WO2024166779A1 - Communication control method - Google Patents

Abstract

Provided is a communication control method in a mobile communication system. The communication control method has a step in which a transmission entity creates a trained model by using prescribed data and a code indicating the prescribed data as training data. Additionally, the communication control method has a step in which the transmission entity transmits the trained model to a reception entity. Further, the communication control method has a step in which the transmission entity infers a code from the prescribed data by using the trained model. Furthermore, the communication control method has a step in which the transmission entity transmits the code to the reception entity. Moreover, the communication control method has a step in which the reception entity acquires the prescribed data from the code by using the trained model.

Description

Communication Control Method

This disclosure relates to a communication control method.

In recent years, the Third Generation Partnership Project (3GPP) (registered trademark), a standardization project for mobile communications systems, has been considering applying artificial intelligence (AI) technology, and in particular machine learning (ML) technology, to the wireless communications (air interface) of mobile communications systems.

3GPP contribution: RP-213599, “New SI: Study on Artificial Intelligence (AI)/Machine Learning (ML) for NR Air Interface”

A communication control method according to one embodiment is a communication control method in a mobile communication system. The communication control method includes a step in which a transmitting entity creates a trained model using predetermined data and a code representing the predetermined data as training data. The communication control method also includes a step in which the transmitting entity transmits the trained model to a receiving entity. The communication control method further includes a step in which the transmitting entity infers a code from the predetermined data using the trained model. The communication control method further includes a step in which the transmitting entity transmits the code to the receiving entity. The communication control method further includes a step in which the receiving entity obtains the predetermined data from the code using the trained model.

A communication control method according to one embodiment is a communication control method in a mobile communication system. The communication control method includes a step in which a network node creates a trained model for inferring data transmission timing in intermittent reception. The communication control method also includes a step in which the network node transmits the trained model to a user device. The communication control method further includes a step in which the user device infers data transmission timing using the trained model. The communication control method further includes a step in which the user device performs data reception processing at the data transmission timing.

FIG. 1 is a diagram showing an example of the configuration of a mobile communication system according to the first embodiment. FIG. 2 is a diagram illustrating an example of the configuration of a UE (user equipment) according to the first embodiment. Figure 3 is a diagram showing an example configuration of a gNB (base station) according to the first embodiment. FIG. 4 is a diagram illustrating an example of the configuration of a protocol stack according to the first embodiment. FIG. 5 is a diagram illustrating an example of the configuration of a protocol stack according to the first embodiment. FIG. 6 is a diagram illustrating an example of a functional block configuration of the AI/ML technology according to the first embodiment. FIG. 7 is a diagram illustrating an example of an operation in the AI/ML technique according to the first embodiment. FIG. 8 is a diagram illustrating an example of an arrangement of functional blocks of the AI/ML technology according to the first embodiment. FIG. 9 is a diagram illustrating an example of reducing CSI-RS according to the first embodiment. FIG. 10 is a diagram illustrating an example of reducing CSI-RS according to the first embodiment. FIG. 11 is a diagram illustrating an example of an operation according to the first embodiment. FIG. 12 is a diagram illustrating an example of an arrangement of functional blocks of the AI/ML technology according to the first embodiment. FIG. 13 is a diagram illustrating an example of an arrangement of functional blocks of the AI/ML technology according to the first embodiment. FIG. 14 is a diagram illustrating an example of an arrangement of functional blocks of the AI/ML technology according to the first embodiment. FIG. 15 is a diagram illustrating an example of an operation according to the first embodiment. FIG. 16 is a diagram illustrating an example of an arrangement of functional blocks of the AI/ML technology according to the first embodiment. FIG. 17 is a diagram illustrating an example of an operation according to the first embodiment. FIG. 18 is a diagram illustrating an example of an operation according to the first embodiment. FIG. 19 is a diagram illustrating an example of a setting message according to the first embodiment. FIG. 20 is a diagram showing the correspondence between codes and CSI according to the first embodiment. FIG. 21 is a diagram illustrating an example of an arrangement of functional blocks of the AI/ML technology according to the first embodiment. FIG. 22 is a diagram illustrating an example of an operation according to the first embodiment. FIG. 23 is a diagram illustrating another operation example according to the first embodiment. FIG. 24 is a diagram showing the correspondence between codes and CSI according to the first embodiment. FIG. 25 is a diagram illustrating another operation example according to the first embodiment. FIG. 26 is a diagram illustrating an example of transmission timing and reception timing according to the second embodiment. FIG. 27 is a diagram showing an example of an arrangement of functional blocks of the AI/ML technology according to the second embodiment. FIG. 28 is a diagram illustrating an example of operation according to the second embodiment. FIG. 29 is a diagram illustrating an example of a margin time according to the second embodiment.

The purpose of this disclosure is to provide a communication control method that can reduce the amount of information.

[First embodiment]
The mobile communication system according to the first embodiment will be described with reference to the drawings. In the description of the drawings, the same or similar parts are denoted by the same or similar reference numerals.

(Configuration of a mobile communication system)
A configuration of a mobile communication system according to the first embodiment will be described. FIG. 1 is a diagram showing an example of the configuration of a mobile communication system 1 according to the first embodiment. The mobile communication system 1 complies with the 5th generation system (5GS: 5th Generation System) of the 3GPP standard. In the following, 5GS will be described as an example, but an LTE (Long Term Evolution) system may be applied at least partially to the mobile communication system. A sixth generation (6G) system or later system may be applied at least partially to the mobile communication system.

The mobile communication system 1 has a user equipment (UE) 100, a 5G radio access network (NG-RAN: Next Generation Radio Access Network) 10, and a 5G core network (5GC: 5G Core Network) 20. In the following, the NG-RAN 10 may be simply referred to as the RAN 10. Also, the 5GC 20 may be simply referred to as the core network (CN) 20.

UE100 is a mobile wireless communication device. UE100 may be any device that is used by a user. For example, UE100 is a mobile phone terminal (including a smartphone) and/or a tablet terminal, a notebook PC, a communication module (including a communication card or chipset), a sensor or a device provided in a sensor, a vehicle or a device provided in a vehicle (Vehicle UE), or an aircraft or a device provided in an aircraft (Aerial UE).

NG-RAN10 includes base station (called "gNB" in 5G system) 200. gNB200 are connected to each other via Xn interface, which is an interface between base stations. gNB200 manages one or more cells. gNB200 performs wireless communication with UE100 that has established a connection with its own cell. gNB200 has a radio resource management (RRM) function, a routing function for user data (hereinafter simply referred to as "data"), a measurement control function for mobility control and scheduling, etc. "Cell" is used as a term indicating the smallest unit of a wireless communication area. "Cell" is also used as a term indicating a function or resource for performing wireless communication with UE100. One cell belongs to one carrier frequency (hereinafter simply referred to as "frequency").

In addition, gNBs can also be connected to the Evolved Packet Core (EPC), which is the core network of LTE. LTE base stations can also be connected to 5GC. LTE base stations and gNBs can also be connected via a base station-to-base station interface.

5GC20 includes an AMF (Access and Mobility Management Function) and a UPF (User Plane Function) 300. The AMF performs various mobility controls for the UE 100. The AMF manages the mobility of the UE 100 by communicating with the UE 100 using NAS (Non-Access Stratum) signaling. The UPF controls data forwarding. The AMF and the UPF 300 are connected to the gNB 200 via an NG interface, which is an interface between a base station and a core network. The AMF and the UPF 300 may be core network devices included in the CN 20.

FIG. 2 is a diagram showing an example of the configuration of a UE 100 (user equipment) according to the first embodiment. The UE 100 includes a receiver 110, a transmitter 120, and a controller 130. The receiver 110 and the transmitter 120 constitute a communication unit that performs wireless communication with the gNB 200. The UE 100 is an example of a communication device.

The receiving unit 110 performs various types of reception under the control of the control unit 130. The receiving unit 110 includes an antenna and a receiver. The receiver converts the radio signal received by the antenna into a baseband signal (received signal) and outputs it to the control unit 130.

The transmitting unit 120 performs various transmissions under the control of the control unit 130. The transmitting unit 120 includes an antenna and a transmitter. The transmitter converts the baseband signal (transmission signal) output by the control unit 130 into a radio signal and transmits it from the antenna.

The control unit 130 performs various controls and processes in the UE 100. Such processes include the processes of each layer described below. The control unit 130 includes at least one processor and at least one memory. The memory stores programs executed by the processor and information used in the processes by the processor. The processor may include a baseband processor and a CPU (Central Processing Unit). The baseband processor performs modulation/demodulation and encoding/decoding of baseband signals. The CPU executes programs stored in the memory to perform various processes. Note that the processes or operations performed in the UE 100 may be performed in the control unit 130.

FIG. 3 is a diagram showing an example of the configuration of a gNB 200 (base station) according to the first embodiment. The gNB 200 includes a transmitter 210, a receiver 220, a controller 230, and a backhaul communication unit 250. The transmitter 210 and the receiver 220 constitute a communication unit that performs wireless communication with the UE 100. The backhaul communication unit 250 constitutes a network communication unit that performs communication with the CN 20. The gNB 200 is another example of a communication device.

The transmitting unit 210 performs various transmissions under the control of the control unit 230. The transmitting unit 210 includes an antenna and a transmitter. The transmitter converts the baseband signal (transmission signal) output by the control unit 230 into a radio signal and transmits it from the antenna.

The receiving unit 220 performs various types of reception under the control of the control unit 230. The receiving unit 220 includes an antenna and a receiver. The receiver converts the radio signal received by the antenna into a baseband signal (received signal) and outputs it to the control unit 230.

The control unit 230 performs various controls and processes in the gNB 200. Such processes include the processes of each layer described below. The control unit 230 includes at least one processor and at least one memory. The memory stores programs executed by the processor and information used in the processes by the processor. The processor may include a baseband processor and a CPU. The baseband processor performs modulation/demodulation and encoding/decoding of baseband signals. The CPU executes programs stored in the memory to perform various processes. Note that the processes or operations performed in the gNB 200 may be performed by the control unit 230.

The backhaul communication unit 250 is connected to adjacent base stations via an Xn interface, which is an interface between base stations. The backhaul communication unit 250 is connected to the AMF/UPF 300 via an NG interface, which is an interface between a base station and a core network. Note that the gNB 200 may be composed of a central unit (CU) and a distributed unit (DU) (i.e., functionally divided), and the two units may be connected via an F1 interface, which is a fronthaul interface.

Figure 4 shows an example of the protocol stack configuration for the wireless interface of the user plane that handles data.

The user plane radio interface protocol has a physical (PHY) layer, a medium access control (MAC) layer, a radio link control (RLC) layer, a packet data convergence protocol (PDCP) layer, and a service data adaptation protocol (SDAP) layer.

The PHY layer performs encoding/decoding, modulation/demodulation, antenna mapping/demapping, and resource mapping/demapping. Data and control information are transmitted between the PHY layer of UE100 and the PHY layer of gNB200 via a physical channel. The PHY layer of UE100 receives downlink control information (DCI) transmitted from gNB200 on a physical downlink control channel (PDCCH). Specifically, UE100 performs blind decoding of PDCCH using a radio network temporary identifier (RNTI) and acquires successfully decoded DCI as DCI addressed to the UE. The DCI transmitted from gNB200 has CRC (Cyclic Redundancy Code) parity bits scrambled by the RNTI added.

In NR, UE100 can use a bandwidth narrower than the system bandwidth (i.e., the cell bandwidth). gNB200 sets a bandwidth portion (BWP) consisting of consecutive PRBs (Physical Resource Blocks) to UE100. UE100 transmits and receives data and control signals in the active BWP. For example, up to four BWPs may be set to UE100. Each BWP may have a different subcarrier spacing. The BWPs may overlap each other in frequency. When multiple BWPs are set to UE100, gNB200 can specify which BWP to apply by controlling the downlink. As a result, gNB200 dynamically adjusts the UE bandwidth according to the amount of data traffic of UE100, etc., thereby reducing UE power consumption.

The gNB200 can, for example, configure up to three control resource sets (CORESETs) for each of up to four BWPs on the serving cell. The CORESET is a radio resource for control information to be received by the UE100. Up to 12 or more CORESETs may be configured on the serving cell for the UE100. Each CORESET may have an index of 0 to 11 or more. The CORESET may consist of six resource blocks (PRBs) and one, two, or three consecutive OFDM (Orthogonal Frequency Division Multiplex) symbols in the time domain.

The MAC layer performs data priority control, retransmission processing using Hybrid Automatic Repeat reQuest (HARQ), and random access procedures. Data and control information are transmitted between the MAC layer of UE100 and the MAC layer of gNB200 via a transport channel. The MAC layer of gNB200 includes a scheduler. The scheduler determines the uplink and downlink transport format (transport block size, modulation and coding scheme (MCS)) and the resource blocks to be assigned to UE100.

The RLC layer uses the functions of the MAC layer and PHY layer to transmit data to the RLC layer on the receiving side. Data and control information are transmitted between the RLC layer of UE100 and the RLC layer of gNB200 via logical channels.

The PDCP layer performs header compression/decompression, encryption/decryption, etc.

The SDAP layer maps IP flows, which are the units for which the core network controls QoS (Quality of Service), to radio bearers, which are the units for which the access stratum (AS) controls QoS. Note that if the RAN is connected to the EPC, SDAP is not necessary.

Figure 5 shows the configuration of the protocol stack for the wireless interface of the control plane that handles signaling (control signals).

The protocol stack of the radio interface of the control plane has a radio resource control (RRC) layer and a non-access stratum (NAS) instead of the SDAP layer shown in Figure 4.

RRC signaling for various settings is transmitted between the RRC layer of UE100 and the RRC layer of gNB200. The RRC layer controls logical channels, transport channels, and physical channels in response to the establishment, re-establishment, and release of radio bearers. When there is a connection (RRC connection) between the RRC of UE100 and the RRC of gNB200, UE100 is in an RRC connected state. When there is no connection (RRC connection) between the RRC of UE100 and the RRC of gNB200, UE100 is in an RRC idle state. When the connection between the RRC of UE100 and the RRC of gNB200 is suspended, UE100 is in an RRC inactive state.

The NAS, which is located above the RRC layer, performs session management, mobility management, etc. NAS signaling is transmitted between the NAS of UE100 and the NAS of AMF300. In addition to the radio interface protocol, UE100 also has an application layer, etc. The layer below the NAS is called the AS (Access Stratum).

(AI/ML technology)
Next, the AI/ML technology according to the embodiment will be described. Fig. 6 is a diagram showing an example of the configuration of functional blocks of the AI/ML technology in the mobile communication system 1 according to the first embodiment.

The functional block configuration example shown in FIG. 6 includes a data collection unit A1, a model learning unit A2, a model inference unit A3, and a data processing unit A4.

The data collection unit A1 collects input data, specifically, learning data and inference data. The data collection unit A1 outputs the learning data to the model learning unit A2. The data collection unit A1 also outputs the inference data to the model inference unit A3. The data collection unit A1 may acquire data in the device on which the data collection unit A1 is provided as input data. The data collection unit A1 may acquire data in another device as input data.

The model learning unit A2 performs model learning. Specifically, the model learning unit A2 optimizes parameters of a learning model by machine learning using learning data, and derives (or generates, or updates) a learned model. The model learning unit A2 outputs the derived learned model to the model inference unit A3. For example,
y = ax + b
In this case, a (slope) and b (intercept) are parameters, and optimizing these corresponds to machine learning. Generally, machine learning includes supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a method in which correct answer data is used for learning data. Unsupervised learning is a method in which correct answer data is not used for learning data. For example, in unsupervised learning, feature points are memorized from a large amount of learning data, and a correct answer is determined (range is estimated). Reinforcement learning is a method in which a score is assigned to an output result, and a method of maximizing the score is learned. In the following, supervised learning will be described, but as machine learning, unsupervised learning or reinforcement learning may be applied.

The model inference unit A3 performs model inference. Specifically, the model inference unit A3 infers an output from inference data using a trained model, and outputs inference result data to the data processing unit A4. For example,
y = ax + b
In this case, x corresponds to inference data and y corresponds to inference result data. Note that "y = ax + b" is a model. A model with optimized slope and intercept, for example, "y = 5x + 3", is a trained model. There are various approaches to the model, including linear regression analysis, neural network, and decision tree analysis. The above "y = ax + b" can be considered as a type of linear regression analysis. The model inference unit A3 may provide model performance feedback to the model learning unit A2.

The data processing unit A4 receives the inference result data and performs processing that utilizes the inference result data.

FIG. 7 shows an example of the operation of the AI/ML technology according to the first embodiment.

The transmitting entity TE is, for example, an entity where machine learning is performed. The transmitting entity TE performs machine learning to derive a trained model. The transmitting entity TE then uses the trained model to generate inference result data as an inference result. The transmitting entity TE transmits the inference result data to the receiving entity RE.

On the other hand, the receiving entity RE is, for example, an entity in which machine learning is not performed. The transmitting entity TE performs various processes using the inference result data received from the transmitting entity TE.

Note that the entity may be, for example, a device. The entity may be a function block included in the device. The entity may be a hardware block included in the device.

For example, the transmitting entity TE may be a UE 100, and the receiving entity RE may be a gNB 200 or a core network device. Alternatively, the transmitting entity TE may be a gNB 200 or a core network device, and the receiving entity RE may be a UE 100.

As shown in FIG. 7, in step S1, the transmitting entity TE transmits control data related to AI/ML technology to the receiving entity RE and receives the control data from the receiving entity RE. The control data may be an RRC message, which is signaling of the RRC layer (i.e., layer 3). The control data may be a MAC Control Element (CE), which is signaling of the MAC layer (i.e., layer 2). The control data may be Downlink Control Information (DCI), which is signaling of the PHY layer (i.e., layer 1). The downlink signaling may be UE-specific signaling. The downlink signaling may be broadcast signaling. The control data may be a control message in a control layer (e.g., an AI/ML layer) specialized for artificial intelligence or machine learning.

(Deployment examples and use cases)
Next, a description will be given of how the functional blocks shown in Fig. 6 are arranged in the mobile communication system 1. Below, an example of the arrangement of the functional blocks will be described along with a specific use case.

For example, there are three use cases where AI/ML technology can be applied:

(1.1) "CSI (Channel State Information) Feedback Enhancement"

(1.2) "Beam management"

(1.3) “Positioning accuracy enhancement”
Below, an example of the arrangement of functional blocks will be explained for each use case.

(1.1) Example of functional block arrangement in "CSI feedback improvement""CSI feedback improvement" represents a use case in which machine learning technology is applied to CSI fed back from UE100 to gNB200, for example. CSI is information on the channel state in the downlink between UE100 and gNB200. CSI includes at least one of a channel quality indicator (CQI), a precoding matrix indicator (PMI), and a rank indicator (RI). Based on the CSI feedback from UE100, gNB200 performs, for example, downlink scheduling.

Figure 8 is a diagram showing an example of the arrangement of each functional block in "CSI feedback improvement". In the example of "CSI feedback improvement" shown in Figure 8, a data collection unit A1, a model learning unit A2, and a model inference unit A3 are included in the control unit 130 of the UE 100. On the other hand, a data processing unit A4 is included in the control unit 230 of the gNB 200. In other words, model learning and model inference are performed in the UE 100. Figure 8 shows an example in which the transmitting entity TE is the UE 100 and the receiving entity RE is the gNB 200.

In "improved CSI feedback," the gNB 200 transmits a reference signal for the UE 100 to estimate the downlink channel state. In the following, the reference signal will be described using a CSI reference signal (CSI-RS) as an example, but the reference signal may also be a demodulation reference signal (DMRS).

First, in model learning, UE100 (receiving unit 110) receives a first reference signal from gNB200 using a first resource. Then, UE100 (model learning unit A2) derives a learned model for inferring CSI from the reference signal using learning data including the first reference signal and CSI. Such a first reference signal may be referred to as a full CSI-RS.

For example, the CSI generation unit 131 performs channel estimation using the received signal (CSI-RS) received by the receiving unit 110, and generates CSI. The transmitting unit 120 transmits the generated CSI to the gNB 200. The model learning unit A2 performs model learning using a set of the received signal (CSI-RS) and CSI as learning data, and derives a learned model for inferring CSI from the received signal (CSI-RS).

Secondly, in model inference, the receiving unit 110 receives a second reference signal from the gNB 200 using a second resource that is less than the first resource. Then, the model inference unit A3 uses the learned model to infer the CSI as inference result data using the second reference signal as inference data. Hereinafter, such a second reference signal may be referred to as a partial CSI-RS or a punctured CSI-RS.

For example, the model inference unit A3 inputs the partial CSI-RS received by the receiving unit 110 into the trained model as inference data, and infers CSI from the CSI-RS. The transmitting unit 120 transmits the inferred CSI to the gNB 200.

This enables UE100 to feed back (or transmit) accurate (complete) CSI to gNB200 from the small amount of CSI-RS (partial CSI-RS) received from gNB200. For example, gNB200 can reduce (puncture) CSI-RS when intended to reduce overhead. In addition, UE100 can respond to situations where the radio conditions deteriorate and some CSI-RS cannot be received normally.

FIGS. 9 and 10 are diagrams showing an example of reducing CSI-RS according to the first embodiment.

FIG. 9 shows an example of reducing CSI-RS by reducing the number of antenna ports that transmit CSI-RS. For example, gNB200 performs the following process. That is, when UE100 is in a mode in which model learning is performed (hereinafter, may be referred to as "learning mode"), gNB200 transmits CSI-RS from all antenna ports of the antenna panel. On the other hand, when UE100 is in a mode in which model inference is performed (hereinafter, may be referred to as "inference mode"), gNB200 reduces the number of antenna ports that transmit CSI-RS and transmits CSI-RS from half the antenna ports of the antenna panel. This reduces overhead, improves the utilization efficiency of antenna ports, and can reduce power consumption. Note that antenna ports are an example of resources.

On the other hand, FIG. 10 shows an example in which the radio resources used to transmit the CSI-RS, specifically, the gNB 200 reduces the time-frequency resources. For example, the gNB 200 performs the following process. That is, when the UE 100 is in the learning mode, the gNB 200 transmits the CSI-RS using a predetermined time-frequency resource. On the other hand, when the UE 100 is in the inference mode, the gNB 200 transmits the CSI-RS using a time-frequency resource that is less than the predetermined time-frequency resource. This reduces overhead, improves the utilization efficiency of the radio resources, and reduces power consumption.

As shown in Figures 9 and 10, gNB200 transmits full CSI-RS using a predetermined amount of first resources, and transmits partial CSI-RS using second resources that have a smaller amount of resources than the first resources.

FIG. 11 shows an example of the operation of "CSI feedback improvement" according to the first embodiment.

As shown in FIG. 11, in step S101, gNB200 may notify or set the transmission pattern (puncture pattern) of CSI-RS in inference mode to UE100 as control data. For example, gNB200 transmits to UE100 the antenna port and/or time-frequency resource that transmits or does not transmit CSI-RS in inference mode.

In step S102, gNB200 may send a switching notification to UE100 to start the learning mode.

In step S103, UE100 starts the learning mode.

In step S104, gNB200 transmits the full CSI-RS. The receiver 110 of UE100 receives the full CSI-RS, and the CSI generator 131 generates (or estimates) CSI based on the full CSI-RS. In the learning mode, the data collector A1 collects the full CSI-RS and CSI. The model learning unit A2 creates a learned model using the full CSI-RS and the CSI as learning data.

In step S105, UE100 transmits the generated CSI to gNB200.

Then, in step S106, when the model learning is completed, the UE 100 transmits a completion notification to the gNB 200 indicating that the model learning is completed. The UE 100 may also transmit a completion notification when the creation of the trained model is completed.

In step S107, in response to receiving the completion notification, gNB200 transmits a switching notification to UE100 to cause UE100 to switch from learning mode to inference mode.

In step S108, in response to receiving the switching notification, UE 100 switches from the learning mode to the inference mode.

In step S109, gNB200 transmits partial CSI-RS. Receiver 110 of UE100 receives the partial CSI-RS. In the inference mode, data collection unit A1 collects partial CSI-RS. Model inference unit A3 inputs the partial CSI-RS as inference data into the trained model, and obtains CSI as the inference result.

In step S110, UE100 feeds back (or transmits) the CSI, which is the inference result, to gNB200 as inference result data. In UE100, by repeating model learning during the learning mode, a trained model with a predetermined accuracy or higher can be generated. It is expected that the inference result using the trained model thus generated will also have a predetermined accuracy or higher.

In addition, in step S111, if UE100 determines that model learning is necessary, it may transmit a notification indicating that model learning is necessary to gNB200 as control data.

In the example shown in FIG. 11, the training data is "(full) CSI-RS" and "CSI," and the inference data is "(partial) CSI-RS." Hereinafter, the training data and/or the inference data may be referred to as a "dataset."

In "improving CSI feedback," in addition to "CSI-RS" and "CSI," at least one of the following data or information may be used as a data set:

(X1) RSRP (Reference Signals Received Power), RSRQ (Reference Signal Received Quality), SINR (Signal-to-interference-plus-noise ratio), or output waveform of an AD converter (These measurements may be CSI-RS. The measurements may be other received signals received from gNB200.)

(X2) Bit Error Rate (BER) or Block Error Rate (BLER) (BER (or BLER) may be measured based on the CSI-RS with the total number of transmitted bits (or total number of transmitted blocks) being known.)

(X3) Moving speed of UE 100 (may be measured by a speed sensor in UE 100)
It may be set as to what is used as the data set used for machine learning. For example, the following processing may be performed. That is, the UE 100 transmits capability information indicating which type of input data the UE 100 can handle in machine learning to the gNB 200 as control data. The capability information may represent, for example, any of the data or information shown in (X1) to (X3). The capability information may be information in which the learning data and the inference data are separately specified. Then, the gNB 200 transmits the data type information used as the data set to the UE 100 as control data. The data type information may represent, for example, any of the data or information shown in (X1) to (X3). In addition, the data type information may be separately specified as the data type information used as the learning data and the data type information used as the inference data.

(1.2) Example of functional block arrangement in "beam management" Next, an example of functional block arrangement in "beam management" will be described. "Beam management" represents a use case in which, for example, machine learning technology is used to manage which beam is the optimal beam among the beams transmitted from gNB200.

In "beam management", gNB200 sequentially transmits beams with different directivities. Each beam includes, for example, a reference signal. UE100 measures the reception quality of each beam using the reference signal included in each beam. UE100 determines, for example, the beam with the best reception quality as the optimal beam.

FIG. 12 is a diagram showing an example of the arrangement of each functional block in "beam management". In the example of "beam management" shown in FIG. 12, a data collection unit A1, a model learning unit A2, and a model inference unit A3 are included in the control unit 130 of the UE 100. On the other hand, a data processing unit A4 is included in the control unit 230 of the gNB 200. That is, FIG. 12 shows an example in which model learning and model inference are performed in the UE 100. FIG. 12 shows an example in which the transmitting entity TE is the UE 100 and the receiving entity RE is the gNB 200.

As shown in FIG. 12, UE 100 has an optimal beam determination unit 132. The optimal beam determination unit 132 determines the optimal beam based on, for example, the reception quality for the reference signal included in each beam. As with "CSI feedback," an example will be described in which CSI-RS is used as the reference signal, but a demodulation reference signal (DMRS) may also be used as the reference signal. The transmission unit 120 transmits information representing the determined optimal beam to gNB 200 as the "optimal beam."

An example of "beam management" operation can be implemented by replacing "CSI feedback" with "optimal beam" in Figure 11.

In the learning mode (step S103), the gNB 200 sequentially transmits beams with different directivities to the UE 100 (step S104). Each beam includes a full CSI-RS. In the learning mode, the data collection unit A1 of the UE 100 collects the full CSI-RS and (information representing) the optimal beam. The model learning unit A2 creates a learned model using the CSI-RS and (information representing) the optimal beam as learning data. The full CSI-RS is an example of a first reference signal, and the partial CSI-RS is an example of a second reference signal.

In the inference mode (step S108), the gNB 200 sequentially transmits beams with different directivities. Each beam includes a partial CSI-RS. In the inference mode, the data collection unit A1 collects the partial CSI-RS. The model inference unit A3 inputs the partial CSI-RS as inference data into the trained model, and obtains the optimal beam (information representing the optimal beam) as the inference result. The UE 100 transmits the inference result (optimal beam) to the gNB 200 as inference result data.

In "beam management", in addition to "CSI-RS" and "optimum beam", at least one of the following data or information may be used as data in the data set.

(Y1) SSB (Synchronization Signal Block) received from gNB200

(Y2) RSRP, RSRQ, SINR, or output waveform of the AD converter (the measurement target may be CSI-RS. The measurement target may be other received signals received from gNB200)

(Y3) BER or BLER (BER (or BLER) may be measured based on CSI-RS with the total number of transmitted bits (or total number of transmitted blocks) known)

(Y4) Number of beams or beam pattern

(Y5) Beam measurement value (including multiple values)

(Y6) Moving speed of UE 100 (may be measured by a speed sensor in UE 100)
The UE 100 may transmit capability information indicating which type of input data the UE 100 can handle in machine learning to the gNB 200 as control data. The capability information may include any of the information or data from (Y1) to (Y6), or may include any of the information or data from (Y1) to (Y6) separately from the learning data and the inference data. The gNB 200 may also transmit data type information used as a data set to the UE 100 as control data. The data type information may include, for example, any of the data or information shown in (Y1) to (Y6). The data type information may include, for example, any of the information or data from (Y1) to (Y6) separately from the learning data and the inference data.

(1.3) Example of Arrangement of Functional Blocks in "Improvement of Location Accuracy" Next, an example of arrangement of functional blocks in "Improvement of Location Accuracy" will be described. "Improvement of location accuracy" represents a use case in which, for example, the accuracy of location information measured by the UE 100 is improved by using machine learning technology.

FIG. 13 shows an example of the arrangement of each functional block in "improving location accuracy". In the example of "improving location accuracy" shown in FIG. 9, the data collection unit A1, model learning unit A2, and model inference unit A3 are included in the control unit 130 of the UE 100. On the other hand, the data processing unit A4 is included in the control unit 230 of the gNB 200. That is, FIG. 13 shows an example in which model learning and model inference are performed in the UE 100. FIG. 13 shows an example in which the transmitting entity TE is the UE 100, and the receiving entity RE is the gNB 200.

As shown in FIG. 13, UE 100 includes a location information generation unit 133. UE 100 may include a Global Navigation Satellite System (GNSS) receiver 150. The location information generation unit 133 generates location data for UE 100 based on a Positioning Reference Signal (PRS) (full PRS or partial PRS) received from gNB 200. The location information generation unit 133 may receive a GNSS signal (full GNSS signal or partial GNSS signal) received by the GNSS receiver 150, and generate location data for UE 100 based on the GNSS signal.

In addition, gNB200 transmits full PRS using a predetermined amount of first resources (e.g., all antenna ports as shown in FIG. 9, or a predetermined amount of time-frequency resources as shown in FIG. 10) in the same manner as full CSI-RS. Also, gNB200 transmits partial PRS using second resources having a smaller amount of resources than the first resources (e.g., half the antenna ports in an antenna panel as shown in FIG. 9, or half the predetermined amount of time-frequency resources as shown in FIG. 10) in the same manner as partial CSI-RS.

The full GNSS signal may be a GNSS signal received by the GNSS receiver 150 continuously over time. Furthermore, the partial GNSS signal may be a GNSS signal received by the GNSS receiver 150 intermittently. That is, a predetermined amount of first resources may be used for the full GNSS signal, and a second resource having a smaller amount than the first resources may be used for the partial GNSS signal.

An example of the operation for "improving location accuracy" can be implemented by replacing "full CSI-RS" with "full PRS," "partial CSI-RS" with "partial PRS," and "CSI feedback" with "location data" in FIG. 11.

In the learning mode (step S103), the location information generation unit 133 generates location data for the UE 100 based on the full PRS received from the gNB 200. The location information generation unit 133 may receive a full GNSS signal received by the GNSS receiver 150 and generate location data for the UE 100 based on the full GNSS signal. The transmission unit 120 feeds back (or transmits) the location data to the gNB 200. The data collection unit A1 collects the full PRS (or full GNSS signal) and location data. The model learning unit A2 creates a learned model using the full PRS (or full GNSS signal) and location data as learning data.

In the inference mode (step S108), the data collection unit A1 collects the partial PRS received by the receiving unit 110 (or the partial GNSS signal received by the GNSS receiver 150). The model inference unit A3 inputs the partial PRS (or the partial GNSS signal) as inference data into the trained model, and obtains location data as the inference result. The UE 100 transmits the inference result (location data) to the gNB 200 as inference result data.

In "improving position accuracy," in addition to "PRS" (or "GNSS signal") and "position data," the data used in the data set may include, for example, at least one of the following data or information:

(Z1) RSRP, RSRQ, SINR (signal-to-interference-plus-noise ratio), or output waveform of an AD converter (these measurements may be PRS. The measurements may be other received signals received from gNB200.)

(Z2) LOS (Line of Sight) or NLOS (Non Line of Sight)

(Z3) Measurement timing, accuracy, likelihood

(Z4) RF fingerprint (cell ID and reception quality in the cell with that cell ID)

(Z5) Angle of arrival of received signal (AOA: Angle of Arrival), reception level for each antenna, reception phase for each antenna, reception time difference for each antenna (OTDOA: Observed Time Difference Of Arrival)

(Z6) Received information from beacons used in wireless LANs (Local Area Networks) such as Wi-Fi (registered trademark) or short-range wireless communications such as Bluetooth (registered trademark)

(Z7) Moving speed of UE 100 (The moving speed may be measured by GNSS receiver 150. The moving speed may be measured by a speed sensor in UE 100.)
The UE 100 may transmit capability information indicating which type of input data the UE 100 can handle in machine learning to the gNB 200 as control data. The capability information may include any of the information or data from (Z1) to (Z7), or may include any of the information or data from (Z1) to (Z7) separately from the learning data and the inference data. The gNB 200 may also transmit data type information used as a data set to the UE 100 as control data. The data type information may include, for example, any of the data or information shown in (Z1) to (Z7), or may include any of the information or data from (Z1) to (Z7) separately from the learning data and the inference data.

(1.4) Other Arrangement Examples Next, other arrangement examples will be described.

FIG. 14 is a diagram showing another example of the arrangement of "CSI feedback improvement" according to the first embodiment. FIG. 14 shows an example in which a data collection unit A1, a model learning unit A2, a model inference unit A3, and a data processing unit A4 are included in a gN200. That is, FIG. 14 shows an example in which model learning and model inference are performed in a gNB200. FIG. 14 shows an example in which a transmitting entity TE is a gNB200, and a receiving entity RE is a UE100.

Figure 14 shows an example in which AI/ML technology is introduced into CSI estimation performed by gNB200 based on SRS (Sounding Reference Signal). Therefore, gNB200 has a CSI generation unit 231 that generates CSI based on SRS. The CSI is information indicating the channel state of the uplink between UE100 and gNB200. gNB200 (e.g., data processing unit A4) performs, for example, uplink scheduling based on the CSI generated based on SRS.

FIG. 15 shows an example of operation in another arrangement example according to the first embodiment.

As shown in FIG. 15, in step S201, the gNB 200 performs SRS transmission configuration for the UE 100. The SRS transmission configuration may include type information of the reference signal transmitted by the UE 100.

In step S202, gNB200 starts learning mode.

In step S203, UE 100 transmits the full SRS to gNB 200 according to the SRS transmission setting (step S201). The receiver 220 of gNB 200 receives the full SRS. In the learning mode, the CSI generator 231 generates (or estimates) CSI based on the full SRS. The data collector A1 collects the full SRS and CSI. The model learning unit A2 creates a learned model using the full SRS and CSI as learning data.

In step S204, gNB200 identifies an SRS transmission pattern (puncture pattern) to be input to the learned model as inference data, and sets the identified SRS transmission pattern to UE100. gNB200 may transmit an SRS transmission setting including the identified SRS transmission pattern to gNB200.

In step S205, gNB200 switches from learning mode to inference mode. gNB200 starts model inference using the trained model.

In step S206, UE100 transmits a partial SRS according to the SRS transmission setting (step S204). gNB200 inputs the SRS as inference data into the trained model to obtain a channel estimation result, and then uses the channel estimation result to perform uplink scheduling for UE100 (e.g., control of uplink transmission weight, etc.). Note that gNB200 may reconfigure UE100 to transmit a full SRS if the inference accuracy of the trained model deteriorates.

(1.5) Example of Arrangement When Federated Learning is Performed Next, an example of the arrangement of each functional block when federated learning is performed will be described. Federated learning is, for example, a machine learning technique in which machine learning is performed in a distributed state without consolidating data (or a data set). In federated learning, each entity does not need to transmit data, so the security of each entity can be ensured. In addition, it is said that federated learning can obtain learning results with the same accuracy as conventional centralized machine learning.

FIG. 16 is a diagram showing an example of a configuration in which federated learning according to the first embodiment is performed. The example shown in FIG. 16 shows an example in which location estimation of UE100 is performed using federated learning. FIG. 16 shows an example in which UE100 has a data collection unit A1, a model learning unit A2, and a model inference unit A3. That is, it shows an example in which model learning and model inference are performed in UE100. FIG. 16 shows an example in which UE100 is the transmitting entity TE, and gNB200 and/or location server 400 are the receiving entity RE.

The federated learning shown in Figure 16 is carried out, for example, in the following steps.

First, the location server 400 transmits the model that serves as the basis for model learning to the UE 100.

Secondly, UE100 (model learning unit A2) performs model learning using data present in UE100. The data present in UE100 is, for example, the PRS received from gNB200 and/or output data (GNSS signal) of GNSS receiver 150. The data present in UE100 may include location data generated by location information generation unit 133 based on the reception result of PRS and/or output data of GNSS receiver 150.

Thirdly, UE100 applies the learned model, which is the result of learning, in model inference unit A3, and transmits variable parameters included in the learned model (hereinafter, sometimes referred to as "learned parameters") to location server 400. In the above example, the optimized a (slope) and b (intercept) correspond to the learned parameters.

Fourth, the location server 400 (associated learning unit A5) collects learned parameters from multiple UEs 100 and integrates them. The location server 500 may transmit the learned model obtained by the integration to the UE 100. The location server 400 can estimate the location of the UE 100 based on the learned model and the measurement report from the UE 100.

FIG. 17 shows an example of operation in associative learning according to the first embodiment.

As shown in FIG. 17, in step S301, gNB200 may notify UE100 of a model that serves as a basis for learning. Location server 400 may notify the model via gNB200.

In step S302, gNB200 instructs UE100 to learn the model. gNB200 may set the report timing (trigger condition) of the learned parameters. The report timing may be periodic. The report timing may be triggered by the learning proficiency satisfying a condition (i.e., an event trigger).

In step S303, UE 100 starts a learning mode. UE 100 performs model learning using the full PRS (or full GNSS signal) and the location data generated by location information generating unit 133 as learning data.

In step S304, when the reporting timing condition is met, the UE 100 transmits the learned parameters at that time to the network (gNB 200 or location server 400).

In step S305, the location server 400 integrates the learned parameters reported from multiple UEs 100.

(1.6) Model transfer example

In (1.1) to (1.5), examples of the layout of each functional block of AI/ML technology were explained. Below, an example of model transfer is explained. The model to be transferred may be a trained model used in model inference. The model may also be an untrained (or untrained) model used in model training.

(1.6.1) First operation pattern related to model forwarding Figure 18 is a diagram showing an example of an operation of the first operation pattern related to model forwarding according to the first embodiment. In the example shown in Figure 18, the receiving entity RE is mainly described as the UE 100, but the receiving entity RE may be the gNB 200 or the AMF 300. In addition, in the example shown in Figure 18, the transmitting entity TE is described as the gNB 200, but the transmitting entity TE may be the UE 100 or the AMF 300.

As shown in FIG. 18, in step S401, gNB200 transmits a capability inquiry message to UE100 to request transmission of a message including an information element (IE) indicating the execution capability for machine learning processing. UE100 receives the capability inquiry message. However, gNB200 may transmit the capability inquiry message when executing machine learning processing (when it has determined that the execution will be performed).

In step S402, UE100 transmits a message including an information element indicating execution capability for machine learning processing (or, from another perspective, execution environment for machine learning processing) to gNB200. gNB200 receives the message. The message may be an RRC message (e.g., a "UE Capability" message, or a newly defined message (e.g., a "UE AI Capability" message, etc.)). Alternatively, the transmitting entity TE may be AMF300 and the message may be a NAS message. Alternatively, if a new layer for performing or controlling machine learning processing (AI/ML processing) is defined, the message may be a message of the new layer.

The information element indicating the execution capability for machine learning processing may be an information element indicating the capability of a processor for executing machine learning processing and/or an information element indicating the capability of a memory for executing machine learning processing. Specifically, the information element indicating the processor capability may be an information element indicating the product number (or model number) of the AI processor. Also, specifically, the information element indicating the memory capability may be an information element indicating the memory capacity.

Alternatively, the information element indicating the execution capability regarding machine learning processing may be an information element indicating the execution capability of inference processing (model inference). Specifically, the information element indicating the execution capability of inference processing may be an information element indicating whether or not a deep neural network model is supported. The information element may be an information element indicating the time (or response time) required to execute the inference processing.

Alternatively, the information element indicating the execution capability related to machine learning processing may be an information element indicating the execution capability of learning processing (model learning). Specifically, the information element indicating the execution capability of learning processing may be an information element indicating the number of learning processing operations being executed simultaneously. The information element may be an information element indicating the processing capacity of the learning processing.

In step S403, gNB200 determines the model to be configured (or deployed) in UE100 based on the information elements contained in the message received in step S402.

In step S404, gNB200 transmits a message including the model determined in step S403 to UE100. UE100 receives the message and performs machine learning processing (i.e., model learning processing and/or model inference processing) using the model included in the message. A specific example of step S404 will be described in the following second operation pattern.

(1.6.2) Second operation pattern regarding model transfer FIG. 19 is a diagram showing an example of a configuration message including a model and additional information according to the first embodiment. The configuration message may be an RRC message (e.g., an "RRC Reconfiguration" message, or a newly defined message (e.g., an "AI Deployment" message or an "AI Reconfiguration" message, etc.)) transmitted from the gNB 200 to the UE 100. Alternatively, the configuration message may be a NAS message transmitted from the AMF 300A to the UE 100. Alternatively, when a new layer for performing or controlling machine learning processing (AI / ML processing) is defined, the message may be a message of the new layer.

In the example of FIG. 19, the setting message includes three models (Model #1 to #3). Each model is included as a container in the setting message. However, the setting message may include only one model. The setting message further includes, as additional information, three individual additional information (Info #1 to #3) that is provided individually corresponding to each of the three models (Model #1 to #3), and common additional information (Meta-Info) that is commonly associated with the three models (Model #1 to #3). Each of the individual additional information (Info #1 to #3) includes information unique to the corresponding model. The common additional information (Meta-Info) includes information common to all models in the setting message.

The individual additional information may be a model index that indicates an index (index number) assigned to each model. The individual additional information may be a model execution condition that indicates the performance (e.g., processing delay) required to apply (execute) the model.

The individual additional information or the common additional information may be a model application that specifies a function to which a model is to be applied (e.g., "CSI feedback," "beam management," "positioning," etc.). The individual additional information or the common additional information may be a model selection criterion that applies (executes) a corresponding model when a specified criterion (e.g., moving speed) is satisfied.

(2) Communication Control Method According to First Embodiment Next, a communication control method according to the first embodiment will be described.

As explained in the above "CSI Feedback", UE100 groups the CQI value, the PMI value, and the RI value into one set (this set may be referred to as (CQI, PMI, RI)) and feeds back (or transmits) the three values as one CSI to gNB200. For example, UE100 feeds back (CQI#1, PMI#1, RI#1) as CSI to gNB200 at a certain timing, and feeds back (CQI#2, PMI#2, RI#2) as CSI to gNB200 at another timing.

In this case, consider the case where three values are represented by one code in UE 100. For example, (CQI#1, PMI#1, RI#1) is represented as code "1", (CQI#2, PMI#2, RI#2) is represented as code "2", etc.

If UE100 can transmit a set of three values (hereinafter, sometimes referred to as "CSI") as a CSI status report using one code, the amount of information transmitted by UE100 in one CSI transmission can be reduced compared to when transmitting CSI. In other words, coding makes it possible to compress information compared to when coding is not performed.

FIG. 20 is a diagram showing an example of a table according to the first embodiment. The table shown in FIG. 20 shows the correspondence between codes and CSI. For example, (CQI#1, PMI#1, RI#1) corresponds to code "1", (CQI#1, PMI#1, RI#2) corresponds to code "2", and (CQI#1, PMI#1, RI#3) corresponds to code "3".

If such a table can be shared between UE100 and gNB200, UE100 can feed back (or transmit) a code to gNB200, and gNB200 can obtain each value of the CSI status report from the code.

However, the number of possible values for CSI is enormous. It is not realistic to use a table to assign codes to all possible values for CSI. This is because the amount of information contained in the table becomes enormous depending on the range of possible values for CSI.

The first embodiment aims to reduce the amount of information compared to when a table is used.

Therefore, in the first embodiment, instead of a table, a learned model based on machine learning technology is used. Specifically, first, a transmitting entity (e.g., UE 100) creates a learned model using predetermined data (e.g., CSI) and a code representing the predetermined data as learning data. Second, the transmitting entity transmits the learned model to a receiving entity (e.g., gNB 200). Third, the transmitting entity infers a code from the predetermined data using the learned model. Fourth, the transmitting entity transmits the code to the receiving entity. Fifth, the receiving entity acquires the predetermined data from the code using the learned model.

As a result, for example, UE 100 uses a learned model to acquire (or infer) a code, which can reduce the amount of information compared to when a table is used (i.e., when machine learning technology is not used). In addition, since UE 100 does not transmit CSI but transmits a code representing the CSI, the amount of information transmitted can be reduced.

(2.1) Example of Arrangement of Each Functional Block in the First Embodiment FIG. 21 is a diagram showing an example of arrangement of each functional block according to the first embodiment. As shown in FIG. 21, the UE 100 has a data collection unit A1, a model learning unit A2, and a model inference unit A3. That is, the example shown in FIG. 21 is an example in which model learning and model inference are performed in the UE 100. The example shows that the transmitting entity TE is the UE 100 and the receiving entity RE is the gNB 200.

As shown in FIG. 21, UE 100 has a code generation unit 135. The code generation unit 135 generates a code representing CSI. The code has a one-to-one correspondence with each CSI. However, the code has a smaller number of bits than the CSI. The correspondence between the code and the CSI may be as shown in FIG. 20. The code generation unit 135 may generate a code each time the CSI generation unit 131 generates CSI.

First, in the learning mode, the data collection unit A1 collects the CSI and the code. The model learning unit A2 performs model learning using the CSI and the code as learning data, and creates a learned model for inferring the code from the CSI.

The model learning unit A2 creates a learned model for each region. The region may be a region represented by one or more cells. The region may be a region represented by one or more Tracking Areas (TAs). Alternatively, the region may be a region represented by one or more Registration Areas (RAs). The region may be a region represented by one or more Public Land Mobile Networks (PLMNs). A TA includes one or more cells and indicates an area in which a UE 100 in an RRC idle state can move without updating the MME. An RA includes one or more cells and is defined as a collection of TAs. Furthermore, a PLMN indicates the range in which a telecommunications carrier can provide services.

The region in which the trained model should be created may be set by gNB200 using control data, for example. The control data may specify the region in which the trained model should be created by an identifier representing the region (e.g., a cell ID, a Tracking Area Identity (TAI), an identifier representing each RA, a PLMN ID, etc.).

In this way, by creating a trained model for each region in UE100, the amount of information (or size) of the trained model can be reduced compared to creating a trained model without taking the region into consideration. Also, by creating a trained model for each region in UE100, the overhead when UE100 transmits to gNB200 can be reduced and communication efficiency can be improved compared to creating a trained model without taking the region into consideration.

Secondly, in the inference mode, the transmitting unit 120 transmits the trained model to the gNB 200. In the learning mode, the transmitting unit 120 may transmit the trained model to the gNB 200. As described above, the transmitting unit 120 may include the trained model in an RRC message and transmit it. The transmitting unit 120 may include the trained model in a new message and transmit it. Furthermore, the transmitting unit 120 may include the trained model in a NAS message and transmit it to the AMF 300. As described above, the transmitting unit 120 may further add common additional information and/or individual additional information to the message to be transmitted.

In the inference mode, the data collection unit A1 collects CSI. The model inference unit A3 uses the learned model to infer a code from the CSI, and outputs the code as inference result data. The transmission unit 120 transmits the code to the gNB 200. At this time, the transmission unit 120 transmits the code together with regional identification information that identifies the region when the learned model was created. The regional identification information may be represented by an identifier that represents each region. Meanwhile, the gNB 200 (control unit 230) receives the learned model, and uses the learned model to obtain CSI from the code received from the UE 100.

(2.2) Example of Operation in First Embodiment Next, an example of operation in the first embodiment will be described.

FIG. 22 shows an example of operation according to the first embodiment.

As shown in FIG. 22, in step S501, gNB200 may notify or set the transmission pattern (puncture pattern) of CSI-RS in inference mode to UE100 as control data. In addition, gNB200 may notify or set data type information (here, data type information representing a data set of CSI and code) indicating the type of data used as learning data to UE100 as control data. Furthermore, gNB200 may transmit a switching notification to UE100 as control data to start the learning mode.

In step S502, UE100 starts the learning mode.

In step S503, gNB200 transmits full CSI-RS.

In step S504, UE 100 creates CSI based on the full CSI-RS. UE 100 creates a set of CQI, PMI, and RI (i.e., CSI) as a CSI status report based on the full CSI-RS.

In step S505, UE100 transmits CSI to gNB200.

In step S506, UE100 performs model learning using the CSI and the code as learning data to create a learned model. At this time, UE100 creates a learned model for each region. UE100 may transmit a completion notification indicating that the learned model has been created to gNB200 as control data.

In step S507, UE100 switches from the learning mode to the inference mode. UE100 may switch to the inference mode in accordance with the switching notification received from gNB200.

In step S508, UE100 transmits the learned model created in step S506 to gNB200. As described above, UE100 may transmit an RRC message (or a newly defined message) including the learned model to gNB200. The RRC message may include regional identification information. gNB200 receives the learned model.

In step S509, gNB200 transmits partial CSI-RS (or full CSI-RS). gNB200 may transmit partial CSI-RS in response to a completion notification received from UE100.

In step S510, UE 100 infers a code from the learned model created in step S506. For example, CSI generation unit 131 generates CSI based on partial CSI-RS, and model inference unit A3 infers a code by inputting the CSI as inference data into the learned model.

In step S511, UE100 transmits the code and regional identification information to gNB200.

In step S512, gNB200 acquires CSI using a learned model corresponding to the regional identification information. The learned model is a model that inputs CSI and outputs (or infers) a code. Alternatively, the learned model may be a model that inputs a code and outputs (or infers) CSI. In this way, gNB200 can input the code received in step S511 to the learned model and obtain the CSI that UE100 intends to report. In general, a learned model is capable of outputting a code from CSI, outputting CSI from a code, and performing processing in both directions.

Alternatively, gNB200 may input several CSIs into the learned model until the learned model outputs the same code as the code received in step S511, and may treat the CSI at the time when the same code as the code received in step S511 is output as the CSI reported by UE100 (i.e., CSI status report). Alternatively, in step S508, UE100 transmits at least a portion of the data set used to create the learned model to gNB200 together with the learned model. Then, gNB200 may use the data set to input CSI into the learned model, and may treat the CSI at the time when the same code as the code received in step S511 is output as the CSI reported by UE100.

Then, gNB200 may use the acquired CSI to perform scheduling control or beam control.

(2.3.1) Another Operation Example 1 According to the First Embodiment
Next, another operation example according to the first embodiment will be described.

There are cases where multiple UEs 100 cooperate to create a trained model. For example, the above-mentioned federated learning is one such case.

FIG. 23 is a diagram showing another example of operation according to the first embodiment. FIG. 23 shows an example in which the learning model used in the first embodiment is used. For example, the following case is assumed.

That is, UE100-1 performs model learning using code #1 and CSI #1 as learning data to create model #1. Model #1 is, for example, a model being learned by model learning. Thereafter, UE100-2 performs model learning using model #1 in order to further improve the accuracy of the model. At this time, UE100-2 performs model learning using the same code #1 used by UE100-1 and CSI #2 (or code #2) that is different from CSI #1 used by UE100.

In such a case, the learning data used in UE100-1 may be overwritten by the learning data used in UE100-2 through model learning in UE100-2, and a learned model that does not reflect the learning results of UE#1 may be created. For example, even if UE100-1 performs model learning using code #1 and CSI #1 as learning data, UE100-2 performs model learning using code #1 and CSI #2 as learning data, so that even if CSI #1 is input, code #1 is not output as an inference result, and a learned model is created in which code #1 is output as an inference result only after CSI #2 is input. Alternatively, even if code #1 is input, CSI #1 is not output as an inference result, and a learned model is generated in which CSI #2 is output as an inference result. In this way, by overwriting the learning data, a learned model that infers code #1 from CSI #1 is not created, and a learned model that does not reflect the learning results of UE100-1 is created.

In another example of the first embodiment, two solutions are used to create an appropriate trained model.

The first solution is an example in which identification information of each UE 100 is added to the code. Specifically, the code includes user equipment identification information (e.g., identification information of each UE) that identifies the user equipment (e.g., UE 100).

Figure 24 is a diagram showing the correspondence between codes and CSI when a UEID is added to the code as identification information for each UE 100. As shown in Figure 24, when model learning is performed in UE 100-1, UEID#1 is added to the code as the UEID of UE 100-1. Therefore, in UE 100-1, model learning is performed using a code including its own UEID (e.g., UEID#1) and CSI as learning data. On the other hand, in UE 100-2, model learning is performed using a code including its own UEID (e.g., UEID#2) and CSI as learning data.

As a result, for example, in FIG. 23, UE 100-1 uses learning data in which its own UEID is added to code #1, and UE 100-2 uses learning data in which its own UEID is added to code #1. Therefore, a learned model using different codes is created. For example, in the example of FIG. 23, a model is created in UE 100-1 using "1_UEID#1" and "CSI#1" as learning data, and a model is created in UE 100-2 using "1_UEID#2" and "CSI#2" as learning data, and the learning data is not overwritten. Therefore, an appropriate learned model can be created.

Then, the UE 100-1 can transmit the code instead of the CSI, so that, as in the first embodiment, it is possible to reduce the amount of information compared to when a table is used.

The UE identification information added to (or included in) the code may be (temporarily) assigned by the network. The identification information may be identification information pre-installed in each UE. Specifically, the identification information may be IMSI (International Mobile Subscriber Identity), SUCI (Subscription Concealed Identifier), GUTI (Globally Unique Temporary UE Identity), TMSI (Temporary Mobile Subscriber Identity), RNTI (Radio Network Temporary Identifier), etc.

The second solution is an example in which the range of codes used for training data is made different between UEs 100. Specifically, first, a base station (e.g., gNB 200) sets the range to be used for codes in a user device (e.g., UE 100). Second, the user device creates a trained model using predetermined data (e.g., CSI) and codes within the set range as training data.

As a result, for example, in FIG. 23, the range of codes used by UE 100-1 when performing model learning (e.g., code "1" to code "10") is different from the range of codes used by UE 100-2 when performing model learning (e.g., code "11" to code "20"). Therefore, the codes used as learning data differ between UE 100-1 and UE 100-2, and therefore the learning data created by UE 100-1 is not overwritten by UE 100-2. Therefore, an appropriate learned model can be created.

Next, specific examples of other operation examples according to the first embodiment will be described.

FIG. 25 shows another example of operation according to the first embodiment.

In step S601, the gNB 200 transmits control data including information indicating the code range to the UE 100. As in step S501 of the first embodiment, the gNB 200 may notify or set the CSI-RS transmission pattern in the inference mode, the type of data used as learning data, and switching to the learning mode.

In step S602, UE100 starts the learning mode.

In step S603, gNB200 transmits full CSI-RS.

In step S604, UE100 creates CSI based on the full CSI-RS.

In step S605, UE100 transmits CSI to gNB200.

In step S606, UE100 performs model learning by adding a UEID to the code. If information indicating the code range is set by gNB200, UE100 does not need to add a UEID to the code. In this case, UE100 performs model learning using a code within the range set by gNB200. UE100 may perform model learning for each region to create a learned model, as in the first embodiment.

　From then on, it operates in the same way as in the first embodiment.

(2.3.2) Another Operation Example 2 According to the First Embodiment
In the first embodiment, an example in which the UE 100 creates a learned model for each region has been described, but the present invention is not limited thereto. For example, the UE 100 may create a learned model for each time period or at a certain timing. Alternatively, the UE 100 may create a learned model according to the moving speed of the UE 100. In such a case, the UE 100 may transmit time information, timing information, or moving speed information of the UE 100 to the gNB 200 instead of the region identification information (step S511).

(2.3.3) Other Operation Example 3 According to First Embodiment
In the CSI feedback using a codebook currently adopted in wireless communication systems, the CSI-RS in the downlink is measured, and the measurement result (channel state) is discretized and coded for each index such as CQI, PMI, and RI according to a predetermined codebook. While the amount of data can be reduced by feeding back the code (digital value), the problem is that it contains an error with respect to the actual channel state (analog amount). This problem can be solved by using a machine learning model. For example, in FIG. 8, the receiving unit 110 receives the CSI-RS and transfers the measurement result of the channel state to the data collecting unit A1. The model inference unit A3 outputs reproducible information for making the channel state reproducible in the gNB 200 as the measurement result (input data) and the inference result (output data). The transmitting unit 120 transmits (feeds back) the reproducible information to the receiving unit 220. The gNB 200 inputs the reproducible information to its own model inference unit (e.g., data processing unit A4), and the model inference unit estimates (reproduces) the channel state. This allows the channel state (channel estimation) measured by the UE 100 to be reproduced in analog quantities (or a resolution close to this) in the gNB 200. The gNB 200 can appropriately (with reduced error) determine the MCS, beam/antenna weighting, MIMO rank, etc. based on the channel state. Note that this method may be implemented not only for the reproduction of the channel state described here, but also for each of the indicators (e.g., PMI or beam/antenna weighting) (individually and independently).

(2.3.4) Other Operation Example 4 According to First Embodiment
In the first embodiment, the gNB 200 is assumed to detect a case in which the accuracy of the CSI obtained from the inference result of the trained model is poor by comparing it with the CSI reported from the UE 100 or the most recent CSI. That is, a case is assumed in which the gNB 200 detects a deviation between the trained model and actual operation. In such a case, the gNB 200 may stop the use of the trained model for the UE 100 as a relief measure. Alternatively, the gNB 200 may start transmitting a trained model other than the trained model being used as a relief measure in order to start using the trained model other than the trained model being used.

[Second embodiment]
Next, a second embodiment will be described, focusing on the differences from the first embodiment.

In the first embodiment, CSI is used as an example of data corresponding to a code. In the second embodiment, data transmission timing is used as an example of data corresponding to a code.

DRX (Discontinuous Reception) may be used in wireless communication. When DRX is set for UE100, UE100 goes into wake-up mode during the On-duration period of the DRX cycle to monitor the PDCCH from the network, and goes into sleep mode during periods other than the On-duration period to turn off some of the functions of UE100 so that there is no need to attempt to receive data from the network. The periodic repetition of sleep mode and wake-up mode is called DRX, for example. DRX can reduce the power consumption of UE100 compared to UE100 that always operates in wake-up mode.

Note that DRX includes connected mode DRX (C-DRX) in which UE100 performs DRX operation in an RRC connected state, and idle mode DRX (I-DRX) in which UE100 performs DRX operation in an RRC idle state or an RRC inactive state. The above-mentioned operation is the operation in C-DRX. In the case of I-DRX, UE100 and gNB200 use the UE100 identifier (IMSI: International Mobile Subscriber Identity) to calculate a paging occasion (PO), which is a subframe in which a paging message is transmitted, and a paging frame (PF), which is a radio frame containing the PO. In I-DRX, the gNB 200 transmits a paging message in a periodic PF, and the UE 100 receives the paging message, thereby performing discontinuous reception.

In C-DRX, the DRX setting (drx-Config) is set in the UE 100 from the gNB 200 using an RRC message (such as an RRCConnectionReconfiguration message or an RRCConnectionSetup message). On the other hand, in I-DRX, parameters used in the calculation are notified using SIB. The UE 100 can calculate the PO and PF using the notified parameters.

Note that the following explanation uses the example of C-DRX, but it can also be applied to I-DRX.

FIG. 26 is a diagram showing an example of the transmission timing and reception timing of DL data according to the second embodiment. For example, the gNB 200 transmits DL data at a certain timing during the DRX On Duration period (i.e., the reception timing of the UE in FIG. 26), and the UE 100 receives the DL data at the same timing during the On Duration period.

Here, we consider a case in which UE 100 uses a table to determine the timing of receiving DL data. For example, the table stores whether or not DL data is to be transmitted at each timing, which represents the time of a day in subframe units starting from midnight. UE 100 can accurately determine the timing of transmitting DL data by using the table.

However, using a table that shows the time of a day in subframe units is not necessarily realistic, as the amount of information that would be stored in the table would be enormous.

In the second embodiment, therefore, the timing of receiving DL data is determined using AI/ML technology. Specifically, first, a base station (e.g., gNB200) creates a trained model for inferring the timing of transmitting data in discontinuous reception. Second, the base station transmits the trained model to a user device (e.g., UE100). Third, the user device infers the timing of transmitting data using the trained model. Fourth, the user device performs a data reception process at the timing of transmitting data.

As a result, for example, in the UE 100, since the reception process is not performed using a table, it is possible to reduce the amount of information compared to the case where a table is used. In addition, in the UE 100, it is also possible to infer the transmission timing of the DL data, and it is possible to receive the DL data in synchronization with the transmission timing of the gNB 200.

FIG. 27 is a diagram showing an example of the arrangement of an AI/ML model according to the second embodiment. In FIG. 27, the gNB 200 performs model learning and model inference, so the gNB 200 becomes the transmitting entity TE, and the UE 100 becomes the receiving entity RE.

As shown in FIG. 27, the gNB 200 has a user data generation unit 240 and a timing generation unit 236.

The user data generation unit 240 generates user data (DL data) addressed to the UE 100. The transmission unit 210 transmits the user data to the UE 100.

The timing generation unit 236 generates timing (or code) that indicates the elapsed time from a reference time. The reference time may be January 1st of every year, the 1st of every month, or 0:00 every day. The elapsed time may be expressed in a predetermined time unit. Specifically, the elapsed time may be expressed in subframe units. The elapsed time may be expressed in slot units. The elapsed time may be expressed in radio frame units or seconds.

Furthermore, when the timing generation unit 236 receives user data from the user data generation unit 240, it outputs information indicating the execution of transmission of the user data to the data collection unit A1 at the timing of receipt. Furthermore, the timing generation unit 236 outputs the user data transmission timing at which the transmission of the user data is executed to the data collection unit A1. The transmission timing may be represented by a code. The timing generation unit 236 may output information indicating that the transmission of the user data will not be executed at any timing other than the timing at which the user data is received to the data collection unit A1.

The learning data used by the model learning unit A2 is information (or predetermined data) indicating the execution of transmission of user data, and the transmission timing (or code) of the user data. When the model learning unit A2 receives information indicating the execution of transmission of user data, it creates a learned model that infers the transmission timing of the user data. Alternatively, when the model learning unit A2 receives information indicating a timing (or code), it creates a learned model that infers whether it is the transmission timing. For example, when the learned model receives "10:40:30" (timing), it can infer "transmission timing" or "non-transmission timing" (whether it is the transmission timing). The learned model may be a model for inferring the transmission timing of user data. The transmission unit 210 transmits the learned model to the UE 100.

(Operation example according to the second embodiment)
Next, an operation example according to the second embodiment will be described.

FIG. 28 shows an example of operation according to the second embodiment.

As shown in FIG. 28, in step S701, gNB200 performs DRX configuration for UE100. The DRX configuration is configured using an RRC message. gNB200 may notify UE100 of information indicating that it will create a trained model.

In step S702, gNB200 starts learning mode.

In step S703, gNB200 creates a trained model. As described above, gNB200 creates a trained model in which information representing the execution of transmission of user data is used as inference data and the transmission timing (or code) of the user data is the inference result. Alternatively, gNB200 creates a trained model that infers whether it is a transmission timing when information representing a timing (or code) is input. For example, with this trained model, when "10:40:30" (timing) is input, it can infer "transmission timing" or "non-transmission timing" (whether it is a transmission timing).

In step S704, gNB200 switches from learning mode to inference mode.

In step S705, gNB200 transmits the trained model created in step S703 to UE100.

In step S706, the gNB 200 uses control data to transmit information indicating where the current time is from the reference timing to the UE 100. The information may be information indicating the current time.

In step S707, discontinuous reception is started according to the DRX setting from gNB200 (step S701).

In step S708, UE100 infers the transmission timing of user data using the learned model received in step S705. UE100 may set the transmission timing as the reception timing. UE100 determines the reception timing (or the transmission timing) based on information indicating the current position from the reference timing (step S706). UE100 may set the reception timing to a time with a margin time secured for the transmission timing. Figure 29 shows an example in which a margin time is secured for the reception timing with respect to the transmission timing. The margin time may be set to UE100 by gNB200 using control data in step S701.

Returning to FIG. 28, in step S709, UE 100 receives DL data at the transmission timing (or reception timing) inferred in step S708.

(Another Example 1 According to the Second Embodiment)
In the second embodiment, an example was described in which the gNB 200 creates a trained model that infers the transmission timing of DL data. For example, the UE 100 may create a trained model that infers the transmission timing of UL data. In this case, the UE 100 performs model learning using information representing the execution of transmission of user data and the transmission timing (or code) of the user data as learning data. The UE 100 creates a trained model in which the transmission timing of the user data is the inference result, using information representing the execution of transmission of user data (UL data) as inference data, by model learning. The UE 100 transmits the trained model to the gNB 200, and the gNB 200 infers the transmission timing of the UL data using the trained model. The gNB 200 receives the UL data from the UE 100 at the inferred transmission timing (or reception timing).

(Another Example 2 According to the Second Embodiment)
In the second embodiment, it is assumed that the gNB 200 detects a difference between the actual uplink data and the trained model. It is also assumed that a case is detected in which downlink data cannot be transmitted from the gNB 200 to the UE 100 due to the difference, and the transmission buffer accumulates. In other words, it is assumed that a deviation is detected between the trained model and the actual operation. In such a case, as a relief measure, the gNB 200 may stop using the trained model at the next reception timing of the UE 100 for the UE 100. Alternatively, as a relief measure, the gNB 200 may start transmitting another trained model to start using the trained model other than the currently used trained model.

In addition, in another example 1 according to the second embodiment, a case is also assumed in which UE100 detects that it continues not to receive downlink data at its own reception timing. Even in such a case, as a rescue measure, UE100 may notify gNB200 of the situation and request the suspension of use of the trained model or the transmission of another trained model. The notification and the request may be transmitted using an RRC message (or a newly defined message) or the like. In addition, gNB200 may specify in advance the implementation conditions of the notification, such as notifying gNB200 of the absence of downlink data at a predetermined number of reception timings (e.g., five times) for UE100. The notification and the specification of the implementation conditions may also be transmitted using an RRC message (or a newly defined message) or the like.

[Other embodiments]
In the above-described first and second embodiments, supervised learning has been mainly described, but the present invention is not limited thereto. For example, the first to third embodiments may be applied to unsupervised learning or reinforcement learning.

Each of the above-mentioned operation flows can be implemented not only separately but also by combining two or more operation flows. For example, some steps of one operation flow can be added to another operation flow, or some steps of one operation flow can be replaced with some steps of another operation flow. In each flow, it is not necessary to execute all steps, and only some of the steps can be executed.

In the above-mentioned embodiment and example, an example in which the base station is an NR base station (gNB) has been described, but the base station may be an LTE base station (eNB) or a 6G base station. The base station may also be a relay node such as an IAB (Integrated Access and Backhaul) node. The base station may be a DU of an IAB node. The UE 100 may also be an MT (Mobile Termination) of an IAB node.

The term "network node" primarily refers to a base station, but may also refer to a core network device or part of a base station (CU, DU, or RU). A network node may also be composed of a combination of at least part of a core network device and at least part of a base station.

Also, a program (e.g., an information processing program) that causes a computer to execute each process or each function according to the above-mentioned embodiment may be provided. Or, a program (e.g., a mobile communication program) that causes the mobile communication system 1 to execute each process or each function according to the above-mentioned embodiment may be provided. The program may be recorded in a computer-readable medium. Using a computer-readable medium, it is possible to install the program in a computer. Here, the computer-readable medium on which the program is recorded may be a non-transient recording medium. The non-transient recording medium is not particularly limited, and may be, for example, a recording medium such as a CD-ROM or a DVD-ROM. Such a recording medium may be a memory included in the UE 100 and the gNB 200. Also, a circuit that executes each process performed by the UE 100 or the gNB 200 may be integrated, and at least a part of the UE 100 or the gNB 200 may be configured as a semiconductor integrated circuit (chip set, SoC: System on a chip).

The functions realized by UE100 or gNB200 (network node) may be implemented in circuitry or processing circuitry, including general-purpose processors, application-specific processors, integrated circuits, ASICs (Application Specific Integrated Circuits), CPUs (Central Processing Units), conventional circuits, and/or combinations thereof, programmed to realize the described functions. A processor includes transistors and other circuits and is considered to be circuitry or processing circuitry. A processor may be a programmed processor that executes a program stored in a memory. In this specification, circuitry, unit, and means are hardware that is programmed to realize the described functions or hardware that executes them. The hardware may be any hardware disclosed herein or any hardware known to be programmed or capable of performing the described functions. If the hardware is a processor considered to be a type of circuitry, the circuitry, means, or unit is a combination of hardware and software used to configure the hardware and/or processor.

As used in this disclosure, the terms "based on" and "depending on/in response to" do not mean "based only on" or "only in response to" unless otherwise specified. The term "based on" means both "based only on" and "based at least in part on". Similarly, the term "in response to" means both "only in response to" and "at least in part on". The terms "include", "comprise", and variations thereof do not mean including only the recited items, but may include only the recited items or may include additional items in addition to the recited items. In addition, the term "or" as used in this disclosure is not intended to mean an exclusive or. Furthermore, any reference to elements using designations such as "first", "second", etc. as used in this disclosure is not intended to generally limit the quantity or order of those elements. These designations may be used herein as a convenient way to distinguish between two or more elements. Thus, a reference to a first and second element does not imply that only two elements may be employed therein, or that the first element must precede the second element in some manner. In this disclosure, where articles are added by translation, such as, for example, a, an, and the in English, these articles are intended to include the plural unless the context clearly indicates otherwise.

The above describes the embodiments in detail with reference to the drawings, but the specific configuration is not limited to the above, and various design changes can be made without departing from the gist of the invention. Furthermore, it is also possible to combine the various embodiments, operation examples, or processes, etc., as long as they are not inconsistent.

This application claims priority from Japanese Patent Application No. 2023-016449 (filed February 6, 2023), the entire contents of which are incorporated herein by reference.

(Additional Note)
(Appendix 1)
A communication control method in a mobile communication system, comprising:
A transmitting entity creates a trained model using predetermined data and a code representing the predetermined data as training data;
the transmitting entity transmitting the trained model to a receiving entity;
the transmitting entity inferring the code from the given data using the trained model;
the transmitting entity transmitting the code to the receiving entity;
The receiving entity obtains the predetermined data from the code using the learned model.

(Appendix 2)
the transmitting entity is a user equipment and the receiving entity is a network node;
The communication control method according to claim 1, wherein the predetermined data is a set of a Channel Quality Indicator (CQI), a Precoding Matrix Indicator (PMI), and a Rank Indicator (RI) used in a Channel State Information (CSI) status report.

(Appendix 3)
The communication control method according to claim 1 or 2, wherein the creating step includes a step in which the user device creates the trained model for each region.

(Appendix 4)
The communication control method according to any one of Supplementary Note 1 to Supplementary Note 3, wherein the step of transmitting the code includes a step of the user equipment transmitting the code and area identification information identifying the area to the network node.

(Appendix 5)
the transmitting entity is a user equipment and the receiving entity is a network node;
The communication control method according to any one of Supplementary Note 1 to Supplementary Note 4, wherein the creating step includes a step in which the user device creates the trained model using the code including user device identification information.

(Appendix 6)
the transmitting entity is a user equipment and the receiving entity is a network node;
The method further comprises the step of: the network node configuring the user equipment with a range to be used for the code;
The communication control method according to any one of Supplementary Note 1 to Supplementary Note 5, wherein the creating step includes a step in which the user device creates the trained model using the specified data and the code within the set range as the training data.

(Appendix 7)
A communication control method in a mobile communication system, comprising:
A network node creates a trained model for inferring data transmission timing in discontinuous reception;
The network node transmits the trained model to a user equipment;
The user equipment infers a transmission timing of the data using the trained model;
The user device performs a receiving process of the data at a timing to transmit the data.

(Appendix 8)
the transmission timing represents an elapsed time from a reference timing,
The communication control method according to claim 7, further comprising a step of the network node transmitting to the user equipment information indicating a position of a current time from the reference timing, and the inferring step includes a step of the user equipment determining the transmission timing based on the information.

1: Mobile communication system 20: 5GC (CN)
100: UE
110: Receiving unit 120: Transmitting unit 130: Control unit 131: CSI generating unit 132: Optimal beam determining unit 133: Position information generating unit 135: Code generating unit 150: GNSS receiver 200: gNB
210: Transmitter 220: Receiver 230: Controller 231: CSI generator 236: Timing generator 240: User data generator A1: Data collector A2: Model learning unit A3: Model inference unit A4: Data processor TE: Transmitting entity RE: Receiving entity

Claims (8)

A communication control method in a mobile communication system, comprising:
A transmitting entity creates a trained model using predetermined data and a code representing the predetermined data as training data;
the transmitting entity transmitting the trained model to a receiving entity;
the transmitting entity inferring the code from the given data using the trained model;
the transmitting entity transmitting the code to the receiving entity;
The receiving entity obtains the predetermined data from the code using the learned model. the transmitting entity is a user equipment and the receiving entity is a network node;
The communication control method according to claim 1 , wherein the predetermined data is a set of a Channel Quality Indicator (CQI), a Precoding Matrix Indicator (PMI), and a Rank Indicator (RI) used in a Channel State Information (CSI) status report. The communication control method according to claim 2 , wherein the creating step includes the user device creating the trained model for each region. The communication control method according to claim 3 , wherein transmitting the code includes the user equipment transmitting the code and area identification information that identifies the area to the network node. the transmitting entity is a user equipment and the receiving entity is a network node;
The communication control method according to claim 1 , wherein the creating step includes the user device creating the trained model using the code including user device identification information. the transmitting entity is a user equipment and the receiving entity is a network node;
The method further comprises the network node configuring a range for use with the code in the user equipment;
The communication control method according to claim 1 , wherein the creating step includes the user device creating the learned model using the specified data and the code within the set range as the learning data. A communication control method in a mobile communication system, comprising:
A network node creates a trained model for inferring data transmission timing in discontinuous reception;
The network node transmits the trained model to a user equipment; and
The user equipment infers a transmission timing of the data using the trained model;
The user device performs a receiving process of the data at a timing for transmitting the data. the transmission timing represents an elapsed time from a reference timing,
8. The method of claim 7, further comprising the network node transmitting to the user equipment information indicating where a current time is from the reference timing, and the inferring includes the user equipment determining the transmission timing based on the information.

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