Method and apparatus for wireless communication

Abstract

A method and apparatus for wireless communication are provided. The method comprises: a first device receiving first indication information, the first indication information being used for indicating a first QCL relationship; the first device executes a first operation according to the first QCL relationship; wherein the first operation is one or more of the following operations: determining a beam set for model training and / or model reasoning, and the element of the first QCL relationship comprising an identifier of the beam set; and determining a plurality of optimal beams in the model prediction result.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and more specifically, to a method and device for wireless communication. Background Art

[0002] In some beam management scenarios, the terminal device can predict the downlink transmission beam through the model and report it to the network device. The model needs to be trained before it can be used for inference prediction. However, there is a certain time interval between the training phase and the inference phase. If the transmission conditions of the beam change, the model may be wrong during the inference phase. Therefore, how to ensure the consistency of the model in the training phase and the inference phase is a technical problem that needs to be solved urgently. Summary of the invention

[0003] The present application provides a method and apparatus for wireless communication. The following introduces various aspects involved in the embodiments of the present application.

[0004] In a first aspect, a method for wireless communication is provided, comprising: a first device receives first indication information, wherein the first indication information is used to indicate a first quasi co-located (QCL) relationship; the first device performs a first operation according to the first QCL relationship; wherein the first operation is one or more of the following: determining a beam set for model training and / or model inference, the elements of the first QCL relationship including an identifier of the beam set; determining multiple optimal beams in a model prediction result.

[0005] According to a second aspect, a method for wireless communication is provided, comprising: a second device sends first indication information to a first device, the first indication information is used to indicate a first QCL relationship, and the first QCL relationship is used for the first device to perform a first operation; wherein the first operation is one or more of the following: determining a beam set for model training and / or model inference, the elements of the first QCL relationship including an identifier of the beam set; determining multiple optimal beams in a model prediction result.

[0006] According to a third aspect, a device for wireless communication is provided, wherein the device is a first device and comprises: a transceiver unit for receiving first indication information, wherein the first indication information is used to indicate a first QCL relationship; a processing unit for performing a first operation according to the first QCL relationship; wherein the first operation is one or more of the following: determining a beam set for model training and / or model inference, wherein the elements of the first QCL relationship include an identifier of the beam set; determining multiple optimal beams in a model prediction result.

[0007] In a fourth aspect, a device for wireless communication is provided, which is a second device and includes: a transceiver unit, used to send first indication information to a first device, the first indication information is used to indicate a first QCL relationship, and the first QCL relationship is used for the first device to perform a first operation; wherein the first operation is one or more of the following: determining a beam set for model training and / or model inference, the elements of the first QCL relationship include an identifier of the beam set; determining multiple optimal beams in the model prediction results.

[0008] In a fifth aspect, a communication device is provided, comprising a memory and a processor, wherein the memory is used to store a program, and the processor is used to call the program in the memory to execute the method described in the first aspect or the second aspect.

[0009] In a sixth aspect, a device is provided, comprising a processor, configured to call a program from a memory to execute the method described in the first aspect or the second aspect.

[0010] In a seventh aspect, a chip is provided, comprising a processor for calling a program from a memory so that a device equipped with the chip executes the method described in the first aspect or the second aspect.

[0011] According to an eighth aspect, a computer-readable storage medium is provided, on which a program is stored, wherein the program enables a computer to execute the method as described in the first aspect or the second aspect.

[0012] According to a ninth aspect, a computer program product is provided, comprising a program, wherein the program enables a computer to execute the method as described in the first aspect or the second aspect.

[0013] In a tenth aspect, a computer program is provided, wherein the computer program enables a computer to execute the method as described in the first aspect or the second aspect.

[0014] The first device (e.g., a terminal device) in an embodiment of the present application can determine the first QCL relationship according to the first indication information. When the element of the first QCL relationship includes an identifier of a beam set, the first QCL relationship can be used to determine the beam set for model training and / or model inference, thereby improving the accuracy of beam prediction by ensuring the consistency of the beam set. The first QCL relationship can also be used to determine multiple optimal beams in the model prediction results, which helps to reduce the number of beams that need to be independently measured. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a wireless communication system applied in the embodiments of the present application.

[0016] Figure 2 It is a schematic diagram of the model processing process applied in the embodiment of the present application.

[0017] Figure 3 It is a schematic diagram of the beam relationship during model training and model inference.

[0018] Figure 4 It is a flowchart of a method for wireless communication provided in an embodiment of the present application.

[0019] Figure 5 yes Figure 4 A schematic diagram of another possible implementation of the method shown.

[0020] Figure 6 It is a structural diagram of a device for wireless communication provided in an embodiment of the present application.

[0021] Figure 7 It is a structural diagram of another device for wireless communication provided in an embodiment of the present application.

[0022] Figure 8 It is a structural diagram of a wireless communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application are described below in conjunction with the accompanying drawings.

[0024] Figure 1 FIG. 1 is a schematic diagram of the architecture of a wireless communication system 100 used in an embodiment of the present application. Figure 1 As shown, the wireless communication system 100 may include a network device 110 and a terminal device 120. The network device 110 may be a device that communicates with the terminal device 120. The network device 110 may provide communication coverage for a specific geographical area and may communicate with terminal devices located in the coverage area.

[0025] Figure 1 One network device and two terminal devices are shown exemplarily. Optionally, the wireless communication system 100 may include multiple network devices and each network device may include other number of terminal devices within its coverage area, which is not limited. That is, the wireless communication system may include one or more network devices, each of which may support wireless communication of one or more terminal devices.

[0026] In the embodiments of the present application, Figure 1 The communication system shown may also include other network entities such as a mobility management entity (MME), an access and mobility management function (AMF), and a network controller, but this is not limited in the embodiments of the present application.

[0027] It should be understood that the embodiments of the present application can be applied to various communication systems. For example, the embodiments of the present application can be applied to global system of mobile communication (GSM) system, code division multiple access (CDMA) system, wideband code division multiple access (WCDMA) system, general packet radio service (GPRS) system, long term evolution (LTE) system, advanced long term evolution (LTE-A) system, new radio (NR) system, NR system evolution system, LTE-based access to unlicensed spectrum (LTE-U) system, NR-based access to unlicensed spectrum (NR-U) system, universal mobile telecommunication system (UMTS), wireless local area network (WLAN) system, wireless fidelity (WiFi) system, fifth generation communication (5G) system. The embodiments of the present application can also be applied to other communication systems, such as the sixth-generation (6G) mobile communication system, or future communication systems such as satellite communication systems.

[0028] Traditional communication systems support a limited number of connections and are easy to implement. However, with the development of communication technology, communication systems can not only support traditional cellular communications, but also support one or more other types of communications. For example, a communication system can support one or more of the following communications: device to device (D2D) communication, machine to machine (M2M) communication, machine type communication (MTC), enhanced machine type communication (eMTC), vehicle to vehicle (V2V) communication, and vehicle to everything (V2X) communication, etc. The embodiments of the present application can also be applied to communication systems that support the above communication methods.

[0029] The communication system in the embodiment of the present application can be applied to a carrier aggregation (CA) scenario, a dual connectivity (DC) scenario, or a standalone (SA) networking scenario.

[0030] The communication system in the embodiment of the present application can be applied to an unlicensed spectrum. The unlicensed spectrum can also be considered as a shared spectrum. Alternatively, the communication system in the embodiment of the present application can also be applied to an authorized spectrum. The authorized spectrum can also be considered as a dedicated spectrum.

[0031] The embodiments of the present application can be applied to a non-terrestrial network (NTN) system. As an example, the NTN system can be a 4G-based NTN system, a NR-based NTN system, an Internet of Things (IoT)-based NTN system, or a narrowband Internet of Things (NB-IoT)-based NTN system.

[0032] The wireless communication system in the embodiment of the present application can use the following resources to support wireless communication with one or more communication devices: time resources (e.g., symbols, sub-time slots, time slots, subframes, frames, etc.) or frequency resources (e.g., subcarriers, carriers). Additionally, the wireless communication system can support wireless communication across various radio access technologies (radio access technology, RAT), including third generation (3G) radio access technology, fourth generation (4G) radio access technology, fifth generation (5G) radio access technology, and other suitable radio access technologies beyond 5G.

[0033] The terminal device in the embodiments of the present application may also be referred to as user equipment (UE), access terminal, user unit, user station, mobile station, mobile station (MS), mobile terminal (MT), remote station, remote terminal, mobile device, user terminal, terminal, user communication equipment, wireless communication equipment, user agent or user device, etc.

[0034] In some embodiments, the terminal device in the embodiment of the present application may be a device that provides voice and / or data connectivity to a user, and may be used to connect people, objects, and machines, such as a handheld device with wireless connection function, a vehicle-mounted device, etc. The terminal device in the embodiment of the present application may be a mobile phone, a tablet computer (Pad), a laptop computer, a PDA, a mobile Internet device (mobile internet device, MID), a wearable device, a virtual reality (virtual reality, VR) device, an augmented reality (augmented reality, AR) device, a wireless terminal in industrial control (industrial control), a wireless terminal in self-driving, a wireless terminal in remote medical surgery, a wireless terminal in smart grid (smart grid), a wireless terminal in transportation safety (transportation safety), a wireless terminal in smart city (smart city), a wireless terminal in smart home (smart home), etc. Optionally, UE may be used to act as a base station. For example, UE may act as a scheduling entity that provides sidelink signals between UEs in V2X or D2D, etc. For example, a cellular phone and a car communicate with each other using sidelink signals. Cellular phones and smart home devices communicate with each other without relaying the communication signal through a base station.

[0035] In some embodiments, the terminal device may be a station (STATION, ST) in a WLAN. In some embodiments, the terminal device may be a cellular phone, a cordless phone, a session initiation protocol (SIP) phone, a wireless local loop (WLL) station, a personal digital assistant (PDA) device, a handheld device with wireless communication function, a computing device or other processing device connected to a wireless modem, a vehicle-mounted device, a wearable device, a terminal device in a next-generation communication system (such as an NR system), or a terminal device in a future-evolved public land mobile network (PLMN) network, etc.

[0036] The network device in the embodiment of the present application may be a device for communicating with a terminal device, and the network device may also be referred to as an access network device or a radio access network device. The network device may be, for example, a base station. The network device in the embodiment of the present application may refer to a radio access network (RAN) node (or device) that connects a terminal device to a wireless network. The base station can broadly cover various names as follows, or be replaced with the following names, such as: NodeB, evolved NodeB (eNB), next generation NodeB (gNB), relay station, access point, transmission point (TRP), transmission point (TP), master station (MeNB), secondary station (SeNB), multi-standard radio (MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, base band unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), positioning node, network communication equipment, etc. The base station can be a macro base station, a micro base station, a relay node, a donor node or the like, or a combination thereof. The base station may also refer to a communication module, modem or chip used to be set in the aforementioned equipment or device. The base station may also be a mobile switching center and a device that performs the base station function in D2D, V2X, and M2M communications, a network-side device in a 6G network, and a device that performs the base station function in a future communication system. The base station may support networks with the same or different access technologies. The embodiments of the present application do not limit the specific technology and specific device form used by the network equipment.

[0037] Base stations can be fixed or mobile. For example, a helicopter or drone can be configured to act as a mobile base station, and one or more cells can move based on the location of the mobile base station. In other examples, a helicopter or drone can be configured to act as a device that communicates with another base station.

[0038] In some deployments, the network device in the embodiments of the present application may refer to a CU or a DU, or the network device includes a CU and a DU. The gNB may also include an AAU.

[0039] The network equipment and terminal equipment can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on the water surface; they can also be deployed on aircraft, balloons and satellites in the air. The embodiments of the present application do not limit the scenarios in which the network equipment and terminal equipment are located.

[0040] It should be understood that all or part of the functions of the communication device in the present application may also be implemented by software functions running on hardware, or by virtualization functions instantiated on a platform (eg, a cloud platform).

[0041] In an embodiment of the present application, a network device may provide services for a cell, and a terminal device may communicate with the network device through transmission resources (e.g., frequency domain resources, or spectrum resources) used by the cell. The cell may be a cell corresponding to a network device (e.g., a base station). The cell may belong to a macro base station or a base station corresponding to a small cell. The small cells here may include: metro cells, micro cells, pico cells, femto cells, etc. These small cells have the characteristics of small coverage and low transmission power, and are suitable for providing high-speed data transmission services.

[0042] It should be understood that the device with communication function in the network / system in the embodiment of the present application can be referred to as a communication device. Figure 1 Taking the wireless communication system 100 shown as an example, the communication equipment may include a network device 110 and a terminal device 120 with communication functions, and may also include other devices in the wireless communication system 100, such as a network controller, a mobile management entity and other network entities, which is not limited in the embodiments of the present application.

[0043] For ease of understanding, some relevant technical knowledge involved in the embodiments of the present application is first introduced. The following related technologies can be arbitrarily combined with the technical solutions of the embodiments of the present application as optional solutions, and they all belong to the protection scope of the embodiments of the present application. The embodiments of the present application include at least part of the following contents.

[0044] In a wireless communication system, a terminal device may acquire a beam (also referred to as a spatial beam) to achieve a wireless connection between the terminal device and a wireless network. Exemplarily, the terminal device may perform beam scanning for an available beam sent by a wireless network and measure the properties of the beam. The properties of the beam include signal strength and signal quality. Exemplarily, after performing beam scanning, the terminal device may also perform beam refinement to achieve a set of potentially narrower beams for wireless connection to the wireless network. The beam may not only achieve a wireless connection between the terminal device and the wireless network, but also achieve high directional accuracy and high signal quality for wireless signal transmission between the terminal device and the wireless network.

[0045] With the development of communication technology, research on artificial intelligence (AI) / machine learning (ML) technology based on the air interface of communication systems (e.g., NR systems) has become one of the directions. The goals of this research include exploring how to enhance the advantages of the air interface. For example, the relevant performance of the air interface can be enhanced by enhancing support for AI / ML algorithms. For another example, the complexity and / or overhead of the air interface can be reduced by enhancing support for AI / ML algorithms.

[0046] Research on AI / ML technology can also enhance beam management (BM) related functions. As an example, AI / ML enhancements related to beam management can support reducing overhead and reducing beam measurement and reporting latency. As an example, applying AI / ML models can predict beams to improve the transmission efficiency of the air interface.

[0047] The entire process of applying AI / ML models for enhancement includes: model training, model reasoning, and model monitoring. In this process, after being trained, the AI / ML model can generate a set of outputs based on a set of inputs. The input can be a set of beam measurements, and the output can be a set of beam groups that are different from / larger than the input.

[0048] During model inference, the AI / ML model can predict the best beam from a set of different / larger beam groups based on a set of beam measurements.

[0049] In some embodiments, the AI / ML model may be located at the terminal device side or the terminal device may perform model training and / or model reasoning. The model may be referred to as a UE-side model. For example, the AI ​​model is located at the terminal device, and the training of the AI ​​model and / or the generation of the best beam using the reasoning of the AI ​​model may be performed at the terminal device or by the terminal device.

[0050] As an example, the terminal device may use the beams in Set B (also called beam group B) as input to the ML model. The ML model may predict the best beam in Set A (also called beam group A), which is not fully measured by the terminal device.

[0051] In the above example, set B may be the beam group that the terminal device first measures. Set B may be multiple beams sent by a base station (e.g., a gNB). Each beam may correspond to a different direction or angle to cover multiple spatial directions. Each beam may also correspond to a measurement signal for obtaining a measurement value, such as reference signal received power (RSRP). The purpose of set B is to provide preliminary environmental information and channel conditions for the model.

[0052] In the above example, set A may be the group of beams that need to be predicted. The number of beams in set A is usually larger than that in set B, or the beam directions may be more concentrated. The AI / ML model can predict the best beam in set A through measurements of set B to improve the efficiency of data transmission.

[0053] Optionally, the beams of set A and set B can be in the same frequency range. The selection of set B can be given by the base station or determined by the terminal device itself. The relationship between set A and set B can be: set A and set B are different (set B is not a subset of set A), or set B is a subset of set A (set A and set B are different), or set A and set B are the same. For the first two, set B can be transmitted in both the measurement window and the prediction window, or it can be transmitted only in the measurement window. The last case can save the reference signal (RS) transmission overhead, and set B as a measurement resource can be transmitted only in the measurement window.

[0054] Optionally, 64 or even more beams can be used as the size of beam set A. For future-oriented networks, network devices will be able to transmit 64 more highly directional narrow beams. More narrow beams can also scan a larger set A, for example, the number of beams in set A may be as high as 256.

[0055] Optionally, the network device may send a channel state information reference signal (CSI-RS) or a synchronization signal block (SSB) as a reference signal. It should be understood that SSB may also represent a synchronization signal / physical broadcast channel block (SS / PBCH block). SSB includes a primary synchronization signal (PSS) and a secondary synchronization signal (SSS).

[0056] Optionally, the terminal device estimates the channel quality of each beam by measuring the RSRP of the received CSI-RS / SSS.

[0057] Optionally, during the model training process, the AI / ML model can adjust its weights by minimizing the loss function so that the model can accurately predict the best beam in set A from the RSRP measurement results of set B.

[0058] In some embodiments, AI / ML may be located at a network device (such as a base station) or the network device may perform model training and / or model reasoning. The model may be referred to as a network-side model (NW-side model). For example, the AI ​​model is located at a base station, and the training of the AI ​​model and / or the generation of the best beam using the reasoning of the AI ​​model may be performed at or by the base station.

[0059] In some embodiments, the network can fully control the data collection process for model training on the terminal device side, including the initiation, termination and management of data collection and data transmission.

[0060] In the model monitoring process, AI / ML model monitoring is used for at least the following purposes: model activation, deactivation, selection, switching, rollback, and update (including retraining). Model monitoring can also be referred to as the process of monitoring the inference performance of the AI / ML model. There must be a time interval between the training process and the inference process of the model. When radio parameters / conditions change in the network, there is a high probability of errors in the inference phase of the model, so the model needs to be continuously corrected and trained based on the results of model monitoring. In addition, in some cases, model monitoring may be required before using the model in a new radio environment / conditions / parameters.

[0061] As an example, due to the differences in mobile environments, each model has a model identification (identity, ID) to facilitate identification of the model.

[0062] Compared with other beam management technologies, through AI / ML-enabled beam management, terminal devices can experience reduced latency, reduced overhead, reduced power consumption, and improved signal quality based on beam prediction.

[0063] For ease of understanding, the following Figure 2 , the entire process of model processing on the terminal device side is introduced. Figure 2 This is explained from the perspective of the interaction between the terminal device (such as UE) and the network (NW). The network side uses 4 beams as an example. Figure 2 It can be seen that the whole process may include a model training process, a model inference process and a reporting process. The model training process includes steps S210 and S220, and the model inference process includes steps S230 and S240.

[0064] See also Figure 2 In step S210, the terminal device reports training-related information (UE report training-related information).

[0065] In step S220, the network side performs beam scanning based on four beams.

[0066] In step S230, the terminal device reports inference-related information (UEreport inference-related information).

[0067] In step S240, the network side selects two beams from four beams to perform beam scanning according to the report from the terminal device.

[0068] In step S250, the terminal device reports the optimal K beams (top-K beam report).

[0069] In step S260, the network side performs beam scanning according to the beam in the beam report. Figure 2 It can be seen that the two beams used for beam scanning at step S260 on the network side may be different from the two beams used for beam scanning at step S240 .

[0070] In step S270, the terminal device sends a beam report to facilitate the network side to determine the optimal beam.

[0071] In step S280, the network side sends a beam indication to the terminal device.

[0072] Combination of the above Figure 2 The process of processing the model when it is located on the terminal device side is introduced. In order to complete the whole process, the terminal device also needs to collect and analyze data to perform model training. The model inference process is mainly used for beam prediction.

[0073] In relevant scenarios, the terminal device can support beam prediction in the spatial domain and / or time domain, namely BM-Case1 and / or BM-Case2. Based on AI / ML enhancements, beam prediction in the spatial domain (BM-Case1) and beam prediction in the time domain (BM-Case2) can reduce the overhead of the terminal device and reduce beam measurement and reporting delays.

[0074] BM-Case1 is to perform spatial domain downlink (DL) beam prediction for Set A based on the measurement results of Set B. For BM-case1, the measurements based on Set B of beams are used as model input to predict the Top-1 / Top-K beams in Set A.

[0075] BM-Case2 is a time-domain DL beam prediction for Set A based on historical measurements of Set B. For BM-Case2, measurements based on Set B of beams at historic time instance(s) can be used as model input to predict the time-domain DL beams for Set A. The prediction of DL Tx beams and the prediction of DL Tx / Rx beams can also be used to evaluate the performance of the prediction.

[0076] For BM-Case1 and BM-Case2, the terminal device can report the prediction results to the NW based on the output of the terminal device side model, or the NW can predict the Top-1 / Top-K beams based on the measurement report of the set B of the NW side model.

[0077] Still taking the terminal device side model as an example, for the training and reasoning process of the AI / ML model on the terminal device side, the traditional transmission configuration indicator (TCI) state mechanism can be used to perform beam indication of the beam. Optionally, the terminal device can report the measurement results of more than 4 beams in one reporting instance.

[0078] As an example, in the reasoning process of beam management, beam indication is an important information. After the terminal device reports the Top-K predicted beam, the NW will further indicate the beam used for the second step of measurement or the terminal device can directly trigger the second measurement. For BM-Case2, the model needs to obtain the Top-K beams corresponding to multiple time instances. Therefore, the beam indication on the network device side needs to consider time information. For example, for BM-Case2 with an AI / ML model on the terminal device side, the terminal device can report the following information of AI / ML model reasoning to the NW in one report example: the beams used for N future times based on the output of AI / ML model reasoning; timestamp information corresponding to the reported beam; information about the measurement values ​​of multiple past time instances.

[0079] As an example, the TCI state can be used to indicate the beam information used for physical downlink shared channel (PDSCH) or physical downlink control channel (PDCCH) transmission.

[0080] As an example, in the 5G NR system, the TCI state defines the beam selection and transmission configuration of the downlink. The main role of the TCI state is to help the terminal device understand the beam or antenna configuration used by the base station (e.g., gNB) when transmitting on certain physical channels (such as PDSCH, PDCCH). Each TCI state corresponds to a specific beam configuration. In general, each TCI state is associated with a specific beam or multiple beams (through QCL relationship). For example, for a unified TCI framework, if there is no PDSCH transmission when the best beam between the base station and the terminal device changes, the network will indicate the corresponding TCI state to the terminal device through downlink control information (DCI). If there is a PDSCH for transmission, the base station will send a DCI with a downlink (DL) allocation to the terminal device, and the TCI field will always be present in the DCI.

[0081] Optionally, the network device may transmit control information through the PDCCH, that is, transmit the DCI through the PDCCH.

[0082] The QCL relationship can also be called a quasi-co-location relationship. In order to ensure that the demodulation reference signal (DMRS) of PDSCH / PDCCH in NR is consistent between different timestamps, this can be achieved through the mechanism of QCL relationship, especially in the training and inference process of the model.

[0083] The following describes the QCL configuration used to indicate the QCL relationship in beam indication.

[0084] QCL relationships are often used to mark the similarity of channel characteristics between certain transmission resources (such as CSI-RS resources). Through QCL configuration, the terminal device can use the same set of measurement results to infer the channel status of multiple resources without the need to independently measure and evaluate each beam. The QCL relationship defines the similarity of channel characteristics of two (or more) beams or resources in terms of space, frequency, time, etc. That is, when two or more beams have a QCL relationship, the channel characteristics of the two or more beams are similar.

[0085] In some embodiments, QCL is typically defined in the following dimensions: delay spread, angle spread, and Doppler spread. Delay spread means that the difference in signal arrival time between the two beams is very small and the same time domain processing can be used. Angle spread means that the arrival angles of the signal sources are almost the same, and spatial processing can be shared between beams. Doppler spread means that due to the similar speed of the signal sources, the Doppler frequency shift is similar, and frequency domain processing can be shared. In beam management, if two resources have the same QCL level in these characteristics, the terminal device can infer that their channel states are similar, so there is no need to measure the channel state of each beam separately.

[0086] In some embodiments, for a set of antenna ports across, the QCL type can be used to indicate which channel attributes are common. For example, multiple QCL types supported by the protocol can be used to indicate the QCL relationship between the DMRS and the tracking reference signal (TRS) in the PDSCH.

[0087] As an example, for a given QCL association, at most one or two QCL types can be used to indicate the QCL relationship. When two QCL types are used, the same RS can be used to indicate the QCL. Table 1 is the definition of four different QCL types.

[0088] Table 1

[0089] QCL Type Description QCL Type A Doppler shift, Doppler spread, average delay, delay spread QCL Type B Doppler shift, Doppler spread QCL Type C Average delay, Doppler shift QCL Type D Spatial receiver parameters

[0090] As shown in Table 1, the QCL type may be defined as QCL type A (QCL-TypeA), QCL type B (QCL-TypeB), QCL type C (QCL-TypeC), and QCL type D (QCL-TypeD). Referring to Table 1, QCL type A indicates a common Doppler shift, Doppler spread, average delay, and delay spread. QCL type B indicates a common Doppler shift and Doppler spread. QCL type C indicates a common average delay and Doppler shift. QCL type D indicates a common spatial receiver parameter (spatial Rx parameter).

[0091] Optionally, the spatial receiver parameters may include beamforming properties of the downlink received signal, such as the main angle of arrival of the signal, the average angle of arrival at the terminal device, or other beamforming properties.

[0092] Optionally, the QCL information can help the terminal device perform beam tracking or time-frequency offset tracking, as well as demodulation. For example, beam tracking can be performed using QCL type D. For another example, time / frequency offset tracking can be performed using QCL types A / B / C.

[0093] As mentioned above, the training and reasoning process of AI / ML models involves two beam sets, set A and set B. The beams in set A and set B can have a certain QCL relationship. For example, during training, the beams in set A have a QCL relationship with the beams in set A during reasoning. For another example, during training, the beams in set B have a QCL relationship with the beams in set B during reasoning.

[0094] As an example, when performing model training and inference based on QCL, the QCL type can be configured for the resources of each beam in set A and its associated transmit beam. During the training and inference process, the network device uses the beams of set A to have similar channel characteristics in the spatial, time, and frequency domains. Since the beam characteristics remain consistent, the channel state of the CSI-RS resources measured by the terminal device in the training phase can be used in the inference phase. For the resources of set B, the terminal device can also assume that the transmit beams used in the training and inference processes have the same QCL relationship. The beam measurement results of the training phase can be directly applied to the inference phase.

[0095] As an example, when the transmission (Tx) beam of the network device has the QCL relationship in Table 1, the consistency of the channel characteristics can be ensured. Figure 3The QCL relationship between the set B beam and the set A beam is illustrated by way of example.

[0096] See also Figure 3 During the model training process, the beams in set A are A′j, where 1≤j≤8; the beams in set B are B′ k , where 1≤k≤3. During model inference, the beams in set A are A j , the beam in set B is B k .like Figure 3 As shown, the beam A′ in the model training j and beam A in model inference j The channel characteristics of the beam B′ in the model training are similar. k and beam B in model inference k That is, during training, beam A′ j During inference, beam A j With the QCL relationship, beam B′ during training k and during reasoning B k Has a QCL relationship.

[0097] Combination of the above Figure 2 and Figure 3 The process of beam management based on AI / ML model and the QCL relationship between beams are introduced. Beam management enhancement based on AI / ML still faces some problems that need to be solved or studied.

[0098] As an example, when a terminal device uses an AI algorithm or ML to process beams detected and measured from a wireless network to infer other beams that may have higher strength and / or higher quality, the terminal device needs to ensure the consistency of the model in the training phase and the inference phase. As mentioned earlier, there is a certain time interval between the training phase and the inference phase, and changes in radio parameters / conditions may cause errors in the inference phase. For example, for a beam prediction use case, the terminal device can use a limited set of beam measurements (such as beam group B) as input to the ML model. When the ML model predicts the best beam or optimal beam from a set of beams that are not fully measured by the terminal device (such as beam group A), there is a high probability of errors in the inference phase of the model. This is because in the time interval between the model training process and the model inference process, the network may experience changes in radio parameters / conditions, resulting in a higher error rate.

[0099] As an example, when QCL relationships are introduced, the various QCL types corresponding to traditional QCL relationships may not ensure beam consistency between model training and model inference. This is because the elements of traditional QCL types change rapidly in some scenarios. When QCL elements change, changes in QCL relationships trigger new beam sets, and the consistency of beam order / index during model training and model inference may not be guaranteed, and even the consistency of beam shape cannot be guaranteed, so the model needs to be updated. For example, in a scenario where the terminal device moves at high speed, changes in geographical characteristics will lead to rapid changes in Doppler shift. Once the Doppler shift changes, the model trained based on the previous beam set needs to be updated, but frequent updates of the model are not conducive to model-based beam prediction. It can be seen that beam order / index consistency and beam shape consistency cannot be ensured by relying on existing QCL types (such as QCL type D).

[0100] As an example, if the terminal device is moving at high speed, the best beam between the network device and the terminal device may change frequently, and the network needs to send DCI frequently to indicate the new TCI status, resulting in large overhead.

[0101] As an example, in the relevant 3rd generation partnership project (3GPP) standard, the maximum number of trigger states for aperiodic CSI reporting regarding TCI states is 128. This number includes beam reports and other triggers. However, the optimal beam combination output by the model far exceeds this number. For example, if there are 32 beams in set A and the model output is the top-4 predicted beams in set A, then all possible combinations of AI / ML output are C3 4 2 = 35960. It is impossible for the network to configure all possible combinations of the four beams in set A, which also brings complexity to the network triggering the second round of beam scanning of the Top-4 beams.

[0102] Based on this, an embodiment of the present application proposes a method for wireless communication. In this method, the terminal device can determine a beam set for model training and / or model inference based on a first quasi-co-location (QCL) relationship in the first indication information, and can also determine multiple optimal beams in the prediction result based on the first QCL relationship. Wherein, when the first QCL relationship is used to determine the beam set, the element corresponding to the first QCL relationship includes the ID of the beam set to more accurately maintain the consistency of the beam, thereby improving the prediction accuracy.

[0103] Combine the following Figure 4 The method for wireless communication proposed in the embodiment of the present application is described in detail. Figure 4It is introduced from the perspective of the interaction between the first device and the second device.

[0104] The first device may be any terminal device described above, or may include any terminal device. For example, the first device is a UE. For another example, the first device may include a terminal device and any processing device for beam prediction.

[0105] In some embodiments, the first device supports function enhancement based on AI / ML operation. For example, the first device has the function of enhancing beam management based on AI / ML operation.

[0106] In some embodiments, a first model is deployed on the first device to implement beam prediction. When the second device is a network device, the first device performs DL beam prediction. When the second device is a terminal device, the first device performs side beam prediction.

[0107] As an example, the first model is a model that supports an AI algorithm or an ML algorithm, that is, the first model is an AI / ML model.

[0108] As an example, the beam prediction implemented by the first model may be BM-Case1 described above, or BM-Case2, or other beam prediction types in the future, which are not limited here.

[0109] In some embodiments, a first model is deployed on the first device side. The first model may not be on the terminal device, but on a server that communicates with the terminal device. For example, the first model is on an over the top (OTT) server that directly communicates with the terminal device.

[0110] The second device can be any network device that communicates with the first device, or a terminal device that communicates with the first device. When the first device is within the coverage of the network device, the second device can be a network device. In a sideline communication system, when the first device communicates with other terminal devices, the second device can be other terminal devices.

[0111] In some embodiments, the second device may monitor the process of the first device processing the first model. For example, the second device may determine whether the current stage is the training stage or the inference stage of the first model based on the report sent by the first device.

[0112] In some embodiments, the second device supports AI / ML operations. The first model may be deployed on the second device side.

[0113] In some embodiments, the second device may send multiple beams to the first device multiple times to facilitate the first device to perform measurements, and perform model training and model inference on the first model based on the measurement results.

[0114] In the above embodiment, no matter the first model is deployed on the terminal device side or the network device side, the first model can be one of the multiple models deployed on the side. Multiple models can be used to predict the transmission beams of different scenarios or different beam transmitting devices.

[0115] See also Figure 4 In step S410, the first device receives first indication information. As an example, the first device may receive first indication information sent by a network device (second device). As an example, the first device may receive first indication information indicated by a higher layer signaling.

[0116] The first indication information is used to indicate a first QCL relationship. As an example, the first indication information may indicate that the QCL relationship of a beam or a beam resource is a first QCL relationship. For example, the first indication information is used to indicate that a first beam or a first beam resource has a first QCL relationship. For another example, the first indication information is used to indicate that multiple beams or multiple beam resources have a first QCL relationship.

[0117] In some embodiments, the first indication information is used to indicate that all beams in the beam set have a first QCL relationship. The beam set may be a beam set related to the training and inference process of the first model. For example, the beam set with the first QCL relationship may be the set A or the set B described above, where the set B may be referred to as the first beam set and the set A may be referred to as the second beam set.

[0118] In some embodiments, the first indication information is an indication information indicating the beam transmission configuration, that is, the first indication information may include TCI information. For example, the first indication information may be TCI information to indicate the TCI status of one or more beams. For example, the first indication information is first TCI information. For another example, the first indication information may include TCI information to indicate the TCI status and other information. Figure 5 The TCI status of one or more beams in the first indication information is introduced in detail and will not be elaborated here.

[0119] In some embodiments, the first indication information may be carried in control signaling. As an example, the first indication information may be carried in one of the following information: DCI, radio resource control (RRC) signaling. For example, the network device may send indication information of one or more beams in DCI format. For another example, the network device may send a beam indication through RRC signaling, and the beam indication may be associated with multiple TCI states.

[0120] The element of the first QCL relationship may refer to one or more elements used to describe the first QCL relationship. The one or more elements may represent beam characteristics or channel characteristics or spatial characteristics of beam resources. That is, the first indication information may indicate which characteristics of the beams are similar or related to the first QCL relationship of the beam based on the one or more elements.

[0121] In some embodiments, the element of the first QCL relationship includes an identifier of a beam set, i.e., a beam set ID. The beam set is used for model training and / or model reasoning of the first model. As can be seen from the foregoing, the consistency of the beam order / index in the beam set cannot be ensured based on the traditional QCL type. In order to achieve consistency between training and reasoning, it is necessary to add new elements to the QCL relationship or configure a new QCL type. When the element of the first QCL relationship includes the ID of the beam set, the consistency of training and reasoning can be ensured in the AI / ML beam management mechanism by using this QCL framework, and the consistency of beams corresponding to different timestamps can be ensured.

[0122] As an example, the above beam set may be the first beam set mentioned above, or the second beam set, or the first beam set and the second beam set. The above beam set may also be one or more beam sets in different processing stages of the model.

[0123] As an example, the ID of a beam set may also be referred to as a beam ID or a transmission ID.

[0124] As an example, the identifier of the beam set can be an association ID between beam sets. Based on the same association ID and the same set A (or set B) resources, the consistency of beam index / ordering can be achieved. That is, the first device's assumption of the transmit beam with the same association ID and the same resource index can replace the first device's assumption of beam shape consistency and beam index / ordering consistency. For example, for each resource of set A, it can be assumed that the transmit beams used in the training and reasoning process have a certain QCL relationship (first QCL relationship) based on the same association ID in training and reasoning. For another example, for each resource of set B, it can also be assumed that the transmit beams used in training and reasoning have a first QCL relationship based on the same association ID.

[0125] As an example, the elements of the first QCL relationship include a beam set ID to implement a beam ID tracking mechanism. The beam ID tracking mechanism can ensure that the first device knows which beam or transmission is being received, even in the case where the signal is slightly adjusted due to channel changes. The beam ID tracking mechanism can also maintain the beam identity across time slots to provide continuity in signal processing and demodulation. It can be seen that the first device can always have a coherent understanding of the same beam at different timestamps based on the first QCL relationship, that is, to ensure that the channel properties of the same beam remain consistent at different timestamps, especially in scenarios with Doppler effect and delay evolution.

[0126] In the above example, the beam ID tracking mechanism may involve an explicit beam or CSI-RS resource identifier carried by high-level signaling or embedded in the DMRS signal. Through this indication method, it is ensured that the first device can maintain understanding of the consistent transmission source even in the case of beam switching or reconfiguration. In addition, since the transmission identity of the beam can be guaranteed over time, the first device can accurately track a specific beam regardless of changes in channel conditions or minor beam adjustments. The beam can maintain a consistent transmission identity even if there are minor changes in the transmission (such as Doppler frequency shift, channel changes, etc.), which is critical for long-term data transmission and high mobility scenarios.

[0127] In some embodiments, the elements of the first QCL relationship further include one or more of the following: a time instance related to model training and / or model inference; and a spatial filter parameter related to the beam set.

[0128] As an example, time instances related to model training and / or model reasoning can be used to determine the time domain resources of the beam, thereby determining the beam information during the model training or model reasoning process.

[0129] As an example, the spatial filter parameters may refer to spatial receiver parameters and / or spatial transmitter parameters. The spatial filter parameters associated with a beam set include an ID spatial parameter associated with a beam set ID.

[0130] As an example, when the elements of the first QCL relationship include spatial filter parameters, model training and model reasoning correspond to the same spatial filter, respectively. That is, the spatial transmit filter solution based on the beam set ID can ensure beam consistency. For example, for each set A resource, the same association ID in model training and model reasoning can correspond to the network device using the same spatial Tx filter during model training and model reasoning. For another example, for each set B resource, the first device may assume that the network device uses the same spatial Tx filter during model training and model reasoning. From a practical point of view, using the same spatial Tx filter in reasoning and training helps ensure beam consistency.

[0131] It should be noted that the elements indicating the first QCL relationship may include not only the beam set ID, time instance, and spatial filter parameter mentioned above, but also elements of multiple existing QCL types, and other elements associated with the beam set ID. In some scenarios, the elements of the first QCL relationship may also include only elements of traditional QCL types, which will be explained later in conjunction with the first operation in step S420.

[0132] In some embodiments, the first QCL relationship may be indicated by one or more QCL types. The one or more QCL types may be conventional QCL types in Table 1 or newly designed QCL types for beam consistency. Among them, the four conventional QCL types are QCL type A, QCL type B, QCL type C, and QCL type D in Table 1. In addition, in order to ensure beam consistency between different timestamps, a new QCL type may be designed to indicate the first QCL relationship, namely, QCL type E. QCL type E may be a type that achieves temporal and spatial consistency to ensure robust beam tracking and state awareness across time slots.

[0133] As an example, the first QCL relationship may be indicated by one QCL type, ie, the first QCL type.

[0134] As an example, the first QCL relationship may be indicated by a plurality of QCL types. For example, the first QCL relationship may be indicated by two QCL types. The two QCL types may include a first QCL type and a second QCL type.

[0135] As an example, QCL Type E can integrate elements of existing QCL types and then add additional mechanisms to support timestamp-level consistency of channel attributes and beam identification.

[0136] As an example, QCL type E can be described by a new element. The new element can include the beam set ID mentioned above, and can also include a time instance related to model training and / or model inference, and a spatial filter parameter related to the beam set.

[0137] In the above example, the newly added QCL type E can ensure the consistent description of time and space through ID. When QCL type E includes beam set ID, time instance and spatial filter parameters, it integrates time (based on time instance) and space (based on beam) consistency mechanisms to ensure the ID consistency of a given transmission at multiple timestamps. In high-mobility environments (such as vehicles and high-speed trains) with frequent Doppler effects and beam switching, maintaining consistent beam identification across timestamps is critical for stable data transmission. For example, for beam tracking of multi-slot PDSCH, when the PDSCH data spans multiple time slots, QCL type E can ensure that the first device can consistently demodulate the signal using the same beam identifier, thereby improving reliability. For another example, when the consistency of the beam over multiple frames is relatively important, QCL type E can ensure that the first device remains connected to a consistent beam, even if rapid reconfiguration is required due to signal blocking or beam scanning.

[0138] In the above example, QCL type E may include some or all elements indicating the first QCL relationship. For example, Table 2 shows a plurality of QCL types after QCL type E is added, wherein QCL type E includes all elements indicating the first QCL relationship.

[0139] Table 2

[0140] QCL Type describe QCL Type A Doppler shift, Doppler spread, average delay, delay spread QCL Type B Doppler shift, Doppler spread QCL Type C Average delay, Doppler shift QCL Type D Spatial receiver parameters QCL Type E Beam set ID, time instance, spatial filter parameters

[0141] In Table 2, the new QCL Type E ensures that the first device can maintain a consistent transmission identity (ID) at different timestamps while dealing with changes in channel properties, Doppler effects, and spatial variations. By tracking the beam set ID over time, even if channel conditions change, the identification of the beam or beam set can be consistent between the model training and model processing phases.

[0142] As an example, instead of adding a new QCL type, the elements of the first QCL relationship, such as beam set ID, time instance, and spatial filter parameters, may be integrated into the existing QCL types. When these elements are partially or fully integrated into QCL type A, QCL type B, QCL type C, or QCL type D, the first QCL relationship can not only capture traditional parameters such as Doppler shift, delay spread, and spatial characteristics, but also introduce a new beam ID tracking mechanism.

[0143] In the above examples, multiple elements indicating the first QCL relationship can be added to QCL type A, QCL type B, QCL type C and / or QCL type D in various combinations to form multiple new QCL types. Tables 3 and 4 are used as examples for illustration below. It should be understood that the adding methods in Tables 3 and 4 are only two examples and do not constitute a limitation.

[0144] In Table 3, multiple elements indicating the first QCL relationship are added to QCL type D to form a new QCL type D.

[0145] Table 3

[0146] QCL Type describe QCL Type A Doppler shift, Doppler spread, average delay, delay spread QCL Type B Doppler shift, Doppler spread QCL Type C Average delay, Doppler shift New QCL Type D Spatial receiver parameters, beam set ID, time instance, spatial filter parameters

[0147] In Table 4, a plurality of elements indicating the first QCL relationship are added to QCL type B, QCL type C, and QCL type D, respectively, forming new QCL type B, QCL type C, and QCL type D.

[0148] Table 4

[0149]

[0150]

[0151] In some embodiments, the first QCL relationship may be indicated by a first QCL type. When multiple elements indicating the first QCL relationship are respectively added to the traditional QCL type, the first QCL type may be one of a new QCL type A, QCL type B, QCL type C, and QCL type D including one of the multiple elements. When some or all of the elements indicating the first QCL relationship constitute QCL type E, the first QCL type may be QCL type E. Therefore, when the first QCL relationship is indicated by the first QCL type, the first QCL type is one of the following: QCL type A, QCL type B, QCL type C, QCL type D, and QCL type E. Among them, QCL type A, QCL type B, QCL type C, and QCL type D are new QCL types with new elements added.

[0152] In some embodiments, the first QCL relationship may be indicated by multiple QCL types, such as a first QCL type and a second QCL type. In some scenarios, for a given QCL association, up to two QCL types may be used to indicate the QCL relationship. For both QCL types, the same RS may be used to indicate the QCL relationship.

[0153] As an example, the first QCL type may include some or all elements indicating the first QCL relationship, and the second QCL type may be a conventional QCL type. For example, when the first QCL type is QCL type E, the second QCL type includes at least one of conventional QCL type A, QCL type B, QCL type C, and QCL type D.

[0154] In the above example, QCL type E is configured to be used together with any one of QCL type B, QCL type C and QCL type D, or any two types, or all three types to better indicate the first QCL relationship and ensure beam consistency.

[0155] As an example, for the DMRS of the PDSCH, the first device may expect the TCI state to indicate the following QCL type. For example, it may be configured by a higher layer parameter trs-Info in a CSI-RS resource set with non-zero power (NZP). This parameter may configure the CSI-RS resource to correspond to QCL type A and, if applicable, QCL type E configured with the same CSI-RS resource.

[0156] As an example, when multiple elements indicating the first QCL relationship are added to QCL type D, the CSI-RS resources configured by the higher layer parameter trs-Info in the NZP CSI-RS resource set correspond to QCL type A and QCL type D configured with the same CSI-RS resources where applicable.

[0157] Continue to see Figure 4 , in step S420, the first device performs a first operation according to the first QCL relationship. That is, the first device needs to consider whether the beam or the beam set has the first QCL relationship when performing the first operation. The first operation can be to determine a beam set for model training and / or model reasoning, or to determine multiple optimal beams (also referred to as optimal beams) in the model prediction results, or to determine a beam set for model training and / or model reasoning and multiple optimal beams in the model prediction results.

[0158] In some embodiments, when the element of the first QCL relationship includes a beam set ID or other new elements, the first operation may be to determine a beam set for model training and / or model inference. That is, the first device may determine a beam set for training and inference of the first model based on the first QCL relationship including the beam set ID. By introducing the first QCL relationship including the beam set ID, the first device may ensure beam consistency during model training and model inference, thereby improving prediction accuracy.

[0159] As an example, the first QCL relationship may be used to determine the first beam set (set B) and / or the second beam set (set A). That is, the first device may determine set B and / or set A using the first QCL relationship.

[0160] As an example, in the model training and model reasoning process of the first model, the correlation can be represented by the first QCL relationship. For the resources of set A and set B, having the first QCL relationship can ensure the correlation of the beams in the model training and model reasoning stages.

[0161] As an example, the beam set used for model training and / or model reasoning also satisfies one or more of the following conditions: the first beam set used for model training and the first beam set used for model reasoning have a first QCL relationship; the second beam set used for model training and the second beam set used for model reasoning have a first QCL relationship; the first beam set used for model training and the second beam set have a first QCL relationship; the first beam set used for model reasoning and the second beam set have a first QCL relationship. As described above, the first beam set is used to determine multiple optimal beams in the second beam set.

[0162] In some embodiments, the first operation may be to determine multiple optimal beams (e.g., K optimal beams, K is a positive integer) in the model prediction results according to the first QCL relationship. The first QCL relationship may be a dynamic QCL relationship to ensure that multiple optimal beams have the same or similar channel characteristics / spatial characteristics. Exemplarily, the dynamic QCL relationship may refer to the system dynamically adjusting the QCL relationship between different beams through channel measurement or AI / ML prediction. The dynamic nature of the QCL relationship is that it can change with changes in channel conditions. The network device needs to dynamically calculate and adjust the QCL state based on the channel measurement results or the update of the AI / ML model.

[0163] As an example, multiple optimal beams have a first QCL relationship, and the first QCL relationship is used to determine one or more of the following: multiple optimal beams correspond to the same channel characteristics; multiple optimal beams correspond to the same delay characteristics; multiple optimal beams correspond to the same beam set identifier; multiple optimal beams correspond to the same spatial parameters or spatial filter parameters. It can be seen that the multiple optimal beams predicted by the AI / ML model can share part or all of the channel characteristics. That is, when multiple optimal beams are determined based on the first QCL relationship, the first device can share channel information between beams, thereby reducing the overhead of independent beam measurements. For example, for each combination of Top-K beams, a QCL relationship can be dynamically defined so that the first device shares CSI measurement results between multiple beams.

[0164] As an example, the first QCL relationship may be used for the first model to output K optimal beams in the second beam set.

[0165] As an example, after the first model outputs the prediction information of all beams in the second beam set, the first QCL relationship can be used by the first device to determine the K best beams in the second beam set. Through the estimation and calculation of the dynamic QCL relationship, the network device can generate a new trigger state for each predicted Top-K beam combination and configure it to the first device together with the corresponding QCL relationship.

[0166] It should be understood that when the first operation is to determine multiple optimal beams, the elements of the first QCL relationship are not limited. For example, the elements of the first QCL relationship may include at least one of the beam set ID, time instance, and spatial filter parameter described above. For another example, the elements of the first QCL relationship may not include new elements, and the first device only determines the optimal beam based on traditional QCL elements.

[0167] As an example, the AI / ML model can predict the Top-K beams based on historical data and real-time measurements, thereby reducing unnecessary beam scanning. The dynamic QCL relationship allows the system to flexibly adjust the correlation between beams based on channel changes and the mobility pattern of the first device, thereby improving the adaptability of the system. For example, the AI / ML model located on the first device side can use historical CSI-RS, RSRP measurements and / or environmental information (such as geographic location information, movement speed, etc.) to predict the most likely Top-K beam in the second beam set.

[0168] As an example, the predicted Top-K beam can be expressed as: pred = {B1, B2, B3, ... B K Based on the predicted Top-K beams, the network equipment can dynamically determine the QCL relationship between these beams through measurement or channel model analysis. Assuming that the QCL relationship between beams can be determined based on their channel characteristics (such as angle, delay, Doppler shift, etc.), for example, if the angle distributions of multiple beams are similar and the delay difference on the propagation path is small or less than a certain threshold, then these beams may have a QCL relationship.

[0169] In the above example, the first QCL relationship at time t can be expressed as:

[0170]

[0171] Among them, B i , B j Belongs to B pred ; B i and B jHaving the first QCL relationship indicates that the two beams share the same channel characteristics.

[0172] As an example, the multiple optimal beams include a first beam and a second beam, and the difference between two channel characteristics corresponding to the first beam and the second beam is less than a first threshold. The first threshold is, for example, a set threshold δ. If the channel difference between the beams is less than δ, it can be determined that the two beams have a first QCL relationship.

[0173] In some embodiments, the introduction of the first QCL relationship can be used as a new trigger state. Through the calculation of the dynamic QCL relationship, the network device can generate a new trigger state for each predicted Top-K beam combination and configure it to the first device together with the corresponding QCL relationship. The new CSI trigger state can include not only the Top-K beam combination, but also the dynamic QCL relationship of these beams.

[0174] As an example, the network device may trigger the measurement of the Top-K beam periodically or non-periodically based on timing or events (such as changes in the moving speed of the first device or changes in channel conditions) through PDCCH or other control signaling.

[0175] In some embodiments, the first QCL relationship can be used to trigger the first device to perform beam measurement. That is, the first device can perform CSI measurement according to the newly configured trigger state. By introducing a new trigger state of a dynamic QCL relationship, the network device can efficiently predict and measure the Top-K beams, reducing signaling overhead and the complexity of CSI measurement. After measuring the CSI, the first device feeds back the CSI reports of these beams to the network device.

[0176] As an example, determining multiple optimal beams based on the first QCL relationship may be equivalent to introducing a new trigger state with a dynamic QCL relationship for the predicted Top-K beam.

[0177] As an example, when multiple optimal beams have a first QCL relationship at the current moment, the first device measures one of the multiple optimal beams, thereby determining the measurement results of multiple optimal beams based on the measurement results of one beam. Since the QCL relationship can indicate that the beams have similar spatial characteristics (such as angle or path delay), the first device can infer the channel states of other beams among the K optimal beams based on the CSI measurement of one beam. It can be seen that when multiple optimal beams are determined and measured based on the first QCL relationship, the number of independent CSI measurements can be reduced, that is, the number of beams that need to be independently measured is reduced.

[0178] As an example, when multiple optimal beams do not have a first QCL relationship at the current moment, the first device updates the multiple optimal beams and measures the updated multiple optimal beams. The first device updates the multiple optimal beams so that the updated multiple optimal beams have a first QCL relationship. It should be noted that the network device can also readjust the QCL relationship of the Top-K beams based on regular channel measurement results or beam updates predicted by the AI ​​model. For example, as the channel conditions or the mobility pattern of the first device change, the first QCL relationship can be updated periodically or based on event triggers. For another example, when the network device detects a significant change in the channel environment (such as an increase in the speed of the first device, deterioration of the channel quality, etc.), the first QCL relationship can be updated immediately and the first device can be notified through control signaling. When the first QCL relationship is a QCL relationship that can be dynamically adjusted, the system can adaptively respond to changes in the channel environment and the mobility pattern of the first device, thereby improving the accuracy of beam management and selection.

[0179] Combination of the above Figure 4 A plurality of new elements indicating a first QCL relationship and a method for determining a beam set or predicting a plurality of optimal beams based on the first QCL relationship are introduced. The first QCL relationship may be indicated by TCI information. The TCI state included in the TCI information may be regarded as a set of specific beams or channel configurations, which are used to indicate which beam the first device should use for communication at a specific time instance. In some embodiments, maintaining consistency of the TCI state may ensure that the TCI states used in different timestamps are associated with the same CSI-RS resources, so that the first device uses the same channel estimate to demodulate the DMRS when processing transmissions at different timestamps.

[0180] In some embodiments, in the NZP CSI-RS resources configured with trs-Info, the first device can rely on these resources to infer the time-frequency characteristics of the transmission beam (for example, through QCL type A in Table 2) and potential spatial characteristics (for example, through QCL type D / QCL type E in Table 2). During the inference process, the network device can indicate the same CSI-RS resources as before for PDSCH, and the first device can use the trained beam information without having to re-perform channel measurement and estimation to ensure consistency. If the CSI-RS resources are configured with high-level parameters trs-Info or repeated configurations, the first device can maintain consistent channel estimates at multiple timestamps by using these repeatedly configured CSI-RS resources. For example, at multiple timestamps, if the same NZP CSI-RS resource set is used, and the CSI-RS resources in the resource set have established channel correlation through QCL type A and QCL type D / QCL type E, the first device can maintain a consistent channel model between these timestamps.

[0181] In some embodiments, when the high-level parameter trs-Info is not configured, the first device can ensure consistency between different timestamps by combining the QCL type E and the traditional QCL type in Table 2. That is, based on the beam set ID, time instance, and spatial filter parameters indicating the first QCL relationship, consistent beam transmission characteristics can be guaranteed without repetition of high-level parameters.

[0182] In order to reduce transmission overhead, a single beam indication can be used to indicate the TCI status of multiple time instances. That is, the first indication information is an indication information indicating the beam transmission configuration, and the first indication information includes multiple transmission configuration indication TCI status corresponding to multiple time instances. In particular, for BM-Case2, the TCI status of multiple time instances is indicated by a single TCI indication signaling, which can reduce the transmission frequency of DCI without DL allocation, thereby reducing DCI overhead.

[0183] In some embodiments, the network device may perform a beam indication through control signaling, and the beam indication may be associated with multiple TCI states. The control signaling is also the first indication information. Through the first indication information, the network device can manage beam selection more flexibly without frequently indicating the detailed configuration of each beam.

[0184] As mentioned above, the first indication information can be carried in PDCCH or RRC signaling. RRC signaling can be used for configuration on a longer time scale. For example, the network device can use RRC signaling to pre-configure the use time of these beams in periodic reports, such as indicating the specific effective time of the TCI state (for example, a specific time slot or symbol). For another example, the network device can indicate the use time slot of the beam and the TCI state used by the first device in the corresponding time slot during the actual transmission process through dynamic PDCCH signaling.

[0185] In some embodiments, the network device may indicate different TCI states that can be applied in different time instances through the first indication information. That is, the network device may notify the first device of the application time slots of each TCI state in different time instances in some way.

[0186] As an example, the system may predefine or indicate the time application instances of certain beams of the first device through configuration signaling. For example, the network device may indicate the TCI states corresponding to different time instances through a predefined time table. In this way, the network device may use different TCI states in different time instances. For the TCI states used in different time instances, the first device needs to know the specific beam corresponding to each TCI state and its effective time.

[0187] As an example, when the network device uses the first indication information to indicate multiple TCI states of multiple time instances, the beam application time of each TCI state should be predefined or indicated to the first device. For example, the time window for the first device to perform beam prediction can be configured by the network.

[0188] As an example, the first device can determine the effective time of each TCI state through a timing synchronization mechanism. For example, when the system is initialized, the network device can configure a set of beams and their corresponding TCI states through RRC signaling, and instruct different time instances to use these beams. Through predefined time application rules, the system can define in advance the mapping rules between a beam indication and different beams in multiple time instances through RRC signaling. For another example, the network device can specify that a beam corresponds to different TCI states in a specific time subframe or time slot. After receiving the first indication information, the first device can know the TCI state applied in each time instance according to the configured rules or signaling, and will automatically apply the corresponding TCI state according to the time instance without receiving a separate beam indication each time.

[0189] As an example, the first indication information can be used by the first device to determine a TCI state table. The TCI state table may include a beam and an effective time corresponding to each TCI state in a plurality of TCI states. For example, the first device may maintain a TCI state table, which records the beam and effective time corresponding to each TCI state.

[0190] In the above example, when the network device sends a beam indication via control signaling, the first device can determine the beam that should be used in the current time slot by looking up the TCI state table. By associating multiple TCI states with a single beam indication, the network device can significantly reduce the frequent control signaling transmission overhead.

[0191] In the above example, when the beam indication sent by the network device is associated with multiple TCI states, the first device needs to understand which TCI states are available according to the indication of the network device and confirm the applicable time according to the TCI state table. For example, for the beams in the first beam set, the first device can automatically switch to the beam used by the corresponding TCI state according to the time instance.

[0192] As an example, each beam indication can be reused in multiple time instances to reduce the need for separate indications for each time slot or beam, and also to ensure consistency of training process and inference process IDs.

[0193] In some embodiments, the first indication information may associate a single beam indication to different beams in multiple time instances. The multiple TCI states in the first indication information may correspond to multiple beams or to one beam. As an example, the multiple TCI states correspond one-to-one to the multiple beams. As an example, at least two TCI states in the multiple TCI states correspond to one beam.

[0194] As an example, the multiple beams corresponding to the multiple TCIs may be the multiple optimal beams mentioned above.

[0195] In some embodiments, multiple TCI states correspond to beams at multiple time instances. Before the first device applies a beam at a certain time instance, the network device may decide whether to change the TCI state of the Tx beam used for transmission.

[0196] For ease of understanding, the following Figure 5 Describes scenarios where multiple TCI states may change. Figure 5 , the AI / ML model can predict the best beams at four future time instances T5, T6, T7, and T8 based on the measurement results at time points T1, T2, T3, and T4. After model reasoning, the network can send TCI status indication 1 (TCI indication 1) to notify the first device of multiple TCI states at T5, T6, T7, and T8. If the network decides to change the TCI state of T7 and T8 before the beam application time of T7 and T8, the network will send TCI status indication 2 (TCI indication 2) to update the TCI state of T7 and T8.

[0197] exist Figure 5 In the example, TCI status indication 1 may be the first indication information, and TCI status indication 2 may be the second indication information.

[0198] In some embodiments, some or all of the TCI states in the first indication information may be updated based on the dynamic indication. The dynamic indication may be referred to as the second indication information. The second indication information may be used to indicate that at least one of the multiple TCI states has changed. As an example, the first device may receive the second indication information sent by the network device to determine the latest TCI state.

[0199] As an example, when at least one TCI state among multiple TCI states changes, the first device receives second indication information, and the second indication information is used by the first device to update at least one TCI state.

[0200] As an example, the second indication information may be carried in physical layer signaling. The physical layer signaling is, for example, PDCCH. For example, the network device may dynamically indicate which TCI state to use at each time instance through physical layer signaling.

[0201] In some embodiments, the first device may send an uplink reference signal to the second device so that the second device determines whether to adjust the multiple TCI states in the first indication information. For example, when the second device is a network device, it may receive an uplink reference signal.

[0202] As an example, the uplink reference signal is, for example, a sounding reference signal (SRS). SRS can quickly reflect changes in channel conditions of the first device. The network device can configure SRS resources for the first device, and the first device sends SRS periodically or on demand. SRS can be transmitted in multiple beam directions, which helps the network device to measure the quality of the channel at different angles. SRS is usually sent through the uplink and can be sent across multiple antenna ports so that the network device can obtain complete channel state information. The network device evaluates the uplink channel quality through the received SRS. Since the uplink and downlink channels have a certain symmetry (especially in time division duplex (TDD) systems), the network device can infer the channel quality of the downlink based on the results of the SRS measurement.

[0203] As an example, the network device can measure the channel of the first device through SRS and dynamically adjust the TCI state to enhance the accuracy and efficiency of beam indication. By dynamically adjusting the TCI state, it can be ensured that beam management can follow the changes in the channel. The network device can infer the channel conditions of the first device in different directions through the SRS measurement results, including parameters such as channel gain, delay, Doppler spread in different beam directions. For example, based on the SRS measurement results, the network device can dynamically adjust the beam configuration associated with each TCI state. In other words, the network device can select an appropriate beam based on the channel measurement results and associate it with different TCI states. For another example, by analyzing the SRS signal, the network device can determine which beam directions have the best channel quality for the first device.

[0204] As an example, the network device may notify the first device to update the TCI state through RRC or PDCCH signaling. After receiving the TCI state indication of the network device, the first device may use the corresponding beam to demodulate and decode the data transmission according to the configuration of the network device, and thus determine the best first beam set. For example, when the network device selects a new set of TCI states, the first device updates its received configuration so that physical channels such as PDSCH can be received using the updated beam direction. This mechanism can manage and optimize multiple beams by binding multiple TCI states to different beams.

[0205] For example, by combining beam scanning and SRS feedback, the network device can always select the optimal first beam set and send it to the first device.

[0206] As an example, the network device can configure the first device to periodically send SRS to dynamically track the channel changes of the first device and adjust the TCI state in real time. By updating the TCI state in a timely manner, it can be ensured that the beam transmission of the downlink is highly matched with the channel characteristics, especially in an environment of high-speed movement or rapid channel changes.

[0207] Combination of the above Figures 1 to 5 , describes in detail the method embodiment of the present application. Figures 6 to 8 , describes the device embodiment of the present application in detail. It should be understood that the description of the device embodiment corresponds to the description of the method embodiment, so the parts not described in detail can refer to the previous method embodiment.

[0208] Figure 6 600 is a schematic block diagram of a device for wireless communication according to an embodiment of the present application. The device 600 may be any first device described above. The first device may be a terminal device. Figure 6 The device 600 shown includes a transceiver unit 610 and a processing unit 620 .

[0209] The transceiver unit 610 can be used to receive first indication information, where the first indication information is used to indicate a first QCL relationship; the processing unit 620 can be used to perform a first operation based on the first QCL relationship; wherein the first operation is one or more of the following: determining a beam set for model training and / or model inference, where the elements of the first QCL relationship include an identifier of the beam set; determining multiple optimal beams in the model prediction results.

[0210] Optionally, the first QCL relationship further includes one or more of the following elements: a time instance related to model training and / or model inference; and a spatial filter parameter related to the beam set.

[0211] Optionally, when the elements of the first QCL relationship include spatial filter parameters, the model training and the model inference correspond to the same spatial filter, respectively.

[0212] Optionally, the first QCL relationship is indicated by a first QCL type, and the first QCL type is one of the following: QCL type A, QCL type B, QCL type C, QCL type D and QCL type E.

[0213] Optionally, the first QCL relationship is indicated by a first QCL type, and when the first QCL type is QCL type E, the first QCL relationship is also indicated by a second QCL type, and the second QCL type includes at least one of QCL type A, QCL type B, QCL type C and QCL type D.

[0214] Optionally, the beam set used for model training and / or model reasoning also satisfies one or more of the following conditions: the first beam set used for model training and the first beam set used for model reasoning have a first QCL relationship; the second beam set used for model training and the second beam set used for model reasoning have a first QCL relationship; the first beam set and the second beam set used for model training have a first QCL relationship; the first beam set and the second beam set used for model reasoning have a first QCL relationship; wherein, the first beam set is used to determine multiple optimal beams in the second beam set.

[0215] Optionally, the first indication information is an indication information indicating a beam transmission configuration, and the first indication information includes multiple transmission configuration indication TCI states corresponding to multiple time instances.

[0216] Optionally, multiple TCI states correspond one-to-one to multiple beams, or at least two TCI states among the multiple TCI states correspond to one beam.

[0217] Optionally, the first indication information is used by the first device to determine a TCI state table, where the TCI state table includes a beam and an effective time corresponding to each TCI state in multiple TCI states.

[0218] Optionally, the transceiver unit 610 is also used to send an uplink reference signal, which is used to determine whether multiple TCI states are adjusted; when at least one TCI state among the multiple TCI states changes, a second indication information is received, and the second indication information is used for the first device to update at least one TCI state.

[0219] Optionally, the second indication information is carried in physical layer signaling.

[0220] Optionally, multiple optimal beams have a first QCL relationship, and the first QCL relationship is used to determine one or more of the following: multiple optimal beams correspond to the same channel characteristics; multiple optimal beams correspond to the same delay characteristics; multiple optimal beams correspond to the same beam set identifier; multiple optimal beams correspond to the same spatial parameters or spatial filter parameters.

[0221] Optionally, the multiple optimal beams include a first beam and a second beam, and a difference between two channel characteristics respectively corresponding to the first beam and the second beam is less than a first threshold.

[0222] Optionally, the processing unit 620 is also used to: when multiple optimal beams have a first QCL relationship at the current moment, measure one beam among the multiple optimal beams; or, when multiple optimal beams do not have the first QCL relationship at the current moment, update the multiple optimal beams and measure the updated multiple optimal beams.

[0223] Optionally, the first indication information is carried in one of the following information: downlink control information, radio resource control signaling.

[0224] Optionally, the first operation is related to a first model, and the first model is an artificial intelligence or machine learning model.

[0225] Figure 7 700 is a schematic block diagram of another apparatus for wireless communication according to an embodiment of the present application. The apparatus 700 may be any second device described above. The second device may be a network device or a terminal device. Figure 7 The illustrated device 700 includes a transceiver unit 710 .

[0226] The transceiver unit 710 can be used to send first indication information to the first device, where the first indication information is used to indicate a first QCL relationship, and the first QCL relationship is used for the first device to perform a first operation; wherein the first operation is one or more of the following: determining a beam set for model training and / or model inference, the elements of the first QCL relationship include an identifier of the beam set; determining multiple optimal beams in the model prediction results.

[0227] Optionally, the first QCL relationship further includes one or more of the following elements: a time instance related to model training and / or model inference; and a spatial filter parameter related to the beam set.

[0228] Optionally, when the elements of the first QCL relationship include spatial filter parameters, the model training and the model inference correspond to the same spatial filter, respectively.

[0229] Optionally, the first QCL relationship is indicated by a first QCL type, and the first QCL type is one of the following: QCL type A, QCL type B, QCL type C, QCL type D and QCL type E.

[0230] Optionally, the first QCL relationship is indicated by a first QCL type, and when the first QCL type is QCL type E, the first QCL relationship is also indicated by a second QCL type, and the second QCL type includes at least one of QCL type A, QCL type B, QCL type C and QCL type D.

[0231] Optionally, the beam set used for model training and / or model reasoning also satisfies one or more of the following conditions: the first beam set used for model training and the first beam set used for model reasoning have a first QCL relationship; the second beam set used for model training and the second beam set used for model reasoning have a first QCL relationship; the first beam set and the second beam set used for model training have a first QCL relationship; the first beam set and the second beam set used for model reasoning have a first QCL relationship; wherein, the first beam set is used to determine multiple optimal beams in the second beam set.

[0232] Optionally, the first indication information is an indication information indicating a beam transmission configuration, and the first indication information includes multiple transmission configuration indication TCI states corresponding to multiple time instances.

[0233] Optionally, multiple TCI states correspond one-to-one to multiple beams, or at least two TCI states among the multiple TCI states correspond to one beam.

[0234] Optionally, the first indication information is used by the first device to determine a TCI state table, where the TCI state table includes a beam and an effective time corresponding to each TCI state in multiple TCI states.

[0235] Optionally, the transceiver unit 710 is also used to receive an uplink reference signal, which is used to determine whether multiple TCI states are adjusted; when at least one TCI state among the multiple TCI states changes, a second indication information is sent, and the second indication information is used for the first device to update at least one TCI state.

[0236] Optionally, the second indication information is carried in physical layer signaling.

[0237] Optionally, multiple optimal beams have a first QCL relationship, and the first QCL relationship is used to determine one or more of the following: multiple optimal beams correspond to the same channel characteristics; multiple optimal beams correspond to the same delay characteristics; multiple optimal beams correspond to the same beam set identifier; multiple optimal beams correspond to the same spatial parameters or spatial filter parameters.

[0238] Optionally, the multiple optimal beams include a first beam and a second beam, and a difference between two channel characteristics respectively corresponding to the first beam and the second beam is less than a first threshold.

[0239] Optionally, the first QCL relationship is also used to trigger the first device to perform beam measurement. When multiple optimal beams have the first QCL relationship at the current moment, protection measurement is performed on one of the multiple optimal beams; or, when multiple optimal beams do not have the first QCL relationship at the current moment, beam measurement is performed on the updated multiple optimal beams.

[0240] Optionally, the first indication information is carried in one of the following information: downlink control information, radio resource control signaling.

[0241] Optionally, the first operation is related to a first model, and the first model is an artificial intelligence or machine learning model.

[0242] Figure 8 Shown is a schematic diagram of the structure of a communication device according to an embodiment of the present application. Figure 8 The dotted line in the figure indicates that the unit or module is optional. The device 800 can be used to implement the method described in the above method embodiment. The device 800 can be a chip, a terminal device or a network device.

[0243] The device 800 may include one or more processors 810. The processor 810 may support the device 800 to implement the method described in the method embodiment above. The processor 810 may be a general-purpose processor or a special-purpose processor. For example, the processor may be a central processing unit (CPU). Alternatively, the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0244] The apparatus 800 may further include one or more memories 820. The memory 820 stores a program, which can be executed by the processor 810, so that the processor 810 executes the method described in the above method embodiment. The memory 820 may be independent of the processor 810 or integrated in the processor 810.

[0245] The apparatus 800 may further include a transceiver 830. The processor 810 may communicate with other devices or chips through the transceiver 830. For example, the processor 810 may transmit and receive data with other devices or chips through the transceiver 830.

[0246] The present application also provides a computer-readable storage medium for storing a program. The computer-readable storage medium can be applied to a terminal device or a network device provided in the present application, and the program enables a computer to execute the method performed by the terminal device or the network device in each embodiment of the present application.

[0247] The computer-readable storage medium may be any available medium that can be read by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid state disk (SSD)).

[0248] The embodiment of the present application also provides a computer program product. The computer program product includes a program. The computer program product can be applied to the terminal device or network device provided in the embodiment of the present application, and the program enables the computer to execute the method performed by the terminal device or network device in each embodiment of the present application.

[0249] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.

[0250] The embodiment of the present application also provides a computer program. The computer program can be applied to the terminal device or network device provided in the embodiment of the present application, and the computer program enables a computer to execute the method executed by the terminal or network device in each embodiment of the present application.

[0251] The terms "system" and "network" in this application can be used interchangeably. In addition, the terms used in this application are only used to explain the specific embodiments of the present application, and are not intended to limit the present application. The terms "first", "second", "third" and "fourth" in the specification and claims of this application and the accompanying drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions.

[0252] In the embodiments of the present application, the "indication" mentioned can be a direct indication, an indirect indication, or an indication of an association relationship. For example, A indicates B, which can mean that A directly indicates B, for example, B can be obtained through A; it can also mean that A indirectly indicates B, for example, A indicates C, and B can be obtained through C; it can also mean that there is an association relationship between A and B.

[0253] In the embodiments of the present application, the term "corresponding" may indicate that there is a direct or indirect correspondence between the two, or an association relationship between the two, or a relationship of indication and being indicated, configuration and being configured, etc.

[0254] In the embodiments of the present application, "pre-definition" or "pre-configuration" can be implemented by pre-saving corresponding codes, tables or other methods that can be used to indicate relevant information in a device (for example, including a terminal device and a network device), and the present application does not limit the specific implementation method. For example, pre-definition can refer to what is defined in the protocol.

[0255] In the embodiments of the present application, the “protocol” may refer to a standard protocol in the communication field, for example, it may include an LTE protocol, an NR protocol, and related protocols used in future communication systems, and the present application does not limit this.

[0256] In the embodiments of the present application, determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0257] In the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0258] In the embodiments of the present application, the sequence numbers of the above processes do not mean the order of execution. The order of execution 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 the present application.

[0259] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0260] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0261] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0262] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for wireless communication, characterized in that: include: The first device receives first indication information, where the first indication information is used to indicate a first quasi co-location QCL relationship; The first device performs a first operation according to the first QCL relationship; The first operation is one or more of the following: Determine a beam set for model training and / or model inference, the element of the first QCL relationship comprising an identifier of the beam set; Determine multiple optimal beams in the model prediction results.

2. The method according to claim 1, characterized in that The first QCL relationship further includes one or more of the following elements: A time instance associated with said model training and / or said model inference; Spatial filter parameters associated with the set of beams.

3. The method according to claim 2, characterized in that When the elements of the first QCL relationship include the spatial filter parameters, the model training and the model inference correspond to the same spatial filter, respectively.

4. The method according to any one of claims 1 to 3, characterized in that The first QCL relationship is indicated by a first QCL type, and the first QCL type is one of the following: QCL type A, QCL type B, QCL type C, QCL type D and QCL type E.

5. The method according to any one of claims 1 to 3, characterized in that: The first QCL relationship is indicated by a first QCL type. When the first QCL type is QCL type E, the first QCL relationship is also indicated by a second QCL type, and the second QCL type includes at least one of QCL type A, QCL type B, QCL type C and QCL type D.

6. The method according to any one of claims 1 to 5, characterized in that The beam set used for model training and / or model inference also meets one or more of the following conditions: The first beam set used for the model training and the first beam set used for the model inference have the first QCL relationship; The second beam set used for the model training and the second beam set used for the model inference have the first QCL relationship; The first beam set and the second beam set used for the model training have the first QCL relationship; The first beam set and the second beam set used for the model inference have the first QCL relationship; The first beam set is used to determine multiple optimal beams in the second beam set.

7. The method according to any one of claims 1 to 6, characterized in that The first indication information is an indication information indicating beam transmission configuration, and the first indication information includes multiple transmission configuration indication TCI states corresponding to multiple time instances.

8. The method according to claim 7, characterized in that The multiple TCI states correspond one-to-one to the multiple beams, or at least two TCI states among the multiple TCI states correspond to one beam.

9. The method according to claim 7 or 8, characterized in that: The first indication information is used by the first device to determine a TCI state table, where the TCI state table includes a beam and an effective time corresponding to each TCI state in the multiple TCI states.

10. The method according to any one of claims 7 to 9, characterized in that: The method further comprises: The first device sends an uplink reference signal, where the uplink reference signal is used to determine whether the multiple TCI states are adjusted; When at least one TCI state among the multiple TCI states changes, the first device receives second indication information, where the second indication information is used by the first device to update the at least one TCI state.

11. The method according to claim 10, characterized in that The second indication information is carried in physical layer signaling.

12. The method according to any one of claims 1 to 11, characterized in that The plurality of optimal beams have the first QCL relationship, and the first QCL relationship is used to determine one or more of the following: The multiple optimal beams correspond to the same channel characteristics; The multiple optimal beams correspond to the same delay characteristic; The multiple optimal beams correspond to the same beam set identifier; The multiple optimal beams correspond to the same spatial parameters or spatial filter parameters.

13. The method according to claim 12, characterized in that The multiple optimal beams include a first beam and a second beam, and a difference between two channel characteristics respectively corresponding to the first beam and the second beam is less than a first threshold.

14. The method according to any one of claims 1 to 13, characterized in that The first QCL relationship is also used to trigger the first device to perform beam measurement. The method further includes: When the multiple optimal beams have the first QCL relationship at the current moment, the first device measures one beam among the multiple optimal beams; or, When the multiple optimal beams do not have the first QCL relationship at a current moment, the first device updates the multiple optimal beams and measures the updated multiple optimal beams.

15. The method according to any one of claims 1 to 14, characterized in that The first indication information is carried in one of the following information: downlink control information and radio resource control signaling.

16. The method according to any one of claims 1 to 15, characterized in that The first operation is related to a first model, which is an artificial intelligence or machine learning model.

17. A method for wireless communication, characterized in that: include: The second device sends first indication information to the first device, where the first indication information is used to indicate a first quasi co-located QCL relationship, and the first QCL relationship is used for the first device to perform a first operation; The first operation is one or more of the following: Determine a beam set for model training and / or model inference, the element of the first QCL relationship comprising an identifier of the beam set; Determine multiple optimal beams in the model prediction results.

18. The method according to claim 17, characterized in that The first QCL relationship further includes one or more of the following elements: A time instance associated with said model training and / or said model inference; Spatial filter parameters associated with the set of beams.

19. The method according to claim 18, characterized in that When the elements of the first QCL relationship include the spatial filter parameters, the model training and the model inference correspond to the same spatial filter, respectively.

20. The method according to any one of claims 17 to 19, characterized in that The first QCL relationship is indicated by a first QCL type, and the first QCL type is one of the following: QCL type A, QCL type B, QCL type C, QCL type D and QCL type E.

21. The method according to any one of claims 17 to 19, characterized in that: The first QCL relationship is indicated by a first QCL type. When the first QCL type is QCL type E, the first QCL relationship is also indicated by a second QCL type, and the second QCL type includes at least one of QCL type A, QCL type B, QCL type C and QCL type D.

22. The method according to any one of claims 17 to 21, characterized in that The beam set used for model training and / or model inference also meets one or more of the following conditions: The first beam set used for the model training and the first beam set used for the model inference have the first QCL relationship; The second beam set used for the model training and the second beam set used for the model inference have the first QCL relationship; The first beam set and the second beam set used for the model training have the first QCL relationship; The first beam set and the second beam set used for the model inference have the first QCL relationship; The first beam set is used to determine multiple optimal beams in the second beam set.

23. The method according to any one of claims 17 to 22, characterized in that The first indication information is an indication information indicating beam transmission configuration, and the first indication information includes multiple transmission configuration indication TCI states corresponding to multiple time instances.

24. The method according to claim 23, characterized in that The multiple TCI states correspond one-to-one to the multiple beams, or at least two TCI states among the multiple TCI states correspond to one beam.

25. The method according to claim 23 or 24, characterized in that The first indication information is used by the first device to determine a TCI state table, where the TCI state table includes a beam and an effective time corresponding to each TCI state in the multiple TCI states.

26. The method according to any one of claims 23 to 25, characterized in that The method further comprises: The second device receives an uplink reference signal, where the uplink reference signal is used to determine whether the multiple TCI states are adjusted; When at least one TCI state among the multiple TCI states changes, the second device sends second indication information, where the second indication information is used by the first device to update the at least one TCI state.

27. The method according to claim 26, characterized in that The second indication information is carried in physical layer signaling.

28. The method according to any one of claims 17 to 27, characterized in that The plurality of optimal beams have the first QCL relationship, and the first QCL relationship is used to determine one or more of the following: The multiple optimal beams correspond to the same channel characteristics; The multiple optimal beams correspond to the same delay characteristic; The multiple optimal beams correspond to the same beam set identifier; The multiple optimal beams correspond to the same spatial parameters or spatial filter parameters.

29. The method according to claim 28, characterized in that The multiple optimal beams include a first beam and a second beam, and a difference between two channel characteristics respectively corresponding to the first beam and the second beam is less than a first threshold.

30. The method according to any one of claims 17 to 29, characterized in that The first QCL relationship is also used to trigger the first device to perform beam measurement. When the multiple optimal beams have the first QCL relationship at the current moment, the beam measurement is performed on one of the multiple optimal beams; or, when the multiple optimal beams do not have the first QCL relationship at the current moment, the beam measurement is performed on the updated multiple optimal beams.

31. The method according to any one of claims 17 to 30, characterized in that The first indication information is carried in one of the following information: downlink control information and radio resource control signaling.

32. The method according to any one of claims 17 to 31, characterized in that The first operation is related to a first model, which is an artificial intelligence or machine learning model.

33. A device for wireless communication, characterized in that: The device is a first device, and the device includes: A transceiver unit, configured to receive first indication information, where the first indication information is used to indicate a first quasi-co-location QCL relationship; a processing unit, configured to perform a first operation according to the first QCL relationship; The first operation is one or more of the following: Determine a beam set for model training and / or model inference, the element of the first QCL relationship comprising an identifier of the beam set; Determine multiple optimal beams in the model prediction results.

34. The device according to claim 33, characterized in that The first QCL relationship further includes one or more of the following elements: A time instance associated with said model training and / or said model inference; Spatial filter parameters associated with the set of beams.

35. The device according to claim 34, characterized in that When the elements of the first QCL relationship include the spatial filter parameters, the model training and the model inference correspond to the same spatial filter, respectively.

36. The device according to any one of claims 33 to 35, characterized in that The first QCL relationship is indicated by a first QCL type, and the first QCL type is one of the following: QCL type A, QCL type B, QCL type C, QCL type D and QCL type E.

37. The device according to any one of claims 33 to 35, characterized in that The first QCL relationship is indicated by a first QCL type. When the first QCL type is QCL type E, the first QCL relationship is also indicated by a second QCL type, and the second QCL type includes at least one of QCL type A, QCL type B, QCL type C and QCL type D.

38. The device according to any one of claims 33 to 37, characterized in that The beam set used for model training and / or model inference also meets one or more of the following conditions: The first beam set used for the model training and the first beam set used for the model inference have the first QCL relationship; The second beam set used for the model training and the second beam set used for the model inference have the first QCL relationship; The first beam set and the second beam set used for the model training have the first QCL relationship; The first beam set and the second beam set used for the model inference have the first QCL relationship; The first beam set is used to determine multiple optimal beams in the second beam set.

39. The device according to any one of claims 33 to 38, characterized in that The first indication information is an indication information indicating beam transmission configuration, and the first indication information includes multiple transmission configuration indication TCI states corresponding to multiple time instances.

40. The device according to claim 39, characterized in that The multiple TCI states correspond one-to-one to the multiple beams, or at least two TCI states among the multiple TCI states correspond to one beam.

41. The device according to claim 39 or 40, characterized in that The first indication information is used by the first device to determine a TCI state table, where the TCI state table includes a beam and an effective time corresponding to each TCI state in the multiple TCI states.

42. The device according to any one of claims 39 to 41, characterized in that The transceiver unit is also used for: Sending an uplink reference signal, where the uplink reference signal is used to determine whether the multiple TCI states are adjusted; When at least one TCI state among the multiple TCI states changes, second indication information is received, where the second indication information is used by the first device to update the at least one TCI state.

43. The device according to claim 42, characterized in that The second indication information is carried in physical layer signaling.

44. The device according to any one of claims 33 to 43, characterized in that The plurality of optimal beams have the first QCL relationship, and the first QCL relationship is used to determine one or more of the following: The multiple optimal beams correspond to the same channel characteristics; The multiple optimal beams correspond to the same delay characteristic; The multiple optimal beams correspond to the same beam set identifier; The multiple optimal beams correspond to the same spatial parameters or spatial filter parameters.

45. The device according to claim 44, characterized in that The multiple optimal beams include a first beam and a second beam, and a difference between two channel characteristics respectively corresponding to the first beam and the second beam is less than a first threshold.

46. ​​The device according to any one of claims 33 to 45, characterized in that The first QCL relationship is further used to trigger the first device to perform beam measurement, and the processing unit is further used to: When the multiple optimal beams have the first QCL relationship at the current moment, measuring one beam among the multiple optimal beams; or, When the multiple optimal beams do not have the first QCL relationship at the current moment, the multiple optimal beams are updated and the updated multiple optimal beams are measured.

47. The device according to any one of claims 33 to 46, characterized in that The first indication information is carried in one of the following information: downlink control information and radio resource control signaling.

48. The device according to any one of claims 33 to 47, characterized in that The first operation is related to a first model, which is an artificial intelligence or machine learning model.

49. An apparatus for wireless communication, characterized in that: The device is a second apparatus, and the device includes: A transceiver unit, configured to send first indication information to a first device, where the first indication information is used to indicate a first quasi-co-located QCL relationship, where the first QCL relationship is used for the first device to perform a first operation; The first operation is one or more of the following: Determine a beam set for model training and / or model inference, the element of the first QCL relationship comprising an identifier of the beam set; Determine multiple optimal beams in the model prediction results.

50. The device according to claim 49, characterized in that The first QCL relationship further includes one or more of the following elements: A time instance associated with said model training and / or said model inference; Spatial filter parameters associated with the set of beams.

51. The device according to claim 50, characterized in that When the elements of the first QCL relationship include the spatial filter parameters, the model training and the model inference correspond to the same spatial filter, respectively.

52. The device according to any one of claims 49 to 51, characterized in that The first QCL relationship is indicated by a first QCL type, and the first QCL type is one of the following: QCL type A, QCL type B, QCL type C, QCL type D and QCL type E.

53. The device according to any one of claims 49 to 51, characterized in that The first QCL relationship is indicated by a first QCL type. When the first QCL type is QCL type E, the first QCL relationship is also indicated by a second QCL type, and the second QCL type includes at least one of QCL type A, QCL type B, QCL type C and QCL type D.

54. The device according to any one of claims 49 to 53, characterized in that The beam set used for model training and / or model inference also meets one or more of the following conditions: The first beam set used for the model training and the first beam set used for the model inference have the first QCL relationship; The second beam set used for the model training and the second beam set used for the model inference have the first QCL relationship; The first beam set and the second beam set used for the model training have the first QCL relationship; The first beam set and the second beam set used for the model inference have the first QCL relationship; The first beam set is used to determine multiple optimal beams in the second beam set.

55. The device according to any one of claims 49 to 54, characterized in that The first indication information is an indication information indicating beam transmission configuration, and the first indication information includes multiple transmission configuration indication TCI states corresponding to multiple time instances.

56. The device according to claim 55, characterized in that The multiple TCI states correspond one-to-one to the multiple beams, or at least two TCI states among the multiple TCI states correspond to one beam.

57. The device according to claim 55 or 56, characterized in that The first indication information is used by the first device to determine a TCI state table, where the TCI state table includes a beam and an effective time corresponding to each TCI state in the multiple TCI states.

58. The device according to any one of claims 55 to 57, characterized in that The transceiver unit is also used for: receiving an uplink reference signal, where the uplink reference signal is used to determine whether the multiple TCI states are adjusted; When at least one TCI state among the multiple TCI states changes, second indication information is sent, where the second indication information is used by the first device to update the at least one TCI state.

59. The device according to claim 58, characterized in that The second indication information is carried in physical layer signaling.

60. The device according to any one of claims 49 to 59, characterized in that The plurality of optimal beams have the first QCL relationship, and the first QCL relationship is used to determine one or more of the following: The multiple optimal beams correspond to the same channel characteristics; The multiple optimal beams correspond to the same delay characteristic; The multiple optimal beams correspond to the same beam set identifier; The multiple optimal beams correspond to the same spatial parameters or spatial filter parameters.

61. The device according to claim 60, characterized in that The multiple optimal beams include a first beam and a second beam, and a difference between two channel characteristics respectively corresponding to the first beam and the second beam is less than a first threshold.

62. The device according to any one of claims 49 to 61, characterized in that The first QCL relationship is also used to trigger the first device to perform beam measurement. When the multiple optimal beams have the first QCL relationship at the current moment, the beam measurement is performed on one of the multiple optimal beams; or, when the multiple optimal beams do not have the first QCL relationship at the current moment, the beam measurement is performed on the updated multiple optimal beams.

63. The device according to any one of claims 49 to 62, characterized in that The first indication information is carried in one of the following information: downlink control information and radio resource control signaling.

64. The device according to any one of claims 49 to 63, characterized in that The first operation is related to a first model, which is an artificial intelligence or machine learning model.

65. A communication device, characterized in that: The invention comprises a memory and a processor, wherein the memory is used to store a program, and the processor is used to call the program in the memory to execute the method according to any one of claims 1 to 32.

66. A device, characterized in that The invention comprises a processor, which is used to call a program from a memory to execute the method according to any one of claims 1 to 32.

67. A chip, characterized in that: It comprises a processor, which is used to call a program from a memory, so that a device equipped with the chip executes a method as claimed in any one of claims 1 to 32.

68. A computer-readable storage medium, characterized in that A program is stored thereon, the program causing a computer to execute the method according to any one of claims 1 to 32.

69. A computer program product, characterized in that The method comprises a program which causes a computer to execute the method according to any one of claims 1 to 32.

70. A computer program, characterized in that The computer program causes a computer to execute the method according to any one of claims 1 to 32.

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