Communication method and device, storage medium and electronic equipment

Abstract

The invention provides a communication method and device, a storage medium and electronic equipment, and relates to the technical field of communication. The method is applied to terminal UE, and comprises the steps that a message or a signaling is sent to target equipment, and the target equipment executes model management according to a first message included in the message or the signaling; the first message comprises UE capability report. According to the embodiment of the invention, under the condition that the UE has the AI function, the UE reports the UE capability report in a message or signaling manner to the target equipment at the network side, so that the UE capability report and transmission are realized.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a communication method and apparatus, a storage medium and an electronic device. Background Technology

[0002] The 6G wireless air interface will be designed with AI in its core. Compared with 5G, the communication scenarios will include the integration of artificial intelligence and communication. The key capabilities will be expanded compared with 5G, including new indicators related to perception, AI, sustainability, coverage, interoperability and positioning.

[0003] For the artificial intelligence functions newly introduced in the 6G wireless air interface, the terminal needs to design corresponding functional parameters to realize the reporting process mechanism and response transmission process of various terminal capabilities.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] This disclosure provides a communication method and apparatus, a storage medium and an electronic device, which at least to some extent realizes terminal capability reporting.

[0006] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0007] According to one aspect of this disclosure, a communication method is provided, applied to a terminal UE, the method comprising:

[0008] A message or signaling is sent to the target device, and the target device performs model management according to a first message included in the message or signaling; the first message includes: UE capability reporting.

[0009] In some embodiments, the UE capability reporting includes at least one of: AI / ML capability information elements, UE-supported feature / feature group information elements, and beam management information elements.

[0010] In some embodiments, sending a message or signaling to the target device includes:

[0011] The terminal UE sends messages or signaling to the target device.

[0012] In some embodiments, the terminal UE sends a message or signaling to the target device, including:

[0013] The terminal UE actively sends messages or signaling to the target device, or;

[0014] The terminal UE responds to the AI / ML capability request sent by the target device by passively sending messages or signaling to the target device.

[0015] In some embodiments, sending a message or signaling to the target device includes:

[0016] The terminal UE receives the AI / ML capability request sent by the target device;

[0017] The terminal UE sends messages or signaling to the target device.

[0018] In some embodiments, the first message is at least one of a user assistance information message, a terminal capability information message, and a location provision capability message.

[0019] In some embodiments, the target device includes at least one of the following: a base station, a location server, a location management function, a location network element, a core network element, an intelligent computing unit, an intelligent unit, a RAN AI layer, a network data analysis function, a centralized unit, a distributed unit, a wireless AI management layer, a RAN AI management unit, a network element or network device responsible for location, and an AI / ML related network element or function or entity.

[0020] In some embodiments, the signaling includes at least one of the following: management plane messages / signaling, control plane messages / signaling, user plane messages / signaling, intelligent plane messages / signaling, data plane messages / signaling, computing plane messages / signaling, system messages, NAS / AS signaling, dedicated configuration messages / signaling, RRC / higher layer protocol signaling, MAC CE, access control layer signaling, open interface protocol signaling, and DCI / physical layer signaling.

[0021] In some embodiments, if the AI / ML capability information element appears, then the UE supports AI / ML functions, or the UE capability reporting includes AI / ML capability reporting, or the UE supports AI / ML capability reporting.

[0022] In some embodiments, the information elements included in the UE capability reporting are fixed or optional.

[0023] In some embodiments, at least one of the AI / ML capability information elements, UE-supported feature / feature group information elements, and beam management information elements is fixed, or at least one information element is optional, or all information elements are fixed, or all information elements are optional.

[0024] In some embodiments, the first message further includes: an AI / ML model management information element; the AI / ML model management information element is used to indicate whether the UE supports at least one of the operations of downloading, updating, and deleting AI / ML models.

[0025] In some embodiments, the first message further includes: an AI / ML inference information element; the AI / ML inference information element is used to indicate whether the UE supports performing AI / ML inference tasks locally, and / or deploying AI / ML models locally and / or supporting cloud-edge / edge-device / cloud collaborative inference.

[0026] In some embodiments, the first message further includes: an AI / ML data collection information element; the AI / ML data collection information element is used to indicate whether the UE supports data acquisition function and whether it supports collecting and / or transmitting data for at least one of AI / ML training / inference / performance testing.

[0027] In some embodiments, the first message further includes: an AI / ML monitoring information element; the AI / ML monitoring information element is used to indicate whether the UE supports model performance monitoring and / or reporting of AI / ML model performance metrics and / or service quality.

[0028] In some embodiments, the first message further includes: an AI / ML energy-saving information element; the AI / ML energy-saving information element is used to indicate whether the UE supports using AI / ML terminal and / or network energy saving.

[0029] In some embodiments, the first message further includes: a terminal storage capacity information element; the terminal storage capacity information element is used to indicate the storage space size supporting AI model training or inference.

[0030] In some embodiments, the first message further includes: a computing power capability information element; the computing power capability information element is used to indicate the amount of computing power supporting AI model training or inference.

[0031] In some embodiments, the first message further includes: an AI / ML function information element; the AI / ML function information element is used to indicate the AIML function or feature / feature group supported by the UE.

[0032] In some embodiments, when the AI / ML capability information element appears, the AI / ML capability information element or the first message includes at least one of the following: AI / ML model management information element, AI / ML inference information element, AI / ML data collection information element, AI / ML monitoring information element, AI / ML energy saving information element, terminal storage capability information element, computing power capability information element, and AI / ML function information element.

[0033] In some embodiments, if the feature / feature group information element supported by the UE appears, then the UE supports the feature / feature group; the feature / feature group information element or the first message includes at least one of the following: AI / ML direct positioning information element, AI / ML assisted positioning information element, AI / ML beam management information element, AI / ML predicted CSI management information element, AI / ML compressed CSI feedback information element, AI / ML assisted interference prediction information element, AI / ML assisted handover optimization information element, AI / ML network status prediction information element, and AI / ML network security enhancement information element.

[0034] In some embodiments, at least one of the following is included: the AI / ML direct positioning information element is used to indicate whether the UE supports direct AI / ML positioning technology; the AI / ML assisted positioning information element is used to indicate whether the UE supports AI / ML assisted positioning technology; the AI / ML beam management information element is used to indicate whether the UE supports AI / ML beam management technology; the AI / ML predicted CSI management information element is used to indicate whether the UE supports AI / ML predicted CSI management technology; the AI / ML compressed CSI feedback information element is used to indicate whether the UE supports AI / ML compressed CSI feedback technology; the AI / ML assisted interference prediction information element is used to indicate whether the UE supports AI / ML assisted interference prediction technology; the AI / ML assisted handover optimization information element is used to indicate whether the UE supports AI / ML assisted handover optimization technology; the AI / ML network state prediction information element is used to indicate whether the UE supports network state prediction; and the AI / ML network security enhancement information element is used to indicate whether the UE supports using AI / ML technology to enhance network security.

[0035] In some embodiments, when one of the beam management conditions is met, the first message further includes at least one of the following: aperiodic beam information reporting element, beam assist information reporting element, uplink / downlink beam prediction element, beam information reporting framework element, and beam scan measurement reporting element; the beam management conditions include at least one of the following: the UE capability reporting includes a beam management element, AI / ML beam management element is defined / occurs, and the UE supports / has AI beam prediction capability.

[0036] In some embodiments, at least one of the following is included: the aperiodic beam information reporting element is used to indicate whether the UE supports at least one of aperiodic beam data acquisition or beam prediction performance detection reporting; the beam auxiliary information reporting element is used to indicate whether the UE supports reporting auxiliary information for model training; the uplink / downlink beam prediction element is used to indicate whether the UE supports at least one of uplink / downlink beam prediction, inter-cell beam prediction, or inter-frequency beam prediction; the beam information reporting framework element is used to indicate whether the UE supports beam prediction model training; and the beam scan measurement reporting element is used by the UE to indicate whether it supports scanning reporting of all / partial beams, or whether it supports single / dual-sided beam measurement reporting on the base station side and / or the UE side.

[0037] In some embodiments, the beam information reporting framework element includes: a maximum number of configurations for periodic / semi-persistent and aperiodic beam measurement reporting, whether auxiliary information is supported, the types and / or number of supported beam auxiliary information reports, whether data acquisition access layer and application layer caching are supported, a maximum number of (transmit / receive) beam (measurement) reports, a maximum number of (store / cachate / activate / deactivate / inactive / monitor) beam prediction models, a minimum / maximum number of beam measurements: the number of beam measurements input to the model, and at least one of the maximum / minimum number of inter-cell / inter-frequency beam reports for L1 / L3 RSRP measurements.

[0038] In some embodiments, model management includes at least one of: model identification, model delivery, model deployment, model training, model activation / deactivation, and model performance testing.

[0039] In some embodiments, model management includes at least one of: AI / ML model management, AI model management, ML model management, model lifecycle management, AI management, and ML management.

[0040] According to another aspect of this disclosure, a communication method is also provided, applied to a target device, the method comprising:

[0041] Model management is performed based on a first message included in the message or signaling sent by the terminal UE; the first message includes: UE capability reporting.

[0042] According to another aspect of this disclosure, a communication device is also provided for use in a terminal UE, the device comprising:

[0043] The sending module is used to send messages or signaling to the target device, wherein the target device performs model management according to a first message included in the message or signaling; the first message includes: UE capability reporting.

[0044] According to another aspect of this disclosure, a communication device is also provided for use with a target device, the device comprising:

[0045] The model management module is used to perform model management based on a first message included in the message or signaling sent by the terminal UE; the first message includes: UE capability reporting.

[0046] According to another aspect of this disclosure, an electronic device is also provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the communication method described in any of the preceding claims by executing the executable instructions.

[0047] According to another aspect of this disclosure, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the communication method described in any of the preceding claims.

[0048] According to another aspect of this disclosure, a computer program product is also provided, including a computer program that, when executed by a processor, implements the communication method of any of the above.

[0049] The communication method, apparatus, storage medium, and electronic device provided in the embodiments of this disclosure, when applied to a terminal UE, include: sending a message or signaling to a target device, wherein the target device performs model management according to a first message included in the message or signaling; the first message includes: UE capability reporting.

[0050] In this embodiment, when the UE has AI functionality, the UE reports its capabilities to the target device on the network side via messages or signaling, thus realizing UE capability reporting and transmission. This enables subsequent function activation / deactivation and information exchange processes for UEs supporting AI functionality on the radio side. It allows the network side to know the UE's support for AI functions and technologies, supports the lifecycle management of the radio interface model, and ensures the model's self-organization, self-operation, self-optimization, and self-evolution on the RAN side. This facilitates the lifecycle management of the radio interface model, enabling the network to manage and control the AI ​​model, and allowing AI to intrinsically improve the performance of the wireless communication system.

[0051] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0052] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0053] Figure 1 A schematic diagram of the system structure of a communication method according to an embodiment of this disclosure is shown.

[0054] Figure 2 A first schematic diagram of a communication method according to an embodiment of the present disclosure is shown.

[0055] Figure 3 A second schematic diagram of a communication method according to an embodiment of this disclosure is shown.

[0056] Figure 4 A first schematic diagram of a communication device according to an embodiment of the present disclosure is shown.

[0057] Figure 5 A second schematic diagram of a communication device according to an embodiment of the present disclosure is shown.

[0058] Figure 6 A structural block diagram of a computer device for a communication method according to an embodiment of the present disclosure is shown. Detailed Implementation

[0059] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0060] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0061] To facilitate understanding, before introducing the embodiments of this disclosure, the following explanations are provided for several terms involved in the embodiments of this disclosure:

[0062] In 6G (sixth generation mobile communication technology), AI / ML (Artificial Intelligence and Machine Learning) will play an even more central and indispensable role. As networks become more complex and data volumes explode, traditional rule-based network management and optimization methods may no longer be sufficient. AI / ML will provide support in several areas, including at least: intelligent resource management, automated network slicing, enhanced user experience, ultra-reliable low-latency communication (URLLC), self-organizing networks (SON), security and privacy protection, edge computing, and full-duplex communication.

[0063] The specific implementation methods of the embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0064] Figure 1 A schematic diagram of an exemplary application system architecture to which the communication methods of the embodiments of this disclosure can be applied is shown. For example... Figure 1 As shown, the system architecture includes terminal device 101, network 102, and network-side device 103.

[0065] Network 102 is a medium used to provide a communication link between terminal device 101 and network-side device 103, and can be a wired network or a wireless network.

[0066] Optionally, the aforementioned wireless or wired networks use standard communication technologies and / or protocols. The network is typically the Internet, but can also be any network, including but not limited to Local Area Networks (LANs), Metropolitan Area Networks (MANs), Wide Area Networks (WANs), mobile, wired or wireless networks, private networks, or any combination of virtual private networks. In some embodiments, technologies and / or formats including Hyper Text Markup Language (HTML), Extensible Markup Language (XML), etc., are used to represent data exchanged over the network. Furthermore, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Networks (VPNs), and Internet Protocol Security (IPSec) can be used to encrypt all or some links. In other embodiments, custom and / or dedicated data communication technologies can be used to replace or supplement the aforementioned data communication technologies.

[0067] Optionally, the terminal device in this embodiment may also be referred to as UE (User Equipment). In specific implementation, the terminal device may be a mobile phone, tablet personal computer, laptop computer, personal digital assistant (PDA), mobile internet device (MID), wearable device, or vehicle-mounted device, etc. It should be noted that the specific type of terminal device is not limited in the embodiments of the present invention.

[0068] Network-side equipment can be base stations, relays, or access points, etc. Base stations can be 5G and later versions of base stations (e.g., 5G NR NB), or base stations in other communication systems (e.g., eNB base stations). It should be noted that the specific type of network-side equipment is not limited in the embodiments disclosed herein.

[0069] Those skilled in the art will know that Figure 1 The number of terminals, networks, and network-side devices shown is merely illustrative; any number of terminals, networks, and network-side devices can be included as needed. This disclosure does not limit the scope of the embodiments.

[0070] Under the above system architecture, this disclosure provides a communication method that can be executed by any electronic device with computing capabilities.

[0071] In some embodiments, the communication method provided in this disclosure can be executed by a terminal device in the above-described system architecture; in other embodiments, the communication method provided in this disclosure can be executed by a server in the above-described system architecture; in still other embodiments, the communication method provided in this disclosure can be implemented by the terminal device and the server in the above-described system architecture through interaction.

[0072] Figure 2 This diagram illustrates a first schematic diagram of a communication method according to an embodiment of the present disclosure, such as... Figure 2 As shown, the communication method provided in this embodiment includes the following steps:

[0073] Step S202: Send a message or signaling to the target device. The target device performs model management according to the first message included in the message or signaling. The first message includes: UE capability reporting.

[0074] In this embodiment, when the UE has AI functionality, the UE reports its capabilities to the target device on the network side via messages or signaling, thus realizing UE capability reporting and transmission. This enables subsequent function activation / deactivation and information exchange processes for UEs supporting AI functionality on the radio side. It allows the network side to know the UE's support for AI functions and technologies, supports the lifecycle management of the radio interface model, and ensures the model's self-organization, self-operation, self-optimization, and self-evolution on the RAN side. This facilitates the lifecycle management of the radio interface model, enabling the network to manage and control the AI ​​model, and allowing AI to intrinsically improve the performance of the wireless communication system.

[0075] In the evolution from 5G to 6G communication, it was explicitly stated that the 6G radio interface will be an AI-inherent design; the three major scenarios of 5G have expanded to six major scenarios of 6G, with the addition of the integration of artificial intelligence and communication; the nine key capabilities of 5G have expanded to fifteen capability indicators of 6G, with the addition of indicators related to perception, AI, sustainability, coverage, interoperability, and positioning. For the AIML function newly introduced into the radio interface, the terminal needs to design corresponding new functional parameters to define the reporting mechanisms and processes for various new terminal capabilities. Therefore, it is necessary to study and define the functional parameters that the UE can report to the network side, as well as the corresponding process mechanisms and response transmission procedures, when the UE has AI and ML capabilities.

[0076] In this embodiment, a set of specific UE capability parameters for AI / ML functions is designed based on the UE capability framework. This can enable / disable the corresponding functions and perform information interaction for UEs that support AI and ML functions on the radio side. This design is a necessary design for the radio side to implement AI and ML beam management schemes.

[0077] In this embodiment, UE capability reporting includes at least one of the following: AI / ML capability information elements, UE-supported feature / feature group information elements, and beam management information elements.

[0078] In this embodiment, User Equipment Capability Reporting (UE Capability Reporting) is a key piece of information in mobile communication networks. The UE uses UE Capability Reporting to report its supported functions and technical parameters to the network side. In this embodiment, UE Capability Reporting includes at least one of the following: AI / ML capability information elements, UE-supported feature / feature group information elements, and beam management information elements. UE Capability Reporting has multiple configuration options and can be used in different scenarios. The descriptions of the above information elements can take other forms, all of which are within the protection scope of this disclosure. AI / ML capability information elements can be equivalent to various other information elements such as AI / ML capability indication information elements, AI capability information elements, ML capability information elements, AI functional capability information elements, and model-related information elements.

[0079] In this embodiment, sending a message or signaling to the target device includes: the terminal UE sending a message or signaling to the target device.

[0080] When a UE reports its capabilities to a target device, it includes the UE capability report in the first message and sends it to the target device via a message or signaling.

[0081] In this embodiment, the terminal UE sends a message or signaling to the target device, including:

[0082] The terminal UE actively sends a message or signaling to the target device, or;

[0083] The terminal UE responds to the AI / ML capability request sent by the target device by passively sending messages or signaling to the target device.

[0084] The UE capability report is sent to the target device, and there are two reporting processes: active reporting and passive reporting.

[0085] When the UE actively reports, the terminal UE actively sends a message or signaling to the target device, and the target device performs model management based on the first message included in the message or signaling.

[0086] When the UE reports passively or responds, the UE receives an AI / ML capability request sent by the target device. The UE passively sends a message or signaling to the target device based on the AI / ML capability request. The target device performs model management based on the first message included in the message or signaling.

[0087] In this embodiment, sending a message or signaling to the target device includes:

[0088] The terminal UE receives an AI / ML capability request sent by the target device;

[0089] The terminal UE sends messages or signaling to the target device.

[0090] The target device sends an AI / ML capability request to the terminal UE. The terminal UE receives the AI / ML capability confirmation message or signaling, and then sends a message or signaling back to the target device. The target device performs model management based on the first message included in the message or signaling. The AI / ML capability request sent by the target device to the terminal UE can be a UECapabilityEnqiry message. An AI / ML capability request can be equivalent to a UE capability request, a UE capability information request, a terminal capability request, or a model-related capability request, etc.

[0091] In this embodiment, the first message is at least one of user assistance information message, terminal capability information message, and location provision capability message.

[0092] When the UE communicates with the target device, the first message can be a UE Assistance Information (UAI) message, a UE Capability Information (UECapabilityInformation) message, or a Location Provide Capabilities (LPP) message. The first message can be at least one or more of the following combinations: UE Assistance Information message, UE Capability Information message, and LPP Provide Capabilities message.

[0093] In this embodiment, the target device includes at least one of the following: a base station, a location server, a positioning management function, a positioning network element, a core network element, an intelligent computing unit, an intelligent unit, a RAN AI layer (Radio Access Network), a network data analysis function, a centralized unit, a distributed unit, a wireless AI management layer, a RAN AI management unit, a network element or network device responsible for positioning, and an AI / ML related network element or function or entity.

[0094] The terminal UE sends messages or signaling to the target device to achieve communication between the terminal UE and the target device, so as to report the UE capabilities of the terminal UE to the target device. The target device can be a physical network device on the network side, or a network element or functional unit on the network side. Specifically, the target device can be at least one of the following: base station, location server, location management function (LMF), positioning network element, core network element, intelligent computing unit, intelligent unit, RAN AI layer, network data analysis function (NWDAF), central unit (CU), distributed unit (DU), wireless AI management layer, RAN AI management unit, network element or network device responsible for positioning, and AI / ML related network element or function or entity.

[0095] In the embodiments, the aforementioned signaling includes at least one of the following: management plane messages / signaling, control plane messages / signaling, user plane messages / signaling, intelligent plane messages / signaling, data plane messages / signaling, computing plane messages / signaling, system messages, NAS / AS (Non-Access Stratum / Access Stratum) signaling, dedicated configuration messages / signaling, RRC (Radio Resource Control) / higher layer protocol signaling, MAC CE (Medium Access Control Control Element), access control layer signaling, open interface protocol signaling, and DCI (Downlink Control Information) / physical layer signaling.

[0096] In the embodiments described above, the signaling sent by the terminal UE to the target device can be carried in various forms of messages or signaling. Specifically, the signaling sent by the terminal UE to the target device may include at least one of the following: management plane messages / signaling, control plane messages / signaling, user plane messages / signaling, intelligent plane messages / signaling, data plane messages / signaling, computing plane messages / signaling, system messages, NAS / AS signaling, dedicated configuration messages / signaling, RRC / higher layer protocol signaling, MAC CE, access control layer signaling, open interface protocol signaling, and DCI / physical layer signaling.

[0097] In the embodiment, the first message further includes at least one of the following: a reported UE-side supported function, an available / applicable function, a supported function, a feature / feature group, and an additional condition.

[0098] The UE capability reporting in the first message also includes at least one of the following: AI / ML capabilities, positioning capabilities, beam-related capabilities, and CSI-related capabilities. UE capability reporting is associated with an associated ID. The process of the terminal sending UE capability reporting to the target device is used to support the target device in performing at least one of model identification and function identification on the terminal.

[0099] In the embodiment, if AI / ML capability information elements appear, then the UE supports AI / ML functions or the UE capability reporting includes AI / ML capability reporting or the UE supports AI / ML capability reporting.

[0100] The presence of AI / ML capability information elements indicates that these elements are enabled or defined. The AI / ML capability information element (General Parameters) can be aiML-Support-r19. The presence of aiML-Support-r19 indicates that the AI / ML capability information element is enabled or defined, suggesting that the UE supports AI / ML functionality, or that the UE's capability reporting includes AI / ML capability reporting, or that the UE supports AI / ML capability reporting.

[0101] In the embodiments, it is also included that the information elements included in the UE capability reporting are fixed or optional.

[0102] The UE capability report includes at least AI / ML capability information elements, UE-supported feature / feature group information elements, and beam management information elements. These AI / ML capability information elements, UE-supported feature / feature group information elements, and beam management information elements can be either fixed (Mandatory) or optional (Optional).

[0103] In the embodiments, at least one of the AI / ML capability information elements, UE-supported feature / feature group information elements, and beam management information elements is fixed, or at least one information element is optional, or all information elements are fixed, or all information elements are optional.

[0104] The definition of UE capability parameters follows these rules: When a UE supports AI / ML functions, the functional information elements of that terminal capability are fixed, and for specific / dedicated functional parameters, the subordinate AI / ML related feature parameters are optional.

[0105] Different terminals may have different information element configurations (optional or mandatory). For example, some terminals may have fixed information elements, while others may have optional information elements. Different terminals may send fixed or optional AI / ML related information elements in the UE capability reporting according to two methods: active and passive transmission.

[0106] For smart terminal UEs with AI wireless air interface communication capabilities, a fixed cell configuration can be used to reduce the network side's requests for related capability information to the terminal capability, making the reporting of AI-related UE capability information a mandatory option.

[0107] For ordinary terminals or low-capability terminals such as Redcap, the use of optional cell configuration can reduce the number of cells sent for UE capability reporting and reduce the signaling load per call. When the terminal has the corresponding function or the network side needs to enable AI capability, AI / ML capability is reported in the form of UE capability request.

[0108] In this embodiment, the first message further includes: an AI / ML model management information element; the AI / ML model management information element is used to indicate whether the UE supports at least one of the download, update, and deletion operations of AI / ML models.

[0109] The first message, in addition to the information elements mentioned above, also includes AI / ML model management information elements. Specifically, aiML-ModelManagement-r19 is defined as the AI / ML model management information element, used to indicate whether the UE supports at least one of the following operations: downloading, updating, and deleting AI / ML models. Specifically, the AI / ML model management information element uses binary bits or Boolean type identifiers to indicate whether it supports receiving model downloads, model updates, and model deletions from the network side.

[0110] In the embodiment, the first message further includes: AI / ML inference information element; the AI / ML inference information element is used to indicate whether the UE supports performing AI / ML inference tasks locally, and / or local deployment of AI / ML models and / or supports cloud-edge-device / edge-device / cloud-based collaborative inference.

[0111] The first message, in addition to the above information elements, also includes AI / ML inference information elements. By defining aiML-Inference-r19 as the AI / ML inference information element, it indicates whether the UE supports executing AI / ML inference tasks locally, and / or local deployment of AI / ML models, and / or supports cloud-edge / edge-device / cloud-based collaborative inference. Specifically, the AI / ML inference information element uses binary bit strings to sequentially represent whether local model deployment and cloud-edge / edge-device / cloud-based collaborative inference are supported.

[0112] In this embodiment, the first message further includes: an AI / ML data collection information element; the AI / ML data collection information element is used to indicate whether the UE supports the data acquisition function and whether it supports the collection and / or transmission of data for at least one of AI / ML training / inference / performance testing.

[0113] First Message, in addition to the above information elements, also includes AI / ML data collection information elements. By defining aiML-DataCollection-r19 as the AI / ML data collection information element, it indicates whether the UE supports data acquisition functionality and whether it supports collecting and / or transmitting data for at least one of AI / ML training / inference / performance testing. Specifically, the AI / ML data collection information element uses binary bits or an enumeration type to identify whether data acquisition functionality is supported. It uses a binary bit string or an enumeration array to indicate whether data acquisition for training, inference, and performance testing is supported; 1 indicates support, and 0 indicates no support. The string indication can distinguish that some terminals (IoT terminals, redcap terminals) support latency-insensitive data acquisition, while some latency-sensitive data acquisition, such as inference or performance testing, is not supported.

[0114] In this embodiment, the first message further includes: an AI / ML monitoring information element; the AI / ML monitoring information element is used to indicate whether the UE supports model performance monitoring and / or reporting of AI / ML model performance metrics and / or service quality.

[0115] The first message, in addition to the above information elements, also includes AI / ML monitoring information elements. By defining aiML-Monitoring-r19 as the AI / ML monitoring information element, it is used to indicate whether the UE supports model performance monitoring and / or reporting of AI / ML model performance metrics and / or Quality of Service (QoS). Specifically, the AI / ML monitoring information element uses binary bits or an enumeration type, where 0 indicates that model performance detection and reporting is not supported, and 1 indicates that model performance detection and reporting is supported.

[0116] In this embodiment, the first message further includes: an AI / ML energy-saving information element; the AI / ML energy-saving information element is used to indicate whether the UE supports using AI / ML terminal and / or network energy saving.

[0117] The first message, in addition to the above information elements, also includes an AI / ML energy-saving information element; by defining aiML-EnergySaving-r19 as the AI / ML energy-saving information element, it is used to indicate whether the UE supports using AI / ML terminal and / or network energy saving. Specifically, the AI / ML energy-saving information element uses binary bits or an enumeration type, with 1 indicating support and 0 indicating no support.

[0118] In this embodiment, the first message further includes: a terminal storage capacity information element; the terminal storage capacity information element is used to indicate the size of the storage space supporting AI model training or inference.

[0119] In addition to the information elements mentioned above, the first message also includes a terminal storage capacity information element; this element indicates the size of the storage space supporting AI model training or inference. Specifically, the terminal storage capacity information element is divided into M storage space levels using a standard predefined format, with each level representing a different storage space size.

[0120] In this embodiment, the first message further includes: a computing power capability information element; the computing power capability information element is used to indicate the amount of computing power supporting AI model training or inference.

[0121] The first message, in addition to the above information elements, also includes computing power capability information elements; these information elements indicate the amount of computing power supporting AI model training or inference. Specifically, they are divided into M computing power capability levels in a standard predefined format, with each level representing a different computing power capability; alternatively, they can be an array of multiple indices, where different elements represent heterogeneous computing power capabilities such as CPU computing power, GPU computing power, and NPU computing power.

[0122] In the embodiment, the first message further includes: AI / ML function information element; the AI / ML function information element is used to indicate the AIML function or feature / feature group supported by the UE.

[0123] The first message, in addition to the above information elements, also includes AI / ML function information elements; by defining aiML-Functionality-r19 as the AI / ML function information element, it is used to indicate the AIML functions or features / feature groups supported by the UE.

[0124] In the embodiments, when an AI / ML capability information element appears, the AI / ML capability information element or the first message includes at least one of the following: AI / ML model management information element, AI / ML inference information element, AI / ML data collection information element, AI / ML monitoring information element, AI / ML energy saving information element, terminal storage capability information element, computing power capability information element, and AI / ML function information element.

[0125] When AI / ML capability information elements appear, these elements may include at least one of the following: AI / ML model management information elements, AI / ML inference information elements, AI / ML data collection information elements, AI / ML monitoring information elements, AI / ML energy-saving information elements, terminal storage capability information elements, computing power capability information elements, and AI / ML function information elements. The functions and definitions of these elements have been described above and will not be elaborated upon further here.

[0126] Furthermore, when an AI / ML capability information element appears, the first message can be at least one of the following: AI / ML model management information element, AI / ML inference information element, AI / ML data collection information element, AI / ML monitoring information element, AI / ML energy saving information element, terminal storage capacity information element, computing power capacity information element, and AI / ML function information element. The functions and definitions of the above information elements have been described above and will not be elaborated further here.

[0127] When AI / ML capability information elements appear, the aforementioned AI / ML model management information elements, AI / ML inference information elements, AI / ML data collection information elements, AI / ML monitoring information elements, AI / ML energy saving information elements, terminal storage capability information elements, computing power capability information elements, and AI / ML function information elements can be either information elements belonging to the first message or information elements belonging to the AI / ML capability information elements.

[0128] In the embodiment, if a feature / feature group information element supported by the UE appears, then the UE supports the feature / feature group; the feature / feature group information element or the first message includes at least one of the following: AI / ML direct positioning information element, AI / ML assisted positioning information element, AI / ML beam management information element, AI / ML predicted CSI management information element, AI / ML compressed CSI feedback information element, AI / ML assisted interference prediction information element, AI / ML assisted handover optimization information element, AI / ML network status prediction information element, and AI / ML network security enhancement information element.

[0129] The appearance of a UE-supported feature / feature group information element indicates that the UE-supported feature / feature group information element is enabled or defined, and that the UE supports that feature / feature group. At this time, the feature / feature group information element or the first message includes at least one of the following: AI / ML direct positioning information element, AI / ML assisted positioning information element, AI / ML beam management information element, AI / ML predicted CSI management information element, AI / ML compressed CSI feedback information element, AI / ML assisted interference prediction information element, AI / ML assisted handover optimization information element, AI / ML network status prediction information element, and AI / ML network security enhancement information element.

[0130] In the embodiment, the feature / feature group information element supported by the UE is defined as optional or mandatory. When the information element appears present, it indicates that the UE supports the feature / feature group, which includes the relevant information element definition.

[0131] In the embodiments, at least one of the following is included: AI / ML direct positioning information element to indicate whether the UE supports direct AI / ML positioning technology; AI / ML assisted positioning information element to indicate whether the UE supports AI / ML assisted positioning technology; AI / ML beam management information element to indicate whether the UE supports AI / ML beam management technology; AI / ML predicted CSI management information element to indicate whether the UE supports AI / ML predicted CSI management technology; AI / ML compressed CSI feedback information element to indicate whether the UE supports AI / ML compressed CSI feedback technology; AI / ML assisted interference prediction information element to indicate whether the UE supports AI / ML assisted interference prediction technology; AI / ML assisted handover optimization information element to indicate whether the UE supports AI / ML assisted handover optimization technology; AI / ML network state prediction information element to indicate whether the UE supports network state prediction; and AI / ML network security enhancement information element to indicate whether the UE supports using AI / ML technology to enhance network security.

[0132] In this embodiment, aiML-DirectPositioning-r19 is defined as an AI / ML direct positioning information element to indicate whether the UE supports direct AI / ML positioning technology. The estimated UE direct position information is obtained through model training and inference deployed on the UE side or LMF side.

[0133] In this embodiment, aiML-AssistedPositioning-r19 is defined as an AI / ML-assisted positioning information element to indicate whether the UE supports AI / ML-assisted positioning technology, thereby improving the accuracy and reliability of positioning. This element can indicate the measurement quantity used in the form of a string or an array. For example, each bit of the string indicates the supported measurement quantity CIR / TOA / TDOA / Multi-RTT, etc.

[0134] In this embodiment, aiML-BeamPrediction-r19 is defined as an AI / ML beam management information element to indicate whether the UE supports AI / ML beam management technology. This allows for optimized beam selection through time-domain or spatial-domain beam prediction, reducing beam measurement and selection trials, improving signal coverage, and minimizing interference. One approach is to define separate spatial-domain beam prediction elements, time-domain beam prediction elements, and joint space-time-frequency beam prediction elements. Another approach uses a single element to indicate the supported beam prediction type through different bit positions (0 / 1) in a binary bit string or an enumerated array.

[0135] In this embodiment, aiML-PredictiveCSIManagement-r19 is defined as the AI / ML predictive CSI management information element to indicate whether the UE supports AI / ML predictive CSI (Channel State Information) management technology, so as to predict changes in channel state information in advance and improve CSI accuracy.

[0136] In the embodiment, aiML-CompressedCSIFeedback-r19 is defined as the AI / ML compressed CSI feedback information element to indicate whether the UE supports AI / ML compressed CSI feedback technology, so as to reduce feedback overhead and improve system capacity. It also includes whether it supports centralized / single-sided model training, dual-sided model training, etc. For example, a string or enumeration type is used to indicate the model training type of CSI compressed feedback supported by the terminal side.

[0137] In this embodiment, aiML-InterferencePrediction-r19 is defined as an AI / ML-assisted interference prediction information element to indicate whether the UE supports AI / ML-assisted interference prediction technology, so as to dynamically adjust the transmission strategy, reduce interference and improve signal quality.

[0138] In this embodiment, aiML-HandoverOptimization-r19 is defined as an AI / ML-assisted handover optimization information element to indicate whether the UE supports AI / ML-assisted handover optimization technology, so as to achieve smoother network handover and reduce the drop rate.

[0139] In this embodiment, aiML-NetworkPrediction-r19 is defined as an AI / ML network state prediction information element to indicate whether the UE supports network state prediction, such as traffic prediction and signal quality prediction.

[0140] In this embodiment, aiML-SecurityEnhancement-r19 is defined as an AI / ML network security enhancement information element to indicate whether the UE supports using AI / ML technology to enhance network security, such as anomaly detection and intrusion prevention.

[0141] In the embodiment, when one of the beam management conditions is met, the first message further includes at least one of the following: aperiodic beam information reporting element, beam auxiliary information reporting element, uplink / downlink beam prediction element, beam information reporting framework element, and beam scan measurement reporting element; the beam management conditions include at least one of the following: the UE capability report includes a beam management element, an AI / ML beam management element is defined / occurs, and the UE supports / has AI beam prediction capability.

[0142] When the beam management conditions are met in the UE capability reporting, including at least one of the following: beam management information element, AI / ML beam management information element being defined / occurring, and UE supporting / possessing AI beam prediction capability, the first message corresponding to the beam management conditions is satisfied, and further includes at least one of the following: aperiodic beam information reporting information element, beam auxiliary information reporting information element, uplink / downlink beam prediction information element, beam information reporting framework information element, and beam scan measurement reporting information element.

[0143] In the embodiment, when the UE capability report includes beam management-related information elements, when the UE supports / has AI beam prediction capability, or when the aiML-BeamPrediction-r19 information element is defined / occurs, the first message also includes the definition of beam management-related information elements.

[0144] The embodiment includes at least one of the following: an aperiodic beam information reporting element used to indicate whether the UE supports at least one of aperiodic beam data acquisition or beam prediction performance detection reporting; a beam auxiliary information reporting element used to indicate whether the UE supports reporting auxiliary information for model training; an uplink / downlink beam prediction element used to indicate whether the UE supports at least one of uplink / downlink beam prediction, inter-cell beam prediction, or inter-frequency beam prediction; a beam information reporting framework element used to indicate whether the UE supports beam prediction model training; and a beam scan measurement reporting element used by the UE to indicate whether it supports scanning reporting of all / partial beams, or single / dual-sided beam measurement reporting on the base station side and / or the UE side.

[0145] The aperiodic beam information reporting cell indicates whether the UE supports aperiodic beam data acquisition or beam prediction performance testing reporting. The beam auxiliary information reporting cell indicates whether the UE supports reporting auxiliary information used for model training, such as UE reception angle and UE beam direction information. The uplink / downlink beam prediction cell indicates whether the UE supports uplink / downlink beam prediction, inter-cell beam prediction, and inter-frequency beam prediction. The beam information reporting framework cell indicates whether the UE supports beam prediction model training. The beam scan measurement reporting cell is used by the UE to indicate whether it supports scanning reporting of all / partial beams and whether it supports single / dual-sided beam measurement reporting on the base station side and / or the UE side.

[0146] In the embodiment, the beam information reporting framework information element includes: the maximum number of beam measurement periodic / semi-persistent and aperiodic reporting configurations, whether auxiliary information is supported, the types and / or number of supported beam auxiliary information reports, whether data acquisition access layer and application layer caching are supported, the maximum number of (transmit / receive) beam (measurement) reports, the maximum number of (store / cachate / activate / deactivate / inactive / monitor) beam prediction models, the minimum / maximum number of beam measurements: the number of beam measurements input to the model, and at least one of the maximum / minimum number of inter-cell / inter-frequency beam reports for L1 / L3 RSRP measurements.

[0147] In this embodiment, the beam information reporting framework indicates whether the UE supports beam prediction model training. This capability signaling includes the following parameters / elements: the maximum number of beam measurement periodic / semi-persistent and aperiodic reporting configurations; whether auxiliary information is supported, the types and / or number of supported beam auxiliary information reports; whether data acquisition access layer and application layer caching are supported; the maximum number of (transmit / receive) beam (measurement) reports; the maximum number of (store / cache / activate / deactivate / inactive / monitor) beam prediction models; the minimum / maximum number of beam measurements: the number of beam measurements input to the model; and the maximum / minimum number of inter-cell / inter-frequency beam reports for L1 / L3 RSRP measurements.

[0148] In this embodiment, model management includes at least one of the following: model identification, model delivery, model deployment, model training, model activation / deactivation, and model performance testing.

[0149] When the target device performs model management based on the first message, the target device performs at least one of the following: model identification, model transfer, model deployment, model training, model activation / deactivation, and model performance testing.

[0150] In this embodiment, model management includes at least one of: AI / ML model management, AI model management, ML model management, model lifecycle management, AI management, and ML management.

[0151] When the target device performs model management based on the first message, the target device performs at least one of the following: AI / ML model management, AI model management, ML model management, model lifecycle management, AI management, and ML management.

[0152] Figure 3 This diagram illustrates a second schematic of a communication method according to an embodiment of the present disclosure, such as... Figure 3 As shown in the embodiments of this disclosure, a communication method is also provided, applied to a target device, the method comprising:

[0153] Step 302: Perform model management based on the first message included in the message or signaling sent by the terminal UE; the first message includes: UE capability reporting.

[0154] This disclosure, through its embodiments, defines how a UE transmits terminal-side AI / ML capabilities to a second device (e.g., a base station). This enables the network side to understand the support status of terminal AI / ML related functions and technical features, supports the lifecycle management of the radio interface AI / ML model, and is a foundational patent for the radio interface AI / ML technology framework. It ensures the self-organization, self-operation, self-optimization, and self-evolution of the AI / ML model on the RAN side. By defining reporting parameters related to AI beam management capabilities in AI / ML terminal capability reporting, the second device (e.g., a base station, network management system) understands the beam management functions supported by the terminal side (e.g., temporal beam prediction, spatial beam prediction), support for intra-cell / inter-cell / inter-frequency prediction, data acquisition and reporting, and cache support. This enables the AI-inherent RAN to support AI-based beam prediction technical features / functions. The AI / ML terminal capability reporting is defined to have both active and passive modes, supporting multiple reporting configurations such as periodic / semi-persistent / aperiodic, providing high flexibility.

[0155] The embodiments disclosed herein are applicable to the reporting of AI / ML terminal capabilities on the wireless side. They can be configured on the RAN side through the network side to enable the terminal to report relevant AI / ML capabilities to management network elements / layers such as base stations and network management systems, thereby facilitating the lifecycle management of AI / ML models on the wireless air interface, enabling the network to manage and control AI models, and enabling AI to intrinsically improve the performance of wireless communication systems.

[0156] This disclosure provides a method for reporting AI / ML capabilities of 5G-Advanced and 6G wireless terminals. It is applicable to 5G-A and future 6G networks, enabling base stations, wireless network management, related network elements, and AI / ML management entities / logical functions / layers to obtain specific information about the terminal's AI / ML-related functions / technical features through terminal AI / ML capability reporting. This enables wireless intelligent air interface AI / ML model lifecycle management and improves the performance of wireless communication systems through endogenous AI technology.

[0157] It should be noted that the acquisition, storage, use, and processing of data in this disclosed technical solution comply with the relevant provisions of national laws and regulations. The various types of data, such as personal identity data, operational data, and behavioral data related to individuals, customers, and groups, obtained in the embodiments of this disclosure have all been authorized.

[0158] Based on the same inventive concept, this disclosure also provides a communication device, as described in the following embodiments. Since the principle by which this device embodiment solves the problem is similar to that of the above-described method embodiments, the implementation of this device embodiment can refer to the implementation of the above-described method embodiments, and repeated details will not be elaborated further.

[0159] Figure 4 This diagram illustrates a first schematic of a communication device according to an embodiment of the present disclosure, such as... Figure 4 As shown, this device is applied to a terminal UE and includes:

[0160] The sending module 401 is used to send a message or signaling to the target device, wherein the target device performs model management according to a first message included in the message or signaling; the first message includes: UE capability reporting.

[0161] It should be noted that the sending module 401 mentioned above corresponds to S202 in the method embodiment. The examples and application scenarios implemented by the above module and the corresponding steps are the same, but are not limited to the content disclosed in the above method embodiment. It should be noted that the above module, as part of the apparatus, can be executed in a computer system such as a set of computer-executable instructions.

[0162] Based on the same inventive concept, this disclosure also provides a communication device, as described in the following embodiments. Since the principle by which this device embodiment solves the problem is similar to that of the above-described method embodiments, the implementation of this device embodiment can refer to the implementation of the above-described method embodiments, and repeated details will not be elaborated further.

[0163] Figure 5 This diagram illustrates a second schematic of a communication device according to an embodiment of the present disclosure, such as... Figure 5 As shown, the device is applied to the target equipment and includes:

[0164] The model management module 501 is used to perform model management based on a first message included in the message or signaling sent by the terminal UE; the first message includes: UE capability reporting.

[0165] It should be noted that the model management module 501 mentioned above corresponds to S302 in the method embodiment. The examples and application scenarios implemented by the above module and the corresponding steps are the same, but are not limited to the content disclosed in the above method embodiment. It should be noted that the above module, as part of the apparatus, can be executed in a computer system such as a set of computer-executable instructions.

[0166] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0167] The following reference Figure 6 To describe an electronic device 600 according to such an embodiment of the present disclosure. Figure 6 The electronic device 600 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0168] like Figure 6 As shown, the electronic device 600 is manifested in the form of a general-purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, and a bus 630 connecting different system components (including storage unit 620 and processing unit 610).

[0169] The storage unit stores program code that can be executed by the processing unit 610, causing the processing unit 610 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure. For example, the processing unit 610 can perform the following steps of the above method embodiments: applied to a terminal UE, including: sending a message or signaling to a target device, wherein the target device performs model management according to a first message included in the message or signaling; the first message includes: UE capability reporting.

[0170] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0171] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0172] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0173] Electronic device 600 can also communicate with one or more external devices 640 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. As shown, network adapter 660 communicates with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0174] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0175] In particular, according to embodiments of this disclosure, the process described above with reference to the flowchart can be implemented as a computer program product, which includes a computer program that, when executed by a processor, implements the above-described communication method.

[0176] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, which may be a readable signal medium or a readable storage medium. A program product capable of implementing the methods described above is stored thereon. In some possible implementations, various aspects of this disclosure may also be implemented as a program product including program code, which, when run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0177] More specific examples of computer-readable storage media in this disclosure may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0178] In this disclosure, a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting a program for use by or in connection with an instruction execution system, apparatus, or device.

[0179] Optionally, the program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0180] In practical implementation, program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0181] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0182] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0183] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0184] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A communication method, characterized in that, Applied to a terminal UE, the method includes: A message or signaling is sent to the target device, and the target device performs model management according to a first message included in the message or signaling; the first message includes: UE capability reporting.

2. The communication method according to claim 1, characterized in that, The UE capability reporting includes at least one of the following: AI / ML capability information elements, UE-supported feature / feature group information elements, and beam management information elements.

3. The communication method according to claim 1, characterized in that, Sending messages or signaling to the target device includes: The terminal UE sends messages or signaling to the target device.

4. The communication method according to claim 3, characterized in that, The terminal UE sends messages or signaling to the target device, including: The terminal UE actively sends messages or signaling to the target device, or; The terminal UE responds to the AI / ML capability request sent by the target device by passively sending messages or signaling to the target device.

5. The communication method according to claim 1, characterized in that, Sending messages or signaling to the target device includes: The terminal UE receives the AI / ML capability request sent by the target device; The terminal UE sends messages or signaling to the target device.

6. The communication method according to claim 1, characterized in that, The first message is at least one of user assistance information message, terminal capability information message, and location provision capability message.

7. The communication method according to claim 1, characterized in that, The target device includes at least one of the following: base station, location server, positioning management function, positioning network element, core network element, intelligent computing unit, intelligent unit, RAN AI layer, network data analysis function, centralized unit, distributed unit, wireless AI management layer, RAN AI management unit, network element or network device responsible for positioning, and AI / ML related network element or function or entity.

8. The communication method according to claim 1, characterized in that, The signaling includes at least one of the following: management plane messages / signaling, control plane messages / signaling, user plane messages / signaling, intelligent plane messages / signaling, data plane messages / signaling, computing plane messages / signaling, system messages, NAS / AS signaling, dedicated configuration messages / signaling, RRC / higher layer protocol signaling, MAC CE, access control layer signaling, open interface protocol signaling, and DCI / physical layer signaling.

9. The communication method according to claim 1, characterized in that, If an AI / ML capability information element appears, then the UE supports AI / ML functions, or the UE capability reporting includes AI / ML capability reporting, or the UE supports AI / ML capability reporting.

10. The communication method according to claim 2, characterized in that, Also includes: The information elements included in the UE capability reporting can be fixed or optional.

11. The communication method according to claim 10, characterized in that, At least one of the AI / ML capability information elements, UE-supported feature / feature group information elements, and beam management information elements is fixed, or at least one information element is optional, or all information elements are fixed, or all information elements are optional.

12. The communication method according to claim 1, characterized in that, The first message further includes: an AI / ML model management information element; the AI / ML model management information element is used to indicate whether the UE supports at least one of the operations of downloading, updating and deleting AI / ML models.

13. The communication method according to claim 1, characterized in that, The first message also includes: AI / ML inference information element; the AI / ML inference information element is used to indicate whether the UE supports performing AI / ML inference tasks locally, and / or local deployment of AI / ML models and / or supports cloud-edge-device / edge-device / cloud-based collaborative inference.

14. The communication method according to claim 1, characterized in that, The first message also includes: an AI / ML data collection information element; the AI / ML data collection information element is used to indicate whether the UE supports data acquisition function and whether it supports collecting and / or transmitting data for at least one of AI / ML training / inference / performance testing.

15. The communication method according to claim 1, characterized in that, The first message also includes: an AI / ML monitoring information element; the AI / ML monitoring information element is used to indicate whether the UE supports model performance monitoring and / or reporting of AI / ML model performance metrics and / or service quality.

16. The communication method according to claim 1, characterized in that, The first message also includes: AI / ML energy-saving information element; the AI / ML energy-saving information element is used to indicate whether the UE supports using AI / ML terminal and / or network energy saving.

17. The communication method according to claim 1, characterized in that, The first message also includes: a terminal storage capacity information element; the terminal storage capacity information element is used to indicate the size of the storage space supporting AI model training or inference.

18. The communication method according to claim 1, characterized in that, The first message also includes: a computing power capability information element; the computing power capability information element is used to indicate the amount of computing power supporting AI model training or inference.

19. The communication method according to claim 1, characterized in that, The first message also includes: AI / ML function information element; the AI / ML function information element is used to indicate the AIML function or feature / feature group supported by the UE.

20. The communication method according to claim 1, characterized in that, When an AI / ML capability information element appears, the AI / ML capability information element or the first message includes at least one of the following: AI / ML model management information element, AI / ML inference information element, AI / ML data collection information element, AI / ML monitoring information element, AI / ML energy saving information element, terminal storage capability information element, computing power capability information element, and AI / ML function information element.

21. The communication method according to claim 1, characterized in that, If the feature / feature group information element supported by the UE appears, then the UE supports the feature / feature group; The feature / feature group information element or the first message includes at least one of the following: AI / ML direct positioning information element, AI / ML assisted positioning information element, AI / ML beam management information element, AI / ML predicted CSI management information element, AI / ML compressed CSI feedback information element, AI / ML assisted interference prediction information element, AI / ML assisted handover optimization information element, AI / ML network status prediction information element, and AI / ML network security enhancement information element.

22. The communication method according to claim 21, characterized in that, Includes at least one of the following: the AI / ML direct positioning information element indicates whether the UE supports direct AI / ML positioning technology; the AI / ML assisted positioning information element indicates whether the UE supports AI / ML assisted positioning technology; the AI / ML beam management information element indicates whether the UE supports AI / ML beam management technology; the AI / ML predicted CSI management information element indicates whether the UE supports AI / ML predicted CSI management technology; the AI / ML compressed CSI feedback information element indicates whether the UE supports AI / ML compressed CSI feedback technology; the AI / ML assisted interference prediction information element indicates whether the UE supports AI / ML assisted interference prediction technology; the AI / ML assisted handover optimization information element indicates whether the UE supports AI / ML assisted handover optimization technology; the AI / ML network state prediction information element indicates whether the UE supports network state prediction; and the AI / ML network security enhancement information element indicates whether the UE supports using AI / ML technology to enhance network security.

23. The communication method according to claim 1, characterized in that, When one of the beam management conditions is met, the first message further includes at least one of the following: aperiodic beam information reporting element, beam assist information reporting element, uplink / downlink beam prediction element, beam information reporting framework element, and beam scan measurement reporting element; the beam management conditions include at least one of the following: the UE capability reporting includes a beam management element, AI / ML beam management element is defined / occurs, and the UE supports / has AI beam prediction capability.

24. The communication method according to claim 23, characterized in that, Including at least one of the following: the aperiodic beam information reporting element is used to indicate whether the UE supports at least one of aperiodic beam data acquisition or beam prediction performance detection reporting; the beam auxiliary information reporting element is used to indicate whether the UE supports the reporting of auxiliary information for model training. The uplink / downlink beam prediction information element is used to indicate whether the UE supports at least one of uplink / downlink beam prediction, inter-cell beam prediction, and inter-frequency beam prediction; the beam information reporting framework information element is used to indicate whether the UE supports beam prediction model training; the beam scan measurement reporting information element is used by the UE to indicate whether it supports scanning reporting of all / partial beams, and whether it supports single / dual-sided beam measurement reporting on the base station side and / or the UE side.

25. The communication method according to claim 24, characterized in that, The beam information reporting framework information element includes: the maximum number of beam measurement periodic / semi-persistent and aperiodic reporting configurations, whether auxiliary information is supported, the types and / or number of supported beam auxiliary information reports, whether data acquisition access layer and application layer caching are supported, the maximum number of (transmit / receive) beam (measurement) reports, the maximum number of (store / cachate / activate / deactivate / inactive / monitor) beam prediction models, the minimum / maximum number of beam measurements: the number of beam measurements input to the model, and at least one of the maximum / minimum number of inter-cell / inter-frequency beam reports for L1 / L3 RSRP measurements.

26. The communication method according to claim 1, characterized in that, Model management includes at least one of the following: model identification, model delivery, model deployment, model training, model activation / deactivation, and model performance testing.

27. The communication method according to claim 1, characterized in that, Model management includes at least one of the following: AI / ML model management, AI model management, ML model management, model lifecycle management, AI management, and ML management.

28. A communication method, characterized in that, Applied to a target device, the method includes: Model management is performed based on a first message included in the message or signaling sent by the terminal UE; the first message includes: UE capability reporting.

29. A communication device, characterized in that, The device, applied to a terminal UE, includes: The sending module is used to send messages or signaling to the target device, wherein the target device performs model management according to a first message included in the message or signaling; the first message includes: UE capability reporting.

30. A communication device, characterized in that, Applied to a target device, the device includes: The model management module is used to perform model management based on a first message included in the message or signaling sent by the terminal UE; the first message includes: UE capability reporting.

31. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the communication method according to any one of claims 1 to 27 or claim 28 by executing the executable instructions.

32. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the communication method according to any one of claims 1 to 27 or claim 28.

33. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the communication method according to any one of claims 1 to 27 or claim 28.

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