (entities) that make up a fifth-generation mobile communication system (5G network). In Figure 1 (A), UE (User Equipment) 2 is a user (subscriber) terminal. RAN (Radio Access Network) 3 is an access network to the 5G core network (5GC). RAN 3 is composed of a base station (gNB) 3A. The 5G network consists of 5GC and RAN 3, and UE 2, DN (Data Network) 5, and AF (Application Function) 12 are connected to the 5G network. DN 5 is a network outside 5GC, such as the Internet. AF 12 is a general-purpose network function that provides auxiliary services outside 5GC. For example, AF 12 is an application executed on an external server. Each of NFs 11a to 11e and 11g to 11k is a function realized, for example, by one or more computers (information processing devices) executing a program. Although a 5G network is used as an example in this specification, the mobile communication system may be an LTE or a mobile communication network beyond 5G.

[0012] 5GC is composed of a set of components (called network nodes) having predetermined functions called NFs (Network Functions). FIG. 1A shows the following NFs 11 that make up 5GC: UPF (User Plane Function) 11a, AMF (Access and Mobility Management Function) 11b, SMF (Session Management Function) 11c, PCF (Policy Control Function) 11d, NEF (Network Exposure Function) 11e, NRF (Network Repository Function) 11g, NSSF (Network Slice Selection Function) 11h, AUSF (Authentication Server Function) 11i, UDM (Unified Data Management) 11j, and NWDAF (Network Data Analytics Function) 11k.

[0013] The UPF 11a performs routing and forwarding of user packets (user plane packets transmitted and received by the UE2), packet inspection, and QoS processing. The AMF 11b is a device accommodating the UE2 in the area where it is located in 5GC. The AMF 11b accommodates the RAN3 and performs subscriber authentication control and location (mobility) management of the UE2. The UDM 11j provides subscriber information, or acquires, registers, deletes, and changes the status of the UE2.

[0014] The SMF 11c manages a PDU (Protocol Data Unit) session and controls the UPF 11a to implement QoS control and policy control. The PDU session is a virtual communication path for transmitting and receiving data between the UE 2 and a DN (Data Network) 5. The DN 5 is a data network (such as the Internet) outside the 5GC.

[0015] The PCF 11d performs QoS control, policy control, billing control, etc. under the control of the SMF 11c. QoS control controls the quality of communication, such as priority packet forwarding. Policy control controls communication, such as QoS based on network or subscriber information, packet forwarding availability, and billing. The NEF 11e mediates communication between external nodes such as the AF (Application Function) 12 and nodes within the control plane. The AF 12 is, for example, an information processing device (server, terminal, etc.) that has an application installed outside the 5GC.

[0016] The NRF 11g stores and manages information on NFs (for example, AMF, SMF, UPF, etc.) within 5GC. In response to an inquiry regarding an NF desired to be used, the NRF 11g can return multiple NF candidates to the inquiry source.

[0017] The NSSF 11h has a function of selecting a network slice to be used by a subscriber from among the network slices generated by network slicing. A network slice is a virtual network having specifications according to the application.

[0018] The AUSF 11i is a subscriber authentication server that performs subscriber authentication under the control of the AMF 11b. The UDM 11j is a database that stores subscriber-related information, etc. The NWDAF 11k is an NF that has the function of collecting and analyzing data from each NF 11, the OAM terminal 8 (FIG. 1B), the AF 12, etc., and provides network analysis information.

[0019] Each NF forming the 5GC is composed of one or more information processing devices (general-purpose devices or appliances (dedicated devices)). The information processing devices are installed in a special building called a data center. A data center is also called a station building. As shown in FIG. 1(B), one or more data centers 6 are placed within the communication area of ​​the 5GC (FIG. 1(B) shows an example of 3), and the data centers 6 are connected by communication lines 7. Each data center 6 is provided with an OAM (Operations, Administration, and Maintenance) terminal 8. The OAM terminal 8 has the function of operating, managing, and maintaining the network (5GC).

[0020] In 5GC, multiple NFs of the same type may be prepared. For example, an NF 11 may be prepared for each data center 6. Also, one NF 11 may be shared between data centers 6. Also, multiple NFs 11 of the same type may be configured in one data center 6. The number of data centers 6, the number of NFs 11, and the correspondence between the NFs 11 and the data centers 6 may be set as appropriate.

[0021] <Configuration of Information Processing Device> Figure 2 is a diagram showing an example configuration of an information processing device that can operate as each of the UE 2, NFs 11a to 11k, OAM terminal 8, and AF 12. In Figure 2, the information processing device 20 can be configured using a dedicated or general-purpose information processing device (computer) such as a personal computer (PC), a workstation (WS), or a server machine. However, the information processing device 20 may also be a collection (cloud) of one or more computers.

[0022] The information processing device 20 includes a processor 21 as a processing unit or control unit (controller), a storage device 22, a communication interface 23 (communication IF23), an input device 24, and a display 25, all of which are interconnected via a bus 26.

[0023] The storage device 22 includes a main storage device and an auxiliary storage device. The main storage device is used as at least one of a storage area for programs and data, a program development area, a program work area, and a buffer area for communication data. The main storage device is configured with RAM (Random Access Memory) or a combination of RAM and ROM (Read Only Memory). The auxiliary storage device is used as a storage area for data and programs. A non-volatile storage medium is used as the auxiliary storage device. Examples of non-volatile storage media include a hard disk, a solid state drive (SSD), a flash memory, and an EEPROM (Electrically Erasable Programmable Read-Only Memory). The storage device 22 may also include a drive device for a disk recording medium.

[0024] The communication IF 23 is a circuit that performs communication processing. For example, the communication IF 23 is a network interface card (NIC). The communication IF 23 may also be a wireless communication circuit that performs wireless communication (5G, wireless LAN (Wi-Fi), BLE, etc.). The communication IF 23 may also be a combination of a circuit that performs wired communication processing and a wireless communication circuit.

[0025] The input device 24 includes keys, buttons, a pointing device, a touch panel, etc., and is used to input information. The display 25 is, for example, a liquid crystal display, etc., and displays information and data.

[0026] The processor 21 performs various processes by executing various programs stored in the storage device 22. By the processor 21 executing the programs stored in the storage device 22, the information processing device 20 can operate as each of the UE 2, NFs 11a to 11k, OAM terminal 8, and AF 12 (external server).

[0027] The processor 21 is, for example, a central processing unit (CPU). A CPU is also called a microprocessor unit (MPU). The processor 21 may have a single processor configuration or a multiprocessor configuration. Furthermore, a single physical CPU connected via a single socket may have a multi-core configuration. The processor 21 may include various arithmetic devices with various circuit configurations, such as a digital signal processor (DSP) or a graphics processing unit (GPU). The processor 21 may also be configured to cooperate with at least one of an integrated circuit (IC), other digital circuit, and analog circuit. Examples of integrated circuits include an LSI, an application-specific integrated circuit (ASIC), and a programmable logic device (PLD). Examples of PLDs include a CPLD and a field-programmable gate array (FPGA). The processor 21 also includes, for example, what is called a microcontroller (MCU), a system-on-a-chip (SoC), a system LSI, or a chipset. The above-mentioned processor 21, ASIC, PLD, MCU, SoC, chipset, and other processing execution entities are examples of "circuitry."

[0028] The communication system of this embodiment extends the communication quality prediction function provided by the 5GC NWDAF11k and provides a mechanism for simulating the impact of changes to control parameters by the NF11 on the quality of service experience (QoE) before the changes are applied.

[0029] When the NF 11 sets the network control parameters, it is required to apply an option that has a good balance between resource consumption and QoE from among multiple setting candidates. However, QoE is a product of the combined effects of the properties and behaviors of many network components, including the AF 12, and it is difficult for each NF 11 to accurately predict changes in QoE caused by changes in the control parameters.

[0030] Therefore, in the communication system according to this embodiment, a new interface is defined for notifying the NWDAF 11k of candidate control parameters that the NF 11, referred to as a "consumer NF (NF)," can apply in the future. The NWDAF 11k predicts the conditional QoE assuming that each candidate control parameter is applied, using a prediction model (an example of a learning model) learned based on communication performance and QoE observation information collected from the NF 11 and the AF 12, referred to as a "producer NF (NF)." The NWDAF 11k responds to the consumer NF with predicted performance values ​​for these multiple hypotheses. The consumer NF can refer to the predicted performance values, select the optimal candidate setting, and apply it to actual network control.

[0031] 3 is a sequence diagram showing a first operation example of the communication system shown in FIG. 1A. The first operation example is performed by a consumer NF, an NWDAF 11k, a producer NF, an NEF 11e, and an AF 12 (one or more information processing devices operating as the consumer NF, the NWDAF 11k, the producer NF, the NEF 11e, and the AF 12, respectively). The number of consumer NFs and producer NFs is an appropriate number of one or more. The consumer NFs and producer NFs may be existing NFs or new NFs. Furthermore, the operation of the NWDAF 11k in the first operation example may be performed by an existing NF other than the NWDAF 11k or a new NF.

[0032] The operation performed in the first operation example is composed of a learning phase and an inference phase. In the learning phase, the NWDAF 11k (the information processing device 20 operating as the NWDAF 11k) performs the following operations.

[0033] 1. The NWD AF 11k collects QoE observation information and communication performance observation information. The QoE observation information can be collected from the AF 12, and the communication performance observation information can be collected from the AF 12 and / or one or more producer NFs.

[0034] The QoE observation information is information indicating the QoE obtained by the AF 12 observing the target communication. The communication performance observation information is information indicating the communication performance (transfer speed, latency, etc.) obtained by the AF 12 and / or the producer NF observing the target communication. The QoE observation information is an example of first information indicating the observation result of the QoE related to the target communication, and the communication performance observation information is an example of second information indicating the observation result of the communication performance related to the communication.

[0035] For example, a message requesting QoE observation information or communication performance observation information is transmitted from the NWDAF 11k to a target NF, and a response message corresponding to the request is transmitted from the target NF to the NWDAF 11k.

[0036] When the collection target is QoE observation information, the target NF is the AF 12, and when the collection target is communication performance observation information, the target NF is at least one of the AF 12 and the producer NF. Note that the message requesting communication performance observation information may be obtained from both the AF 12 and the producer NF, or may be obtained from only one of them.

[0037] When the NWDAF 11k communicates with the AF 12, the request message is "Naf_EventExposure_Subscribe (EventID = Service Experience Information)" and the response message is "Naf_EventExposure_Notify". When the NWDAF 11k communicates with the producer NF, the request message is "Nnf_EventExposure_Subscribe (EventID = Service Experience Information)" and the response message is "Nnf_EventExposure_Notify".

[0038] 2. The NWDAF 11k uses the history of QoE observation information and communication performance observation information as learning data to learn a "conditional QoE prediction model." The conditional QoE prediction model has a function of predicting changes in QoE when specific network control parameters are applied in a given communication environment.

[0039] In the inference phase, the following operations are performed: 1. A consumer NF (NF (consumer)) initiates a network control parameter change procedure when the required communication quality (QoS) for the target communication is not satisfied or is predicted to become unsatisfied in the future. 2. The consumer NF generates one or more candidates for new network control parameters (referred to as candidate parameters). 3. The consumer NF sends a message to the NWDAF 11k requesting a parameter update simulation test (conditional QoE prediction) with one or more candidate parameters (network control parameter sets) as arguments (<1> in FIG. 3). 4. Upon receiving the request, the NWDAF 11k collects QoE observation information from the AF 12 and communication performance observation information from at least one of the AF 12 and the producer NF (NF (producer)) (<2> in FIG. 3). That is, QoE observation information and communication performance observation information are collected through message exchanges similar to those performed in 1 of the learning phase. 5. The NWDAF 11k receives the QoE observation information, communication performance observation information, and candidate parameters as input, and uses a conditional QoE prediction model to calculate the conditional QoE expected when the candidate parameters are applied. 6. The NWDAF 11k returns a set of the candidate parameters and the conditional predicted QoE value to the consumer NF. 7. The consumer NF selects the candidate parameters that offer an excellent balance between resource consumption and predicted QoE, and applies them to actual network control.

[0040] The communication system according to the embodiment performs the operation shown in FIG. 3 , thereby achieving the following advantageous effects. That is, the consumer NF can select and apply optimal or suitable network control parameter settings after estimating in advance how a change in the setting parameters (e.g., an increase or decrease in the amount of communication bandwidth allocated to a certain network slice) will affect the QoE. This makes it possible to quickly adapt to changes in communication conditions and provide a stable QoE, compared to a control method that searches for optimal network control parameters by trial and error while repeatedly changing the setting of the network control parameters and observing the actual QoE.

[0041] Examples of entities that utilize the above-mentioned communication system include automobile manufacturers that operate communication services for connected cars (for example, controlling the behavior of the communication network via NEF11e), or communication network operators.

[0042] In the operation example shown in Fig. 3, the following two service operations are newly defined between the consumer NF and the NWDAF 11k. The service operations are performed by sending and receiving messages. (1) Nnwdaf_Simulation_Request: This message requests the NWDAF 11k to predict the QoE assuming that the candidate parameters specified by the arguments are applied. In <1> of Fig. 3, this message is sent from the consumer NF to the NWDAF 11k. (2) Nnwdaf_Simulation_Request Response: This message responds to the requesting NF with the QoE prediction result. In <4> of Fig. 3, this message is sent from the NWDAF 11k to the consumer NF.

[0043] Examples of candidate parameters include, but are not limited to, the following: communication bands allocated to UEs 2 or network slices; UEs 2 or UE groups whose registration with the network is denied; UEs or UE groups whose communication with the network is cut off; and communication quality (QoS) settings.

[0044] For example, in 5GC, if the required communication quality is not met currently or in the future, it is possible to reduce the number of UEs 2 connected to the 5G network by refusing to register one or more specified UEs 2 with the 5G network (location registration). Alternatively, it is possible to reduce the load on the 5G network by throttling (reducing) the communication bandwidth of one or more specified UEs 2. The consumer NF may provide advance notification via the NEF11e and AF12 to UEs 2 whose registration with the 5G network is to be refused or whose communication bandwidth is to be throttled. Upon receiving the notification, the AF12 and NEF11e can take measures to prevent a decrease in QoE, such as expanding the reception buffer for video data provided to the UEs 2 or pre-caching web content that the user is likely to view in the future.

[0045] The NWDAF 11k can provide the following as examples of information collected from producer NFs and AFs: [Example of QoE observation information] "Service data related to Observed Service Experience" specified in Table 6.4.2-1 of 3GPP (registered trademark) TS 23.288, and "Performance information" specified in Table 6.4.2-1a (collected from the AF 12).

[0046] "Service Data for Observed Service Experience" may include, for example: - Application ID: collected, for example, from the AF. An identifier for identifying the service and supporting analysis for each service type (desired service level). - IP filter information: collected, for example, from the AF. Used to identify the UE's service flows for the application. - Application location: collected, for example, from the AF / NEF. The location of the application represented by a list of DNAIs. If the DNAIs used by the application are statically defined, the NEF can map the AF-Service-Identifier information to the list of DNAIs. - Service Experience: collected, for example, from the AF. Refers to the SLA and the QoE for each service flow established during the flight. For example, this can be either the MOS or video MOS specified in ITU-T P.1203.3

[0011] , or a customized MOS for any type of service, including those not related to video or voice. - UE ID: collected, for example, from the AF. A list of UE IDs associated with the Service Experience value. If the AF is not trusted, GPSI is provided. If the AF is trusted, a SUPI is provided. Service experience contribution weights: e.g., collected from the AF. A list of service experience contribution weights associated with each provided UE ID. QoE metrics: e.g., collected from the UE via the AF. QoE metrics observed at the UE. QoE metrics and measurements described in TS 26.114

[0027] , TS 26.247

[0028] , TS 26.118

[0029] , TS 26.346

[0030] , TS 26.512

[0031] , or ASP-specific QoE metrics in TS 26.512

[0031] agreed in the SLA with the MNO may be used. Timestamp: e.g., collected from the AF. A timestamp associated with the service experience provided by the AF. Required if the service experience is provided by an ASP.Application Server Instance: Collected e.g. from the AF. The IP address or FQDN of the application server with which the UE had a communication session when the measurement was made.

[0047] The performance information may include, for example: ・UE identifier: for example, the IP address of the UE being measured. ・UE location: for example, the location of the UE when the performance measurement is made. ・Application ID: for example, the service and support analysis (desired level of service) for each service type. ・IP filter information: for example, used to identify the UE's service flow for the application. ・Locations of Application: for example, indicating the location of the application indicated by the list of DNAIs. ・Application Server Instance address: for example, the IP address / FQDN of the application server with which the UE had a communication session when the measurement was made. ・Performance Data: for example, performance related to the communication session between the UE and the application server: average packet delay, average loss rate, throughput, etc. ・Timestamp: for example, a timestamp associated with the performance data provided by the AF.

[0048] [Example of communication performance observation information] Other data includes the history of communication traffic generated by the UE 2, future communication demand, and data that serves as an indicator of QoE (collected from the AF 12). "QoS flow-level network data" specified in Table 6.4.2-2 of 3GPP (registered trademark) TS 23.288 (collected from the NF). "QoS flow-level network data" may include the following: - Timestamp: collected from the 5GC NF. A timestamp associated with the collected information. - Location information: collected, for example, from the AMF. UE location information (such as cell ID or TAI). - Finer granularity location (1 to maximum): collected, for example, from the GMLC. Indicates the location of the UE. ->UE location: GAD shape or location coordinates (see TS 23.032

[0034] ). ->Timestamp: A timestamp when the location was measured. ->LCS: The accuracy of the QoS measurement. QoS flow. ・UE ID: Collected, for example, from the AMF. List of SUPIs. If no UE ID is provided as the target of the Slice Service Experience Analytics Report, the AMF returns UE IDs that match the AMF event filter. ・DNN: Collected, for example, from the SMF. DNN of the PDU session including the QoS flow. ・S-NSSAI: Collected, for example, from the SMF. S-NSSAI of the PDU session including the QoS flow. ・Application ID: Collected, for example, from the SMF, used by the NWDAF to identify the application service provider and application of the QoS flow. ・DNAI: Collected, for example, from the SMF. Identifies the access to the DN to which the PDU session connects. ・PDU Session type: Collected, for example, from the SMF. Type of the PDU session. ・SSC Mode: Collected, for example, from the SMF. SSC mode selected for the PDU session. ・Access Type: Collected, for example, from the SMF. List of access types used in the PDU session. IP filter information: collected, for example, from the SMF.Used by the NWDAF to identify service data flows for QoS flow policy control and / or differentiated charging, and provided by the SMF. QFI: Collected, for example, from the SMF. QoS flow identifier. QoS Flow Bit Rate: Collected, for example, from the UPF. Observed bit rate in the uplink (UL) direction and observed bit rate in the downlink (DL) direction. QoS Flow Packet Delay: Collected, for example, from the UPF. Observed packet delay in the UL direction and observed packet delay for the DL direction. Packet Transmission: Collected, for example, from the UPF. Indicates the number of observed packet transmissions. Packet Retransmissions: Collected, for example, from the UPF or AF. Indicates the number of observed packet retransmissions.

[0049] "UE-level network data" (collection source is NF) specified in Tables 6.4.2-3 and 6.4.2-4 of 3GPP (registered trademark) TS 23.288. "UE-level network data" may include the following. The description of collection source is an example. - Timestamp: collected, for example, from OAM. Timestamp related to the collected information. - Reference signal received power: collected, for example, from OAM. Per-UE measurement of received power level in network cell. Includes SS-RSRP, CSI-RSRP specified in clause 5.5 of TS 38.331

[0014] , and E-UTRA RSRP specified in clause 5.5.5 of TS 36.331

[0015] . - Reference signal received quality: collected, for example, from OAM. - UE-specific measurements of received quality in the network cell, including SS-RSRQ, CSI-RSRQ as specified in TS 38.331

[0014] clause 5.5, and E-UTRA RSRQ as specified in TS 36.331

[0015] clause 5.5.5. - Signal-to-noise and interference ratio: collected, for example, from OAM. UE-specific measurements of received signal-to-noise and interference ratio in the network cell, including SS-SINR, CSI-SINR, and E-UTRA RS-SINR as specified in TS 38.215

[0012] clause 5.1. - DL and UL RAN throughput: collected, for example, from OAM. UE-specific measurements of DL and UL throughput as specified in TS 37.320

[0020] clauses 5.2.1.1 and 5.4.1.1. DL and UL RAN packet delay: collected, for example, from OAM. Per-UE measurements of DL and UL packet delay, including per-UE per-QCI packet delay as specified in TS 37.320

[0020] clause 5.2.1.1, and per-UE per-DRB packet delay as specified in TS 37.320

[0020] clause 5.4.1.1. DL and UL RAN packet loss rate: collected, for example, from OAM.Measured packet loss rates for DL ​​and UL per UE, including the packet loss rate per QCI per UE as specified in clause 5.2.1.1 of TS 37.320

[0020] , and the packet loss rate per DRB per UE as specified in clause 5.4.1.1 of TS 37.320

[0020] . Cell ID and frequency mapping information: collected, for example, from OAM. Cell ID and frequency mapping information. Cell energy saving status: collected, for example, from OAM. A list of cells in the target area that are in energy saving status, as specified in clauses 3.1 and 6.2 of TS 28.310

[0024] .

[0050] Furthermore, "UE level network data" may include the following. The description of the collection source is an example. - Timestamp: collected from 5GC NF. A timestamp associated with the collected information. - Location information: collected, for example, from AMF. UE location information (such as cell ID or TAI). - Finer granularity location: collected from GMLC. Indicates the location of the UE. - UE ID: collected, for example, from AMF. A list of SUPIs. - RAT Type: collected, for example, from SMF. The RAT type on which the UE is camped.

[0051] Examples of predicted QoE information that the NWDAF 11k responds to the consumer NF include the following: - The predicted QoE value specified in Table 6.4.3-2 of TS 23.288 of 3GPP (registered trademark) (a "conditional" predicted value is responded when the candidate parameters are applied) - The predicted QoE value may be expressed as a numerical value in the range from 0 to a predetermined maximum value max, or other expression methods may be adopted (for example, the probability distribution of the predicted value may be used to express the uncertainty of the predicted result).

[0052] QoE prediction values ​​may include the following: - Slice Instance Service Experience (0 to max): A list of observed service experience information for each network slice instance. -> S-NSSAI: Identifies the network slice. -> NSI ID: Identifies the network slice instance within the network slice. -> Network Slice Instance Service Experience: The service experience (average, variance) across applications of the network slice instance during the analytics period. -> SUPI List (0 to SUPImax): A list of SUPIs to which the slice instance's service experience applies. -> Ratio: An estimated proportion of UEs with similar service experiences (within a group or among all UEs). -> Spatial Validity: The area to which the network slice service experience analysis applies. -> Validity Period: The validity period of the network slice service experience analysis as defined in Section 6.1.3. -> Confidence: The confidence level of this prediction. - Application Service Experience (0 to max): A list of predicted service experience information for each application. -> S-NSSAI: Identifies the network slice used to access the application. -> Application ID: Identifier of the application. -> Service Experience Type: Type of service experience analysis, such as voice, video, etc. -> UE Location: Indicates UE location information (TAI list, gNB ID, location coordinates, etc.) when the UE service is delivered. -> UPF Information: Indicates the UPF providing the service to the UE. -> DNAI: Indicates which DNAI the UE service uses / camps on. -> DNN: DNN of the PDU session including the QoS flow. -> Application Server Instance Address: Indicates the application server instance (IP address of the application server) or the FQDN of the application server. -> Service Experience: Service experience (average, variance) for the period covered by the analytics. -> SUPI List (0 to SUPImax): List of SUPIs with the same application service experience.> Ratio: Estimated proportion of UEs with similar service experience (within a group or among all UEs). > Spatial validity: Area to which the application usage experience analysis applies. > Validity period: Validity period of the application service experience analysis as defined in subclause 6.1.3. > Confidence: Confidence level of this prediction. > RAT type: Indicates a list of RAT types to which the application service experience analysis applies. > Frequency: Indicates a list of carrier frequency values ​​of the UE's serving cell to which the application service experience analysis applies. > SSC mode: SSC mode selected for the PDU session used for association with the application. > PDU session type: Type of PDU session used for association with the application. > Access type: List of access types used for the application's PDU session.

[0053] 4 is a sequence diagram showing a second operation example of the communication system, in which the PCF 11d triggers processing as a consumer NF. In FIG. 4, the PCF 11d sends a message to the NWDAF 11k requesting a parameter update simulation test (conditional QoE prediction) with one or more candidate parameters as arguments (<1> in FIG. 4).

[0054] <2> to <4> in Fig. 4 are the same operations as in the first operation example. However, in <4> in Fig. 4, a response message from the NWDAF 11k is sent to the PCF 11d. In <5> in Fig. 4, the PCF 11d selects candidate parameters that provide the best balance between conditional QoE and resource consumption. In <6> in Fig. 4, the PCF 11d applies the selected candidate parameters to the producer NF.

[0055] FIG. 5 is a sequence diagram showing a third operation example of the communication system. In the third operation example, the NWDAF 11k triggers the process. In <0> of FIG. 5, the PCF 11 sets a trigger condition for the conditional QoE prediction. In <1> of FIG. 5, the PCF 11 starts the conditional QoE prediction when a predetermined trigger condition is met (for example, when a certain time has passed since the previous QoE prediction and the QoE or communication performance falls below a threshold). In the third operation example, the candidate parameters are generated by the NWDAF 11k. The operations from <2> to <6> of FIG. 5 are the same as those in the second operation example (FIG. 4), and therefore will not be described here.

[0056] Fig. 6 is a sequence diagram showing a fourth operation example of the communication system. In the fourth operation example, the AF 12 triggers the processing. In <0> of Fig. 6, the AF 12 detects a decrease in QoE and notifies the PCF 11d of the decrease in QoE via the NEF 11e. The operations of <1> to <6> of Fig. 6 are the same as those of the second operation example, and therefore will not be described here.

[0057] In the first to fourth operation examples described above, the information processing device 20 operating as NWDAF11k is an example of a first information processing device, and the consumer NF in the first operation example and the PCF11d in the second to fourth operation examples are examples of a second information processing device.

[0058] The above-described embodiment and modifications are merely examples, and the present disclosure may be modified as appropriate within the scope of the present disclosure. Furthermore, the processes and means described in the present disclosure may be freely combined and implemented as long as no technical contradiction occurs.

[0059] Furthermore, a process described as being performed by one device may be shared and executed by multiple devices. Alternatively, a process described as being performed by different devices may be executed by a single device. In a computer system, the hardware configuration (server configuration) by which each function is realized can be flexibly changed.

[0060] The present disclosure can also be realized by supplying a computer program that implements the functions described in the above embodiments to a computer, and having one or more processors in the computer read and execute the program. Such a computer program may be provided to the computer on a non-transitory computer-readable storage medium connectable to the computer's system bus or via a network. Non-transitory computer-readable storage media include any type of medium suitable for storing electronic instructions, such as any type of disk, including magnetic disks (e.g., floppy disks, hard disk drives (HDDs), etc.), optical disks (e.g., CD-ROMs, DVDs, Blu-ray disks), read-only memory (ROM), random access memory (RAM), EPROM, EEPROM, magnetic cards, flash memory, or optical cards.

Claims

1. An information processing device including a control unit that executes the following: acquiring, from at least one network node, at least one of first information indicating observation results of quality of service experience (QoE) related to a target communication and second information indicating observation results of communication performance related to the communication; and using at least one of the first information and the second information, generating a learning model that predicts changes in QoE when specific network control parameters are applied.

2. An information processing method in which an information processing device acquires, from at least one network node, at least one of first information indicating observed results of quality of service experience (QoE) related to a target communication and second information indicating observed results of communication performance related to the communication, and uses at least one of the first information and the second information to generate a learning model that predicts changes in QoE when specific network control parameters are applied.

3. An information processing device including a control unit that executes the following: transmitting one or more network control parameter sets related to a target communication to a network node; receiving a response to the one or more network control parameter sets from the network node; and selecting a network control parameter set to be used for controlling the communication from the one or more network control parameter sets based on the response.

4. The information processing device according to claim 3, wherein the response includes a predicted value of QoE for the communication calculated using the one or more network control parameter sets, and the control unit makes the selection using the predicted value of QoE.

5. The information processing device according to claim 4, wherein the control unit makes the selection taking into consideration a balance between resource consumption related to the communication and a predicted value of QoE.

6. An information processing method in which an information processing device transmits one or more network control parameter sets related to a target communication to a network node, receives a response to the one or more network control parameter sets from the network node, and selects a network control parameter set to be used for controlling the communication from the one or more network control parameter sets based on the response.

7. The information processing method according to claim 6, wherein the response includes a predicted value of QoE for the communication calculated using the one or more network control parameter sets, and the information processing device makes the selection using the predicted value of QoE.

8. The information processing method according to claim 7, wherein the information processing device makes the selection taking into consideration a balance between resource consumption related to the communication and a predicted value of QoE.

9. A communication system comprising a first information processing device which is the information processing device according to claim 1 and a second information processing device which is the information processing device according to claim 3, wherein the network node according to claim 3 is the first information processing device.

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