Method of wireless communication and wireless communication device

Abstract

This application provides a wireless communication method and a wireless communication device. The method is executed on a user equipment (UE) and includes sending, via a first message, first predicted measurement information predicted by the UE based on an artificial intelligence / machine learning (AI / ML) model to at least one network node. The first predicted measurement information is the UE's mobility information at a future time, and the UE's mobility information at the future time includes at least one of the following: AI / ML model identification information, handover node identification information, handover node access resource information, handover node access resource validity information, handover node access resource prediction accuracy information, predicted measurement results, predicted additional measurement results, predicted neighboring cell handover beams, predicted measurement result time, and / or accuracy.

Description

Wireless communication method and wireless communication device Technical Field

[0001] The embodiments of the present application relate to the field of mobile communication technology, and more particularly to a wireless communication method and a wireless communication device. Background Art

[0002] In existing technologies, artificial intelligence / machine learning (AI / ML) is a system that can replace human labor through computational learning. AI / ML can be used to solve various problems, such as natural language processing, computing, and graphics processing. In recent years, AI / ML has been applied in the communications field. However, there are unresolved issues regarding the application of AI / ML in communications to enhance mobility. Therefore, there is a need to propose a wireless communication method and wireless communication device to address these and other issues in existing technologies.

[0003] Summary of the Invention

[0004] Embodiments of the present application provide a wireless communication method and a wireless communication device.

[0005] An embodiment of the present application provides a method for wireless communication, which is executed on a user equipment (UE), wherein the method includes: sending first predicted measurement information of the UE based on an artificial intelligence / machine learning (AI / ML) model to at least one network node through a first message, wherein the first predicted measurement information is the mobility information of the UE at a future time, and the mobility information of the UE at a future time includes at least one of the following information: AI / ML model identification information, switching node identification information, switching node access resource information, switching node access resource validity information, switching node access resource prediction accuracy information, predicted measurement results, predicted additional measurement results, predicted adjacent cell switching beam, predicted measurement result time, and / or accuracy.

[0006] Through the above technical solution, first predicted measurement information of the UE, predicted based on an artificial intelligence / machine learning (AI / ML) model, is transmitted to at least one network node. The first predicted measurement information represents the UE's mobility parameters at a future time. This allows the AI / ML algorithm to predict the UE's trajectory, thereby enabling better wireless resource allocation and mobility management for the UE.

[0007] An embodiment of the present application provides a wireless communication method, which is executed on a user equipment (UE), wherein the method includes: receiving UE trajectory prediction information sent by at least one network node, which is predicted by the at least one network node based on an artificial intelligence / machine learning (AI / ML) model, wherein the UE trajectory prediction information includes at least one of the following information: UE identification information, predicted UE RRM measurement results, predicted measurement result time, prediction accuracy information, node information, node access resource information, node access resource validity information, and / or node access resource prediction accuracy information.

[0008] The above technical solution receives UE trajectory prediction information from at least one network node, which is generated by the at least one network node and is based on an artificial intelligence / machine learning (AI / ML) model. This allows the AI / ML algorithm to predict the UE's trajectory, thereby enabling better allocation of wireless resources and mobility management for the UE.

[0009] An embodiment of the present application provides a method for wireless communication, which is executed on at least one network node, wherein the method includes: receiving first predicted measurement information of the UE based on an artificial intelligence / machine learning AI / ML model and sent by a user equipment UE through a first message, wherein the first predicted measurement information is the mobility information of the UE at a future time, and the mobility information of the UE at a future time includes at least one of the following information: AI / ML model identification information, switching node identification information, switching node access resource information, switching node access resource validity information, switching node access resource prediction accuracy information, predicted measurement results, predicted additional measurement results, predicted adjacent cell switching beam, predicted measurement result time, and / or accuracy.

[0010] The above technical solution receives first predicted measurement information from a user equipment (UE) based on an artificial intelligence (AI) or machine learning (ML) model, where the first predicted measurement information represents the UE's mobility information at a future time. This allows the AI / ML algorithm to predict the UE's trajectory, thereby enabling better allocation of radio resources and mobility management for the UE.

[0011] An embodiment of the present application provides a wireless communication method, which is executed on at least one network node, wherein the method includes: sending UE trajectory prediction information predicted by the at least one network node based on an artificial intelligence / machine learning AI / ML model to a user equipment UE, wherein the UE trajectory prediction information includes at least one of the following information: UE identification information, predicted UE RRM measurement results, predicted measurement result time, prediction accuracy information, node information, node access resource information, node access resource validity information, and / or node access resource prediction accuracy information.

[0012] Through the above technical solution, the UE trajectory prediction information, which is predicted by at least one network node based on an artificial intelligence / machine learning AI / ML model, is sent to the user equipment (UE). In this way, the UE's movement trajectory can be predicted through the AI / ML algorithm, thereby better allocating radio resources and managing mobility for the UE.

[0013] A wireless communication device provided in an embodiment of the present application includes: a processor and a memory, the memory being used to store a computer program, and the processor being used to call and run the computer program stored in the memory to execute the above-mentioned wireless communication method.

[0014] The user equipment provided in the embodiment of the present application includes a processor and a memory. The memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to perform the above-mentioned wireless communication method.

[0015] The base station provided in the embodiment of the present application includes a processor and a memory. The memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to perform the above-mentioned wireless communication method.

[0016] The network element provided in the embodiment of the present application includes a processor and a memory. The memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to perform the above-mentioned wireless communication method.

[0017] The chip provided in the embodiment of the present application is used to implement the above-mentioned wireless communication method.

[0018] Specifically, the chip includes: a processor, which is used to call and run a computer program from a memory, so that a device equipped with the chip executes the above-mentioned wireless communication method.

[0019] The computer-readable storage medium provided in an embodiment of the present application is used to store a computer program, which enables a computer to execute the above-mentioned wireless communication method.

[0020] The computer program product provided in the embodiments of the present application includes computer program instructions, which enable a computer to execute the above-mentioned wireless communication method.

[0021] The computer program provided in the embodiment of the present application, when executed on a computer, enables the computer to execute the above-mentioned method for wireless communication.

[0022] In the above technical solution, the UE sends first predicted measurement information of the UE based on an artificial intelligence / machine learning AI / ML model to at least one network node, wherein the first predicted measurement information is the mobility information of the UE at a future moment. The UE receives UE trajectory prediction information sent by at least one network node, which is predicted by the at least one network node based on the artificial intelligence / machine learning AI / ML model. In this way, the UE movement trajectory can be predicted through the AI / ML algorithm, thereby better allocating wireless resources and mobility management for the UE. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0024] FIG1 is a schematic diagram of a wireless communication system architecture provided in an embodiment of the present application;

[0025] FIG2A is a schematic diagram of a flow chart of a wireless communication method provided in an embodiment of the present application;

[0026] FIG2B is a schematic flow chart of a wireless communication method according to an embodiment of the present application;

[0027] FIG2C is a schematic diagram of a flow chart of a wireless communication method provided in an embodiment of the present application;

[0028] FIG2D is a schematic diagram of a flow chart of a wireless communication method provided in an embodiment of the present application;

[0029] FIG2E is a schematic flow chart of a wireless communication method according to an embodiment of the present application;

[0030] FIG2F is a schematic diagram of a flow chart of a wireless communication method provided in an embodiment of the present application;

[0031] FIG2G is a schematic diagram of a flow chart of a wireless communication method provided in an embodiment of the present application;

[0032] FIG3A is a schematic diagram of a flow chart of a wireless communication method provided in an embodiment of the present application;

[0033] FIG3B is a schematic diagram of a flow chart of a wireless communication method provided in an embodiment of the present application;

[0034] FIG3C is a schematic diagram of a wireless communication method provided in an embodiment of the present application;

[0035] FIG4A is a schematic diagram of a flow chart of a wireless communication method provided in an embodiment of the present application;

[0036] FIG4B is a schematic flow chart of a wireless communication method according to an embodiment of the present application;

[0037] FIG4C is a schematic diagram of a wireless communication method provided in an embodiment of the present application;

[0038] FIG4D is a schematic diagram of a wireless communication method provided in an embodiment of the present application;

[0039] FIG5A is a schematic flow chart of a wireless communication method according to an embodiment of the present application;

[0040] FIG5B is a schematic diagram of a flow chart of a wireless communication method provided in an embodiment of the present application;

[0041] FIG6 is a schematic flow chart of a wireless communication method according to an embodiment of the present application;

[0042] FIG7 is a schematic structural diagram of a wireless communication device provided in an embodiment of the present application;

[0043] FIG8 is a schematic structural diagram of a chip according to an embodiment of the present application;

[0044] FIG9 is a schematic block diagram of a wireless communication system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0045] The following will describe the technical solutions in the embodiments of this application in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0046] The technical solutions of the embodiments of the present application can be applied to various wireless communication systems, such as: Long Term Evolution (LTE) system, LTE Frequency Division Duplex (FDD) system, LTE Time Division Duplex (TDD) system, 5G communication system or future wireless communication systems, etc.

[0047] Exemplarily, a wireless communication system 100 used in an embodiment of the present application is shown in FIG1 . The wireless communication system 100 may include a base station 110, which may be a device that communicates with a user equipment 120 (User Equipment, UE). The base station 110 may provide communication coverage for a specific geographical area and may communicate with user equipment located within the coverage area. Optionally, the base station 110 may be an evolved base station (eNB or eNodeB) in an LTE system, or the base station may be a mobile switching center, a relay station, an access point, an in-vehicle device, a wearable device, a hub, a switch, a bridge, a router, a network-side device in a 5G network, or a base station in a future communication system, etc.

[0048] The wireless communication system 100 also includes at least one user equipment 120 located within the coverage area of ​​the base station 110. As used herein, "user equipment" includes, but is not limited to, a device configured to receive / send communication signals via a wired connection, such as a Public Switched Telephone Network (PSTN), a Digital Subscriber Line (DSL), a digital cable, a direct cable connection; and / or another data connection / network; and / or via a wireless interface, such as a cellular network, a Wireless Local Area Network (WLAN), a digital television network such as a DVB-H network, a satellite network, an AM-FM broadcast transmitter; and / or another user equipment; and / or an Internet of Things (IoT) device. A user equipment configured to communicate via a wireless interface may be referred to as a "wireless communication terminal," "wireless terminal," or "mobile terminal." Examples of mobile terminals include, but are not limited to, satellite or cellular telephones; Personal Communications System (PCS) terminals that can combine cellular radiotelephones with data processing, fax, and data communication capabilities; PDAs that can include radiotelephones, pagers, Internet / Intranet access, web browsers, notepads, calendars, and / or Global Positioning System (GPS) receivers; and conventional laptop and / or palmtop receivers or other electronic devices that include radiotelephone transceivers. User equipment can refer to access terminals, subscriber units, subscriber stations, mobile stations, mobile stations, remote stations, remote user equipment, mobile devices, wireless communication devices, or user agents. An access terminal can be a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA), a handheld device with wireless communication capabilities, a computing device or other processing device connected to a wireless modem, an in-vehicle device, a wearable device, a user device in a 5G network, or a user device in a future evolved PLMN, etc.

[0049] In some embodiments of the present invention, user equipment 120 transmits first predicted measurement information of the user equipment 120 based on an artificial intelligence / machine learning (AI / ML) model to base station 110, where the first predicted measurement information is a mobility parameter of the user equipment 120 at a future time. In this way, the movement trajectory of the user equipment 120 can be predicted using the AI / ML algorithm, thereby improving wireless resource allocation and mobility management for the user equipment 120.

[0050] In some embodiments of the present invention, user equipment 120 receives UE trajectory prediction information sent by base station 110, which is predicted by base station 110 based on an artificial intelligence / machine learning (AI / ML) model. In this way, the movement trajectory of user equipment 120 can be predicted using the AI / ML algorithm, thereby better allocating wireless resources and performing mobility management for user equipment 120.

[0051] In some embodiments of the present invention, the user equipment 120 transmits first predicted measurement information predicted by the user equipment 120 based on an artificial intelligence / machine learning (AI / ML) model to the base station 110, and the user equipment 120 receives UE trajectory prediction information sent by the base station 110, which is predicted by the base station 110 based on the artificial intelligence / machine learning (AI / ML) model. In this way, the movement trajectory of the user equipment 120 can be predicted through the AI / ML algorithm, thereby better allocating wireless resources and mobility management for the user equipment 120.

[0052] Optionally, the user equipments 120 may perform device-to-device (D2D) communication with each other.

[0053] Optionally, the 5G communication system or 5G network may also be referred to as a New Radio (NR) system or NR network.

[0054] The wireless communication system 100 also includes a network 130. Network 130 may be an IP mobile communication network operated by a mobile communication operator. For example, network 130 may be a core network used by a mobile communication operator that operates and manages the wireless communication system 100, or a core network used by a virtual mobile communication operator such as an MVNO (Mobile Virtual Network Operator).

[0055] The network 130 can be connected to the base station 110 and serve as a relay device for transmitting user data. The user equipment 120 transmits and receives user data via the network 130. It should be noted that the communication of user data is not limited to IP communication and can also be non-IP communication.

[0056] FIG1 exemplarily shows a base station 110 , two user equipments 120 and a network 130 . Optionally, the wireless communication system 100 may include multiple base stations and each base station may include other numbers of user equipments within its coverage area, which is not limited in the embodiments of the present application.

[0057] The multiple base stations may be, for example, a first base station and a second base station. The first base station may be, for example, a source base station. The second base station may be, for example, a target base station. In some embodiments of the present invention, second predicted measurement information is sent from the source base station to the target base station, where the second predicted measurement information is a mobility parameter of the user equipment 120 at a future time. In this way, the movement trajectory of the user equipment 120 can be predicted using an AI / ML algorithm, thereby improving wireless resource allocation and mobility management for the user equipment 120.

[0058] Optionally, the wireless communication system 100 may further include other network entities such as a network controller, a mobility management entity, and a network element, which is not limited in this embodiment of the present application. For example, the network 130 may include other network entities such as a network controller, a mobility management entity, and a network element, which is not limited in this embodiment of the present application.

[0059] It should be understood that in the embodiments of the present application, a device having wireless communication capabilities in a network / system may be referred to as a wireless communication device. Taking the wireless communication system 100 shown in Figure 1 as an example, the wireless communication device may include a base station 110 having communication capabilities, a user device 120, and a network 130. The base station 110 and the user device 120 may be the specific devices described above and will not be described in detail here. The wireless communication device may also include other devices (network 130) in the wireless communication system 100. For example, the network 130 may include other network entities such as a network controller and a mobility management entity, which is not limited in the embodiments of the present application.

[0060] It should be understood that the terms "system" and "network" are often used interchangeably herein. The term "and / or" is simply a description of an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " generally indicates that the related objects are in an "or" relationship.

[0061] To facilitate understanding of the technical solutions of the embodiments of the present application, the technical solutions related to the embodiments of the present application are described below.

[0062] First embodiment: UE reports first predicted measurement information predicted by UE based on AI / ML model

[0063] FIG2A is a flow chart of a wireless communication method provided in an embodiment of the present application. As shown in FIG2A , the wireless communication method is executed on a user equipment (UE) and includes at least one of the following operations: Operation 201: Sending first predicted measurement information of the UE based on an artificial intelligence / machine learning AI / ML model prediction to at least one network node through a first message. The first predicted measurement information is the mobility information of the UE at a future time, and the mobility information of the UE at a future time includes at least one of the following information: AI / ML model identification information, switching node identification information, switching node access resource information, switching node access resource validity information, switching node access resource prediction accuracy information, predicted measurement results, predicted additional measurement results, predicted adjacent cell switching beam, predicted measurement result time, and / or accuracy.

[0064] FIG2B is a flow chart of a wireless communication method provided in an embodiment of the present application. As shown in FIG2B , the wireless communication method is executed on at least one network node and includes at least one of the following operations: Operation 202: Receive first predicted measurement information of the UE based on an artificial intelligence / machine learning AI / ML model predicted by the user equipment UE through a first message. The first predicted measurement information is the mobility information of the UE at a future time, and the mobility information of the UE at a future time includes at least one of the following information: AI / ML model identification information, switching node identification information, switching node access resource information, switching node access resource validity information, switching node access resource prediction accuracy information, predicted measurement results, predicted additional measurement results, predicted adjacent cell switching beam, predicted measurement result time, and / or accuracy.

[0065] Specifically, the UE is, for example, user equipment 120 shown in Figure 1 . At least one network node is, for example, base station 110 and / or network 130 (first network node) shown in Figure 1 . Base station 110 is, for example, a gNB. For example, in some embodiments of the present invention, the UE sends first predicted measurement information, predicted by the UE based on an artificial intelligence / machine learning (AI / ML) model, to at least one network node. This first predicted measurement information is used to represent relevant information about a target handover node within a future period of time. In this way, the movement trajectory of the user equipment 120 can be predicted using the AI / ML algorithm, thereby better allocating radio resources and mobility management for the user equipment 120. This can also solve conventional technical problems. Conventional technical problems, for example, are that existing radio resource management (RRM) measurement results do not support measurement reporting at future times. The UE predicts possible RRM measurement results at a certain point in the future based on historical information, which can generate misleading information. Furthermore, in traditional handovers, handover delays can occur.

[0066] In some embodiments of the present invention, the first message includes radio resource management (RRM) signaling, radio resource control (RRC) signaling or UE assistance information.

[0067] Specifically, in some embodiments of the present invention, the UE may send first predicted measurement information based on the AI / ML model prediction of the UE to at least one network node via RRM signaling. In some embodiments of the present invention, the UE may send first predicted measurement information based on the AI / ML model prediction of the UE to at least one network node via RRC signaling. In some embodiments of the present invention, the UE may send first predicted measurement information based on the AI / ML model prediction of the UE to at least one network node via UE assistance information. The RRC signaling is, for example, an RRC-specific predicted RRM reporting message.

[0068] In some embodiments of the present invention, the AI / ML model identification information is used to indicate the AI / ML model used by the UE, and the AI / ML model identification information includes at least one of the following information: model ID, functional ID, feature ID, and / or RRC configuration ID.

[0069] In some embodiments of the present invention, the switching node identification information is used to indicate identification information of the switching node, and the switching node identification information includes at least one of the following information: area ID, cell ID, sending and receiving point TRP ID, and / or tracking area TA.

[0070] In some embodiments of the present invention, the handover node access resource information indicates resource information used by the handover node for handover or access in the future. The handover node access resource information includes pilot signal period, time interval, frequency domain location, density information, transmission duration, and / or transmission power. Specifically, in some embodiments of the present invention, the resource includes at least one of a beam, a spectrum, and a bandwidth. Additionally, the resource information may further include pilot configuration information requirements for data transmission or measurement after the handover. The expression of the future period of time is similar to, but not limited to, the handover node access resource validity information. In some embodiments of the present invention, the resource information in the handover node access resource information may further include pilot configuration information requirements for data transmission or measurement after the handover, including at least one of pilot signal period, time interval, frequency domain location, density information, transmission duration, and transmission power. This information is used to guide the pilot transmission strategy of the target handover node to better help the target node serve the current UE during data transmission or handover, thereby reducing the probability of handover failure or providing higher data transmission capacity after the handover is completed.

[0071] In some embodiments of the present invention, the handover node access resource validity information is used to indicate the handover node access resource corresponding to the handover node, or the validity period or validity area of ​​the handover node. Specifically, in some embodiments of the present invention, the validity information may be a time description, including one or more of a start time, an end time, and a duration. Furthermore, the validity information may also be expressed as a coverage range, such as a TA; as a UE trajectory range; or in other ways.

[0072] In some embodiments of the present invention, the switching node access resource validity information includes a first implementation method or a second implementation method, wherein the first implementation method refers to an access resource validity information corresponding to each access resource, which is used to characterize the effective time of each access resource, and the second implementation method refers to the order of the access resources used to represent the effective time sequence of the access resources, as well as the corresponding time interval. Specifically, in some embodiments of the present invention, in order to further reduce the configuration overhead, and taking into account that the measurement resources (CSI-RS or SSB, etc.) are in a periodic transmission state, the implementation method of the node access resource validity message includes two implementation methods. As described below.

[0073] First implementation: Direct representation: In this solution, each access resource is associated with access resource validity information, which represents the effective time of the resource. For example, {(Beam 1, time 1), (Beam 2, time 2)} indicates that the time corresponding to Beam 1 is time 1, and the effective time corresponding to Beam 2 is time 2.

[0074] Second implementation: Sequential representation: In this solution, the order of access resources represents the order in which they become effective, along with the corresponding time intervals. For example, {Beam 1, Beam 2, Beam 3} indicates that Beam 1 is available at time T0, Beam 2 at time T0+DeltaT, and Beam 2 at time T0+2*DeltaT. DeltaT can be predefined or configured for the UE by the base station.

[0075] In some embodiments of the present invention, the switching node access resource prediction accuracy information is used to represent the access resources corresponding to the switching node or the prediction accuracy of the switching node.

[0076] In some embodiments of the present invention, the switching node access resource prediction accuracy information includes a first mode, a second mode, or a third mode. The first mode means that all switching node access resources share one accuracy, the second mode means that each switching node access resource corresponds to one accuracy, and the third mode means that one or more of all switching node access resources share one accuracy. Specifically, in some embodiments of the present invention, for the switching node access resource prediction accuracy information, its expression can be three modes. One mode is that all switching node access resources share one accuracy, that is, the accuracy corresponding to all switching node access resources. The second mode is that each switching node access resource corresponds to a separate accuracy. The third mode is that one or more of all switching node access resources share one accuracy. The three accuracy modes provided can greatly reduce the feedback overhead and provide relatively high flexibility for the implementation of AI / ML on the UE side. For example, the acquisition of switching node access resources can be obtained by one AI / ML model or by different AI / ML models.

[0077] In some embodiments of the present invention, the above information representation is illustrated by way of example: {AI Model #1, Cell #1, Beam #1, Time window #1, 90%} indicates that when AI Model #1 is used, the accuracy of Beam #1 corresponding to Cell #1 during Time window #1 is 90%; and {AI Model #1, Cell #1, Time window #1, 90%} indicates that when AI Model #1 is used, the accuracy of Cell #1 during Time window #1 is 90%. Other examples are not described further here.

[0078] In some embodiments of the present invention, the UE identification information is used to identify which UE the information belongs to. In some embodiments of the present invention, the UE identification information is a user globally unique identifier or a radio network terminal identifier (RNTI) information.

[0079] In some embodiments of the present invention, the predicted measurement result refers to a measurement result predicted by the UE based on an AI / ML model.

[0080] In some embodiments of the present invention, the predicted additional measurement result refers to the UE's measurement result of the beam when the serving cell is not configured for beam measurement. Specifically, in some embodiments of the present invention, serving cell 1 may not be configured for beam 2 measurement. In this case, the UE may include the measurement result of beam 2 in the first predicted measurement information and report it to at least one network node, predicting that beam 2 is the beam to be switched and reporting it to serving cell 1.

[0081] In some embodiments of the present invention, the predicted neighboring cell handover beam refers to the beam expected to be used after the neighboring cell is switched. In some embodiments of the present invention, the predicted measurement result time refers to the time range of the expected measurement result, and the predicted measurement result time is an absolute time or a relative time. In some embodiments of the present invention, the predicted measurement result time includes at least one of the following times: the predicted measurement result time range start time, the predicted measurement result time range end time, and / or the valid time window.

[0082] Specifically, in some embodiments of the present invention, the predicted measurement result time includes the start time of the time range of the expected measurement result, which can be an absolute time or a delta relative to the current time. The predicted measurement result time includes the end time of the time range of the expected measurement result. The predicted measurement result time includes a valid time window.

[0083] In some embodiments of the present invention, the predicted measurement result accuracy refers to a measurement error range of the Reference Signal Received Power (RSRP) of the expected measurement result or a measurement error range of the time of the expected measurement result. Specifically, in some embodiments of the present invention, the predicted measurement result accuracy may be an RSRP error of 10%, 20%, or a time error of 1s, 2s, or so on.

[0084] In some embodiments of the present invention, the UE assists the at least one network node with configuration via UE assistance information. Specifically, in some embodiments of the present invention, the UE may also assist the at least one network node with configuration of measurement reporting via UE assistance information-assisted / (predicted) measurement configuration. Preferably, the measurement configuration includes which SSBs of which neighboring cells are measured. In other words, the predicted neighboring cell beam switching is reported in the UE assistance information, rather than in the first predicted measurement information.

[0085] The UE assists the at least one network node in configuring the information element (IE) for measurement reporting through the UE assistance information. The example is as follows: UEAssistanceInformation-v1910-IEs::=SEQUENCE{ preferredMeasconfig preferredPredictedMeasconfig}

[0086] Example: gNB configures UE to report predicted RRM measurement report conditions

[0087] Figure 2C is a flow chart of the wireless communication method provided in an embodiment of the present application. As shown in Figure 2C, the wireless communication method is executed on a user equipment (UE) and includes at least one of the following operations: Operation 203: Receive the predicted configuration sent by the at least one network node.

[0088] Figure 2D is a flow chart of a wireless communication method provided in an embodiment of the present application. As shown in Figure 2D, the wireless communication method is executed on a user equipment (UE) and includes at least one of the following operations: Operation 204: Receive the predicted configuration sent by the at least one network node. Operation 205: Send the first predicted measurement information predicted by the UE based on an artificial intelligence / machine learning (AI / ML) model to at least one network node through a first message. The first predicted measurement information is the mobility parameter of the UE at a future moment, and the first predicted measurement information includes at least one of the following parameters: UE identification information, predicted measurement results, predicted additional measurement results, predicted adjacent cell switching beams, predicted measurement result time, and / or predicted measurement result accuracy.

[0089] Specifically, the UE is, for example, the user equipment 120 shown in Figure 1. The at least one network node is, for example, the base station 110 and / or the network 130 (first network node) shown in Figure 1. The base station 110 is, for example, a gNB.

[0090] Illustratively, in some embodiments of the present invention, the UE sends first predicted measurement information predicted by the UE based on an artificial intelligence / machine learning (AI / ML) model to at least one network node, and the first predicted measurement information is used to express relevant information of the target switching node within a period of time in the future. In this way, the movement trajectory of the user equipment 120 can be predicted through the AI / ML algorithm, so as to better allocate wireless resources and mobility management for the user equipment 120. This can also solve conventional technical problems. Conventional technical problems are, for example, that existing radio resource management (RRM) measurement results do not support measurement reporting at future times. The UE will predict possible RRM measurement results at a certain point in the future based on historical information, which will generate misleading information. In addition, in traditional switching, there is a problem of switching delay.

[0091] In some embodiments of the present invention, before sending the first predicted measurement information predicted by the UE based on the AI / ML model to the at least one network node through the first message, the method includes: receiving a prediction configuration sent by the at least one network node.

[0092] In some embodiments of the present invention, the prediction configuration includes a trigger condition, where the trigger condition is a condition that triggers the UE to send the first predicted measurement information to the at least one network node, and the trigger condition includes at least one of the following: a predicted measurement event, a predicted measurement time, a predicted measurement advance time, and / or a current measurement result. In some embodiments of the present invention, if the prediction configuration is at the beam level, the first predicted measurement information is also at the beam level.

[0093] Specifically, in some embodiments of the present invention, to support UE-side mobility handover measurements, the base station needs to configure an RRM measurement reporting event for the UE to minimize UE feedback overhead while ensuring real-time measurement time. After the introduction of AI / ML-based predicted RRM measurement reporting, the configuration information for predictive RRM measurements on the UE includes at least one or more of the following information.

[0094] Predicted Measurement Event: This is a prediction-based measurement event definition that describes the measurement reporting process performed by the UE upon detecting the measurement event. Detailed implementation details are described below.

[0095] Predicted measurement time: This indicates the duration for which the predicted measurement event needs to occur, and then reports the predicted measurement results. This time can be expressed as one or more of the following: start time, end time, minimum measurement time window, maximum measurement time window, or measurement duration window.

[0096] Predicted measurement lead time: Indicates how long in advance a predicted measurement event is detected before a predicted measurement report is submitted. This time can be expressed in absolute or relative terms.

[0097] The current measurement result is used as a trigger for predictive RRM measurement: The current measurement result may indicate the current location of the UE, for example, the UE has moved close to the cell edge. Therefore, the current measurement result can be used as a trigger for predictive RRM measurement.

[0098] In some embodiments of the present invention, the predicted measurement configuration can be at the cell level or the beam level. If the measurement configuration is at the beam level, the predicted measurement report is also at the beam level (SSB-INDEX).

[0099] To illustrate the above method, an example is provided here, but this example is not intended to limit other implementations of this solution. For example, if the gNB configures a predictive measurement configuration (predicted measurement event A3, start time 10s, end time 20s), the UE reports the predicted measurement event when it predicts that the A3 measurement event will occur 10-20 seconds in advance. For example, if the gNB configures a predictive measurement configuration (predicted measurement event A3, time advance 10s), the UE reports the predicted measurement event when it predicts that the A3 measurement event will occur 10 seconds in advance.

[0100] Example: UE capability reporting

[0101] FIG2E is a flow chart of a wireless communication method according to an embodiment of the present application. As shown in FIG2E , the wireless communication method, executed on a user equipment (UE), includes at least one of the following operations: Operation 206: Sending predicted measurement capability information to the at least one network node. The predicted measurement capability information includes at least one of the following: handover failure prediction support, radio link failure prediction support, cell-level RRM prediction support, beam-level RRM prediction support, cell-level handover prediction support, and / or beam-level handover prediction support.

[0102] FIG2F is a flow chart illustrating a wireless communication method according to an embodiment of the present application. As shown in FIG2F , the wireless communication method, executed on a user equipment (UE), includes at least one of the following operations: Operation 207: Sending predicted measurement capability information to the at least one network node. The predicted measurement capability information includes at least one of the following: handover failure prediction support, radio link failure prediction support, cell-level RRM prediction support, beam-level RRM prediction support, cell-level handover prediction support, and / or beam-level handover prediction support. Operation 208: Receive the predicted configuration sent by the at least one network node.

[0103] FIG2G is a flow chart of a wireless communication method provided in an embodiment of the present application. As shown in FIG2G , the wireless communication method, executed on a user equipment (UE), includes at least one of the following operations: Operation 209: Receive a predicted measurement capability request sent by the at least one network node. Operation 210: Send predicted measurement capability information to the at least one network node. The predicted measurement capability information includes at least one of the following information: handover failure prediction support, radio link failure prediction support, cell-level RRM prediction support, beam-level RRM prediction support, cell-level handover prediction support, and / or beam-level handover prediction support. Operation 211: Receive a predicted configuration sent by the at least one network node.

[0104] Specifically, the UE is, for example, the user equipment 120 shown in Figure 1. At least one network node is, for example, the base station 110 and / or the network 130 (first network node) shown in Figure 1. The base station 110 is, for example, a gNB. Exemplarily, in some embodiments of the present invention, the UE sends predicted measurement capability information to the at least one network node. In this way, the movement trajectory of the user equipment 120 can be predicted through the AI / ML algorithm, so as to better allocate wireless resources and mobility management to the user equipment 120. This can also solve conventional technical problems. Conventional technical problems are, for example, that existing radio resource management (RRM) measurement results do not support measurement reporting at future times. The UE will predict the possible RRM measurement results at a certain point in the future based on historical information, which will generate misleading information. In addition, in traditional switching, there will be a problem of switching delay.

[0105] In some embodiments of the present invention, before receiving the prediction configuration sent by the at least one network node, the method includes: sending predicted measurement capability information to the at least one network node, wherein the predicted measurement capability information includes at least one of the following information: handover failure prediction support, radio link failure prediction support, cell-level RRM prediction support, beam-level RRM prediction support, cell-level handover prediction support, and / or beam-level handover prediction support.

[0106] In some embodiments of the present invention, before sending the predicted measurement capability information to the at least one network node, the method includes: receiving a predicted measurement capability request sent by the at least one network node. In some embodiments of the present invention, the predicted measurement capability request is used to request the UE to report RRM predicted capabilities.

[0107] In some embodiments of the present invention, the handover failure prediction support refers to the UE's ability to support handover failure prediction, including whether it supports the ability to predict when the UE will experience a handover failure, and the handover failure prediction support includes node information and / or resource information and / or AI / ML model information. In some embodiments of the present invention, the radio link failure prediction support refers to the UE's ability to support radio link failure prediction, including whether it supports the ability to predict when the UE will experience a radio link failure, and the radio link failure prediction support includes node information and / or resource information and / or AI / ML model information. In some embodiments of the present invention, the cell-level RRM prediction support refers to the UE's ability to support predicted RRM, including whether it supports the ability to predict cell-level RRM, and the cell-level RRM prediction support includes node information and / or resource information and / or AI / ML model information.

[0108] In some embodiments of the present invention, the beam-level RRM prediction support refers to the UE's ability to support RRM prediction at the beam level, including whether it supports the ability to predict beam-level RRM, and the beam-level RRM prediction support includes node information and / or resource information and / or AI / ML model information. In some embodiments of the present invention, the cell-level handover prediction support refers to the UE's support for handover prediction at the cell level, including whether it supports the ability to predict cell-level handover, and the cell-level handover prediction support includes node information and / or resource information and / or AI / ML model information. In some embodiments of the present invention, the beam-level handover prediction support refers to the UE's support for handover prediction at the beam level, including whether it supports the ability to predict beam-level handover, and the cell-level handover prediction support includes node information and / or resource information and / or AI / ML model information.

[0109] In some embodiments of the present invention, the node information is used to indicate that the prediction capability supports the prediction capability for a specific node, and the resource information is used to indicate that the prediction capability supports the prediction capability for a specific resource. The node information includes cell ID, TRP ID, TA, scene ID and / or area ID; the resource information includes beam information, bandwidth information, and / or frequency information.

[0110] Specifically, in some embodiments of the present invention, AI / ML requires UE-side computing resources. Some UEs in the network may support AI / ML-based predictive RRM measurements, while others may not. Alternatively, different UEs may have significantly different predictive RRM measurement capabilities. Therefore, to support predictive RRM measurements, the gNB needs to know whether the UE has predictive RRM measurement capabilities. This allows the gNB to provide appropriate configurations to the UE.

[0111] In some embodiments of the present invention, the prediction-based RRM measurement capability information reported by the UE to the gNB or the network side (gNB, core network element, third party, etc.) includes at least one or more of the following information:

[0112] HO failure prediction support: describes the UE's ability to support handover failure prediction, including whether it supports the ability to predict when the UE will experience a handover failure. This information may further include node information and / or resource information and / or AI / ML model information, where the node information is used to describe that the prediction capability only supports the prediction capability for specific nodes, and the resource information is used to describe that the prediction capability only supports the prediction capability for specific resources. The node information described here may include cell ID, TRP (transmitter receiver port) ID, TA, scenario ID, area ID, etc.; the resource information may include beam information, bandwidth information, frequency information, etc.

[0113] Radio Link Failure Prediction Support (RLF Failure Prediction Support): describes the UE's ability to support RLF failure prediction, including whether it supports the ability to predict when the UE will experience an RLF failure. This information may further include node information and / or resource information and / or AI / ML model information, where the node information is used to describe that the prediction capability only supports prediction capabilities for specific nodes, and the resource information is used to describe that the prediction capability only supports prediction capabilities for specific resources. The node information described here may include cell ID, TRP ID, TA, scenario ID, area ID, etc.; the resource information may include beam information, bandwidth information, frequency information, etc.

[0114] Cell level RRM prediction support: describes the UE's ability to support predicted RRM, including whether it supports the ability to predict cell-level RRM. This information may further include node information and / or resource information and / or AI / ML model information. The node information is used to describe that the prediction capability only supports prediction capabilities for specific nodes, and the resource information is used to describe that the prediction capability only supports prediction capabilities for specific resources. The node information described here may include cell ID, TRP ID, TA, scenario ID, area ID, etc.; the resource information may include beam information, bandwidth information, frequency information, etc.

[0115] Beam level RRM prediction support: describes the UE's ability to support beam-level RRM prediction, including whether it supports the ability to predict beam-level RRM. This information may further include node information and / or resource information and / or AI / ML model information, where the node information is used to describe that the prediction capability only supports prediction capabilities for specific nodes, and the resource information is used to describe that the prediction capability only supports prediction capabilities for specific resources. The node information described here may include cell ID, TRP ID, TA, scenario ID, area ID, etc.; the resource information may include beam information, bandwidth information, frequency information, etc.

[0116] Cell level HO prediction support: describes the UE's support for cell-level handover prediction, including whether it supports the ability to predict cell-level handover. The information may further include node information and / or resource information and / or AI / ML model information, where the node information is used to describe that the prediction capability only supports prediction capabilities for specific nodes, and the resource information is used to describe that the prediction capability only supports prediction capabilities for specific resources. The node information described here may include cell ID, TRP ID, TA, scenario ID, area ID (this model is only valid for this area), etc.; resource information may include beam information, bandwidth information, frequency information, etc.

[0117] ·Beam level HO prediction support: describes the UE's support for handover prediction at the Beam level, including whether it supports the ability to predict beam-level handover. This capability may also not contain any parameters and is a general capability of the UE. The information may further include node information and / or resource information and / or AI / ML model information, where the node information is used to describe that the prediction capability only supports prediction capabilities for specific nodes, and the resource information is used to describe that the prediction capability only supports prediction capabilities for specific resources. The node information described here may include cell ID, TRP ID, TA, scenario ID, area ID, etc.; the resource information may include beam information, bandwidth information, frequency information, etc.

[0118] Note 1: All of the above capabilities may further include predicting time, that is, the ability to predict the start time (start time) and end time (end time) of the time range in which an event occurs.

[0119] Note 2: All of the above capabilities may further include accuracy, that is, the probability of predicting an event to occur, 10%, 20%, 30%...

[0120] Note 3: For all the above capabilities, if they include both area ID and cell ID, then this capability is valid for a specific area ID or a specific cell ID.

[0121] Example: Predicting RRM measurement capability triggering reporting

[0122] The gNB sends an RRM (Mobility) Prediction Capability Request message to the UE, requesting the UE to report its RRM (Mobility) prediction capabilities. After receiving the RRM (Mobility) Prediction Capability Request, the UE responds with an RRM (Mobility) Prediction Capability Report (including at least one of the following information: handover failure prediction support, radio link failure prediction support, cell-level RRM prediction support, beam-level RRM prediction support, cell-level handover prediction support, and / or beam-level handover prediction support) to the gNB.

[0123] Example: Carrying in UE capability report

[0124] The UE carries an RRM (mobility) prediction capability report in the capability report (including at least one of the following information: handover failure prediction support, radio link failure prediction support, cell-level RRM prediction support, beam-level RRM prediction support, cell-level handover prediction support, and / or beam-level handover prediction support). The specific information format is not described here.

[0125] Examples of information elements (IEs) in the RRM (mobility) prediction capability report are as follows: UE-NR-Capability information element UE-NR-Capability ::= SEQUENCE { accessStratumRelease AccessStratumRelease, pdcp-Parameters PDCP-Parameters, rlc-Parameters RLC-Parameters OPTIONAL, mac-Parameters MAC-Parameters OPTIONAL, phy-Parameters Phy-Parameters, rf-Parameters RF-Parameters, measAndMobParameters MeasAndMobParameters OPTIONAL, fdd-Add-UE-NR-Capabilities UE-NR-CapabilityAddXDD-Mode OPTIONAL, tdd-Add-UE-NR-Capabilities UE-NR-CapabilityAddXDD-Mode OPTIONAL, fr1-Add-UE-NR-Capabilities UE-NR-CapabilityAddFRX-Mode OPTIONAL, fr2-Add-UE-NR-Capabilities UE-NR-CapabilityAddFRX-Mode OPTIONAL, featureSets FeatureSets OPTIONAL, featureSetCombinations SEQUENCE(SIZE(1..maxFeatureSetCombinations)) OF FeatureSetCombination OPTIONAL, lateNonCriticalExtension OCTET STRING(CONTAINING UE-NR-Capability-v15c0) OPTIONAL, nonCriticalExtension UE-NR-Capability-v1530 OPTIONAL}...........................................................................MBS-Parameters-r17::=SEQUENCE{ maxMRB-Add-r17INTEGER(1..16)OPTIONAL RRMprediction-r19::=SEQUENCE{ HO failure prediction support(cell ID){supported}OPTIONAL RLF failure prediction support{supported}OPTIONAL cell level RRM prediction support(cell ID){supported}OPTIONAL beam level RRM prediction support(cell ID,SSB index){supported}OPTIONAL cell level HO prediction support(cell ID)ENUMERATED{supported}OPTIONAL beam level HO prediction support(cell ID,SSB index){supported}OPTIONAL}.

[0126] The network may also send a request message to the UE with a cell ID / SSB-index to inquire whether the UE supports specific cells and beams, such as handover failure and RRM prediction.

[0127] Second embodiment: At least one network node predicts UE trajectory prediction information based on an AI / ML model

[0128] Figure 3A is a flow chart of a wireless communication method provided in an embodiment of the present application. As shown in Figure 3A, the wireless communication method is executed on a user equipment (UE) and includes at least one of the following operations: Operation 301: Receive UE trajectory prediction information sent by at least one network node, which is predicted by the at least one network node based on an artificial intelligence / machine learning AI / ML model, wherein the UE trajectory prediction information includes at least one of the following information: UE identification information, predicted UE RRM measurement results, predicted measurement result time, prediction accuracy information, node information, node access resource information, node access resource validity information, and / or node access resource prediction accuracy information.

[0129] Figure 3B is a flow chart of a wireless communication method provided in an embodiment of the present application. As shown in Figure 3B, the wireless communication method is executed on at least one network node and includes at least one of the following operations: Operation 302: Sending UE trajectory prediction information predicted by the at least one network node based on an artificial intelligence / machine learning AI / ML model to a user equipment UE, wherein the UE trajectory prediction information includes at least one of the following information: UE identification information, predicted UE RRM measurement results, predicted measurement result time, prediction accuracy information, node information, node access resource information, node access resource validity information, and / or node access resource prediction accuracy information.

[0130] Specifically, the UE is, for example, the user equipment 120 shown in Figure 1. At least one network node is, for example, the base station 110 and / or the network 130 (first network node) shown in Figure 1. The base station 110 is, for example, a gNB. Exemplarily, in some embodiments of the present invention, the UE 120 receives UE trajectory prediction information sent by at least one network node, which is predicted by at least one network node based on an artificial intelligence / machine learning AI / ML model. In this way, the movement trajectory of the user equipment 120 can be predicted through the AI / ML algorithm, thereby better allocating wireless resources and mobility management to the user equipment 120. This can also solve conventional technical problems. Conventional technical problems, for example, are that existing radio resource management (RRM) measurement results do not support measurement reporting at future times. The UE will predict possible RRM measurement results at a certain point in the future based on historical information, which will generate misleading information. In addition, in traditional switching, there will be a problem of switching delay.

[0131] In some embodiments of the present invention, the UE identification information is used to identify the UE to which the information belongs. In some embodiments of the present invention, the UE identification information is a user globally unique identifier or a radio network terminal identifier (RNTI). In some embodiments of the present invention, the predicted UE RRM measurement result is the same as the measurement result reported by the UE.

[0132] In some embodiments of the present invention, the predicted UE RRM measurement result time refers to a time range of an expected measurement result, and the predicted measurement result time is an absolute time or a relative time. In some embodiments of the present invention, the predicted measurement result time includes at least one of the following times: a predicted measurement result time range start time, a predicted measurement result time range end time, and / or a valid time window.

[0133] Specifically, in some embodiments of the present invention, the predicted measurement result time includes the start time of the time range of the expected measurement result, which can be an absolute time or a delta relative to the current time. The predicted measurement result time includes the end time of the time range of the expected measurement result. The predicted measurement result time includes a valid time window.

[0134] In some embodiments of the present invention, the prediction accuracy information includes at least one of the following: a measurement error range of the Reference Signal Received Power (RSRP) of the expected measurement result and / or a measurement error range of the time of the expected measurement result. Specifically, in some embodiments of the present invention, the predicted measurement result accuracy may be an RSRP error of 10%, 20%, or a time error of 1 second, 2 seconds, or the like.

[0135] In some embodiments of the present invention, the node information is used to indicate identification information of the switching node, and the node information includes at least one of the following information: area ID, cell ID, transmitting and receiving point TRP ID, and / or tracking area TA. In some embodiments of the present invention, the node access resource information is used to indicate resources used by the switching node for switching or access in the future, and the switching node access resource information includes at least one of the following resources: beam, spectrum, and / or bandwidth.

[0136] In some embodiments of the present invention, the node access resource validity information is used to indicate the handover node access resource corresponding to the handover node, or the effective time or effective area of ​​the handover node. Specifically, in some embodiments of the present invention, the validity information can be a time description, including one or more of a start time, an end time, and a duration. In addition, the validity information can also be expressed as a coverage range, such as a TA; it can also be expressed as a UE trajectory range; or it can be expressed in other ways.

[0137] In some embodiments of the present invention, the node access resource validity information includes a first implementation method or a second implementation method. The first implementation method means that each access resource corresponds to an access resource validity information, which is used to characterize the effective time of each access resource. The second implementation method means that the order of access resources is used to represent the effective time order of the access resources, and the corresponding time interval.

[0138] Specifically, in some embodiments of the present invention, in order to further reduce configuration overhead and taking into account that measurement resources (CSI-RS or SSB, etc.) are in a periodic transmission state, the implementation of the node access resource validity message includes two implementations, as described below.

[0139] First implementation: Direct representation: In this solution, each access resource is associated with access resource validity information, which represents the effective time of the resource. For example, {(Beam 1, time 1), (Beam 2, time 2)} indicates that the time corresponding to Beam 1 is time 1, and the effective time corresponding to Beam 2 is time 2.

[0140] Second implementation: Sequential representation: In this solution, the order of access resources represents the order in which they become effective, along with the corresponding time intervals. For example, {Beam 1, Beam 2, Beam 3} indicates that Beam 1 is available at time T0, Beam 2 at time T0+DeltaT, and Beam 2 at time T0+2*DeltaT. DeltaT can be predefined or configured for the UE by the base station.

[0141] In some embodiments of the present invention, the node access resource prediction accuracy information is used to represent the access resources corresponding to the switching node or the prediction accuracy of the switching node. In some embodiments of the present invention, the node access resource prediction accuracy information includes a first mode, a second mode, or a third mode, wherein the first mode means that all switching node access resources share one accuracy, the second mode means that each switching node access resource corresponds to one accuracy, and the third mode means that one or more of all switching node access resources share one accuracy. In some embodiments of the present invention, the UE assists the at least one network node in configuring through UE assistance information.

[0142] Specifically, in some embodiments of the present invention, for the prediction accuracy information of the switching node access resources, its expression can be three modes. One mode is that all switching node access resources share one accuracy, that is, the accuracy corresponding to all switching node access resources. The second mode is that each switching node access resource corresponds to a separate accuracy. The third mode is that one or more of all switching node access resources share one accuracy. The three accuracy modes provided can greatly reduce the feedback overhead and provide relatively high flexibility for the implementation of AI / ML on the UE side. For example, the acquisition of the switching node access resources can be obtained by one AI / ML model or by different AI / ML models.

[0143] In some embodiments of the present invention, the above information representation is illustrated by way of example: {AI Model #1, Cell #1, Beam #1, Time window #1, 90%} indicates that when AI Model #1 is used, the accuracy of Beam #1 corresponding to Cell #1 during Time window #1 is 90%; and {AI Model #1, Cell #1, Time window #1, 90%} indicates that when AI Model #1 is used, the accuracy of Cell #1 during Time window #1 is 90%. Other examples are not described further here.

[0144] Specifically, in some embodiments of the present invention, the UE may also assist the at least one network node in configuring measurement reporting through UE assistance information / (predicted) measurement configuration. Preferably, the measurement configuration includes which SSBs of which neighboring cells are measured. In other words, the predicted neighboring cell beam switching is reported in the UE assistance information rather than in the first predicted measurement information.

[0145] As shown in Figure 3C , in some embodiments of the present invention, AI / ML-based mobility prediction is deployed on the network side. After the network-side AI / ML model completes the UE's mobility handover prediction, it sends the prediction result to the UE. The UE then initiates a mobility handover action to the target node based on the obtained result. Both the network and the gNB can predict the UE's measurement results.

[0146] As shown in Figure 3C, in some embodiments of the present invention, the at least one network node includes a base station 110 and a first network node, where base station 110 is, for example, a gNB and the first network node is, for example, network 130. The method also includes base station 110 obtaining the UE trajectory prediction information from the first network node. In some embodiments of the present invention, the UE trajectory prediction information obtained by base station 110 from the first network node includes at least one of the following information: the UE identification information, the predicted UE RRM measurement result, the predicted measurement result time, and / or the prediction accuracy information.

[0147] Specifically, as shown in Figure 3C, the base station 110 (for example, a gNB) obtains UE trajectory prediction information from the first network node (for example, the network 130).

[0148] 1) Message 1: The gNB obtains UE trajectory prediction information from the network

[0149] The gNB obtains UE trajectory prediction information from the network side, which includes one or more entities such as AMF, SMF, UPF, NEF, DWDAF, MEC, and a third party. For example, if the gNB obtains UE trajectory prediction information from the AMF, the UE trajectory prediction information can be carried in the INITIAL CONTEXT SETUP REQUEST message. If the gNB obtains UE trajectory prediction information directly from a third party, the message can be carried in the UDP or TCP protocol. Other methods are not described here. The UE trajectory prediction information obtained by the gNB includes at least one of the following information.

[0150] UE identification information: This message is used to identify the UE to which the information belongs. The identification information can be a user globally unique identifier or RNTI information, without any limitation.

[0151] Predicted UE measurement RRM result: This parameter is the same as the measurement result reported by the UE.

[0152] Predicted measurement result time: The effective time of the prediction result, which is equivalent to the start time and end time of the first embodiment.

[0153] Prediction accuracy and information accuracy.

[0154] Based on the acquired information, the gNB configures the UE's current mobility handover measurement process. This process is implemented in the same manner as the RRM measurement configuration process and is not described further here. For example, after obtaining the predicted UE trajectory, the gNB configures a measurement object for the UE and sends the predicted target cell's SSB index to the measurement object.

[0155] As shown in Figure 3C, in some embodiments of the present invention, the at least one network node includes a base station 110 and a first network node, where base station 110 is, for example, a gNB and the first network node is, for example, network 130. The method further includes, after base station 110 obtains the UE trajectory prediction information from the first network node, sending the UE trajectory prediction information to UE 120. In some embodiments of the present invention, after base station 110 obtains the UE trajectory prediction information from the first network node, the UE trajectory prediction information sent by base station 110 to UE 120 includes at least one of the following information: the node information, the node access resource information, the node access resource validity information, and / or the node access resource prediction accuracy information.

[0156] Specifically, as shown in Figure 3C, after the base station 110 (for example, a gNB) obtains the UE predicted RRM from the first network node (for example, the network 130), it sends the UE prediction result.

[0157] 2) Message 2: After the gNB obtains the UE predicted RRM from the network, it sends the UE prediction result.

[0158] In this solution, the network side (including base stations, core network elements, MEC, and third-party implementation entities) obtains the current UE's RRM measurement prediction information through algorithms or AI / ML, and configures the UE's RRM measurement process based on the obtained prediction information. The information configured to the UE by the network side includes at least one or more of the following:

[0159] Node information: This information is used to indicate the identification information of the handover node. This information can be one or more of area ID, cell ID, TRP ID, TA, etc.

[0160] Node access resource information: used to indicate the resources that the switching node will use for switching or access in the future. The resources here include at least one of beam, spectrum, and bandwidth.

[0161] Node access resource validity information: This information describes the validity period or area of ​​the handover node access resource corresponding to the handover node. This validity information can be expressed as a time description, including one or more of the start time, end time, and duration. Furthermore, this validity information can also be expressed as a coverage area, such as a TA; as the UE's trajectory range; or in other ways.

[0162] Node access resource prediction accuracy information: used to describe the prediction accuracy of the access resource corresponding to the handover node.

[0163] In order to further reduce configuration overhead and considering that measurement resources (CSI-RS or SSB, etc.) are in a periodic transmission state, the implementation of the node access resource validity message includes two methods, as described below.

[0164] Direct representation: In this scheme, each access resource is associated with a specific access resource validity information, which represents the effective time of the resource. For example, {(Beam 1, time 1), (Beam 2, time 2)} indicates that the effective time corresponding to beam 1 is time 1, and the effective time corresponding to beam 2 is time 2.

[0165] Sequential representation: In the solution, the order of access resources indicates the order in which they become available and the corresponding time intervals. For example, {Beam 1, Beam 2, Beam 3} indicates that Beam 1 is available at time T0, Beam 2 is available at time T0+DeltaT, and Beam 2 is available at time T0+2*DeltaT. DeltaT can be predefined or configured for the UE by the base station.

[0166] Third embodiment: The first base station sends second predicted measurement information to the second base station

[0167] FIG4A is a flow chart of a wireless communication method provided in an embodiment of the present application. As shown in FIG4A , the wireless communication method is executed on at least one network node and includes at least one of the following operations: Operation 401: The first base station sends second predicted measurement information to the second base station. The second predicted measurement information is the mobility information of the UE at a future time, and the mobility information of the UE at a future time includes at least one of the following information: AI / ML model identification information, switching node identification information, switching node access resource information, switching node access resource validity information, switching node access resource prediction accuracy information, predicted measurement results, predicted additional measurement results, predicted adjacent cell switching beam, predicted measurement result time, and / or accuracy.

[0168] FIG4B is a flow chart of a wireless communication method provided in an embodiment of the present application. As shown in FIG4B , the wireless communication method is executed on at least one network node and includes at least one of the following operations: Operation 402: Receive, via a first message, first predicted measurement information of the UE predicted based on an artificial intelligence / machine learning AI / ML model, sent by the UE. The first predicted measurement information is the mobility information of the UE at a future time, and the mobility information of the UE at a future time includes at least one of the following information: AI / ML model identification information, switching node identification information, switching node access resource information, switching node access resource validity information, switching node access resource prediction accuracy information, predicted measurement results, predicted additional measurement results, predicted adjacent cell switching beams, predicted measurement result time, and / or accuracy. Operation 403: The first base station sends second predicted measurement information to the second base station. The second predicted measurement information is the mobility information of the UE at a future time, and the mobility information of the UE at a future time includes at least one of the following information: AI / ML model identification information, switching node identification information, switching node access resource information, switching node access resource validity information, switching node access resource prediction accuracy information, predicted measurement results, predicted additional measurement results, predicted neighboring cell switching beam, predicted measurement result time, and / or accuracy. Specifically, the embodiment shown in FIG4B may be some examples dependent on the first embodiment, and the details of the first embodiment are not repeated here.

[0169] FIG4C is a flow chart of a wireless communication method provided in an embodiment of the present application. As shown in FIG4C , the wireless communication method, executed by at least one network node, includes at least one of the following operations: Operation 404: Sending UE trajectory prediction information predicted by the at least one network node based on an artificial intelligence / machine learning (AI / ML) model to a user equipment (UE). The UE trajectory prediction information includes at least one of the following information: UE identification information, predicted UE RRM measurement results, predicted measurement result time, prediction accuracy information, node information, node access resource information, node access resource validity information, and / or node access resource prediction accuracy information. Operation 405: The first base station sends second predicted measurement information to the second base station. The second predicted measurement information is mobility information of the UE at a future time. The mobility information of the UE at a future time includes at least one of the following information: AI / ML model identification information, handover node identification information, handover node access resource information, handover node access resource validity information, handover node access resource prediction accuracy information, predicted measurement results, predicted additional measurement results, predicted neighboring cell handover beam, predicted measurement result time, and / or accuracy. Specifically, the embodiment shown in FIG. 4C may be some examples dependent on the second embodiment, and the details of the second embodiment will not be repeated here.

[0170] In some embodiments of the present invention, the at least one network node also includes a first network node, and the method also includes the first base station receiving the second predicted measurement information sent by the UE, where the second predicted measurement information is predicted measurement information predicted by the UE based on the AI / ML model or predicted measurement information predicted by the first network node based on the AI / ML model.

[0171] In some embodiments of the present invention, the at least one network node also includes a first network node, and the method also includes the first base station receiving the second predicted measurement information sent by the first network node, where the second predicted measurement information is predicted measurement information predicted by the UE based on the AI / ML model or predicted measurement information predicted by the first network node based on the AI / ML model.

[0172] Specifically, the UE is, for example, user equipment 120 shown in Figure 1 . The at least one network node is, for example, base station 110 and / or network 130 (first network node) shown in Figure 1 . Base station 110 is, for example, a gNB. Base station 110 includes, for example, a first base station and a second base station. The first base station is, for example, a source gNB. The second base station is, for example, a target gNB.

[0173] As shown in Figure 4D , the source gNB illustratively sends the predicted measurement results received from the UE to the target gNB so that the target gNB can pre-allocate handover resources to the UE. The definition of the predicted measurement results is described in Example 1 and will not be repeated here. In some embodiments of the present invention, the predicted measurement results (e.g., including at least one of the following: cell ID, predicted measurement result (measObject), predicted neighboring cell beam to be handed over, additional measurement results, start time, end time, and / or accuracy) are provided.

[0174] This message must be distinguished as being sent by the UE to the source gNB or by the network. A new parameter, UE Prediction / NW Prediction Indication, is added.

[0175] In some embodiments of the present invention, the source gNB sends the predicted measurement information to the target gNB. In some embodiments of the present invention, the source gNB receives the predicted measurement information sent by the UE via a first indication. In some embodiments of the present invention, the first indication is a UE predicted indication.

[0176] In some embodiments of the present invention, the source gNB sends UE trajectory prediction information to the target gNB. In some embodiments of the present invention, the source gNB receives the UE trajectory prediction information sent by the first network node via a first indication. In some embodiments of the present invention, the first indication is a network prediction indication.

[0177] Figure 5A is a flow chart of a wireless communication method provided in an embodiment of the present application. As shown in Figure 5A, the wireless communication method is executed on a user equipment (UE) and includes at least one of the following operations: Operation 501: sending first predicted measurement information of the UE based on an artificial intelligence / machine learning AI / ML model prediction to at least one network node through a first message and receiving UE trajectory prediction information of the at least one network node based on an artificial intelligence / machine learning AI / ML model prediction sent by at least one network node. The first predicted measurement information is the mobility information of the UE at a future moment, and the mobility information of the UE at a future moment includes at least one of the following information: AI / ML model identification information, switching node identification information, switching node access resource information, switching node access resource validity information, switching node access resource prediction accuracy information, predicted measurement results, predicted additional measurement results, predicted adjacent cell switching beams, predicted measurement result time, and / or accuracy, wherein the UE trajectory prediction information includes at least one of the following information: UE identification information, predicted UE RRM measurement results, predicted measurement result time, prediction accuracy information, node information, node access resource information, node access resource validity information, and / or node access resource prediction accuracy information.

[0178] FIG5B is a flow chart of a wireless communication method provided in an embodiment of the present application. As shown in FIG5B , the wireless communication method is executed on at least one network node and includes at least one of the following operations: Operation 502: receiving first predicted measurement information of the UE based on an artificial intelligence / machine learning AI / ML model predicted by the user equipment UE through a first message and sending UE trajectory prediction information predicted by the at least one network node based on the artificial intelligence / machine learning AI / ML model to the user equipment UE. Wherein, the first predicted measurement information is the mobility information of the UE at a future time, and the mobility information of the UE at a future time includes at least one of the following information: AI / ML model identification information, switching node identification information, switching node access resource information, switching node access resource validity information, switching node access resource prediction accuracy information, predicted measurement result, predicted additional measurement result, predicted adjacent cell switching beam, predicted measurement result time, and / or accuracy, wherein the UE trajectory prediction information includes at least one of the following information: UE identification information, predicted UE RRM measurement result, predicted measurement result time, prediction accuracy information, node information, node access resource information, node access resource validity information, and / or node access resource prediction accuracy information.

[0179] Specifically, the UE is, for example, the user equipment 120 shown in Figure 1. The at least one network node is, for example, the base station 110 and / or the network 130 (first network node) shown in Figure 1. The base station 110 is, for example, a gNB. For example, in some embodiments of the present invention, the UE sends first predicted measurement information predicted by the UE based on an artificial intelligence / machine learning (AI / ML) model to at least one network node, and receives UE trajectory prediction information sent by at least one network node, which is predicted by the at least one network node based on the artificial intelligence / machine learning (AI / ML) model. The first predicted measurement information and / or UE trajectory prediction information is used to represent relevant information about a target handover node within a future period of time. In this way, the movement trajectory of the user equipment 120 can be predicted using the AI / ML algorithm, thereby better allocating radio resources and mobility management for the user equipment 120. This can also solve conventional technical problems. Conventional technical problems, for example, are that existing radio resource management (RRM) measurement results do not support measurement reporting at future times. The UE predicts possible RRM measurement results at a certain point in the future based on historical information, which can generate misleading information. In addition, in traditional switching, there is a problem of switching delay.

[0180] FIG6 is a flow chart of a wireless communication method according to an embodiment of the present application. As shown in FIG6 , the wireless communication method is applicable to an RRM measurement mechanism for AI / ML mobility switching, and includes at least one of the following operations:

[0181] ·UE AI / ML mobility capability reporting: including whether the UE supports AI / ML-based predictive RRM measurement, and / or the requirements for base station side resources (base station load, base station side pilot resource configuration) in order to support the predictive RRM measurement on the UE side. For detailed embodiments, please refer to the fifth embodiment. UE AI / ML mobility capability reporting includes at least one of the following operations: Operation 1: Receive a predictive measurement capability request sent by the at least one network node. Operation 2: Send predictive measurement capability information to the at least one network node, wherein the predictive measurement capability information includes at least one of the following information: handover failure prediction support, radio link failure prediction support, cell-level RRM prediction support, beam-level RRM prediction support, cell-level handover prediction support, and / or beam-level handover prediction support.

[0182] Predicted RRM measurement configuration: The gNB configures parameters related to AI / ML RRM measurement prediction, including events that trigger reporting. For detailed implementations, please refer to the second and fourth embodiments.

[0183] Predicted RRM measurement reporting content: defines the reporting content required to support AI / ML mobility on the UE side to help the base station better perform handover operations. For detailed implementation, please refer to the first embodiment.

[0184] The neighboring gNB (e.g., the source gNB) reserves resources for the UE. The serving gNB (e.g., the target gNB) forwards the predicted RRM measurement results to the relevant neighboring gNBs. For detailed embodiments, please refer to the third embodiment.

[0185] Figure 7 is a schematic structural diagram of a wireless communication device 700 provided in an embodiment of the present application. The wireless communication device can be a user equipment, a base station, or a network element. The wireless communication device 700 shown in Figure 7 includes a processor 710, which can call and execute a computer program from a memory to implement the method in the embodiment of the present application.

[0186] Optionally, as shown in FIG8 , the wireless communication device 700 may further include a memory 720. The processor 710 may call and execute a computer program from the memory 720 to implement the method in the embodiment of the present application. The memory 720 may be a separate device independent of the processor 710 or may be integrated into the processor 710.

[0187] Optionally, as shown in FIG7 , the wireless communication device 700 may further include a transceiver 730. The processor 710 may control the transceiver 730 to communicate with other devices. Specifically, the transceiver 730 may send information or data to other devices or receive information or data sent by other devices. The transceiver 730 may include a transmitter and a receiver. The transceiver 730 may further include one or more antennas.

[0188] Optionally, the wireless communication device 700 may specifically be a base station in an embodiment of the present application, and the wireless communication device 700 may implement the corresponding processes implemented by the base station in each method in the embodiment of the present application. For the sake of brevity, they will not be repeated here.

[0189] Optionally, the wireless communication device 700 may specifically be a mobile user device / user device in an embodiment of the present application, and the wireless communication device 700 may implement the corresponding processes implemented by the mobile user device / user device in each method of the embodiment of the present application. For the sake of brevity, they will not be repeated here.

[0190] Optionally, the wireless communication device 700 may specifically be a network element in an embodiment of the present application, and the wireless communication device 700 may implement the corresponding processes implemented by the network element in each method in the embodiment of the present application. For the sake of brevity, they will not be repeated here.

[0191] Figure 8 is a schematic structural diagram of a chip according to an embodiment of the present application. The chip 800 shown in Figure 8 includes a processor 810, which can call and run a computer program from a memory to implement the method according to the embodiment of the present application.

[0192] Optionally, as shown in FIG8 , the chip 800 may further include a memory 820. The processor 810 may call and execute computer programs from the memory 820 to implement the methods in the embodiments of the present application. The memory 820 may be a separate device independent of the processor 810 or may be integrated into the processor 810.

[0193] Optionally, the chip 800 may further include an input interface 830. The processor 910 may control the input interface 830 to communicate with other devices or chips, and specifically, may obtain information or data sent by other devices or chips.

[0194] Optionally, the chip 800 may further include an output interface 840. The processor 810 may control the output interface 840 to communicate with other devices or chips, and specifically, may output information or data to other devices or chips.

[0195] Optionally, the chip can be applied to the base station in the embodiments of the present application, and the chip can implement the corresponding processes implemented by the base station in each method of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0196] Optionally, the chip can be applied to the mobile user device / user device in the embodiments of the present application, and the chip can implement the corresponding processes implemented by the mobile user device / user device in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0197] Optionally, the chip can be applied to the network element in the embodiment of the present application, and the chip can implement the corresponding processes implemented by the mobile network element in each method of the embodiment of the present application. For the sake of brevity, it will not be repeated here.

[0198] Figure 9 is a schematic block diagram of a wireless communication system 100 provided in an embodiment of the present application. As shown in Figure 9, the communication system 100 includes a user equipment 120 and a base station 110. The user equipment 120 can be used to implement the corresponding functions implemented by the user equipment in the above method, and the base station 110 can be used to implement the corresponding functions implemented by the base station in the above method. For the sake of brevity, these functions are not further described here.

[0199] It should be understood that the processor of the embodiment of the present application may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiment may be completed by hardware integrated logic circuits in the processor or software instructions.

[0200] It is understood that the memory in the embodiments of the present application may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory. The embodiments of the present application also provide a computer-readable storage medium for storing a computer program.

[0201] Optionally, the computer-readable storage medium may be applied to the base station in the embodiments of the present application, and the computer program causes the computer to execute the corresponding processes implemented by the base station in the various methods in the embodiments of the present application. For the sake of brevity, no further description is given here. Optionally, the computer-readable storage medium may be applied to the mobile user equipment / user equipment in the embodiments of the present application, and the computer program causes the computer to execute the corresponding processes implemented by the mobile user equipment / user equipment in the various methods in the embodiments of the present application. For the sake of brevity, no further description is given here.

[0202] An embodiment of the present application also provides a computer program product, including computer program instructions.

[0203] Optionally, the computer program product may be applied to the base station in the embodiments of the present application, and the computer program instructions cause the computer to execute the corresponding processes implemented by the base station in the various methods of the embodiments of the present application. For the sake of brevity, they are not described in detail here. Optionally, the computer program product may be applied to the mobile user equipment / user equipment in the embodiments of the present application, and the computer program instructions cause the computer to execute the corresponding processes implemented by the mobile user equipment / user equipment in the various methods of the embodiments of the present application. For the sake of brevity, they are not described in detail here.

[0204] The embodiment of the present application also provides a computer program.

[0205] Optionally, the computer program may be applied to the base station in the embodiments of the present application. When the computer program is executed on a computer, the computer executes the corresponding processes implemented by the base station in the various methods of the embodiments of the present application. For the sake of brevity, no further details are given here. Optionally, the computer program may be applied to the mobile user equipment / user equipment in the embodiments of the present application. When the computer program is executed on a computer, the computer executes the corresponding processes implemented by the mobile user equipment / user equipment in the various methods of the embodiments of the present application. For the sake of brevity, no further details are given here.

[0206] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0207] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for wireless communication, which is executed on a user equipment UE, wherein, The method includes: Sending, via a first message, first predicted measurement information predicted by the UE based on an artificial intelligence / machine learning (AI / ML) model to at least one network node, where the first predicted measurement information is mobility information of the UE at a future time, and the mobility information of the UE at the future time includes at least one of the following information: AI / ML model identification information, handover node identification information, handover node access resource information, handover node access resource availability information, handover node access resource prediction accuracy information, predicted measurement result, predicted additional measurement result, predicted adjacent cell handover beam, predicted measurement result time, and / or accuracy.

2. The method according to claim 1, wherein The first message includes radio resource management (RRM) signaling, radio resource control (RRC) signaling, or UE assistance information.

3. The method according to claim 1 or 2, wherein The AI / ML model identification information is used to represent the AI / ML model used by the UE, and the AI / ML model identification information includes at least one of the following information: model ID, functional ID, feature ID, and / or RRC configuration ID.

4. The method according to any one of claims 1 to 3, wherein The handover node identification information is used to indicate the identification information of a handover node, and the identification information of the handover node includes at least one of the following information: area ID, cell ID, transmit and receive point (TRP) ID, and / or tracking area (TA).

5. The method according to any one of claims 1 to 4, wherein The handover node access resource information is used to indicate resource information for handover or access by a handover node in a future period of time, and the handover node access resource information includes at least one of the following information: pilot signal period, time interval, frequency domain location, density information, transmission duration, and / or transmission power.

6. The method according to any one of claims 1 to 5, wherein The handover node access resource availability information is used to represent at least one of the following information: the handover node access resource corresponding to a handover node, or the effective time or effective area of the handover node.

7. The method according to any one of claims 1 to 6, wherein The handover node access resource availability information includes a first implementation or a second implementation. The first implementation means that each access resource corresponds to an access resource availability information for characterizing the effective time of each access resource. The second implementation means that the order of access resources is used to represent the effective time order of the access resources and the corresponding time intervals.

8. The method according to any one of claims 1 to 7, wherein The handover node access resource prediction accuracy information is used to represent the access resource corresponding to a handover node or the prediction accuracy of the handover node.

9. The method according to any one of claims 1 to 8, wherein, The handover node access resource prediction accuracy information includes a first mode, a second mode, or a third mode. The first mode means that all handover node access resources share one accuracy. The second mode means that each handover node access resource corresponds to one accuracy. The third mode means that one or more of all handover node access resources share one accuracy.

10. The method according to any one of claims 1 to 9, wherein, The UE identification information is identified as a globally unique user identifier or radio network terminal identifier (RNTI) information.

11. The method according to any one of claims 1 to 10, wherein The predicted additional measurement result refers to the measurement result of a beam by the UE when the serving cell is not configured with beam measurement.

12. The method according to any one of claims 1 to 11, wherein The predicted adjacent cell handover beam refers to the beam expected to be used after an adjacent cell handover.

13. The method according to any one of claims 1 to 12, wherein, The predicted measurement result time refers to the time range of the expected measurement result, and the predicted measurement result time is an absolute time or a relative time.

14. The method according to any one of claims 1 to 13, wherein The predicted measurement result time includes at least one of the following times: the start time of the predicted measurement result time range, the end time of the predicted measurement result time range, and / or a valid time window.

15. The method according to any one of claims 1 to 14, wherein, The accuracy refers to at least one of the following accuracies: the measurement error range of the reference signal received power (RSRP) of the expected measurement result and / or the measurement error range of the time of the expected measurement result.

16. The method according to any one of claims 1 to 15, wherein Before sending the first predicted measurement information predicted by the UE based on the AI / ML model to the at least one network node through the first message, the method includes: Receiving a prediction configuration sent by the at least one network node.

17. The method according to claim 16, wherein The prediction configuration includes a trigger condition, which refers to the condition for triggering the UE to send the first predicted measurement information to the at least one network node. The trigger condition includes at least one of the following: a predicted measurement event, a predicted measurement time, a predicted measurement advance time, and / or a current measurement result.

18. The method according to claim 16 or 17, wherein, If the prediction configuration is at the beam level, the first predicted measurement information is also at the beam level.

19. The method according to any one of claims 16 to 18, wherein Before receiving the prediction configuration sent by the at least one network node, the method includes: Sending prediction measurement capability information to the at least one network node, where the prediction measurement capability information includes at least one of the following information: handover failure prediction support, radio link failure prediction support, cell-level RRM prediction support, beam-level RRM prediction support, cell-level handover prediction support, and / or beam-level handover prediction support.

20. The method according to claim 19, wherein, The handover failure prediction support refers to the UE's ability to support handover failure prediction, including the ability to predict when the UE will experience a handover failure. The handover failure prediction support includes node information and / or resource information and / or AI / ML model information.

21. The method according to claim 19 or 20, wherein, The radio link failure prediction support refers to the UE's ability to support radio link failure prediction, including the ability to predict when the UE will experience a radio link failure. The radio link failure prediction support includes node information and / or resource information and / or AI / ML model information.

22. The method according to any one of claims 19 to 21, wherein The cell-level RRM prediction support refers to the UE's ability to support RRM prediction, including the ability to predict cell-level RRM. The cell-level RRM prediction support includes node information and / or resource information and / or AI / ML model information.

23. The method according to any one of claims 19 to 22, wherein The beam-level RRM prediction support refers to the UE's ability to support RRM prediction at the beam level, including the ability to predict beam-level RRM. The beam-level RRM prediction support includes node information and / or resource information and / or AI / ML model information.

24. The method according to any one of claims 19 to 23, wherein The cell-level handover prediction support refers to the situation of the UE's support for handover prediction at the cell level, including the ability to support predicting cell-level handovers. The cell-level handover prediction support includes node information and / or resource information and / or AI / ML model information.

25. The method according to any one of claims 19 to 24, wherein, The beam-level handover prediction support refers to the situation of the UE's support for handover prediction at the beam level, including the ability to support predicting beam-level handovers. The cell-level handover prediction support includes node information and / or resource information and / or AI / ML model information.

26. The method according to any one of claims 19 to 25, wherein The node information is used to indicate that the prediction ability supports having prediction ability for specific nodes, and the resource information is used to indicate that the prediction ability supports having prediction ability for specific resources. The node information includes cell ID, TRP ID, TA, scenario ID, and / or region ID; the resource information includes beam information, bandwidth information, and / or frequency point information.

27. The method according to any one of claims 19 to 26, wherein, Before sending the prediction measurement ability information to the at least one network node, the method includes: Receiving a prediction measurement ability request sent by the at least one network node.

28. The method according to claim 27, wherein, The prediction measurement ability request is used to request the UE to report the ability of RRM prediction.

29. The method according to any one of claims 1 to 28, wherein The UE assists the at least one network node in configuration through UE assistance information.

30. A method for wireless communication, which is executed on a user equipment UE, wherein, The method includes: Receiving UE trajectory prediction information predicted by the at least one network node based on an artificial intelligence / machine learning AI / ML model, where the UE trajectory prediction information includes at least one of the following information: UE identification information, predicted UE RRM measurement results, predicted measurement result time, prediction accuracy information, node information, node access resource information, node access resource availability information, and / or node access resource prediction accuracy information.

31. The method according to claim 30, wherein The UE identification information is identified as the user globally unique identifier or radio network terminal identifier RNTI information.

32. The method according to claim 30 or 31, wherein, The predicted UE RRM measurement result time refers to the time range of the expected measurement result, and the predicted measurement result time is an absolute time or a relative time.

33. The method according to any one of claims 30 to 32, wherein, The predicted measurement result time includes at least one of the following times: the start time of the predicted measurement result time range, the end time of the predicted measurement result time range, and / or the valid time window.

34. The method according to any one of claims 30 to 33, wherein, The prediction accuracy information refers to at least one of the following information: the measurement error range of the reference signal received power RSRP of the expected measurement result and / or the measurement error range of the time of the expected measurement result.

35. The method according to any one of claims 30 to 34, wherein The node information is used to indicate the identification information of the handover node, and the node information includes at least one of the following information: region ID, cell ID, transmit and receive point TRP ID, and / or tracking area TA.

36. The method according to any one of claims 30 to 35, wherein, The node access resource information is used to indicate the resources used by the handover node for handover or access in a future period of time. The handover node access resource information includes at least one of the following resources: beam, spectrum, and / or bandwidth.

37. The method according to any one of claims 30 to 36, wherein The node access resource validity information is used to represent the handover node access resources corresponding to the handover node, or the valid time or valid area of the handover node.

38. The method according to any one of claims 30 to 37, wherein, The node access resource validity information includes a first implementation method or a second implementation method. The first implementation method means that each access resource corresponds to an access resource validity information, which is used to characterize the effective time of each access resource. The second implementation method means that the order of the access resources is used to represent the effective time order of the access resources and the corresponding time interval.

39. The method according to any one of claims 30 to 38, wherein, The node access resource prediction accuracy information is used to represent the access resources corresponding to the handover node or the prediction accuracy of the handover node.

40. The method according to any one of claims 30 to 39, wherein, The node access resource prediction accuracy information includes a first mode, a second mode, or a third mode. The first mode means that all handover node access resources share one accuracy. The second mode means that each handover node access resource corresponds to one accuracy. The third mode means that one or more of all handover node access resources share one accuracy.

41. The method according to any one of claims 30 to 40, wherein, The UE assists the at least one network node in configuration through UE assistance information.

42. A method for wireless communication, which is executed by at least one network node, wherein, The method includes: Receiving, through a first message, the first predicted measurement information predicted by the UE based on an artificial intelligence / machine learning (AI / ML) model sent by the user equipment (UE), where the first predicted measurement information is the mobility information of the UE at a future moment, and the mobility information of the UE at the future moment includes at least one of the following information: AI / ML model identification information, handover node identification information, handover node access resource information, handover node access resource validity information, handover node access resource prediction accuracy information, predicted measurement result, predicted additional measurement result, predicted handover beam of an adjacent cell, predicted measurement result time, and / or accuracy.

43. The method according to claim 42, wherein, The first message includes radio resource management (RRM) signaling, radio resource control (RRC) signaling, or UE assistance information.

44. The method according to claim 42 or 43, wherein, The AI / ML model identification information is used to represent the AI / ML model used by the UE, and the AI / ML model identification information includes at least one of the following information: model ID, functional ID, feature ID, and / or RRC configuration ID.

45. The method according to claims 42 to 44, wherein, The handover node identification information is used to indicate the identification information of the handover node, and the identification information of the handover node includes at least one of the following information: area ID, cell ID, transmit and receive point (TRP) ID, and / or tracking area (TA).

46. The method according to any one of claims 42 to 45, wherein The handover node access resource information is used to indicate the resource information for handover or access by the handover node in a future period of time, and the handover node access resource information includes at least one of the following information: pilot signal period, time interval, frequency domain position, density information, transmission duration, and / or transmission power.

47. The method according to any one of claims 42 to 46, wherein, The handover node access resource validity information is used to represent at least one of the following information: the handover node access resources corresponding to the handover node, or the valid time or valid area of the handover node.

48. The method according to any one of claims 42 to 47, wherein The access resource validity information of the handover node includes a first implementation method or a second implementation method. The first implementation method means that each access resource corresponds to an access resource validity information, which is used to characterize the effective time of each access resource. The second implementation method means that the order of the access resources is used to represent the effective time order of the access resources and the corresponding time interval.

49. The method according to any one of claims 42 to 48, wherein, The access resource prediction accuracy information of the handover node is used to represent the access resources corresponding to the handover node or the prediction accuracy of the handover node.

50. The method according to any one of claims 42 to 49, wherein, The access resource prediction accuracy information of the handover node includes a first mode, a second mode or a third mode. The first mode means that all the access resources of the handover node share an accuracy. The second mode means that each access resource of the handover node corresponds to an accuracy. The third mode means that one or more of all the access resources of the handover node share an accuracy.

51. The method according to any one of claims 42 to 50, wherein The UE identification information is identified as the globally unique identifier of the user or the radio network terminal identifier (RNTI) information.

52. The method according to any one of claims 42 to 51, wherein, The predicted additional measurement result refers to the measurement result of the beam by the UE when there is no measurement of the beam configured for the serving cell.

53. The method according to any one of claims 42 to 52, wherein, The predicted handover beam of the neighboring cell refers to the beam expected to be used after the handover to the neighboring cell.

54. The method according to any one of claims 42 to 53, wherein, The predicted measurement result time refers to the time range of the expected measurement result, and the predicted measurement result time is an absolute time or a relative time.

55. The method according to any one of claims 42 to 54, wherein, The predicted measurement result time includes at least one of the following times: the start time of the predicted measurement result time range, the end time of the predicted measurement result time range, and / or the valid time window.

56. The method according to any one of claims 42 to 55, wherein, The accuracy refers to at least one of the following accuracies: the measurement error range of the reference signal received power (RSRP) of the expected measurement result and / or the measurement error range of the time of the expected measurement result.

57. The method according to any one of claims 42 to 56, wherein, Before receiving, through the first message, the first prediction measurement information predicted by the UE based on the AI / ML model sent by the UE, the method includes: Sending a prediction configuration to the UE.

58. The method according to claim 57, wherein, The prediction configuration includes a trigger condition, which refers to the condition for triggering the UE to send the first prediction measurement information to the at least one network node. The trigger condition includes at least one of the following: a prediction measurement event, a prediction measurement time, a prediction measurement advance time, and / or a current measurement result.

59. The method according to claim 57 or 58, wherein, If the prediction configuration is at the beam level, the first prediction measurement information is also at the beam level.

60. The method according to any one of claims 57 to 59, wherein, Before sending the prediction configuration to the UE, the method includes: Receiving the prediction measurement capability information sent by the UE, where the prediction measurement capability information includes at least one of the following information: handover failure prediction support, radio link failure prediction support, cell-level RRM prediction support, beam-level RRM prediction support, cell-level handover prediction support, and / or beam-level handover prediction support.

61. The method according to claim 60, wherein, The handover failure prediction support refers to the ability of the UE to support handover failure prediction, including the ability to predict when the UE will experience a handover failure. The handover failure prediction support includes node information and / or resource information and / or AI / ML model information.

62. The method according to claim 60 or 61, wherein The radio link failure prediction support refers to the ability of the UE to support radio link failure prediction, including the ability to predict when the UE will experience a radio link failure. The radio link failure prediction support includes node information and / or resource information and / or AI / ML model information.

63. The method according to any one of claims 60 to 62, wherein, The cell-level RRM prediction support refers to the ability of the UE to support RRM prediction, including the ability to predict cell-level RRM. The cell-level RRM prediction support includes node information and / or resource information and / or AI / ML model information.

64. The method according to any one of claims 60 to 63, wherein, The beam-level RRM prediction support refers to the ability of the UE to support RRM prediction at the beam level, including the ability to predict beam-level RRM. The beam-level RRM prediction support includes node information and / or resource information and / or AI / ML model information.

65. The method according to any one of claims 60 to 64, wherein, The cell-level handover prediction support refers to the support situation of the UE for cell-level handover prediction, including the ability to predict cell-level handovers. The cell-level handover prediction support includes node information and / or resource information and / or AI / ML model information.

66. The method according to any one of claims 60 to 65, wherein, The beam-level handover prediction support refers to the support situation of the UE for beam-level handover prediction, including the ability to predict beam-level handovers. The cell-level handover prediction support includes node information and / or resource information and / or AI / ML model information.

67. The method according to any one of claims 60 to 66, wherein, The node information is used to indicate that the prediction ability support has the prediction ability for a specific node, and the resource information is used to indicate that the prediction ability support has the prediction ability for a specific resource. The node information includes cell ID, TRP ID, TA, scenario ID, and / or area ID; the resource information includes beam information, bandwidth information, and / or frequency point information.

68. The method according to any one of claims 60 to 67, wherein Before receiving the prediction measurement ability information sent by the UE, the method includes: Sending a prediction measurement ability request to the UE.

69. The method according to claim 68, wherein, The prediction measurement ability request is used to request the UE to report its RRM prediction ability.

70. The method according to any one of claims 42 to 69, wherein, The at least one network node receives the UE assistance information sent by the UE, where the UE assistance information is used to assist the at least one network node in configuration.

71. The method according to any one of claims 42 to 70, wherein, The at least one network node includes a first base station and a second base station, and the method further includes the first base station sending second predicted measurement information to the second base station, where the second predicted measurement information is mobility information of the UE at a future time, and the mobility information of the UE at the future time includes at least one of the following information: AI / ML model identification information, handover node identification information, handover node access resource information, handover node access resource availability information, handover node access resource prediction accuracy information, predicted measurement results, predicted additional measurement results, predicted handover beams of neighboring cells, predicted measurement result time, and / or accuracy.

72. The method according to claim 71, wherein, The at least one network node further includes a first network node, and the method further includes the first base station receiving the second predicted measurement information sent by the UE, where the second predicted measurement information is predicted measurement information predicted by the UE based on an AI / ML model or predicted measurement information predicted by the first network node based on an AI / ML model.

73. The method according to claim 71, wherein, The at least one network node further includes a first network node, and the method further includes the first base station receiving the second predicted measurement information sent by the first network node, where the second predicted measurement information is predicted measurement information predicted by the UE based on an AI / ML model or predicted measurement information predicted by the first network node based on an AI / ML model.

74. A method for wireless communication, which is executed on at least one network node, wherein, The method includes: Sending UE trajectory prediction information predicted by the at least one network node based on an artificial intelligence / machine learning (AI / ML) model to a user equipment (UE), where the UE trajectory prediction information includes at least one of the following information: UE identification information, predicted UE RRM measurement results, predicted measurement result time, prediction accuracy information, node information, node access resource information, node access resource availability information, and / or node access resource prediction accuracy information.

75. The method according to claim 74, wherein, The UE identification information is identified as a globally unique user identifier or radio network terminal identifier (RNTI) information.

76. The method according to claim 74 or 75, wherein, The predicted UE RRM measurement result time refers to the time range of the expected measurement results, and the predicted measurement result time is an absolute time or a relative time.

77. The method according to any one of claims 74 to 76, wherein, The predicted measurement result time includes at least one of the following times: start time of the predicted measurement result time range, end time of the predicted measurement result time range, and / or valid time window.

78. The method according to any one of claims 74 to 77, wherein, The prediction accuracy information refers to at least one of the following information: measurement error range of the reference signal received power (RSRP) of the expected measurement results and / or measurement error range of the time of the expected measurement results.

79. The method according to any one of claims 74 to 78, wherein, The node information is used to indicate the identification information of the handover node, and the node information includes at least one of the following information: area ID, cell ID, transmit and receive point (TRP) ID, and / or tracking area (TA).

80. The method according to any one of claims 74 to 79, wherein, The node access resource information is used to indicate the resources used by the handover node for handover or access in a future period of time, and the handover node access resource information includes at least one of the following resources: beam, spectrum, and / or bandwidth.

81. The method according to any one of claims 74 to 80, wherein, The node access resource validity information is used to represent the handover node access resources corresponding to the handover node, or the valid time or valid area of the handover node.

82. The method according to any one of claims 74 to 81, wherein, The node access resource validity information includes a first implementation method or a second implementation method. The first implementation method means that each access resource corresponds to an access resource validity information, which is used to characterize the effective time of each access resource. The second implementation method means that the order of the access resources is used to represent the effective time order of the access resources and the corresponding time interval.

83. The method according to any one of claims 74 to 82, wherein, The node access resource prediction accuracy information is used to represent the access resources corresponding to the handover node or the prediction accuracy of the handover node.

84. The method according to any one of claims 74 to 83, wherein, The node access resource prediction accuracy information includes a first mode, a second mode, or a third mode. The first mode means that all handover node access resources share an accuracy. The second mode means that each handover node access resource corresponds to an accuracy. The third mode means that one or more of all handover node access resources share an accuracy.

85. The method according to any one of claims 74 to 84, wherein The at least one network node receives UE assistance information sent by the UE, where the UE assistance information is used to assist the at least one network node in configuration.

86. The method according to any one of claims 74 to 85, wherein, The at least one network node includes a base station and a first network node, and the method further includes the base station obtaining the UE trajectory prediction information from the first network node.

87. The method according to claim 86, wherein, The UE trajectory prediction information obtained by the base station from the first network node includes at least one of the following information: the UE identification information, the predicted UE RRM measurement result, the predicted measurement result time, and / or the prediction accuracy information.

88. The method according to any one of claims 74 to 85, wherein, The at least one network node includes a base station and a first network node, and the method further includes, after the base station obtains the UE trajectory prediction information from the first network node, the base station sending the UE trajectory prediction information to the UE.

89. The method according to claim 88, wherein, After the base station obtains the UE trajectory prediction information from the first network node, the UE trajectory prediction information sent by the base station to the UE includes at least one of the following information: the node information, the node access resource information, the node access resource validity information, and / or the node access resource prediction accuracy information.

90. The method according to any one of claims 74 to 85, wherein, The at least one network node includes a first base station and a second base station, and the method further includes the first base station sending second prediction measurement information to the second base station, where the second prediction measurement information is the mobility information of the UE at a future moment, and the mobility information of the UE at a future moment includes at least one of the following information: AI / ML model identification information, handover node identification information, handover node access resource information, handover node access resource validity information, handover node access resource prediction accuracy information, predicted measurement result, predicted additional measurement result, predicted adjacent cell handover beam, predicted measurement result time, and / or accuracy.

91. The method according to claim 90, wherein, The at least one network node includes a first network node, and the method further includes the first base station receiving the second predicted measurement information sent by the UE, where the second predicted measurement information is predicted measurement information predicted by the UE based on an AI / ML model or predicted measurement information predicted by the first network node based on an AI / ML model.

92. The method according to claim 90, wherein, The at least one network node further includes a first network node, and the method further includes the first base station receiving the second predicted measurement information sent by the first network node, where the second predicted measurement information is predicted measurement information predicted by the UE based on an AI / ML model or predicted measurement information predicted by the first network node based on an AI / ML model.

93. A wireless communication device, comprising: A processor and a memory, where the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 92.

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