COMMUNICATION CONTROL METHOD, NETWORK NODE, AND USER EQUIPMENT

Abstract

Provided is a communication control method for a mobile communication system. The communication control method includes a step in which a base station sets, for a user device, a prescribed range for wireless quality. The prescribed range for wireless quality represents a wireless quality range in which a user device is permitted to determine the timing for executing conditional handover using an AI / ML model.

Description

Communication Control Method

[0001] The present disclosure relates to a communication control method.

[0002] In recent years, the Third Generation Partnership Project (3GPP) (registered trademark; the same applies hereinafter), a standardization project for mobile communication systems, has been studying the application of artificial intelligence (AI) technology, particularly machine learning (ML) technology, to wireless communication (air interface) in mobile communication systems.

[0003] For example, the following Non-Patent Document 1 discusses AI-based mobility management. That is, Non-Patent Document 1 discusses the possibility that mobility characteristics may deteriorate due to reasons such as increased network deployment density caused by higher frequencies, and discusses that high accuracy was achieved in simulating AI inference for mobility failures including handover command loss and handover failure (HOF). Furthermore, the following Non-Patent Document 2 discusses the need to consider generalizing models for mobility management from the perspective of high-speed movement.

[0004] 3GPP contribution: RP-223079, “Study on AI / ML for NR air interface higher layer” 3GPP contribution: RP-222954, “Motivation on AI / ML for NR air interface high layer”

[0005] A communication control method according to one aspect is a communication control method in a mobile communication system, the communication control method comprising a step of setting a predetermined range of radio quality for a user equipment by a network node, wherein the predetermined range of radio quality represents a range of radio quality within which the user equipment is permitted to determine the timing of execution of a conditional handover using an AI / ML model.

[0006] According to another aspect, there is provided a communication control method in a mobile communication system, the communication control method including a step of transmitting, by a user equipment, log information including a result of a handover execution and execution trigger information indicating that the handover has been executed using an AI / ML model or that the handover has been executed using a network setting without using the AI / ML model, to a network node.

[0007] FIG. 1 is a diagram showing an example of the configuration of a mobile communication system according to the first embodiment. FIG. 2 is a diagram showing an example of the configuration of a UE (user equipment) according to the first embodiment. FIG. 3 is a diagram showing an example of the configuration of a gNB (base station) according to the first embodiment. FIG. 4 is a diagram showing an example of the configuration of a protocol stack according to the first embodiment. FIG. 5 is a diagram showing an example of the configuration of a protocol stack according to the first embodiment. FIG. 6 is a diagram showing an example of the configuration of functional blocks of the AI / ML technology according to the first embodiment. FIG. 7 is a diagram showing an example of operation in the AI / ML technology according to the first embodiment. FIG. 8 is a diagram showing an example of the arrangement of functional blocks of the AI / ML technology according to the first embodiment. FIG. 9 is a diagram showing an example of reducing CSI-RS according to the first embodiment. FIG. 10 is a diagram showing an example of reducing CSI-RS according to the first embodiment. FIG. 11 is a diagram showing an example of operation according to the first embodiment. FIG. 12 is a diagram showing an example of the arrangement of functional blocks of the AI / ML technology according to the first embodiment. FIG. 13 is a diagram showing an example of the arrangement of functional blocks of the AI / ML technology according to the first embodiment. FIG. 14 is a diagram illustrating an example of an arrangement of functional blocks of AI / ML technology according to the first embodiment. FIG. 15 is a diagram illustrating an example of an operation according to the first embodiment. FIG. 16 is a diagram illustrating an example of an arrangement of functional blocks of AI / ML technology according to the first embodiment. FIG. 17 is a diagram illustrating an example of an operation according to the first embodiment. FIG. 18 is a diagram illustrating an example of an operation according to the first embodiment. FIG. 19 is a diagram illustrating an example of a setting message according to the first embodiment. FIG. 20 is a diagram illustrating an example of an operation according to the first embodiment. FIG. 21 is a diagram illustrating an example of an operation according to the second embodiment.

[0008] The present disclosure aims to enable a user device to properly perform wireless communication using an AI / ML model.

[0009] [First embodiment] A mobile communication system according to a first embodiment will be described with reference to the drawings. In the description of the drawings, the same or similar parts are denoted by the same or similar reference numerals.

[0010] (Configuration of mobile communication system) The configuration of a mobile communication system according to the first embodiment will be described. FIG. 1 is a diagram showing an example of the configuration of a mobile communication system 1 according to the first embodiment. The mobile communication system 1 conforms to the 5th Generation System (5GS) of the 3GPP standard. Although 5GS will be described below as an example, the mobile communication system may also be at least partially applied to an LTE (Long Term Evolution) system. The mobile communication system may also be at least partially applied to a sixth generation (6G) system or later 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.

[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 300 are connected to the gNB 200 via an NG interface, which is an interface between a base station and a core network. The AMF and the UPF 300 may be core network devices included in the CN 20.

[0016] 2 is a diagram showing an example of the configuration of a UE 100 (user equipment) according to the first embodiment. The UE 100 includes a 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. Such processes include processes of each layer described below. The control unit 130 includes at least one processor and at least one memory. The memory stores programs executed by the processor and information used in the processes by the processor. The processor may include a baseband processor and a CPU (Central Processing Unit). The baseband processor performs modulation / demodulation, encoding / decoding, etc. of baseband signals. The CPU executes programs stored in the memory to perform various processes. Note that the processes or operations performed in the UE 100 may be performed in the control unit 130.

[0020] 3 is a diagram showing an example of the configuration of a gNB 200 (base station) according to the first embodiment. The gNB 200 includes a transmitter 210, a receiver 220, a controller 230, and a backhaul communication unit 250. The transmitter 210 and the receiver 220 constitute a communication unit that performs wireless communication with the UE 100. The backhaul communication unit 250 constitutes a network communication unit that 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. Such processes include processes for each layer described below. The control unit 230 includes at least one processor and at least one memory. The memory stores programs executed by the processor and information used in the processes by the processor. The processor may include a baseband processor and a CPU. The baseband processor performs modulation / demodulation, encoding / decoding, etc. of baseband signals. The CPU executes programs stored in the memory to perform various processes. Note that the processes or operations performed in the gNB 200 may be performed by the control unit 230.

[0024] The backhaul communication unit 250 is connected to adjacent base stations via an Xn interface, which is an interface between base stations. The backhaul communication unit 250 is connected to the AMF / UPF 300 via an NG interface, which is an interface between a base station and a core network. 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 an example of 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 300. Note that the UE 100 has an application layer and the like in addition to the radio interface protocol. Also, the layer below the NAS is called an Access Stratum (AS).

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

[0039] The functional block configuration example shown in FIG. 6 includes a data collection unit A1, a model learning unit A2, a model inference unit A3, and a data processing unit A4.

[0040] The data collection unit A1 collects input data, specifically, learning data and inference data. The data collection unit A1 outputs the learning data to the model learning unit A2. The data collection unit A1 also outputs the inference data to the model inference unit A3. The data collection unit A1 may acquire data in the device on which the data collection unit A1 is provided as input data. The data collection unit A1 may 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 the learning model through machine learning using the learning data, and derives (or generates, or updates) a learned model. The model learning unit A2 outputs the derived learned model to the model inference unit A3. For example, 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 answer data as learning data. For example, in unsupervised learning, feature points are memorized from a large amount of learning data, and the correct answer is determined (range estimation). Reinforcement learning is a method of assigning a score to an output result and learning how to maximize the score. Although supervised learning will be described below, unsupervised learning or reinforcement learning may also be applied as machine learning.

[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. Here, there are various model approaches, such as 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] FIG. 7 is a diagram illustrating an example of operation in the AI / ML technology according to the first embodiment.

[0045] The transmitting entity TE is, for example, an entity in which machine learning is performed. The transmitting entity TE performs machine learning to derive a trained model. The transmitting entity TE then generates inference result data as an inference result using the trained model. The transmitting entity TE transmits the inference result data to the receiving entity RE.

[0046] On the other hand, the receiving entity RE is, for example, an entity in which machine learning is not performed. The transmitting entity TE performs various processes using the inference result data received from the transmitting entity TE.

[0047] The entity may be, for example, a device, a functional block included in the device, or a hardware block included in the device.

[0048] For example, the transmitting entity TE may be the UE 100, and the receiving entity RE may be the gNB 200 or a core network device. Alternatively, the transmitting entity TE may be the gNB 200 or a core network device, and the receiving entity RE may be the UE 100.

[0049] As shown in Fig. 7, in step S1, the transmitting entity TE transmits control data related to AI / ML technology to the receiving entity RE and receives the control data from the receiving entity RE. The control data may be an RRC message, which is signaling of the RRC layer (i.e., Layer 3). The control data may be a MAC Control Element (CE), which is signaling of the MAC layer (i.e., Layer 2). The control data may be Downlink Control Information (DCI), which is signaling of the PHY layer (i.e., Layer 1). The downlink signaling may be UE-specific signaling. The downlink signaling may be broadcast signaling. The control data may be a control message in a control layer (e.g., AI / ML layer) dedicated to artificial intelligence or machine learning.

[0050] (Layout Examples and Use Cases) Next, a description will be given of how the functional blocks shown in Fig. 6 are arranged in the mobile communication system 1. Below, layout examples of the functional blocks will be described along with specific use cases.

[0051] For example, there are three use cases in which AI / ML technology is applied:

[0052] (1.1) "CSI (Channel State Information) Feedback Enhancement"

[0053] (1.2) "Beam management"

[0054] (1.3) "Positioning Accuracy Enhancement" Below, an example of the placement of functional blocks for each use case will be described.

[0055] (1.1) Example of functional block arrangement in "CSI feedback improvement" "CSI feedback improvement" represents a use case in which, for example, machine learning technology is applied to CSI fed back from UE100 to gNB200. CSI is information about the channel state in the downlink between UE100 and gNB200. The CSI includes at least one of a channel quality indicator (CQI), a precoding matrix indicator (PMI), and a rank indicator (RI). The gNB200 performs, for example, downlink scheduling based on the CSI feedback from UE100.

[0056] 8 is a diagram showing an example of the arrangement of each functional block in "CSI feedback improvement". In the example of "CSI feedback improvement" shown in FIG. 8, a data collection unit A1, a model learning unit A2, and a model inference unit A3 are included in the control unit 130 of the UE 100. On the other hand, a data processing unit A4 is included in the control unit 230 of the gNB 200. That is, model learning and model inference are performed in the UE 100. FIG. 8 shows an example in which the transmitting entity TE is the UE 100 and the receiving entity RE is the gNB 200.

[0057] In "CSI feedback improvement", the gNB 200 transmits a reference signal for the UE 100 to estimate the downlink channel state. As the reference signal, a CSI reference signal (CSI-RS) will be described as an example below, but the reference signal may be a demodulation reference signal (DMRS).

[0058] First, in model learning, UE100 (receiving unit 110) receives a first reference signal from gNB200 using a first resource. Then, UE100 (model learning unit A2) derives a learned model for inferring CSI from the reference signal using learning data including the first reference signal and CSI. Such a first reference signal is sometimes referred to as a full CSI-RS.

[0059] For example, the CSI generation unit 131 performs channel estimation using the received signal (CSI-RS) received by the receiving unit 110 to generate CSI. The transmitting unit 120 transmits the generated CSI to the gNB 200. The model learning unit A2 performs model learning using a set of the received signal (CSI-RS) and the CSI as learning data, and derives a learned model for inferring the CSI from the received signal (CSI-RS).

[0060] Second, in model inference, the receiver 110 receives a second reference signal from the gNB 200 using a second resource that is less than the first resource. Then, the model inference unit A3 uses the trained model to infer CSI as inference result data using the second reference signal as inference data. Hereinafter, such a second reference signal may be referred to as a partial CSI-RS or a punctured CSI-RS.

[0061] For example, the model inference unit A3 inputs the partial CSI-RS received by the receiving unit 110 as inference data into the trained model, and infers CSI from the CSI-RS. The transmitting unit 120 transmits the inferred CSI to the gNB 200.

[0062] This enables UE 100 to feed back (or transmit) 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.

[0063] 9 and 10 are diagrams illustrating an example of reducing CSI-RS according to the first embodiment.

[0064] FIG. 9 shows an example of reducing CSI-RS by reducing the number of antenna ports that transmit CSI-RS. For example, the gNB 200 performs the following process. That is, when the UE 100 is in a mode in which model learning is performed (hereinafter, sometimes referred to as the "learning mode"), the gNB 200 transmits CSI-RS from all antenna ports of the antenna panel. On the other hand, when the UE 100 is in a mode in which model inference is performed (hereinafter, sometimes referred to as the "inference mode"), the gNB 200 reduces the number of antenna ports that transmit CSI-RS and transmits CSI-RS from half the antenna ports of the antenna panel. This reduces overhead, improves antenna port utilization efficiency, and achieves a reduction in power consumption. Note that the antenna port is an example of a resource.

[0065] On the other hand, FIG. 10 shows an example in which the radio resources used for transmitting the CSI-RS, specifically, the gNB 200, reduces the time-frequency resources. For example, the gNB 200 performs the following process. That is, when the UE 100 is in learning mode, the gNB 200 transmits the CSI-RS using predetermined time-frequency resources. On the other hand, when the UE 100 is in inference mode, the gNB 200 transmits the CSI-RS using time-frequency resources that are less than the predetermined time-frequency resources. This reduces overhead, improves the utilization efficiency of radio resources, and reduces power consumption.

[0066] As shown in Figures 9 and 10, gNB200 transmits full CSI-RS using a predetermined amount of first resources and transmits partial CSI-RS using second resources that have a smaller amount of resources than the first resources.

[0067] FIG. 11 is a diagram illustrating an example of operation in "CSI feedback improvement" according to the first embodiment.

[0068] 11, in step S101, the gNB 200 may notify or set 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 transmits to the UE 100 the antenna port and / or time-frequency resource that transmits or does not transmit the CSI-RS in the inference mode.

[0069] In step S102, gNB200 may send a switching notification to UE100 to start learning mode.

[0070] In step S103, the UE 100 starts the learning mode.

[0071] In step S104, the gNB 200 transmits the full CSI-RS. The receiver 110 of the UE 100 receives the full CSI-RS, and the CSI generator 131 generates (or estimates) CSI based on the full CSI-RS. In the learning mode, the data collector A1 collects the full CSI-RS and CSI. The model learning unit A2 creates a learned model using the full CSI-RS and the CSI as learning data.

[0072] In step S105, UE100 transmits the generated CSI to gNB200.

[0073] Thereafter, in step S106, when the model learning is completed, the UE 100 transmits a completion notification indicating that the model learning is completed to the gNB 200. The UE 100 may transmit a completion notification when the creation of the learned model is completed.

[0074] In step S107, in response to receiving the completion notification, gNB200 sends a switching notification to UE100 to switch UE100 from learning mode to inference mode.

[0075] In step S108, in response to receiving the switching notification, the UE 100 switches from the learning mode to the inference mode.

[0076] In step S109, the gNB 200 transmits a partial CSI-RS. The receiver 110 of the UE 100 receives the partial CSI-RS. In the inference mode, the data collector A1 collects the partial CSI-RS. The model inference unit A3 inputs the partial CSI-RS as inference data into the trained model, and obtains CSI as the inference result.

[0077] In step S110, the UE 100 feeds back (or transmits) the CSI, which is the inference result, to the gNB 200 as inference result data. In the UE 100, by repeating model learning in the learning mode, a trained model with a predetermined accuracy or higher can be generated. It is expected that the inference result using the trained model generated in this way will also have a predetermined accuracy or higher.

[0078] In addition, in step S111, if UE100 determines that model learning is necessary, it may send a notification indicating that model learning is necessary to gNB200 as control data.

[0079] In the example shown in Fig. 11, an example has been described in which the training data is "(full) CSI-RS" and "CSI", and the inference data is "(partial) CSI-RS". Hereinafter, the training data and / or the inference data may be referred to as a "dataset".

[0080] In "improving CSI feedback," in addition to "CSI-RS" and "CSI," at least one of the following data or information may be used as a data set:

[0081] (X1) RSRP (Reference Signals Received Power), RSRQ (Reference Signal Received Quality), SINR (Signal-to-interference-plus-noise ratio), or AD converter output waveform (These measurements may be CSI-RS. These measurements may also be other received signals received from the gNB 200.)

[0082] (X2) Bit Error Rate (BER) or Block Error Rate (BLER) (The total number of transmitted bits (or the total number of transmitted blocks) is known, and the BER (or BLER) may be measured based on the CSI-RS.)

[0083] (X3) The movement speed of UE100 (which may be measured by a speed sensor within UE100). The data set to be used for machine learning may be set. For example, the following processing may be performed. That is, UE100 transmits capability information indicating which type of input data it can handle in machine learning to gNB200 as control data. The capability information may represent, for example, any of the data or information shown in (X1) to (X3). The capability information may be information in which learning data and inference data are separately specified. Then, gNB200 transmits data type information to be used as the data set to UE100 as control data. The data type information may represent, for example, any of the data or information shown in (X1) to (X3). Furthermore, the data type information may specify separately data type information to be used as learning data and data type information to be used as inference data.

[0084] (1.2) Example of functional block arrangement in "beam management" Next, an example of functional block arrangement in "beam management" will be described. "Beam management" represents a use case in which, for example, machine learning technology is used to manage which beam is the optimal beam among the beams transmitted from gNB200.

[0085] In "beam management," gNB200 sequentially transmits beams with different directivities. Each beam includes, for example, a reference signal. UE100 measures the reception quality of each beam using the reference signal included in each beam. UE100, for example, determines the beam with the best reception quality as the optimal beam.

[0086] Figure 12 is a diagram showing an example of the arrangement of each functional block in "beam management". In the example of "beam management" shown in Figure 12, a data collection unit A1, a model learning unit A2, and a model inference unit A3 are included in the control unit 130 of UE100. On the other hand, a data processing unit A4 is included in the control unit 230 of gNB200. That is, Figure 12 shows an example in which model learning and model inference are performed in UE100. Figure 12 shows an example in which the transmitting entity TE is UE100 and the receiving entity RE is gNB200.

[0087] As shown in FIG. 12, the UE 100 has an optimal beam determination unit 132. The optimal beam determination unit 132 determines the optimal beam based on, for example, the reception quality for the reference signal included in each beam. As with "CSI feedback," an example will be described in which a CSI-RS is used as the reference signal, but a demodulation reference signal (DMRS) may also be used as the reference signal. The transmitter 120 transmits information representing the determined optimal beam to the gNB 200 as the "optimal beam."

[0088] An example of the operation in "beam management" can be implemented by replacing "CSI feedback" with "optimal beam" in Figure 11.

[0089] In the learning mode (step S103), the gNB 200 sequentially transmits beams with different directivities to the UE 100 (step S104). Each beam includes a full CSI-RS. In the learning mode, the data collection unit A1 of the UE 100 collects the full CSI-RS and the optimal beam (information representing the optimal beam). The model learning unit A2 creates a learned model using the CSI-RS and the optimal beam (information representing the optimal beam) as learning data. The full CSI-RS is an example of a first reference signal, and the partial CSI-RS is an example of a second reference signal.

[0090] In the inference mode (step S108), the gNB 200 sequentially transmits beams with different directivities. Each beam includes a partial CSI-RS. In the inference mode, the data collection unit A1 collects the partial CSI-RS. The model inference unit A3 inputs the partial CSI-RS as inference data into the trained model, and obtains the optimal beam (information representing the optimal beam) as the inference result. The UE 100 transmits the inference result (optimal beam) to the gNB 200 as inference result data.

[0091] In "beam management," in addition to "CSI-RS" and "optimal beam," at least one of the following data or information may be used as data in the data set:

[0092] (Y1) SSB (Synchronization Signal Block) received from gNB200

[0093] (Y2) RSRP, RSRQ, SINR, or AD converter output waveform (these measurements may be CSI-RS. These measurements may also be other received signals received from gNB200)

[0094] (Y3) BER or BLER (BER (or BLER) may be measured based on CSI-RS with the total number of transmission bits (or the total number of transmission blocks) known)

[0095] (Y4) Number of beams or beam pattern

[0096] (Y5) Beam measurement value(s)

[0097] (Y6) UE100's movement speed (may be measured by a speed sensor within UE100) UE100 may transmit capability information indicating which type of input data it can handle in machine learning to gNB200 as control data. The capability information may include any of the information or data from (Y1) to (Y6), or may include any of the information or data from (Y1) to (Y6) separately for learning data and inference data. In addition, gNB200 may transmit data type information used as a data set to UE100 as control data. The data type information may include, for example, any of the data or information shown in (Y1) to (Y6), or may include any of the information or data from (Y1) to (Y6) separately for learning data and inference data.

[0098] (1.3) Example of Arrangement of Functional Blocks in "Improvement of Location Accuracy" Next, an example of arrangement of functional blocks in "Improvement of Location Accuracy" will be described. "Improvement of Location Accuracy" represents a use case in which, for example, the accuracy of location information measured by the UE 100 is improved by using machine learning technology.

[0099] Figure 13 is a diagram showing an example of the arrangement of each functional block in "improving location accuracy". In the example of "improving location accuracy" shown in Figure 9, the data collection unit A1, model learning unit A2, and model inference unit A3 are included in the control unit 130 of the UE 100. On the other hand, the data processing unit A4 is included in the control unit 230 of the gNB 200. That is, Figure 13 shows an example in which model learning and model inference are performed in the UE 100. Figure 13 shows an example in which the transmitting entity TE is the UE 100 and the receiving entity RE is the gNB 200.

[0100] As shown in FIG. 13 , the UE 100 includes a location information generation unit 133. The UE 100 may include a GNSS (Global Navigation Satellite System) receiver 150. The location information generation unit 133 generates location data for the UE 100 based on a positioning reference signal (PRS) (full PRS or partial PRS) received from the gNB 200. The location information generation unit 133 may receive a GNSS signal (full GNSS signal or partial GNSS signal) received by the GNSS receiver 150, and generate location data for the UE 100 based on the GNSS signal.

[0101] Note that the gNB 200 transmits the full PRS using a predetermined amount of first resources (for example, all antenna ports as shown in FIG. 9, or a predetermined amount of time-frequency resources as shown in FIG. 10), similar to the full CSI-RS. Also, the gNB 200 transmits the partial PRS using second resources (for example, half the antenna ports in the antenna panel as shown in FIG. 9, or half the predetermined amount of time-frequency resources as shown in FIG. 10) that have a smaller amount of resources than the first resources, similar to the partial CSI-RS.

[0102] The full GNSS signal may also be a GNSS signal that is received continuously over time by the GNSS receiver 150. Furthermore, the partial GNSS signal may also be a GNSS signal that is received intermittently by the GNSS receiver 150. That is, a predetermined amount of first resources may be used for the full GNSS signal, and second resources having a smaller amount than the first resources may be used for the partial GNSS signal.

[0103] An example of the operation for "improving location accuracy" can be implemented by replacing "full CSI-RS" with "full PRS," "partial CSI-RS" with "partial PRS," and "CSI feedback" with "location data" in Figure 11.

[0104] In the learning mode (step S103), the location information generation unit 133 generates location data for the UE 100 based on the full PRS received from the gNB 200. The location information generation unit 133 may receive a full GNSS signal received by the GNSS receiver 150 and generate location data for the UE 100 based on the full GNSS signal. The transmission unit 120 feeds back (or transmits) the location data to the gNB 200. The data collection unit A1 collects the full PRS (or full GNSS signal) and location data. The model learning unit A2 creates a learned model using the full PRS (or full GNSS signal) and location data as learning data.

[0105] In the inference mode (step S108), the data collection unit A1 collects the partial PRS received by the receiving unit 110 (or the partial GNSS signal received by the GNSS receiver 150). The model inference unit A3 inputs the partial PRS (or the partial GNSS signal) as inference data into the trained model, and obtains location data as an inference result. The UE 100 transmits the inference result (location data) to the gNB 200 as inference result data.

[0106] In "improving position accuracy," in addition to "PRS" (or "GNSS signal") and "position data," at least one of the following data or information may be used in the data set:

[0107] (Z1) RSRP, RSRQ, SINR (Signal-to-interference-plus-noise ratio), or AD converter output waveform (these measurements may be PRS. These measurements may also be other received signals received from gNB200.)

[0108] (Z2) LOS (Line of Sight) or NLOS (Non Line of Sight)

[0109] (Z3) Measurement timing, accuracy, likelihood

[0110] (Z4) RF Fingerprint (Cell ID and reception quality in the cell of the cell ID)

[0111] (Z5) Angle of Arrival (AOA), reception level for each antenna, reception phase for each antenna, and observed time difference of arrival (OTDOA) for each antenna

[0112] (Z6) Received information of beacons used in wireless LANs (Local Area Networks) such as Wi-Fi (registered trademark) or short-range wireless communications such as Bluetooth (registered trademark)

[0113] (Z7) Movement speed of UE100 (The movement speed may be measured by the GNSS receiver 150. The movement speed may be measured by a speed sensor within UE100.) UE100 may transmit capability information indicating which type of input data it can handle in machine learning as control data to gNB200. The capability information may include any of the information or data from (Z1) to (Z7). The capability information may include any of the information or data from (Z1) to (Z7), separating the learning data and the inference data. In addition, gNB200 may transmit data type information to be used as a data set as control data to UE100. The data type information may include, for example, any of the data or information shown in (Z1) to (Z7). The data type information may include any of the information or data from (Z1) to (Z7), separating the learning data and the inference data.

[0114] (1.4) Other Arrangement Examples Next, other arrangement examples will be described.

[0115] Figure 14 is a diagram showing another example of the arrangement of "CSI feedback improvement" according to the first embodiment. Figure 14 shows an example in which a data collection unit A1, a model learning unit A2, a model inference unit A3, and a data processing unit A4 are included in a gN200. That is, Figure 14 shows an example in which model learning and model inference are performed in the gNB200. Figure 14 shows an example in which the transmitting entity TE is the gNB200 and the receiving entity RE is the UE100.

[0116] 14 shows an example in which AI / ML technology is introduced into CSI estimation performed by gNB200 based on SRS (Sounding Reference Signal). Therefore, gNB200 has a CSI generation unit 231 that generates CSI based on SRS. The CSI is information indicating the channel state of the uplink between UE100 and gNB200. gNB200 (e.g., data processing unit A4) performs, for example, uplink scheduling based on the CSI generated based on SRS.

[0117] FIG. 15 is a diagram illustrating an example of operation in another arrangement example according to the first embodiment.

[0118] 15 , in step S201, the gNB 200 performs SRS transmission configuration for the UE 100. The SRS transmission configuration may include type information of the reference signal transmitted by the UE 100.

[0119] In step S202, gNB200 starts learning mode.

[0120] In step S203, UE100 transmits the full SRS to gNB200 according to the SRS transmission setting (step S201). The receiver 220 of gNB200 receives the full SRS. In the learning mode, the CSI generator 231 generates (or estimates) CSI based on the full SRS. The data collector A1 collects the full SRS and CSI. The model learning unit A2 creates a learned model using the full SRS and CSI as learning data.

[0121] In step S204, the gNB 200 identifies an SRS transmission pattern (puncture pattern) to be input to the learned model as inference data, and sets the identified SRS transmission pattern to the UE 100. The gNB 200 may transmit an SRS transmission configuration including the identified SRS transmission pattern to the gNB 200.

[0122] In step S205, gNB200 switches from learning mode to inference mode. gNB200 starts model inference using the trained model.

[0123] In step S206, UE100 transmits a partial SRS in accordance with the SRS transmission setting (step S204). 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.

[0124] (1.5) Example of Arrangement When Federated Learning is Performed Next, an example of the arrangement of each functional block when federated learning is performed will be described. Federated learning is, for example, a machine learning technique in which machine learning is performed in a distributed state without aggregating data (or data sets). In federated learning, each entity does not need to transmit data, so the security of each entity can be ensured. Furthermore, federated learning is said to be able to obtain learning results with the same accuracy as conventional centralized machine learning.

[0125] Figure 16 is a diagram showing an example of a configuration when federated learning according to the first embodiment is performed. The example shown in Figure 16 shows an example where location estimation of UE100 is performed using federated learning. Figure 16 shows an example where UE100 has a data collection unit A1, a model learning unit A2, and a model inference unit A3. That is, it shows an example where model learning and model inference are performed in UE100. Figure 16 shows an example where UE100 is the transmitting entity TE, and gNB200 and / or location server 400 is the receiving entity RE.

[0126] The associative learning shown in FIG. 16 is performed, for example, in the following procedure.

[0127] First, the location server 400 transmits to the UE 100 a model that is the basis for model learning.

[0128] Second, UE 100 (model learning unit A2) performs model learning using data present in UE 100. The data present in UE 100 is, for example, the PRS received from gNB 200 and / or output data (GNSS signal) of GNSS receiver 150. The data present in UE 100 may include location data generated by location information generation unit 133 based on the reception result of the PRS and / or the output data of GNSS receiver 150.

[0129] Third, UE 100 applies the learned model, which is the learning result, in model inference unit A3, and transmits variable parameters (hereinafter, sometimes referred to as "learned parameters") included in the learned model to location server 400. In the above example, the optimized a (slope) and b (intercept) correspond to the learned parameters.

[0130] 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 and measurement reports from UE 100.

[0131] FIG. 17 is a diagram illustrating an example of operation in the federated learning according to the first embodiment.

[0132] 17, in step S301, the gNB 200 may notify the UE 100 of a model that serves as a basis for learning. The location server 400 may notify the model via the gNB 200.

[0133] In step S302, the gNB 200 instructs the UE 100 to learn the model. The gNB 200 may set the reporting timing (trigger condition) of the learned parameters. The reporting timing may be periodic. The reporting timing may be triggered by the learning proficiency satisfying the condition (i.e., an event trigger).

[0134] In step S303, the UE 100 starts a learning mode. The UE 100 performs model learning using the full PRS (or full GNSS signal) and the position data generated by the position information generating unit 133 as learning data.

[0135] In step S304, when the reporting timing condition is met, UE100 transmits the learned parameters at that time to the network (gNB200 or location server 400).

[0136] In step S305, the location server 400 integrates the learned parameters reported from the plurality of UEs 100.

[0137] (1.6) Model transfer example

[0138] In (1.1) to (1.5), examples of the arrangement of each functional block of AI / ML technology have been described. Below, an example of model transfer will be described. The model to be transferred may be a trained model used in model inference. The model may also be an untrained (or training) model used in model training.

[0139] (1.6.1) First operation pattern related to model forwarding Figure 18 is a diagram showing an example of an operation of the first operation pattern related to model forwarding according to the first embodiment. In the example shown in Figure 18, the description will be given assuming that the receiving entity RE is mainly the UE 100, but the receiving entity RE may be the gNB 200 or the AMF 300. Also, in the example shown in Figure 18, the description will be given assuming that the transmitting entity TE is the gNB 200, but the transmitting entity TE may be the UE 100 or the AMF 300.

[0140] 18, in step S401, the gNB 200 transmits a capability inquiry message to the UE 100 to request transmission of a message including an information element (IE) 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 is to be executed).

[0141] In step S402, the UE 100 transmits to the gNB 200 a message including an information element indicating execution capability 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 or a newly defined message (for example, a "UE AI Capability" message, etc.)). Alternatively, the transmitting entity TE may be the AMF 300, and the message may be a NAS message. Alternatively, if a new layer for performing or controlling machine learning processing (AI / ML processing) is defined, the message may be a message of the new layer.

[0142] The information element indicating the execution capability related to the machine learning process may be an information element indicating the capability of a processor for executing the machine learning process and / or an information element indicating the capability of a memory for executing the machine learning process. Specifically, the information element indicating the processor capability may be an information element indicating the product number (or model number) of the AI ​​processor. Specifically, the information element indicating the memory capability may be information indicating the memory capacity.

[0143] Alternatively, the information element indicating the execution capability of machine learning processing may be an information element indicating the execution capability of inference processing (model inference). Specifically, the information element indicating the execution capability of inference processing may be an information element indicating whether a deep neural network model is supported. The information element may also be an information element indicating the time (or response time) required to execute the inference processing.

[0144] Alternatively, the information element indicating the execution capability related to the machine learning process may be an information element indicating the execution capability of the learning process (model learning). Specifically, the information element indicating the execution capability of the learning process may be an information element indicating the number of concurrent executions of the learning process. The information element may be an information element indicating the processing capacity of the learning process.

[0145] In step S403, gNB200 determines the model to be configured (or deployed) in UE100 based on the information elements contained in the message received in step S402.

[0146] In step S404, gNB200 transmits a message including the model determined in step S403 to UE100. UE100 receives the message and performs machine learning processing (i.e., model learning processing and / or model inference processing) using the model included in the message. A specific example of step S404 will be described in the following second operation pattern.

[0147] (1.6.2) 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 first embodiment. The configuration message may be an RRC message transmitted from the gNB 200 to the UE 100 (for example, an "RRC Reconfiguration" message, or a newly defined message (for example, an "AI Deployment" message or an "AI Reconfiguration" message, etc.). Alternatively, the configuration message may be a NAS message transmitted from the AMF 300 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.

[0148] 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.

[0149] The individual additional information may be a model index indicating an index (index number) assigned to each model, or may be a model execution condition indicating the performance (e.g., processing delay) required to apply (execute) the model.

[0150] The individual additional information or the common additional information may be a model usage that specifies a function to which a model is to be applied (e.g., "CSI feedback," "beam management," "positioning," etc.). The individual additional information or the common additional information may be a model selection criterion that applies (executes) a corresponding model depending on whether a specified criterion (e.g., a moving speed) is satisfied.

[0151] (1.7) Communication Control Method According to First Embodiment Next, a communication control method according to the first embodiment will be described.

[0152] As mentioned above, regarding AI-based mobility management, Non-Patent Document 1 discusses that high accuracy was achieved for mobility failures as a result of simulating AI inference for mobility failures, including handover command loss and handover failure. However, Non-Patent Document 1 does not discuss how the AI / ML model was specifically deployed to obtain the simulation results. Non-Patent Document 2 also does not discuss the specific deployment of the AI / ML model.

[0153] Here, let us consider handover as mobility management. When an AI / ML model exists on the UE 100 side, a normal handover is determined by the gNB 200. Therefore, the involvement of the AI / ML model is much smaller than when an AI / ML model exists on the gNB 200 side.

[0154] On the other hand, consider a conditional handover as a handover. In this case, since the trigger conditions for the conditional handover are determined on the UE 100 side, if an AI / ML model exists on the UE 100 side, the AI ​​ / ML model is more likely to be involved than if the AI ​​ / ML model exists on the gNB 200 side.

[0155] However, the trigger conditions for the conditional handover are clearly defined. Therefore, even if the AI / ML model exists on the UE 100 side, it is expected that the UE 100 has little room to make its own judgment.

[0156] On the other hand, if all the decisions regarding the conditional handover are made on the UE 100 side, it may not necessarily be appropriate from the viewpoint of network control.

[0157] Therefore, the first embodiment aims to enable the UE 100 (the AI / ML model thereof) to appropriately determine the timing of conditional handover while retaining network control. The first embodiment also aims to enable the UE 100 to appropriately perform wireless communication using the AI / ML model by appropriately determining the timing of conditional handover.

[0158] Therefore, in the first embodiment, gNB200 sets to UE100 a range of radio quality for which the timing of executing a conditional handover may be determined using the AI / ML model.

[0159] Specifically, a base station (e.g., gNB 200) sets a predetermined range of radio quality to a user equipment (e.g., UE 100). Here, the predetermined range of radio quality represents a range of radio quality within which the user equipment is permitted to determine the timing of executing a conditional handover using the AI / ML model.

[0160] In this way, UE100 can determine the execution timing of conditional handover using the AI / ML model within a predetermined range of wireless quality, so UE100 can appropriately determine the execution timing of conditional handover. Moreover, since the predetermined range of wireless communication is determined by gNB200 and transmitted to UE100, UE100 can appropriately determine the timing of conditional handover while retaining network control. Therefore, UE100 can appropriately perform wireless communication using the AI / ML model.

[0161] (1.8) Conditional Handover (CHO) Here, a conditional handover according to the first embodiment will be described. A conditional handover is a handover executed by the UE 100 when one or more handover execution conditions are satisfied. The UE 100 starts evaluating the handover execution conditions when it receives a conditional configuration (ConditionalReconfiguration) from the gNB 200. The handover execution conditions include one or two trigger conditions. The conditional configuration includes a candidate cell and a trigger condition. If at least one candidate cell satisfies the handover execution condition, the UE 100 detaches from the source gNB and starts connection to the selected candidate cell. Note that the conditional configuration is notified to the UE 100 from the gNB 200 by dedicated signaling (for example, an RRC reconfiguration message).

[0162] In this way, conditional handover differs from normal handover (hereinafter sometimes referred to as "legacy handover") in which UE100 reports radio condition measurements to gNB200, and gNB200 decides to handover to a neighboring cell based on the report, and can autonomously perform handover to a candidate cell that meets the trigger conditions.

[0163] (1.9) Example of Operation According to First Embodiment Next, an example of operation according to the first embodiment will be described.

[0164] The AI / ML model used in the operation example according to the first embodiment exists in the UE 100 ("UE-side one-sided model"). The input of the AI / ML model is information about the radio environment. Specifically, it may be measurement information for a serving cell, a candidate cell, or the like. The input may also be movement information indicated by the speed or direction of the UE 100. Alternatively, the input may be location information of the UE 100. On the other hand, the output of the AI / ML model is the execution timing of a conditional handover (i.e., the timing of access to the target cell).

[0165] That is, the AI / ML model used in the operation example of the first embodiment is a model that inputs information about the radio environment and outputs the execution timing of a conditional handover.

[0166] In the following, the terms "AI / ML model" and "inference model" may be used interchangeably. An "inference model" may also be a "trained model." Furthermore, the "input" of an inference model may be referred to as "inference data," and the "output" of an inference model may be referred to as "inference result."

[0167] FIG. 20 is a diagram illustrating an example of operation according to the first embodiment.

[0168] 20, in step S501, the UE 100 may notify the gNB 200 that it has the capability to determine (or infer) the execution timing of the conditional handover. For this notification, an RRC message (e.g., a UECapability message) may be used.

[0169] In step S502, the gNB 200 configures a conditional handover for the UE 100. Specifically, the gNB 200 may configure the conditional handover using the above-described conditional reconfiguration.

[0170] First, the conditional handover configuration includes a range of radio qualities that permits the UE 100 to make a conditional handover decision. Hereinafter, the range of radio qualities that permits the UE 100 to make a conditional handover decision may be referred to as a "predetermined range of radio qualities." The predetermined range of radio qualities may represent a range of radio qualities that permits the UE 100 to determine the execution timing of a conditional handover using an AI / ML model. For example, the predetermined range of radio qualities may be a serving cell RSRP of "-80 dBm to -90 dBm." If the serving cell RSRP is within this range, the UE 100 can infer (or determine) the execution timing of a conditional handover using the AI / ML model. The radio quality is any one of RSRP (Reference Signal Received Power), RSRQ (Reference Signal Received Quality), and SINR (Signal to Interference plus Noise Ratio).

[0171] Specifically, a first condition including a predetermined range of wireless quality is included in the setting of the conditional handover.

[0172] The first condition is, for example, as follows:

[0173] (X1) When the event A2 (Serving becomes worse than threshold) is included in the conditional handover setting, when the radio quality of the serving cell (the radio quality within the predetermined range) becomes worse than the event A2 threshold, the UE 100 is permitted to determine the timing of executing the conditional handover using the AI / ML model.

[0174] (X2) When event A3 (Neighbor becomes offset better than PCel) is included in the conditional handover configuration, it indicates that the UE 100 is permitted to determine the timing of executing the conditional handover using the AI / ML model when the radio quality of the neighboring cell (the radio quality within the predetermined range) is better than the radio quality of the serving cell (the radio quality within the predetermined range) plus an offset.

[0175] (X3) When the event A4 (Neighbor becomes better than threshold) is included in the conditional handover setting, if the wireless quality of the neighboring cell (wireless quality within the specified range) is better than the event A3 threshold, the UE 100 is allowed to determine the timing of executing the conditional handover using the AI / ML model.

[0176] (X4) When event A5 (PCell becomes worse than threshold1 and neighbor becomes better than threshold2) is included in the conditional handover configuration, when the radio quality of the serving cell is worse than the event A5 first threshold and the radio quality of the neighboring cell is better than the event A5 second threshold, the UE 100 is allowed to determine the timing of executing the conditional handover using the AI / ML model.

[0177] The predetermined range of wireless communication may be a range in which a conditional handover based on model inference of the UE 100 is permitted. This range may be a first condition. Specifically, this range may be, for example, a range indicated by an upper limit value and a lower limit value. This range may be a range indicated by only an upper limit value. This range may be a range indicated by only a lower limit value. This range may be expressed as an offset to a second condition described below.

[0178] Second, the conditional handover configuration may include a radio quality condition for executing the conditional handover regardless of the determination of the execution timing of the conditional handover by the UE 100. For example, if the AI / ML model does not determine the execution timing of the conditional handover for some reason, the radio quality condition is used to forcibly execute the conditional handover when a specific radio quality is reached in the UE 100. The radio quality condition is used, for example, as a rescue measure when the UE 100 is unable to determine the execution timing. The radio quality condition may be expressed by a radio quality threshold that causes the UE 100 to execute the conditional handover without using the AI / ML model. For example, the radio quality condition is used such that the UE 100 always executes the conditional handover when the RSRP of the serving cell becomes "-90 dBm." The radio quality condition may be a radio quality threshold that causes the UE 100 to execute the conditional handover without using the AI / ML model.

[0179] The wireless quality condition is included in the second condition. The second condition may be the same as the trigger condition of an existing conditional handover (e.g., Events A2 to A5). The wireless quality condition is, for example, a threshold value included in the trigger condition.

[0180] In this way, gNB200 sets a first condition for conditional handover that includes a predetermined range of wireless quality and a second condition for conditional handover that includes a threshold value of wireless quality (i.e., a wireless quality condition) to UE100.

[0181] In step S503, the UE 100 measures the radio quality of the serving cell and / or the neighboring cell.

[0182] In step S504, if the wireless quality satisfies the first condition, the UE 100 infers (or determines) the execution timing of the conditional handover using the AI / ML model.

[0183] Then, in step S505, if the AI / ML model infers appropriate execution timing, the UE 100 executes a conditional handover at the execution timing and starts accessing the target cell.

[0184] On the other hand, in step S506, when the AI / ML model cannot appropriately infer the execution timing and the wireless quality satisfies the second condition, the UE 100 executes the conditional handover without using the AI / ML model. The UE 100 may stop model inference using the AI / ML model.

[0185] (1.10) Other Examples According to the First Embodiment In the first embodiment, the execution timing according to the AI / ML model is permitted for a range of wireless quality, but this is not limiting. For example, the range may be permitted not only based on wireless quality but also on distance or time, and the execution timing of a conditional handover according to the AI / ML model may be permitted within a set range. For example, the trigger conditions included in the first condition may include the following two:

[0186] (X5) Conditional event D1 (CondEvent D1): When the conditional event D1 is included in the conditional handover configuration, if the distance between the UE 100 and the first reference position is greater than the event D1 first threshold and the distance between the UE 100 and the second reference position of the conditional reconfiguration candidate is shorter than the event D1 second threshold, the UE 100 is permitted to determine the execution timing of the conditional handover using the AI / ML model.

[0187] (X6) Conditional Event T1 (CondEvent D1): When the conditional event T1 is included in the conditional handover configuration, if the time measured by the UE 100 is longer than the event T1 threshold and shorter than (event T1 threshold - predetermined threshold (Threshold) + duration), the UE 100 is permitted to determine the timing of executing the conditional handover using the AI / ML model.

[0188] For example, a distance range within which execution timing based on the AI / ML model is permitted is set for the distance in the conditional event D1. Also, a distance range within which execution timing based on the AI / ML model is permitted is set for the measurement time in the conditional event T1. When the conditional event D1 or the conditional event T1 is used in the second condition, a range within which a conditional handover is forcibly executed may be set for each threshold.

[0189] Second Embodiment Next, a second embodiment will be described, focusing on the differences from the first embodiment.

[0190] As described in the first embodiment, when the UE 100 makes a decision regarding the conditional handover, the network may need to collect information on whether the handover failure due to the conditional handover was due to a decision made by the UE 100 or a network instruction. In the second embodiment, an example will be described in which the UE 100 transmits to the gNB 200 as log information whether the decision was made by the UE 100 or an instruction from the network.

[0191] Specifically, first, the user equipment transmits to the base station log information including the execution result of the conditional handover and execution trigger information indicating either that the conditional handover was executed using the AI / ML model or that the conditional handover was executed using the network settings without using the AI / ML model.

[0192] Such information collection enables the network to optimize the area and the inference model control in the UE 100. Such optimization also enables the UE 100 to appropriately perform wireless communication using the AI / ML model, for example.

[0193] (2.1) MDT Here, the MDT used in the second embodiment will be described.

[0194] An operator may measure the radio conditions in a coverage area through a drive test. Information collected through the drive test can be used to optimize base station settings and base station antenna tilt. However, it may require labor and cost for the operator to perform the drive test. Therefore, 3GPP is considering having the UE 100 measure and report information collected through the drive test. This can reduce operating expenses (OPEX). A general term for techniques used to minimize the execution of drive tests is, for example, MDT (Minimization of Drive Test).

[0195] In MDT, two methods are specified for the acquisition and reporting of measurement information in UE100: immediate MDT and logged MDT. Immediate MDT is a method in which UE100 in an RRC connected state acquires and reports measurement information. In immediate MDT, processing is performed based on the RRC configuration (MeasurementConfiguration) related to measurement and the reporting procedure. On the other hand, logged MDT is a method in which UE100 in an RRC idle state or an RRC inactive state acquires and reports measurement information. In logged MDT, UE100 records (logs) the measurement results, that is, the measurement results are not immediately reported to gNB200, but are reported to gNB200 in response to a request from gNB200 after the measurement results have been acquired. In the case of logged MDT, the UE 100 performs processing based on the RRC configuration (LoggedMeasurementConfiguration).

[0196] (2.2) Example of Operation According to Second Embodiment Next, an example of operation according to the second embodiment will be described.

[0197] 21 is a diagram illustrating an example of operation according to the second embodiment. Note that it is assumed that the UE 100 is configured for MDT by the gNB 200.

[0198] In step S601, the UE 100 executes a conditional handover and starts accessing the target cell.

[0199] In step S602, the UE 100 records log information upon completion of the execution of the conditional handover. The log information may be any of the following information.

[0200] (Y1) Execution result of conditional handover: Specifically, for example, this is information indicating either that the conditional handover was successful or that the conditional handover was unsuccessful.

[0201] (Y2) Trigger of conditional handover: Specifically, for example, it may be information indicating that an inference based on an AI / ML model has been used as a trigger for executing a conditional handover, or that a network setting has been used as a trigger for executing a conditional handover. Alternatively, for example, it may be information indicating that a conditional handover has been executed using an AI / ML model, or that a conditional handover has been executed using a network setting without using an AI / ML model. The information indicating a trigger for a conditional handover may be referred to as "execution trigger information."

[0202] (Y3) Identification information of the AI / ML model used in the UE 100: The identification information may be, for example, a model ID of the AI / ML model. Alternatively, the identification information may be represented by a model attribute of the AI / ML model (for example, either a proprietary model or an open format model).

[0203] (Y4) Current radio information: for example, the radio quality of the source cell and the target cell.

[0204] (Y5) A timestamp, or location information indicating the location of UE 100, etc.

[0205] In step S603, the UE 100 may notify the gNB 200 that the log information is being recorded. The UE 100 may perform the notification using an RRC message or a MAC CE.

[0206] In step S604, the gNB 200 may request the UE 100 to transmit log information. The gNB 200 may make the request using an RRC message, a MAC Control Element (CE), or a DCI.

[0207] In step S605, the UE 100 transmits the log information to the gNB 200. The UE 100 may transmit the log information by using a measurement report. The UE 100 may transmit the log information by using another RRC message.

[0208] In step S606, gNB200 uses the received log information to perform area optimization and optimize AI / ML model control in UE100.

[0209] (2.3) Other Examples of the Second Embodiment In the second embodiment, an example in which a conditional handover is performed in the UE 100 has been described, but the present invention is not limited to this. For example, the second embodiment can be implemented even when a legacy handover is performed in the UE 100. In this case, the second embodiment can be implemented by replacing "execution of conditional handover" in step S601 with "execution of legacy handover." Furthermore, the UE 100 may use an AI / ML model to infer the execution timing of the legacy handover. The input (inference data) to the AI / ML model is the same as in the first embodiment. Furthermore, the second embodiment can be implemented by replacing "(Y1) execution result of conditional handover" with "execution result of legacy handover."

[0210] That is, the user equipment (e.g., UE100) transmits log information to the base station (e.g., gNB200) that includes the result of the handover execution and execution trigger information indicating either that the handover was executed using the AI / ML model or that the handover was executed using network settings without using the AI / ML model.

[0211] This allows the network to optimize the area and the inference model control in the UE 100 based on the log information even in the case of legacy handover. Such optimization also allows the UE 100 to appropriately perform wireless communication using the AI / ML model, for example.

[0212] [Other Embodiments] In the first embodiment described above, supervised learning has been mainly described, but the present invention is not limited to this. For example, the first embodiment may be applied to unsupervised learning or reinforcement learning.

[0213] 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.

[0214] In the above-described embodiments and examples, an example in which the base station is an NR base station (gNB) has been described, but the base station may be an LTE base station (eNB) or a 6G base station. The base station may also be a relay node such as an IAB (Integrated Access and Backhaul) node. The base station may also be a DU of the IAB node. The UE 100 may also be an MT (Mobile Termination) of the IAB node.

[0215] The term "network node" primarily refers to a base station, but may also refer to a core network device or a part of a base station (CU, DU, or RU). A network node may also be configured by a combination of at least a part of a core network device and at least a part of a base station.

[0216] Furthermore, a program (e.g., an information processing program) that causes a computer to execute each process or function according to the above-described embodiments may be provided. Alternatively, a program (e.g., a mobile communication program) that causes the mobile communication system 1 to execute each process or function according to the above-described embodiments may be provided. 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. Such a recording medium may be memory included in the UE 100 and the gNB 200. Furthermore, circuits that execute each process performed by the UE 100 or the gNB 200 may be integrated, and at least a portion of the UE 100 or the gNB 200 may be configured as a semiconductor integrated circuit (chip set, SoC: System on a chip).

[0217] The functions performed by the UE 100 or the gNB 200 (network node) may be implemented in circuitry or processing circuitry, including general-purpose processors, application-specific processors, integrated circuits, ASICs (Application Specific Integrated Circuits), CPUs (Central Processing Units), conventional circuits, and / or combinations thereof, programmed to perform the described functions. A processor includes transistors and other circuits and is considered to be circuitry or processing circuitry. A processor may also be a programmed processor that executes a program stored in memory. In this specification, circuitry, unit, or means refers to hardware that is programmed to perform the described functions or hardware that executes them. The hardware may be any hardware disclosed herein or any hardware known to be programmed or capable of performing the described functions. If the hardware is a processor, the circuitry, means, or unit is a combination of hardware and software used to configure the hardware and / or processor.

[0218] As used in this disclosure, the terms "based on" and "depending on / in response to" 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 "depending only on" and "depending at least in part on." The terms "include," "comprise," and variations thereof do not mean including only the listed items, but may mean including only the listed items or may include additional items in addition to the listed items. Additionally, the term "or," as used in this disclosure, is not intended to mean an exclusive or. Furthermore, any reference to elements using designations such as "first," "second," etc., as used in this disclosure 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, a 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, where articles are added by translation, such as a, an, and the in English, these articles shall include the plural unless the context clearly indicates otherwise.

[0219] Although the embodiments have been described in detail above with reference to the drawings, the specific configuration is not limited to the above, and various design changes can be made without departing from the spirit of the invention. Furthermore, it is also possible to combine the embodiments, operation examples, or processes within a consistent range.

[0220] This application claims priority from Japanese Patent Application No. 2023-017192 (filed February 7, 2023), the entire contents of which are incorporated herein by reference.

[0221] (Supplementary Note) (Supplementary Note 1) A communication control method in a mobile communication system, comprising a step in which a network node sets a predetermined range of wireless quality to a user equipment, wherein the predetermined range of wireless quality represents a range of wireless quality within which the user equipment is permitted to determine the timing of executing a conditional handover using an AI / ML model.

[0222] (Supplementary Note 2) The communication control method according to Supplementary Note 1, wherein the setting step includes a step of the network node setting, in the user equipment, a radio quality threshold that causes the conditional handover to be executed without using the AI / ML model.

[0223] (Supplementary Note 3) The communication control method according to Supplementary Note 1 or Supplementary Note 2, wherein the setting step includes a step of setting, in the user equipment, a first condition for the conditional handover including a predetermined range of the wireless quality and a second condition for the conditional handover including a threshold value of the wireless quality, and further includes a step of, by the user equipment, executing the conditional handover at the execution timing inferred using the AI / ML model if the wireless quality satisfies the first condition, and, if the AI / ML model was unable to appropriately infer the execution timing, executing the conditional handover without using the AI / ML model if the wireless quality satisfies the second condition.

[0224] (Supplementary Note 4) The communication control method according to any one of Supplementary Notes 1 to 3, further comprising a step in which the user equipment transmits to the network node log information including an execution result of the conditional handover and execution trigger information indicating that the conditional handover has been executed using the AI / ML model or that the conditional handover has been executed using a network setting without using the AI / ML model.

[0225] (Supplementary Note 5) A communication control method in a mobile communication system, comprising: a step in which a user equipment transmits, to a network node, log information including a result of handover execution and execution trigger information indicating that the handover has been executed using an AI / ML model or that the handover has been executed using a network setting without using the AI / ML model.

[0226] 1: Mobile communication system 20: 5GC (CN) 100: UE 110: Receiving unit 120: Transmitting unit 130: Control unit 200: gNB 210: Transmitting unit 220: Receiving unit 230: Control unit A1: Data collection unit A2: Model learning unit A3: Model inference unit A4: Data processing unit TE: Transmitting entity RE: Receiving entity

Claims

1. A communication control method in a mobile communication system, comprising: The network node sets a predetermined range of radio quality for the user equipment; The predetermined range of wireless quality represents a range of wireless quality that is permitted for determining the execution timing of a conditional handover using an AI (Artificial Intelligence) / ML (Machine Learning) model in the user equipment. Communication control method.

2. The setting includes the network node setting a radio quality threshold for the user equipment that causes the conditional handover to be performed without using the AI / ML model. The communication control method according to claim 1.

3. the setting includes setting, in the user equipment, a first condition for the conditional handover including a predetermined range of the wireless quality and a second condition for the conditional handover including a threshold value of the wireless quality; The user equipment further comprises: when the wireless quality satisfies the first condition, executing the conditional handover at the execution timing inferred using the AI / ML model; and when the AI / ML model cannot appropriately infer the execution timing and the wireless quality satisfies the second condition, executing the conditional handover without using the AI / ML model. The communication control method according to claim 2.

4. The method further comprises the user equipment transmitting, to the network node, log information including an execution result of the conditional handover and execution trigger information indicating that the conditional handover was executed using the AI / ML model or that the conditional handover was executed using a network setting without using the AI / ML model. The communication control method according to claim 1.

5. A communication control method in a mobile communication system, comprising: The user equipment transmits, to the network node, log information including a result of the handover execution and execution trigger information indicating that the handover has been executed using an AI / ML model or that the handover has been executed using a network setting without using the AI / ML model. Communication control method.

6. A network node in a mobile communication system, comprising: a control unit that sets a predetermined range of wireless quality to a user device; The predetermined range of wireless quality represents a range of wireless quality that is permitted for determining the execution timing of a conditional handover using an AI (Artificial Intelligence) / ML (Machine Learning) model in the user equipment. Network node.

7. A user device in a mobile communication system, comprising: a receiving unit that receives information including a predetermined range of wireless quality from a network node; The predetermined range of wireless quality represents a range of wireless quality that is permitted for determining the execution timing of a conditional handover using an AI (Artificial Intelligence) / ML (Machine Learning) model in the user equipment. User equipment.