Wireless communication method and related device

Abstract

The embodiment of the invention provides a wireless communication method and a related device, and the wireless communication method comprises the steps: receiving a first message which is used for indicating the configuration of a monitoring report; sending a monitoring report, wherein the monitoring report is used for indicating the number of monitoring reference signal opportunities meeting the first condition in the monitoring window; the first condition comprises that an optimal beam in a monitoring set indicated by the monitoring result comprises K optimal beams indicated by the channel prediction result, the monitoring set comprises the K optimal beams indicated by the channel prediction result and N first beams, and the spatial distribution of the first beams and any beam in the K optimal beams indicated by the channel prediction result has continuity. The monitoring set comprises the K optimal beams and the N first beams indicated by the channel prediction result, and the number of the beams in the monitoring set is increased, so that the spatial dimension of monitoring is increased, the AI model is effectively monitored, and the prediction accuracy of the AI model is enhanced by monitoring whether the performance of the AI model is reduced or not.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a wireless communication method and related apparatus. Background Technology

[0002] With the development of artificial intelligence technology, in some application scenarios, the functions of a terminal can be realized by artificial intelligence (AI) models. For example, a terminal can use an AI model to perform channel prediction. Specifically, the terminal can perform channel measurements based on a first set of reference signals to obtain measurement results. These measurement results can be used as input to an AI model, which then uses the input information to obtain channel prediction results for a second set of reference signals.

[0003] The terminal can also perform channel measurements based on the monitoring set, obtain measurement results, and then use the measurement results as a measured tag to compare with the channel prediction results of the second reference signal set in order to monitor the accuracy of the channel prediction results of the second reference signal set.

[0004] The monitoring set is a subset of the second reference signal set. In some cases, the optimal beam (i.e., the Top 1 beam) selected by the terminal based on the channel prediction results of the second reference signal set may not be the same as the optimal beam selected by the terminal based on the measurement results of the monitoring set. This indicates that the optimal beam predicted by the AI ​​model is not the actual optimal beam, and the accuracy of the AI ​​model prediction is not high. Summary of the Invention

[0005] This application provides a wireless communication method and related apparatus, with the aim of enhancing the accuracy of AI model predictions.

[0006] To achieve the above objectives, this application provides the following technical solution:

[0007] Firstly, this application provides a wireless communication method, which can be executed by a terminal, or by a component (such as a circuit, chip, or chip system) configured in the terminal, or by a logic module or software capable of implementing all or part of the terminal's functions. This application does not limit the scope of the method. The following description uses a terminal as an example.

[0008] The wireless communication method includes: receiving a first message, the first message being used to indicate a monitoring report configuration, the monitoring report configuration being used to indicate performance monitoring of channel prediction results; and sending a monitoring report, the monitoring report being used to indicate the number of monitoring reference signal opportunities within a monitoring window that satisfy a first condition; wherein: the first condition includes: the optimal beams in the monitoring set indicated by the monitoring results are included in the K optimal beams indicated by the channel prediction results, the monitoring results are channel measurement results obtained based on the monitoring set at the monitoring reference signal opportunities, the monitoring set includes the K optimal beams indicated by the channel prediction results and N first beams, the spatial distribution of the first beams and any one of the K optimal beams indicated by the channel prediction results is continuous, and K and N are both positive integers.

[0009] In the above technical solution, the monitoring set includes K optimal beams and N first beams indicated by the channel prediction results, which expands the number of beams in the monitoring set, increases the spatial dimension of monitoring, and realizes more accurate monitoring of channel prediction results and effective monitoring of AI models. By quickly and sensitively detecting whether the performance of AI models has deteriorated, timely measures can be taken to improve the robustness of AI models and enhance the accuracy of AI model predictions.

[0010] Furthermore, since the spatial distribution of the first beam and any of the K optimal beams indicated by the channel prediction results is continuous, it also ensures that the beams extended to the monitoring set are also beams with better channel quality, thereby improving the efficiency of the monitoring set in monitoring the channel prediction results.

[0011] In one possible implementation, the spatial distribution of the first beam and any one of the K optimal beams indicated by the channel prediction results having continuity includes: the reference signal transmitted by the first beam and the reference signal transmitted by any one of the K optimal beams indicated by the channel prediction results having a quasi-co-located QCL relationship.

[0012] In one possible implementation, N is determined by: screening out the number of first beams corresponding to confidence information in the mapping relationship, the mapping relationship including multiple confidence information and the number of first beams corresponding to each confidence information; the confidence information is used to indicate the credibility of the channel prediction result.

[0013] In one possible implementation, the confidence information includes the difference between the probability that the Kth optimal beam indicated by the channel prediction result is the optimal beam and the probability that the (K+1)th beam indicated by the channel prediction result is the optimal beam.

[0014] In one possible implementation, the confidence information includes: the difference between the probability that the first optimal beam indicated by the channel prediction result is the optimal beam and the first probability sum, where the first probability sum is the sum of the probabilities that the second optimal beam indicated by the channel prediction result is the optimal beam up to the Kth optimal beam being the optimal beam.

[0015] In one possible implementation, the confidence information includes: the KL divergence of distribution P relative to distribution Q, where distribution P is the probability distribution of multiple beams as the optimal beams indicated by the channel prediction results, and distribution Q is the ideal probability distribution of multiple beams as the optimal beams.

[0016] In one possible implementation, the mapping relationship is included in the first message.

[0017] Secondly, this application provides a wireless communication method, which can be executed by a network device, or by a component (such as a circuit, chip, or chip system) configured in the network device, or by a logic module or software capable of implementing all or part of the functions of the network device. This application does not limit the scope of this method. The following description uses a network device as an example.

[0018] The wireless communication method includes: sending a first message, the first message being used to indicate a monitoring report configuration, the monitoring report configuration being used to indicate performance monitoring of channel prediction results; receiving a monitoring report, the monitoring report being used to indicate the number of monitoring reference signal opportunities within a monitoring window that satisfy a first condition; wherein: the first condition includes: the optimal beams in the monitoring set indicated by the monitoring results are included in the K optimal beams indicated by the channel prediction results, the monitoring results are channel measurement results obtained based on the monitoring set at the monitoring reference signal opportunities, the monitoring set includes the K optimal beams indicated by the channel prediction results and N first beams, the spatial distribution of the first beams and any one of the K optimal beams indicated by the channel prediction results is continuous, and K and N are both positive integers.

[0019] Thirdly, this application provides a communication device including a transceiver module for receiving a first message indicating a monitoring report configuration, the monitoring report configuration indicating performance monitoring of channel prediction results; and for sending a monitoring report indicating the number of monitoring reference signal opportunities within a monitoring window that satisfy a first condition; wherein the first condition includes: the optimal beams in the monitoring set indicated by the monitoring results are included in the K optimal beams indicated by the channel prediction results, the monitoring results are channel measurement results obtained based on the monitoring set at the monitoring reference signal opportunities, the monitoring set includes the K optimal beams indicated by the channel prediction results and N first beams, the spatial distribution of the first beams and any one of the K optimal beams indicated by the channel prediction results is continuous, and K and N are both positive integers.

[0020] Fourthly, this application provides a communication device, which includes a transceiver module for transmitting a first message, the first message for instructing a monitoring report configuration, the monitoring report configuration for instructing performance monitoring of channel prediction results; and for receiving a monitoring report, the monitoring report for instructing the number of monitoring reference signal opportunities within a monitoring window that satisfy a first condition; wherein: the first condition includes: the optimal beams in the monitoring set indicated by the monitoring results are included in the K optimal beams indicated by the channel prediction results, the monitoring results are channel measurement results obtained based on the monitoring set at the monitoring reference signal opportunities, the monitoring set includes the K optimal beams indicated by the channel prediction results and N first beams, the spatial distribution of the first beams and any one of the K optimal beams indicated by the channel prediction results is continuous, and K and N are both positive integers.

[0021] Fifthly, this application provides a communication device including a processor coupled to a memory, which can be used to execute instructions or data in the memory to implement the method in the first aspect above.

[0022] In one possible implementation, the communication device also includes a memory.

[0023] In one possible implementation, the communication device further includes a communication interface, to which the processor is coupled. In one implementation, the communication interface may be a transceiver, or an input / output interface.

[0024] In another implementation, the communication device is a chip configured in the terminal. When the communication device is a chip configured in the terminal, the communication interface can be an input / output interface.

[0025] In a sixth aspect, this application provides a communication device including a processor coupled to a memory, which can be used to execute instructions or data in the memory to implement the method in the second aspect above.

[0026] In one possible implementation, the communication device also includes a memory.

[0027] In one possible implementation, the communication device further includes a communication interface, to which the processor is coupled. In one implementation, the communication interface may be a transceiver, or an input / output interface.

[0028] In another implementation, the communication device is a chip configured in a network device. When the communication device is a chip configured in a network device, the communication interface can be an input / output interface.

[0029] In a seventh aspect, this application provides a processor, including: an input circuit, an output circuit, and a processing circuit. The processing circuit is used to receive signals through the input circuit and transmit signals through the output circuit, causing the processor to execute the method in any of the aspects.

[0030] In specific implementation, the processor can be one or more chips, the input circuit can be input pins, the output circuit can be output pins, and the processing circuit can be transistors, gate circuits, flip-flops, and various logic circuits. The input signal received by the input circuit can be received and input by, for example, but not limited to, a receiver, and the signal output by the output circuit can be, for example, but not limited to, output to and transmitted by a transmitter. Furthermore, the input circuit and the output circuit can be the same circuit, which is used as both the input circuit and the output circuit at different times. This application does not limit the specific implementation of the processor and various circuits.

[0031] Eighthly, this application provides a computer program product comprising: a computer program (also referred to as code or instructions) that, when run, causes a computer to perform the methods described in any of the preceding aspects.

[0032] Ninthly, a computer-readable storage medium is provided that stores a computer program (also referred to as code or instructions) that, when run on a computer, causes the computer to perform the methods of any of the preceding aspects.

[0033] In a tenth aspect, this application provides a chip system including one or more processors for calling and executing instructions stored in memory, causing the methods in any of the above aspects or possible implementations to be executed. The chip system may be composed of a chip or may include chips and other discrete devices. The chip system may include input circuitry or interfaces for transmitting information or data, and output circuitry or interfaces for receiving information or data.

[0034] Eleventhly, a communication system is provided, including the aforementioned terminal and network equipment.

[0035] In one possible implementation, the communication system may also include other devices that communicate with the terminal and / or network devices.

[0036] The technical effects of the solutions provided in the second to eleventh aspects can be found in the content of the first aspect. Attached Figure Description

[0037] Figure 1 A schematic diagram of the architecture of a communication system provided in an embodiment of this application;

[0038] Figure 2 This is a schematic diagram of the network device provided in the embodiments of this application;

[0039] Figure 3 and Figure 4 A schematic diagram illustrating two use case examples of spatial beam prediction provided in the embodiments of this application;

[0040] Figure 5 and Figure 6 A schematic diagram illustrating two use case examples of time-domain beam prediction provided in the embodiments of this application;

[0041] Figure 7 A flowchart illustrating the wireless communication method provided in an embodiment of this application;

[0042] Figure 8 This is a schematic diagram illustrating the process of constructing a monitoring set provided in an embodiment of this application;

[0043] Figure 9 A schematic diagram illustrating the process of sorting the probability distribution of beams in SetA, provided in an embodiment of this application;

[0044] Figure 10 This is a structural example diagram of another communication device disclosed in the embodiments of this application;

[0045] Figure 11 This is a structural example diagram of another communication device disclosed in an embodiment of this application. Detailed Implementation

[0046] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. The terminology used in the following embodiments is for the purpose of describing specific embodiments only and is not intended to be a limitation of this application. As used in the specification and appended claims of this application, the singular expressions "a," "an," "the," "the," "the," and "this" are intended to also include expressions such as "one or more," unless the context clearly indicates otherwise. It should also be understood that in the embodiments of this application, "one or more" refers to one, two, or more; "and / or" describes the relationship between related objects, indicating that three relationships may exist; for example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship.

[0047] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0048] The "multiple" mentioned in the embodiments of this application refers to two or more. It should be noted that in the description of the embodiments of this application, terms such as "first" and "second" are used only for the purpose of distinguishing descriptions and should not be construed as indicating or implying relative importance, nor should they be construed as indicating or implying order.

[0049] The technical solutions provided in this application can be applied to communication systems, which may include, but are not limited to, the following systems: second-generation (2G) communication systems, third-generation (3G) communication systems, long-term evolution (LTE) systems, universal mobile telecommunication system (UMTS), worldwide interoperability for microwave access (WiMAX) communication systems, fifth-generation (5G) systems or new radio (NR) systems, 5.5G systems or sixth-generation (6G) systems, and future mobile communication systems; vehicle-to-X (V2X); V2X may include vehicle-to-network (V2N), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P), long-term evolution-vehicle (LTE-V) technology, vehicle-to-everything (V2X), machine-type communication (MTC), and the Internet of Things (IoT). Things (IoT), ambient internet of things (AIOT), long term evolution-machine (LTE-M), machine to machine (M2M), etc.

[0050] The communication system can be applied to scenarios including: terrestrial cellular communication, non-terrestrial network (NTN), satellite communication, high altitude platform station (HAPS) communication, vehicle-to-everything (V2X) communication, integrated access and backhaul (IAB) communication, and reconfigurable intelligent surface (RIS) communication, etc.

[0051] For example, Figure 1 A schematic diagram of the architecture of a communication system provided in an embodiment of this application is shown.

[0052] like Figure 1 As shown, the communication system includes a first device 100 and a second device 200.

[0053] The first device 100 may be a network-side device used to provide network communication functions. In some cases, it may also be called a network device or network element. The network device may typically be a base station (including functional units of the base station, or a combination of functional units of the base station) or a core network unit. The core network unit may be a functional unit in the core network, including but not limited to access and mobility management function (AMF) units or session management function (SMF) units.

[0054] In this embodiment, the base station can be any device with wireless transceiver capabilities, including but not limited to: evolved Node B (NodeB, eNB, or e-NodeB) in Long Term Evolution (LTE), base station (gNodeB or gNB) or transmission receiving point / transmission reception point (TRP) in New Radio (NR), base stations in subsequent 3GPP evolutions, access nodes, wireless relay nodes, and wireless backhaul nodes in Wi-Fi systems. The base station can be: macro base station, micro base station, pico base station, small cell, relay station, or balloon station, etc. The base station can include one or more co-located or non-co-located transmission reception points (TRPs). The base station can also be a radio controller, centralized unit (CU), and / or distributed unit (DU) in a cloud radioaccess network (CRAN) scenario. The base station can communicate with terminal 200, or it can communicate with terminal 200 through a relay station. The terminal can communicate with multiple base stations using different technologies. For example, the terminal can communicate with base stations that support LTE networks, base stations that support 5G networks, and can also establish dual connections with both LTE and 5G base stations.

[0055] In practical applications, when network devices function as access network devices, multiple network devices can collaborate to assist terminals in achieving wireless access, with different network devices each implementing some of the functions of a base station. For example, network devices can be central units (CUs), distributed units (DUs), CUs (control planes, CPs), CUs (user planes, UPs), or radio units (RUs), etc. CUs and DUs can be set up separately or included in the same network element, such as a baseband unit (BBU). RUs can be included in radio frequency devices or radio frequency units, such as remote radio units (RRUs), active antenna units (AAUs), or remote radio heads (RRHs).

[0056] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an ORAN system, CU can also be called O-CU (Open CU), DU can also be called O-DU, CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called O-RU. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software and hardware modules. CU (or CU-CP and CU-UP), DU, and RU can implement different protocol layer functions.

[0057] Figure 2 This is a schematic diagram of the structure of an access network device. As an implementation example, such as... Figure 2As shown, the access network device may include at least one CU and at least one DU. This design can be referred to as CU and DU separation. One CU can be connected to one or more DUs. CU and DU can be separated according to the protocol layer of the wireless network: for example, the functions of the PDCP layer and above (such as the RRC layer and SDAP layer, etc.) are set in the CU, and the functions of the protocol layers below the PDCP layer (such as the RLC layer, media access control (MAC) layer, and PHY layer, etc.) are set in the DU; or, for another example, the functions of the protocol layers above the PDCP layer are set in the CU, and the functions of the protocol layers below the PDCP layer are set in the DU, without limitation. When the CU includes CU-CP and CU-UP, CU-CP is used to implement the control plane functions of the CU, and CU-UP is used to implement the user plane functions of the CU. For example, when the CU is configured to implement the functions of the PDCP layer, RRC layer, and SDAP layer, CU-CP is used to implement the RRC layer functions and the PDCP layer control plane functions, and CU-UP is used to implement the SDAP layer functions and the PDCP layer user plane functions. This application does not limit the names of CU and DU. The above division of CU and DU processing functions according to the protocol layer is just one example; other methods can also be used.

[0058] The CU can be connected to the core network. Optionally, the CU can have some of the functions of the core network.

[0059] Furthermore, some functions of the DU can be separated and configured. For example... Figure 2As shown, this functionality can be implemented by a radio unit (RU). The RU can have radio frequency (RF) capabilities. This application does not limit the name of the RU. The DU and RU can be split or separated within the PHY layer. For example, the DU can implement higher-level functions in the PHY layer, and the RU can implement lower-level functions in the PHY layer, or implement both lower-level and RF functions. Higher-level functions in the PHY layer include functions closer to the MAC layer, and lower-level functions in the PHY layer include functions closer to the RF layer. For example, higher-level functions in the PHY layer include one or more of the following: forward error correction (FEC) encoding / decoding, scrambling, or modulation / demodulation. Lower-level functions in the PHY layer include one or more of the following: fast Fourier transform (FFT) / inverse fast Fourier transform (IFFT), beamforming, or extraction and filtering of the physical random access channel (PRACH), etc. The RU can communicate with the terminal device via the air interface using RF signals. The pre-coding function of the PHY layer code can be located in the DU or the RU. The separation between the DU and RU can be done in various ways without restriction. An interface exists between the DU and RU. For example, depending on the separation method, the interface between the DU and RU can be a Common Public Radio Interface (CPRI) interface or an Enhanced Common Public Radio Interface (eCPRI) interface.

[0060] Optionally, any one of CU, CU-CP, CU-UP, DU, and RU can be a software module, a hardware structure, or a combination of software and hardware structures, without limitation. The different entities can exist in the same or different forms. For example, CU, CU-CP, CU-UP, and DU are software modules, and RU is a hardware structure. For the sake of brevity, all possible combinations are not listed here. These modules and the methods they execute are also within the protection scope of the embodiments of this application. For example, when the method of the embodiments of this application is executed by an access network device, it can be specifically executed by at least one of CU, CU-CP, CU-UP, DU, or RU.

[0061] The second device 200 can be a device that accesses the network, typically a terminal.

[0062] In the embodiments of this application, the terminal can take various forms, such as a mobile phone, tablet computer, computer with wireless transceiver capabilities, virtual reality (VR) terminal device, augmented reality (AR) terminal device, wireless terminal in industrial control, vehicle-mounted terminal device, wireless terminal in self-driving, wireless terminal in remote medical care, wireless terminal in smart grid, wireless terminal in transportation safety, wireless terminal in smart city, wireless terminal in smart home, wearable terminal device, etc. The terminal may also be referred to as terminal equipment, user equipment (UE), access terminal equipment, vehicle-mounted terminal, industrial control terminal, UE unit, UE station, mobile station, mobile station, remote station, remote terminal equipment, mobile device, UE terminal equipment, wireless communication equipment, UE agent, or UE device, etc. The terminal can also be a fixed terminal or a mobile terminal.

[0063] In some embodiments, the communication system may also include other devices that communicate with the first device and / or the second device, which is not a limitation of this application.

[0064] To facilitate understanding, the concepts involved in this application will be explained below.

[0065] 1. Spatial beam prediction.

[0066] Spatial beam prediction refers to prediction based on spatial dimensions. Terminals can utilize artificial intelligence (AI) models for spatial beam prediction. For example, spatial beam prediction involves the terminal predicting the measurement results of a second reference signal set (Set 2 or Set A) based on the measurement results of a first reference signal set (Set 1 or Set B). The reference signals in the first and second reference signal sets may include: channel state information reference signal (CSI-RS), synchronization signal block (SSB), etc.

[0067] Different reference signals typically correspond to different beams. Optionally, the terminal uses an AI model to predict the signal quality of M beams (such as the received power to interference plus noise ratio (RSRP) and / or the signal to interference plus noise ratio (SINR)) based on the signal quality of L beams, or predicts the best beam among the M beams, or predicts the best K beams among the M beams, where K is an integer greater than 1.

[0068] Figure 3 In one use case of spatial beam prediction, the first reference signal set is a subset of the second reference signal set, that is, the number of reference signals L in the first reference signal set is less than the number of reference signals M in the second reference signal set.

[0069] Figure 4 In another use case of spatial beam prediction, the reference signals in the first and second reference signal sets may belong to different reference signal types. For example, the beam corresponding to Set 1 is typically a wide beam, and the beam corresponding to Set 2 is typically a narrow beam. Optionally, to ensure accurate prediction, the narrow beam corresponding to Set 2 is typically located within the coverage area of ​​the wide beam corresponding to Set 1.

[0070] 2. Time-domain beam prediction.

[0071] Temporal beam prediction refers to prediction based on the time dimension. Terminals can also use artificial intelligence (AI) models for temporal beam prediction. For example, temporal beam prediction is when a terminal predicts the measurement results of a second reference signal set (Set 2 or Set A) in the future based on the measurement results of a first reference signal set (Set 1 or Set B) in historical time.

[0072] In one use case, the first reference signal set and the second reference signal set can be the same reference signal set; or, the first reference signal set and the second reference signal set can be different reference signal sets, such as the first reference signal set being a subset of the second reference signal set.

[0073] like Figure 5As shown, the terminal uses an AI model to predict the signal quality of the second reference signal set's beam at future T2 times based on the measurement results of the signal quality of the first reference signal set at historical times T1, or to predict the optimal beam of the second reference signal set at each of the T2 times, or to predict the best K beams of the second reference signal set at each of the T2 times. T1 and T2 are integers greater than 1, and T1 can be greater than T2, less than T2, or equal to T2; K is an integer greater than 1.

[0074] In another use case, the reference signals in the first and second reference signal sets can belong to different reference signal types. For example, the beam corresponding to Set 1 is typically a wide beam, and the beam corresponding to Set 2 is typically a narrow beam. Optionally, the narrow beam corresponding to Set 2 is typically located within the coverage area of ​​the wide beam corresponding to Set 1.

[0075] like Figure 6 As shown, the terminal uses an AI model to predict the signal quality of the second reference signal set's beam at future T2 times based on the measurement results of the signal quality of the first reference signal set at historical times T1, or to predict the optimal beam of the second reference signal set at each of the T2 times, or to predict the best K beams of the second reference signal set at each of the T2 times. T1 and T2 are integers greater than 1, and T1 can be greater than T2, less than T2, or equal to T2; K is an integer greater than 1.

[0076] 3. Quasi-co-location (QCL): Its main function is to allow a terminal to infer the channel characteristics of another reference signal using a known reference signal. If two reference signals have a QCL relationship, it means that they have similar propagation characteristics in space.

[0077] The descriptions of spatial beam prediction, temporal beam prediction, and quasi-colocation are provided only to facilitate understanding of the technical solutions in this application and do not constitute any limitation on this application.

[0078] With the development of artificial intelligence technology, in some application scenarios, the functions of a terminal can be realized by an AI model. For example, a terminal uses an AI model to perform channel prediction. Specifically, the network device configures a first reference signal set and a second reference signal set (i.e., Set A) to the terminal. The terminal can measure the first reference signal set to perform channel measurement and obtain the measurement result. This measurement result can be used as input to the AI ​​model, and the AI ​​model obtains the channel prediction result of Set A based on the input information.

[0079] The terminal can also utilize a third reference signal set (or monitoring set) to monitor the accuracy of the prediction results of Set A. Specifically, the terminal can perform channel measurements based on the monitoring set, obtain channel measurement results, and use these results as a measured label to compare with the channel prediction results of Set A to monitor the accuracy of Set A's channel prediction results. Set A and the monitoring set typically have a corresponding relationship, such as the monitoring set being a subset of Set A or the monitoring set being the entire set of Set A.

[0080] The monitoring set is a subset of Set A. In some cases, the optimal beam (Top 1 beam) selected by the terminal based on the prediction results of Set A is not the same as the optimal beam selected by the terminal based on the measurement results of the monitoring set. This indicates that the optimal beam predicted by the terminal through the AI ​​model is not the actual optimal beam, and the accuracy of the AI ​​model prediction is not high.

[0081] In response to this, embodiments of this application provide a wireless communication method that can enhance the accuracy of AI model predictions.

[0082] Figure 7 This application illustrates a wireless communication method provided by an embodiment of the present application, which can be applied to a communication system including network devices and terminals. The description of the network devices and terminals can be found in the foregoing content and will not be repeated here.

[0083] like Figure 7 As shown, the wireless communication method provided in this application embodiment includes:

[0084] S701, the network device sends a first message to the terminal, and the terminal receives the first message.

[0085] The first message is used to instruct the monitoring report configuration, which instructs the performance monitoring of the channel prediction results.

[0086] The channel prediction result is the channel prediction result obtained by the terminal based on the prediction report configuration. That is, the terminal can perform channel measurement based on the first reference signal set, obtain the measurement result, and call the AI ​​model to obtain the channel prediction result of Set A based on the measurement result.

[0087] For example, the prediction report configuration may include: measurement resources, i.e., a set of reference signals (or beam sets) used for beam prediction, and may also include report content, reporting time, etc. Beam prediction may include spatial domain beam prediction and / or temporal domain beam prediction. The reference signal set may include: a first set of reference signals used for beam measurement and Set A used for beam prediction. The report content may include the channel prediction results performed by the terminal, such as predicting K optimal beams in Set A based on signal quality. The reporting time may be periodic, semi-persistent, or aperiodic.

[0088] The explanations of spatial beam prediction, temporal beam prediction, the first reference signal set, and Set A can be found in the previous text and will not be repeated here.

[0089] In some embodiments, the first message is a monitoring report configuration for the network device and the terminal to cooperate in evaluating the AI ​​model's predicted scenario, thus saving signaling between the network device and the terminal. For example, the first message may be referred to as a monitoring report configuration.

[0090] Optionally, the configuration of the first message indication monitoring report may include: the content of the first message indication report, the reporting time, etc.

[0091] The report content, or reporting quantity, is used to indicate the content that the terminal needs to report through the monitoring report. For example, the monitoring report carries Np, which indicates the number of samples that meet the prediction accuracy conditions within the monitoring window. Set A and the monitoring set are a pair of samples, and the number of samples that meet the prediction accuracy conditions is: the number of sample pairs of Set A and the monitoring set that meet the prediction accuracy conditions.

[0092] It can be understood that a monitoring window may include one or more monitoring reference signal opportunities. During a monitoring reference signal opportunity, the terminal performs a channel measurement using the monitoring set to obtain the channel measurement result. This channel measurement result is then used as a measured label and compared with the channel prediction result of Set A to monitor the accuracy of the channel prediction result of Set A. Therefore, Np indicates the number of samples within the monitoring window that meet the prediction accuracy condition, and also indicates the number of monitoring reference signal opportunities within the monitoring window that meet the prediction accuracy condition.

[0093] Optionally, the duration of the monitoring window and / or the number of monitoring reference signal opportunities included in the monitoring window may be configured by the network device to the terminal in the first message or other messages. Alternatively, the terminal may also decide on its own the duration of the monitoring window and / or the number of monitoring reference signal opportunities included in the monitoring window.

[0094] The accurate prediction condition, also referred to as the first condition, may include: CRI_measured_best exists in the Top K beam set predicted by the AI ​​model; where:

[0095] CRI_measured_best refers to the optimal beam in the monitoring set, indicated by the channel measurement results obtained by the terminal based on the monitoring set at a monitoring reference signal timing. The reference signal transmitted by this optimal beam has the best signal quality. The channel measurement results obtained based on the monitoring set at a monitoring reference signal timing can be simply referred to as monitoring results.

[0096] The Top K-beam set includes the K optimal beams indicated by the channel prediction results, i.e., the K optimal beams in Set A indicated by the channel prediction results.

[0097] The report may also include content other than Np; please refer to the relevant agreement for details, which will not be elaborated here.

[0098] The reporting time, also known as the time-domain configuration, is used to indicate when the terminal reports the monitoring report. Optionally, the reporting time may not be included in the first message.

[0099] Optionally, the first message indicating the monitoring report configuration may further include: the first message indicating the prediction report configuration associated with the monitoring report configuration, such as the first message including an identifier (CSI-ResourceConfigId) of the prediction report configuration, such that the monitoring report configuration is associated with the prediction report configuration indicated by the identifier.

[0100] As mentioned above, the prediction report configuration includes measurement resources, namely a first reference signal set for beam measurement and Set A for beam prediction. The first message indicates the prediction report configuration associated with the monitoring report configuration. Thus, the terminal can specify the resources used for channel measurement based on the monitoring set, such as the beam index in the monitoring set and time-frequency domain resources, based on the measurement resources in the prediction report configuration associated with the monitoring report configuration.

[0101] Optionally, the first message indicating monitoring report configuration may further include: the first message indicating report size (reportSize), i.e., the value of K, used to indicate the K optimal beams in Set A predicted by the AI ​​model. Further optionally, the first message may also indicate the use of the K optimal beams in Set A predicted by the AI ​​model to construct a monitoring set for channel measurements.

[0102] Optionally, the first message can also be used to instruct the terminal to provide a monitoring report obtained from performance monitoring of the channel prediction results. This monitoring report is a CSI (channel state information) report.

[0103] In other embodiments, the first message and the monitoring report in the prediction scenario of evaluating the AI ​​model are configured as two independent messages. The first message is a new message and is also applied to the network device and terminal to cooperate in evaluating the prediction scenario of the AI ​​model, which can enhance the flexibility of network device configuration.

[0104] Optionally, the configuration of the first message indication monitoring report may include: the content of the first message indication report, the reporting time, etc. Optionally, it may also include: the configuration of the prediction report associated with the first message indication monitoring report configuration, and optionally, it may also include: the size of the first message indication report (reportSize).

[0105] The report content, report time, the configuration of the prediction report associated with the first message indication monitoring report, and the description of the first message indication report size can be found in the previous text and will not be repeated here.

[0106] Alternatively, in order to reduce the content carried by the first message, in a scenario where the first message and the monitoring report in the prediction scenario of evaluating the AI ​​model are configured as two independent messages, the content included in the monitoring report configuration in the prediction scenario of evaluating the AI ​​model may not be included in the first message.

[0107] For example, if one or more of the following are included in the monitoring report configuration for the prediction scenario of evaluating the AI ​​model: report content, report time, monitoring report configuration associated with the prediction report configuration, and report size, then the first message will not include the content included in the monitoring report configuration for the prediction scenario of evaluating the AI ​​model, but will only include the content not included in the monitoring report configuration for the prediction scenario of evaluating the AI ​​model.

[0108] Alternatively, the first message indication monitoring report configuration may further include: a first message indication parameter, such as called dynamic monitoring-enabled, used to activate the method provided in the embodiments of this application, that is, to use the method provided in the embodiments of this application to perform performance monitoring of channel prediction results and send a prediction report.

[0109] For example, the first message may include radio resource control reconfiguration (RRC) signaling. That is, the network device sends RRC signaling, which instructs the terminal to perform performance monitoring on the channel prediction results.

[0110] As another example, the first message may include a medium access control element (MAC CE). That is, the network device sends a MAC CE, which instructs the terminal to perform performance monitoring on the channel prediction results.

[0111] S702: The terminal sends a monitoring report to the network device, and the corresponding network device receives the monitoring report.

[0112] The monitoring report is used to indicate the number of monitoring reference signals that meet the first condition within the monitoring window.

[0113] The first condition, as described above, includes: the optimal beams in the monitoring set indicated by the monitoring results are included in the K optimal beams indicated by the channel prediction results; the monitoring results are channel measurement results obtained based on the monitoring set at the time of monitoring the reference signal; the monitoring set includes the K optimal beams indicated by the channel prediction results and N first beams, the spatial distribution of the first beams and any one of the K optimal beams indicated by the channel prediction results is continuous, and K and N are both positive integers.

[0114] In some embodiments, after the network device sends the first message, it may also transmit reference signals to the terminal in the beam directions included in the beam set Set A when monitoring the reference signal timing. The terminal can obtain the monitoring result of the reference signal timing based on the reference signals transmitted by the network device in the beam directions included in Set A.

[0115] The monitoring results can indicate the signal quality of the reference signal in each beam direction included in the monitoring set, such as the RSRP of the reference signal in each beam direction included in the monitoring set.

[0116] Optionally, the monitoring result can also indicate the optimal beam in the monitoring set. For example, the terminal measures the RSRP of the reference signal in each beam direction included in the monitoring set, and takes the beam in the monitoring set corresponding to the maximum RSRP of the reference signal as the optimal beam in the monitoring set. That is, the reference signal transmitted by the optimal beam in the monitoring set has the maximum RSRP measured by the terminal.

[0117] In this embodiment, the monitoring set includes K optimal beams and N first beams indicated by the channel prediction results, which expands the number of beams in the monitoring set and increases the spatial dimension of monitoring. This enables more accurate monitoring of the channel prediction results and effective monitoring of the AI ​​model. By quickly and sensitively detecting whether the performance of the AI ​​model has deteriorated, timely measures can be taken to improve the robustness of the AI ​​model and enhance the accuracy of AI model predictions.

[0118] Furthermore, since the spatial distribution of the first beam and any of the K optimal beams indicated by the channel prediction results is continuous, it also ensures that the beams extended to the monitoring set are also beams with better channel quality, thereby improving the efficiency of the monitoring set in monitoring the channel prediction results.

[0119] In some embodiments, the terminal obtains the monitoring result of the timing of monitoring the reference signal based on the reference signal transmitted by the network device in the beam direction included in Set A, as follows:

[0120] Implementation Method 1. When monitoring reference signals, the network device transmits reference signals to the terminal in the beam directions included in Set A respectively; the terminal obtains the beam index and time-frequency domain resources of the monitoring set by configuring the prediction report associated with the monitoring report configuration based on the first message instruction; the terminal measures the signal quality (such as RSRP) of the reference signal in each beam direction included in the monitoring set based on the beam index and time-frequency domain resources of the monitoring set; the terminal can also obtain the optimal beam in the monitoring set based on the signal quality of the reference signal in each beam direction included in the monitoring set.

[0121] Implementation Method 2. The terminal can send the beams included in the monitoring set (such as beam indices) to the network device; when monitoring reference signals, the network device transmits reference signals to the terminal in the beam directions included in the monitoring set respectively; the terminal obtains the beam indices and time-frequency domain resources in the monitoring set by configuring the prediction report associated with the monitoring report configuration based on the first message instruction; the terminal measures the signal quality (such as RSRP) of the reference signal in each beam direction included in the monitoring set based on the beam indices and time-frequency domain resources in the monitoring set; the terminal can also obtain the optimal beam in the monitoring set based on the signal quality of the reference signal in each beam direction included in the monitoring set.

[0122] In some embodiments, the terminal may also construct a monitoring set.

[0123] For example, after obtaining the channel prediction results of Set A, such as the K optimal beams in Set A, the terminal can also construct a monitoring set, which, as described above, includes the K optimal beams (referred to as Top-K beams) and N first beams in Set A.

[0124] Figure 8 The flowchart for building a monitoring set on the terminal is shown.

[0125] like Figure 8 As shown, the method for constructing this monitoring set includes:

[0126] S801, The terminal obtains the channel prediction result of Set A output by the AI ​​model.

[0127] S802. Based on the probability distribution of beams in Set A indicated by the channel prediction results, obtain the probability ranking of the beams.

[0128] Optionally, the channel prediction results of Set A can be used to indicate the probability distribution of beams in Set A, which includes the probability that each beam in Set A is predicted as the optimal beam by the AI ​​model.

[0129] The Top-K beams predicted by the AI ​​model are the top K beams ranked according to the probability that the AI ​​model predicts them as the optimal beams.

[0130] For example, Figure 9 This demonstrates how the terminal obtains the probability that the beams in Set A, sorted by probability, are the optimal beams.

[0131] like Figure 9 As shown in (a), the terminal uses the measurement results of the first reference signal set as input to the AI ​​model. Based on the measurement results of the first reference signal set, the AI ​​model predicts the channel prediction result for Set A. Optionally, the channel prediction result for Set A may include the probability distribution of beams in Set A, which includes the probability that a beam in Set A is the optimal beam in Set A.

[0132] Taking the channel prediction result of Set A, which includes the RSRP of the reference signal in each beam direction contained in Set A, as an example, the optimal beam in Set A is the beam in Set A corresponding to the maximum value of the RSRP of the reference signal. That is, the reference signal transmitted by the optimal beam in Set A has the maximum RSRP measured by the terminal.

[0133] Figure 9 In example (b), the probability distribution of beams in Set A is shown. In this example, the beams in Set A include 6 beams with indices #1 to #6, and each beam has a probability of being the optimal beam of any value in the interval [0,1].

[0134] Depend on Figure 9 As shown in (b): the probability of beam #1 being the optimal beam is 0.15, the probability of beam #2 being the optimal beam is 0.2, the probability of beam #3 being the optimal beam is 0.5, the probability of beam #4 being the optimal beam is 0.8, the probability of beam #5 being the optimal beam is 0.03, and the probability of beam #6 being the optimal beam is 0.01.

[0135] The six beams in Set A, sorted by probability, are as follows: Figure 9 As shown in (c), the beams are: beam #4, beam #3, beam #2, beam #1, beam #5 and beam #6.

[0136] S803. Based on the probability ranking of beams in Set A, obtain confidence information.

[0137] In some embodiments, the terminal obtains confidence information in the following ways:

[0138] Implementation Method 1. Confidence information includes: the difference between the probability that the Kth optimal beam indicated by the channel prediction result is the optimal beam and the probability that the (K+1)th optimal beam indicated by the channel prediction result is the optimal beam.

[0139] If K is assumed to be 4, then K+1 would be 5. Figure 9 In the example shown in (c), the fourth optimal beam indicated by the channel prediction result is the fourth beam in the sequence of six beams in Set A, sorted by probability, i.e., beam #1, which has a probability of 0.15. Similarly, the fifth optimal beam indicated by the channel prediction result is the fifth beam in the sequence of six beams in Set A, sorted by probability, i.e., beam #5, which has a probability of 0.03. The confidence information is 0.15 - 0.03 = 0.12.

[0140] In this implementation, the higher the confidence information value, the clearer the probability boundary of the Top K beams and the higher the reliability of the channel prediction result of Set A. Conversely, the lower the confidence information value, the less clear the probability boundary of the Top K beams and the lower the reliability of the channel prediction result of Set A.

[0141] Implementation method 2. The confidence information includes: the difference between the probability that the first optimal beam indicated by the channel prediction result is the optimal beam and the first probability sum, where the first probability sum is the sum of the probabilities that the second optimal beam indicated by the channel prediction result is the optimal beam up to the Kth optimal beam is the optimal beam.

[0142] Similarly, assuming K is 4, Figure 9 In the example shown in (c), the probability that the first optimal beam indicated by the channel prediction result is the optimal beam is the probability corresponding to the first beam in the sequence of 6 beams in Set A, which is sorted by probability, i.e., the probability of beam #4 is 0.8; the first probability sum is the sum of the probabilities corresponding to the second to fourth beams in the sequence of 6 beams in Set A, which is sorted by probability, i.e., the probability sum of the three beams #3, #2 and #1, which is 0.5 + 0.2 + 0.15 = 0.85; the confidence information is 0.8 - 0.85 = -0.05.

[0143] In this implementation, a negative confidence value or a value close to 0 indicates that the Top 1 advantage indicated by the channel prediction result is very weak, and the difference with the following beams is very small, resulting in low credibility of the channel prediction result for Set A. Conversely, a large confidence value indicates that the Top 1 advantage indicated by the channel prediction result is very significant, resulting in high credibility of the channel prediction result for Set A.

[0144] Implementation method 3. Confidence information includes: the KL divergence of distribution P relative to distribution Q, i.e.:

[0145] Formula 1

[0146] In Equation 1, distribution P is the probability distribution of multiple beams in Set A as the optimal beam, as indicated by the channel prediction results, and distribution Q is the ideal probability distribution of multiple beams as the optimal beam.

[0147] Distribution Q is the ideal probability distribution of multiple beams that are the optimal beams. Optionally, in distribution Q, the probability of the optimal beam in Set A is 1, and the probability of the other beams is 0. That is, the probability of the first beam in the beam sequence of Set A, which is sorted by probability, is 1, and the probability of the other beams is 0.

[0148] Based on this, since Q(x) is 1 only at the optimal beam, the KL divergence of distribution P relative to distribution Q can be simplified to:

[0149] Formula 2

[0150] In Equation 2, P(TOP1) is the probability of the first beam in the beam sequence of Set A, which is sorted by probability.

[0151] exist Figure 9 In the example shown in (c), the probability that the first optimal beam indicated by the channel prediction result is the optimal beam is the probability corresponding to the first beam in the sequence of 6 beams in Set A sorted by probability, i.e., the probability of beam #4 is 0.8; P(TOP1)=0.8, confidence information= -log(0.8). 0.097.

[0152] In this implementation, the confidence information is inversely proportional to the KL divergence. The smaller the KL divergence, the higher the confidence information, meaning that the channel prediction results of SetA are more reliable.

[0153] S804. Screen out the number N of the first beams corresponding to the confidence information in the mapping relationship.

[0154] In some embodiments, the terminal determines the value of N in the following ways:

[0155] The number of first beams corresponding to the confidence information is screened out from the mapping relationship; whereby the confidence information is used to indicate the credibility of the channel prediction results of Set A.

[0156] The mapping relationship includes multiple confidence levels and the number of first beams corresponding to each confidence level.

[0157] Optionally, the mapping relationship may be included in the first message, that is, the first message sent by the network device may indicate the mapping relationship. The confidence information included in the mapping relationship and the number of its corresponding first beams may be negatively correlated, that is, the higher the confidence information, the smaller the number of its corresponding first beams.

[0158] When aiming at a robust network, the number of first beams corresponding to the confidence information in the mapping relationship can be set to a larger value; when aiming to improve efficiency, the number of first beams corresponding to the confidence information in the mapping relationship can be set to a smaller value.

[0159] As mentioned earlier, the mapping relationship includes multiple confidence level information and the number of first beams corresponding to each confidence level information. After obtaining the confidence level information based on any of the three implementation methods described above, the terminal can screen it in the mapping relationship to obtain the N value corresponding to the confidence level information calculated by the terminal.

[0160] An example of a mapping relationship is as follows:

[0161] {threshold: 15, numSpace: 2};

[0162] {threshold: 5, numSpace: 4};

[0163] {threshold: 0, numSpace: 8}.

[0164] Here, threshold indicates confidence information, and numSpace is used to indicate the number N of the first beam.

[0165] Taking the confidence information obtained through implementation method 1 as an example, in the above mapping example, 15 can refer to the integer corresponding to the confidence information. For example, 15 is the integer corresponding to 0.15. It can be understood that: {threshold: 15, numSpace: 2} means: P(TopK) – P(Top K+1) > 0.15, and the number of the first beam N is 2; {threshold: 5, numSpace: 4} means: 0.15≥P(Top K) – P(Top K+1)>0.05, and the number of the first beam N is 4; {threshold: 0, numSpace: 8} means: 0.05≥P(Top K) – P(Top K+1)>0, and the number of the first beam N is 8.

[0166] The threshold shown in the above example is merely an example and does not constitute a limitation on the confidence information in the mapping relationship.

[0167] exist Figure 9 In the example shown in (c), the confidence level obtained according to implementation method 1 is 0.12. Based on this confidence level, the number of first beams N obtained by filtering in the mapping relationship is 2.

[0168] S805. The monitoring set is constructed including K optimal beams indicated by the channel prediction results and N first beams. The spatial distribution of the first beams is continuous with that of any one of the K optimal beams indicated by the channel prediction results.

[0169] The principle behind the terminal constructing N first beams as part of the monitoring set is:

[0170] In the field of wireless communication technology, the channel state of a wireless channel is not independently and randomly distributed in space, but has a high degree of spatial correlation. That is, the quality of the reference signal transmitted by multiple beams that are spatially continuous is basically the same. Simply put, the optimal beam rarely exists in isolation; usually, multiple optimal beams exist in spatial continuity.

[0171] Therefore, the rule for constructing the monitoring set is the Top-K beam and the beam with spatial correlation to it (i.e., the first beam), as follows:

[0172] The monitoring set = Top-K beams predicted by the AI ​​model + N first beams.

[0173] Optionally, as mentioned above, the first message indicates the value of K. Based on this, the value of K in the Top-K beam predicted by the AI ​​model used by the terminal to construct the monitoring set can be specified by the terminal through the first message. Of course, this does not constitute a limitation on the terminal specifying the value of K, and the terminal can also choose the value of K on its own.

[0174] In some embodiments, the terminal determines the index (or designation) of the first beam in the following ways:

[0175] The first beam is the beam whose spatial distribution is continuous with any of the K optimal beams indicated by the channel prediction results. That is, the first beam has continuous spatial distribution with any of the K optimal beams indicated by the channel prediction results, or the first beam has continuous spatial distribution with any of the Top-K beams. Of course, it can also have continuous spatial distribution with multiple beams in the Top-K beams.

[0176] Optionally, the first beam is included in Set A, that is, the terminal selects a beam from the beams in Set A whose spatial distribution is continuous with any of the K optimal beams indicated by the channel prediction result as the first beam.

[0177] As described above, if the number of first beams is N, and the number of beams selected by the terminal that have spatial continuity with any beam in the Top-K beams is greater than N, the terminal can randomly select N first beams, or it can preferentially select the beam that has spatial continuity with the highest-ranked beam in the Top-K beams as the first beam. This application does not limit the scope of the embodiments.

[0178] Optionally, one implementation where the spatial distribution of the first beam and any of the K optimal beams indicated by the channel prediction results is continuous includes:

[0179] The reference signal transmitted by the first beam has a QCL relationship with the reference signal transmitted by any of the K optimal beams indicated by the channel prediction results. That is, the reference signal transmitted by the first beam has a QCL relationship with the reference signal transmitted by any of the Top-K beams, or has a QCL relationship with the reference signals transmitted by multiple beams in the Top-K beams.

[0180] Optionally, the aforementioned prediction report configuration can also indicate the QCL relationship of beams in Set A; based on this, the terminal can select a beam whose spatial distribution is continuous with any beam in the Top-K beams as the first beam based on the prediction report configuration.

[0181] In some embodiments, before the terminal receives the first message, step S703 can be performed: the terminal sends a prediction report to the network device, and the network device can receive the prediction report.

[0182] The prediction report is obtained by the terminal through channel prediction based on the prediction report configuration. It may include channel prediction results, such as the K optimal beams in Set A, where the K optimal beams in Set A can be indicated by beam indexes.

[0183] In some other embodiments, before the terminal executes step S702, it may also execute step S704, in which the network device sends a second message to the terminal. Correspondingly, the terminal receives the second message, which is used to trigger the terminal to perform performance monitoring on the channel prediction results. Optionally, the second message may also be used to trigger the terminal to send a monitoring report.

[0184] Optionally, the reporting time indicated by the first message is non-periodic, and the network device can trigger the terminal to perform performance monitoring on the channel prediction results based on the second message, and can also trigger the terminal to report a monitoring report.

[0185] Optionally, the second message may also indicate that it is associated with the monitoring report configuration indicated by the first message. For example, the second message may include the identifier of the first message or the identifier of the monitoring report configuration indicated by the first message. In this case, the second message is associated with the monitoring report configuration indicated by the first message. Based on the second message, the terminal can explicitly perform performance monitoring on the channel prediction results based on the monitoring report configuration associated with the second message, and send a monitoring report to the network device after the performance monitoring is completed.

[0186] For example, the second message may include downlink control information (DCI).

[0187] Figure 10 This is a schematic block diagram of a communication device provided in an embodiment of this application.

[0188] like Figure 10 As shown, the communication device 1000 may include a communication module 1020. The communication module 1020 can implement corresponding communication functions, which can be internal communication functions of the communication device 1000 or communication functions between the communication device 1000 and other devices. Optionally, the communication module 1020 may also be referred to as a communication interface, transceiver module, or transceiver unit.

[0189] Optionally, the communication device 1000 further includes a processing module 1010. The processing module 1010 can perform corresponding processing functions, and optionally, the processing module 1010 can also be referred to as a processing unit.

[0190] Optionally, the communication device 1000 further includes a storage module, which can be used to store instructions and / or data; the processing module 1010 can read the instructions and / or data in the storage module so that the communication device 1000 can implement the aforementioned method embodiments.

[0191] In one possible design, the communication device 1000 may correspond to the terminal in the above method embodiments, or to a component (such as a circuit, chip, or chip system) configured in the terminal. The communication device 1000 may be used to execute the steps or processes performed by the terminal in any of the above method embodiments.

[0192] For example, the communication module 1020 is used to receive a first message, which is used to indicate a monitoring report configuration, which is used to indicate performance monitoring of the channel prediction results; and to send a monitoring report, which is used to indicate the number of monitoring reference signal opportunities that meet the first condition within the monitoring window; wherein: the first condition includes: the optimal beams in the monitoring set indicated by the monitoring results are included in the K optimal beams indicated by the channel prediction results, the monitoring results are channel measurement results obtained based on the monitoring set at the monitoring reference signal opportunities, the monitoring set includes the K optimal beams indicated by the channel prediction results and N first beams, the spatial distribution of the first beams and any one of the K optimal beams indicated by the channel prediction results is continuous, and K and N are both positive integers.

[0193] For example, the continuity of the spatial distribution of the first beam and any one of the K optimal beams indicated by the channel prediction results includes: the reference signal transmitted by the first beam and the reference signal transmitted by any one of the K optimal beams indicated by the channel prediction results have a quasi-co-located QCL relationship.

[0194] For example, the determination of N includes: screening out the number of first beams corresponding to the confidence information in the mapping relationship, the mapping relationship including multiple confidence information and the number of first beams corresponding to each confidence information; the confidence information is used to indicate the credibility of the channel prediction results.

[0195] For example, the confidence information includes: the difference between the probability that the Kth optimal beam indicated by the channel prediction result is the optimal beam and the probability that the (K+1)th beam indicated by the channel prediction result is the optimal beam;

[0196] Alternatively, the confidence information includes: the difference between the probability that the first optimal beam indicated by the channel prediction result is the optimal beam and the first probability sum, where the first probability sum is the sum of the probabilities that the second optimal beam indicated by the channel prediction result is the optimal beam up to the Kth optimal beam being the optimal beam.

[0197] Alternatively, the confidence information includes: the KL divergence of distribution P relative to distribution Q, where distribution P is the probability distribution of multiple beams as the optimal beams indicated by the channel prediction results, and distribution Q is the ideal probability distribution of multiple beams as the optimal beams.

[0198] For example, the mapping relationship is included in the first message.

[0199] The above are merely examples; for detailed steps or procedures, please refer to the descriptions in the foregoing embodiments.

[0200] In one possible design, the communication device 1000 may correspond to a network device (which may be a RAN, a core network device, or a functional unit within a core network device) in the above method embodiments, or a component (such as a circuit, chip, or chip system) configured within a network device. The communication device 1000 can be used to execute the steps or processes performed by the network device in any of the above method embodiments.

[0201] For example, the communication module 1020 is used to send a first message, which is used to indicate a monitoring report configuration, which is used to indicate performance monitoring of the channel prediction results; and to receive a monitoring report, which is used to indicate the number of monitoring reference signal moments that meet the first condition within the monitoring window; wherein: the first condition includes: the optimal beams in the monitoring set indicated by the monitoring results are included in the K optimal beams indicated by the channel prediction results, the monitoring results are channel measurement results obtained based on the monitoring set at the monitoring reference signal moments, the monitoring set includes the K optimal beams indicated by the channel prediction results and N first beams, the spatial distribution of the first beams and any one of the K optimal beams indicated by the channel prediction results is continuous, and K and N are both positive integers.

[0202] For example, the continuity of the spatial distribution of the first beam and any one of the K optimal beams indicated by the channel prediction results includes: the reference signal transmitted by the first beam and the reference signal transmitted by any one of the K optimal beams indicated by the channel prediction results have a quasi-co-located QCL relationship.

[0203] For example, the determination of N includes: screening out the number of first beams corresponding to the confidence information in the mapping relationship, the mapping relationship including multiple confidence information and the number of first beams corresponding to each confidence information; the confidence information is used to indicate the credibility of the channel prediction results.

[0204] For example, the confidence information includes: the difference between the probability that the Kth optimal beam indicated by the channel prediction result is the optimal beam and the probability that the (K+1)th beam indicated by the channel prediction result is the optimal beam;

[0205] Alternatively, the confidence information includes: the difference between the probability that the first optimal beam indicated by the channel prediction result is the optimal beam and the first probability sum, where the first probability sum is the sum of the probabilities that the second optimal beam indicated by the channel prediction result is the optimal beam up to the Kth optimal beam being the optimal beam.

[0206] Alternatively, the confidence information includes: the KL divergence of distribution P relative to distribution Q, where distribution P is the probability distribution of multiple beams as the optimal beams indicated by the channel prediction results, and distribution Q is the ideal probability distribution of multiple beams as the optimal beams.

[0207] For example, the mapping relationship is included in the first message.

[0208] The above are merely examples; for detailed steps or procedures, please refer to the descriptions in the foregoing embodiments.

[0209] Figure 11 This is another schematic block diagram of the communication device 1100 provided in the embodiments of this application.

[0210] The communication device 1100 may be a terminal, a network device, a chip, a chip system, or a processor that implements the above methods. The communication device 1100 can be used to implement the methods described in the above method embodiments; for details, please refer to the descriptions in the above method embodiments.

[0211] like Figure 11 As shown, the communication device 1100 may include one or more processors 1110, which may also be referred to as processing units or processing modules, and can implement certain control functions. The processor 1110 may be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, while the central processing unit can be used to control the communication device 1100 (e.g., a base station, baseband chip, user, user chip), execute software programs, and process data from the software programs.

[0212] In an alternative design, the processor 1110 may also store instructions and / or data, which can be executed by the processor 1110 to cause the communication device 1100 to perform the methods described in the above method embodiments.

[0213] In another alternative design, the communication device 1100 may include a communication interface 1120 for implementing receiving and transmitting functions. For example, the communication interface 1120 may be a transceiver circuit, interface, interface circuit, or transceiver. The transceiver circuit, interface, interface circuit, or transceiver for implementing receiving and transmitting functions may be separate or integrated. The aforementioned transceiver circuit, interface, interface circuit, or transceiver may be used for reading and writing code / data, or it may be used for transmitting or relaying signals.

[0214] Optionally, the communication device 1100 may include one or more memories 1130, which may store instructions that can be executed on the processor 1110, causing the communication device 1100 to perform the methods described in the above method embodiments. Optionally, the memories 1130 may also store data. Optionally, the processor 1110 may also store instructions and / or data. The processor 1110 and the memories 1130 may be provided separately or integrated together.

[0215] It should be understood that, in one possible design, the steps in the method embodiments provided in this application can be implemented by integrated logic circuits in the processor's hardware or by instructions in software form. The steps of the methods disclosed in the embodiments of this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, detailed descriptions are not provided here.

[0216] In one implementation, the communication device 1100 may correspond to the terminal in the above method embodiments and may be used to execute the various steps and / or processes executed by the terminal in the above method embodiments. The processor 1110 may be used to execute instructions stored in the memory 1130, and when the processor 1110 executes the instructions stored in the memory, the processor 1110 is used to execute the various steps and / or processes of the above method embodiments corresponding to the terminal.

[0217] In another implementation, the communication device 1100 may correspond to the network device in the above method embodiments and may be used to execute the various steps and / or processes executed by the network device in the above method embodiments. The processor 1110 may be used to execute instructions stored in the memory 1130, and when the processor 1110 executes the instructions stored in the memory, the processor 1110 is used to execute the various steps and / or processes of the above method embodiments corresponding to the network device.

[0218] It is understood that the aforementioned processor can be one or more chips. For example, the processor can be a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a system-on-chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0219] It is understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0220] This application also provides a computer-readable storage medium storing instructions that, when executed on one or more computing devices, cause the one or more computing devices to perform the data instruction method described in the above embodiments.

[0221] Computer-readable storage media can be non-transitory computer-readable storage media, such as read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage devices.

[0222] This application also provides a computer program product. When executed by one or more computing devices, the computer program product enables the computing devices to perform any of the aforementioned data indication methods. The computer program product can be a software installation package. When any of the aforementioned data indication methods needs to be used, the computer program product can be downloaded and executed on a computer.

[0223] This application also provides a processor, including: an input circuit, an output circuit, and a processing circuit. The processing circuit receives signals through the input circuit and transmits signals through the output circuit, causing the processor to execute the data indication method described in the above embodiments.

[0224] In specific implementation, the processor can be one or more chips, the input circuit can be input pins, the output circuit can be output pins, and the processing circuit can be transistors, gate circuits, flip-flops, and various logic circuits. The input signal received by the input circuit can be received and input by, for example, but not limited to, a receiver, and the signal output by the output circuit can be output to, for example, but not limited to, a transmitter and transmitted by the transmitter. Furthermore, the input circuit and the output circuit can be the same circuit, which is used as the input circuit and the output circuit at different times. This application does not limit the specific implementation of the processor and various circuits.

[0225] This application also provides a chip system including one or more processors for calling and executing instructions stored in memory, causing the data indication method described in the above embodiments to be executed. The chip system may be composed of a chip or may include chips and other discrete devices. The chip system may include input circuitry or interfaces for transmitting information or data, and output circuitry or interfaces for receiving information or data.

[0226] In the embodiments of this application, the terms and English abbreviations are exemplary examples given for ease of description and should not be construed as limiting the application in any way. This application does not preclude the possibility of defining other terms that can achieve the same or similar functions in existing or future agreements.

[0227] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When these computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated.

[0228] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0229] It should be understood that in the various embodiments of this application, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0230] In summary, the above description is merely a preferred embodiment of the technical solution of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A wireless communication method, characterized in that, include: Receive a first message, the first message being used to instruct monitoring report configuration, the monitoring report configuration being used to instruct performance monitoring of channel prediction results; A monitoring report is sent, which indicates the number of monitoring reference signal moments that meet a first condition within the monitoring window; wherein: the first condition includes: the optimal beams in the monitoring set indicated by the monitoring results are included in the K optimal beams indicated by the channel prediction results, the monitoring results are channel measurement results obtained based on the monitoring set at the monitoring reference signal moment, the monitoring set includes the K optimal beams indicated by the channel prediction results and N first beams, the spatial distribution of the first beams and any one of the K optimal beams indicated by the channel prediction results is continuous, and K and N are both positive integers.

2. The method according to claim 1, characterized in that, The spatial distribution of the first beam and any of the K optimal beams indicated by the channel prediction result is continuous, including: The reference signal transmitted by the first beam has a quasi-co-located (QCL) relationship with the reference signal transmitted by any of the K optimal beams indicated by the channel prediction result.

3. The method according to claim 1 or 2, characterized in that, The methods for determining N include: The number of first beams corresponding to confidence information is screened out in the mapping relationship, which includes multiple confidence information and the number of first beams corresponding to each confidence information; the confidence information is used to indicate the credibility of the channel prediction result.

4. The method according to claim 3, characterized in that, The confidence information includes: the difference between the probability that the Kth optimal beam indicated by the channel prediction result is the optimal beam and the probability that the (K+1)th beam indicated by the channel prediction result is the optimal beam; Alternatively, the confidence information includes: the difference between the probability that the first optimal beam indicated by the channel prediction result is the optimal beam and the first probability sum, where the first probability sum is the sum of the probabilities that the second optimal beam indicated by the channel prediction result is the optimal beam up to the Kth optimal beam being the optimal beam. Alternatively, the confidence information includes: the KL divergence of distribution P relative to distribution Q, where distribution P is the probability distribution of multiple beams indicating that they are the optimal beams according to the channel prediction results, and distribution Q is the ideal probability distribution of the multiple beams being the optimal beams.

5. The method according to claim 3, characterized in that, The mapping relationship is included in the first message.

6. A wireless communication method, characterized in that, include: Send a first message, the first message being used to instruct the monitoring report configuration, the monitoring report configuration being used to instruct performance monitoring of the channel prediction results; A monitoring report is received, the monitoring report being used to indicate the number of monitoring reference signal opportunities that meet a first condition within the monitoring window; wherein: the first condition includes: the optimal beams in the monitoring set indicated by the monitoring results are included in the K optimal beams indicated by the channel prediction results, the monitoring results are channel measurement results obtained based on the monitoring set at the monitoring reference signal opportunity, the monitoring set includes the K optimal beams indicated by the channel prediction results and N first beams, the spatial distribution of the first beams and any one of the K optimal beams indicated by the channel prediction results is continuous, and K and N are both positive integers.

7. The method according to claim 6, characterized in that, The spatial distribution of the first beam and any of the K optimal beams indicated by the channel prediction result is continuous, including: The reference signal transmitted by the first beam has a quasi-co-located (QCL) relationship with the reference signal transmitted by any of the K optimal beams indicated by the channel prediction result.

8. The method according to claim 6 or 7, characterized in that, The methods for determining N include: The number of first beams corresponding to confidence information is screened out in the mapping relationship, which includes multiple confidence information and the number of first beams corresponding to each confidence information; the confidence information is used to indicate the credibility of the channel prediction result.

9. The method according to claim 8, characterized in that, The confidence information includes: the difference between the probability that the Kth optimal beam indicated by the channel prediction result is the optimal beam and the probability that the (K+1)th beam indicated by the channel prediction result is the optimal beam; Alternatively, the confidence information includes: the difference between the probability that the first optimal beam indicated by the channel prediction result is the optimal beam and the first probability sum, where the first probability sum is the sum of the probabilities that the second optimal beam indicated by the channel prediction result is the optimal beam up to the Kth optimal beam being the optimal beam. Alternatively, the confidence information includes: the KL divergence of distribution P relative to distribution Q, where distribution P is the probability distribution of multiple beams indicating that they are the optimal beams according to the channel prediction results, and distribution Q is the ideal probability distribution of the multiple beams being the optimal beams.

10. The method according to claim 8, characterized in that, The mapping relationship is included in the first message.

11. A communication device, characterized in that, The communication device includes a processing module and a transceiver module, and is used to perform the method as described in any one of claims 1 to 5, or the method as described in any one of claims 6 to 10.

12. A communication device, characterized in that, include: Memory, used to store computer instructions; A processor for executing a computer program or computer instructions stored in the memory, causing the communication device to perform the method as described in any one of claims 1 to 5, or the method as described in any one of claims 6 to 10.

13. A communication system, characterized in that, Includes the communication device as described in claim 12.

14. A computer storage medium, characterized in that, Used to store a computer program, which, when executed, is used to implement the method as described in any one of claims 1 to 5, or the method as described in any one of claims 6 to 10.

15. A computer program product, characterized in that, The computer program thereunder, when the computer program is run, causes the method as described in any one of claims 1 to 5, or the method as described in any one of claims 6 to 10, to be performed.

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