Communication method and user equipment

Abstract

This communication method applies an artificial intelligence or machine learning (AI / ML) technology to wireless communication between a user device and a network in a mobile communication system, and comprises: a step in which the user device receives, from the network, environment information indicating communication environment of a coverage area corresponding to a position of the user device; and a step in which the user device performs, on the basis of the environment information, an AI / ML process which is at least one of a learning process and an inference process that use an AI / ML model.

Description

Communication Method

[0001] The present disclosure relates to a communication method for use in a mobile communication system.

[0002] 3GPP (Third Generation Partnership Project) (registered trademark; the same applies hereinafter), a standardization project for mobile communication systems, is considering applying artificial intelligence or machine learning (also referred to as "AI / ML") technology to wireless communication (air interface) of mobile communication systems.

[0003] 3GPP contribution: RP-213599, “New SI: Study on Artificial Intelligence (AI) / Machine Learning (ML) for NR Air Interface”

[0004] A communication method according to a first aspect is a communication method that applies artificial intelligence or machine learning (AI / ML) technology to wireless communication between a user device and a network in a mobile communication system, and includes the steps of: the user device receiving, from the network, environmental information indicating the communication environment of a coverage area corresponding to the location of the user device; and the user device performing, based on the environmental information, AI / ML processing, which is at least one of a learning process and an inference process using an AI / ML model.

[0005] A communication method according to a second aspect is a communication method that applies artificial intelligence or machine learning (AI / ML) technology to wireless communication between a user device and a network in a mobile communication system, and includes the steps of: the user device transmitting model information indicating attributes of an AI / ML model possessed by the user device to the network; and the user device receiving information from the network indicating whether the user device can use the AI / ML model.

[0006] A communication method according to a third aspect is a communication method that applies artificial intelligence or machine learning (AI / ML) technology to wireless communication between a user device and a network in a mobile communication system, and includes a step in which the user device receives, from the network, configuration information that configures a transmission path used to transfer an AI / ML model from the network to the user device, and a step in which the user device receives the AI / ML model from the network via the transmission path.

[0007] 1 is a diagram showing a configuration of a mobile communication system according to an embodiment. FIG. 1 is a diagram showing a configuration of a UE (user equipment) according to an embodiment. FIG. 2 is a diagram showing a configuration of a gNB (base station) according to an embodiment. FIG. 3 is a diagram showing a configuration of a protocol stack of a radio interface of a user plane that handles data. FIG. 4 is a diagram showing a configuration of a protocol stack of a radio interface of a control plane that handles signaling (control signals). FIG. 5 is a diagram showing a functional block configuration of AI / ML technology (machine learning technology) in a mobile communication system according to an embodiment. FIG. 6 is a diagram showing an overview of operations related to each operation scenario according to an embodiment. FIG. 7 is a diagram showing a first operation scenario according to an embodiment. FIG. 8 is a diagram showing a first example of reducing CSI-RS according to an embodiment. FIG. 9 is a diagram showing a second example of reducing CSI-RS according to an embodiment. FIG. 10 is an operation flow diagram showing a first operation pattern related to the first operation scenario according to an embodiment. FIG. 11 is an operation flow diagram showing a second operation pattern related to the first operation scenario according to an embodiment. FIG. 12 is a diagram showing a second operation scenario according to an embodiment. FIG. 13 is an operation flow diagram showing an operation example related to the second operation scenario according to an embodiment. FIG. 14 is a diagram showing a third operation scenario according to an embodiment. FIG. 15 is an operation flow diagram showing an operation example related to the third operation scenario according to an embodiment. FIG. 16 is a diagram showing a first operation pattern related to model transfer according to an embodiment. 1 is a diagram showing an example of a setting message including a model and additional information according to the embodiment. FIG. 2 is a diagram showing a second operation pattern related to model transfer according to the embodiment. FIG. 3 is a diagram showing a third operation pattern related to model transfer according to the embodiment. FIG. 4 is a diagram showing an example of model management according to the embodiment. FIG. 5 is a diagram showing details of model management according to the embodiment. FIG. 6 is a diagram showing an example of a UE-side model possessed by a UE according to the embodiment. FIG. 7 is a diagram showing another example of a UE-side model possessed by a UE according to the embodiment. FIG. 8 is a diagram showing an operation example of a first operation pattern taking into account an area communication environment according to the embodiment. FIG. 9 is a diagram showing an operation example of a second operation pattern taking into account an area communication environment according to the embodiment. FIG. 10 is a diagram for explaining a transmission path used for model transfer according to the embodiment. FIG. 11 is a diagram showing an operation example related to setting of a transmission path used for model transfer according to the embodiment.

[0008] The present disclosure provides a communication method that enables utilization of AI / ML technology in a mobile communication system.

[0009] A mobile communication system according to an 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.

[0010] (1) Configuration of a Mobile Communication System First, the configuration of a mobile communication system according to an embodiment will be described. FIG. 1 is a diagram showing the configuration of a mobile communication system 1 according to an embodiment. The mobile communication system 1 conforms to the 5th Generation System (5GS) of the 3GPP standard. In the following description, 5GS will be used as an example, but the LTE (Long Term Evolution) system may also be applied at least in part to the mobile communication system. The 6th Generation (6G) system may also be applied at least in part to the mobile communication system.

[0011] The mobile communication system 1 includes 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. Hereinafter, the NG-RAN 10 may be simply referred to as the RAN 10. Furthermore, the 5GC 20 may be simply referred to as the core network (CN) 20. The RAN 10 and the CN 20 constitute a network 5 of the mobile communication system 1. The UE 100 performs wireless communication with the network 5.

[0012] The UE 100 is a mobile wireless communication device. The UE 100 may be any device that is used by a user. For example, the UE 100 may be a mobile phone terminal (including a smartphone) and / or a tablet terminal, a notebook PC, a communication module (including a communication card or a 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).

[0013] The NG-RAN 10 includes a base station (called a "gNB" in a 5G system) 200. The gNBs 200 are connected to each other via an Xn interface, which is an interface between base stations. The gNB 200 manages one or more cells. The gNB 200 performs wireless communication with a UE 100 that has established a connection with its own cell. The gNB 200 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, and the like. The term "cell" is used to indicate the smallest unit of a wireless communication area. The term "cell" is also used to indicate a function or resource for wireless communication with the UE 100. One cell belongs to one carrier frequency (hereinafter simply referred to as "frequency").

[0014] 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 an inter-base station interface.

[0015] The 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 are connected to the gNB 200 via an NG interface, which is an interface between a base station and a core network.

[0016] 2 is a diagram showing the configuration of a UE 100 (user equipment) according to an embodiment. The UE 100 has a receiving unit 110, a transmitting unit 120, and a control unit 130. The receiving unit 110 and the transmitting unit 120 constitute a communication unit that performs wireless communication with the gNB 200. The UE 100 is an example of a communication device.

[0017] 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 a radio signal received by the antenna into a baseband signal (received signal) and outputs the baseband signal to the control unit 130.

[0018] 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 a baseband signal (transmission signal) output by the control unit 130 into a radio signal and transmits it from the antenna.

[0019] The control unit 130 performs various controls and processes in the UE 100. The operations of the UE 100 described above and below may be operations controlled by the control unit 130. 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 processing 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.

[0020] 3 is a diagram showing the configuration of a gNB 200 (base station) according to an embodiment. The gNB 200 has a transmitter 210, a receiver 220, a controller 230, and a backhaul communication unit 240. The transmitter 210 and the receiver 220 constitute a communication unit that performs wireless communication with the UE 100. The backhaul communication unit 240 constitutes a network communication unit that communicates with the CN 20. The gNB 200 is another example of a communication device.

[0021] 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 a baseband signal (transmission signal) output by the control unit 230 into a radio signal and transmits it from the antenna.

[0022] 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 a radio signal received by the antenna into a baseband signal (received signal) and outputs the baseband signal to the control unit 230.

[0023] The control unit 230 performs various controls and processes in the gNB 200. The operations of the gNB 200 described above and below may be operations under the control of the control unit 130. 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 processing 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.

[0024] The backhaul communication unit 240 is connected to adjacent base stations via an Xn interface, which is an interface between base stations. The backhaul communication unit 240 is connected to the AMF / UPF 300 via an NG interface, which is an interface between a base station and a core network. The gNB 200 may be configured (i.e., functionally divided) with a central unit (CU) and a distributed unit (DU), and the two units may be connected via an F1 interface, which is a fronthaul interface.

[0025] FIG. 4 is a diagram showing the configuration of a protocol stack of a radio interface of a user plane that handles data.

[0026] The user plane air interface protocol includes 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.

[0027] 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 the 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 a CRC (Cyclic Redundancy Code) parity bit scrambled by the RNTI added.

[0028] In NR, UE100 can use a bandwidth narrower than the system bandwidth (i.e., the cell bandwidth). The gNB200 configures the UE100 with a bandwidth portion (BWP) consisting of contiguous PRBs (Physical Resource Blocks). The UE100 transmits and receives data and control signals in the active BWP. For example, up to four BWPs may be configurable for the UE100. Each BWP may have a different subcarrier spacing. The BWPs may overlap in frequency. When multiple BWPs are configured for the UE100, the gNB200 can specify which BWP to apply by controlling the downlink. This allows the gNB200 to dynamically adjust the UE bandwidth according to the amount of data traffic of the UE100, etc., thereby reducing UE power consumption.

[0029] The gNB 200 can configure, for example, 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 UE 100. Up to 12 or more CORESETs may be configured on the serving cell for the UE 100. 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 Orthogonal Frequency Division Multiplex (OFDM) symbols in the time domain.

[0030] The MAC layer performs data priority control, retransmission processing using Hybrid Automatic Repeat reQuest (HARQ), random access procedures, etc. Data and control information are transmitted between the MAC layer of the UE 100 and the MAC layer of the gNB 200 via a transport channel. The MAC layer of the gNB 200 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 allocated to the UE 100.

[0031] The RLC layer transmits data to the receiving RLC layer using the functions of the MAC layer and PHY layer. Data and control information are transmitted between the RLC layer of the UE 100 and the RLC layer of the gNB 200 via a logical channel.

[0032] The PDCP layer performs header compression / decompression, encryption / decryption, and the like.

[0033] The SDAP layer maps IP flows, which are units for Quality of Service (QoS) control by the core network, to radio bearers, which are units for QoS control by the access stratum (AS). Note that if the RAN is connected to the EPC, SDAP may not be required.

[0034] FIG. 5 is a diagram showing the configuration of a protocol stack of a radio interface of a control plane that handles signaling (control signals).

[0035] The protocol stack of the radio interface of the control plane includes a radio resource control (RRC) layer and a non-access stratum (NAS) instead of the SDAP layer shown in FIG.

[0036] 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 according 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.

[0037] The NAS, which is located above the RRC layer, performs session management, mobility management, etc. NAS signaling is transmitted between the NAS of the UE 100 and the NAS of the AMF 300A. Note that the UE 100 has an application layer and the like in addition to the radio interface protocol. Also, a layer lower than the NAS is called an AS (Access Stratum).

[0038] (2) Overview of AI / ML Technology Next, the AI / ML technology according to the embodiment will be described. Fig. 6 is a diagram showing a functional block configuration of the AI / ML technology in the mobile communication system 1 according to the embodiment.

[0039] The functional block configuration 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.

[0040] The data collection unit A1 collects input data, specifically, learning data and inference data, outputs the learning data to the model learning unit A2, and 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 also acquire data in another device as input data.

[0041] The model learning unit A2 performs model learning. Specifically, the model learning unit A2 optimizes parameters of a learning model (hereinafter also referred to as a "model" or an "AI / ML model") through machine learning using learning data, derives (generates and updates) a learned model, and outputs the learned model to the model inference unit A3. The model is a data-driven algorithm that applies AI / ML technology to generate a set of outputs based on a set of inputs. For example, considering y = ax + b, a (slope) and b (intercept) are parameters, and optimizing these corresponds to machine learning. Generally, machine learning is classified into supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is a method that uses correct answer data as learning data. Unsupervised learning is a method that does not use correct data for training data. For example, in unsupervised learning, feature points are memorized from a large amount of training data and the correct answer is determined (range estimation). Reinforcement learning is a method that assigns a score to the output result and learns how to maximize the score.

[0042] 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 the inference result data to the data processing unit A4. For example, in the case of y = ax + b, x corresponds to the inference data and y corresponds to the inference result data. Note that "y = ax + b" is a model. A model in which the slope and intercept are optimized, for example, "y = 5x + 3", is a trained model. There are various modeling techniques, including linear regression analysis, neural networks, and decision tree analysis. The above "y = ax + b" can be considered a type of linear regression analysis. The model inference unit A3 may provide model performance feedback to the model learning unit A2.

[0043] The data processing unit A4 receives the inference result data and performs processing that utilizes the inference result data.

[0044] 7 is a diagram showing an overview of the operation related to each operation scenario according to the embodiment. In FIG. 7, one of the UE 100 and the gNB 200 corresponds to the first communication device, and the other corresponds to the second communication device.

[0045] In step S1, the UE 100 transmits or receives control data related to AI / ML technology to or from the gNB 200. 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.

[0046] (3) Example Operation Scenario Next, an example operation scenario according to the embodiment will be described.

[0047] (3.1) First Operation Scenario Figure 8 is a diagram showing a first operation scenario according to the embodiment. In the first operation scenario, the data collection unit A1, the model learning unit A2, and the model inference unit A3 are arranged in the UE 100 (e.g., the control unit 130), and the data processing unit A4 is arranged in the gNB 200 (e.g., the control unit 230). In other words, model learning and model inference are performed on the UE 100 side.

[0048] In the first operation scenario, AI / ML technology is introduced into the channel state information (CSI) feedback from the UE 100 to the gNB 200. The CSI (CSI feedback information) transmitted (feedback) from the UE 100 to the gNB 200 is information regarding the downlink channel state between the UE 100 and the gNB 200. The CSI includes at least one of a channel quality indicator (CQI), a precoding matrix indicator (PMI), and a rank indicator (RI). The gNB 200 performs, for example, downlink scheduling based on the CSI feedback from the UE 100.

[0049] The gNB 200 transmits a reference signal for the UE 100 to estimate the downlink channel state. Such a reference signal may be, for example, a CSI reference signal (CSI-RS). Such a reference signal may be a demodulation reference signal (DMRS). For example, the reference signal is assumed to be a CSI-RS.

[0050] 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. Such a first reference signal is sometimes referred to as a full CSI-RS.

[0051] For example, UE 100 (CSI generation unit 131) performs channel estimation using a received signal (CSI-RS) received by receiving unit 110 from gNB 200, and generates CSI. UE 100 (transmission unit 120) transmits the generated CSI to gNB 200. Model learning unit A2 performs model learning using multiple sets of received signals (CSI-RS) and CSI as learning data, and derives a learned model for inferring CSI from the received signal (CSI-RS).

[0052] Second, in model inference, UE100 (receiving unit 110) receives a second reference signal from gNB200 using second resources that are less than the first resources. Then, UE100 (model inference unit A3) uses the learned model to infer CSI from inference data including the second reference signal as inference result data. In the description of the first operating scenario, such a second reference signal may be referred to as a partial CSI-RS or a punctured CSI-RS.

[0053] For example, the UE 100 (model inference unit A3) uses the received signal (CSI-RS) received by the receiver 110 from the gNB 200 as inference data, and infers CSI from the received signal (CSI-RS) using a trained model. The UE 100 (transmitter 120) transmits the inferred CSI to the gNB 200.

[0054] This enables UE 100 to feed back accurate (complete) CSI to gNB 200 from the small amount of CSI-RS (partial CSI-RS) received from gNB 200. For example, gNB 200 can reduce (puncture) CSI-RS when intended to reduce overhead. In addition, UE 100 can respond to situations where the radio conditions deteriorate and some CSI-RS cannot be received normally.

[0055] FIG. 9 is a diagram illustrating a first example of reducing CSI-RS according to the embodiment. In the first example, the gNB 200 reduces the number of antenna ports that transmit the CSI-RS. For example, in a mode in which the UE 100 performs model learning, the gNB 200 transmits the CSI-RS from all antenna ports of the antenna panel. On the other hand, in a mode in which the UE 100 performs model inference, the gNB 200 reduces the number of antenna ports that transmit the CSI-RS and transmits the CSI-RS from half the antenna ports of the antenna panel. Note that the antenna port is an example of a resource. This reduces overhead, improves antenna port utilization efficiency, and reduces power consumption.

[0056] 10 is a diagram showing a second example of reducing CSI-RS according to the embodiment. In the second example, the gNB 200 reduces the number of radio resources, specifically, time-frequency resources, that transmit the CSI-RS. For example, in a mode in which the UE 100 performs model learning, the gNB 200 transmits the CSI-RS using predetermined time-frequency resources. On the other hand, in a mode in which the UE 100 performs model inference, the gNB 200 transmits the CSI-RS using a smaller amount of time-frequency resources than the predetermined time-frequency resources. This reduces overhead, improves the utilization efficiency of radio resources, and reduces power consumption.

[0057] Next, a first operation pattern related to the first operation scenario will be described. In this first operation pattern, the gNB 200 transmits a switching notification to the UE 100 as control data, notifying the UE 100 of mode switching between a mode for performing model learning (hereinafter also referred to as "learning mode") and a mode for performing model inference (hereinafter also referred to as "inference mode"). The UE 100 receives the switching notification and switches between the learning mode and the inference mode. This makes it possible to appropriately switch between the learning mode and the inference mode. The switching notification may be setting information for setting a mode in the UE 100. The switching notification may also be a switching command for instructing the UE 100 to switch modes.

[0058] In this first operation pattern, when the model learning is completed, the UE 100 transmits a completion notification indicating that the model learning is completed to the gNB 200 as control data. The gNB 200 receives the completion notification. This allows the gNB 200 to understand that the model learning has been completed on the UE 100 side.

[0059] 11 is an operation flow diagram showing a first operation pattern according to a first operation scenario of the embodiment. This flow may be performed after the UE 100 establishes an RRC connection with a cell of the gNB 200. In the following operation flow diagram, optional steps are indicated by dashed lines.

[0060] In step S101, the gNB 200 may notify or set the input data pattern in the inference mode, for example, the transmission pattern (puncture pattern) of the CSI-RS in the inference mode, as control data to the UE 100. For example, the gNB 200 notifies the UE 100 of the antenna port and / or time-frequency resource from which the CSI-RS is or is not transmitted in the inference mode.

[0061] In step S102, gNB200 may send a switching notification to UE100 to start the learning mode.

[0062] In step S103, the UE 100 starts the learning mode.

[0063] In step S104, the gNB 200 transmits the full CSI-RS. The UE 100 receives the full CSI-RS and generates CSI based on the received CSI-RS. In the learning mode, the UE 100 can perform supervised learning using the received CSI-RS and the corresponding CSI. The UE 100 may derive and manage learning results (trained models) for each of its communication environments, for example, for each reception quality (RSRP, RSRQ, SINR) and / or movement speed.

[0064] In step S105, the generated CSI is transmitted (feedback) to gNB200.

[0065] Thereafter, 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 transmit a completion notification to the gNB 200 when the derivation (generation, update) of the learned model is completed. Here, the UE 100 may notify that the learning is completed for each of its own communication environments (e.g., movement speed, reception quality). In this case, the UE 100 includes information indicating which communication environment the completion notification is for in the notification.

[0066] In step S107, gNB200 sends a switching notification to UE100 to switch from learning mode to inference mode.

[0067] In step S108, in response to receiving the switching notification in step S107, the UE 100 switches from the learning mode to the inference mode.

[0068] In step S109, the gNB 200 transmits a partial CSI-RS. When the UE 100 receives the partial CSI-RS, it infers CSI from the received CSI-RS using a trained model. The UE 100 may select a trained model corresponding to its own communication environment from the trained models managed for each communication environment, and infer CSI using the selected trained model.

[0069] In step S110, UE100 transmits (feeds back) the inferred CSI to gNB200.

[0070] In step S111, if UE 100 determines that model learning is necessary, it may transmit a notification that model learning is necessary as control data to gNB 200. For example, when UE 100 moves, when its movement speed changes, when its reception quality changes, when the cell in which it is located changes, or when the bandwidth portion (BWP) used for communication changes, it assumes that the accuracy of the inference result can no longer be guaranteed, and transmits the notification to gNB 200.

[0071] Next, a second operation pattern related to the first operation scenario will be described. This second operation pattern may be used in combination with the above-mentioned operation pattern. In this second operation pattern, the gNB 200 transmits a completion condition notification indicating the completion condition of model learning to the UE 100 as control data. The UE 100 receives the completion condition notification and determines the completion of model learning based on the completion condition notification. This allows the UE 100 to appropriately determine the completion of model learning. The completion condition notification may be setting information that sets the completion condition of model learning in the UE 100. The completion condition notification may be included in a switching notification that notifies (instructs) the UE 100 to switch to the learning mode.

[0072] FIG. 12 is an operation flow diagram showing a second operation pattern according to a first operation scenario according to the embodiment.

[0073] In step S201, the gNB 200 transmits a completion condition notification indicating the completion condition of the model learning as control data to the UE 100. The completion condition notification may include at least one of the following completion condition information.

[0074] - Acceptable error range for correct answer data: For example, this is the acceptable range of error between the CSI generated using a normal CSI feedback calculation method and the CSI inferred by model inference. When learning has progressed to a certain extent, the UE 100 infers the CSI using the trained model at that time, compares it with the correct CSI, and can determine that learning is complete based on whether the error is within the acceptable range.

[0075] Number of training data: The number of data used for training, for example, the number of received CSI-RSs corresponds to the number of training data. The UE 100 can determine that training is complete when the number of received CSI-RSs in the training mode reaches the notified (set) number of training data.

[0076] Number of learning attempts: The number of times model learning is performed using learning data. The UE 100 can determine that learning is complete when the number of learning attempts in the learning mode reaches a notified (set) number of times.

[0077] Output score threshold: For example, a score in reinforcement learning. The UE 100 can determine that the learning is completed when the score reaches a notified (set) score.

[0078] The UE 100 continues learning based on the full CSI-RS until it is determined that the learning is completed (steps S203 and S204).

[0079] In step S205, when UE100 determines that model learning has been completed, it may send a completion notification to gNB200 indicating that model learning has been completed.

[0080] Next, a third operation pattern related to the first operation scenario will be described. This third operation pattern may be used in conjunction with the above-described operation pattern. When it is desired to improve the accuracy of CSI feedback, not only CSI-RS but also other types of data, such as the reception characteristics of the physical downlink shared channel (PDSCH), can be used as training data and inference data. In this third operation pattern, the gNB 200 transmits data type information specifying at least the type of data to be used as training data to the UE 100 as control data. In other words, the gNB 200 specifies to the UE 100 what type of training data and inference data to use (type of input data). The UE 100 receives the data type information and performs model training using the specified type of data. This allows the UE 100 to perform appropriate model training.

[0081] FIG. 13 is an operation flow diagram showing a third operation pattern according to the first operation scenario according to the embodiment.

[0082] In step S301, the UE 100 may transmit capability information indicating which type of input data the UE 100 can handle by machine learning as control data to the gNB 200. Here, the UE 100 may further notify accompanying information such as the accuracy of the input data.

[0083] In step S302, the gNB 200 transmits data type information to the UE 100. The data type information may be setting information for setting the type of input data to the UE 100. Here, the type of input data may be reception quality and / or UE movement speed for CSI feedback. The reception quality may be reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference-and-noise ratio (SINR), bit error rate (BER), block error rate (BLER), analog-to-digital converter output waveform, etc.

[0084] In addition, assuming UE positioning, which will be described later, the type of input data may be location information (latitude, longitude, and altitude) of the GNSS (Global Navigation Satellite System), an RF fingerprint (cell ID and its reception quality, etc.), the angle of arrival (AoA) of the received signal, the reception level, reception phase, and reception time difference (OTDOA) for each antenna, round trip time, wireless LAN (Local Area Network) or other short-range wireless reception information.

[0085] The gNB200 may specify the type of input data independently for each of the training data and the inference data. The gNB200 may specify the type of input data independently for each of the CSI feedback and the UE positioning.

[0086] (3.2) Second Operation Scenario Next, the second operation scenario will be described, focusing on differences from the first operation scenario. In the first operation scenario, the downlink reference signal (i.e., downlink CSI estimation) has been mainly described. In the second operation scenario, the uplink reference signal (i.e., uplink CSI estimation) will be described. In the description of the second operation scenario, the uplink reference signal is a sounding reference signal (SRS), but it may also be an uplink DMRS, etc.

[0087] 14 is a diagram showing a second operation scenario according to the embodiment. In the second operation scenario, the data collection unit A1, the model learning unit A2, the model inference unit A3, and the data processing unit A4 are arranged in the gNB 200 (e.g., the control unit 230). In other words, model learning and model inference are performed on the gNB 200 side.

[0088] In the second operation scenario, AI / ML technology is introduced into the CSI estimation performed by gNB200 based on the SRS from UE100. Therefore, gNB200 (e.g., control unit 230) has a CSI generation unit 231 that generates CSI based on the SRS received by receiver 220 from UE100. This 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 the SRS.

[0089] First, in model learning, gNB200 (receiving unit 220) receives a first reference signal from UE100 using a first resource. Then, gNB200 (model learning unit A2) derives a learned model for inferring CSI from a reference signal (SRS) using learning data including the first reference signal. In the description of the second operating scenario, such a first reference signal may be referred to as a full SRS.

[0090] For example, gNB200 (CSI generation unit 231) performs channel estimation using the received signal (SRS) received by receiver 220 from UE100 to generate CSI. Model learning unit A2 performs model learning using multiple sets of received signals (SRS) and CSI as learning data, and derives a learned model for inferring CSI from the received signal (SRS).

[0091] Second, in model inference, gNB200 (receiving unit 220) receives a second reference signal from UE100 using a second resource that is less than the first resource. Then, UE100 (model inference unit A3) uses the learned model to infer CSI as inference result data from inference data including the second reference signal. In the description of the second operating scenario, such a second reference signal may be referred to as a partial SRS or a punctured SRS. For the SRS puncturing pattern, a pattern similar to that in the first operating scenario can be used (see Figures 9 and 10).

[0092] For example, gNB200 (model inference unit A3) uses the received signal (SRS) received by the receiving unit 220 from UE100 as inference data, and infers CSI from the received signal (SRS) using a learned model.

[0093] This enables the gNB 200 to generate accurate (complete) CSI from the small number of SRSs (partial SRSs) received from the UE 100. For example, the UE 100 can reduce (puncture) the SRS when intended to reduce overhead. In addition, the gNB 200 can respond to situations where the radio conditions deteriorate and some SRSs cannot be received normally.

[0094] In such an operating scenario, the "CSI-RS" in the operation of the first operating scenario described above can be replaced with "SRS," "gNB200" with "UE100," and "UE100" with "gNB200."

[0095] In addition, in the second operation scenario, gNB200 transmits reference signal type information indicating the type of reference signal to be transmitted by UE100, between the first reference signal (full SRS) and the second reference signal (partial SRS), as control data to UE100. UE100 receives the reference signal type information and transmits the SRS specified by gNB200 to gNB200. This allows UE100 to transmit an appropriate SRS.

[0096] FIG. 15 is an operational flow diagram showing an example of an operation according to a second operation scenario according to the embodiment.

[0097] In step S501, gNB200 configures UE100 for SRS transmission.

[0098] In step S502, gNB200 starts learning mode.

[0099] In step S503, UE100 transmits the full SRS to gNB200 according to the setting in step S501. gNB200 receives the full SRS and performs model learning for channel estimation.

[0100] In step S504, gNB200 identifies an SRS transmission pattern (puncture pattern) to be input as inference data into the learned model, and sets the identified SRS transmission pattern to UE100.

[0101] In step S505, gNB200 transitions to inference mode and starts model inference using the trained model.

[0102] In step S506, UE100 transmits a partial SRS in accordance with the SRS transmission setting in step S504. When gNB200 inputs the SRS as inference data into the trained model to obtain a channel estimation result, it uses the channel estimation result to perform uplink scheduling of UE100 (for example, control of uplink transmission weight, etc.). Note that if the inference accuracy using the trained model deteriorates, gNB200 may reconfigure UE100 to transmit a full SRS.

[0103] (3.3) Third Operation Scenario Next, the third operation scenario will be described, mainly focusing on the differences from the first and second operation scenarios. The third operation scenario is an embodiment in which the position of the UE 100 is estimated (so-called UE positioning) using federated learning. Fig. 16 is a diagram showing the third operation scenario according to the embodiment. In such an application example of federated learning, for example, the following procedure is performed.

[0104] First, the location server 400 transmits the model to the UE 100 .

[0105] Secondly, the UE 100 performs model learning on the UE 100 (model learning unit A2) side using data stored in the UE 100. The data stored in the UE 100 is, for example, a positioning reference signal (PRS) received by the UE 100 from the gNB 200 and / or output data of the GNSS receiver 140. The data stored in the UE 100 may include location information (including latitude and longitude) generated by the location information generation unit 132 based on the reception result of the PRS and / or the output data of the GNSS receiver 140.

[0106] Third, the UE 100 applies the learned model, which is the learning result, in the UE 100 (model inference unit A3), and transmits variable parameters (hereinafter also referred to as "learned parameters") included in the learned model to the location server 400. In the above example, the optimized a (slope) and b (intercept) correspond to the learned parameters.

[0107] Fourth, location server 400 (associated learning unit A5) collects learned parameters from multiple UEs 100 and integrates them. Location server 400 may transmit the learned model obtained by the integration to UE 100. Location server 400 can estimate the location of UE 100 based on the learned model obtained by the integration and measurement reports from UE 100.

[0108] In the third operation scenario, gNB200 transmits trigger setting information to UE100 as control data, which sets a transmission trigger condition for UE100 to transmit the learned parameters. UE100 receives the trigger setting information, and transmits the learned parameters to gNB200 (location server 400) when the set transmission trigger condition is satisfied. This allows UE100 to transmit the learned parameters at an appropriate timing.

[0109] FIG. 17 is an operational flow diagram showing an example of an operation according to a third operation scenario according to the embodiment.

[0110] In step S601, the gNB 200 may notify the UE 100 of the base model to be learned. Here, the base model may be a model that has been learned in the past. As described above, the gNB 200 may transmit data type information indicating what the input data is to be to the UE 100.

[0111] In step S602, the gNB 200 instructs the UE 100 to perform model learning and sets the report timing (trigger condition) of the learned parameters. The set report timing may be periodic. The report timing may be triggered (i.e., an event trigger) when the learning proficiency meets the condition.

[0112] In the case of periodic timing, the gNB 200 sets, for example, a timer value to the UE 100. The UE 100 starts the timer when learning starts (step S603), and when it expires, reports the learned parameters to the gNB 200 (location server 400) (step S604). Alternatively, the gNB 200 may specify the radio frame or time to report to the UE 100. The radio frame may be specified as an absolute value, for example, SFN = 512. The radio frame may be calculated by modulo arithmetic. For example, the gNB 200 sets N as a setting value, and the UE 100 reports the learned parameters with an SFN such that "SFN mod N = 0" (step S604).

[0113] In the case of an event trigger, the completion condition as described above is set in the UE 100. When the completion condition is satisfied, the UE 100 reports the learned parameters to the gNB 200 (location server 400) (step S604). The UE 100 may trigger the reporting of the learned parameters, for example, when the accuracy of the model inference becomes better than the previously transmitted model. Here, an offset may be introduced, and the reporting may be triggered when "current accuracy > previous accuracy + offset." The UE 100 may trigger the reporting of the learned parameters, for example, when the learning data has been input (learned) N or more times. Such an offset and / or the value of N may be set to the UE 100 by the gNB 200.

[0114] In step S604, when the reporting timing conditions are met, UE100 reports the learned parameters at that time to the network (gNB200).

[0115] In step S605, the network (location server 400) integrates the learned parameters reported from the multiple UEs 100.

[0116] (4) Example of Model Transfer Next, an example of model transfer according to the embodiment will be described.

[0117] (4.1) First Operation Pattern Related to Model Transfer FIG. 18 is a diagram showing a first operation pattern related to model transfer according to the embodiment. In the drawings referred to in the following embodiments, non-essential processes are indicated by dashed lines. In the following embodiments, the communication device 501 is primarily assumed to be the UE 100, but the communication device 501 may be the gNB 200 or the AMF 300A. In addition, the communication device 502 is primarily assumed to be the gNB 200, but the communication device 502 may be the UE 100 or the AMF 300A.

[0118] As shown in FIG. 18, in step S701, the gNB 200 transmits a capability inquiry message to the UE 100 to request transmission of a message including an information element indicating execution capability for machine learning processing. The capability inquiry message is an example of a transmission request requesting transmission of a message including an information element indicating execution capability for machine learning processing. The UE 100 receives the capability inquiry message. However, the gNB 200 may transmit the capability inquiry message when executing the machine learning processing (when determining that the processing will be executed).

[0119] In step S702, the UE 100 transmits to the gNB 200 a message including an information element indicating execution capabilities for machine learning processing (or, from another perspective, the execution environment for machine learning processing). The gNB 200 receives the message. The message may be an RRC message, for example, a "UE Capability" message defined in the RRC technical specifications, or a newly defined message (for example, a "UE AI Capability" message, etc.). Alternatively, the communication device 502 may be an AMF 300A, and the message may be a NAS message. Alternatively, if a new layer is defined for executing or controlling machine learning processing (AI / ML processing), the message may be a message of the new layer. The new layer will be referred to as the "AI / ML layer" as appropriate.

[0120] The information element indicating the execution capability for machine learning processing is at least one of the following information elements (A1) to (A3).

[0121] Information Element (A1) The information element (A1) is an information element indicating the processor's capability for executing machine learning processing and / or an information element indicating the memory's capability for executing machine learning processing.

[0122] The information element indicating the processor's capability for executing machine learning processing may be an information element indicating whether the UE 100 has an AI processor. If the UE 100 has the processor, the information element may include the AI ​​processor product number (model number). The information element may be an information element indicating whether the UE 100 can use a GPU (Graphics Processing Unit). The information element may be an information element indicating whether the machine learning processing must be executed by a CPU. By transmitting the information element indicating the processor's capability for executing machine learning processing from the UE 100 to the gNB 200, the network side can determine, for example, whether the UE 100 can use a neural network model as a model. The information element indicating the processor's capability for executing machine learning processing may be an information element indicating the clock frequency and / or the number of parallel executions of the processor.

[0123] The information element indicating the memory capacity for executing the machine learning process may be an information element indicating the memory capacity of a volatile memory (e.g., RAM: Random Access Memory) among the memories of the UE 100. The information element may also be an information element indicating the memory capacity of a non-volatile memory (e.g., ROM: Read Only Memory) among the memories of the UE 100. The information element may be both of these. The information element indicating the memory capacity for executing the machine learning process may be specified for each type, such as a memory for storing a model, a memory for an AI processor, or a memory for a GPU.

[0124] The information element (A1) may be defined as an information element for inference processing (model inference). The information element (A1) may be defined as an information element for learning processing (model learning). The information element (A1) may be defined as both an information element for inference processing and an information element for learning processing.

[0125] Information element (A2) Information element (A2) is an information element indicating the execution capability of the inference process. Information element (A2) may be an information element indicating a model supported in the inference process. The information element may be an information element indicating whether a deep neural network model is supported. In this case, the information element may include at least one of information indicating the number of layers (stages) of a neural network that can be supported, information indicating the number of neurons that can be supported (which may be the number of neurons per layer), and information indicating the number of synapses that can be supported (which may be the number of input or output synapses per layer or per neuron).

[0126] The information element (A2) may be an information element indicating the execution time (response time) required to execute the inference process. The information element (A2) may be an information element indicating the number of inference processes that can be executed simultaneously (e.g., how many inference processes can be executed in parallel). The information element (A2) may be an information element indicating the processing capacity of the inference process. For example, if the processing load of a certain standard model (standard task) is determined to be 1 point, the information element indicating the processing capacity of the inference process may be information indicating how many points its own processing capacity is.

[0127] Information element (A3) Information element (A3) is an information element indicating the execution capability of the learning process. Information element (A3) may be an information element indicating a learning algorithm supported in the learning process. The learning algorithm indicated by the information element includes supervised learning (e.g., linear regression, decision tree, logistic regression, k-nearest neighbor method, support vector machine, etc.), unsupervised learning (e.g., clustering, k-means method, principal component analysis, etc.), reinforcement learning, and deep learning. When UE 100 supports deep learning, the information element may include at least one of information indicating the number of layers (stages) of a neural network that can be supported, information indicating the number of neurons that can be supported (which may be the number of neurons per layer), and information indicating the number of synapses that can be supported (which may be the number of input or output synapses per layer or per neuron).

[0128] The information element (A3) may be an information element indicating the execution time (response time) required to execute the learning process. The information element (A3) may be an information element indicating the number of concurrent executions of the learning process (e.g., how many learning processes can be executed in parallel). The information element (A3) may be an information element indicating the processing capacity of the learning process. For example, if the processing load of a certain standard model (standard task) is determined to be 1 point, the information element indicating the processing capacity of the learning process may be information indicating how many points its own processing capacity is. Note that, with regard to the number of concurrent executions, since the learning process generally has a higher processing load than the inference process, the information may be information indicating the number of concurrent executions with the inference process (e.g., two inference processes and one learning process).

[0129] In step S703, gNB200 determines a model to be configured (deployed) in UE100 based on the information elements included in the message received in step S702. The model may be a trained model used by UE100 in the inference process. The model may also be an untrained model used by UE100 in the learning process.

[0130] In step S704, gNB200 transmits a message including the model determined in step S703 to UE100. UE100 receives the message and performs machine learning processing (learning processing and / or inference processing) using the model included in the message. A specific example of step S704 will be described in the following second operation pattern.

[0131] (4.2) Second Operation Pattern Related to Model Transfer FIG. 19 is a diagram showing an example of a configuration message including a model and additional information according to the embodiment. The configuration message may be an RRC message transmitted from the gNB 200 to the UE 100, for example, an "RRC Reconfiguration" message defined in the RRC technical specifications, or a newly defined message (for example, an "AI Deployment" message or an "AI Reconfiguration" message). 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.

[0132] 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) provided individually corresponding to each of the three models (Model #1 to #3), and common additional information (Meta-Info) 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.

[0133] FIG. 20 is a diagram illustrating a second operation pattern regarding model transfer according to the embodiment.

[0134] In step S711, gNB200 transmits a configuration message including a model and additional information to UE100. UE100 receives the configuration message. The configuration message includes at least one of the following information elements (B1) to (B6).

[0135] (B1) Model The "model" may be a trained model used by UE100 in the inference process. The "model" may be an untrained model used by UE100 in the training process. In the configuration message, the "model" may be encapsulated (containerized). If the "model" is a neural network model, the "model" may be expressed by the number of layers (stages), the number of neurons per layer, and the synapses (weighting) between each neuron. For example, a trained (or untrained) neural network model may be expressed by a combination of matrices.

[0136] A single setting message may contain multiple "models." In this case, the multiple "models" may be included in the setting message in list form. The multiple "models" may be set for the same purpose, or may be set for different purposes. Details of the use of the models will be described later.

[0137] (B2) Model Index (also referred to as "Model ID") A "model index" is an example of additional information (for example, individual additional information). A "model index" is an index (index number) assigned to a model. In the activation command and deletion message described below, a model can be specified by the "model index". When the model settings are changed, the model can also be specified by the "model index".

[0138] (B3) Model Use "Model Use" is an example of additional information (individual additional information or common additional information). "Model Use" specifies the function to which the model is applied. For example, functions to which the model is applied include CSI feedback, beam management (beam estimation, overhead / latency reduction, beam selection accuracy improvement), positioning, modulation / demodulation, encoding / decoding (CODEC), and packet compression. The content of the model use and its index (identifier) ​​may be pre-defined in the 3GPP technical specifications, and the "model use" may be specified by an index. For example, the model use and its index (identifier) ​​are defined such that CSI feedback is specified as use index #A and beam management is specified as use index #B. The UE 100 deploys a model for which a "model use" is specified in a functional block corresponding to the specified use. Note that the "model use" may also be an information element specifying the input data and output data of the model.

[0139] (B4) Model Execution Requirements "Model execution requirements" are an example of additional information (for example, individual additional information). "Model execution requirements" are information elements that indicate the performance (required performance) required to apply (execute) the model, for example, the processing delay (required latency).

[0140] (B5) Model Selection Criteria The "model selection criteria" is an example of additional information (individual additional information or common additional information). UE 100 applies (executes) a corresponding model in response to the criteria specified in the "model selection criteria" being satisfied. The "model selection criteria" may be the movement speed of UE 100. In this case, the "model selection criteria" may be specified as a speed range such as "low speed movement" or "high speed movement." The "model selection criteria" may be specified as a movement speed threshold. The "model selection criteria" may be radio quality (e.g., RSRP / RSRQ / SINR) measured by UE 100. In this case, the "model selection criteria" may be specified as a radio quality range. The "model selection criteria" may be specified as a radio quality threshold. The "model selection criteria" may be the location (latitude / longitude / altitude) of UE 100. The "model selection criteria" may be set to follow sequential notifications from the network (an activation command, described later), or may specify autonomous selection by UE 100.

[0141] (B6) Necessity of Learning Process "Necessity of Learning Process" is an information element indicating whether or not a learning process (or re-learning) is necessary or possible for the corresponding model. If a learning process is necessary, the type of parameter to be used in the learning process may be further set. For example, in the case of CSI feedback, it is set so that CSI-RS and UE movement speed are used as parameters. If a learning process is necessary, the method of the learning process, for example, supervised learning, unsupervised learning, reinforcement learning, or deep learning, may be further set. It may also be set whether or not the learning process is to be performed immediately after the model is set. If it is not to be performed immediately, the execution of the learning may be controlled by an activation command described below. For example, in the case of federated learning, it may also be set whether or not the result of the learning process of the UE100 is to be notified to the gNB200. When it is necessary to notify the gNB200 of the results of the learning process of the UE100, the UE100 may, after executing the learning process, encapsulate the learned model or learned parameters and transmit them to the gNB200 by an RRC message, etc. The information element indicating "whether or not learning process is required" may be an information element indicating, in addition to whether or not learning process is required, whether or not the corresponding model is to be used only for model inference.

[0142] In step S712, the UE 100 determines whether the model set in step S711 can be deployed (executed). The UE 100 may make this determination when activating the model, which will be described later, and step S713, which will be described later, may be a message notifying an error at the time of activation. Furthermore, this determination may be made not at the time of deployment or activation, but while the model is in use (during execution of the machine learning process). If it is determined that the model cannot be deployed (step S712: NO), that is, if an error occurs, in step S713, the UE 100 transmits an error message to the gNB 200. The error message may be an RRC message transmitted from the UE 100 to the gNB 200, for example, a "Failure Information" message specified in the RRC technical specifications, or a newly specified message (for example, an "AI Deployment Failure Information" message). The error message may be UCI (Uplink Control Information) defined in the physical layer or MAC CE (Control Element) defined in the MAC layer. Alternatively, the error message may be a NAS message transmitted from the UE 100 to the AMF 300A. Alternatively, when a new layer (AI / ML layer) for performing machine learning processing (AI / ML processing) is defined, the message may be a message of the new layer.

[0143] The error message includes at least one of the following information elements (C1) to (C3):

[0144] (C1) Model Index This is the model index of the model that has been determined to be undeployable.

[0145] (C2) Usage Index This is the usage index of the model that has been determined to be undeployable.

[0146] - (C3) Error cause This is an information element regarding the cause of the error. The "error cause" may be, for example, "unsupported model," "exceeding processing capacity," "phase of error occurrence," or "other error." The "unsupported model" includes, for example, the UE 100 cannot support a neural network model, or the UE 100 cannot support machine learning processing (AI / ML processing) of a specified function. "Exceeding processing capacity" may be due to, for example, overload (processing load and / or memory load exceeding capacity), inability to satisfy requested processing time, interrupt processing or priority processing of an application (upper layer), etc. The "phase of error occurrence" is information indicating when the error occurred. The "phase of error occurrence" may be categorized as deployment (setting), activation, or operation. The "phase of error occurrence" may be classified as during inference processing or during learning processing. "Other errors" are other causes.

[0147] When an error occurs, the UE 100 may automatically delete the corresponding model. The UE 100 may delete the model when it confirms that an error message has been received by the gNB 200, for example, when it receives an ACK in a lower layer. When the gNB 200 receives an error message from the UE 100, it may recognize that the model has been deleted.

[0148] On the other hand, if it is determined that the model set in step S711 can be deployed (step S712: YES), that is, if no error occurs, in step S714, the UE 100 deploys the model according to the setting. "Deployment" may mean making the model applicable. "Deployment" may mean actually applying the model. In the former case, the model is not applied simply by being deployed, but is applied when the model is activated by an activation command described below. In the latter case, once the model is deployed, the model becomes in use.

[0149] In step S715, the UE 100 transmits a response message to the gNB 200 in response to the completion of the model deployment. The gNB 200 receives the response message. The UE 100 may transmit the response message when the activation of the model is completed by an activation command described below. The response message may be an RRC message transmitted from the UE 100 to the gNB 200, for example, an "RRC Reconfiguration Complete" message defined in the RRC technical specifications, or a newly defined message (for example, an "AI Deployment Complete" message). The response message may be a MAC CE defined in the MAC layer. Alternatively, the response message may be a NAS message transmitted from the UE 100 to the AMF 300A. Alternatively, if a new layer for performing machine learning processing (AI / ML processing) is defined, the message may be a message of the new layer.

[0150] In step S716, the UE 100 may transmit a measurement report message, which is an RRC message including measurement results of the radio environment, to the gNB 200. The gNB 200 receives the measurement report message.

[0151] In step S717, the gNB 200 selects a model to be activated, for example, based on the measurement report message, and transmits an activation command (selection command) to the UE 100 to activate the selected model. The UE 100 receives the activation command. The activation command may be a DCI, MAC CE, RRC message, or an AI / ML layer message. The activation command may include a model index indicating the selected model. The activation command may include information specifying whether the UE 100 performs an inference process or a learning process.

[0152] The gNB 200 selects a model to be deactivated, for example, based on a measurement report message, and transmits a deactivation command (selection command) to the UE 100 to deactivate the selected model. The UE 100 receives the deactivation command. The deactivation command may be a DCI, MAC CE, RRC message, or a message of the AI ​​ / ML layer. The deactivation command may include a model index indicating the selected model. When the UE 100 receives the deactivation command, the UE 100 may deactivate (discontinue application of) the specified model without deleting it.

[0153] In step S718, in response to receiving the activation command, the UE 100 applies (activates) the designated model. The UE 100 performs an inference process and / or a learning process using the activated model from among the deployed models.

[0154] Then, in step S719, the gNB 200 transmits a deletion message to the UE 100 to delete the model. The UE 100 receives the deletion message. The deletion message may be a MAC CE, an RRC message, a NAS message, or a message of the AI ​​ / ML layer. The deletion message may include a model index of the model to be deleted. When the UE 100 receives the deletion message, it deletes the specified model.

[0155] (4.3) Third Operation Pattern Regarding Model Transfer In this third operation pattern, the UE 100 notifies the network of the load status of the machine learning process (AI / ML process). As a result, the network (e.g., gNB 200) can determine how many more models can be deployed (or activated) in the UE 100 based on the notified load status. This third operation pattern does not need to be based on the first operation pattern regarding the above-mentioned model transfer. This third operation pattern may be based on the first operation pattern.

[0156] FIG. 21 is a diagram illustrating a third operation pattern regarding model transfer according to the embodiment.

[0157] In step S751, the gNB 200 transmits a message to the UE 100 including a request for information provision of the AI / ML processing load status or a setting for reporting the AI / ML processing load status. The UE 100 receives the message. The message may be a MAC CE, an RRC message, a NAS message, or a message of the AI / ML layer. The setting for reporting the AI / ML processing load status may include information for setting a report trigger (transmission trigger), for example, "Periodic" or "Event triggered." "Periodic" sets the reporting period, and the UE 100 reports at that period. "Event triggered" sets a threshold value to be compared with a value indicating the AI / ML processing load status in the UE 100 (processing load value and / or memory load value), and the UE 100 reports when that value satisfies the condition of the threshold. Here, the threshold value may be set for each model. For example, the message may associate a model index with a threshold value.

[0158] In step S752, the UE 100 transmits a message (report message) including information indicating the AI / ML processing load status to the gNB 200. The message may be an RRC message, for example, a "UE Assistance Information" message or a "Measurement Report" message. The message may be a newly defined message (for example, an "AI Assistance Information" message). The message may be a NAS message. The message may be an AI / ML layer message.

[0159] The message includes a "processing load status" and / or a "memory load status." The "processing load status" may indicate what percentage of the processing capacity (processor capacity) is being used or what percentage is remaining available. Alternatively, the "processing load status" may express the load in points as described above, and notify how many points are being used and how many points are remaining available. The UE 100 may notify the "processing load status" for each model. For example, the UE 100 may include at least one set of a "model index" and a "processing load status" in the message. The "memory load status" may be memory capacity, memory usage, or remaining memory. The UE 100 may notify the "memory load status" for each type, such as memory for storing models, memory for the AI ​​processor, memory for the GPU, etc.

[0160] In step S752, if the UE 100 wants to stop using a particular model, for example, because of a high processing load or inefficiency, the UE 100 may include information (model index) indicating the model whose setting is to be deleted or deactivated in the message. When the UE 100's processing load becomes critical, the UE 100 may include alert information in the message and transmit it to the gNB 200.

[0161] In step S753, gNB200 determines a model setting change, etc. based on the message received from UE100 in step S752, and transmits a message for the model setting change to UE100. The message may be a MAC CE, an RRC message, a NAS message, or an AI / ML layer message. gNB200 may transmit the above-mentioned activation command or deactivation command to UE100.

[0162] (5) Example of Model Management Next, an example of model management according to the embodiment will be described. Fig. 22 is a diagram showing an example of model management according to the embodiment.

[0163] In step S801, the communication device 501 executes AI / ML processing (machine learning processing). The machine learning processing is one of the steps shown in FIG. 23, which will be described later.

[0164] In step S802, the communication device 501 transmits a notification regarding the machine learning process as control data to the communication device 502. The communication device 502 receives the notification.

[0165] In step S802, the communication device 501 sends a notification to the communication device 502 indicating at least one of, for example, that it has an untrained model, that it has a model in training, and that it has a trained model that has been inspected.

[0166] In step S803, the communication device 502 transmits a response corresponding to the notification in step S802 as control data to the communication device 501. The communication device 501 receives the response.

[0167] The notification in step S802 may be a notification indicating that the communication device 501 has an unlearned model. In this case, step S803 may include at least one of a dataset and setting parameters used for model learning.

[0168] The notification in step S802 may be a notification indicating that the communication device 501 has a model that is being trained. In this case, the response in step S803 may include a dataset for continuing model training.

[0169] The notification in step S802 may be a notification indicating that the communication device 501 has a trained model for which inspection has been completed. The response in step S803 may include information for starting use of the trained model for which inspection has been completed.

[0170] The notification in step S802 and the response in step S803 may each include an index of the corresponding model and / or identification information for identifying the type or use of the corresponding model (e.g., for CSI feedback, for beam management, for positioning, etc.). Hereinafter, this information is also referred to as "model use information, etc."

[0171] FIG. 23 is a diagram showing details of model management according to this embodiment, specifically, step S801 in FIG. 22.

[0172] In step S811, the communication device 501 executes a model deployment process. Here, the communication device 501 notifies the communication device 502 that it has an untrained model, i.e., a model that requires training. For example, the untrained model may be pre-installed when the communication device 501 is shipped. The communication device 501 may acquire the untrained model from the communication device 502. The communication device 501 may notify the communication device 502 that it has an untrained model if model training is not complete, for example, if a certain level of quality is not satisfied. For example, this may apply to a case where model training was once completed, but the quality of the model can no longer be guaranteed in monitoring due to movement to a different environment (e.g., from indoors to outdoors). The communication device 502 may provide a training dataset to the communication device 501 based on the notification. The communication device 502 may also perform associated settings on the communication device 501. The communication device 502 may deactivate, for example, discard, deconfigure, or deactivate the model.

[0173] In step S812, the communication device 501 executes a model learning process. The communication device 501 notifies the communication device 502 that model learning is in progress. The notification may include, as described above, model usage information, etc. The communication device 502 continues to provide the communication device 501 with a learning dataset based on the notification. Note that, when the communication device 502 receives a notification indicating that learning is in progress or before learning, the communication device 502 may recognize that the communication device 501 is applying a conventional method that does not apply a model.

[0174] In step S813, the communication device 501 executes a model verification process. The model verification process is a sub-process of the model training process. The model verification process is a process for evaluating the quality of an AI / ML model using a dataset different from the dataset used for model training and selecting (adjusting) model parameters. The communication device 501 may notify the communication device 502 that model training is in progress or that model verification has been completed.

[0175] In step S814, the communication device 501 executes a model checking process. The model checking process is a sub-process of the model learning process. In the model checking process, a dataset different from the datasets used in model learning and model validation is used to evaluate the performance of the final AI / ML model. Unlike model validation, model checking does not involve adjusting the model. The communication device 501 notifies the communication device 502 that it has a validated model (i.e., a model that can guarantee a certain level of quality). The notification may include, as described above, information on the use of the model. Based on the notification, the communication device 502 performs a process to start using the model, such as configuring or activating the model. The communication device 502 may decide to provide an inference dataset and perform the necessary settings on the communication device 501.

[0176] In step S815, the communication device 501 executes a model sharing process. For example, the communication device 501 transmits (uploads) the trained model to the communication device 502.

[0177] In step S816, the communication device 501 executes a model activation process. The model activation process is a process for activating (enabling) a model for a specific function. The communication device 501 may notify the communication device 502 that the model has been activated. The notification may include, as described above, information about the use of the model.

[0178] In step S817, the communication device 501 executes a model inference process. The model inference process is a process of generating a set of outputs based on a set of inputs using a trained model. The communication device 501 may notify the communication device 502 that model inference has been executed. The notification may include, as described above, information about the use of the model.

[0179] In step S818, the communication device 501 executes a model monitoring process. The model monitoring process is a process for monitoring the inference performance of the AI / ML model. The communication device 501 may transmit a notification regarding the model monitoring process to the communication device 502. The notification may include, as described above, model usage information, etc. Specific examples of the notification will be described later.

[0180] In step S819, the communication device 501 executes a model deactivation process. The model deactivation process is a process of deactivating (disabling) a model for a specific function. The communication device 501 may notify the communication device 502 that the model has been deactivated. The notification may include information about the model's use, as described above. The model deactivation process may be a process of deactivating a currently active model and activating another model. This process is also called model switching.

[0181] (6) AI / ML Control Taking Area Communication Environment into Account Next, AI / ML control taking area communication environment into account according to the embodiment will be described.

[0182] The AI / ML models used for inference processing (model inference) include: 1) a "UE-side model" in which the inference processing is entirely performed by UE 100; 2) a "network-side model" in which the inference processing is entirely performed by network 5; and 3) a "two-sided model" in which the inference processing is jointly performed by UE 100 and network 5.

[0183] Here, each of the "UE-side model" and the "network-side model" is also referred to as a "one-sided model." In the "two-sided model," the first part of the inference process is executed by the UE 100, and then the remaining part of the inference process is executed by the gNB 200.

[0184] When the AI / ML model used in the inference process is a UE-side model, it is assumed that the network 5 (gNB 200) does not understand the attributes of the model (e.g., use and / or performance, etc.). Therefore, it is difficult for the network 5 (gNB 200) to control the model, specifically, to control the AI / ML process using the model.

[0185] 24 is a diagram illustrating an example of a UE-side model included in the UE 100. In the illustrated example, the UE 100 has different model groups for different applications (CSI feedback, beam management, and positioning). Each model group includes multiple AI / ML models optimized for each communication environment. Under such a premise, the UE 100 needs to be able to appropriately select the AI / ML model to be used for the inference process (and learning process) according to the current communication environment.

[0186] FIG. 25 is a diagram showing another example of a UE-side model possessed by the UE 100. In the illustrated example, the AI / ML model uses environmental information related to the current communication environment as one of the inference datasets (input data). For example, the UE 100 has one AI / ML model that is applicable to all communication environments for a certain application, and uses the environmental information as additional information for performing accurate inference. The UE 100 inputs the environmental information into the AI / ML model and acquires inference result data output by the AI / ML model. Note that, before performing such inference processing, the UE 100 may perform a learning process using the environmental information as one of the learning datasets (input data).

[0187] (6.1) First operation pattern taking into account area communication environment Under the assumptions shown in Figures 24 and 25, network 5 provides UE 100 with environmental information that UE 100 uses to perform at least one of AI / ML processing, namely, learning processing and inference processing, using an AI / ML model.

[0188] That is, UE 100 receives, from network 5, environmental information indicating the communication environment of a coverage area (also referred to as "area communication environment") corresponding to the position of UE 100. The coverage area corresponding to the position of UE 100 may be a cell in which UE 100 is located, a tracking area in which UE 100 is located, or a registration area in which UE 100 is located. However, the coverage area corresponding to the position of UE 100 may be a peripheral area of ​​UE 100, and may be an area unit smaller than a cell. For example, the coverage area corresponding to the position of UE 100 may be a beam. The beam is identified by, for example, an SSB (Synchronization Signal / PBCH block) index. UE 100 performs AI / ML processing, which is at least one of a learning process and an inference process using an AI / ML model, based on the environmental information received from network 5. This allows the UE 100 to perform the AI / ML processing in consideration of the environmental information. Note that the environmental information provided by the network 5 is information that assists the AI / ML processing in the UE 100, and may be referred to as assist information.

[0189] The environmental information is a parameter indicating a geographical characteristic of the coverage area and is at least one environmental parameter that affects wireless propagation. For example, the environmental information may include at least one of information indicating the density of buildings in the coverage area, information indicating the population density in the coverage area, information indicating whether the coverage area is indoors, information indicating the size of cells that make up the coverage area, and information indicating the height of antennas of the cells.

[0190] 24 , the UE 100 that has received environmental information from the network 5 may select, in accordance with the environmental information, an AI / ML model to be used in the AI / ML processing from among a plurality of AI / ML models that the UE 100 has. This enables the UE 100 to select an appropriate AI / ML model in consideration of the environmental information and to perform the AI / ML processing using the selected AI / ML model.

[0191] 25 , the UE 100 that has received environmental information from the network 5 may perform AI / ML processing by using the environmental information as an input to the AI / ML model. This allows the UE 100 to perform inference processing with high accuracy using, for example, the AI / ML model.

[0192] The UE 100 may receive information permitting the use of the AI / ML model by the UE itself from the network 5. Based on the permission to use the AI / ML model, the UE 100 may perform AI / ML processing using the AI / ML model. This allows the UE 100 to perform AI / ML processing under the management of the network 5.

[0193] The UE 100 may transmit request information requesting transmission of environmental information to the network 5. This allows the UE 100 to acquire the environmental information from the network 5 in an appropriate situation and at an appropriate timing.

[0194] The UE 100 may receive information permitting the transmission of the request information by the UE itself from the network 5. The UE 100 may transmit the request information to the network 5 based on the permission of the transmission of the request information. This allows the UE 100 to acquire the environmental information under the management of the network 5.

[0195] 26 is a diagram showing an example of operation of a first operation pattern taking into account the area communication environment according to the embodiment. In this example of operation, the UE 100 may have multiple AI / ML models that are UE implementation-dependent / vendor-dependent. Such an AI / ML model is also referred to as a proprietary model. In this example of operation, the network entity that provides the auxiliary information is the gNB 200. However, the network entity that provides the auxiliary information may be another network entity, for example, the AMF 300. The gNB 200 in FIG. 26 may be replaced with the AMF 300.

[0196] In step S901, gNB200 may grant UE100 permission to use model inference (and / or learning). For example, gNB200 transmits a message including at least one of the following information to UE100: - Information indicating whether a one-sided model may be used; - Information indicating whether a proprietary model may be used; Here, gNB200 may grant permission / non-permission individually for each application (CSI feedback, beam management, positioning). The message of step S901 may be a system information block (SIB) transmitted by broadcast. The message may be dedicated signaling (e.g., an RRC Reconfiguration message) transmitted by unicast. UE100 determines whether to use its AI / ML model based on the received message.

[0197] In step S902, gNB200 may permit UE100 to request environmental information related to model inference (and / or learning). For example, gNB200 transmits a message including at least one of the following information to UE100: - Information indicating whether UE100 is allowed to request environmental information; - Information indicating what items of environmental information gNB200 can provide (list of items); Here, gNB200 may individually grant permission / non-permission for each application (CSI feedback, beam management, positioning). The items of environmental information will be described later. The message of step S902 may be an SIB transmitted by broadcast. The message may be dedicated signaling (e.g., an RRC Reconfiguration message). UE100 determines whether to request environmental information based on the received message.

[0198] In step S903, UE100 may transmit a request for environmental information to gNB200. For example, UE100 transmits a message to gNB200 including at least one of the following information: - Information indicating the purpose (CSI feedback, beam management, or positioning); - Information indicating whether the environmental information is to be used for inference or learning; - Information indicating whether the environmental information is to be used for a one-sided model (UE-side model) or a two-sided model; - Information indicating whether the environmental information is to be used for a proprietary model or a (managed) model provided by network 5; - Information specifying the items of environmental information required; - Information regarding the frequency of providing environmental information: This may be information indicating whether one-shot provision or periodic provision is desired. If periodic provision is desired, the frequency of provision (time interval, number of times, etc.) may be further notified. The message of step S903 may be, for example, an RRC Setup Request message, an RRC Resume Request message, or a UE Assistance Information message. The gNB 200 receives the message. Alternatively, the UE 100 may transmit a request for environmental information by transmitting a random access preamble to the gNB 200 using a physical random access channel (PRACH) resource reserved for requesting environmental information. The UE 100 may be permitted to transmit the request of step S903 only if at least one of the following conditions is met: Model inference (and / or learning) is permitted in step S901; The request for environmental information is permitted in step S902.

[0199] In step S904, gNB200 provides UE100 with environmental information of the cell (serving cell) in which UE100 is located. For example, gNB200 transmits to gNB200 a message including at least one of the following information (items): Information on the layout of buildings such as buildings: Urban, Suburban, Rural; Information on the layout of reflectors: Indoor, Outdoor; Cell radius, cell type (femto, pico, micro, macro, etc.), transmission power (class); Antenna height, LOS (Line of Sight: Line of Sight) / NLOS (Line of Sight: Non-Line of Sight); This information may be information on neighboring cells in addition to information on the serving cell. The message in step S904 may be, for example, an SIB or dedicated signaling (for example, an RRC Reconfiguration message).

[0200] In step S905, the UE 100 performs at least one of the following processes based on the environmental information received in step S904: Based on the environmental information, the UE 100 selects an appropriate model from among the multiple AI / ML models it owns, specifically, an AI / ML model that matches the communication environment indicated by the environmental information. For example, a UE 100 in an urban, outdoor communication environment selects an AI / ML model for urban / outdoor use; The UE 100 inputs the environmental information into the AI / ML model as inference data (and / or learning data). For example, the UE 100 inputs the environmental information as inference data, in addition to radio measurement data, into a trained model for CSI feedback.

[0201] When the AI / ML process is completed successfully, the UE 100 may notify the gNB 200 that the process has been completed successfully. For example, when the selection of an appropriate model is completed, the UE 100 may notify the gNB 200 that the selection of an appropriate model has been completed. On the other hand, the UE 100 may notify the gNB 200 when the AI / ML process is terminated abnormally (or not completed successfully). For example, the UE 100 may notify the gNB 200 that an appropriate model could not be selected.

[0202] In this operation example, new information not specified in the existing 3GPP technical specifications is introduced as environmental information. However, information specified in the existing 3GPP technical specifications may be used as at least part of the environmental information. For example, at least one of the following information provided by gNB200 in the current specifications may be used as environmental information: SIB9: Time Info (time information); SIB19: Reference Location (location information for NTN (Non-Terrestrial Network)); SIB21: MBS FSAI (MBS area for MBS (Multicast / Broadcast Service)).

[0203] (6.2) Second operation pattern taking into account the area communication environment In the first operation pattern described above, it was assumed that the gNB 200 would assist the UE 100 implementation-dependent AI / ML processing with environmental information. However, from the perspective of the operator and / or vendor of the network 5, it is not desirable to disclose information about the network 5 (to provide information to the UE 100) from the viewpoint of security, etc. Therefore, in this second operation pattern, the UE 100 notifies the network 5 of its AI / ML model and uses the model in response to instructions from the gNB 200.

[0204] That is, UE 100 transmits model information indicating attributes of the AI / ML model that UE 100 has to network 5. Then, UE 100 receives information indicating whether or not UE 100 can use the AI / ML model from network 5. This enables network 5 to cause UE 100 to use an appropriate AI / ML model, for example, taking into consideration the communication environment at the location of UE 100 (for example, the communication environment of the cell in which UE 100 is located).

[0205] The model information transmitted from UE 100 to network 5 may include at least one of information indicating the type of AI / ML model, information indicating the dependency of the AI / ML model on network 5, information indicating whether learning of the AI / ML model is necessary, information indicating whether environmental information from network 5 is used for at least one of the learning process and inference process using the AI / ML model, information indicating the purpose of the AI / ML model, and information indicating the application environment of the AI / ML model.

[0206] 27 is a diagram showing an example of operation of a second operation pattern taking into account the area communication environment according to the embodiment. In this example of operation, the UE 100 may have multiple AI / ML models that are UE implementation-dependent / vendor-dependent. Such an AI / ML model is also referred to as a proprietary model. In this example of operation, the notification destination of the model information (from another perspective, the registration destination) is the gNB 200. However, the notification destination of the model information may be another network entity, for example, the AMF 300, and the gNB 200 in FIG. 27 may be read as the AMF 300.

[0207] In step S931, gNB200 may broadcast information indicating that model notification (model registration) from UE100 is possible or that gNB200 supports the AI ​​ / ML function to UE100, for example, in an SIB. The information may be notified (set) individually to the UE by dedicated signaling (e.g., an RRC Reconfiguration message).

[0208] In step S932, the UE 100 may transmit information (for example, 1-bit flag information) indicating that the UE 100 has an AI / ML model that it can notify (register) to the gNB 200. For example, the UE 100 may transmit the information in a random access procedure message (Msg) 5. The UE 100 may transmit the information in a UE Assistance Information message.

[0209] In step S933, gNB200 may transmit request information to UE100 requesting (or permitting) UE100 to notify gNB200 of model information. gNB200 may broadcast the request information in an SIB. gNB200 may transmit the request information by dedicated signaling.

[0210] In step S934, UE100 transmits model information indicating the attributes of the AI / ML model that UE100 possesses to gNB200. For example, UE100 transmits a message including at least one of the following information to gNB200: Information indicating whether the AI / ML model is a one-sided model or a two-sided model; Information indicating whether the AI / ML model is a proprietary model (an AI / ML model that is UE implementation-dependent and vendor-dependent), an open format model (an AI / ML model based on a format standardized and / or published outside of 3GPP), or a model provided by the 3GPP network (an AI / ML model that is network implementation-dependent and network vendor-dependent and transferred from network 5 to UE100); Information indicating the dependency (collaboration level) of the AI / ML model on network 5. For example, collaboration levels include level X (no collaboration), level Y (signaling-based collaboration without model transfer from network 5), and level Z (signaling-based collaboration with model transfer from network 5); - Information indicating whether the AI / ML model is a trained model. This information may be information indicating whether training is necessary or whether training is possible; - Information indicating whether the AI / ML model requires the above-mentioned environmental information; - Information indicating the purpose of the AI / ML model (CSI feedback, beam management, or positioning); - Information regarding the communication environment to which the AI / ML model is applied; - Model ID of the AI / ML model. This is an ID assigned to the AI / ML model by UE100. This ID may be a temporary ID that can be updated by gNB200. Specifically, gNB200 has the authority to issue a regular model ID and may replace the temporary ID with the regular model ID. Alternatively, the ID may be an ID that is not updated on the gNB 200 side. The model ID may be the name of the AI / ML model; the UE 100 may notify the gNB 200 of such model information for each AI / ML model.For example, the UE 100 may transmit to the gNB 200 a message including, in list form, model information for each of a plurality of AI / ML models that the UE 100 has. In this case, model IDs may be implicitly assigned in the order of the entries in the list, such as 0, 1, 2, .... The message of step S934 may be, for example, a UE Capability message, a UE Assistance Information message, or a new message (for example, an AI / ML Assistance Information message). The gNB 200 receives the message. The gNB 200 may assign a new model ID to each notified model. The gNB 200 may use the notified model ID as is.

[0211] In step S935, the gNB 200 may transmit to the UE 100 a notification indicating that the model notification (model registration) has been accepted. When the gNB 200 assigns a new model ID to the model, the gNB 200 may transmit to the UE 100 information associating the temporary ID of the model with the new ID. Alternatively, the gNB 200 may notify the UE 100 that the model registration has not been accepted (model registration failure).

[0212] In step S936, gNB200 selects a model to be used by UE100 from the models notified by UE100 according to the current communication environment (i.e., determines whether to use or not to use each model). For example, if the communication environment of its coverage (specifically, serving of UE100) is urban, gNB200 determines that UE100 should use the urban model.

[0213] In step S937, the gNB200 transmits information indicating the determination result of step S936 to the UE100. For example, the gNB200 transmits to the UE100 a set of a model ID and a model deployment instruction or a model activation instruction for a model to be used by the UE100. The gNB200 may transmit to the UE100 a set of a model ID and a model de-deployment / release instruction or a model deactivation instruction for a model that the UE100 is not to use. The message of step S937 may be dedicated signaling, for example, an RRC Reconfiguration message or a MAC CE. The UE100 receives the message.

[0214] In step S938, the UE 100 executes the operation of the model in accordance with the instruction in step S937. For example, the UE 100 may deploy or activate a model for which model deployment or model activation is instructed. The UE 100 may de-deploy or de-activate a model for which model de-deployment / release or model deactivation is instructed.

[0215] When handing over UE100 from the cell of gNB200 to a cell of another gNB (target gNB), gNB200 may notify the target gNB of the model information acquired in step S934. For example, gNB200 may transmit a Handover Request message including the model information as part of the UE context information to the target gNB.

[0216] (7) Transmission Path Used for Model Transfer Next, a transmission path used for model transfer according to the embodiment will be described with reference to FIG.

[0217] When an AI / ML model is provided from the network 5 to the UE 100 (model transfer), candidate transmission paths for the model transfer include a signaling radio bearer (SRB) and a data radio bearer (DRB). Specifically, an SRB is used when model transfer is performed on the control plane, and a DRB is used when model transfer is performed on the user plane. Multiple types of SRB are defined in the technical specifications. The DRB is appropriately set from the network 5 to the UE 100. When an SRB is used as the transmission path for model transfer, it is difficult to transfer a large-sized model. Therefore, for large-sized models, it is expected that an SRB will be used as the transmission path for model transfer.

[0218] Under the assumption that such various transmission path candidates exist, it is desirable for UE 100 to be able to grasp which transmission path will be used for model transfer. Therefore, it is assumed that network 5 sets, in UE 100, a transmission path to be used for model transfer. That is, UE 100 receives, from network 5, configuration information for setting a transmission path to be used for transferring the AI / ML model from network 5 to UE 100. UE 100 receives the AI / ML model from network 5 via the transmission path. This allows UE 100 to appropriately receive the AI / ML model from network 5.

[0219] The setting information for setting a transmission path may include information indicating whether an SRB or a DRB is to be set as the transmission path. The setting information for setting a transmission path may include information identifying an SRB to be set as the transmission path. The setting information for setting a transmission path may include at least one of information identifying a DRB to be set as the transmission path and a source address on the transmission path.

[0220] FIG. 29 is a diagram illustrating an example of an operation related to setting a transmission path used for model transfer according to the embodiment.

[0221] In step S1001, gNB200 transmits configuration information for setting up a transmission path for transmitting the model to UE100 by dedicated signaling (e.g., an RRC Reconfiguration message). UE100 may receive the configuration information and establish the transmission path.

[0222] The setting information may include information indicating whether to use an SRB or a DRB as the transmission path.

[0223] When an SRB is used as the transmission path, the setting information may include information for identifying the type of the SRB. SRB types include SRB1 to SRB4. SRB1 is an SRB mainly used for SIBs. SRB2 is an SRB mainly used for dedicated RRC messages. SRB2 is an SRB mainly used for NAS messages. SRB3 is an SRB used for signaling from a secondary node during dual connectivity. SRB4 is an SRB used for application-related messages, for example, QoE reports.

[0224] When a DRB is used as the transmission path, the configuration information may include at least one of a DRB ID and a source IP address. For example, the gNB 200 may include information indicating that the DRB configuration (including the DRB ID configuration) for the UE 100 is for model transmission. The source IP address may be the IP address of the server from which the model is transmitted or the gNB 200. The configuration information may include information indicating whether the model is managed and controlled by the control plane. For example, the gNB 200 may notify the UE 100 of the model ID via the control plane (SRB) and transfer the model to the UE 100 via the user plane (DRB), and the UE 100 may associate the model with the model ID. Note that the UE 100 may notify the gNB 200 of its user plane IP address. The IP address may be the destination IP address as seen from the server.

[0225] In step S1002, gNB200 transfers the model to UE100 using the transmission path set in step S1001. UE100 receives and stores the model. When transmitting the model in the user plane (DRB), gNB200 may include identification information in the SDAP header or PDCP header of the packet storing the model. The identification information includes at least one of information indicating whether the model is managed in the control plane and a model ID when managed in the control plane. UE100 acquires the identification information and associates the model with the control plane (RRC).

[0226] In step S1003, gNB200 specifies the model ID and controls the deployment, activation, deactivation, etc. of the model in the control plane (RRC message). For example, when a model is transmitted in the user plane (DRB), gNB200 may notify UE100 of metadata (model header, additional information) linked to the model ID in an RRC message. UE100 may associate the model received in the user plane with the metadata received in the control plane based on the model ID.

[0227] (8) Other Embodiments In the above-described embodiments, communication between the UE100 and the gNB200 has been mainly described, but the operations according to the above-described embodiments may be applied to communication between the gNB200 and the AMF300A (i.e., communication between a base station and a core network). The above-described signaling may be transmitted from the gNB200 to the AMF300A over the NG interface. The above-described signaling may be transmitted from the AMF300A to the gNB200 over the NG interface. A request for execution of federated learning and / or the learning result of federated learning may be exchanged between the AMF300A and the gNB200. The operations of each of the above-described operational scenarios may be applied to communication between the gNB200 and another gNB200 (i.e., communication between base stations). The above-described signaling may be transmitted from the gNB200 to another gNB200 over the Xn interface. A request to perform federated learning and / or a learning result of federated learning may be exchanged between gNB200 and another gNB200. Each of the above operations may be applied to communication between UE100 and another UE100 (i.e., communication between user equipments). The above signaling may be transmitted from UE100 to another UE100 on a side link. A request to perform federated learning and / or a learning result of federated learning may be exchanged between UE100 and another UE100.

[0228] The above-described operational flows are not limited to being implemented independently, but can also be implemented by combining two or more operational flows. For example, some steps of one operational flow may be added to another operational flow, or some steps of one operational flow may be replaced with some steps of another operational flow. In each flow, it is not necessary to execute all steps, and only some steps may be executed.

[0229] In the above embodiment, an example in which the base station is an NR base station (gNB) has been described, but the base station may also be an LTE base station (eNB). The base station may also be a relay node such as an IAB (Integrated Access and Backhaul) node. The base station may also be a DU (Distributed Unit) of the IAB node. The user equipment (terminal device) may also be a relay node such as an IAB node, or an MT (Mobile Termination) of the IAB node.

[0230] Also, the term "network node" primarily refers to a base station, but may also refer to a device in the core network or part of a base station (CU, DU, or RU).

[0231] A program may be provided that causes a computer to execute each process performed by a communication device (for example, UE 100 or gNB 200). The program may be recorded on a computer-readable medium. Using a computer-readable medium, the program can be installed on a computer. Here, the computer-readable medium on which the program is recorded may be a non-transitory recording medium. The non-transitory recording medium is not particularly limited, and may be, for example, a recording medium such as a CD-ROM or DVD-ROM. Furthermore, circuits that execute each process performed by the communication device may be integrated, and at least a part of the communication device may be configured as a semiconductor integrated circuit (chip set, SoC: System on a chip).

[0232] As used in this disclosure, the terms "based on" and "depending on" do not mean "based only on" or "depending only on," unless expressly stated otherwise. The term "based on" means both "based only on" and "based at least in part on." Similarly, the term "depending on" means both "based only on" and "at least in part on." Furthermore, "obtain" may mean obtaining information from stored information, obtaining information from information received from another node, or obtaining information by generating the information. The terms "include," "comprise," and variations thereof do not mean including only the listed items, but may also mean including only the listed items or including additional items in addition to the listed items. Furthermore, as used in this disclosure, the term "or" is not intended to mean an exclusive or. Furthermore, any reference to an element using a designation such as "first," "second," etc., as used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used herein as a convenient method of distinguishing between two or more elements. Thus, reference to a first and a 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 way. In this disclosure, when articles are added by translation, such as a, an, and the in English, these articles are intended to include the plural unless the context clearly dictates otherwise.

[0233] The above describes the embodiments in detail with reference to the drawings, but the specific configuration is not limited to that described above, and various design changes can be made within the scope that does not deviate from the gist of the invention.

[0234] This application claims priority from Japanese Patent Application No. 2022-175872 (filed November 1, 2022), the entire contents of which are incorporated herein by reference.

[0235] (9) Supplementary Notes The following are additional notes regarding the features of the above-described embodiment.

[0236] (Supplementary Note 1) A communication method that applies artificial intelligence or machine learning (AI / ML) technology to wireless communication between a user device and a network in a mobile communication system, the communication method comprising: a step in which the user device receives, from the network, environmental information indicating the communication environment of a coverage area corresponding to the location of the user device; and a step in which the user device performs AI / ML processing, which is at least one of a learning process and an inference process using an AI / ML model, based on the environmental information.

[0237] (Supplementary Note 2) The communication method according to Supplementary Note 1, wherein the environmental information includes at least one of information indicating a building density in the coverage area, information indicating a population density in the coverage area, information indicating whether the coverage area is indoors, information indicating a size of a cell constituting the coverage area, and information indicating a height of an antenna of the cell.

[0238] (Supplementary Note 3) The communication method according to Supplementary Note 1 or 2, wherein the step of performing the AI / ML processing includes a step of selecting the AI / ML model to be used in the AI / ML processing from a plurality of AI / ML models held by the user device in accordance with the environmental information.

[0239] (Supplementary Note 4) The communication method according to any one of Supplementary Notes 1 to 3, wherein the step of performing the AI / ML processing includes a step of performing the AI / ML processing using the environmental information as an input to the AI / ML model.

[0240] (Supplementary Note 5) The communication method according to any one of Supplementary Notes 1 to 4, further comprising a step in which the user device receives, from the network, information permitting the user device to use the AI / ML model, and the step of performing the AI / ML processing includes a step of performing the AI / ML processing based on the permission to use the AI / ML model.

[0241] (Supplementary Note 6) The communication method according to any one of Supplementary Notes 1 to 5, further comprising the step of the user device transmitting, to the network, request information requesting transmission of the environmental information.

[0242] (Supplementary Note 7) The communication method according to any one of Supplementary Notes 1 to 6, further comprising the step of receiving, from the network, information permitting the user device to transmit the request information, wherein the step of transmitting the request information includes the step of transmitting the request information based on the permission to transmit the request information.

[0243] (Supplementary Note 8) A communication method for applying artificial intelligence or machine learning (AI / ML) technology to wireless communication between a user device and a network in a mobile communication system, the communication method comprising: a step in which the user device transmits model information indicating attributes of an AI / ML model possessed by the user device to the network; and a step in which the user device receives information indicating whether the user device can use the AI / ML model from the network.

[0244] (Supplementary Note 9) The communication method according to Supplementary Note 8, wherein the model information includes at least one of information indicating the type of the AI / ML model, information indicating the dependency of the AI / ML model on the network, information indicating whether learning of the AI / ML model is necessary, information indicating whether environmental information from the network is used for at least one of the learning process and the inference process using the AI / ML model, information indicating the application of the AI / ML model, and information indicating the application environment of the AI / ML model.

[0245] (Supplementary Note 10) A communication method for applying artificial intelligence or machine learning (AI / ML) technology to wireless communication between a user device and a network in a mobile communication system, the communication method comprising: a step in which the user device receives, from the network, setting information for setting a transmission path used for transferring an AI / ML model from the network to the user device; and a step in which the user device receives the AI / ML model from the network via the transmission path.

[0246] (Supplementary Note 11) The communication method according to Supplementary Note 10, wherein the setting information includes information indicating whether a signaling radio bearer (SRB) or a data radio bearer (DRB) is to be set as the transmission path.

[0247] (Supplementary Note 12) The communication method according to Supplementary Note 10 or 11, wherein the setting information includes information for identifying a signaling radio bearer (SRB) to be set as the transmission path.

[0248] (Supplementary Note 13) The communication method according to Supplementary Note 10 or 11, wherein the setting information includes at least one of information for identifying a data radio bearer (DRB) to be set as the transmission path and a source address of the transmission path.

[0249] 1: Mobile communication system 5: Network 10: RAN (NG-RAN) 20: CN (5GC) 100: UE 110: Receiving unit 120: Transmitting unit 130: Control unit 131: CSI generating unit 132: Location information generating unit 140: GNSS receiver 200: gNB 210: Transmitting unit 220: Receiving unit 230: Control unit 231: CSI generating unit 240: Backhaul communication unit 400: Location server 501: Communication device 502: Communication device

Claims

1. A communication method for applying artificial intelligence or machine learning (AI / ML) technology to wireless communication between a user device and a network in a mobile communication system, comprising: a step in which the user device receives, from the network, environment information indicating a communication environment of a coverage area corresponding to the position of the user device; a step in which the user device performs at least one of learning processing and inference processing using an AI / ML model based on the environment information; The communication method.

2. The environment information includes at least one of information indicating the density of buildings in the coverage area, information indicating the population density in the coverage area, information indicating whether the coverage area is indoor, information indicating the size of a cell constituting the coverage area, and information indicating the height of an antenna of the cell. The communication method according to Claim 1.

3. The step of performing the AI / ML processing includes a step of selecting the AI / ML model to be used in the AI / ML processing from among a plurality of AI / ML models possessed by the user device according to the environment information. The communication method according to Claim 1 or 2.

4. The step of performing the AI / ML processing includes a step of performing the AI / ML processing using the environment information as an input to the AI / ML model. The communication method according to Claim 1 or 2.

5. The user device further has a step of receiving, from the network, information permitting the use of the AI / ML model by the user device, The step of performing the AI / ML processing includes a step of performing the AI / ML processing based on the fact that the use of the AI / ML model is permitted. The communication method according to Claim 1 or 2.

6. The user device further has a step of transmitting, to the network, request information for requesting transmission of the environment information. The communication method according to Claim 1 or 2.

7. The user device further has a step of receiving, from the network, information permitting transmission of the request information by the user device, The step of transmitting the request information includes a step of transmitting the request information based on the fact that transmission of the request information is permitted. The communication method according to Claim 6.

8. A communication method for applying artificial intelligence or machine learning (AI / ML) technology to wireless communication between a user device and a network in a mobile communication system, comprising: The step in which the user device transmits model information indicating the attributes of the AI / ML model possessed by the user device to the network; The step in which the user device receives, from the network, information indicating whether the user device can use the AI / ML model, and A communication method.

9. The model information includes at least one of information indicating the type of the AI / ML model, information indicating the degree of dependence of the AI / ML model on the network, information indicating the necessity of learning of the AI / ML model, information indicating whether to use environmental information from the network for at least one of learning processing and inference processing using the AI / ML model, information indicating the use of the AI / ML model, and information indicating the application environment of the AI / ML model. The communication method according to claim 8.

10. A communication method for applying artificial intelligence or machine learning (AI / ML) technology to wireless communication between a user device and a network in a mobile communication system, The step in which the user device receives, from the network, setting information for setting a transmission path used for transferring an AI / ML model from the network to the user device, The step in which the user device receives the AI / ML model from the network via the transmission path, and A communication method.

11. The setting information includes information indicating which of a signaling radio bearer (SRB) and a data radio bearer (DRB) is to be set as the transmission path. The communication method according to claim 10.

12. The setting information includes information for identifying a signaling radio bearer (SRB) to be set as the transmission path. The communication method according to claim 10 or 11.

13. The setting information includes at least one of information for identifying a data radio bearer (DRB) to be set as the transmission path and the source address in the transmission path. The communication method according to claim 10 or 11.

14. A user device used in a mobile communication system to which artificial intelligence or machine learning (AI / ML) technology is applied, A receiving unit that receives, from the network, environmental information indicating the communication environment of a coverage area corresponding to the position of the user device, A control unit that performs at least one AI / ML process of learning processing and inference processing using an AI / ML model based on the environmental information, and A user device. A user device used in a mobile communication system to which artificial intelligence or machine learning (AI / ML) technology is applied, comprising: a transmission unit that transmits model information indicating the attributes of an AI / ML model possessed by the user device to a network; a reception unit that receives from the network information indicating whether the user device can use the AI / ML model. A user device. A user device used in a mobile communication system to which artificial intelligence or machine learning (AI / ML) technology is applied, comprising: a reception unit that receives from the network setting information for setting a transmission path used for transferring an AI / ML model from the network to the user device, and receives the AI / ML model from the network via the transmission path. A user device.