Method and apparatus for wireless communication

Abstract

A method and apparatus for wireless communication are provided. The method comprises the following steps: a first device receives a plurality of beams sent by a second device in an observation window, wherein the plurality of beams are used for determining a first beam set; the first device performs beam prediction on a second beam set in a prediction window based on the measurement value of the first beam set; the first device sends a first report to the second device; wherein the first report is used for determining one or K optimal beams in the second beam set, K is a positive integer, the first beam set is determined according to first information, and the first information comprises at least two of measurement values of the plurality of beams, capability of the first device and network configuration.

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. For example, the terminal device can measure the beams in the received set B to predict the optimal beam in set A. However, there is no clear regulation on how the terminal device determines set B based on the beams sent by the network device. 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 multiple beams sent by a second device within an observation window, and the multiple beams are used to determine a first beam set; the first device performs beam prediction on a second beam set within a prediction window based on measurement values ​​of the first beam set; the first device sends a first report to the second device; wherein the first report is used to determine one or K optimal beams in the second beam set, K is a positive integer, and the first beam set is determined based on first information, and the first information includes at least two of the measurement values ​​of the multiple beams, the capabilities of the first device, and the network configuration.

[0005] In a second aspect, a method for wireless communication is provided, comprising: a second device sends multiple beams within an observation window, and the multiple beams are used to determine a first beam set; the second device receives a first report sent by the first device; wherein the first report is used to determine one or K optimal beams in a second beam set within a prediction window, K is a positive integer, and the beam prediction of the second beam set is performed based on the measurement value of the first beam set, and the first beam set is determined according to first information, and the first information includes at least two of the measurement values ​​of the multiple beams, the capability of the first device, and the network configuration.

[0006] According to a third aspect, a device for wireless communication is provided, which is a first device and includes: a transceiver unit, used to receive multiple beams sent by a second device within an observation window, and the multiple beams are used to determine a first beam set; a processing unit, used to perform beam prediction on a second beam set within a prediction window based on the measurement values ​​of the first beam set; the transceiver unit is also used to send a first report to the second device; wherein the first report is used to determine one or K optimal beams in the second beam set, K is a positive integer, and the first beam set is determined based on first information, and the first information includes at least two of the measurement values ​​of the multiple beams, the capabilities of the first device, and the network configuration.

[0007] In a fourth aspect, a device for wireless communication is provided, which is a first device, comprising: a transceiver unit, used to send multiple beams within an observation window, and the multiple beams are used to determine a first beam set; the transceiver unit is also used to receive a first report sent by the first device; wherein the first report is used to determine one or K optimal beams in a second beam set within a prediction window, K is a positive integer, and the beam prediction of the second beam set is based on the measurement value of the first beam set, and the first beam set is determined according to first information, and the first information includes at least two of the measurement values ​​of the multiple beams, the capabilities of the first device, and the network configuration.

[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 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] After receiving multiple beams in the observation window, the first device in the embodiment of the present application can determine the first beam set according to at least two of the measured values ​​of the multiple beams, the capabilities of the first device, and the network configuration. The first beam set can be used by the first device to perform beam prediction on the second beam set. It can be seen that when the second device sends multiple beams, the first device does not need to confirm the purpose of these beams, but directly determines and measures the first beam set, thereby improving the efficiency of model reasoning. 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 flowchart of model reasoning on the terminal device side used in the embodiment of the present application.

[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 flowchart of a possible implementation of the method shown.

[0020] Figure 6 yes Figure 4 A flowchart of another possible implementation of the method is shown.

[0021] Figure 7 yes Figure 4 A schematic diagram of a possible implementation of the method shown.

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

[0023] Fig. 9 It is a structural diagram of another device for wireless communication provided in an embodiment of the present application.

[0024] Fig.10 It is a structural diagram of a wireless communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present application are described below in conjunction with the accompanying drawings.

[0026] 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 1As 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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 (MID), a wearable device, a virtual reality (VR) device, an augmented reality (AR) device, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical surgery, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, etc. Optionally, the UE may be used to act as a base station. For example, the 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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).

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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 may be located at the terminal device or the terminal device may perform AI model training and / or generate the best beam using AI model reasoning.

[0052] 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.

[0053] 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., 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 a reference signal received power (RSRP). The role of set B is to provide preliminary environmental information and channel conditions for the model. In the above example, set A may be the beam group that needs 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 by measuring set B to improve the efficiency of data transmission.

[0054] 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.

[0055] 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.

[0056] 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 (PSS).

[0057] Optionally, the terminal device estimates the channel quality of each beam by measuring the RSRP of the received CSI-RS / SSS.

[0058] 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.

[0059] 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. For example, the AI ​​model may be located at a base station or the base station may perform AI model training and / or generate the best beam using AI model reasoning.

[0060] 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.

[0061] 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 AI / ML models. 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.

[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 2In 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 This article introduces the process of processing the model when it is located on the terminal device side. In order to complete the entire process, the terminal device needs to collect and analyze data. As mentioned above, the data collection process can include data collection, initiation of data transmission, transmission, and management. The following introduces data collection and analysis using the terminal device side model as an example.

[0073] For the terminal device side model, the behavior of the terminal device or the content of the terminal device report may vary depending on whether it is a measurement resource for training, reasoning, or monitoring. In other words, the content may be different for different terminal device behaviors or terminal device reports. Therefore, when configuring AI / ML operation-related measurements for the terminal device side model, it is necessary to indicate to the terminal device the purpose of the measurement configuration or the implied terminal device behavior (such as training, reasoning, monitoring, or non-AI / ML operation).

[0074] As an example, for the training process, the terminal device may only need to measure the beams of the configured resources. For example, the model input and labels generated according to set A / set B can be used for internal training on the terminal device side, and there is no need to report to the network device.

[0075] As an example, for the inference process, the terminal device may need to measure the transmit (Tx) beams from set B and use them as model input, while the terminal device should also report the model output regarding the predicted beam / RSRP value.

[0076] For data collection, in the embodiment of the present application, data collection can be started / triggered by network configuration, and data collection executed by terminal device request can also be supported. Data collection for different purposes can correspond to different behaviors of terminal devices.

[0077] As an example, the collected data can be used to train AI models or give terminal devices a preliminary understanding of network performance. When performing data collection based on a training data set, the terminal device needs to be equipped with a mechanism to determine when and what data to collect for effective training. Since the network side may not know the specific data requirements of the terminal device or the best time for data collection, the terminal device can autonomously trigger the collection process and report the relevant configuration. For example, a terminal device-side AI / ML model, whose input may include layer 1 (L1) RSRP of 8 SSBs, and whose output may include predicted L1-RSRP of 32 CSI-RSs. The 8 SSBs can correspond to set B for training purposes, and the 32 CSI RSs can correspond to set A for training purposes.

[0078] Taking the AI / ML model in the above example as an example, in order to collect data for training the AI / ML model, the network can configure the first SSB resource set and the first CSI-RS resource set to the terminal device. In the first SSB resource set, the SSB resource indicator (resource indicator, RI) or resource index (resource index, RI) can be from 1 to 8. SSBRI can be defined based on an identifier (identity, ID) associated with each SSB in the SSB resource set. In the first CSI-RS resource set, the CSI-RS resource indicator or index (CSI-RS RI, CRI) can be from 1 to 32. CRI can be defined based on an ID associated with each CSI-RS in the CSI-RS resource set.

[0079] Optionally, the CSI-RS in the first CSI-RS resource set will share the same periodicity with the SSB in the first SSB resource set.

[0080] Optionally, the terminal device can collect L1-RSRP about SSB and CSI-RS at various time occasions, and upload the data to the over the top (OTT) server for offline model training.

[0081] Optionally, during the model training process, the L1-RSRP corresponding to SSB RI=m (1≤m≤8) in the first SSB resource set can be used to determine the input value of the mth input feature of the AI / ML model, and the L1-RSRP corresponding to CRI=n (1≤n≤32) in the first CSI-RS resource set can be used to determine the value label associated with the nth output feature of the AI / ML model. This offline trained AI / ML model can then be downloaded back to the terminal device for future model reasoning.

[0082] The following still takes the terminal device side model as an example to introduce the model reasoning and reporting process, which also includes the beam management reasoning process.

[0083] For the inference process of the model on the terminal device side, the terminal device measures the RS of the set B beams and predicts the Top-K beams of set A, and reports the prediction results to the network. In the traditional L1-RSRP report, the terminal device should report the L1-RSRP value of the channel measurement resource (CMR) associated with the CSI report. However, for artificial intelligence-based beam management, the beam group used for measurement (set B) and the beam group used for reporting (set A) may be different, so in order to notify the terminal device to report the prediction results, the association between set A and set B should be indicated to the terminal device.

[0084] It should be noted that the model training process also needs to confirm the association between set A and set B. For model training, the measurement results of set B will be used as model input, and the measurement results of set A will be used as true value labels. Therefore, the method of associating set B with set A can be to configure both the resources of set A and the resources of set B to the terminal device in advance. However, for model reasoning, the terminal device only needs to measure the beams in set B, and the resources or indexes of the beams in set A will only be used for reporting. Therefore, the key issue in the model reasoning process is how to represent the resources or indexes of set A.

[0085] During the inference process, the terminal device will not measure the beams in set A, so there is no need to configure the RS for the beams in set A. However, in some scenarios, the RS for the beams in set A can be configured to the terminal device for other purposes, such as performance monitoring. In this case, both the resource set of set B and the resource set of set A will be configured to the terminal device. It can be seen that the network can configure the resource set of set B and the resource set of set A based on different purposes. Taking CSI resources as an example, the terminal device needs to know the intention of the configured CSI resources (whether it is for training, inference, monitoring or non-AI / ML operations), because the corresponding CSI report types may be different.

[0086] As an example, for monitoring, depending on the discussion of monitoring types, the terminal device may need to measure the monitoring resources configured by the network equipment and report the model output / labels, or report the calculated metrics (e.g., beam prediction accuracy). For traditional non-AI / ML operations, the terminal device may need to measure Tx beams and report the measured beams / RSRP.

[0087] During the inference process of the AI / ML model, the measurements based on the Set B beams can be used as model input. In addition, the beam ID information can also be provided as input to the model. Based on the model output, the Top-1 / Top-K beams among Set A of beams can be obtained, or the Top-1 / Top-K beams among Set A of beams can be determined using the predicted L1-RSRP (depending on the label).

[0088] Optionally, the output of the model is, for example, the probability of each beam in Set A to be the Top-1 beam, or the predicted L1-RSRPs.

[0089] In some scenarios, the terminal device may 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, reduce beam measurement and reporting delays. BM-Case1 is a downlink (DL) beam prediction in the spatial domain for set A based on the measurement results of set B. For BM-case1, the measurements of set B (measurements based on Set B of beams) are used as model input to predict the Top-1 / Top-K beams in set A. BM-Case2 is a DL beam prediction in the time domain for set A based on the historical measurement results of set B. For BM-Case2, the measurements of set B beams at historical time instances (measurements based on SetB of beams at historic time instance(s)) can be used as model input to predict the time domain DL beams of set A beams. The prediction of DL Tx beams and the prediction of DL Tx / Rx beams may also be used to evaluate the performance of the prediction.

[0090] 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.

[0091] Still taking the terminal device side model as an example, for BM-Case1 with 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. For BM-Case2 with the AI / ML model on the terminal device side, the terminal device can report the following information of AI / ML model reasoning to NW in one reporting 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.

[0092] In the reasoning process of beam management, beam indication is also 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 measurement or the terminal device can directly trigger the second measurement. Unlike BM-case1, BM-Case2 can obtain Top-K beams corresponding to multiple time instances. The beam indication on the network device side needs to consider the time information.

[0093] As an example, the network device may use multiple indications to indicate beams to the terminal device. The advantage of this is that the network device may select a more appropriate beam according to real-time channel changes.

[0094] Combine the following Figure 3 , taking the UE side model as an example, the model reasoning process based on beam prediction is introduced based on the interaction between UE and gNB.

[0095] See also Figure 3 In step S310, the gNB performs scanning based on the beams in the sparse set B (sparse Set Bsweeping).

[0096] In step S320, the model on the UE side performs Top-K beam prediction. The UE inputs the measurement value of the set B beam into the AI ​​model, and outputs the index and L1-RSRP of the top K best beams among all measured beams and the beam ID or beam index. The UE side model can also output the best beam LI-RSRP and the difference between the RSRP of the other K-1 beams and the best beam L1-RSRP to the base station. The K value can be configured by the base station, or the UE can determine the value of K based on the mobile speed, location information, and business mode.

[0097] In step S330, report CRI and / or predicted quality of Top K beam.

[0098] In step S340, the gNB may continue to initiate scanning with the best Top-K beams (Top-K beam sweeping). The Top-K beam sweeping procedure can be configured by the gNB. Step S340 is an optional step.

[0099] In step S350, the UE reports the beam quality (beam quality report). The UE may report the actual top 1 best beam, or may report the index and L1-RSRP of part or all of the top K best beams. The UE may reuse the traditional beam reporting mechanism.

[0100] In step S360, the gNB indicates the beam used for DL ​​data transmission. The gNB can reuse the traditional TCI beam indication mechanism.

[0101] Combination of the above Figure 2 and Figure 3 The process of beam management or beam prediction based on AI / ML models is introduced. Beam management enhancement based on AI / ML still faces some problems that need to be solved or studied.

[0102] As an example, when a terminal device uses an AI algorithm 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 above, 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.

[0103] As an example, as can be seen from the previous text, set A usually has more beams than set B. Among them, set B usually represents a measurement set or a partial set of downlink reference signals, which is used to assist the network side in beam prediction and selection. However, the terminal device may not need to know whether the beam scanning performed by the network device is for the beams of set A or for the beams of set B, or even for non-AI / ML-based BM. Therefore, how the terminal device determines the beams in set B among the beams sent by the network device is a question that needs to be considered.

[0104] It should be understood that the above introduces the existing problems using the terminal device side model as an example, and the embodiments of the present application can also be used for the network side model.

[0105] Based on this, an embodiment of the present application proposes a method for wireless communication. In this method, after receiving multiple beams sent by a second device, a first device can determine a first beam set (set B) based on at least two of the beam measurement value, the capability of the first device, and the network configuration to perform beam prediction on the second beam set (set A). Through this method, the first device can determine the first beam set by itself according to actual conditions, thereby improving the flexibility of beam prediction.

[0106] Combine the following Figure 4 The method for wireless communication proposed in the embodiment of the present application is described in detail. Figure 4 It is introduced from the perspective of the interaction between the first device and the second device.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] As an example, the first model is a model that supports AI algorithms or ML, that is, the first model is an AI / ML model.

[0111] 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.

[0112] In some embodiments, the first model is deployed on the first device side. That is, the first model is not on the terminal device, but on a server that communicates with the terminal device. For example, the first model is on an OTT server that directly communicates with the terminal device.

[0113] 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 the sideline communication system, the first device communicates with the terminal device, and the second device can be a terminal device.

[0114] 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.

[0115] In some embodiments, the second device supports AI / ML operations. The first model may be deployed on the second device side.

[0116] 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.

[0117] See also Figure 4 In step S410, the first device receives multiple beams sent by the second device. The window in which the first device receives multiple beams or the window in which the second device sends multiple beams is an observation window. That is, the first device receives multiple beams sent by the second device within the observation window.

[0118] The observation window is used for the first device to receive multiple beams. The observation window includes RS resources of the multiple beams, and the resources need to be configured.

[0119] In some embodiments, the observation window of the first device may be periodically configured. For example, when the second device periodically transmits multiple beams, the first device may set one or more observation windows based on the transmission period of the multiple beams. When the second device periodically transmits multiple beams, the multiple beams transmitted periodically may be shared by multiple terminal devices including the first device. That is, the multiple beams transmitted periodically may correspond to multiple observation windows, and the observation windows of multiple terminal devices may correspond to the multiple observation windows respectively. Figure 5 Provide explanation.

[0120] As an example, each terminal device has a corresponding observation window, so the observation window for the first device to receive multiple beams is different from the observation window for other terminal devices to receive beams. Optionally, the observation window for the first device to receive multiple beams can partially overlap with the observation window for other terminal devices to receive beams.

[0121] As an example, when the second device sends multiple beams based on a periodicity, the multiple beams are sent even in the prediction window where the first device performs model inference. For example, when model inference is performed based on periodic beam group B, NW is still transmitting periodic beam group B even during the prediction window. In this scenario, the observation window can overlap with the prediction window. In the overlapping time domain resources, the first device can choose to receive the beam sent by the second device or not.

[0122] In some embodiments, periodic beam transmission can be triggered based on the triggering of periodic prediction reports. The network can configure the first device with RS resources of the first beam set for first model inference. According to the resource periodicity and offset of configuring the RS resources of the first beam set, the first device can identify the time slot of the RS corresponding to each scheduled first beam set, so as to measure the beam at one or more corresponding time points. The RS resources can also be referred to as RS resources of multiple beams transmitted by the second device.

[0123] As an example, the first beam set RS resources may be located in an observation window of the first device. Beam resources corresponding to multiple time instances may be configured in the observation window. Considering that the first beam set may be different in each time instance, the resources of the first beam set for each time instance in the observation window may be configured as an RS set.

[0124] In periodic reporting or periodic beam transmission, the observation window is one of a plurality of observation windows. The observation window of the first device may be one or more windows of the plurality of observation windows. The positions of the plurality of observation windows may be determined based on the position of the initial observation window and the first period. The first beam set is used for repeated transmission in the plurality of observation windows.

[0125] As an example, the first period may be equal to the reporting period or may not be equal to the reporting period.

[0126] As an example, the first device may be one of a plurality of devices that share periodic RS resources. The plurality of devices may perform beam prediction based on a first beam set in any observation window in a plurality of observation windows.

[0127] In some embodiments, the network may configure the length of the observation window for the first device. For example, how many time slots are needed as RS resources. The observation window may be a set of RSs that the network configures to the first device so that the first device can perform measurements for CSI reporting or other reporting. These resources are configured as periodic resources, and different time instances may correspond to different RS configurations.

[0128] In some embodiments, the length of the observation window can be configured or adjusted according to one or more of the following methods: configured by a third parameter, which is used to define the length of the observation window; determined by a reporting offset and a resource offset; increasing the length of the observation window by increasing the reporting period or reducing the reference signal resource offset; reducing the length of the observation window by reducing the reporting period or increasing the reference signal resource offset; predicting the length of the observation window based on the number of time instances corresponding to the first beam set.

[0129] As an example, the window length (duration) of the observation window may be indicated by the CSI framework or may be configurable.

[0130] As an example, when the length of the observation window can be configured according to the third parameter, the third parameter can directly define the length of the observation window. For example, the observation window length can be a new information element (IE) IE defined in csi-ReportConfig. The new IE can directly define a window length, for example, set to 20 time slots or 50 time slots. This configuration allows network operators to more directly control the length of the observation window without relying on the difference between the report offset and the resource offset.

[0131] As an example, the length of the observation window can be determined based on the report offset and resource offset of the periodic report. For example, the value of the parameter ReportPeriodityAndOffset configured by the network can determine the report offset; the value of the parameter ResourcePeriodityandOffset can determine the resource offset. Among them, ResourcePeriodityandOffset is used to indicate the period and offset of the RS resource of the first beam set, defining which time slot the terminal device starts measuring the RS resource, and how many time slots the resource will be repeated.

[0132] For example, the first device may determine the length of the observation window by using the values ​​of ReportPeriodityAndOffset and ResourcePeriodityandOffset. The definition of the observation window length may be described as: L w =T re ―T RS Among them, L w is the length of the observation window, T re is the first reported time slot, T RS It is the first time slot of the first beam set RS resource and also the starting time slot of the observation window.

[0133] Optionally, T RS It can be described as: T RS =slot0+offset1; where slot0 is the position of time slot 0; offset1 is the offset (ResourceOffset) of the first beam set RS resource, indicating which time slot the first beam set RS resource starts from.

[0134] Optionally, the first reporting time slot T re It can be described as: T re = slot 0 + offset2; where offset2 is the report offset (ReportOffset), indicating which time slot the report starts from.

[0135] Therefore, the length of the observation window can be described as: L w =T re ―T RS =offset2―offset1.

[0136] As can be seen from the above, the length of the observation window usually depends on the difference between the report offset and the RS resource offset, which determines the amount of measurement information that the first device can accumulate before each report. As an example, in order to configure or adjust the length of the observation window, the CSI framework can add the following elements: report periodicity, report offset, RS resource periodicity, and RS resource offset.

[0137] Optionally, the reporting period can be used to configure how long (in time slots) the terminal device performs a CSI report.

[0138] Optionally, the reporting offset can be used to configure the offset of the reporting time slot relative to the time axis to determine the starting time slot of the report.

[0139] Optionally, the RS resource period can be used to configure the repetition period of the RS resources of the first beam set to determine how many time slots later the first device can measure the same resource.

[0140] Optionally, the RS resource offset may be used to configure a starting time slot of the RS resource to determine a starting point of a measurement window or an observation window.

[0141] As an example, the network side can extend the duration of the observation window by increasing the reporting period or reducing the RS resource offset. This allows the first device to collect more RS resources over a longer period of time. By reducing the RS resource offset, the RS resources can be measured at an earlier time slot. This method is suitable for scenarios that require more accurate channel state information, such as in high interference or high signal-to-noise ratio environments.

[0142] Optionally, the configuration method for extending the observation window length may further include increasing a reporting offset so that the first reporting time slot is later, thereby providing a longer time period for measuring RS resources.

[0143] As an example, the network side may shorten the length of the observation window by reducing the reporting period or increasing the RS resource offset. In some low-load or relatively stable environments, the network may shorten the length of the observation window to reduce the computational burden or resource overhead of the first device.

[0144] As an example, the length of the observation window can be predicted based on the number of time instances corresponding to the first beam set. The number of time instances can be network configured or dynamically adjusted. For example, in BM-Case 2, the observation window can include three time instances of beam transmission.

[0145] In some embodiments, the observation window of the first device may be configured aperiodically. For example, when the second device may transmit multiple beams aperiodically, the observation window of the first device may be set based on the triggering of the aperiodic report. Figure 6 Provide explanation.

[0146] As an example, whenever the network needs a prediction of a future beam, a trigger instruction may be sent. When the beam prediction is triggered, the first device may start the AI / ML model for reasoning and predict the future beam based on the measurement results of the current observation window.

[0147] As an example, the timing of network-triggered beam prediction can be dynamically adjusted based on network conditions, user location, channel changes, etc.

[0148] In some embodiments, when the observation window is configured aperiodically, the observation window of the first device can be determined according to the trigger instruction. As an example, for the observation window length in the aperiodic configuration, it can be defined as the duration from "the time of triggering the aperiodic report" to "the time of the reporting time slot". The length of the observation window may not be fixed, so as to be dynamically adjusted according to the actual situation. For example, the network can set different observation window lengths according to changes in channel conditions. As an example, in the aperiodic configuration, the length of the observation window may not be configured, but only the length of the prediction window may be configured to align the predicted time instances.

[0149] The plurality of beams received by the first device are used to determine a first beam set. The first beam set or a measurement value of the first beam set may be used as an input to the first model. The first beam set may be the set B described above. The first beam set is used to predict the second beam set. The second beam set may be the set A described above.

[0150] In some embodiments, the first beam set is a portion of the multiple beams. That is, after the first device receives multiple beams in the observation window, it selects a portion of the beams from the multiple beams as the first beam set. In this scenario, the first device does not need to determine whether the multiple beams sent by the second device are for set B, set A, or non-AI / ML operations, that is, it does not need to determine the purpose of the transmitted beams, but directly determines the first beam set based on the multiple beams. In addition, since the first device hopes to predict the optimal beam in the second beam set, when the first device screens the multiple received beams in advance, it helps to reduce the prediction error of predicting the optimal beam.

[0151] In some embodiments, the first beam set is all beams in the multiple beams. That is, all the multiple beams received by the first device in the observation window are used as the first beam set. For example, according to the network configuration, the first device may consider that the received multiple beams are for set B. For another example, when the capability of the first device meets the conditions, all the received beams may be used as the first beam set for inference.

[0152] As an example, when multiple beams are sent periodically, the first beam set determined by the multiple beams is also sent periodically. Taking set B as an example, each terminal device has its own prediction window and observation window, and periodic set B can be easily shared with multiple terminal devices. Therefore, when multiple terminal devices including the first device use the AI / ML beam management feature, periodic set B is feasible.

[0153] As an example, when multiple beams are transmitted aperiodically, the first beam set determined by the multiple beams is also transmitted aperiodically.

[0154] The first beam set may be determined based on the first information. In other words, the first device may determine all beams in the first beam set from among the plurality of beams based on the first information. Exemplarily, the first information may be used by the first device to screen the plurality of received beams to determine the first beam set. Exemplarily, the first information may be used by the first device to determine that the plurality of received beams belong to the first beam set.

[0155] The first information includes at least two of the measurement values ​​of multiple beams, the capability of the first device, and the network configuration. As an example, the first device may select a beam with better channel quality from multiple beams according to the capability of the first device and the RSRP measurement value, and use it as the first beam set. As an example, the first device may select a beam with better channel quality from multiple beams as the first beam set according to the network configuration and the RSRP measurement value. As an example, the first device may determine the beam in the first beam set from multiple beams according to the capability of the first device and the network configuration. As an example, the first device may determine the first beam set by comprehensively considering the RSRP measurement value, the capability of the first device, and the network configuration.

[0156] When the first information includes measurement values ​​of multiple beams, the first beam set may include N beams with relatively good beam channel quality, where N is a positive integer. Alternatively, the first beam set may include N beams with beam channel quality within a certain range.

[0157] In some embodiments, the measurement values ​​of multiple beams may be expressed by RSRP, or by signal to interference plus noise ratio (SINR), or by other similar parameters.

[0158] In some embodiments, the measurement values ​​of the multiple beams are measurement values ​​obtained after the multiple beams are received, that is, real-time measurement values. When the first device receives the multiple beams in the observation window, the multiple beams can be directly measured.

[0159] As an example, the measurement values ​​of multiple beams can form a measurement vector group for processing. For example, the RSRP value of each beam in the first beam set can form a vector, such as RSRP B1 、RSRP B2 , ..., RSRP Bn , where n is the number of beams in the first beam set. The RSRP measurements of all beams can form a vector group as input to the AI / ML model for training or inference. For example, the model can predict the best K beams (Top-K) in the second beam set, or the most suitable 1 beam (Top-1) based on these measurements.

[0160] In some embodiments, the measurement values ​​of the multiple beams may include historical measurement values ​​of the multiple beams. The historical measurement values ​​may be represented by a measurement vector or by other means.

[0161] As an embodiment, the first device may select the first beam set by analyzing historical measurement values. For example, the first device may combine RSRP measurement results of several past time steps and determine RSRP measurement values ​​of multiple beams by weighting the historical beam measurement values ​​RSRP, thereby determining the first beam set. For another example, if the geographic location information and movement speed of the first device are known, the network may use this information to further optimize the beam selection.

[0162] When the first information includes the capability of the first device, the capability of the first device may include information such as the type or algorithm of the first model, the number of samples required by the first model, the computing capability of the first device, and the service mode that the first device can support. In some scenarios, the capability information of the first device may also include capability information related to beam reception, such as the moving speed and location information of the first device.

[0163] As an example, the first device may determine the first beam set according to its own capabilities or business model. For example, the first device may report basic information such as the number of samples required, the number of Tx beams required for set A and set B, the type / algorithm of the first model, and the preferred set B mode corresponding to the first model to the network, so that the network configures the first beam set or the first device determines the first beam set among multiple beams. For another example, when requesting to collect data for training, the first device may report a model-specific preferred set B mode.

[0164] As an example, the first beam set may be one of a plurality of beam sets. The plurality of beam sets may correspond to a plurality of beam set modes related to device capabilities. The plurality of beam set modes are determined according to network configuration. For example, in order to reduce RS overhead on the network side, the network may pre-configure a plurality of set B modes, and then select and transmit an appropriate set B mode to the first device to generate input data for the AI ​​model. Alternatively, the network may send a plurality of set B modes, and the first device may take a plurality of set B sets as input.

[0165] Optionally, when sending basic information, the first device may not disclose detailed receive (Rx) beam information to the network.

[0166] When the first information includes network configuration, the network can directly configure the beams in the first beam set, or configure the number of beams in the first beam set, and can also configure different first beam sets for terminal devices in different regions or of different types. For example, the network can mark which beam IDs can be used as beam IDs in the first beam set in the framework information of the CSI. For another example, when the network performs beam scanning based on terminal devices in several different areas, the settings of the first beam set can be different or the same. For another example, the network can determine the type of the first device based on the moving speed, location information, computing power, business model, etc. of the first device, and configure different first beam sets for different types of terminal devices to facilitate selection by the first device. Detailed configuration information can be reported by the network device and / or requested by the first device.

[0167] As an embodiment, all beams in the first beam set are N beams with the highest measurement values ​​among the multiple beams, and N is determined according to the network configuration. For example, the first device can select N beams with the best channel quality (according to RSRP or other quality parameters) as the first beam set, and then report it to the network device. The value of N can be defined according to the specific network configuration.

[0168] As an embodiment, all beams in the first beam set are beams whose measured values ​​are greater than the first threshold among the multiple beams. The first threshold may be a threshold related to signal quality. For example, the first device may be based on the set signal quality threshold RSRP target , select the beam that satisfies the value greater than the threshold as the first beam set.

[0169] In the above embodiment, the first threshold may be determined according to network configuration, or may be determined by the first device itself.

[0170] As an embodiment, the first beam set includes multiple beam subsets, and the multiple beam subsets correspond to multiple thresholds related to the measurement value. For example, the first device can be based on the set multiple quality thresholds RSRP target , selecting a beam that satisfies a value greater than different thresholds as a beam subset in the first beam set. Multiple beam subsets may correspond to a second beam set. The first device may predict the second beam set through some or all of the beam subsets in the first beam set.

[0171] In the above embodiment, the multiple thresholds may be determined according to the network configuration, or may be determined by the first device itself.

[0172] In the above embodiments, the multiple beam subsets may have the same or similar quasi co-location (QCL).

[0173] In the above embodiment, the number of beam subsets in the first beam set is determined according to the moving speed and / or coverage of the first device. That is, the first device can determine how many beam subsets are used to form the first beam set to predict the second beam set according to its own speed and coverage. For example, the higher the speed of the first device, the more beam subsets can be used to form the first beam set to predict the second beam set. For another example, the worse the coverage or at the edge of the cell, the more beam subsets can be used to form the first beam set to predict the second beam set.

[0174] In the above embodiment, in the scenario where the first device is relatively stationary, the position-based beam selection can be more accurate because the channel state changes less. In the high-speed mobile scenario, the movement speed of the first device can help predict the trend of rapid channel changes, thereby optimizing the beam selection. In a multi-path propagation environment, the first device can receive signals from multiple paths, and different beams may cover different propagation paths. The first device can select a path with better signal quality and use the corresponding beam as the beam in the first beam set.

[0175] It should be understood that when the determination of the first beam set takes into account the position, movement or environmental information of the first device, in order to improve the generalization ability of the first model, the training data may also include information such as the position, movement speed, and environment of the first device.

[0176] In step S420, the first device performs beam prediction on the second beam set within the prediction window based on the measurement value of the first beam set.

[0177] The prediction window may include multiple time instances of the beam prediction of the first device. As an example, the prediction window is used by the network to indicate or configure the beam according to the beam prediction result.

[0178] In some embodiments, the prediction window of the first device may be periodically configured. For example, when the first beam set is periodically transmitted, the first device may set one or more prediction windows based on the transmission period of the first beam set. Figure 5 The first beam set transmitted periodically may correspond to multiple prediction windows, and the prediction windows of multiple terminal devices may correspond to the multiple prediction windows respectively.

[0179] As an example, each terminal device has a corresponding prediction window. The prediction window for model inference performed by the first device may be different from the prediction window for model inference performed by other terminal devices, or may be the same or partially overlap, which is not limited here.

[0180] As an example, the configuration of the prediction window length may refer to the configuration or adjustment method of the observation window length, which will not be described in detail here.

[0181] In the periodic configuration, the prediction window and the observation window of the first device share the same periodicity. The periodicity may be the same as the periodicity of the RS resources of the plurality of beams or the first beam set. Therefore, the prediction window may be determined implicitly.

[0182] As an embodiment, the length of the prediction window and the length of the observation window are used to determine the reporting period for sending the report as the first period or the first device. For example, the first period can be the sum of the lengths of the prediction window and the observation window. For example, if there is no gap between the observation window and the prediction window, a reporting period can be completely divided into the observation window and the prediction window. The reporting period can be the first period. Therefore, the duration of the prediction window can be determined by the reporting period, that is, the duration of the observation window minus the reporting period.

[0183] As an embodiment, when the prediction window is periodically configured, the prediction window of the first device may be one or more windows among the multiple prediction windows. The positions of the multiple prediction windows may be determined based on the position of the initial prediction window and the first period.

[0184] In the periodic configuration, the prediction window and the observation window may not have the same periodicity. For example, the first beam set is configured to have a longer periodicity, and the first device needs to predict and report in the time slot between the RS resources of the two beam sets with a shorter periodicity. In this case, the NW can further configure the prediction periodicity and prediction offset in the csi-ReportConfig. Among them, the prediction offset is, for example, to offset the prediction window to the symbol of the last set of RS resources in the observation window.

[0185] As an example, when the observation window and the prediction window have different periods, the prediction window can be determined based on the position of the initial prediction window and the second period. The length of the second period is different from the first period. Optionally, the length of the second period is less than the length of the first period. Optionally, the length of the second period is greater than the length of the first period.

[0186] As an example, the prediction period is a configured prediction window time interval, which defines how long it takes for the first device to make a prediction report.

[0187] As an example, the prediction offset is a configured prediction window start time slot, which can be determined based on the time slot of the last RS resource in the observation window. For example, the prediction window start time slot is the time slot of the last RS resource in the observation window plus the prediction window offset.

[0188] In some embodiments, the prediction window can be configured according to the configuration of the observation window. For example, the prediction window can correspond one-to-one with the observation window.

[0189] In some embodiments, the prediction window of the first device may be configured aperiodically. For example, when the first beam set is transmitted aperiodically, the prediction window of the first device may be set based on the triggering of the aperiodic report, which will be described later in conjunction with Figure 6 Provide explanation.

[0190] In a non-periodic configuration, the network can configure the length of the prediction window for the first device, that is, the future time period that the first device wants to predict. The length of the prediction window can be a fixed length or can be adjusted based on specific conditions (such as measurement error or the performance of the AI / ML model). As an example, the length of the prediction window can be dynamically adjusted based on the prediction error of the AI / ML model.

[0191] Exemplarily, the length of the prediction window may include two future time instances that need to be predicted.

[0192] For example, if the difference reaches β (i.e., the error between the last prediction result and the actual measurement result is large), the AI / ML model needs to be fine-tuned and the prediction window length needs to be increased accordingly to offset the random error. In other words, if the prediction error is large, the network can increase the prediction window length, thereby providing more data for the AI / ML model to correct the error and improve the accuracy of future beam predictions.

[0193] The number of beams in the second beam set is greater than or equal to the number of beams in the first beam set, so that a larger number of beams can be predicted by fewer beams. As can be seen from the foregoing, the second beam set can be set A, and the first beam set can be set B.

[0194] In some embodiments, the first device may perform beam prediction on the second beam set through the first model. For example, the first device may use the measurement value of the first beam set as the input of the first model, and then output the optimal beam in the second beam set through the first model.

[0195] In some embodiments, the first model may perform beam prediction on the second beam set after completing model training. When the first model is located on the terminal device side, the terminal device or a server connected to the terminal device may collect training data and train the first model. When the first model is located on the network device side, the network device may collect training data and train the first model.

[0196] As an example, the training data used to train the first model can be determined according to the purpose of the first model. When the first model is used to predict the second beam set sent by the second device, the training data of the first model can be related to the transmission beam of the second device. For example, the training data is related to multiple beams sent by the second device. For another example, the training data is related to the first beam set sent by the second device. For another example, the training data is related to the second beam set sent by the second device.

[0197] In some embodiments, the first device may generate training data. For example, when the first device is a terminal device, the terminal device may generate measurement values ​​of multiple beams through measurement as training data.

[0198] In some embodiments, the first device may collect training data for model training. For example, when the first device includes a terminal device and a first server for training the first model, the terminal device or the first server may receive training data sent by other devices. The server used by the terminal device to collect training data may be referred to as a data acquisition server.

[0199] As an example, the first device may generate or collect training data related to the transmission beam of the second device. For example, the terminal device may generate or collect training data and then send the training data to the first server.

[0200] In some embodiments, when the first device is a terminal device, the terminal device can directly use the training data to train the first model. That is, when the first device is a terminal device deployed with the first model, the termination point of the training data can include the terminal device.

[0201] In some embodiments, when the first device trains the first model through the first server, the data acquisition server of the terminal device can transmit the training data to the first server to facilitate the first server to perform model training.

[0202] In the above embodiment, the first server is, for example, an OTT server. When the OTT server is used to train the first model, the termination point of the training data may include the OTT server.

[0203] For ease of understanding, the following exemplary description is given by taking the first server as an OTT server as an example.

[0204] As an example, for the terminal device side model, the training data can be generated by the terminal device. The termination point of the training data can include the terminal device or the terminal device side OTT server. Operation administration and maintenance (OAM) or the core network can be used to collect the situation of the terminal device side model training.

[0205] In the above example, the OTT server on the terminal device side collects data and trains the model. Therefore, the OTT server on the terminal device side knows what data it needs. Leaving the data collection on the terminal device side to the OTT server means that the OTT server can collect the data it needs more directly. The required data type does not need to be specified, and when / what to transmit is determined by the terminal device, which can provide full flexibility for terminal device / chipset vendors to train and implement their specific AI / ML models. The terminal device can use the same method to send training data to the OTT server on the terminal device side for model training. This process is transparent to the network side and does not require control / visibility from the network / mobile network operator (MNO).

[0206] As an example, the terminal device itself should be responsible for protecting data privacy and obtaining user consent. More specifically, the training data is reported via the user plane (UP), that is, reported from the application-level data collection client of the terminal device to the application server (that is, the OTT server on the terminal device side). The terminal device vendor can install its own data collection client at the terminal device to collect lower-layer data and report it to its own application server for AI / ML model training. For the data collection application client at the terminal device, data transmission from the terminal device to the application server of the terminal device vendor can be supported in accordance with relevant regulations without the participation of the network side. The collected data type / format does not need to be specified, which provides full flexibility for the terminal device to train and implement its specific AI / ML model.

[0207] As an example, after the terminal device collects the training data, it can first transmit it to a data collection server (within the MNO) for terminal device side model training. Then, the training data can be transmitted from the data collection server to the OTT server (outside the MNO). That is, the terminal device can collect data and transmit it to the server for data collection for terminal device side model training. The data collection server for terminal device side model training can be marked as the first entity.

[0208] In the above example, the first entity can choose whether to send the collected data to the OTT server. The ownership of the OTT server is different from that of the OTT server in the previous example. The terminal device can transmit the collected data to the first entity within the MNO through the UP, and then the first entity can choose to forward the collected data to the OTT server, which depends on the implementation of the terminal device.

[0209] Continue to see Figure 4 In step S430, the first device sends a first report to the second device. The first report is used to determine one or K best beams (ie, Top-1 or Top-K beams) in the second beam set, where K is a positive integer.

[0210] In some embodiments, the first report may be determined based on the output of the first model. When the first model performs the model reasoning phase, the output of the first model may be a reasoning result report. The first report may be determined based on the reasoning result report. For example, the first report may be a reasoning result report, or a new report may be submitted after processing the reasoning result report.

[0211] As an example, the output of the first model may be the best beam ID in the second beam set, or a probability distribution of a beam ID. As an example, the output of the first model may also be a channel quality prediction value of the beam or L1-RSRP and the difference between the RSRP of multiple beams and the best beam RSRP.

[0212] In some embodiments, the first report is used to report one of the following information: reporting beam information of K optimal beams, the beam information including RS indicators or predefined beam indexes of the K optimal beams; reporting beam information and RSRP of the K optimal beams; reporting beam information of the K optimal beams and probability information of the K optimal beams predicted by the second beam set; reporting the RSRP difference between the measured RSRP of the first beam set and the best RSRP in some / all beams of the second beam set; indicating the strongest beam ID in the form of a bitmap; reporting the predicted RSRP of the measured beam and the non-measured beam; reporting the predicted RSRP of the unmeasured beam and the measured RSRP of the measured beam; reporting the error and error threshold between the predicted RSRP and the actual measured RSRP of each measured beam.

[0213] In the above embodiment, RSRP may be L1-RSRP, or may include L1-RSRP.

[0214] In the above embodiments, RSRP may be replaced by SINR or other parameters indicating signal or channel quality.

[0215] As an example, the error threshold between the predicted RSRP and the actual RSRP may be one of multiple thresholds configured by a higher layer, or may be a threshold determined by the terminal device itself and then notified to the network side.

[0216] As an embodiment, the first report is used to report the beam information of the predicted Top-K beam in a group of beams (the second beam set). The beam information of the Top-K beam can be an RS indicator (e.g., traditional CRI / SSB RI) or a predefined beam index.

[0217] As an embodiment, the first report is used to report the beam information of the Top-K beam predicted in the second beam set and the RSRP value of the Top-K beam predicted in the second beam set.

[0218] As an embodiment, the first report is used to report the L1-RSRP difference between the measured L1-RSRP of the first beam set and the best L1-RSRP in the full / subset of the second beam set.

[0219] As an embodiment, the first report is used to report the beam information of the predicted Top-K beam in the second beam set and the predicted Top-K beam probability information of the second beam set, which will be combined with Figure 7 An exemplary description is given.

[0220] As an embodiment, the first report is used to indicate the strongest beam ID in a bitmap format.

[0221] As an embodiment, for the RSRP of the Top-K beams predicted in the inference result report, the first report is used to report the predicted L1-RSRP of the measured beam and the non-measured beam, or to report the predicted RSRP of the non-measured beam and the measured RSRP of the measured beam. If the beam is not configured for corresponding measurement, the predicted RSRP is reported; if the beam is configured for corresponding measurement, the measured L1-RSRP is reported.

[0222] As an embodiment, the first report is used to report the L1-RSRP difference between the measured L1-RSRP of the predicted beam and the best L1-RSRP in the full / subset of the second beam set.

[0223] As an embodiment, the first report is used to report the error between the predicted RSRP and the actual measured RSRP of each measurement beam. In this case, the first report may also include a set error threshold.

[0224] In some embodiments, the first report is configured to report at least two of the following: beam information of the predicted K optimal beams; beam information and RSRP values ​​of the predicted K optimal beams; beam information and probability information of the predicted K optimal beams; the difference between the predicted beam RSRP value and the highest RSRP value among some or all of the beams in the second beam set; indicating one or K optimal beams based on a bitmap.

[0225] As an embodiment, the content of the first report may depend on the options for configuring the report content by the base station.

[0226] In some embodiments, when the first report is configured to report multiple beams, the number of beams in the first report can be determined according to the configuration of the base station, or can be determined according to one or more of the following information: nrofReportedRS; nrofTopK; whether the first report is one of multiple reports in the current reporting period; the weight of the first report in multiple reports in the current reporting period; the prediction error of beam prediction.

[0227] As an embodiment, when nrofReportedRS>1 configured by the network device, or nrofReportedRS is M (M is a positive integer), the beam in the first report may be M or less than M.

[0228] As an embodiment, when the network configuration received by the first device includes a new beam number parameter (e.g., nrofTopK), the number of beams in the first report is determined by the first device. For example, through protocol extension or parameter adjustment (e.g., introducing a new parameter nrofTopK), the first device with AI / ML capabilities is allowed to dynamically adjust the number and strategy of its beam reports. In this way, the network can receive the optimal Top-K beam report of the first device without forcing all first devices to report the same number of beams.

[0229] As a sub-embodiment of the above embodiment, the network device (e.g., gNB) may convey parameter information nrofTopK to the first device through a radio resource control (RRC) connection configuration message to allow the first device to dynamically adjust the number of reported beams. When the network shares this new parameter with the first device, it means that under certain conditions (such as when the position of the first device remains unchanged, network additional conditions remain unchanged, etc.), the first device can report a number of beams less than nrofReportedRS.

[0230] As an embodiment, the number of reports in the first report can be determined based on the prediction error of the beam prediction. For example, the first device can evaluate the credibility of the AI / ML model reasoning result by calculating the error between the predicted L1-RSRP value and the actually measured L1-RSRP value for each beam. If the prediction error is very low, the prediction result of the model has a higher credibility. In this scenario, the first device can report the Top-K beams based on the predicted L1-RSRP value, and K can be less than the configured nrofReportedRS value.

[0231] As a sub-embodiment of the above embodiment, the first device may set an error threshold. If the prediction error of some beams is lower than the threshold, these beams may be considered as "high confidence beams" and a smaller number of Top-K beams may be selected for reporting. If the error is higher than the threshold, the first device needs to further adjust the beam reporting strategy to ensure that all potentially important M beams are covered.

[0232] As an embodiment, when the number of beams in the first report is less than the value of nrofReportedRS, the first device may adopt a multi-round reporting strategy to send the first report. That is, if the number of beams determined by the first device in a beam report is less than the nrofReportedRS value configured by the network, a multi-round reporting strategy may be adopted. For example, the first report is one of multiple reports, and the number of multiple reports is determined according to the value of nrofReportedRS and the number of beams in each report. For another example, the first report includes multiple reports reported in multiple rounds.

[0233] As a sub-embodiment of the above embodiment, the first device may first report the beam with smaller prediction error (Top-K beam) in the first report. If the network has further requirements on the number of beams, the first device may gradually add other beams in subsequent reports.

[0234] As a sub-embodiment of the above embodiment, multiple reports reported in multiple rounds may correspond to multiple sending priorities. When the first report is one of the multiple reports, the sending priority of the first report may be determined according to the prediction error of the beam in the first report.

[0235] As a sub-embodiment of the above embodiment, the first device may assign a weight to each report in the plurality of reports to determine a transmission priority. The weight may be determined based on the size of the L1-RSRP prediction error of the beam. For example, for a beam with a smaller prediction error, the weight is higher and the priority of the report is higher; for a beam with a larger error, the weight is lower and the report may be postponed or not reported.

[0236] For example, when the first report is one of multiple reports, the weight of the first report may be a first weight. The first weight may be used to determine the transmission priority of the first report, and the first weight is determined according to the prediction error of the beam in the first report.

[0237] Combination of the above Figure 4 The method for the first device to determine the first beam set and the content of the first report are described. Figure 4 In the method shown in FIG. 1 , the first beam set may be sent periodically or aperiodically. Accordingly, the first report may be sent periodically or aperiodically. Figure 5 and Figure 6 Two types of periodic reports and non-periodic reports are introduced by way of example. Figure 5 and Figure 6 They are all introduced from the perspective of interaction between UE (first device) and NW (second device), and the first model is located on the UE side.

[0238] During the periodic reporting process, the NW can configure periodic reporting and offset to define how many time slots the first device reports and from which time slot the report starts. From the configured periodic reporting and offset, the first device can also identify the reporting time slot for each reporting instance. For example, the parameter ReportPeriodityAndOffset can configure the network to trigger a periodic prediction report at time slot 0. For another example, the parameter ResourcePeriodityAndOffset can configure the period and offset of the RS resource.

[0239] Optionally, by combining the values ​​of ReportPeriodityAndOffset and ResourcePeriodityAndOffset, the first device may determine the time point of each measurement and report, and thereby calculate the length of the observation window.

[0240] Optionally, when the RS resources of the first beam set are configured as periodic RS resources, a prediction time instance (prediction window) related to sending a periodic report may also be configured.

[0241] Figure 5 This is the entire process of periodic reporting, which can implement periodic model inference for beam management. In this process, the UE can continuously measure the L1-RSRP of the first beam set RS resources of the periodic set.

[0242] See also Figure 5 In step S510, the NW sends a periodic report trigger to the UE in time slot 0.

[0243] In step S520, the NW may send multiple beams in the first observation window (SetB (first beam set) RS resources), and the UE measures the first beam set and inputs it into the AI / ML model.

[0244] In step S530, the UE may send a first periodic report based on the output of the AI / ML model. The first periodic report may be used by the NW to design beam indications P2 at times T1 and T2. The two beam indications P2 may correspond to the beam predictions made by the UE at times T1 and T2 in step S535.

[0245] In step S540, the NW may send multiple beams in the second observation window (SetB (first beam set) RS resources), and the UE measures the first beam set and inputs it into the AI / ML model.

[0246] In step S550, the UE may send a second periodic report based on the output of the AI / ML model. The second periodic report may be used by the NW to design beam indications P2 at times T3 and T4. The two beam indications P2 may correspond to the beam predictions made by the UE at times T3 and T4 in step S555.

[0247] exist Figure 5 In the example, the starting time slot of the RS resource may be time slot n, and time slot n is determined according to the resource offset. The first beam set may be sent in the observation window. The UE may use the measurement values ​​of the first beam set as model inputs to predict the best beams of one or more future time slots, and report to the NW the prediction information of the best beams in the second beam set at times T1, T2, T3, and T4. T1 and T2 are time instances of the first prediction window, and T3 and T4 are time instances of the second prediction window. Based on each future time slot, the NW may perform beam indication and / or configure the P2 / P3 process according to the model output design. The interval between two periodic reports may be determined according to the reporting period.

[0248] Figure 6 This is the whole process of aperiodic reporting, which can realize aperiodic model reasoning for beam management.

[0249] See also Figure 6 , in step S610, the NW sends a non-periodic report trigger to the UE.

[0250] Step S620 and Figure 5 The process is the same as step S520 in the process and will not be described in detail.

[0251] In step S630, the UE sends an aperiodic report to the NW. The aperiodic report can be used by the NW to design the beam indications P2 at times T1 and T2. The two beam indications P2 can correspond to the beam predictions performed by the UE at times T1 and T2 in step S635.

[0252] In step S640, the NW sends an aperiodic report trigger to the UE again.

[0253] Step S650 and Figure 5 The process is the same as step S540 in the process and will not be described in detail.

[0254] In step S660, the UE sends an aperiodic report to the NW. The aperiodic report can be used by the NW to design the beam indications P2 at time T3 and T4. The two beam indications P2 can correspond to the beam predictions made by the UE at time T3 and T4 in step S665.

[0255] exist Figure 6In the CSI-ReportConfig, the NW can only configure the RS resources for the first beam set to be transmitted in the observation window. The RS resources are configured as a combination of multiple non-periodic resources and connected through non-periodic reports. Therefore, the NW does not need to configure the length of the observation window in the csi-ReportConfig. However, for the length of the prediction window, the NW and the UE need to be aligned to determine the duration of the prediction window and where the predicted future time slot is. Therefore, the period and length of the prediction window need to be explicitly configured in the CSI framework.

[0256] The previous article introduced that the first report can include the probability information of the predicted Top-K beam. Figure 7 The probability information in the first report is introduced. The probability information can refer to the probability of a beam becoming a Top-1 or Top-K beam. Based on the probability information, some understanding of the model performance can be obtained. For example, the probability information can be used to rank the candidate beams, and the similarity of the ranked sequence can be compared with the RSRP ranked sequence of the beams. Although different models may output different levels of probability information, if the probability information of the beams is accurate enough, the final ranking of the beams should be the same or similar.

[0257] Figure 7 An example of comparing the beam rankings from model inference and the beam rankings from monitoring measurements on a second subset of beam sets. Figure 7 As shown, for comparison, the subset of beams configured for measurement in the model output may be filtered to compare the probability scores by filtering this subset of beams from model output. The measurement result may be the L1-RSRP value.

[0258] See also Figure 7 , examples of two ranks of the best 8 beams derived from model reasoning and monitoring measurements by comparing the probability scores and the measured L1-RSRP, respectively. Figure 7 The beams in are the beams with even-numbered beam IDs. Figure 7 It can be seen that there are some differences between the 8 optimal beams inferred by the model and the 8 optimal beams obtained by measurement. For example, the optimal (best) beam ID of the model inference is 16, and the suboptimal (2 nd The third best (3 rd The best) beam ID is 14, and the fourth beam ID is 8; the measured optimal beam ID is 16, the second best (2 nd The third best (3 rd The best) beam ID is 8, and the fourth beam ID is 14.

[0259] exist Figure 7 In the comparison, the model inference ranking and the measured ranking are compared to determine the performance of the model. The correlation is, for example, Spearman correlation or Kendall correlation, which effectively evaluates the accuracy of the beam prediction ranking between the model inference and the actual measurement. The higher the correlation coefficient, the more consistent the model prediction is with the actual measurement, and the better the prediction performance of the model.

[0260] Combination of the above Figures 1 to 7 , describes in detail the method embodiment of the present application. Figures 8 to 10 , 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.

[0261] Figure 8 800 is a schematic block diagram of an apparatus for wireless communication according to an embodiment of the present application. The apparatus 800 may be any first device described above. The first device may be a terminal device. Figure 8 The device 800 shown includes a transceiver unit 810 and a processing unit 820 .

[0262] The transceiver unit 810 can be used to receive multiple beams sent by the second device within an observation window, and the multiple beams are used to determine a first beam set; the processing unit 820 is used to perform beam prediction on the second beam set within the prediction window based on the measurement value of the first beam set; the transceiver unit 810 is also used to send a first report to the second device; wherein the first report is used to determine one or K optimal beams in the second beam set, K is a positive integer, and the first beam set is determined based on first information, and the first information includes at least two of the measurement values ​​of the multiple beams, the capabilities of the first device, and the network configuration.

[0263] Optionally, the first report is used to report one of the following information: reporting beam information of K optimal beams, the beam information including reference signal indicators or predefined beam indexes of the K optimal beams; reporting beam information and RSRP of the K optimal beams; reporting beam information of the K optimal beams and probability information of the K optimal beams predicted by the second beam set; reporting the RSRP difference between the measured RSRP of the first beam set and the best RSRP in some / all beams of the second beam set; indicating the strongest beam ID in the form of a bitmap; reporting the predicted RSRP of the measured beam and the non-measured beam; reporting the predicted RSRP of the unmeasured beam and the measured RSRP of the measured beam; reporting the error and error threshold between the predicted RSRP and the actual measured RSRP of each measured beam.

[0264] Optionally, all beams in the first beam set are one of the following: beams with the highest N measurement values ​​among multiple beams, where N is a positive integer and is determined according to network configuration; beams among multiple beams whose measurement values ​​are greater than a first threshold and the first threshold is determined according to network configuration.

[0265] Optionally, the first beam set includes multiple beam subsets, the multiple beam subsets correspond to multiple thresholds related to the measurement value, and the number of beam subsets in the first beam set is determined according to the moving speed and / or coverage of the first device.

[0266] Optionally, the first beam set is one of multiple beam sets, the multiple beam sets correspond to multiple beam set modes related to capabilities, and the multiple beam set modes are determined according to network configuration.

[0267] Optionally, the measurement values ​​of the multiple beams include historical measurement values ​​of the multiple beams, and the historical measurement values ​​are represented by measurement vectors.

[0268] Optionally, the observation window is one of a plurality of observation windows, positions of the plurality of observation windows are determined based on a position of an initial observation window and a first period, and the first beam set is used for repeated transmission in the plurality of observation windows.

[0269] Optionally, the first device is one of a plurality of devices, and the plurality of devices perform beam prediction based on a first beam set in any observation window of a plurality of observation windows.

[0270] Optionally, the length of the observation window is configured or adjusted according to one or more of the following methods: configured by a third parameter, which is used to define the length of the observation window; determined by a reporting offset and a resource offset; increasing the length of the observation window by increasing the reporting period or reducing the reference signal resource offset; reducing the length of the observation window by reducing the reporting period or increasing the reference signal resource offset; predicting the length of the observation window based on the number of time instances corresponding to the first beam set.

[0271] Optionally, the prediction window is one of a plurality of prediction windows, the position of the prediction window is determined based on the position of the initial prediction window and the first period, and the length of the prediction window and the length of the observation window are used to determine the reporting period.

[0272] Optionally, the prediction window is one of a plurality of prediction windows, and the prediction window is determined based on a position of an initial prediction window and a second period, wherein a length of the second period is less than a length of the first period.

[0273] Optionally, the position of the observation window is determined according to a trigger instruction.

[0274] Optionally, the number of beams in the first report is determined based on one or more of the following information: nrofReportedRS; nrofTopK; whether the first report is one of multiple reports in the current reporting period; the weight of the first report in multiple reports in the current reporting period; and the prediction error of beam prediction.

[0275] Optionally, when the number of beams in the first report is less than the value of nrofReportedRS, the first report is one of multiple reports, and the number of multiple reports is determined according to the value of nrofReportedRS and the number of beams in each report.

[0276] Optionally, multiple reports correspond to multiple sending priorities, and a first weight of a first report is used to determine the sending priority of the first report, and the first weight is determined based on a prediction error of a beam in the first report.

[0277] Optionally, when the network configuration received by the first device includes nrofTopK, the number of beams in the first report is determined by the first device.

[0278] Optionally, beam prediction is implemented by a first model, which is an artificial intelligence or machine learning model.

[0279] Optionally, the first model is located on the terminal device side, the first device includes a terminal device and a first server for training the first model, and the processing unit 820 is also used to generate or collect training data related to the transmission beam of the second device; the terminal device is used to send training data to the first server.

[0280] Fig. 9 900 is a schematic block diagram of another apparatus for wireless communication according to an embodiment of the present application. The apparatus 900 may be any second device described above. The second device may be a terminal device or a network device. Fig. 9 The second device 900 shown includes a transceiver unit 910 .

[0281] The transceiver unit 910 can be used to send multiple beams within an observation window, and the multiple beams are used to determine a first beam set; the transceiver unit 910 is also used to receive a first report sent by a first device; wherein the first report is used to determine one or K optimal beams in a second beam set within a prediction window, K is a positive integer, and the beam prediction of the second beam set is based on the measurement value of the first beam set, and the first beam set is determined according to first information, and the first information includes at least two of the measurement values ​​of the multiple beams, the capabilities of the first device, and the network configuration.

[0282] Optionally, the first report is used to report one of the following information: reporting beam information of K optimal beams, the beam information including reference signal indicators or predefined beam indexes of the K optimal beams; reporting beam information and RSRP of the K optimal beams; reporting beam information of the K optimal beams and probability information of the K optimal beams predicted by the second beam set; reporting the RSRP difference between the measured RSRP of the first beam set and the best RSRP in some / all beams of the second beam set; indicating the strongest beam ID in the form of a bitmap; reporting the predicted RSRP of the measured beam and the non-measured beam; reporting the predicted RSRP of the unmeasured beam and the measured RSRP of the measured beam; reporting the error and error threshold between the predicted RSRP and the actual measured RSRP of each measured beam.

[0283] Optionally, all beams in the first beam set are one of the following: beams with the highest N measurement values ​​among multiple beams, where N is a positive integer and is determined according to network configuration; beams among multiple beams whose measurement values ​​are greater than a first threshold and the first threshold is determined according to network configuration.

[0284] Optionally, the first beam set includes multiple beam subsets, the multiple beam subsets correspond to multiple thresholds related to the measurement value, and the number of beam subsets in the first beam set is determined according to the moving speed and / or coverage of the first device.

[0285] Optionally, the first beam set is one of multiple beam sets, the multiple beam sets correspond to multiple beam set modes related to capabilities, and the multiple beam set modes are determined according to network configuration.

[0286] Optionally, the measurement values ​​of the multiple beams include historical measurement values ​​of the multiple beams, and the historical measurement values ​​are represented by measurement vectors.

[0287] Optionally, the observation window is one of a plurality of observation windows, positions of the plurality of observation windows are determined based on a position of an initial observation window and a first period, and the first beam set is used for repeated transmission in the plurality of observation windows.

[0288] Optionally, the first device is one of a plurality of devices, and the plurality of devices perform beam prediction based on a first beam set in any one of a plurality of observation windows.

[0289] Optionally, the length of the observation window is configured or adjusted according to one or more of the following methods: configured by a third parameter, which is used to define the length of the observation window; determined by a reporting offset and a resource offset; increasing the length of the observation window by increasing the reporting period or reducing the reference signal resource offset; reducing the length of the observation window by reducing the reporting period or increasing the reference signal resource offset; predicting the length of the observation window based on the number of time instances corresponding to the first beam set.

[0290] Optionally, the prediction window is one of a plurality of prediction windows, the position of the prediction window is determined based on the position of the initial prediction window and the first period, and the length of the prediction window and the length of the observation window are used to determine the reporting period.

[0291] Optionally, the prediction window is one of a plurality of prediction windows, and the prediction window is determined based on a position of an initial prediction window and a second period, wherein a length of the second period is less than a length of the first period.

[0292] Optionally, the position of the observation window is determined according to a trigger instruction.

[0293] Optionally, the number of beams in the first report is determined based on one or more of the following information: nrofReportedRS; nrofTopK; whether the first report is one of multiple reports in the current reporting period; the weight of the first report in multiple reports in the current reporting period; and the prediction error of beam prediction.

[0294] Optionally, when the number of beams in the first report is less than the value of nrofReportedRS, the first report is one of multiple reports, and the number of multiple reports is determined according to the value of nrofReportedRS and the number of beams in each report.

[0295] Optionally, multiple reports correspond to multiple sending priorities, and a first weight of a first report is used to determine the sending priority of the first report, and the first weight is determined based on a prediction error of a beam in the first report.

[0296] Optionally, when the network configuration received by the first device includes nrofTopK, the number of beams in the first report is determined by the first device.

[0297] Optionally, beam prediction is implemented by a first model, which is an artificial intelligence or machine learning model.

[0298] Optionally, the first model is located on the terminal device side, the first device includes the terminal device and a first server for training the first model, and the training data related to the transmission beam of the second device is sent by the terminal device to the first server.

[0299] Fig.10 Shown is a schematic diagram of the structure of a communication device according to an embodiment of the present application. Fig.10 The dotted line in the figure indicates that the unit or module is optional. The device 1000 can be used to implement the method described in the above method embodiment. The device 1000 can be a chip, a terminal device or a network device.

[0300] The device 1000 may include one or more processors 1010. The processor 1010 may support the device 1000 to implement the method described in the method embodiment above. The processor 1010 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.

[0301] The apparatus 1000 may further include one or more memories 1020. The memory 1020 stores a program, which can be executed by the processor 1010, so that the processor 1010 executes the method described in the above method embodiment. The memory 1020 may be independent of the processor 1010 or integrated in the processor 1010.

[0302] The apparatus 1000 may further include a transceiver 1030. The processor 1010 may communicate with other devices or chips through the transceiver 1030. For example, the processor 1010 may transmit and receive data with other devices or chips through the transceiver 1030.

[0303] 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.

[0304] 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)).

[0305] 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.

[0306] 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.

[0307] 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.

[0308] 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.

[0309] 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.

[0310] 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.

[0311] 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.

[0312] 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.

[0313] 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.

[0314] 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.

[0315] 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.

[0316] 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.

[0317] 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.

[0318] 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.

[0319] 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 a plurality of beams sent by the second device within an observation window, where the plurality of beams are used to determine a first beam set; The first device performs beam prediction on a second beam set within a prediction window based on the measurement value of the first beam set; The first device sends a first report to the second device; The first report is used to determine one or K optimal beams in the second beam set, K is a positive integer, and the first beam set is determined based on first information, which includes at least two of the measurement values ​​of the multiple beams, the capabilities of the first device, and the network configuration.

2. The method according to claim 1, characterized in that The first report is used to report one of the following information: Reporting beam information of the K best beams, where the beam information includes reference signal indicators or predefined beam indexes of the K best beams; Reporting beam information and reference signal received power RSRP of the K optimal beams; Reporting beam information of the K optimal beams and probability information of the K optimal beams predicted by the second beam set; reporting an RSRP difference between a measured RSRP of the first set of beams and a best RSRP in some / all beams of the second set of beams; Indicates the strongest beam ID in a bitmap-based format; Reports the predicted RSRP of the measured and non-measured beams; Reports the predicted RSRP of the unmeasured beam and the measured RSRP of the measured beam; Reports the error and error threshold between the predicted RSRP and the actual measured RSRP for each measurement beam.

3. The method according to claim 1 or 2, characterized in that: All beams in the first beam set are one of the following: N beams with the highest measurement values ​​among the multiple beams, where N is a positive integer and is determined according to a network configuration; A beam among the multiple beams whose measurement value is greater than a first threshold, wherein the first threshold is determined according to a network configuration.

4. The method according to claim 1 or 2, characterized in that: The first beam set includes multiple beam subsets, and the multiple beam subsets correspond to multiple thresholds related to measurement values. The number of beam subsets in the first beam set is determined according to the moving speed and / or coverage of the first device.

5. The method according to any one of claims 1 to 4, characterized in that The first beam set is one of multiple beam sets, and the multiple beam sets correspond to multiple beam set modes related to capabilities, and the multiple beam set modes are determined according to network configuration.

6. The method according to any one of claims 1 to 5, characterized in that The measurement values ​​of the plurality of beams include historical measurement values ​​of the plurality of beams, and the historical measurement values ​​are represented by a measurement vector.

7. The method according to any one of claims 1 to 6, characterized in that The observation window is one of a plurality of observation windows, positions of the plurality of observation windows are determined based on a position of an initial observation window and a first period, and the first beam set is used for repeated transmission in the plurality of observation windows.

8. The method according to claim 7, characterized in that The first device is one of a plurality of devices, and the plurality of devices perform beam prediction based on a first beam set in any observation window of the plurality of observation windows.

9. The method according to claim 7 or 8, characterized in that: The length of the observation window is configured or adjusted according to one or more of the following methods: configured by a third parameter, the third parameter being used to define the length of the observation window; Determined by report offset and resource offset; Increasing the length of the observation window by increasing the reporting period or reducing the reference signal resource offset; Reducing the length of the observation window by reducing the reporting period or increasing the reference signal resource offset; The length of the observation window is predicted according to the number of time instances corresponding to the first beam set.

10. The method according to any one of claims 7 to 9, characterized in that: The prediction window is one of a plurality of prediction windows, positions of the plurality of prediction windows are determined based on a position of an initial prediction window and the first period, and a length of the prediction window and a length of the observation window are used to determine a reporting period.

11. The method according to any one of claims 7 to 9, characterized in that: The prediction window is one of a plurality of prediction windows, and the prediction window is determined based on a position of an initial prediction window and a second period, wherein the length of the second period is smaller than the length of the first period.

12. The method according to any one of claims 1 to 6, characterized in that The position of the observation window is determined according to a trigger instruction.

13. The method according to any one of claims 1 to 12, characterized in that The number of beams in the first report is determined according to one or more of the following information: nrofReportedRS; nrofTopK; whether the first report is one of multiple reports in the current reporting period; a weight of the first report among multiple reports in the current reporting period; The prediction error of the beam prediction.

14. The method according to claim 13, characterized in that When the number of beams in the first report is less than the value of nrofReportedRS, the first report is one of the multiple reports, and the number of the multiple reports is determined according to the value of nrofReportedRS and the number of beams in each report.

15. The method according to claim 14, characterized in that The multiple reports correspond to multiple sending priorities, and the first weight of the first report is used to determine the sending priority of the first report, and the first weight is determined according to the prediction error of the beam in the first report.

16. The method according to claim 13, characterized in that When the network configuration received by the first device includes nrofTopK, the number of beams in the first report is determined by the first device.

17. The method according to any one of claims 1 to 16, characterized in that The beam prediction is implemented by a first model, which is an artificial intelligence or machine learning model.

18. The method according to claim 16, wherein the first model is located at a terminal device side, the first device comprises a terminal device and a first server for training the first model, and the method further comprises: The first device generates or collects training data related to a transmit beam of the second device; The terminal device sends the training data to the first server.

19. A method for wireless communication, characterized in that: include: The second device transmits a plurality of beams within an observation window, wherein the plurality of beams are used to determine a first beam set; The second device receives a first report sent by the first device; The first report is used to determine one or K optimal beams in the second beam set within the prediction window, K is a positive integer, and the beam prediction of the second beam set is performed based on the measurement value of the first beam set. The first beam set is determined according to first information, and the first information includes at least two of the measurement values ​​of the multiple beams, the capabilities of the first device, and the network configuration.

20. The method according to claim 19, characterized in that The first report is used to report one of the following information: Reporting beam information of the K best beams, where the beam information includes reference signal indicators or predefined beam indexes of the K best beams; Reporting beam information and reference signal received power RSRP of the K optimal beams; Reporting beam information of the K optimal beams and probability information of the K optimal beams predicted by the second beam set; reporting an RSRP difference between a measured RSRP of the first set of beams and a best RSRP in some / all beams of the second set of beams; Indicates the strongest beam ID in a bitmap-based format; Reports the predicted RSRP of the measured and non-measured beams; Reports the predicted RSRP of the unmeasured beam and the measured RSRP of the measured beam; Reports the error and error threshold between the predicted RSRP and the actual measured RSRP for each measurement beam.

21. The method according to claim 19 or 20, characterized in that All beams in the first beam set are one of the following: N beams with the highest measurement values ​​among the multiple beams, where N is a positive integer and is determined according to a network configuration; A beam among the multiple beams whose measurement value is greater than a first threshold, wherein the first threshold is determined according to a network configuration.

22. The method according to claim 19 or 20, characterized in that The first beam set includes multiple beam subsets, and the multiple beam subsets correspond to multiple thresholds related to measurement values. The number of beam subsets in the first beam set is determined according to the moving speed and / or coverage of the first device.

23. The method according to any one of claims 19 to 22, characterized in that The first beam set is one of multiple beam sets, and the multiple beam sets correspond to multiple beam set modes related to capabilities, and the multiple beam set modes are determined according to network configuration.

24. The method according to any one of claims 19 to 23, characterized in that The measurement values ​​of the plurality of beams include historical measurement values ​​of the plurality of beams, and the historical measurement values ​​are represented by a measurement vector.

25. The method according to any one of claims 19 to 24, characterized in that The observation window is one of a plurality of observation windows, positions of the plurality of observation windows are determined based on a position of an initial observation window and a first period, and the first beam set is used for repeated transmission in the plurality of observation windows.

26. The method according to claim 25, characterized in that The first device is one of a plurality of devices, and the plurality of devices perform beam prediction based on a first beam set in any observation window of the plurality of observation windows.

27. The method according to claim 25 or 26, characterized in that The length of the observation window is configured or adjusted according to one or more of the following methods: configured by a third parameter, the third parameter being used to define the length of the observation window; Determined by report offset and resource offset; Increasing the length of the observation window by increasing the reporting period or reducing the reference signal resource offset; Reducing the length of the observation window by reducing the reporting period or increasing the reference signal resource offset; The length of the observation window is predicted according to the number of time instances corresponding to the first beam set.

28. The method according to any one of claims 25 to 27, characterized in that The prediction window is one of a plurality of prediction windows, positions of the plurality of prediction windows are determined based on a position of an initial prediction window and the first period, and a length of the prediction window and a length of the observation window are used to determine a reporting period.

29. The method according to any one of claims 25 to 27, characterized in that The prediction window is one of a plurality of prediction windows, and the prediction window is determined based on a position of an initial prediction window and a second period, wherein the length of the second period is smaller than the length of the first period.

30. The method according to any one of claims 19 to 24, characterized in that The position of the observation window is determined according to a trigger instruction.

31. The method according to any one of claims 19 to 30, characterized in that The number of beams in the first report is determined according to one or more of the following information: nrofReportedRS; nrofTopK; whether the first report is one of multiple reports in the current reporting period; a weight of the first report among multiple reports in the current reporting period; The prediction error of the beam prediction.

32. The method according to claim 31, characterized in that When the number of beams in the first report is less than the value of nrofReportedRS, the first report is one of the multiple reports, and the number of the multiple reports is determined according to the value of nrofReportedRS and the number of beams in each report.

33. The method according to claim 32, characterized in that The multiple reports correspond to multiple sending priorities, and the first weight of the first report is used to determine the sending priority of the first report, and the first weight is determined according to the prediction error of the beam in the first report.

34. The method according to claim 31, characterized in that When the network configuration received by the first device includes nrofTopK, the number of beams in the first report is determined by the first device.

35. The method according to any one of claims 19 to 34, characterized in that The beam prediction is implemented by a first model, which is an artificial intelligence or machine learning model.

36. According to the method of claim 35, the first model is located on the terminal device side, the first device includes a terminal device and a first server for training the first model, and the training data related to the transmission beam of the second device is sent by the terminal device to the first server.

37. A device for wireless communication, characterized in that: The device is a first device, and the device includes: a transceiver unit, configured to receive a plurality of beams sent by a second device within an observation window, wherein the plurality of beams are used to determine a first beam set; a processing unit, configured to perform beam prediction on a second beam set within a prediction window based on the measurement value of the first beam set; The transceiver unit is further configured to send a first report to the second device; The first report is used to determine one or K optimal beams in the second beam set, K is a positive integer, and the first beam set is determined based on first information, which includes at least two of the measurement values ​​of the multiple beams, the capabilities of the first device, and the network configuration.

38. The device according to claim 37, characterized in that The first report is used to report one of the following information: Reporting beam information of the K best beams, where the beam information includes reference signal indicators or predefined beam indexes of the K best beams; Reporting beam information and reference signal received power RSRP of the K optimal beams; Reporting beam information of the K optimal beams and probability information of the K optimal beams predicted by the second beam set; reporting an RSRP difference between a measured RSRP of the first set of beams and a best RSRP in some / all beams of the second set of beams; Indicates the strongest beam ID in a bitmap-based format; Reports the predicted RSRP of the measured and non-measured beams; Reports the predicted RSRP of the unmeasured beam and the measured RSRP of the measured beam; Reports the error and error threshold between the predicted RSRP and the actual measured RSRP for each measurement beam.

39. The device according to claim 37 or 38, characterized in that All beams in the first beam set are one of the following: N beams with the highest measurement values ​​among the multiple beams, where N is a positive integer and is determined according to a network configuration; A beam among the multiple beams whose measurement value is greater than a first threshold, wherein the first threshold is determined according to a network configuration.

40. The device according to claim 37 or 38, characterized in that The first beam set includes multiple beam subsets, and the multiple beam subsets correspond to multiple thresholds related to measurement values. The number of beam subsets in the first beam set is determined according to the moving speed and / or coverage of the first device.

41. The device according to any one of claims 37 to 40, characterized in that The first beam set is one of multiple beam sets, and the multiple beam sets correspond to multiple beam set modes related to capabilities, and the multiple beam set modes are determined according to network configuration.

42. The device according to any one of claims 37 to 41, characterized in that The measurement values ​​of the plurality of beams include historical measurement values ​​of the plurality of beams, and the historical measurement values ​​are represented by a measurement vector.

43. The device according to any one of claims 37 to 42, characterized in that The observation window is one of a plurality of observation windows, positions of the plurality of observation windows are determined based on a position of an initial observation window and a first period, and the first beam set is used for repeated transmission in the plurality of observation windows.

44. The device according to claim 43, characterized in that The first device is one of a plurality of devices, and the plurality of devices perform beam prediction based on a first beam set in any observation window of the plurality of observation windows.

45. The device according to claim 43 or 44, characterized in that The length of the observation window is configured or adjusted according to one or more of the following methods: configured by a third parameter, the third parameter being used to define the length of the observation window; Determined by report offset and resource offset; Increasing the length of the observation window by increasing the reporting period or reducing the reference signal resource offset; Reducing the length of the observation window by reducing the reporting period or increasing the reference signal resource offset; The length of the observation window is predicted according to the number of time instances corresponding to the first beam set.

46. ​​The device according to any one of claims 43 to 45, characterized in that The prediction window is one of a plurality of prediction windows, positions of the plurality of prediction windows are determined based on a position of an initial prediction window and the first period, and a length of the prediction window and a length of the observation window are used to determine a reporting period.

47. The device according to any one of claims 43 to 45, characterized in that The prediction window is one of a plurality of prediction windows, and the prediction window is determined based on a position of an initial prediction window and a second period, wherein the length of the second period is smaller than the length of the first period.

48. The device according to any one of claims 37 to 42, characterized in that The position of the observation window is determined according to a trigger instruction.

49. The device according to any one of claims 37-48, characterized in that The number of beams in the first report is determined according to one or more of the following information: nrofReportedRS; nrofTopK; whether the first report is one of multiple reports in the current reporting period; a weight of the first report among multiple reports in the current reporting period; The prediction error of the beam prediction.

50. The device according to claim 49, characterized in that When the number of beams in the first report is less than the value of nrofReportedRS, the first report is one of the multiple reports, and the number of the multiple reports is determined according to the value of nrofReportedRS and the number of beams in each report.

51. The device according to claim 50, characterized in that The multiple reports correspond to multiple sending priorities, and the first weight of the first report is used to determine the sending priority of the first report, and the first weight is determined according to the prediction error of the beam in the first report.

52. The device according to claim 49, characterized in that When the network configuration received by the first device includes nrofTopK, the number of beams in the first report is determined by the first device.

53. The device according to any one of claims 37 to 52, characterized in that The beam prediction is implemented by a first model, which is an artificial intelligence or machine learning model.

54. According to the apparatus according to claim 53, the first model is located on the terminal device side, the first device includes a terminal device and a first server for training the first model, the processing unit is also used to generate or collect training data related to the transmission beam of the second device; the terminal device is used to send the training data to the first server.

55. An apparatus for wireless communication, characterized in that: The device is a second apparatus, and the device includes: A transceiver unit, configured to send a plurality of beams within an observation window, wherein the plurality of beams are used to determine a first beam set; The transceiver unit is further configured to receive a first report sent by a first device; The first report is used to determine one or K optimal beams in the second beam set within the prediction window, K is a positive integer, and the beam prediction of the second beam set is performed based on the measurement value of the first beam set. The first beam set is determined according to first information, and the first information includes at least two of the measurement values ​​of the multiple beams, the capabilities of the first device, and the network configuration.

56. The device according to claim 55, characterized in that The first report is used to report one of the following information: Reporting beam information of the K best beams, where the beam information includes reference signal indicators or predefined beam indexes of the K best beams; Reporting beam information and reference signal received power RSRP of the K optimal beams; Reporting beam information of the K optimal beams and probability information of the K optimal beams predicted by the second beam set; reporting an RSRP difference between a measured RSRP of the first set of beams and a best RSRP in some / all beams of the second set of beams; Indicates the strongest beam ID in a bitmap-based format; Reports the predicted RSRP of the measured and non-measured beams; Reports the predicted RSRP of the unmeasured beam and the measured RSRP of the measured beam; Reports the error and error threshold between the predicted RSRP and the actual measured RSRP for each measurement beam.

57. The device according to claim 55 or 56, characterized in that All beams in the first beam set are one of the following: N beams with the highest measurement values ​​among the multiple beams, where N is a positive integer and is determined according to a network configuration; A beam among the multiple beams whose measurement value is greater than a first threshold, wherein the first threshold is determined according to a network configuration.

58. The device according to claim 55 or 56, characterized in that The first beam set includes multiple beam subsets, and the multiple beam subsets correspond to multiple thresholds related to measurement values. The number of beam subsets in the first beam set is determined according to the moving speed and / or coverage of the first device.

59. The device according to any one of claims 55 to 58, characterized in that The first beam set is one of multiple beam sets, and the multiple beam sets correspond to multiple beam set modes related to capabilities, and the multiple beam set modes are determined according to network configuration.

60. The device according to any one of claims 55 to 59, characterized in that The measurement values ​​of the plurality of beams include historical measurement values ​​of the plurality of beams, and the historical measurement values ​​are represented by a measurement vector.

61. The device according to any one of claims 55 to 60, characterized in that The observation window is one of a plurality of observation windows, positions of the plurality of observation windows are determined based on a position of an initial observation window and a first period, and the first beam set is used for repeated transmission in the plurality of observation windows.

62. The device according to claim 61, characterized in that The first device is one of a plurality of devices, and the plurality of devices perform beam prediction based on a first beam set in any observation window of the plurality of observation windows.

63. The device according to claim 61 or 62, characterized in that The length of the observation window is configured or adjusted according to one or more of the following methods: configured by a third parameter, the third parameter being used to define the length of the observation window; Determined by report offset and resource offset; Increasing the length of the observation window by increasing the reporting period or reducing the reference signal resource offset; Reducing the length of the observation window by reducing the reporting period or increasing the reference signal resource offset; The length of the observation window is predicted according to the number of time instances corresponding to the first beam set.

64. The device according to any one of claims 61 to 63, characterized in that The prediction window is one of a plurality of prediction windows, positions of the plurality of prediction windows are determined based on a position of an initial prediction window and the first period, and a length of the prediction window and a length of the observation window are used to determine a reporting period.

65. The device according to any one of claims 61 to 63, characterized in that The prediction window is one of a plurality of prediction windows, and the prediction window is determined based on a position of an initial prediction window and a second period, wherein the length of the second period is smaller than the length of the first period.

66. The device according to any one of claims 55 to 60, characterized in that The position of the observation window is determined according to a trigger instruction.

67. The device according to any one of claims 55 to 66, characterized in that The number of beams in the first report is determined according to one or more of the following information: nrofReportedRS; nrofTopK; whether the first report is one of multiple reports in the current reporting period; a weight of the first report among multiple reports in the current reporting period; The prediction error of the beam prediction.

68. The device according to claim 67, characterized in that When the number of beams in the first report is less than the value of nrofReportedRS, the first report is one of the multiple reports, and the number of the multiple reports is determined according to the value of nrofReportedRS and the number of beams in each report.

69. The device according to claim 68, characterized in that The multiple reports correspond to multiple sending priorities, and the first weight of the first report is used to determine the sending priority of the first report, and the first weight is determined according to the prediction error of the beam in the first report.

70. The device according to claim 67, characterized in that When the network configuration received by the first device includes nrofTopK, the number of beams in the first report is determined by the first device.

71. The device according to any one of claims 55 to 70, characterized in that The beam prediction is implemented by a first model, which is an artificial intelligence or machine learning model.

72. According to the apparatus according to claim 71, the first model is located on the terminal device side, the first device includes a terminal device and a first server for training the first model, and the training data related to the transmission beam of the second device is sent by the terminal device to the first server.

73. A communication device, characterized in that: It 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 as described in any one of claims 1-36.

74. A device, characterized in that The device comprises a processor, configured to call a program from a memory to execute a method as claimed in any one of claims 1 to 36.

75. 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 described in any one of claims 1 to 36.

76. 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 36.

77. 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 36.

78. A computer program, characterized in that The computer program enables a computer to execute the method according to any one of claims 1 to 36.

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