Method and device for node used for wireless communication

Abstract

The present application discloses a method and device for a node used for wireless communication. In response to any condition in a first condition set being satisfied, a first node sends first signaling, wherein the first signaling comprises capability information of the first node, the capability information of the first node comprises a first-type identifier list, and any first-type identifier in the first-type identifier list indicates a trainable model. One condition in the first condition set is that at least one first-type identifier currently supported by the first node does not belong to the most recently reported first-type identifier list, and the first-type identifier list in the first signaling comprises the at least one first-type identifier currently supported by the first node. The present application can optimize the management of capability information, enhance the flexibility of reporting capability information, and improve the overall performance of a system.

Description

A method and device used in a node for wireless communication

[0001] This application claims priority to a Chinese patent application filed with the State Intellectual Property Office of China on January 11, 2024, with application number 202410044455.7 and invention name “A method and device in a node used for wireless communication”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to a transmission method and apparatus in a wireless communication system, and in particular to a scheme and apparatus related to UE (User Equipment) capability management in a wireless communication system. Background Art

[0003] The application scenarios of future wireless communication systems are becoming increasingly diverse, and different scenarios place varying performance requirements on the systems. To meet these diverse performance demands, the 3GPP (3rd Generation Partner Project) RAN (Radio Access Network) plenary meeting #72 decided to conduct research on New Radio (NR) (or 5G). The 3GPP RAN plenary meeting #75 approved the WI (Work Item) for New Radio, initiating standardization work on NR.

[0004] In cellular systems such as LTE (Long Term Evolution) and NR (New Radio), network devices such as base stations or core network devices send capability query commands to user equipment (UE). The UE then sends capability information to the network device. The network device respects the received UE capability information when configuring or scheduling the UE.

[0005] As wireless communications evolve towards intelligence, artificial intelligence (AI) and machine learning (ML) are being introduced and integrated by 3GPP. They can be used to improve network performance and enhance user experience, and are considered key technologies for 5G-Advanced research and one of the core visions of 6G. At the 3GPP RAN#88e meeting and the 3GPP Rel-18 workshop, the application of AI / ML in the physical layer of wireless communication systems received extensive attention and discussion. In NR R (release) 18, CSI feedback enhancement, beam management, and positioning accuracy enhancement based on AI / ML were established as projects. Summary of the Invention

[0006] To support AI / ML-based technologies in wireless communication systems, UE (User Equipment) must possess corresponding capabilities. Research has found that managing UE (User Equipment) capabilities is a key issue when AI / ML is introduced into wireless communication systems. This application discloses a solution to this technical problem. It should be noted that in the description of this application, the NR (New Radio) system is used as an example. This application is also applicable to scenarios such as future 6G systems, achieving technical effects similar to those of the NR system. In addition, this application only uses the AI / ML technology scenario as a typical application scenario or example. This application can also be applied to other non-AI / ML technology scenarios, such as traditional CSI feedback and beam management. Furthermore, adopting a unified design solution for different scenarios (including but not limited to AI / ML-based CSI feedback, traditional CSI feedback, AI / ML-based beam management, traditional beam management, AI / ML-based technology scenarios, other non-AI / ML technology scenarios, capacity enhancement systems, short-range communication systems, unlicensed spectrum communications, IoT (Internet of Things), URLLC (Ultra Reliable Low Latency Communication) networks, and Internet of Vehicles, etc.) can also help reduce hardware complexity and cost. In the absence of conflict, the embodiments and features of any node in this application can be applied to any other node. In the absence of conflict, the embodiments and features of the embodiments of this application can be combined with each other in any way.

[0007] In particular, the interpretation of terminology, nouns, functions, and variables in this application (unless otherwise specified) may refer to the definitions in the 3GPP specification protocols TS36 series, TS38 series, and TS37 series. If necessary, reference may be made to 3GPP standards TS38.211, TS38.212, TS38.213, TS38.214, TS38.215, TS38.321, TS38.331, TS38.305, TS38.304, and TS37.355 to assist in understanding this application.

[0008] The present application discloses a method in a first node used for wireless communication, characterized by comprising:

[0009] In response to any condition in the first set of conditions being met, sending first signaling;

[0010] In which, the first signaling includes capability information of the first node, the capability information of the first node includes a first-class identifier list, and any first-class identifier in the first-class identifier list indicates a trainable model; one of the conditions in the first condition set is that at least one first-class identifier currently supported by the first node does not belong to the most recently reported first-class identifier list; the first-class identifier list in the first signaling includes at least one first-class identifier currently supported by the first node.

[0011] As an embodiment, the problem to be solved by this application includes: how to manage the capabilities of UE (User Equipment).

[0012] As an embodiment, the problem to be solved by the present application includes: how to determine the timing and conditions for UE (User Equipment) capability reporting; in the above method, by introducing and designing a first condition set, any condition contained therein is satisfied as a trigger for UE (User Equipment) capability reporting, thereby solving the above problem.

[0013] As an embodiment, the problem to be solved by this application includes: how to determine whether the AI / ML function and / or model reported by the user is available; in the above method, whether the AI / ML function and / or model is available depends on the content reported by the UE (User Equipment) capability, which solves the above problem.

[0014] As an embodiment, the essence of the above method includes: in addition to being requested to report, UE (User Equipment) capabilities also support active reporting, and can adopt event triggering and other methods; for example, when the UE's own capabilities or other devices or conditions associated with the UE change, the UE actively reports the user equipment capabilities.

[0015] As an embodiment, the essence of the above method includes: whether certain user equipment capabilities reported by the UE, such as AI / ML functions and / or models, are available depends on certain other user equipment capabilities reported by the UE, that is, there is a dependency relationship between certain user equipment capabilities; for example, the prerequisite for the availability of capability A is that capability B is supported.

[0016] As an embodiment, the essence of the above method includes: whether the AI / ML function and / or model is available depends on certain other user equipment capabilities reported by the UE and certain parameters related to the AI / ML function and / or model.

[0017] As an embodiment, the benefits of the above method include: enhancing the flexibility of UE (User Equipment) capability reporting.

[0018] As an embodiment, the benefits of the above method include: adapting to more complex network environments and application scenarios.

[0019] As an embodiment, the benefits of the above method include: better support for AI / ML functions and / or models, and improved performance of AI / ML technology.

[0020] As an embodiment, the benefits of the above method include: improving the accuracy and timeliness of information of the user equipment and its associated network equipment.

[0021] As an embodiment, the above method has the following benefits: good forward and backward compatibility.

[0022] As an embodiment, the benefits of the above method include: simplifying system design and reducing implementation complexity.

[0023] As an embodiment, the benefits of the above method include: improving system flexibility and overall performance.

[0024] According to one aspect of the present application, one of the conditions in the first condition set is:

[0025] Second signaling is received, where the second signaling requests capability information of the first node.

[0026] As an embodiment, the benefits of the above method include: enhancing the flexibility and forward compatibility of the system.

[0027] According to one aspect of the present application, one of the conditions in the first condition set is that at least one first-category identifier currently supported by the first node does not belong to a most recently reported first-category identifier list, including at least one of the following:

[0028] After the most recently reported first category identifier list, the application server of the first node is replaced;

[0029] After the most recently reported first category identifier list, the first node receives signaling from an application server, where the signaling from the application server indicates the first category identifiers currently supported by the first node.

[0030] As an embodiment, the benefits of the above method include: providing more possibilities and supporting different network environments and application scenarios.

[0031] As an embodiment, the benefits of the above method include: enhancing the reliability and robustness of the system.

[0032] As an embodiment, the benefits of the above method include: enhancing the flexibility and backward compatibility of the system.

[0033] According to one aspect of the present application, it is characterized in that whether the trainable model indicated by at least one first category identifier in the first category identifier list is available for the first frequency band depends on the codebook parameters on the first frequency band indicated by the first signaling.

[0034] As an embodiment, the essence of the above method includes: in the CSI feedback enhancement scenario, whether the AI / ML function and / or model is available depends on the MIMO codebook capability supported by the user.

[0035] As an embodiment, the benefits of the above method include: improving the flexibility of UE (User Equipment) capability management.

[0036] As an embodiment, the benefits of the above method include: enhancing the reliability and stability of the system.

[0037] As an embodiment, the benefits of the above method include: it is possible to more reasonably select and activate AI / ML functions and / or models, thereby improving the performance of AI / ML functions and / or models.

[0038] According to one aspect of the present application, it is characterized in that when the codebook parameters on the first frequency band indicated by the first signaling do not include a first type of codebook, the trainable model indicated by the at least one first type identifier in the first type identifier list is not available for the first frequency band.

[0039] As an embodiment, the essence of the above method includes: in the CSI feedback enhancement scenario, the AI / ML function and / or model is only available when the MIMO codebook capability supported by the user includes certain specific codebook types.

[0040] As an embodiment, the benefits of the above method include: improving the flexibility of UE (User Equipment) capability management.

[0041] As an embodiment, the benefits of the above method include: enhancing the reliability and stability of the system.

[0042] As an embodiment, the benefits of the above method include: it is possible to more reasonably select and activate AI / ML functions and / or models, thereby improving the performance of AI / ML functions and / or models.

[0043] According to one aspect of the present application, it is characterized in that whether the trainable model indicated by at least one first category identifier in the first category identifier list is available for the first frequency band depends on the first beam-related parameter on the first frequency band indicated by the first signaling.

[0044] As an embodiment, the essence of the above method includes: in the beam management scenario, whether the AI / ML function and / or model is available depends on the parameter capabilities related to beam management supported by the user.

[0045] As an embodiment, the benefits of the above method include: improving the flexibility of UE (User Equipment) capability management.

[0046] As an embodiment, the benefits of the above method include: enhancing the reliability and stability of the system.

[0047] As an embodiment, the benefits of the above method include: it is possible to more reasonably select and activate AI / ML functions and / or models, thereby improving the performance of AI / ML functions and / or models.

[0048] According to one aspect of the present application, it is characterized in that when the first beam-related parameters on the first frequency band indicated by the first signaling do not include the second parameter, the trainable model indicated by the at least one first category identifier in the first category identifier list is not available for the first frequency band.

[0049] As an embodiment, the essence of the above method includes: in the beam management scenario, the AI / ML function and / or model is only available when the parameters of the AI / ML function and / or model and the user-supported parameters related to beam management satisfy a certain specific relationship.

[0050] As an embodiment, the benefits of the above method include: improving the flexibility of UE (User Equipment) capability management.

[0051] As an embodiment, the benefits of the above method include: enhancing the reliability and stability of the system.

[0052] As an embodiment, the benefits of the above method include: it is possible to more reasonably select and activate AI / ML functions and / or models, thereby improving the performance of AI / ML functions and / or models.

[0053] The present application discloses a method used in a second node of wireless communication, characterized by comprising:

[0054] receiving a first signaling;

[0055] In which, as a response to any condition in the first condition set being met, the sender of the first signaling sends the first signaling; the first signaling includes capability information of the first node, the capability information of the first node includes a first-class identifier list, and any first-class identifier in the first-class identifier list indicates a trainable model; one condition in the first condition set is that at least one first-class identifier currently supported by the first node does not belong to the most recently reported first-class identifier list; the first-class identifier list in the first signaling includes at least one first-class identifier currently supported by the first node.

[0056] According to one aspect of the present application, one of the conditions in the first condition set is:

[0057] A second signaling is sent, where the second signaling requests capability information of the first node; and a receiver of the second signaling receives the second signaling.

[0058] According to one aspect of the present application, one of the conditions in the first condition set is that at least one first-category identifier currently supported by the first node does not belong to a most recently reported first-category identifier list, including at least one of the following:

[0059] After the most recently reported first category identifier list, the application server of the first node is replaced;

[0060] After the most recently reported first category identifier list, the first node receives signaling from an application server, where the signaling from the application server indicates the first category identifier currently supported by the first node;

[0061] According to one aspect of the present application, it is characterized in that whether the trainable model indicated by at least one first category identifier in the first category identifier list is available for the first frequency band depends on the codebook parameters on the first frequency band indicated by the first signaling.

[0062] According to one aspect of the present application, it is characterized in that when the codebook parameters on the first frequency band indicated by the first signaling do not include a first type of codebook, the trainable model indicated by the at least one first type identifier in the first type identifier list is not available for the first frequency band.

[0063] According to one aspect of the present application, it is characterized in that whether the trainable model indicated by at least one first category identifier in the first category identifier list is available for the first frequency band depends on the first beam-related parameter on the first frequency band indicated by the first signaling.

[0064] According to one aspect of the present application, it is characterized in that when the first beam-related parameters on the first frequency band indicated by the first signaling do not include the second parameter, the trainable model indicated by the at least one first category identifier in the first category identifier list is not available for the first frequency band.

[0065] The present application discloses a first node used for wireless communication, characterized by comprising:

[0066] The first transmitter sends a first signaling in response to any condition in the first condition set being met;

[0067] In which, the first signaling includes capability information of the first node, the capability information of the first node includes a first-class identifier list, and any first-class identifier in the first-class identifier list indicates a trainable model; one of the conditions in the first condition set is that at least one first-class identifier currently supported by the first node does not belong to the most recently reported first-class identifier list; the first-class identifier list in the first signaling includes at least one first-class identifier currently supported by the first node.

[0068] The present application discloses a second node used for wireless communication, characterized by comprising:

[0069] A second receiver receives the first signaling;

[0070] In which, as a response to any condition in the first condition set being met, the sender of the first signaling sends the first signaling; the first signaling includes capability information of the first node, the capability information of the first node includes a first-class identifier list, and any first-class identifier in the first-class identifier list indicates a trainable model; one condition in the first condition set is that at least one first-class identifier currently supported by the first node does not belong to the most recently reported first-class identifier list; the first-class identifier list in the first signaling includes at least one first-class identifier currently supported by the first node.

[0071] As an example, compared with traditional solutions, this application has the following advantages:

[0072] - Enhanced flexibility in UE (User Equipment) capability reporting;

[0073] -Adapt to more complex network environments and application scenarios;

[0074] - Better support for AI / ML functions and / or models, and improved performance of AI / ML technologies;

[0075] - Improve the accuracy and reliability of information about user devices and their associated network devices;

[0076] -Good forward and backward compatibility, with minimal changes to the standard;

[0077] -Simplify system design and reduce implementation complexity;

[0078] - Enhance the robustness, stability and reliability of the system;

[0079] - Improve system flexibility and overall performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] FIG1 shows a flowchart of a first signaling according to an embodiment of the present application;

[0081] FIG2 shows a schematic diagram of a network architecture according to an embodiment of the present application;

[0082] FIG3 is a schematic diagram showing an embodiment of a radio protocol architecture of a user plane and a control plane according to an embodiment of the present application;

[0083] FIG4 shows a schematic diagram of a first communication device and a second communication device according to an embodiment of the present application;

[0084] FIG5 shows a flow chart of wireless transmission according to an embodiment of the present application;

[0085] FIG6 shows a schematic diagram of second signaling according to an embodiment of the present application;

[0086] FIG7 is a schematic diagram showing one condition in the first condition set according to an embodiment of the present application;

[0087] FIG8 shows a schematic diagram of codebook parameters indicated by the first signaling according to an embodiment of the present application;

[0088] FIG9 shows a schematic diagram of a first type of codebook according to an embodiment of the present application;

[0089] FIG10 is a schematic diagram showing a first beam-related parameter indicated by the first signaling according to an embodiment of the present application;

[0090] FIG11 is a schematic diagram showing a relationship between the second parameter and the first beam-related parameter indicated by the first signaling according to an embodiment of the present application;

[0091] FIG12 shows a schematic diagram of a system of trainable models according to one embodiment of the present application;

[0092] FIG13 shows a structural block diagram of a processing device used in a first node according to an embodiment of the present application;

[0093] FIG14 shows a structural block diagram of a processing device used in a second node according to an embodiment of the present application. DETAILED DESCRIPTION

[0094] The technical solutions of this application will be further described below in conjunction with the accompanying drawings. It should be noted that the embodiments and features of the embodiments of this application may be arbitrarily combined with each other, provided that no conflicts exist. Based on considerations such as flexibility, complexity, overhead, and compatibility, those skilled in the art are motivated to flexibly combine the embodiments in different drawings, provided that no conflicts exist.

[0095] Example 1

[0096] Embodiment 1 illustrates a flowchart of a first signaling according to an embodiment of the present application, as shown in FIG1. ​​In 100 shown in FIG1, each box represents a step.

[0097] In Example 1, the first node in the present application sends a first signaling in step 101 as a response to any condition in a first condition set being met; wherein, the first signaling includes capability information of the first node, the capability information of the first node includes a first-category identifier list, and any first-category identifier in the first-category identifier list indicates a trainable model; one of the conditions in the first condition set is that at least one first-category identifier currently supported by the first node does not belong to the most recently reported first-category identifier list; the first-category identifier list in the first signaling includes at least one first-category identifier currently supported by the first node.

[0098] As an embodiment, the first node is a UE (User Equipment).

[0099] As an embodiment, the first node includes a UE and an application server.

[0100] As an embodiment, the first node includes a UE and an OTT (over-the-top) server.

[0101] As an embodiment, the first node includes a UE and a device on the UE side.

[0102] As an embodiment, the first node is in an RRC (Radio Resource Control) connected state.

[0103] As an embodiment, the first node is in a non-RRC (Radio Resource Control) connection state.

[0104] As an embodiment, the second node is a BS (Base Station) device.

[0105] As an embodiment, the second node is a network-side device.

[0106] As an embodiment, the second node is an application server.

[0107] As an embodiment, the second node is an OTT (over-the-top) server.

[0108] As an embodiment, the second node includes a BS (Base Station) device and a network side device.

[0109] As an embodiment, the second node includes a BS (Base Station) device and an application server.

[0110] As an embodiment, the second node includes a BS (Base Station) device and an OTT (over-the-top) server.

[0111] As an embodiment, the second node includes a BS (Base Station) device and a core network function.

[0112] As an embodiment, the present application is directed to NR.

[0113] As an embodiment, the present application is directed to wireless communication networks after NR.

[0114] As an embodiment, the individual content of an IE (Information Element) is called a domain.

[0115] As an embodiment, the first signaling is transmitted via an uplink (UL).

[0116] As an embodiment, the first signaling is transmitted via a side link (Sidelink, SL).

[0117] As an embodiment, the first signaling is air interface signaling.

[0118] As an embodiment, the first signaling is UE specific signaling.

[0119] As an embodiment, the first signaling is configured per frequency band.

[0120] As an embodiment, the first signaling is not configured per frequency band.

[0121] As an embodiment, the first signaling is configured per UE.

[0122] As an embodiment, the first signaling is configured per RAT (Radio Access Technology).

[0123] As an embodiment, the first signaling is sent via a DCCH (Dedicated Control Channel).

[0124] As an embodiment, the first signaling is sent via SRB1 (Signalling Radio Bearer 1).

[0125] As an embodiment, the first signaling is sent via SRB3 (Signalling Radio Bearer 3).

[0126] As an embodiment, the first signaling is sent via PUSCH (Physical Uplink Shared Channel).

[0127] As an embodiment, the first signaling is sent via UL-SCH (Uplink Shared Channel).

[0128] As an embodiment, the first signaling is RRC (Radio Resource Control) signaling.

[0129] As an embodiment, the first signaling is physical layer signaling.

[0130] As an embodiment, the first signaling is MAC layer signaling.

[0131] As an embodiment, the first signaling is higher layer signaling.

[0132] As an embodiment, the first signaling is non-access stratum (NAS) signaling.

[0133] As an embodiment, the first signaling is uplink signaling.

[0134] As an embodiment, the first signaling is UECapabilityInformation signaling.

[0135] As an embodiment, the first signaling includes a UECapabilityInformation message.

[0136] As an embodiment, the first signaling block includes part or all of the fields in IE UECapabilityInformation.

[0137] As an embodiment, the first signaling is UE-NR-Capability signaling.

[0138] As an embodiment, the first signaling includes a UE-NR-Capability message.

[0139] As an embodiment, the first signaling block includes part or all of the fields in IE UE-NR-Capability.

[0140] As an embodiment, the first signaling is UEInformationResponse signaling.

[0141] As an embodiment, the first signaling includes a UEInformationResponse message.

[0142] As an embodiment, the first signaling block includes part or all of the fields in IE UEInformationResponse.

[0143] As an embodiment, the first signaling is ProvideCapabilities signaling.

[0144] As an embodiment, the first signaling includes a ProvideCapabilities message.

[0145] As an embodiment, the first signaling block includes part or all of the fields in IE ProvideCapabilities.

[0146] As an embodiment, the first signaling is feedback for a downlink signaling.

[0147] As an embodiment, the first signaling is feedback for a downlink RRC signaling.

[0148] As an embodiment, the first signaling is sent proactively and is not a feedback for a downlink signaling.

[0149] As an embodiment, the first signaling is sent to a location service center.

[0150] As an embodiment, the first signaling includes capability information of the first node.

[0151] As an embodiment, the first signaling includes all capability information of the first node.

[0152] As an embodiment, the first signaling includes partial capability information of the first node.

[0153] As an embodiment, the first signaling explicitly indicates capability information of the first node.

[0154] As an embodiment, the first signaling implicitly indicates capability information of the first node.

[0155] As an embodiment, the first signaling is an IE (Information Element).

[0156] As an embodiment, the first signaling includes at least one IE (Information Element).

[0157] As an embodiment, the first signaling includes at least one field.

[0158] As an embodiment, the first signaling includes at least BandCombination.

[0159] As an embodiment, the first signaling includes at least one FeatureSetCombination.

[0160] As an embodiment, the first signaling includes the first category identifier list.

[0161] As an embodiment, the content of a field of the first signaling includes the first category identifier list.

[0162] As an embodiment, the contents of the multiple fields of the first signaling include the first category identifier list.

[0163] As an embodiment, the first signaling includes at least one of the first category identifiers.

[0164] As an embodiment, the first signaling includes information about the trainable model that the first node can support.

[0165] As an embodiment, the capability information of the first node is the capability information carried by UECapabilityInformation.

[0166] As an embodiment, the capability information of the first node includes capability information other than UECapabilityInformation.

[0167] As an embodiment, the capability information of the first node includes at least one capability of the first node.

[0168] As an embodiment, the capability information of the first node includes information of at least two capabilities of the first node.

[0169] As an embodiment, the capability information of the first node includes hardware capabilities.

[0170] As an embodiment, the capability information of the first node includes UE radio access capability parameters.

[0171] As an embodiment, the capability information of the first node includes features other than UE radio access capability parameters.

[0172] As an embodiment, the capability information of the first node includes the maximum data rate supported by uplink and / or downlink.

[0173] As an embodiment, the capability information of the first node includes supported transport block sizes.

[0174] As an embodiment, the capability information of the first node includes supported modulation modes.

[0175] As an embodiment, the capability information of the first node includes RLC (Radio Link Control, wireless link layer control protocol) parameters.

[0176] As an embodiment, the capability information of the first node includes MAC (Medium Access Control) parameters.

[0177] As an embodiment, the capability information of the first node includes physical layer parameters.

[0178] As an embodiment, the capability information of the first node includes measurement and mobility parameters.

[0179] As an embodiment, the capability information of the first node includes parameters of inter-radio access technologies (inter RAT).

[0180] As an embodiment, the capability information of the first node includes IMS (IP Multimedia Subsystem) parameters.

[0181] As an embodiment, the capability information of the first node includes the RRC cache size.

[0182] As an embodiment, the capability information of the first node includes self-organizing network parameters.

[0183] As an embodiment, the capability information of the first node includes a performance measurement parameter based on the UE.

[0184] As an embodiment, the capability information of the first node includes a high-speed parameter.

[0185] As an embodiment, the capability information of the first node includes application layer measurement parameters.

[0186] As an embodiment, the capability information of the first node includes a capability reduction parameter.

[0187] As an embodiment, the capability information of the first node includes PWS (public warning system) characteristics.

[0188] As an embodiment, the capability information of the first node includes UE receiver characteristics.

[0189] As an embodiment, the capability information of the first node includes the number of supported cells.

[0190] As an embodiment, the capability information of the first node includes the number of supported cells.

[0191] As an embodiment, the capability information of the first node includes supported frequency and / or bandwidth.

[0192] As an embodiment, the capability information of the first node includes supported frequency bands.

[0193] As an embodiment, the capability information of the first node includes parameters related to the RRC connection.

[0194] As an embodiment, the capability information of the first node includes RRM (radio resource management) measurement characteristics.

[0195] As an embodiment, the capability information of the first node includes an extended DRX (Discontinuous Reception) feature.

[0196] As an embodiment, the capability information of the first node includes MBS (multicast broadcast service) characteristics.

[0197] As an embodiment, the capability information of the first node includes MDT (Minimization of Drive Tests) and SON (self-organized network) characteristics.

[0198] As an embodiment, the capability information of the first node includes AI / ML characteristics.

[0199] As an embodiment, the capability information of the first node includes AI / ML parameters.

[0200] As an embodiment, the capability information of the first node includes supported AI / ML functions.

[0201] As an embodiment, the capability information of the first node includes supported AI / ML models.

[0202] As an embodiment, the capability information of the first node includes supported AI / ML functions and their models.

[0203] As an embodiment, the capability information of the first node includes the category of supported AI / ML models.

[0204] As an embodiment, the capability information of the first node includes the number of supported AI / ML models.

[0205] As an embodiment, the capability information of the first node includes the training method of the supported AI / ML model.

[0206] As an embodiment, the capability information of the first node includes a monitoring method of a supported AI / ML model.

[0207] As an embodiment, the capability information of the first node includes the ability to adjust and optimize the AI / ML model.

[0208] As an embodiment, the capability information of the first node includes supported data collection methods.

[0209] As an embodiment, the capability information of the first node includes parameters related to AI / ML characteristics.

[0210] As an embodiment, the capability information of the first node includes other characteristics in addition to the above characteristics.

[0211] As an embodiment, the capability information of the first node includes multiple sub-capability information, the multiple sub-capability information are respectively for multiple frequency bands, and the first frequency band is any frequency band among the multiple frequency bands.

[0212] As an embodiment, the trainable model is an AI model.

[0213] As an embodiment, the trainable model is an ML model.

[0214] As an embodiment, the trainable model is a nonlinear model.

[0215] As an embodiment, the trainable model is obtained based on training.

[0216] As an embodiment, the trainable model is a neural network model.

[0217] As an embodiment, the trainable model is based on a neural network.

[0218] As an embodiment, the trainable model is a feedforward neural network model.

[0219] As an embodiment, the trainable model is based on a feedforward neural network.

[0220] As an embodiment, the trainable model is a CNN (Conventional Neural Networks) model.

[0221] As an embodiment, the trainable model is based on CNN (Conventional Neural Networks).

[0222] As an embodiment, the trainable model is an RNN (Recurrent Neural Network) model.

[0223] As an embodiment, the trainable model is based on RNN (Recurrent Neural Network).

[0224] As an embodiment, the trainable model is a transformer neural network model.

[0225] As an embodiment, the trainable model is based on a transformer neural network.

[0226] As an embodiment, the trainable model is a GAN (Generative Adversarial Network) model.

[0227] As an embodiment, the trainable model is based on GAN (Generative Adversarial Network).

[0228] As an embodiment, the trainable model is an AE (autoencoder) model.

[0229] As an embodiment, the trainable model is based on AE (autoencoder).

[0230] As an embodiment, the trainable model is obtained by training on the UE side.

[0231] As a sub-embodiment of the above embodiment, the trainable model is obtained by training in the UE.

[0232] As a sub-embodiment of the above embodiment, the trainable model is obtained by training in a device on the UE side.

[0233] As a sub-embodiment of the above embodiment, the trainable model is obtained by training in an application server on the UE side.

[0234] As a sub-embodiment of the above embodiment, the trainable model is obtained by training in an OTT (over-the-top) server on the UE side.

[0235] As a sub-embodiment of the above embodiment, the trainable model is obtained by training in the UE and the device on the UE side.

[0236] As an embodiment, the trainable model is obtained by training on the network side.

[0237] As an embodiment, the trainable model is obtained by training on the BS (base station, BS) side.

[0238] As a sub-embodiment of the above embodiment, the trainable model is obtained by training in the BS.

[0239] As a sub-embodiment of the above embodiment, the trainable model is obtained by training in a device on the network side.

[0240] As a sub-embodiment of the above embodiment, the trainable model is obtained by training in an application server on the network side.

[0241] As a sub-embodiment of the above embodiment, the trainable model is obtained by training in an OTT (over-the-top) server on the network side.

[0242] As a sub-embodiment of the above embodiment, the trainable model is obtained by training in the BS and network-side devices.

[0243] As a sub-embodiment of the above embodiment, the trainable model is obtained by training in a location service center.

[0244] As an embodiment, the trainable model is obtained by joint training on the UE side and the network side.

[0245] As an embodiment, a part of the trainable model is obtained by training on the UE side, and the other part is obtained by training on the network side.

[0246] As an embodiment, the trainable model is deployed on the UE side.

[0247] As a sub-embodiment of the above embodiment, the trainable model is deployed in the UE.

[0248] As a sub-embodiment of the above embodiment, the trainable model is deployed in a device on the UE side.

[0249] As a sub-embodiment of the above embodiment, the trainable model is deployed in an application server on the UE side.

[0250] As a sub-embodiment of the above embodiment, the trainable model is deployed in an OTT (over-the-top) server on the UE side.

[0251] As an embodiment, the trainable model is deployed on the BS side.

[0252] As an embodiment, the trainable model is deployed on the network side.

[0253] As a sub-embodiment of the above embodiment, the trainable model is deployed in the BS.

[0254] As a sub-embodiment of the above embodiment, the trainable model is deployed in a device on the network side.

[0255] As a sub-embodiment of the above embodiment, the trainable model is deployed in an application server on the network side.

[0256] As a sub-embodiment of the above embodiment, the trainable model is deployed in an OTT (over-the-top) server on the network side.

[0257] As a sub-embodiment of the above embodiment, the trainable model is deployed in a location service center.

[0258] As an embodiment, the trainable model is deployed on both the UE side and the network side.

[0259] As an embodiment, a part of the trainable model is deployed on the UE side, and the other part is deployed on the network side.

[0260] As an embodiment, the trainable model is transferred from the first node to the second node.

[0261] As an embodiment, a partial model of the trainable model is transferred from the first node to the second node.

[0262] As an embodiment, the parameters of the trainable model are passed from the first node to the second node.

[0263] As an embodiment, some parameters of the trainable model are transferred from the first node to the second node.

[0264] As an embodiment, the trainable model is transferred from the second node to the first node.

[0265] As an embodiment, a partial model of the trainable model is transferred from the second node to the first node.

[0266] As an embodiment, the parameters of the trainable model are passed from the second node to the first node.

[0267] As an embodiment, some parameters of the trainable model are transferred from the second node to the first node.

[0268] As an embodiment, the trainable model is managed by the first node.

[0269] As an embodiment, the trainable model is managed by the second node.

[0270] As an embodiment, the trainable model is jointly managed by the first node and the second node.

[0271] As an embodiment, the trainable model is managed by the UE-side device.

[0272] As a sub-embodiment of the above embodiment, the trainable model is managed by the UE.

[0273] As a sub-embodiment of the above embodiment, the trainable model is managed by an application server on the UE side.

[0274] As a sub-embodiment of the above embodiment, the trainable model is managed by an OTT (over-the-top) server on the UE side.

[0275] As an embodiment, the trainable model is managed by a network-side device.

[0276] As a sub-embodiment of the above embodiment, the trainable model is managed by the BS.

[0277] As a sub-embodiment of the above embodiment, the trainable model is managed by an application server on the network side.

[0278] As a sub-embodiment of the above embodiment, the trainable model is managed by an OTT (over-the-top) server on the network side.

[0279] As a sub-embodiment of the above embodiment, the trainable model is managed by the BS and network-side devices.

[0280] As a sub-embodiment of the above embodiment, the trainable model is managed by a location service center.

[0281] As an embodiment, the trainable model is jointly managed by the UE-side device management and the network-side device management.

[0282] As an embodiment, the trainable model is used for physical layer related processes.

[0283] As an embodiment, the trainable model is used in MAC (Medium Access Control) layer related processes.

[0284] As an embodiment, the trainable model is used for LLC (logical link control) layer related processes.

[0285] As an embodiment, the trainable model is used in a MIMO scenario.

[0286] As an embodiment, the trainable model is used for system scheduling and control.

[0287] As an embodiment, the trainable model is used for system resource allocation.

[0288] As an embodiment, the trainable model is used for CSI feedback enhancement.

[0289] As an embodiment, the trainable model is used for CSI compression.

[0290] As an embodiment, the trainable model is used for CSI prediction.

[0291] As an embodiment, the trainable model is used for beam management.

[0292] As an embodiment, the trainable model is used for beam prediction.

[0293] As an embodiment, the trainable model is used for beam failure recovery.

[0294] As an embodiment, the trainable model is used for localization.

[0295] As an embodiment, the trainable model is used for positioning accuracy enhancement.

[0296] As an embodiment, the trainable model is used for positioning prediction.

[0297] As an embodiment, the first type of identifier is an index.

[0298] As an embodiment, the first type identifier is a positive integer.

[0299] As an embodiment, the first type identifier is a non-negative integer.

[0300] As an embodiment, the first type of identifier is a field.

[0301] As an embodiment, the first type of identification includes multiple fields.

[0302] As an embodiment, the first type of identifier is a network-side identifier.

[0303] As an embodiment, the first type of identifier is an identifier on the UE side.

[0304] As an embodiment, the first type of logo is a two-sided logo.

[0305] As an embodiment, the first type of identifier is a network identifier and a UE identifier.

[0306] As an embodiment, the first type of identifier is pre-defined.

[0307] As an embodiment, the first type of identification is optional.

[0308] As an embodiment, the first category identifier has at least one candidate value.

[0309] As an embodiment, the first type of identification is network configured.

[0310] As an embodiment, the first type of identifier is configured by the RAN.

[0311] As an embodiment, the first type of identifier is configured by the core network.

[0312] As an embodiment, the first type of identifier is configured by the application server.

[0313] As an embodiment, the first type of identifier is configured by the UE side device.

[0314] As an embodiment, the first type of identifier is configured by RRC signaling.

[0315] As an embodiment, the first type of identifier is configured by higher-layer signaling.

[0316] As an embodiment, the first type of identifier is generated by the first node.

[0317] As an embodiment, the first type of identifier is generated by the second node.

[0318] As an embodiment, the first type of identifier is globally unique.

[0319] As an embodiment, the first type of identification is indicated by a PLMN (Public Land Mobile Network) ID.

[0320] As an embodiment, the first type of identification is indicated by combining multiple basic fields.

[0321] As an embodiment, the first type of identifier is an identifier of the trainable model.

[0322] As an embodiment, the first type of identifier is used to identify one of the trainable models.

[0323] As an embodiment, the first type of identifier is used to select one of the trainable models.

[0324] As an embodiment, the first type of identifier is used to activate one of the trainable models.

[0325] As an embodiment, the first type of identifier is used to switch one of the trainable models.

[0326] As an embodiment, the first type of identifier is used for the trainable model pairing.

[0327] As an embodiment, the first type of identification is based on lifecycle management (LCM).

[0328] As an embodiment, the first type of identification is based on AI / ML features and / or functionality.

[0329] As an embodiment, any first-category identifier in the first-category identifier list indicates a trainable model, including: any first-category identifier in the first-category identifier list indicates a set of parameters, and the set of parameters is used to generate a trainable model.

[0330] As an embodiment, the set of parameters is obtained after training.

[0331] Those skilled in the art should know that for different types of AI / ML models, the content included in the set of parameters may be different. For example, for CNN (Conventional Neural Networks), the set of parameters may include the threshold of the activation function, the size of the convolution kernel, the step size of the convolution kernel, the weights between feature maps, etc.

[0332] As an embodiment, any first-category identifier in the first-category identifier list indicates a trainable model, including: any first-category identifier in the first-category identifier list indicates a set of parameters, and the set of parameters is used to indicate an applicable scenario of a trainable model.

[0333] As an embodiment, any first-category identifier in the first-category identifier list indicates a trainable model, including: any first-category identifier in the first-category identifier list is used to index a trainable model.

[0334] As a sub-embodiment of the above embodiment, the trainable model is maintained separately on the first node side and the network device side, and the trainable model maintained by the first node is different from the trainable model on the network device side.

[0335] As an embodiment, any first-category identifier in the first-category identifier list indicates a trainable model, including: any first-category identifier in the first-category identifier list indicates an executable file, and the executable file is used to generate a trainable model.

[0336] As an embodiment, the first category identifier list in the first signaling does not include any elements.

[0337] As an embodiment, the first category identifier list in the first signaling includes at least one of the first category identifiers.

[0338] As an embodiment, the first category identifier list in the first signaling includes all first category identifiers currently supported by the first node.

[0339] As an embodiment, the first category identifier list in the first signaling includes a portion of all first category identifiers currently supported by the first node.

[0340] As an embodiment, the first category identifier list in the first signaling includes a default first category identifier.

[0341] As an embodiment, the first category identifier list in the first signaling includes a default first category identifier currently supported by the first node.

[0342] As an embodiment, the first category identifier list in the first signaling includes a default first category identifier and at least one non-default first category identifier currently supported by the first node.

[0343] As a sub-embodiment of the above embodiment, the value of the default first-category identifier is a default.

[0344] As a sub-embodiment of the above embodiment, the trainable model indicated by the default first-category identifier is a default one.

[0345] As a sub-embodiment of the above embodiment, the default first-category identifier indicates a specific trainable model.

[0346] As a sub-embodiment of the above embodiment, the trainable model indicated by the default first-category identifier is a default; the trainable model is a generalized AI / ML model.

[0347] As an embodiment, the meaning that the first type of identifier is supported by the first node includes: the identifiers that the first node can support include the first type of identifier.

[0348] As an embodiment, the meaning that the first type of identifier is supported by the first node includes: the first type of identifier does not violate all constraints of the first node.

[0349] As an embodiment, the meaning that the first type of identifier is supported by the first node includes: the value of the first type of identifier is allowed at the first node.

[0350] As an embodiment, the meaning that the first type of identifier is supported by the first node includes: the value of the first type of identifier does not conflict with all constraints of the first node.

[0351] As an embodiment, the meaning that the first type of identifier is supported by the first node includes: the first node supports the trainable model indicated by the first type of identifier.

[0352] As an embodiment, the first condition set includes at least one condition.

[0353] As a sub-embodiment of the above embodiment, the at least one condition is that at least one first-category identifier currently supported by the first node does not belong to the most recently reported first-category identifier list.

[0354] As a sub-embodiment of the above embodiment, the at least one condition is that the first-category identifier list currently held by the first node is different from the first-category identifier list most recently reported.

[0355] As an embodiment, the essence of the above method includes: in addition to being requested to report, UE (User Equipment) capabilities also support active reporting, and can adopt event triggering and other methods; for example, when the first category identifier list of the first node changes, the UE actively reports the user equipment capabilities.

[0356] As an embodiment, the benefits of the above method include: enhancing the flexibility of UE (User Equipment) capability reporting.

[0357] As an embodiment, the benefits of the above method include: better support for AI / ML functions and / or models, and improved performance of AI / ML technology.

[0358] As an embodiment, the benefits of the above method include: improving the accuracy and timeliness of information of the user equipment and its associated network equipment.

[0359] As an embodiment, the benefits of the above method include: adapting to more complex network environments and application scenarios.

[0360] As an embodiment, the benefits of the above method include: improving system flexibility and overall performance.

[0361] As an embodiment, the first condition set includes at least two conditions.

[0362] As a sub-embodiment of the above embodiment, one of the at least two conditions is that at least one first-category identifier currently supported by the first node does not belong to the most recently reported first-category identifier list.

[0363] As a sub-embodiment of the above embodiment, one of the at least two conditions is that the current first-category identifier list of the first node is different from the most recently reported first-category identifier list.

[0364] As a sub-embodiment of the above embodiment, one of the at least two conditions is that the first node includes a first receiver, the first receiver receives second signaling, and the second signaling requests capability information of the first node.

[0365] As a sub-embodiment of the above embodiment, one of the at least two conditions is that at least one first-category identifier currently supported by the first node does not belong to the most recently reported first-category identifier list; the other of the at least two conditions is that the first node includes a first receiver, the first receiver receives second signaling, and the second signaling requests capability information of the first node.

[0366] As an embodiment, the above method has the following benefits: supporting multiple triggering mechanisms for UE capability reporting and enhancing the flexibility of UE (User Equipment) capability reporting.

[0367] As an embodiment, the benefits of the above method include: better support for AI / ML functions and / or models, and improved performance of AI / ML technology.

[0368] As an embodiment, the advantages of the above method include: good forward and backward compatibility and minor changes to the standard.

[0369] As an embodiment, the benefits of the above method include: improving system flexibility and overall performance.

[0370] Example 2

[0371] Example 2 illustrates a schematic diagram of a network architecture according to an embodiment of the present application, as shown in Figure 2.

[0372] FIG2 illustrates a network architecture 200 for LTE (Long-Term Evolution), LTE-A (Long-Term Evolution Advanced), and future 5G systems. The network architecture 200 for LTE, LTE-A, and future 5G systems is referred to as EPS (Evolved Packet System) 200. The 5G NR or LTE network architecture 200 may be referred to as 5GS (5G System) / EPS (Evolved Packet System) 200 or some other appropriate terminology. The 5GS / EPS 200 may include one or more UEs (User Equipment) 201, a UE 241 in sidelink communication with UE 201, an NG-RAN (Next Generation Radio Access Network) 202, a 5G Core Network (5GC) / EPC (Evolved Packet Core) 210, an HSS (Home Subscriber Server) / UDM (Unified Data Management) 220, and Internet services 230. The 5GS / EPS 200 may interconnect with other access networks, but these entities / interfaces are not shown for simplicity. As shown in FIG2 , the 5GS / EPS 200 provides packet-switched services. However, those skilled in the art will readily appreciate that the various concepts presented throughout this disclosure can be extended to networks providing circuit-switched services. The NG-RAN 202 includes an NR (New Radio) Node B (gNB) 203 and other gNBs 204. The gNB 203 provides user and control plane protocol termination towards the UE 201. The gNB 203 can be connected to other gNBs 204 via an Xn interface (e.g., backhaul). The gNB 203 may also be referred to as a base station, a base transceiver station, a radio base station, a radio transceiver, a transceiver function, a basic service set (BSS), an extended service set (ESS), a TRP (transmitter / receiver point), or some other suitable terminology. The gNB 203 provides an access point to the 5GC / EPC 210 for the UE 201. Examples of UE 201 include a cellular phone, a smartphone, a Session Initiation Protocol (SIP) phone, a laptop, a personal digital assistant (PDA), a satellite radio, a global positioning system, a multimedia device, a video device, a digital audio player (e.g., an MP3 player), a camera, a game console, a drone, an aircraft, a narrowband physical network device, a machine type communication device, a land vehicle, an automobile, a wearable device, or any other similarly functional device.Those skilled in the art may also refer to UE 201 as a mobile station, subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or some other appropriate terminology. The gNB 203 connects to the 5GC / EPC 210 via the S1 / NG interface. The 5GC / EPC 210 includes the MME (Mobility Management Entity) / AMF (Authentication Management Field) / SMF (Session Management Function) 211, other MMEs / AMFs / SMFs 214, the S-GW (Service Gateway) / UPF (User Plane Function) 212, and the P-GW (Packet Data Network Gateway) / UPF 213. The MME / AMF / SMF 211 is the control node that handles signaling between the UE 201 and the 5GC / EPC 210. Generally, the MME / AMF / SMF 211 provides bearer and connection management. All user IP (Internet Protocol) packets are transmitted through the S-GW / UPF 212, which is itself connected to the P-GW / UPF 213. The P-GW provides UE IP address allocation and other functions. The P-GW / UPF 213 is connected to the Internet Services 230. Internet Services 230 includes operator-specific Internet Protocol services, specifically including the Internet, intranet, IMS (IP Multimedia Subsystem), and packet switching services.

[0373] As an embodiment, the first node in the present application includes the UE201 or the UE241.

[0374] As an embodiment, the second node in this application includes the gNB203.

[0375] As an embodiment, the second node in the present application includes the gNB203 and a core network function.

[0376] As an embodiment, the core network function is LMF.

[0377] As an embodiment, the one core network function is used to manage AI / ML models.

[0378] As an embodiment, the core network function is connected to the UE201 via AMF.

[0379] As an embodiment, the UE201 or the UE241 is a user equipment (UE).

[0380] As an embodiment, the UE201 or the UE241 includes a user equipment (UE).

[0381] As an embodiment, the UE201 or the UE241 includes a user equipment (UE) and a user-side application server.

[0382] As an embodiment, the UE201 or the UE241 is a relay device.

[0383] As an embodiment, the UE 201 or the UE 241 includes a relay device and a user equipment (UE).

[0384] As an embodiment, the UE201 or the UE241 includes a relay device, a user equipment (UE) and a user-side application server.

[0385] As an embodiment, the UE 201 is a terminal supporting Massive-MIMO.

[0386] As an embodiment, the gNB203 is a base station (BS) device.

[0387] As an embodiment, the gNB203 is a macrocellular base station.

[0388] As an embodiment, the gNB203 is a micro cell base station.

[0389] As an embodiment, the gNB203 is a picocell (PicoCell) base station.

[0390] As an embodiment, the gNB203 is a home base station (Femtocell).

[0391] As an embodiment, the gNB203 is a base station device that supports large delay difference.

[0392] As an embodiment, the gNB203 is a flying platform device.

[0393] As an embodiment, the gNB203 is a satellite device.

[0394] As an embodiment, the gNB203 is a network-side device.

[0395] As an embodiment, the gNB203 is a network-side application server.

[0396] As an embodiment, the gNB203 includes a base station (BS) device and a network side device.

[0397] As an embodiment, the gNB203 includes a base station (BS) device and a network-side application server.

[0398] As an embodiment, the wireless link between the UE201 or the UE241 and the gNB203 includes a cellular network link.

[0399] As an embodiment, the gNB203 supports AI / ML functions and / or models.

[0400] As an embodiment, the UE 201 or the UE 241 supports AI / ML functions and / or models.

[0401] As an embodiment, the UE 201 supports generating reports using AI / ML.

[0402] As an embodiment, the UE 201 supports generating reports using AI / ML in conjunction with an application server on the UE side.

[0403] As a sub-embodiment of the above two embodiments, the reporting is used for purposes such as channel information feedback, beam management, or positioning.

[0404] As an embodiment, the UE 201 supports generating a trained model using training data or generating part of the parameters in the trained model using trained data.

[0405] As an embodiment, the UE 201 supports determining at least part of the parameters of a CNN (Conventional Neural Networks) for CSI reconstruction through training.

[0406] As an embodiment, the UE 201 supports determining a transformer for CSI reconstruction through training.

[0407] As an embodiment, the gNB203 supports Massive-MIMO based transmission.

[0408] As an embodiment, the gNB203 supports decompression of CSI using AI or deep learning.

[0409] As an embodiment, the gNB203 supports mobility prediction using AI or deep learning, such as predicting the beam that the UE201 is about to enter.

[0410] Example 3

[0411] Embodiment 3 illustrates a schematic diagram of an embodiment of a wireless protocol architecture of a user plane and a control plane according to an embodiment of the present application, as shown in FIG3 .

[0412] Embodiment 3 illustrates a schematic diagram of an embodiment of a radio protocol architecture for a user plane and a control plane according to the present application, as shown in FIG3 . FIG3 is a schematic diagram illustrating an embodiment of a radio protocol architecture for a user plane 350 and a control plane 300. FIG3 illustrates the radio protocol architecture of the control plane 300 for communication between a first communication node device (UE, gNB, or RSU in V2X) and a second communication node device (gNB, UE, or RSU in V2X), or between two UEs, using three layers: Layer 1, Layer 2, and Layer 3. Layer 1 (L1 layer) is the lowest layer and implements various PHY (physical layer) signal processing functions. The L1 layer will be referred to herein as PHY 301. Layer 2 (L2 layer) 305, located above PHY 301, is responsible for the link between the first communication node device and the second communication node device, or between two UEs. The L2 layer 305 includes a MAC (Medium Access Control) sublayer 302, an RLC (Radio Link Control) sublayer 303, and a PDCP (Packet Data Convergence Protocol) sublayer 304, which terminate at the second communication node device. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. The PDCP sublayer 304 also provides security by encrypting data packets, and provides support for inter-zone mobility of the first communication node device between the second communication node devices. The RLC sublayer 303 provides segmentation and reassembly of upper layer data packets, retransmission of lost data packets, and reordering of data packets to compensate for out-of-order reception due to HARQ. The MAC sublayer 302 provides multiplexing between logical and transport channels. The MAC sublayer 302 is also responsible for allocating various radio resources (e.g., resource blocks) in a cell between the first communication node devices. The MAC sublayer 302 is also responsible for HARQ operations. The RRC (Radio Resource Control) sublayer 306 in Layer 3 (L3 layer) in the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and configuring lower layers using RRC signaling between the second communication node device and the first communication node device. The radio protocol architecture of the user plane 350 includes Layer 1 (L1 layer) and Layer 2 (L2 layer). The radio protocol architecture for the first communication node device and the second communication node device in the user plane 350 is substantially the same as the corresponding layers and sublayers in the control plane 300 for the physical layer 351, the PDCP sublayer 354 in the L2 layer 355, the RLC sublayer 353 in the L2 layer 355, and the MAC sublayer 352 in the L2 layer 355. However, the PDCP sublayer 354 also provides header compression for upper layer data packets to reduce radio transmission overhead.The L2 layer 355 in the user plane 350 also includes an SDAP (Service Data Adaptation Protocol) sublayer 356. The SDAP sublayer 356 is responsible for mapping between QoS flows and data radio bearers (DRBs) to support service diversity. Although not shown in the figure, the first communication node device may have several upper layers above the L2 layer 355, including a network layer (e.g., an IP layer) terminated at the P-GW on the network side and an application layer terminated at the other end of the connection (e.g., a remote UE, a server, etc.).

[0413] As an embodiment, the wireless protocol architecture in FIG3 is applicable to the first node in this application.

[0414] As an embodiment, the wireless protocol architecture in FIG3 is applicable to the second node in this application.

[0415] As an embodiment, the first signaling is generated in the RRC sublayer 306.

[0416] As an embodiment, the first signaling is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0417] As an embodiment, the first signaling is generated in PHY301 or PHY351.

[0418] As an embodiment, the second signaling is generated in the RRC sublayer 306.

[0419] As an embodiment, the second signaling is generated in the MAC sublayer 302 or the MAC sublayer 352.

[0420] As an embodiment, the second signaling is generated in PHY301 or PHY351.

[0421] As an embodiment, the higher layer in this application refers to a layer above the physical layer.

[0422] As an embodiment, the higher layer in this application refers to the MAC layer.

[0423] As an embodiment, the higher layer in this application refers to the physical layer.

[0424] As an embodiment, the higher layer in this application refers to the MAC layer or the physical layer.

[0425] Example 4

[0426] Embodiment 4 illustrates a schematic diagram of a first communication device and a second communication device according to an embodiment of the present application, as shown in Figure 4. Figure 4 is a block diagram of a first communication device 410 and a second communication device 450 communicating with each other in an access network.

[0427] The first communications device 410 includes a controller / processor 475 , a memory 476 , a receive processor 470 , a transmit processor 416 , a multi-antenna receive processor 472 , a multi-antenna transmit processor 471 , a transmitter / receiver 418 , and an antenna 420 .

[0428] The second communication device 450 includes a controller / processor 459 , a memory 460 , a data source 467 , a transmit processor 468 , a receive processor 456 , a multi-antenna transmit processor 457 , a multi-antenna receive processor 458 , a transmitter / receiver 454 and an antenna 452 .

[0429] In transmission from the first communications device 410 to the second communications device 450, at the first communications device 410, upper layer data packets from the core network are provided to the controller / processor 475. The controller / processor 475 implements the functionality of the L2 layer. In the DL, the controller / processor 475 provides header compression, encryption, packet segmentation and reordering, multiplexing between logical and transport channels, and allocation of radio resources to the second communications device 450 based on various priority metrics. The controller / processor 475 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the second communications device 450. The transmit processor 416 and the multi-antenna transmit processor 471 implement various signal processing functions for the L1 layer (i.e., the physical layer). The transmit processor 416 implements coding and interleaving to facilitate forward error correction (FEC) at the second communication device 450, as well as constellation mapping based on various modulation schemes (e.g., binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), M-phase-shift keying (M-PSK), and M-quadrature amplitude modulation (M-QAM)). The multi-antenna transmit processor 471 performs digital spatial precoding, including codebook-based and non-codebook-based precoding, and beamforming on the coded and modulated symbols to generate one or more parallel streams. The transmit processor 416 then maps each parallel stream to a subcarrier, multiplexes the modulated symbols with reference signals (e.g., pilots) in the time and / or frequency domains, and then uses an inverse fast Fourier transform (IFFT) to generate a physical channel carrying the time-domain multicarrier symbol stream. The multi-antenna transmit processor 471 then performs transmit analog precoding / beamforming operations on the time-domain multicarrier symbol stream. Each transmitter 418 converts the baseband multi-carrier symbol stream provided by the multi-antenna transmit processor 471 into a radio frequency stream, and then provides it to a different antenna 420.

[0430] During transmission from the first communications device 410 to the second communications device 450, each receiver 454 receives a signal at the second communications device 450 via its corresponding antenna 452. Each receiver 454 recovers the information modulated onto the RF carrier and converts the RF stream into a baseband multi-carrier symbol stream, which is provided to the receive processor 456. The receive processor 456 and the multi-antenna receive processor 458 implement various L1 signal processing functions. The multi-antenna receive processor 458 performs receive analog precoding / beamforming operations on the baseband multi-carrier symbol stream from the receiver 454. The receive processor 456 converts the baseband multi-carrier symbol stream, after the receive analog precoding / beamforming operations, from the time domain to the frequency domain using a fast Fourier transform (FFT). In the frequency domain, the physical layer data signal and reference signal are demultiplexed by the receive processor 456, where the reference signal is used for channel estimation. The data signal undergoes multi-antenna detection in the multi-antenna receive processor 458 to recover any parallel streams destined for the second communications device 450. The symbols on each parallel stream are demodulated and recovered in the receive processor 456, and soft decisions are generated. The receive processor 456 then decodes and deinterleaves the soft decisions to recover the upper layer data and control signals transmitted by the first communication device 410 on the physical channel. The upper layer data and control signals are then provided to the controller / processor 459. The controller / processor 459 implements the functions of the L2 layer. The controller / processor 459 may be associated with a memory 460 that stores program code and data. The memory 460 may be referred to as a computer-readable medium. In the DL (Downlink), the controller / processor 459 provides demultiplexing between transport and logical channels, packet reassembly, decryption, header decompression, and control signal processing to recover upper layer data packets from the core network. The upper layer data packets are then provided to all protocol layers above the L2 layer. Various control signals may also be provided to the L3 layer for L3 processing. The controller / processor 459 is also responsible for error detection using an acknowledgement (ACK) and / or negative acknowledgement (NACK) protocol to support HARQ operations.

[0431] During transmission from the second communications device 450 to the first communications device 410, at the second communications device 450, a data source 467 is used to provide upper layer data packets to the controller / processor 459. The data source 467 represents all protocol layers above the L2 layer. Similar to the transmit functionality at the first communications device 410 described in the DL, the controller / processor 459 implements header compression, encryption, packet segmentation and reordering, and multiplexing between logical and transport channels based on the radio resource allocation of the first communications device 410, implementing L2 layer functions for the user plane and control plane. The controller / processor 459 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the first communications device 410. The transmit processor 468 performs modulation mapping and channel coding, while the multi-antenna transmit processor 457 performs digital multi-antenna spatial precoding, including codebook-based and non-codebook-based precoding, and beamforming. The transmit processor 468 then modulates the resulting parallel streams into multi-carrier / single-carrier symbol streams. After analog precoding and beamforming operations in the multi-antenna transmit processor 457, these streams are provided to different antennas 452 via the transmitter 454. Each transmitter 454 first converts the baseband symbol stream provided by the multi-antenna transmit processor 457 into a RF symbol stream before providing it to the antenna 452.

[0432] During transmission from the second communication device 450 to the first communication device 410, the functionality at the first communication device 410 is similar to the reception functionality at the second communication device 450 described for transmission from the first communication device 410 to the second communication device 450. Each receiver 418 receives RF signals via its corresponding antenna 420, converts the received RF signals into baseband signals, and provides the baseband signals to the multi-antenna receive processor 472 and the receive processor 470. The receive processor 470 and the multi-antenna receive processor 472 collectively implement L1 layer functionality. The controller / processor 475 implements L2 layer functionality. The controller / processor 475 may be associated with a memory 476 storing program code and data. The memory 476 may be referred to as a computer-readable medium. The controller / processor 475 provides demultiplexing between transmit and logical channels, packet reassembly, decryption, header decompression, and control signal processing to recover upper layer data packets from the second communication device 450. The upper layer data packets from the controller / processor 475 may be provided to the core network. The controller / processor 475 is also responsible for error detection using an ACK and / or NACK protocol to support HARQ operations.

[0433] As an embodiment, the second communication device 450 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used together with the at least one processor. The second communication device 450 device at least: sends a first signaling in response to any condition in a first condition set being met; wherein the first signaling includes capability information of the first node, the capability information of the first node includes a first-class identifier list, and any first-class identifier in the first-class identifier list indicates a trainable model; one of the conditions in the first condition set is that at least one first-class identifier currently supported by the first node does not belong to the most recently reported first-class identifier list; the first-class identifier list in the first signaling includes the at least one first-class identifier currently supported by the first node.

[0434] As an embodiment, the second communication device 450 includes: a memory storing a computer-readable instruction program, wherein the computer-readable instruction program generates an action when executed by at least one processor, the action including: sending a first signaling in response to any condition in a first condition set being satisfied; wherein the first signaling includes capability information of the first node, the capability information of the first node includes a first-class identifier list, and any first-class identifier in the first-class identifier list indicates a trainable model; one of the conditions in the first condition set is that at least one first-class identifier currently supported by the first node does not belong to the most recently reported first-class identifier list; the first-class identifier list in the first signaling includes the at least one first-class identifier currently supported by the first node.

[0435] As an embodiment, the first communication device 410 includes: at least one processor and at least one memory, the at least one memory including computer program code; the at least one memory and the computer program code are configured to be used together with the at least one processor. The first communication device 410 device at least: receives first signaling; wherein, as a response to any condition in a first condition set being met, the sender of the first signaling sends the first signaling; the first signaling includes capability information of the first node, the capability information of the first node includes a first-class identifier list, and any first-class identifier in the first-class identifier list indicates a trainable model; one of the conditions in the first condition set is that at least one first-class identifier currently supported by the first node does not belong to the most recently reported first-class identifier list; the first-class identifier list in the first signaling includes the at least one first-class identifier currently supported by the first node.

[0436] As an embodiment, the first communication device 410 includes: a memory storing a computer-readable instruction program, wherein the computer-readable instruction program generates an action when executed by at least one processor, the action including: receiving a first signaling; wherein, in response to any condition in a first condition set being satisfied, the sender of the first signaling sends the first signaling; the first signaling includes capability information of the first node, the capability information of the first node includes a first-class identifier list, and any first-class identifier in the first-class identifier list indicates a trainable model; one of the conditions in the first condition set is that at least one first-class identifier currently supported by the first node does not belong to the most recently reported first-class identifier list; the first-class identifier list in the first signaling includes the at least one first-class identifier currently supported by the first node.

[0437] As a sub-embodiment of the above embodiment, the first signaling may be transparent to the second node, or the second node does not parse the first signaling.

[0438] As a sub-embodiment of the above embodiment, the first signaling is distributed by the second node to the core network function on the network side through signaling such as a container.

[0439] As an embodiment, the above method has the following advantages: it is applicable to reporting NAS-related capability information and has good compatibility with existing systems.

[0440] As an embodiment, the first node in the present application includes the second communication device 450.

[0441] As an embodiment, the second node in the present application includes the first communication device 410.

[0442] As an embodiment, the second communication device 450 is a UE, and the first communication device 410 is a base station.

[0443] As an embodiment, at least one of {the antenna 452, the transmitter 454, the transmit processor 468, the multi-antenna transmit processor 457, the controller / processor 459, and the memory 460} is used to send the first signaling in this application; and at least one of {the antenna 420, the receiver 418, the receive processor 470, the multi-antenna receive processor 472, the controller / processor 475, and the memory 476} is used to receive the first signaling in this application.

[0444] As an embodiment, at least one of {the antenna 452, the receiver 454, the receiving processor 456, the multi-antenna receiving processor 458, the controller / processor 459, the memory 460, and the data source 467} is used for the second signaling in this application; and at least one of {the antenna 420, the transmitter 418, the transmitting processor 416, the multi-antenna transmitting processor 471, the controller / processor 475, and the memory 476} is used to send the second signaling in this application.

[0445] As an embodiment, the second communication device 450 is a user equipment.

[0446] As an embodiment, the second communication device 450 is a base station device.

[0447] As an embodiment, the second communication device 450 is a relay device.

[0448] As an embodiment, the first communication device 410 is a user equipment.

[0449] As an embodiment, the first communication device 410 is a base station device.

[0450] As an embodiment, the first communication device 410 is a relay device.

[0451] Example 5

[0452] Example 5 illustrates a flowchart of wireless transmission according to an embodiment of the present application, as shown in FIG5 . In FIG5 , the first node U1 and the second node N2 are two communicating nodes transmitting via an air interface, and the steps in block F51 are optional. In particular, the order of the steps in the blocks does not represent a specific temporal relationship between the steps.

[0453] For the first node U1, the second signaling is received in step S5101; and the first signaling is sent in step S5102.

[0454] For the second node N2, the second signaling is sent in step S5201 and the first signaling is received in step S5202.

[0455] In Example 5, as a response to any condition in the first condition set being met, the sender of the first signaling sends the first signaling; the first signaling includes capability information of the first node, the capability information of the first node includes a first-class identifier list, and any first-class identifier in the first-class identifier list indicates a trainable model; one condition in the first condition set is that at least one first-class identifier currently supported by the first node does not belong to the most recently reported first-class identifier list; the first-class identifier list in the first signaling includes at least one first-class identifier currently supported by the first node.

[0456] As an embodiment, the first node U1 is the first node in this application.

[0457] As an embodiment, the second node N2 is the second node in this application.

[0458] As an embodiment, the first node U1 is a UE (User Equipment).

[0459] As an embodiment, the first node U1 includes a UE and an application server.

[0460] As an embodiment, the first node U1 includes a UE and an OTT (over-the-top) server.

[0461] As an embodiment, the first node U1 includes a UE and a device on the UE side.

[0462] As an embodiment, the second node N2 is a BS (Base Station) device.

[0463] As an embodiment, the second node N2 is a network-side device.

[0464] As an embodiment, the second node N2 is an application server.

[0465] As an embodiment, the second node N2 is an OTT (over-the-top) server.

[0466] As an embodiment, the second node N2 includes a BS (Base Station) device and a network side device.

[0467] As an embodiment, the second node N2 includes a BS (Base Station) device and an application server.

[0468] As an embodiment, the second node N2 includes a BS (Base Station) device and an OTT (over-the-top) server.

[0469] As an embodiment, the second node N2 includes a BS (Base Station) device and a location service center.

[0470] As an embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between a base station and a user equipment.

[0471] As an embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between a relay node and a user equipment.

[0472] As an embodiment, the air interface between the second node N2 and the first node U1 includes a wireless interface between user equipments.

[0473] As an embodiment, the second node N2 is a base station maintaining a serving cell of the first node U1.

[0474] As an embodiment, the base station includes at least one of gNB or TRP.

[0475] As an embodiment, the first signaling is UE specific signaling.

[0476] As an embodiment, the first signaling is air interface signaling.

[0477] As an embodiment, the first signaling is RRC (Radio Resource Control) signaling.

[0478] As an embodiment, the first signaling is physical layer signaling.

[0479] As an embodiment, the first signaling is MAC layer signaling.

[0480] As an embodiment, the first signaling is higher layer signaling.

[0481] As an embodiment, the first signaling is non-access stratum (NAS) signaling.

[0482] As an embodiment, the first signaling is uplink signaling.

[0483] As an embodiment, the first signaling includes capability information of the first node.

[0484] As an embodiment, the first signaling is used to report UE capability information.

[0485] As an embodiment, the first signaling is UECapabilityInformation signaling.

[0486] As an embodiment, the first signaling includes a UECapabilityInformation message.

[0487] As an embodiment, the first signaling is UEInformationResponse signaling.

[0488] As an embodiment, the first signaling includes a UEInformationResponse message.

[0489] As an embodiment, the first signaling is ProvideCapabilities signaling.

[0490] As an embodiment, the first signaling includes a ProvideCapabilities message.

[0491] As an embodiment, the first signaling is feedback for a downlink signaling.

[0492] As an embodiment, the first signaling is sent proactively and is not a feedback for a downlink signaling.

[0493] As an embodiment, the first signaling includes capability information of the first node.

[0494] As an embodiment, the first signaling indicates capability information of the first node.

[0495] As an embodiment, the first signaling includes the first category identifier list.

[0496] As an embodiment, the first signaling includes at least one of the first category identifiers.

[0497] As an embodiment, the first signaling includes information about the trainable model that the first node can support.

[0498] As an embodiment, whether the trainable model indicated by at least one first type identifier included in the first signaling is available depends on the codebook parameters indicated by the first signaling.

[0499] As an embodiment, whether the trainable model supported by the first node included in the first signaling is available depends on the codebook parameters indicated by the first signaling.

[0500] As an embodiment, when the codebook parameters indicated by the first signaling do not include the first type of codebook, the trainable model indicated by at least one first type identifier included in the first signaling is unavailable.

[0501] As an embodiment, when the codebook parameters indicated by the first signaling do not include the first type of codebook, the trainable model supported by the first node included in the first signaling is unavailable.

[0502] As a sub-embodiment of the above embodiment, the first type of codebook is a non-Type I codebook.

[0503] As a sub-embodiment of the above embodiment, the first type of codebook is a Type II codebook.

[0504] As a sub-embodiment of the above embodiment, the first type codebook is an enhanced type II codebook.

[0505] As a sub-embodiment of the above embodiment, the accuracy of the first type of codebook is not lower than that of the enhanced second type of codebook.

[0506] As a sub-embodiment of the above embodiment, the first type of codebook includes an enhanced second type codebook, an enhanced second type port selection codebook (Enhanced Type II Port Selection Codebook), a further enhanced second type port selection codebook (Further enhanced Type II port selection codebook), an enhanced second type codebook for CJT (Coherent Joint Transmission, consistent joint transmission) (Enhanced Type II codebook for CJT), a further enhanced second type port selection codebook for CJT (Further enhanced Type II port selection codebook for CJT), an enhanced second type codebook for predicted PMI (Enhanced Type II codebook for predicted PMI), and a further enhanced second type port selection codebook for predicted PMI (Further enhanced Type II port selection codebook for predicted PMI). One or more.

[0507] As a sub-embodiment of the above embodiment, the first type of codebook is a codebook defined by 3GPP NR R (Release) 15, R16 or R17.

[0508] As a sub-embodiment of the above embodiment, the first type of codebook is a PMI (Precoding Matrix Indicator) codebook defined in 3GPP NR R15, R16 or R17.

[0509] As a sub-embodiment of the above embodiment, the first type of codebook includes at least one of the Type II codebook of 3GPP NR R15, the Type II port selection codebook of 3GPP NR R15, the enhanced Type II codebook of 3GPP NR R16, the enhanced Type II port selection codebook of 3GPP NR R16, or the further enhanced Type II port selection codebook of 3GPP NR R17.

[0510] As a sub-embodiment of the above embodiment, the first type of codebook includes at least one of the codebooks enhanced based on the Type II codebook of 3GPP NR R15, the Type II port selection codebook of 3GPP NR R15, the enhanced Type II codebook of 3GPP NR R16, the enhanced Type II port selection codebook of 3GPP NR R16, or the further enhanced Type II port selection codebook of 3GPP NR R17.

[0511] As a sub-embodiment of the above embodiment, the first type of codebook includes at least one of the codebooks for performing PMI (Precoding Matrix Indicator) parameter enhancement on the basis of the Type II codebook of 3GPP NR R15, the Type II port selection codebook of 3GPP NR R15, the enhanced Type II codebook of 3GPP NR R16, the enhanced Type II port selection codebook of 3GPP NR R16, or the further enhanced Type II port selection codebook of 3GPP NR R17.

[0512] As a sub-embodiment of the foregoing embodiment, the first type of codebook is a Type II codebook in a version later than 3GPP NR R17.

[0513] As an embodiment, whether the trainable model indicated by at least one first type identifier included in the first signaling is available depends on the first beam-related parameter indicated by the first signaling.

[0514] As an embodiment, whether the trainable model that the first node can support included in the first signaling is available depends on the first beam-related parameter indicated by the first signaling.

[0515] As an embodiment, when the first beam-related first parameter on the first frequency band indicated by the first signaling does not include the second parameter, the trainable model indicated by at least one first type identifier contained in the first signaling is unavailable.

[0516] As an embodiment, when the first beam-related first parameter on the first frequency band indicated by the first signaling does not include the second parameter, the trainable model supported by the first node included in the first signaling is not available.

[0517] As a sub-embodiment of the above embodiment, the second parameter is a parameter of the trainable model.

[0518] As a sub-embodiment of the above embodiment, the second parameter is a parameter related to the trainable model.

[0519] As a sub-embodiment of the above embodiment, the second parameter is a parameter related to the training data of the trainable model.

[0520] As a sub-embodiment of the above embodiment, the second parameter is a parameter related to the trainable model output.

[0521] As an embodiment, the essence of the above method includes: whether certain user equipment capabilities reported by the UE, such as AI / ML functions and / or models, are available depends on certain other user equipment capabilities reported by the UE, that is, there is a dependency relationship between certain user equipment capabilities; for example, the prerequisite for the availability of capability A is that capability B is supported.

[0522] As an embodiment, the essence of the above method includes: whether the AI / ML function and / or model is available depends on certain other user equipment capabilities reported by the UE and certain parameters related to the AI / ML function and / or model.

[0523] As an embodiment, the benefits of the above method include: it is possible to more reasonably select and activate AI / ML functions and / or models, thereby improving the performance of AI / ML functions and / or models.

[0524] As an embodiment, the benefits of the above method include: improving the flexibility of UE (User Equipment) capability management.

[0525] As an embodiment, the benefits of the above method include: enhancing the reliability and stability of the system.

[0526] As an embodiment, the second signaling is UE specific signaling.

[0527] As an embodiment, the second signaling is air interface signaling.

[0528] As an embodiment, the second signaling is RRC (Radio Resource Control) signaling.

[0529] As an embodiment, the second signaling is physical layer signaling.

[0530] As an embodiment, the second signaling is MAC layer signaling.

[0531] As an embodiment, the second signaling is higher layer signaling.

[0532] As an embodiment, the second signaling is downlink signaling.

[0533] As an embodiment, the second signaling is used to query UE capabilities.

[0534] As an embodiment, the second signaling requests capability information of the first node.

[0535] As an embodiment, the second signaling is UECapabilityEnquiry signaling.

[0536] As an embodiment, the second signaling includes a UECapabilityEnquiry message.

[0537] As an embodiment, the second signaling is RequestCapabilities signaling.

[0538] As an embodiment, the second signaling includes a RequestCapabilities message.

[0539] As an embodiment, the receipt of the second signaling triggers the sending of an uplink signaling.

[0540] As an embodiment, the first signaling follows the second signaling.

[0541] As an embodiment, the first node sends the first signaling after receiving the second signaling.

[0542] As an embodiment, the second signaling and the first signaling are both RRC layer signaling, and the second signaling and the first signaling are UECapabilityEnquiry message and UECapabilityInformation message respectively.

[0543] As an embodiment, the second signaling and the first signaling are both non-access stratum (NAS) signaling, and the second signaling and the first signaling are RequestCapabilities message and ProvideCapabilities message respectively.

[0544] As an embodiment, in response to any condition in the first condition set being met, the first node sends a first signaling.

[0545] As an embodiment, one of the conditions in the first condition set is: the first node receives the second signaling.

[0546] As an embodiment, the conditions in the first condition set do not include: the first node receiving the second signaling.

[0547] As an embodiment, the step in box F51 in FIG. 5 exists, and the first signaling is feedback for the second signaling.

[0548] As an embodiment, the step in box F51 in FIG. 5 exists, and the reception of the second signaling triggers the sending of the first signaling.

[0549] As an embodiment, the benefits of the above method include: enhancing the forward and backward compatibility of the system and making minimal changes to the calibration.

[0550] As an embodiment, the benefits of the above method include: enhancing the stability and reliability of the system.

[0551] As an embodiment, the benefits of the above method include: simplifying system design and reducing implementation complexity.

[0552] As an embodiment, the step in box F51 in FIG. 5 does not exist, and the first node actively reports the UE capability.

[0553] As an embodiment, the step in box F51 in FIG. 5 does not exist, and the capability reporting of the first node is triggered by an event.

[0554] As an embodiment, the step in box F51 in FIG. 5 does not exist, and when any condition in the first condition set is met, the first node sends the first signaling.

[0555] As an embodiment, the step in box F51 in FIG. 5 does not exist, and when at least one first-category identifier currently supported by the first node does not belong to the most recently reported first-category identifier list, the first node sends the first signaling.

[0556] As an embodiment, one of the conditions in the first condition set is that at least one first-category identifier currently supported by the first node does not belong to the most recently reported first-category identifier list.

[0557] As a sub-embodiment of the above embodiment, one of the conditions in the first condition set is that at least one first-class identifier currently supported by the first node does not belong to the most recently reported first-class identifier list, including: after the most recently reported first-class identifier list, the application server of the first node is replaced.

[0558] As a sub-embodiment of the above embodiment, one of the conditions in the first condition set is that at least one first-class identifier currently supported by the first node does not belong to the most recently reported first-class identifier list, including: after the most recently reported first-class identifier list, the first node receives signaling from the application server, and the signaling from the application server indicates the first-class identifier currently supported by the first node.

[0559] As an embodiment, the essence of the above method includes: in addition to being requested to report, UE (User Equipment) capabilities also support active reporting, and can adopt event triggering and other methods; for example, when the UE's own capabilities or other devices or conditions associated with the UE change, the UE actively reports the user equipment capabilities.

[0560] As an embodiment, the benefits of the above method include: enhancing the flexibility of UE (User Equipment) capability reporting.

[0561] As an embodiment, the benefits of the above method include: adapting to more complex network environments and application scenarios.

[0562] As an embodiment, the benefits of the above method include: better support for AI / ML functions and / or models, and improved performance of AI / ML technology.

[0563] As an embodiment, the benefits of the above method include: improving the accuracy and timeliness of information of the user equipment and its associated network equipment.

[0564] As an embodiment, the benefits of the above method include: improving system flexibility and overall performance.

[0565] Example 6

[0566] Example 6 illustrates a schematic diagram of the second signaling according to an embodiment of the present application; as shown in Figure 6.

[0567] In embodiment 6, the first node includes a first receiver; and one of the conditions in the first condition set is: the first receiver receives second signaling, and the second signaling requests capability information of the first node.

[0568] As an embodiment, the second signaling is transmitted via a downlink (DL).

[0569] As an embodiment, the second signaling is transmitted via a side link (Sidelink, SL).

[0570] As an embodiment, the second signaling is air interface signaling.

[0571] As an embodiment, the second signaling is UE specific signaling.

[0572] As an embodiment, the second signaling is sent via a DCCH (Dedicated Control Channel).

[0573] As an embodiment, the second signaling is sent via SRB1 (Signalling Radio Bearer 1).

[0574] As an embodiment, the second signaling is sent via SRB3 (Signalling Radio Bearer 3).

[0575] As an embodiment, the second signaling is RRC (Radio Resource Control) signaling.

[0576] As an embodiment, the second signaling is physical layer signaling.

[0577] As an embodiment, the second signaling is MAC layer signaling.

[0578] As an embodiment, the second signaling is higher layer signaling.

[0579] As an embodiment, the second signaling is non-access stratum (NAS) signaling.

[0580] As an embodiment, the second signaling is downlink signaling.

[0581] As an embodiment, the second signaling is used to query UE capabilities.

[0582] As an embodiment, the second signaling requests capability information of the first node.

[0583] As an embodiment, the second signaling requests all capability information of the first node.

[0584] As an embodiment, the second signaling requests partial capability information of the first node.

[0585] As an embodiment, the second signaling requests capability information of the first node at least about AI / ML functions and / or models.

[0586] As an embodiment, the second signaling requests information about the trainable model that the first node can support.

[0587] As an embodiment, the second signaling includes frequency band information.

[0588] As an embodiment, the second signaling requests capability information of the first node on a certain frequency band.

[0589] As an embodiment, the second signaling requests capability information of the first node on at least one frequency band.

[0590] As an embodiment, the second signaling requests the first type identifier list of the first node.

[0591] As an embodiment, the second signaling request includes capability information of the first node including the first category identifier list.

[0592] As an embodiment, the second signaling is UECapabilityEnquiry signaling.

[0593] As an embodiment, the second signaling includes a UECapabilityEnquiry message.

[0594] As an embodiment, the second signaling block includes part or all of the fields in the IE UECapabilityEnquiry.

[0595] As an embodiment, the second signaling is RequestCapabilities signaling.

[0596] As an embodiment, the second signaling includes a RequestCapabilities message.

[0597] As an embodiment, the second signaling block includes part or all of the fields in IE RequestCapabilities.

[0598] As an embodiment, the second signaling includes at least one IE bandInformationNR.

[0599] As an embodiment, the second signaling is an IE (Information Element).

[0600] As an embodiment, the second signaling includes at least one IE (Information Element).

[0601] As an embodiment, the second signaling includes at least one field.

[0602] As an embodiment, the receipt of the second signaling triggers the sending of an uplink signaling.

[0603] As an embodiment, the reception of the second signaling triggers the sending of an uplink RRC signaling.

[0604] As an embodiment, the second signaling precedes the first signaling.

[0605] As an embodiment, the receipt of the second signaling triggers the sending of the first signaling.

[0606] As an embodiment, the first signaling is feedback for the second signaling.

[0607] As an embodiment, the first node receives the second signaling before sending the first signaling.

[0608] As an embodiment, the second signaling and the first signaling are both RRC layer signaling, and the second signaling and the first signaling are UECapabilityEnquiry message and UECapabilityInformation message respectively.

[0609] As an embodiment, the second signaling and the first signaling are both non-access stratum (NAS) signaling, and the second signaling and the first signaling are RequestCapabilities message and ProvideCapabilities message respectively.

[0610] As an embodiment, another condition in the first condition set is: at least one first-category identifier currently supported by the first node does not belong to the most recently reported first-category identifier list.

[0611] Example 7

[0612] Example 7 illustrates a schematic diagram of one condition in the first condition set according to an embodiment of the present application; as shown in Figure 7.

[0613] In embodiment 7, one of the conditions in the first condition set is that at least one first-category identifier currently supported by the first node does not belong to a most recently reported first-category identifier list, and includes at least one of the following:

[0614] After the most recently reported first category identifier list, the application server of the first node is replaced;

[0615] After the most recently reported first category identifier list, the first node receives signaling from an application server, where the signaling from the application server indicates the first category identifiers currently supported by the first node.

[0616] As an embodiment, the application server is in a logical sense.

[0617] As an embodiment, the application server is physical.

[0618] As an embodiment, the application server is both logical and physical.

[0619] As an embodiment, the application server is a functional entity.

[0620] As an embodiment, the application server is a hardware entity.

[0621] As an embodiment, the application server is both a functional entity and a hardware entity.

[0622] As an embodiment, the application server includes a hardware part and a software part.

[0623] As an embodiment, the application server only includes the software part.

[0624] As an embodiment, the application server is installed by the UE through an executable file.

[0625] As an embodiment, the application server provides OTT (Over-the-Top) service.

[0626] As an embodiment, the application server is an OTT (over-the-top) server.

[0627] As an embodiment, the hardware part of the application server is independent of UE (User Equipment).

[0628] As an embodiment, the software part of the application server is independent of UE (User Equipment).

[0629] As an embodiment, the hardware part and the software part of the application server are independent of UE (User Equipment).

[0630] As an embodiment, the application server is deployed outside the UE (User Equipment).

[0631] As an embodiment, the application server relies on at least one UE (User Equipment).

[0632] As a sub-embodiment of the above embodiment, the application server and the at least one UE (User Equipment) share at least one of a hardware part and a software part.

[0633] As a sub-embodiment of the above embodiment, the application server is deployed in the at least one UE (User Equipment).

[0634] As a sub-embodiment of the above embodiment, at least a part of the structure and / or function of the application server is deployed in the at least one UE (User Equipment).

[0635] As a sub-embodiment of the above embodiment, at least one function of the application server depends on the at least one UE (User Equipment).

[0636] As an embodiment, UE (User Equipment) relies on the application server.

[0637] As a sub-embodiment of the above embodiment, at least one capability of UE (User Equipment) depends on the application server.

[0638] As a sub-embodiment of the above embodiment, at least one function of UE (User Equipment) depends on the application server.

[0639] As a sub-embodiment of the above embodiment, normal operation of UE (User Equipment) requires participation and / or assistance of the application server.

[0640] As a sub-embodiment of the above embodiment, there is information interaction between UE (User Equipment) and the application server.

[0641] As a sub-embodiment of the above embodiment, signaling is sent and / or received between UE (User Equipment) and the application server.

[0642] As an embodiment, the UE (User Equipment) and the application server are dependent on each other.

[0643] As an embodiment, the AI / ML capability of UE (User Equipment) depends on the application server.

[0644] As a sub-embodiment of the above embodiment, at least one AI / ML capability of the UE (User Equipment) depends on the application server.

[0645] As a sub-embodiment of the above embodiment, at least one AI / ML capability of the UE (User Equipment) depends on at least one of the hardware part and the software part of the application server.

[0646] As a sub-embodiment of the above embodiment, at least one AI / ML capability of the UE (User Equipment) is determined by the application server.

[0647] As a sub-embodiment of the above embodiment, at least one AI / ML capability of UE (User Equipment) is jointly determined by at least one of its own hardware and software parts and the application server.

[0648] As an embodiment, the trainable model that can be supported by UE (User Equipment) depends on the application server.

[0649] As a sub-embodiment of the above embodiment, the type of the trainable model that can be supported by UE (User Equipment) depends on the application server.

[0650] As a sub-embodiment of the above embodiment, the number of the trainable models that can be supported by UE (User Equipment) depends on the application server.

[0651] As a sub-embodiment of the above embodiment, the identifier of the trainable model that can be supported by UE (User Equipment) depends on the application server.

[0652] As an embodiment, the first category identification list depends on the application server.

[0653] As a sub-embodiment of the above embodiment, the first category identification list is determined by the application server.

[0654] As a sub-embodiment of the above embodiment, at least one first-category identifier in the first-category identifier list is determined by the application server.

[0655] As a sub-embodiment of the above embodiment, at least one of the first-category identifiers in the first-category identifier list is determined by signaling from the application server.

[0656] As an embodiment, “the application server of the first node is replaced” includes: the application server of the first node is replaced physically.

[0657] As a sub-embodiment of the above embodiment, the hardware part of the application server of the first node is replaced.

[0658] As a sub-embodiment of the above embodiment, the software portion of the application server of the first node is replaced.

[0659] As a sub-embodiment of the above embodiment, the software portion of the application server of the first node is updated.

[0660] As a sub-embodiment of the above embodiment, the software portion of the application server of the first node is upgraded.

[0661] As a sub-embodiment of the above embodiment, both the hardware part and the software part of the application server of the first node are replaced.

[0662] As an embodiment, “the application server of the first node is replaced” includes: the application server of the first node is replaced in a logical sense.

[0663] As a sub-embodiment of the above embodiment, the first node is switched from a first application server to a second application server.

[0664] As a sub-embodiment of the above embodiment, the first application server and the second application server are two different application servers.

[0665] As a sub-embodiment of the above embodiment, the first application server and the second application server are two different applications.

[0666] As a sub-embodiment of the above embodiment, the first application server and the second application server are two different executable files.

[0667] As a sub-embodiment of the above embodiment, the first application server and the second application server correspond to two different functions.

[0668] As a sub-embodiment of the above embodiment, the first application server and the second application server are deployed in different locations.

[0669] As a sub-embodiment of the above embodiment, the first application server and the second application server are both deployed on the first node.

[0670] As a sub-embodiment of the above embodiment, the first application server and the second application server are both deployed on a remote server outside the first node; switching from the first application server to the second application server is triggered by the movement of the first node, or triggered by the first node executing a new application.

[0671] As an embodiment, “the application server of the first node is replaced” includes: the application server of the first node is replaced both physically and logically.

[0672] As an embodiment, "the signaling from the application server indicates the first type of identifier currently supported by the first node" includes: the signaling from the application server directly indicates the first type of identifier currently supported by the first node.

[0673] As an embodiment, "the signaling from the application server indicates the first type of identifier currently supported by the first node" includes: the signaling from the application server indirectly indicates the first type of identifier currently supported by the first node.

[0674] As an embodiment, "the signaling from the application server indicates the first type of identifier currently supported by the first node" includes: the signaling from the application server indicates all the first type of identifiers currently supported by the first node.

[0675] As an embodiment, "the signaling from the application server indicates the first type of identifier currently supported by the first node" includes: the signaling from the application server indicates at least one first type of identifier newly added by the first node based on the first type of identifier currently supported.

[0676] As an embodiment, "the signaling from the application server indicates the first type of identifier currently supported by the first node" includes: the signaling from the application server indicates that the first node no longer supports at least one first type of identifier among the first type of identifiers currently supported.

[0677] As an embodiment, "the signaling from the application server indicates the first type of identifier currently supported by the first node" includes: the signaling from the application server indicates a change in the first type of identifier currently supported by the first node.

[0678] As an embodiment, "the signaling from the application server indicates the first-class identifier currently supported by the first node" includes: the signaling from the application server simultaneously indicates that the first node no longer supports at least one first-class identifier among the currently supported first-class identifiers and at least one first-class identifier newly added based on the currently supported first-class identifiers.

[0679] As an embodiment, the signaling from the application server is NAS signaling.

[0680] As an embodiment, the signaling from the application server is application layer signaling, that is, outside the scope of 3GPP.

[0681] As an embodiment, the signaling from the application server triggers the sending of the first signaling.

[0682] As an embodiment, the signaling from the application server is deployed in the first node, and the signaling from the application server is transmitted inside the first node.

[0683] As an embodiment, the signaling from the application server is an application installed by the first node, and the signaling from the application server is transmitted within the first node.

[0684] As an embodiment, the signaling from the application server is deployed outside the first node, and the transmission path of the signaling from the application server includes an air interface.

[0685] As an embodiment, the signaling from the application server is a remote server outside the first node, and the transmission path of the signaling from the application server includes an air interface.

[0686] Example 8

[0687] Embodiment 8 illustrates a schematic diagram of the codebook parameters indicated by the first signaling according to an embodiment of the present application; as shown in FIG8 .

[0688] In embodiment 8, whether the trainable model indicated by at least one first category identifier in the first category identifier list is available for the first frequency band depends on the codebook parameters on the first frequency band indicated by the first signaling.

[0689] As an embodiment, the capability information of the first node includes F supported frequency bands.

[0690] As an embodiment, the capability information of the first node includes sub-capability information, the multiple sub-capability information respectively indicate the F frequency bands, and the first frequency band is any one of the F frequency bands.

[0691] As an embodiment, the F frequency bands are F low-frequency bands.

[0692] As an embodiment, the F frequency bands are F high frequency bands.

[0693] As an embodiment, the F frequency bands are F FR1 frequency bands.

[0694] As an embodiment, the F frequency bands are F FR2 frequency bands.

[0695] As an embodiment, the F frequency bands are F 6G frequency bands.

[0696] As an embodiment, the F frequency bands are F NR frequency bands.

[0697] As an embodiment, the first frequency band is any one of the F frequency bands.

[0698] As an embodiment, the first frequency band is the F frequency bands.

[0699] As an embodiment, the first frequency band is at least one frequency band of the F frequency bands.

[0700] As an embodiment, the first frequency band includes at least one frequency band combination of the F frequency bands.

[0701] As an embodiment, the first frequency band includes at least one subcarrier.

[0702] As an embodiment, the sub-capability information is IE BandNR.

[0703] As an embodiment, the sub-capability information includes part or all of the fields in the IE BandNR, and the frequency band is indicated by the FreqBandIndicatorNR in the corresponding sub-capability information.

[0704] As an embodiment, the capability information of the first node is an IE supportedBandListNR.

[0705] As an embodiment, the capability information of the first node is an IE supportedBandListNR, the first signaling is RRC signaling and the first signaling does not belong to the IE supportedBandListNR.

[0706] As a sub-embodiment of the above embodiment, the capability information of the first node and the first signaling belong to an IE RF-Parameters.

[0707] As a sub-embodiment of the above embodiment, the capability information of the first node and the first signaling belong to an IE Phy-Parameters.

[0708] As a sub-embodiment of the above embodiment, the capability information of the first node and the first signaling belong to one IE UECapabilityInformation.

[0709] As a sub-embodiment of the above embodiment, the capability information of the first node and the first signaling respectively belong to IE RF-Parameters in an IE UECapabilityInformation and IE Phy-Parameters in the IE UECapabilityInformation.

[0710] As an embodiment, the first signaling indicates each of the F frequency bands.

[0711] As an embodiment, the first signaling indicates at least one frequency band among the F frequency bands.

[0712] As an embodiment, the first signaling indicates at least one frequency band combination of the F frequency bands.

[0713] As an embodiment, the above method has the following advantages: maintaining good compatibility, avoiding configuring the first signaling for each frequency band, and saving signaling overhead.

[0714] As an embodiment, the codebook parameters belong to MIMO-related parameters in the capability information of the first node.

[0715] As an embodiment, the codebook parameter indicates a codebook type for CSI reporting that the first node can support.

[0716] As an embodiment, the codebook parameters are configured per band (Per Band) or are specific for a certain band (specific for a certain band).

[0717] As an embodiment, the codebook parameters are parameters in IE MIMO-ParametersPerBand.

[0718] As an embodiment, the codebook parameter is all or part of the fields in the IE MIMO-ParametersPerBand.

[0719] As an embodiment, the codebook parameters are parameters in IE CodebookParameters.

[0720] As an embodiment, the codebook parameters are all or part of the fields in IE CodebookParameters.

[0721] As an embodiment, whether the trainable model indicated by at least one first category identifier in the first category identifier list is available for the first frequency band depends on whether the codebook parameters on the first frequency band indicated by the first signaling include a first category codebook.

[0722] As a sub-embodiment of the above embodiment, when the codebook parameters on the first frequency band indicated by the first signaling do not include a first-class codebook, the trainable model indicated by the at least one first-class identifier in the first-class identifier list is not available for the first frequency band.

[0723] As a sub-embodiment of the above embodiment, when the codebook parameters on the first frequency band indicated by the first signaling include a first type of codebook, the trainable model indicated by the at least one first type identifier in the first type identifier list is not available for the first frequency band.

[0724] Example 9

[0725] Embodiment 9 illustrates a schematic diagram of a first type of codebook according to an embodiment of the present application; as shown in FIG9 .

[0726] In embodiment 9, when the codebook parameters on the first frequency band indicated by the first signaling do not include a first type of codebook, the trainable model indicated by the at least one first type identifier in the first type identifier list is not available for the first frequency band.

[0727] As an embodiment, the first type of codebook is a non-Type I codebook.

[0728] As an embodiment, the first type of codebook is a Type II codebook.

[0729] As an embodiment, the first type codebook is an enhanced type II codebook.

[0730] As an embodiment, the accuracy of the first type of codebook is not lower than that of the enhanced second type of codebook.

[0731] As an embodiment, the first type of codebook includes an enhanced second type codebook, an enhanced second type port selection codebook (Enhanced Type II Port Selection Codebook), a further enhanced second type port selection codebook (Further enhanced Type II port selection codebook), an enhanced second type codebook for CJT (Coherent Joint Transmission, consistent joint transmission) (Enhanced Type II codebook for CJT), a further enhanced second type port selection codebook for CJT (Further enhanced Type II port selection codebook for CJT), an enhanced second type codebook for predicted PMI (Enhanced Type II codebook for predicted PMI), and a further enhanced second type port selection codebook for predicted PMI (Further enhanced Type II port selection codebook for predicted PMI) One or more.

[0732] As an embodiment, the first type of codebook is a codebook defined by 3GPP NR R (Release) 15, R16 or R17.

[0733] As an embodiment, the first type of codebook is a PMI (Precoding Matrix Indicator) codebook defined in 3GPP NR R15, R16 or R17.

[0734] As an embodiment, the first type of codebook includes at least one of the Type II codebook of 3GPP NR R15, the Type II port selection codebook of 3GPP NR R15, the enhanced Type II codebook of 3GPP NR R16, the enhanced Type II port selection codebook of 3GPP NR R16, or the further enhanced Type II port selection codebook of 3GPP NR R17.

[0735] As an embodiment, the first type of codebook includes at least one of a codebook enhanced based on the Type II codebook of 3GPP NR R15, the Type II port selection codebook of 3GPP NR R15, the enhanced Type II codebook of 3GPP NR R16, the enhanced Type II port selection codebook of 3GPP NR R16, or the further enhanced Type II port selection codebook of 3GPP NR R17.

[0736] As an embodiment, the first type of codebook includes at least one of the codebooks for performing PMI (Precoding Matrix Indicator) parameter enhancement based on the Type II codebook of 3GPP NR R15, the Type II port selection codebook of 3GPP NR R15, the enhanced Type II codebook of 3GPP NR R16, the enhanced Type II port selection codebook of 3GPP NR R16, or the further enhanced Type II port selection codebook of 3GPP NR R17.

[0737] As an embodiment, the first type of codebook is a Type II codebook in a version later than 3GPP NR R17.

[0738] Example 10

[0739] Embodiment 10 illustrates a schematic diagram of the first beam-related first parameter indicated by the first signaling according to an embodiment of the present application; as shown in FIG10 .

[0740] In embodiment 10, whether the trainable model indicated by at least one first category identifier in the first category identifier list is available for the first frequency band depends on the first beam-related parameter on the first frequency band indicated by the first signaling.

[0741] As an embodiment, the first beam-related parameter is configured per band (Per Band) or is specific for a certain band (specific for a certain band).

[0742] As an embodiment, the first beam-related parameter indicates the capabilities of user equipment (UE) related to beam management (Beam Management).

[0743] As an embodiment, the first beam-related parameter includes a parameter related to beam management (Beam Management).

[0744] As an embodiment, the first beam-related parameter indicates the beam measurement-related capability of user equipment (UE).

[0745] As an embodiment, the first beam-related parameter includes a parameter related to beam measurement.

[0746] As a sub-embodiment of the above embodiment, the first beam-related parameter is the maximum number of RS resources (SSB or CSI-RS) for L1-RSRP.

[0747] As a sub-embodiment of the above embodiment, the first beam-related parameter is the maximum number of CSI-RS resources for L1-RSRP.

[0748] As a sub-embodiment of the above embodiment, the first beam-related parameter is CSI-RS density for L1-RSRP.

[0749] As a sub-embodiment of the above embodiment, the first beam-related parameter is the maximum number of RS resources (SSB or CSI-RS) for L1-SINR.

[0750] As a sub-embodiment of the above embodiment, the first beam-related parameter is the maximum number of CSI-RS resources for L1-SINR.

[0751] As a sub-embodiment of the above embodiment, the first beam-related parameter is a CSI-RS density for L1-SINR.

[0752] As a sub-embodiment of the above embodiment, the first beam-related parameter is the number of repetitions of CSI-RS resources recommended for each resource set.

[0753] As a sub-embodiment of the above embodiment, the first beam-related parameter is all or part of the field in IE beamManagementSSB-CSI-RS.

[0754] As a sub-embodiment of the above embodiment, the first beam-related parameter is maxNumberCSI-RS-Resource.

[0755] As a sub-embodiment of the above embodiment, the first beam-related parameter is maxNumberSSB-CSI-RS-ResourceOneTx.

[0756] As a sub-embodiment of the above embodiment, the first beam-related parameter is maxNumberCSI-RS-ResourceTwoTx.

[0757] As a sub-embodiment of the above embodiment, the first beam-related parameter is supportedCSI-RS-Density.

[0758] As a sub-embodiment of the above embodiment, the first beam-related parameter is maxNumberAperiodicCSI-RS-Resource.

[0759] As a sub-embodiment of the above embodiment, the first beam-related parameter is all or part of the fields in the IE uplinkBeamManagement.

[0760] As a sub-embodiment of the above embodiment, the first beam-related parameter is maxNumberSRS-ResourcePerSet-BM.

[0761] As a sub-embodiment of the above embodiment, the first beam-related parameter is maxNumberSRS-ResourceSet.

[0762] As an embodiment, the first beam-related parameter indicates the capability of user equipment (UE) related to beam failure recovery.

[0763] As an embodiment, the first beam-related parameter includes a parameter related to beam failure recovery.

[0764] As a sub-embodiment of the above embodiment, the first beam-related parameter is maxNumberCSI-RS-BFD.

[0765] As a sub-embodiment of the above embodiment, the first beam-related parameter is maxNumberSSB-BFD.

[0766] As a sub-embodiment of the above embodiment, the first beam-related parameter is maxNumberCSI-RS-SSB-CBD.

[0767] As an embodiment, the first parameter related to the beam indicates the capability of the user equipment (UE) related to beam reporting (Beam Reporting).

[0768] As an embodiment, the first beam-related parameter includes a parameter related to beam reporting.

[0769] As a sub-embodiment of the above embodiment, the first beam-related parameter is maxNumberNonGroupBeamReporting.

[0770] As a sub-embodiment of the above embodiment, the first beam-related parameter is all or part of the fields in IE csi-ReportFramework.

[0771] As a sub-embodiment of the above embodiment, the first beam-related parameter is maxNumberPeriodicCSI-PerBWP-ForBeamReport.

[0772] As a sub-embodiment of the above embodiment, the first beam-related parameter is maxNumberAperiodicCSI-PerBWP-ForBeamReport.

[0773] As a sub-embodiment of the above embodiment, the first beam-related parameter is maxNumberAperiodicCSI-triggeringStatePerCC.

[0774] As a sub-embodiment of the above embodiment, the first beam-related parameter is maxNumberSemiPersistentCSI-PerBWP-ForBeamReport.

[0775] As an embodiment, "whether the trainable model indicated by at least one first category identifier in the first category identifier list is available for the first frequency band depends on the first parameter on the first frequency band indicated by the first signaling" includes: whether the trainable model indicated by at least one first category identifier in the first category identifier list is available for the first frequency band depends on the second parameter and the first parameter on the first frequency band indicated by the first signaling.

[0776] As a sub-embodiment of the above embodiment, the second parameter is related to the AI / ML function and / or model.

[0777] As a sub-embodiment of the above embodiment, the second parameter is an AI / ML capability parameter of the first node.

[0778] As a sub-embodiment of the above embodiment, the second parameter is associated with the trainable model indicated by at least one first category identifier in the first category identifier list.

[0779] As a sub-embodiment of the above embodiment, the second parameter is a parameter of the trainable model.

[0780] As a sub-embodiment of the above embodiment, the second parameter is a parameter related to the trainable model.

[0781] As a sub-embodiment of the above embodiment, the second parameter is a parameter related to the training data of the trainable model.

[0782] As a sub-embodiment of the above embodiment, the second parameter is a parameter related to the training data category of the trainable model.

[0783] As a sub-embodiment of the above embodiment, the second parameter is a parameter related to the trainable model input.

[0784] As a sub-embodiment of the above embodiment, the second parameter is a parameter related to the trainable model output.

[0785] As a sub-embodiment of the above embodiment, the second parameter indicates the number of candidate values ​​of the training data of the trainable model.

[0786] As a sub-embodiment of the above embodiment, the second parameter indicates the number of candidate values ​​for the training data category values ​​of the trainable model.

[0787] As a sub-embodiment of the above embodiment, the second parameter indicates the number of candidate values ​​for the output data of the trainable model.

[0788] As a sub-embodiment of the above embodiment, the second parameter indicates the number of candidate values ​​of the beam index output by the trainable model.

[0789] As an embodiment, the second parameter has different values ​​for different trainable models.

[0790] As an embodiment, the second parameter has the same value for different trainable models.

[0791] As an embodiment, the second parameter may have different values ​​for different trainable models.

[0792] As an embodiment, the second parameter is for the first frequency band.

[0793] As an embodiment, the second parameter is for all frequency bands of the system.

[0794] As an embodiment, the second parameter has different values ​​in different frequency band combinations.

[0795] As an embodiment, the second parameter has the same value in different frequency band combinations.

[0796] As an embodiment, the second parameter may have different values ​​in different frequency band combinations.

[0797] As an embodiment, the first node is unaware of the existence of the second parameter.

[0798] As an embodiment, the first node is aware of the existence of the second parameter.

[0799] As an embodiment, the second node is aware of the existence of the second parameter.

[0800] As an embodiment, both the first node and the second node are aware of the existence of the second parameter.

[0801] As an embodiment, the value of the second parameter is known to the first node.

[0802] As an embodiment, the value of the second parameter is known to the second node.

[0803] As an embodiment, the value of the second parameter is only known to the second node.

[0804] As an embodiment, the value of the second parameter is unknown to the first node.

[0805] As an embodiment, the value of the second parameter is known to both the first node and the second node.

[0806] As an embodiment, the value of the second parameter is determined by the second node and transmitted to the first node.

[0807] As an embodiment, the value of the second parameter is determined by the first node and transmitted to the second node.

[0808] Example 11

[0809] Embodiment 11 illustrates a schematic diagram of the relationship between the second parameter and the first beam-related parameter indicated by the first signaling according to an embodiment of the present application; as shown in Figure 11.

[0810] In embodiment 11, when the first beam-related first parameter on the first frequency band indicated by the first signaling does not include the second parameter, the trainable model indicated by at least one first category identifier in the first category identifier list is not available for the first frequency band.

[0811] As an embodiment, the second parameter is associated with the trainable model.

[0812] As an embodiment, the second parameter is associated with the training of the trainable model.

[0813] As an embodiment, the second parameter is a parameter of the trainable model.

[0814] As an embodiment, the second parameter is a parameter of the training data of the trainable model.

[0815] As an embodiment, the second parameter is the number of RS resources (SSB or CSI-RS) required to obtain training data for the trainable model.

[0816] As an embodiment, the second parameter is the number of RS resources (SSB or CSI-RS) required to obtain the input data of the trainable model.

[0817] As an embodiment, the second parameter is the number of RS resources (SSB or CSI-RS) required for the execution of the trainable model.

[0818] As a sub-embodiment of the above embodiment, the required RS resources (SSB or CSI-RS) are used for channel measurement.

[0819] As a sub-embodiment of the above embodiment, the required RS resources (SSB or CSI-RS) are used for beam measurement.

[0820] As an embodiment, the second parameter is a parameter related to the trainable model output.

[0821] As an embodiment, the second parameter indicates the number of candidate values ​​for the output data of the trainable model.

[0822] As an embodiment, the second parameter indicates the number of candidate values ​​of the beam index output by the trainable model.

[0823] As an embodiment, "the first beam-related parameter on the first frequency band indicated by the first signaling does not include the second parameter" includes: the second parameter does not belong to the first parameter on the first frequency band indicated by the first signaling.

[0824] As an embodiment, "the first beam-related parameter on the first frequency band indicated by the first signaling does not include the second parameter" includes: the second parameter is not equal to the first parameter on the first frequency band indicated by the first signaling.

[0825] As an embodiment, "the first beam-related parameter on the first frequency band indicated by the first signaling does not include the second parameter" includes: the second parameter is different from the first parameter on the first frequency band indicated by the first signaling.

[0826] As an embodiment, "the first beam-related parameter on the first frequency band indicated by the first signaling does not include the second parameter" includes: the second parameter is greater than the first parameter on the first frequency band indicated by the first signaling.

[0827] As an embodiment, "the first beam-related parameter on the first frequency band indicated by the first signaling does not include the second parameter" includes: the second parameter is greater than all parameters of the first parameter on the first frequency band indicated by the first signaling.

[0828] As an embodiment, "the first beam-related parameter on the first frequency band indicated by the first signaling does not include the second parameter" includes: the second parameter is greater than the maximum value of the first parameter on the first frequency band indicated by the first signaling.

[0829] As an embodiment, the determination of whether the first parameter related to the beam on the first frequency band indicated by the first signaling includes the second parameter is performed at the first node.

[0830] As an embodiment, the determination of whether the first parameter related to the beam on the first frequency band indicated by the first signaling includes the second parameter is performed at the second node.

[0831] As an embodiment, the determination of whether the first parameter related to the beam on the first frequency band indicated by the first signaling includes the second parameter is performed simultaneously at the first node and the second node.

[0832] As an embodiment, the determination of whether the first parameter related to the beam on the first frequency band indicated by the first signaling includes the second parameter is performed separately at the first node and the second node.

[0833] Example 12

[0834] Example 12 illustrates a schematic diagram of a system for a trainable model according to one embodiment of the present application, as shown in FIG12 . FIG12 includes a first processor, a second processor, a third processor, and a fourth processor. In FIG12 , the first type of feedback and the second type of feedback are optional.

[0835] In Example 12, the first processor sends a first data set to the second processor, the second processor generates a target first-category parameter group based on the first data set, the second processor sends the generated target first-category parameter group to the third processor, the third processor uses the target first-category parameter group to process the second data set to obtain a first-category output, and then sends the first-category output to the fourth processor.

[0836] As an embodiment, the third processor sends first-type feedback to the second processor, where the first-type feedback is used to trigger recalculation or updating of the target first-type parameter group.

[0837] As an embodiment, the fourth processor sends a second type of feedback to the first processor, and the second type of feedback is used to generate the first data set or the second data set, or the second type of feedback is used to trigger the sending of the first data set or the second data set.

[0838] As an embodiment, the first processor generates the first data set based on measurement of a first wireless signal, where the first wireless signal includes a downlink RS.

[0839] As an embodiment, the first processor generates the first data set and the second data set based on measurements of a first wireless signal, where the first wireless signal includes a downlink RS.

[0840] As an embodiment, the second data set is obtained based on the measurement of the first RS resources.

[0841] As an embodiment, the first processor and the third processor belong to a first node, and the fourth processor belongs to a second node.

[0842] As an embodiment, the first type of output includes the at least first channel information.

[0843] As an embodiment, the first type of output includes multiple non-codebook reports, and the first channel information is one of the multiple non-codebook reports.

[0844] As an embodiment, the first processor belongs to the first node.

[0845] As an embodiment, the second processor belongs to the first node.

[0846] As an embodiment, the third processor belongs to the first node.

[0847] As an embodiment, the second processor belongs to the second node.

[0848] As an embodiment, the fourth processor belongs to the second node.

[0849] As an embodiment, the fourth processor belongs to the second node, and the first node reports the target first-category parameter group to the second node.

[0850] As an embodiment, the benefits of the above embodiment include: avoiding transmitting the first data set to the second node.

[0851] As an embodiment, the benefits of the above embodiment include: reducing the complexity of the first node.

[0852] As an embodiment, the third processor belongs to the second node, and the first node reports the target first-category parameter group to the second node.

[0853] As an embodiment, the benefits of the above embodiment include: supporting joint training and optimizing system performance.

[0854] As an embodiment, the trainable model includes the third processor.

[0855] As an embodiment, the trainable model includes the second processor and the third processor.

[0856] As an embodiment, the trainable model includes the third processor and the fourth processor.

[0857] As an embodiment, the first data set is a training data set, the second data set is an inference data set, the second processor is used to train a model, and the trained model is described by the target first-category parameter group.

[0858] As an embodiment, the first data set includes training data (Training Data), the second data set includes inference data (Inference Data), the third processor is used for model training (Model Training), and the trained model is described by the target first category parameter group.

[0859] As an embodiment, the trained model is described by the target first-category parameter group.

[0860] As an embodiment, the target first-category parameter group includes the trained model.

[0861] As an embodiment, the target first-category parameter group is the trained model.

[0862] As an embodiment, the target first-category parameter group includes: one or more of: convolution kernel size, number of convolution layers, convolution step size, pooling kernel size, pooling kernel step size, pooling function, activation function, or number of feature maps.

[0863] As an embodiment, the target first-category parameter group includes: one or more of: a convolution kernel, a pooling kernel, a pooling function, an activation function, parameters of a pooling function, or parameters of an activation function.

[0864] As an embodiment, the third processor constructs a model according to the target first-category parameter group, then inputs the second data set into the constructed model to obtain the first-category output, and then sends the first-category output to the fourth processor.

[0865] As an embodiment, the third processor calculates the error between the first type of output and actual data to determine the performance of the trained model; the actual data is the data received after the second data set and transmitted by the first processor.

[0866] As an embodiment, the benefits of the above method include: being suitable for prediction-related reporting.

[0867] As an embodiment, the third processing engine restores a reference data set according to the first type of output, and an error between the reference data set and the second data set is used to generate the first type of feedback.

[0868] As an embodiment, the restoration of the reference data set usually adopts an inverse operation similar to the target first-category parameter group.

[0869] As an embodiment, the above method has the following benefits: it is suitable for CSI compression-related reporting.

[0870] As an embodiment, the first type of feedback is used to reflect the performance of the trained model; when the performance of the trained model fails to meet the requirements, the second processor will recalculate the target first type parameter group.

[0871] As an embodiment, when the error is too large or the model has not been updated for too long, the performance of the trained model is considered to be unsatisfactory.

[0872] Example 13

[0873] Embodiment 13 illustrates a structural block diagram of a processing device in a first node according to an embodiment of the present application, as shown in FIG13. In FIG13, the processing device 1300 in the first node includes at least the first transmitter 1302 of a first receiver 1301 and a first transmitter 1302, wherein the first receiver 1301 is optional.

[0874] The first transmitter 1302 sends a first signaling in response to any condition in the first condition set being met.

[0875] In embodiment 13, the first signaling includes capability information of the first node, the capability information of the first node includes a first-class identifier list, and any first-class identifier in the first-class identifier list indicates a trainable model; one of the conditions in the first condition set is that at least one first-class identifier currently supported by the first node does not belong to the most recently reported first-class identifier list; the first-class identifier list in the first signaling includes at least one first-class identifier currently supported by the first node.

[0876] As an embodiment, one of the conditions in the first condition set is:

[0877] The first receiver 1301 receives second signaling, where the second signaling requests capability information of the first node.

[0878] As an embodiment, one of the conditions in the first condition set is that at least one first-category identifier currently supported by the first node does not belong to a most recently reported first-category identifier list, including at least one of the following:

[0879] After the most recently reported first category identifier list, the application server of the first node is replaced;

[0880] After the most recently reported first category identifier list, the first node receives signaling from an application server, where the signaling from the application server indicates the first category identifiers currently supported by the first node.

[0881] As an embodiment, whether the trainable model indicated by at least one first category identifier in the first category identifier list is available for the first frequency band depends on the codebook parameters on the first frequency band indicated by the first signaling.

[0882] As an embodiment, when the codebook parameters on the first frequency band indicated by the first signaling do not include a first type of codebook, the trainable model indicated by the at least one first type identifier in the first type identifier list is not available for the first frequency band.

[0883] As an embodiment, whether the trainable model indicated by at least one first category identifier in the first category identifier list is available for the first frequency band depends on the first beam-related parameter on the first frequency band indicated by the first signaling.

[0884] As an embodiment, when the first beam-related first parameter on the first frequency band indicated by the first signaling does not include the second parameter, the trainable model indicated by the at least one first category identifier in the first category identifier list is not available for the first frequency band.

[0885] As an embodiment, the first node 1300 is a user equipment.

[0886] As an embodiment, the first node 1300 is a relay node device.

[0887] As an embodiment, the first receiver 1301 includes at least one of {antenna 452, receiver 454, multi-antenna receiving processor 458, receiving processor 456, controller / processor 459, memory 460, data source 467} in embodiment 4.

[0888] As an embodiment, the first receiver 1301 includes at least the first five of {antenna 452, receiver 454, multi-antenna receiving processor 458, receiving processor 456, controller / processor 459, memory 460, data source 467} in embodiment 4.

[0889] As an embodiment, the first receiver 1301 includes at least the first four of {antenna 452, receiver 454, multi-antenna receiving processor 458, receiving processor 456, controller / processor 459, memory 460, data source 467} in embodiment 4.

[0890] As an embodiment, the first receiver 1301 includes at least the first three of {antenna 452, receiver 454, multi-antenna receiving processor 458, receiving processor 456, controller / processor 459, memory 460, data source 467} in embodiment 4.

[0891] As an embodiment, the first transmitter 1302 includes at least one of {antenna 452, transmitter 454, transmit processor 468, multi-antenna transmit processor 457, controller / processor 459, memory 460, data source 467} in embodiment 4.

[0892] As an embodiment, the first transmitter 1302 includes all of {antenna 452, transmitter 454, transmit processor 468, multi-antenna transmit processor 457, controller / processor 459, memory 460, data source 467} in Embodiment 4.

[0893] Example 14

[0894] Embodiment 14 illustrates a structural block diagram of a processing device in a second node according to an embodiment of the present application, as shown in FIG14. In FIG14, the processing device 1400 in the second node includes a second transmitter 1401 and a second receiver 1402, at least the second receiver 1402, where the second transmitter 1401 is optional.

[0895] The second receiver 1402 receives the first signaling.

[0896] In Example 14, as a response to any condition in a first condition set being met, the sender of the first signaling sends the first signaling; the first signaling includes capability information of the first node, the capability information of the first node includes a first-class identifier list, and any first-class identifier in the first-class identifier list indicates a trainable model; one condition in the first condition set is that at least one first-class identifier currently supported by the first node does not belong to the most recently reported first-class identifier list; the first-class identifier list in the first signaling includes at least one first-class identifier currently supported by the first node.

[0897] As an embodiment, one of the conditions in the first condition set is:

[0898] The second transmitter 1401 sends a second signaling; the first receiver 1301 in Example 13 receives the second signaling, and the second signaling requests the capability information of the first node.

[0899] As an embodiment, one of the conditions in the first condition set is that at least one first-category identifier currently supported by the first node does not belong to a most recently reported first-category identifier list, including at least one of the following:

[0900] After the most recently reported first category identifier list, the application server of the first node is replaced;

[0901] After the most recently reported first category identifier list, the first node receives signaling from an application server, where the signaling from the application server indicates the first category identifiers currently supported by the first node.

[0902] As an embodiment, whether the trainable model indicated by at least one first category identifier in the first category identifier list is available for the first frequency band depends on the codebook parameters on the first frequency band indicated by the first signaling.

[0903] As an embodiment, when the codebook parameters on the first frequency band indicated by the first signaling do not include a first type of codebook, the trainable model indicated by the at least one first type identifier in the first type identifier list is not available for the first frequency band.

[0904] As an embodiment, whether the trainable model indicated by at least one first category identifier in the first category identifier list is available for the first frequency band depends on the first beam-related parameter on the first frequency band indicated by the first signaling.

[0905] As an embodiment, when the first beam-related first parameter on the first frequency band indicated by the first signaling does not include the second parameter, the trainable model indicated by the at least one first category identifier in the first category identifier list is not available for the first frequency band.

[0906] As an embodiment, the second node 1400 is a base station.

[0907] As an embodiment, the second node 1400 includes a base station device and a core network function.

[0908] As an embodiment, the second node 1400 is user equipment.

[0909] As an embodiment, the second node 1400 is a relay node device.

[0910] As an embodiment, the second transmitter 1401 includes the antenna 420, the transmitter 418, the transmit processor 416, and the controller / processor 475.

[0911] As an embodiment, the second transmitter 1401 includes the antenna 420, the transmitter 418, the multi-antenna transmit processor 471, the transmit processor 416, and the controller / processor 475.

[0912] As an embodiment, the second transmitter 1401 includes the antenna 420, the transmitter 418, the transmit processor 416, and the controller / processor 475.

[0913] As an embodiment, the second transmitter 1401 includes the antenna 420, the transmitter 418, the multi-antenna transmit processor 471, the transmit processor 416, and the controller / processor 475.

[0914] As an embodiment, the second transmitter 1401 includes at least one of {antenna 420, transmitter 418, transmit processor 416, multi-antenna transmit processor 471, controller / processor 475, memory 476} in embodiment 4.

[0915] As an embodiment, the second receiver 1402 includes the antenna 420, the receiver 418, the multi-antenna reception processor 472, the reception processor 470, and the controller / processor 475.

[0916] As an embodiment, the second receiver 1402 includes the controller / processor 475 .

[0917] As an embodiment, the second receiver 1402 includes at least one of {antenna 420, receiver 418, receiving processor 470, multi-antenna receiving processor 472, controller / processor 475, memory 476} in embodiment 4.

[0918] Those skilled in the art will appreciate that all or part of the steps in the above method can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a hard disk or an optical disk. Optionally, all or part of the steps in the above embodiment can also be implemented using one or more integrated circuits. Accordingly, each module unit in the above embodiment can be implemented in the form of hardware or in the form of a software functional module. This application is not limited to any specific form of combination of software and hardware. The user equipment, terminal and UE in this application include but are not limited to drones, communication modules on drones, remote-controlled aircraft, aircraft, small aircraft, mobile phones, tablet computers, notebooks, vehicle-mounted communication equipment, wireless sensors, internet cards, Internet of Things terminals, RFID terminals, NB-IOT terminals, MTC (Machine Type Communication) terminals, eMTC (enhanced MTC) terminals, data cards, internet cards, vehicle-mounted communication equipment, low-cost mobile phones, low-cost tablet computers and other wireless communication devices. The base stations or system devices in this application include but are not limited to macrocell base stations, microcell base stations, home base stations, relay base stations, gNB (NR node B) NR node B, TRP (Transmitter Receiver Point) and other wireless communication devices.

[0919] The above description is only a preferred embodiment of the present application and is not intended to limit the scope of protection of the present application. Any changes and modifications made based on the embodiments described in the specification, if they can achieve similar partial or complete technical effects, should be considered obvious and fall within the scope of protection of the present invention.

Claims

1. A first node used for wireless communication, characterized in that, Including: A first transmitter that, in response to any condition in a first set of conditions being satisfied, transmits a first signaling; Wherein, the first signaling includes capability information of the first node, the capability information of the first node includes a first type of identification list, and any first type of identification in the first type of identification list indicates a trainable model; one condition in the first set of conditions is that at least one first type of identification currently supported by the first node does not belong to the first type of identification list reported most recently; the first type of identification list in the first signaling includes at least one first type of identification currently supported by the first node.

2. The first node according to claim 1, characterized in that The first node includes a first receiver, and one condition in the first set of conditions is: The first receiver receives a second signaling that requests the capability information of the first node.

3. The first node according to claim 1 or 2, characterized in that, One condition in the first set of conditions that at least one first type of identification currently supported by the first node does not belong to the first type of identification list reported most recently includes at least one of the following: After the first type of identification list reported most recently, the application server of the first node is replaced; After the first type of identification list reported most recently, the first node receives a signaling from the application server, and the signaling from the application server indicates the first type of identification currently supported by the first node.

4. The first node according to any one of claims 1 to 3, characterized in that Whether the trainable model indicated by at least one first type of identification in the first type of identification list is available for a first frequency band depends on the codebook parameters on the first frequency band indicated by the first signaling.

5. The first node according to any one of claims 1 to 4, characterized in that, When the codebook parameters on the first frequency band indicated by the first signaling do not include a first type of codebook, the trainable model indicated by at least one first type of identification in the first type of identification list is not available for the first frequency band.

6. The first node according to any one of claims 1 to 3, characterized in that Whether the trainable model indicated by at least one first type of identification in the first type of identification list is available for a first frequency band depends on the first parameter related to a beam on the first frequency band indicated by the first signaling.

7. The first node according to any one of claims 1 to 3 or 6, characterized in that When the first parameter related to the beam on the first frequency band indicated by the first signaling does not include the second parameter, the trainable model indicated by at least one first type of identification in the first type of identification list is not available for the first frequency band.

8. A second node used for wireless communication, characterized in that, Including: A second receiver that receives the first signaling; Wherein, in response to any condition in the first set of conditions being satisfied, the sender of the first signaling transmits the first signaling; The first signaling includes capability information of the sender of the first signaling, the capability information of the sender of the first signaling includes a first type of identification list, and any first type of identification in the first type of identification list indicates a trainable model; one condition in the first set of conditions is that at least one first type of identification currently supported by the sender of the first signaling does not belong to the first type of identification list reported most recently; the first type of identification list in the first signaling includes at least one first type of identification currently supported by the sender of the first signaling.

9. A method used in a first node for wireless communication, characterized in that, Including: In response to any condition in the first set of conditions being satisfied, send a first signaling message; Wherein, the first signaling message includes the capability information of the first node, and the capability information of the first node includes a first type of identifier list, and any first type of identifier in the first type of identifier list indicates a trainable model; one condition in the first set of conditions is that at least one first type of identifier currently supported by the first node does not belong to the first type of identifier list reported most recently; the first type of identifier list in the first signaling message includes at least one first type of identifier currently supported by the first node.

10. A method in a second node for use in wireless communication, characterized in that, including: Receive a first signaling message; Wherein, in response to any condition in the first set of conditions being satisfied, the sender of the first signaling message sends the first signaling message; The first signaling message includes the capability information of the sender of the first signaling message, and the capability information of the sender of the first signaling message includes a first type of identifier list, and any first type of identifier in the first type of identifier list indicates a trainable model; one condition in the first set of conditions is that at least one first type of identifier currently supported by the sender of the first signaling message does not belong to the first type of identifier list reported most recently; the first type of identifier list in the first signaling message includes at least one first type of identifier currently supported by the sender of the first signaling message.

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