Wireless communication method and device

Abstract

The embodiment of the invention provides a wireless communication method and equipment, which are favorable for reducing overhead and time delay caused by a beam scanning process. The wireless communication method comprises the following steps: a first communication device obtains a first measurement data set; the first measurement data set comprises at least one of identification information of spatial filters in M measurement instances and link quality information corresponding to the spatial filters in the M measurement instances; m is a positive integer; the first communication device inputs the first measurement data set into a first network model and outputs a first prediction data set; wherein the first prediction data set comprises at least one of the following information: identification information of K spatial filters predicted in each prediction instance in the F prediction instances, link quality information corresponding to the K spatial filters predicted in each prediction instance in the F prediction instances, and link quality information corresponding to the K spatial filters predicted in each prediction instance in the F prediction instances; durations of K spatial filters predicted in each of the F prediction instances; wherein F and K are positive integers.

Description

Wireless communication method and device Technical Field

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

[0002] In the New Radio (NR) system, millimeter wave frequency band communications are introduced, and the corresponding beam management mechanism is also introduced, which can be specifically divided into uplink and downlink beam management. Among them, downlink beam management may include: downlink beam scanning, optimal beam reporting on the terminal side, downlink beam indication on the network side, etc. Specifically, the network equipment scans all transmit beam directions through the downlink reference signal. The terminal device can use different receive beams for measurement, so that it can traverse all beam pairs.

[0003] It can be seen from this that the terminal device needs to traverse all combinations of transmit beams and receive beams to select the optimal beam, which will bring a lot of overhead and delay.

[0004] Summary of the Invention

[0005] The embodiments of the present application provide a method and device for wireless communication, which are conducive to reducing the overhead and delay caused by the beam scanning process.

[0006] In a first aspect, a wireless communication method is provided, the method comprising:

[0007] The first communication device obtains a first measurement data set; wherein the first measurement data set includes at least one of the following: identification information of the spatial filter in M ​​measurement instances, and link quality information corresponding to the spatial filter in the M measurement instances; wherein M is a positive integer;

[0008] The first communication device inputs the first measurement data set into a first network model and outputs a first prediction data set; wherein, the first prediction data set includes at least one of the following: identification information of K spatial filters predicted in each of F prediction instances, link quality information corresponding to the K spatial filters predicted in each of the F prediction instances, and duration of the K spatial filters predicted in each of the F prediction instances; wherein F and K are both positive integers.

[0009] In a second aspect, a communication device is provided for executing the method in the first aspect.

[0010] Specifically, the communication device includes a functional module for executing the method in the above-mentioned first aspect.

[0011] In a third aspect, a communication device is provided, comprising a processor and a memory; the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory, so that the communication device executes the method in the above-mentioned first aspect.

[0012] In a fourth aspect, a device is provided for implementing the method in the first aspect.

[0013] Specifically, the apparatus includes: a processor, configured to call and run a computer program from a memory, so that a device equipped with the apparatus executes the method in the first aspect described above.

[0014] In a fifth aspect, a computer-readable storage medium is provided for storing a computer program, which enables a computer to execute the method in the first aspect.

[0015] In a sixth aspect, a computer program product is provided, comprising computer program instructions, wherein the computer program instructions enable a computer to execute the method in the first aspect.

[0016] In a seventh aspect, a computer program is provided, which, when executed on a computer, enables the computer to execute the method in the first aspect.

[0017] Through the above technical solution, the first communication device can realize spatial filter prediction in the time domain based on the first network model, and the first communication device does not need to scan all deployed spatial filters, which is conducive to reducing the overhead and delay caused by spatial filter scanning. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] FIG1 is a schematic diagram of a communication system architecture provided in an embodiment of the present application.

[0019] FIG2 is a schematic diagram showing the connections of neurons in a neural network.

[0020] FIG3 is a schematic structural diagram of a neural network.

[0021] FIG4 is a schematic diagram of a convolutional neural network.

[0022] FIG5 is a schematic structural diagram of an LSTM unit.

[0023] FIG6 is a schematic diagram of an LSTM model provided in an embodiment of the present application.

[0024] FIG7 is a schematic diagram of a downlink beam scanning process.

[0025] FIG8 is a schematic diagram of another downlink beam scanning process.

[0026] FIG9 is a schematic diagram of yet another downlink beam scanning process.

[0027] FIG10 is a schematic diagram of a wireless communication method provided according to an embodiment of the present application.

[0028] FIG11 is a schematic diagram of a data set B provided according to an embodiment of the present application.

[0029] FIG12 is a schematic diagram of a data set A provided according to an embodiment of the present application.

[0030] FIG13 is a flowchart of a beam prediction method according to an embodiment of the present application.

[0031] FIG14 is a flowchart of another beam prediction method provided according to an embodiment of the present application.

[0032] FIG15 shows that Set B provided according to an embodiment of the present application has different Tx beam configurations in different measurement instances.

[0033] FIG16 is a schematic diagram of a DCI triggering multiple Set B measurements according to an embodiment of the present application.

[0034] FIG17 is a schematic diagram showing that Set A has different Tx beam configurations in different prediction instances according to an embodiment of the present application.

[0035] FIG18 is a schematic diagram showing that Tx beam configurations X and Y of Set A appear alternately according to an embodiment of the present application.

[0036] FIG19 is a schematic diagram of beam duration in time-domain beam prediction according to an embodiment of the present application.

[0037] Figure 20 is a schematic block diagram of a communication device provided according to an embodiment of the present application.

[0038] Figure 21 is a schematic block diagram of a communication device provided according to an embodiment of the present application.

[0039] Figure 22 is a schematic block diagram of a device provided according to an embodiment of the present application.

[0040] Figure 23 is a schematic block diagram of a communication system provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0041] The following will describe the technical solutions in the embodiments of this application in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of the embodiments. With respect to the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0042] The technical solutions of the embodiments of the present application can be applied to various communication systems, such as: 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), 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 on unlicensed spectrum, NR-based access to unlicensed spectrum (NR-U) system on unlicensed spectrum, Non-Terrestrial Networks (NTN) system, Universal Mobile Telecommunication System (UMTS), Wireless Local Area Networks (WLAN), Internet of Things (IoT), Wireless Fidelity (WFI) system. Fidelity, WiFi), fifth-generation communication (5th-Generation, 5G) system, sixth-generation communication (6G) system or other communication systems.

[0043] Generally speaking, traditional communication systems support a limited number of connections and are easy to implement. However, with the development of communication technology, mobile communication systems will not only support traditional communications, but will also support, for example, device-to-device (D2D) communication, machine-to-machine (M2M) communication, machine type communication (MTC), vehicle-to-vehicle (V2V) communication, sidelink (SL) communication, vehicle-to-everything (V2X) communication, etc. The embodiments of the present application can also be applied to these communication systems.

[0044] In some embodiments, the communication system in the embodiments of the present application can be applied to a carrier aggregation (CA) scenario, a dual connectivity (DC) scenario, an independent (SA) networking scenario, or a non-standalone (NSA) networking scenario.

[0045] In some embodiments, the communication system in the embodiments of the present application can be applied to an unlicensed spectrum, where the unlicensed spectrum can also be considered as a shared spectrum; or, the communication system in the embodiments of the present application can also be applied to an authorized spectrum, where the authorized spectrum can also be considered as an unshared spectrum.

[0046] In some embodiments, the communication system in the embodiments of the present application can be applied to the FR1 frequency band (corresponding to the frequency band range of 410MHz to 7.125GHz), can also be applied to the FR2 frequency band (corresponding to the frequency band range of 24.25GHz to 52.6GHz), and can also be applied to new frequency bands such as high-frequency bands corresponding to the frequency band range of 52.6GHz to 71GHz or the frequency band range of 71GHz to 114.25GHz.

[0047] The embodiments of the present application describe various embodiments in conjunction with network devices and terminal devices, wherein the terminal device may also be referred to as user equipment (UE), access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent or user device, etc.

[0048] The terminal device can be a station (ST) in a WLAN, 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 capabilities, 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 network, or a terminal device in a future evolved Public Land Mobile Network (PLMN) network, etc.

[0049] In an embodiment of the present application, the terminal device can be deployed on land, including indoors or outdoors, handheld, wearable or vehicle-mounted; it can also be deployed on the water surface (such as ships, etc.); it can also be deployed in the air (such as airplanes, balloons and satellites, etc.).

[0050] In an embodiment of the present application, the terminal device may be a mobile phone, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city or a wireless terminal device in a smart home, an in-vehicle communication device, a wireless communication chip / application specific integrated circuit (ASIC) / system on chip (SoC), etc.

[0051] As an example and not a limitation, in the embodiment of the present application, the terminal device may also be a wearable device. Wearable devices may also be called wearable smart devices, which are a general term for wearable devices that are intelligently designed and developed using wearable technology for daily wear, such as glasses, gloves, watches, clothing, and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not only hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are fully functional, large in size, and can achieve complete or partial functions without relying on smartphones, such as smart watches or smart glasses, as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0052] In an embodiment of the present application, the network device may be a device for communicating with a mobile device. The network device may be an access point (AP) in WLAN, a base station (BTS) in GSM or CDMA, a base station (NodeB, NB) in WCDMA, an evolved base station (eNB or eNodeB) in LTE, or a relay station or access point, or a network device or base station (gNB) or transmission reception point (TRP) in a vehicle-mounted device, a wearable device, and an NR network, or a network device in a future evolved PLMN network or a network device in an NTN network, etc.

[0053] As an example and not a limitation, in an embodiment of the present application, the network device may have a mobile feature, for example, the network device may be a mobile device. In some embodiments, the network device may be a satellite or a balloon station. For example, the satellite may be a low earth orbit (LEO) satellite, a medium earth orbit (MEO) satellite, a geostationary earth orbit (GEO) satellite, a high elliptical orbit (HEO) satellite, etc. In some embodiments, the network device may also be a base station set up in a location such as land or water.

[0054] In an embodiment of the present application, the network device can provide services for a cell, and the terminal device communicates with the network device through the transmission resources used by the cell (for example, frequency domain resources, or spectrum resources). The cell can be a cell corresponding to the network device (for example, a base station). The cell can 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.

[0055] For example, a communication system 100 used in an embodiment of the present application is shown in FIG1 . The communication system 100 may include a network device 110, which may be a device that communicates with a terminal device 120 (or a communication terminal or terminal). The network device 110 may provide communication coverage for a specific geographic area and may communicate with terminal devices within the coverage area.

[0056] FIG1 exemplarily shows a network device and two terminal devices. In some embodiments, the communication system 100 may include multiple network devices and each network device may include another number of terminal devices within its coverage area, which is not limited in the embodiments of the present application.

[0057] In some embodiments, the communication system 100 may further include other network entities such as a network controller and a mobility management entity, which is not limited in the embodiments of the present application.

[0058] It should be understood that in the embodiments of the present application, a device having a communication function in a network / system may be referred to as a communication device. Taking the communication system 100 shown in FIG1 as an example, the communication device may include a network device 110 and a terminal device 120 having a communication function. The network device 110 and the terminal device 120 may be the specific devices described above and will not be described in detail here. The communication device may also include other devices in the communication system 100, such as a network controller, a mobility management entity, and other network entities, which are not limited in the embodiments of the present application.

[0059] It should be understood that the terms "system" and "network" are often used interchangeably herein. The term "and / or" is simply a description of an association between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " generally indicates that the related objects are in an "or" relationship.

[0060] It should be understood that this document relates to a first communication device and a second communication device. The first communication device can be a terminal device, such as a mobile phone, machine facilities, customer premises equipment (CPE), industrial equipment, vehicles, etc.; the second communication device can be a peer communication device of the first communication device, such as a network device, mobile phone, industrial equipment, vehicles, etc. In the embodiments of the present application, the first communication device can be a terminal device, and the second communication device can be a network device (i.e., uplink communication or downlink communication); alternatively, the first communication device can be a first terminal, and the second communication device can be a second terminal (i.e., sideline communication).

[0061] The terms used in the embodiments of this application are intended only to explain the specific embodiments of this application and are not intended to limit this application. The terms "first," "second," "third," and "fourth," etc. in the specification and claims of this application and the accompanying drawings are used to distinguish different objects, not to describe a specific order. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions.

[0062] It should be understood that the "indication" mentioned in the embodiments of this application can be a direct indication, an indirect indication, or an indication of an association. For example, "A indicates B" 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 between A and B.

[0063] In the description of the embodiments of the present application, the term "corresponding" may indicate a direct or indirect correspondence between the two, or an association relationship between the two, or a relationship between indication and being indicated, configuration and being configured, etc.

[0064] In the embodiments of the present application, "pre-definition" or "pre-configuration" may be implemented by pre-storing corresponding codes, tables, or other methods that can be used to indicate relevant information in a device (e.g., a terminal device and a network device). The present application does not limit the specific implementation method. For example, pre-definition may refer to information defined in a protocol.

[0065] In the embodiments of the present application, the “protocol” may refer to a standard protocol in the communications field, for example, it may be an evolution of an existing LTE protocol, NR protocol, Wi-Fi protocol, or a protocol related to other communication systems. The present application does not limit the protocol type.

[0066] To facilitate a better understanding of the embodiments of the present application, the neural network and machine learning related to the present application are explained.

[0067] A neural network (NN) is a computational model consisting of multiple interconnected neuron nodes, where the connections between nodes represent weighted values ​​from input signals to output signals, called weights. Each node performs weighted summation (SUM) on different input signals and outputs them through a specific activation function (f). Figure 2 is a schematic diagram of a neuron structure, where a1, a2, …, an represent input signals, w1, w2, …, wn represent weights, f represents the activation function, and t represents the output.

[0068] A simple neural network, shown in Figure 3, consists of an input layer, hidden layers, and an output layer. By using different connections, weights, and activation functions among multiple neurons, different outputs can be generated, thereby fitting the mapping relationship from input to output. Each node in the previous level is connected to all nodes in the next level. This neural network is a fully connected neural network, also known as a deep neural network (DNN).

[0069] The basic structure of a convolutional neural network (CNN) consists of an input layer, multiple convolutional layers, multiple pooling layers, a fully connected layer, and an output layer, as shown in Figure 4. Each neuron in the convolutional kernel of a convolutional layer is locally connected to its input. The introduction of a pooling layer extracts the local maximum or average features of a layer, effectively reducing network parameters and exploiting local features, enabling the CNN to converge quickly and achieve excellent performance.

[0070] Deep learning utilizes deep neural networks with multiple hidden layers, significantly improving the network's ability to learn features and fitting complex, nonlinear mappings from input to output. Consequently, it has found widespread application in speech and image processing. In addition to deep neural networks, deep learning also includes other commonly used basic structures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for different tasks.

[0071] The basic structure of a convolutional neural network includes an input layer, multiple convolutional layers, multiple pooling layers, a fully connected layer, and an output layer, as shown in Figure 4. Each neuron in the convolution kernel of the convolutional layer is locally connected to its input, and the introduction of the pooling layer extracts the local maximum or average features of a certain layer, effectively reducing the network parameters and mining local features, enabling the convolutional neural network to converge quickly and achieve excellent performance.

[0072] RNNs are neural networks that model sequential data and have achieved remarkable success in natural language processing applications such as machine translation and speech recognition. Specifically, network devices memorize information from past moments and use it in the calculation of current outputs. This means that nodes in hidden layers are no longer disconnected but connected, and the input to a hidden layer includes not only the input layer but also the output of the previous hidden layer. Common RNN structures include long short-term memory (LSTM) and gated recurrent unit (GRU). Figure 5 shows a basic LSTM cell structure, which can include a tanh activation function. Unlike RNNs, which only consider the most recent state, the LSTM cell state determines which states should be retained and which should be forgotten, addressing the long-term memory limitations of traditional RNNs.

[0073] To facilitate a better understanding of the embodiments of the present application, the beam management related to the present application is explained.

[0074] The NR system introduces millimeter-wave frequency band communications and corresponding beam management mechanisms, including uplink and downlink beam management. Downlink beam management includes downlink beam scanning, optimal beam reporting on the UE side, and downlink beam indication on the network side.

[0075] The downlink beam scanning process refers to the network device scanning different transmit beam directions using downlink reference signals. The UE can use different receive beams for measurement, thereby traversing all beam pairs. The UE calculates the Layer 1 Reference Signal Received Power (L1-RSRP) value corresponding to each beam pair.

[0076] The downlink reference signal includes a synchronization signal block (Synchronization Signal Block, SSB) and / or a channel state information reference signal (Channel State Information Reference Signal, CSI-RS).

[0077] To facilitate a better understanding of the embodiments of the present application, the beam management based on artificial intelligence (AI) / machine learning (ML) related to the present application is described.

[0078] AI / ML-based beam management can predict downlink beams in both spatial and temporal domains.

[0079] Spatial-domain beam prediction (also known as Beam Management Case 1 (BM-Case 1)): The downlink beams in Dataset A (Set A) are spatially predicted by measuring the beams in Dataset B (Set B). Set B is either a subset of Set A or different sets of beams. Set B can be considered a subset of the beams (pairs), while Set A can be considered the full set of beams (pairs).

[0080] Time-domain beam prediction (also known as Beam Management Case 2 (BM-Case 2)): The downlink beams in Dataset A (Set A) are predicted in the time domain using the beams in Historical Measurement Dataset B (Set B). Set B is either a subset of Set A, the same as Set A, or a subset of Set A. Set B can be considered a partial subset of the beams (pairs); Set A can be considered the full set of beams (pairs).

[0081] In order to facilitate a better understanding of the embodiments of the present application, the neural network model related to the present application is explained.

[0082] A neural network (NN) model can be trained and obtained through the processes of data set construction, training, verification and testing. This application assumes that the NN models have been trained in advance through offline training or online training. It should be noted that offline training and online training are not mutually exclusive. First, the network (NW) can obtain a static training result through offline training of the data set, which can be referred to as offline training here. During the use of the NN model by the network (NW) or the terminal (UE), as the UE further measures and / or reports, the NN model can continue to collect more data and perform real-time online training to optimize the parameters of the NN model to achieve better inference and prediction results.

[0083] This application uses the prediction of time-domain beam (pairs) and their performance as an example, using an LSTM model, as shown in Figure 6. This LSTM model can be understood as extending M instances as input in time series, equivalent to a cascade of M LSTM units. The input to each LSTM unit is the L1-RSRP of the beam (pair) of instance m (Set Bm) in dataset B, where 1 ≤ m ≤ M.

[0084] It should be noted that the beam (pair) index of Set Bm can be implicitly input through the fixed ordering of L1-RSRP. After completing the performance input of M instances, the LSTM model can predict the optimal beam (pair) for the next F instances, the performance of the optimal beam (pair) (i.e., link quality information), and the dwelling time of the optimal beam (pair).

[0085] To facilitate a better understanding of the embodiments of the present application, the beam management of the NR related to the present application is explained.

[0086] The introduction of millimeter-wave frequency band communications in NR systems also introduces beam management mechanisms. These include uplink and downlink beam management. Downlink beam management includes downlink beam sweeping, UE beam measurement and reporting, and downlink beam indication by network equipment.

[0087] The downlink beam scanning process may include three processes, namely P1, P2 and P3 processes. The P1 process refers to the network device scanning different transmit beams and the UE scanning different receive beams; the P2 process refers to the network device scanning different transmit beams and the UE using the same receive beam; the P3 process refers to the network device using the same transmit beam and the UE scanning different receive beams. Generally, the network device completes the above beam scanning process by sending a downlink reference signal. Optionally, the downlink reference signal may include but is not limited to a synchronization signal block (SSB) and / or a channel state information reference signal (CSI-RS).

[0088] FIG7 is a schematic diagram of the P1 process (or called the downlink full scan process), FIG8 is a schematic diagram of the P2 process, and FIG9 is a schematic diagram of the P3 process.

[0089] As shown in FIG7 , in the P1 process, the network device traverses all transmit beams to send downlink reference signals, and the UE side traverses all receive beams to perform measurements and determine the corresponding measurement results.

[0090] As shown in Figure 8, in the P2 process, the network device traverses all transmit beams to send downlink reference signals, and the UE side uses a specific receive beam to perform measurements to determine the corresponding measurement results.

[0091] As shown in Figure 9, in the P3 process, the network device can use a specific transmit beam to send a downlink reference signal, and the UE side traverses all receive beams to perform measurements and determine the corresponding measurement results.

[0092] Traditional beam reporting in NR means that the UE measures the L1-RSRP values ​​of different beams (pairs), selects the K transmit beams with the highest L1-RSRP, and reports them to the NW in the form of uplink control information (UCI). Here, L1-RSRP can also be replaced by other beam link indicators, such as Layer 1 Signal to Interference plus Noise Ratio (L1-SINR) and Layer 1 Reference Signal Received Quality (L1-RSRQ).

[0093] After the network device learns the optimal beam reported by the terminal device, it can carry the Transmission Configuration Indicator (TCI) status (which contains the transmit beam with the downlink reference signal as a reference) through Media Access Control (MAC) or Downlink Control Information (DCI) signaling to complete the beam indication to the UE. The UE uses the receive beam corresponding to the transmit beam for downlink reception.

[0094] In order to facilitate a better understanding of the embodiments of the present application, the problems solved by the present application are explained.

[0095] For the NR beam scanning process, if it is a periodic full-beam scanning process for the downlink, that is, the periodic P1 process, the UE needs to periodically traverse all combinations of transmit beams and receive beams, which will bring a lot of overhead and delay. For example, assuming that the NW deploys 64 different downlink transmission directions in FR2 (carried by up to 64 SSBs), the UE uses multiple antenna panels (including only one receive beam panel) to perform receive beam scanning simultaneously when receiving, and each antenna panel has 4 receive beams. From a time perspective, each SSB period is approximately 20ms, so it takes 4 SSB periods to complete the measurement of the 4 receive beams (assuming that multiple receive antenna panels can be used for beam scanning), which means at least 80ms is required.

[0096] The above process is repeated every 80 milliseconds. In each 80-millisecond period, the UE needs to measure at least 64*4=256 beam pairs, which corresponds to a downlink resource overhead of 256 resources.

[0097] Therefore, how to reduce the overhead and delay of beam scanning is an urgent problem to be solved. Based on this problem, this application proposes a time domain beam prediction solution based on AI / ML model, which is conducive to reducing the overhead and delay caused by the beam scanning process.

[0098] To facilitate understanding of the technical solutions of the embodiments of the present application, the technical solutions of the present application are described in detail below through specific embodiments. The following related technologies can be combined with the technical solutions of the embodiments of the present application as optional solutions, and they all fall within the scope of protection of the embodiments of the present application. The embodiments of the present application include at least part of the following contents.

[0099] FIG10 is a schematic flowchart of a wireless communication method 200 according to an embodiment of the present application. As shown in FIG10 , the wireless communication method 200 may include at least part of the following contents:

[0100] S210: A first communications device obtains a first measurement data set; wherein the first measurement data set includes at least one of the following: identification information of spatial filters in M ​​measurement instances, and link quality information corresponding to the spatial filters in the M measurement instances; wherein M is a positive integer;

[0101] S220, the first communication device inputs the first measurement data set into the first network model and outputs a first prediction data set; wherein, the first prediction data set includes at least one of the following: identification information of the K spatial filters predicted in each of the F prediction instances, link quality information corresponding to the K spatial filters predicted in each of the F prediction instances, and duration of the K spatial filters predicted in each of the F prediction instances; wherein F and K are both positive integers.

[0102] In some embodiments of the present application, a spatial filter may also be referred to as a beam, a beam pair, a spatial relation, a spatial setting, a spatial domain filter, or a reference signal.

[0103] In some embodiments, the spatial filter includes a transmit spatial filter. Optionally, the transmit spatial filter may also be referred to as a transmit beam (Tx beam) or a transmitting-end spatial domain filter, and the above terms are interchangeable.

[0104] In some embodiments, the spatial filter includes a receive spatial filter. Optionally, the receive spatial filter may also be referred to as a transmit beam (Rx beam) or a receive-end spatial filter, and the above terms are interchangeable.

[0105] In some embodiments, the spatial filter includes a transmit spatial filter and a receive spatial filter. Optionally, the combination of the transmit spatial filter and the receive spatial filter can also be called a beam pair, a spatial filter pair, or a spatial filter bank, and the above terms can be used interchangeably.

[0106] In some embodiments, the identification information of the spatial filter may be an index or an identification of the spatial filter.

[0107] For example, the identification information of the transmit spatial filter may be an index or an identification of the transmit spatial filter.

[0108] For another example, the identification information of the receiving spatial filter may be an index or an identification of the receiving spatial filter.

[0109] For another example, the identification information of the combination of the transmit spatial filter and the receive spatial filter may be a combination index.

[0110] In some embodiments, the link quality information may include but is not limited to at least one of the following:

[0111] Layer 1 Reference Signal Receiving Power (L1-RSRP), Layer 1 Reference Signal Receiving Quality (L1-RSRQ), Layer 1 Signal to Interference plus Noise Ratio (L1-SINR).

[0112] In some embodiments, a measurement instance may correspond to one or more time units, where the time unit may include but is not limited to one of the following: a time slot, a mini-time slot, a frame, a subframe, a symbol, a millisecond, and a microsecond.

[0113] In some embodiments, a prediction instance may correspond to one or more time units, where the time unit may include but is not limited to one of the following: time slot, mini-time slot, frame, subframe, symbol, millisecond, microsecond.

[0114] In some embodiments, the K spatial filters are determined based on the link quality information in each prediction instance.

[0115] Specifically, for example, the K spatial filters are obtained based on the descending order of the link quality information in each prediction instance. For example, the spatial filters corresponding to the first K link quality information in the descending order of the link quality information in each prediction instance are the K spatial filters in the prediction instance.

[0116] For another specific example, the K spatial filters are spatial filters corresponding to link quality information in each prediction instance whose link quality information is greater than or equal to a first threshold. Optionally, the first threshold is agreed upon by a protocol, or configured by a network device.

[0117] In some embodiments, the value of K includes but is not limited to one of the following: 1, 2, 4, 8.

[0118] In some embodiments, the dwelling time of the K spatial filters may be the time during which the link quality information corresponding to the K spatial filters remains stable.

[0119] In some embodiments, when the first measurement data set only includes link quality information corresponding to the spatial filters in M ​​measurement instances, the link quality information corresponding to the spatial filters in the M measurement instances may implicitly indicate the identification information of the spatial filters in the M measurement instances.

[0120] In some embodiments, the first communication device is a terminal device, or the first communication device is a network device.

[0121] In some embodiments, the first network model may be an LSTM model. As shown in FIG6 , the LSTM model can be understood as extending M measurement instances as input in a temporal manner, equivalent to a cascade of M LSTM units. The input to each LSTM unit is the link quality information (e.g., L1-RSRP) of the beam (pair) of measurement instance m (Set B m) in dataset B (i.e., the first measurement dataset), where 1≤m≤M.

[0122] In some embodiments, the spatial filters in each of the M measurement instances are part of all spatial filters. Specifically, all spatial filters are all deployed spatial filters. That is, in each measurement instance, not all deployed spatial filters are traversed. For example, assuming that a network device (NW) deploys 64 different downlink transmission beams (Tx beams) in FR2, the number of downlink transmission beams in each of the M measurement instances is less than 64.

[0123] Therefore, in this embodiment, only part of the spatial filter information is measured in each measurement. After merging M measurements, more spatial filter information, or even all the spatial filter information, can be obtained for subsequent prediction, thereby reducing the overhead of each measurement.

[0124] In some embodiments, the configuration of the spatial filter in each of the M measurement instances is the same. That is, the spatial filter in each of the M measurement instances is the same. For example, the identifier and number of the spatial filters in each of the M measurement instances are the same.

[0125] For example, assuming that the first measurement data set is Set B, the M measurement instances are respectively recorded as Set B#1, Set B#2, ..., Set B#M-1, and Set B#M. The transmit-receive (Tx-Rx) beam pair indexes in Set B#1 are from index#0 to index#15, the transmit-receive (Tx-Rx) beam pair indexes in Set B#2 are also from index#0 to index#15, ..., the transmit-receive (Tx-Rx) beam pair indexes in Set B#M-1 are also from index#0 to index#15, and the transmit-receive (Tx-Rx) beam pair indexes in Set B#M are also from index#0 to index#15.

[0126] In some embodiments, the spatial filters in at least some of the M measurement instances have different configurations. That is, the spatial filters in at least some of the M measurement instances have different configurations. For example, the spatial filters in at least some of the M measurement instances have different identifiers. For another example, the number of spatial filters in at least some of the M measurement instances has different numbers. For another example, the identifiers and number of spatial filters in at least some of the M measurement instances have different numbers.

[0127] For example, assuming the first measurement data set is Set B, the M measurement instances are respectively recorded as Set B#1, Set B#2, ..., Set B#M-1, and Set B#M. As shown in Figure 11, the transmit-receive (Tx-Rx) beam pairs in Set B#1 are indexed from index#0 to index#15, the transmit-receive (Tx-Rx) beam pairs in Set B#2 are indexed from index#16 to index#19, ..., and the transmit-receive (Tx-Rx) beam pairs in Set B#M are indexed from index#48 to index#63.

[0128] In some embodiments, some spatial filters in different measurement instances among the M measurement instances are identical. That is, the spatial filters in different measurement instances among the M measurement instances may overlap to a certain extent. Optionally, the overlapping spatial filters may be easily selected. This can improve measurement quality.

[0129] Specifically, for different beam pairs, the possibility of being selected is not exactly the same (generally speaking, the transmitting beam with a more moderate downtilt angle on the NW side is easy to be selected). Therefore, for beam pairs that are easy to be selected by UE, more measurements can be performed; for beam pairs that are not easy to be selected, fewer measurements can be performed, or even no measurement can be performed.

[0130] For example, assuming the first measurement data set is Set B, the M measurement instances are recorded as Set B#1, Set B#2, ..., Set B#M-1, and Set B#M. The Tx-Rx beam pairs in Set B#1 are indexed from index#0 to index#15, and the Tx-Rx beam pairs in Set B#2 are indexed from index#14 to index#19. That is, the Tx-Rx beam pairs in both Set B#1 and Set B#2 include index#14 and index#15.

[0131] In some embodiments, the M measurement instances are set at equal intervals in the time domain. That is, the M measurement instances may be periodic measurement instances. Optionally, period information corresponding to the measurement instances may be configured via RRC signaling.

[0132] In some embodiments, the M measurement instances correspond to periodic spatial filter measurements within a first duration. Optionally, periodic information corresponding to the spatial filter measurements may be configured via RRC signaling.

[0133] In some embodiments, the first duration is agreed upon by a protocol, or the first duration is configured by a network device.

[0134] In some embodiments, the M measurement instances are arranged at unequal intervals in the time domain. For example, as shown in FIG11 , the time intervals (time gaps) between different measurement instances in the M measurement instances are different. This allows for more flexible adaptation to channel changes.

[0135] In some embodiments, the time intervals between the M measurement instances are determined based on the speed of movement of the terminal device. For example, when the UE moves faster, the frequency of measurement can be increased (i.e., the time intervals between different measurement instances are shorter); when the UE moves slower, the frequency of measurement can be reduced (i.e., the time intervals between different measurement instances are longer). This allows the UE to perform approximately the same number of measurements over the same moving distance.

[0136] In some embodiments, different measurement instances in the M measurement instances correspond to the same period and different offsets. Specifically, for example, for measurement instances that are non-uniformly spaced in the time domain, from a network configuration perspective, for periodic measurements, the network may configure the same period and different offsets for different measurement instances.

[0137] In some embodiments, the M measurement instances are measurement instances activated by downlink control information (DCI) or a media access control control element (MAC CE) among multiple measurement instances of semi-persistent scheduling (SPS).

[0138] In some embodiments, the M measurement instances are measurement instances dynamically triggered by DCI from among multiple pre-configured measurement instances. That is, in this embodiment, one DCI can trigger the aperiodic CSI-RS of M measurement instances. The advantage of this is that only one DCI (minimizing signaling overhead) can be used to trigger measurements of M measurement instances.

[0139] In some embodiments, the M measurement instances are measurement instances corresponding to a set of measurement configurations activated by a DCI or MAC CE among multiple measurement configurations, or the M measurement instances are measurement instances corresponding to a current measurement configuration among multiple measurement configurations activated and used in turn by a DCI or MAC CE. Optionally, the multiple measurement configurations are pre-configured or semi-statically configured by the network device.

[0140] In some embodiments, the network device may configure the M measurement instances in the first measurement data set, or the network device may activate the M measurement instances in the first measurement data set, or the network device may update the M measurement instances in the first measurement data set. For example, the network device may configure the multiple measurement instances through RRC signaling and activate the M measurement instances in the first measurement data set through MAC CE signaling.

[0141] In some embodiments, assuming that the first communication device is a terminal device, the first communication device may measure M measurement instances to obtain the first measurement data set.

[0142] In some embodiments, assuming that the first communication device is a network device, the first communication device may obtain measurement results of the terminal device for M measurement instances to obtain the first measurement data set.

[0143] In some embodiments, the spatial filters in each measurement instance in the F prediction instances are part of all spatial filters. Specifically, all spatial filters are all deployed spatial filters. That is, in each prediction instance, not all deployed spatial filters are traversed. For example, assuming that the network equipment (NW) deploys 64 different downlink transmit beams in FR2, the number of downlink transmit beams in each of the F prediction instances is less than 64.

[0144] Specifically, the range of the predicted spatial filter can be limited in each prediction instance, thereby controlling the range of spatial filters that the model can select, focusing only on some key spatial filters, and improving the accuracy of the predicted spatial filter.

[0145] In some embodiments, the configuration of the spatial filters in each of the F prediction instances is the same. That is, the spatial filters in each of the F prediction instances are the same. For example, the identifiers and number of the spatial filters in each of the F prediction instances are the same.

[0146] For example, assuming that the first prediction data set is Set A, the F prediction instances are respectively recorded as Set A#1, Set A#2, ..., Set A#F-1, and Set A#F. Among them, the transmit-receive (Tx-Rx) beam pair indexes (index) in Set A#1 are from index#0 to index#20, the transmit-receive (Tx-Rx) beam pair indexes (index) in Set A#2 are also from index#0 to index#20, ..., the transmit-receive (Tx-Rx) beam pair indexes (index) in Set A#F-1 are also from index#0 to index#20, and the transmit-receive (Tx-Rx) beam pair indexes (index) in Set A#F are also from index#0 to index#20.

[0147] In some embodiments, the configurations of the spatial filters in at least some of the F prediction instances are different. That is, the spatial filters in at least some of the F prediction instances are different. For example, the identifiers of the spatial filters in at least some of the F prediction instances are different. For another example, the number of spatial filters in at least some of the F prediction instances is different. For another example, the identifiers and number of spatial filters in at least some of the F prediction instances are different.

[0148] For example, assuming that the first prediction data set is Set A, the F prediction instances are respectively recorded as Set A#1, Set A#2, ..., Set A#F-1, and Set A#F. As shown in Figure 12, the transmit-receive (Tx-Rx) beam pairs in Set A#1 are indexed from index#0 to index#35, the transmit-receive (Tx-Rx) beam pairs in Set A#2 are indexed from index#0 to index#15, ..., and the transmit-receive (Tx-Rx) beam pairs in Set A#F are indexed from index#0 to index#27.

[0149] In some embodiments, some or all of the spatial filters in different prediction instances among the F prediction instances are the same. That is, the spatial filters in different prediction instances among the F prediction instances may overlap to a certain extent. Optionally, the overlapping spatial filters may be spatial filters that are easily selected. This can improve prediction quality.

[0150] Specifically, for different beam pairs, the possibility of being selected is not exactly the same (generally speaking, the transmitting beam with a more moderate downtilt angle on the NW side is easy to be selected). Therefore, for beam pairs that are easy to be selected by UE, more measurements can be performed; for beam pairs that are not easy to be selected, fewer measurements can be performed, or even no measurement can be performed.

[0151] In some embodiments, the F prediction instances are set at equal intervals in the time domain. That is, the F prediction instances may be periodic prediction instances. Optionally, periodic information corresponding to the prediction instances may be configured via RRC signaling.

[0152] In some embodiments, the F prediction instances correspond to periodic spatial filter predictions within the second duration. Optionally, periodic information corresponding to the spatial filter predictions may be configured via RRC signaling.

[0153] In some embodiments, the second duration is agreed upon by a protocol, or the second duration is configured by a network device.

[0154] In some embodiments, the F prediction instances are arranged at unequal intervals in the time domain. For example, as shown in FIG12 , the time gaps between different prediction instances in the F prediction instances are different. This allows for more flexible adaptation to channel changes.

[0155] In some embodiments, the time intervals between the F prediction instances are determined based on the terminal device's moving speed. For example, when the UE's moving speed is fast, the prediction frequency can be increased (i.e., the time intervals between different prediction instances are smaller); when the UE's moving speed is slow, the prediction frequency can be reduced (i.e., the time intervals between different prediction instances are larger). This allows for approximately the same number of predictions to be made over the same moving distance.

[0156] In some embodiments, different prediction instances in the F prediction instances correspond to the same period and different offsets. Specifically, for example, for prediction instances that are non-uniformly spaced in the time domain, from a network configuration perspective, for periodic predictions, the network may configure the same period and different offsets for different prediction instances.

[0157] In some embodiments, the F prediction instances are prediction instances activated by DCI or MAC CE among multiple prediction instances of the SPS.

[0158] In some embodiments, the F prediction instances are prediction instances dynamically triggered by DCI from among multiple pre-configured prediction instances. That is, in this embodiment, one DCI can trigger F prediction instances from multiple pre-configured prediction instances. The advantage of this is that only one DCI (minimizing signaling overhead) can be used to trigger the prediction of the F prediction instances.

[0159] In some embodiments, the F prediction instances are prediction instances corresponding to a set of prediction configurations activated by a DCI or MAC CE among multiple prediction configurations, or the F prediction instances are prediction instances corresponding to a current prediction configuration among multiple sets of prediction configurations activated and used in turn by a DCI or MAC CE. Optionally, the multiple prediction configurations are pre-configured or semi-statically configured by the network device.

[0160] In some embodiments, for each prediction instance, the first network model may infer identification information of K spatial filters and link quality information corresponding to the K spatial filters.

[0161] In some embodiments, for each prediction instance, the first network model may infer identification information of the K spatial filters, link quality information corresponding to the K spatial filters, and durations of the K spatial filters.

[0162] For example, assuming the spatial filter is a beam pair, for each prediction instance, the first network model can also infer the dwelling time of the optimal beam pair. For example, as shown in Figure 12, there is a period of time between Set A#1 and Set A#2, which can be defined as time gap #1. The first network model infers the optimal K beam pairs for the prediction instance of Set A#1. The first network model can also predict the duration for which the L1-RSRP of these K beam pairs remains stable. This stability can be defined in terms of relative quality, i.e., beam quality ranking, such as the duration for which the top-1 beam pair remains ranked first. It can also be defined in terms of absolute L1-RSRP quality, such as the duration for which the top-1 beam pair's L1-RSRP drops below a certain threshold (e.g., 3dB). This approach allows for a more detailed characterization of the dwelling time of the effective beam pair, providing the network (NW) with more beam information, allowing the NW to adjust the beams in advance or later in subsequent beam instructions.

[0163] In some embodiments, the network device may configure F prediction instances in the first prediction data set, or the network device may activate the F prediction instances in the first prediction data set, or the network device may update the F prediction instances in the first prediction data set. For example, the network device may configure multiple prediction instances via RRC signaling and activate the F prediction instances in the first prediction data set via MAC CE signaling.

[0164] In some embodiments, the first communication device sends first information;

[0165] The first information includes at least one of the following: identification information of the K spatial filters predicted in each of the F prediction instances, link quality information corresponding to the K spatial filters predicted in each of the F prediction instances, and duration of the K spatial filters predicted in each of the F prediction instances.

[0166] That is, the first communication device may report prediction information in F prediction instances.

[0167] For example, the first communication device sends the first information to the second communication device, where the first communication device is a terminal device and the second communication device is a network device. That is, after the first network model completes the prediction of the time-domain spatial filter, the terminal device needs to report the prediction results of the first network model in F prediction instances to the network device.

[0168] In some embodiments, the first information is carried by one of the following:

[0169] Uplink Control Information (UCI), MAC CE signaling, and Radio Resource Control (RRC) signaling.

[0170] In some embodiments, the link quality information corresponding to the K spatial filters is sent in a differential manner.

[0171] For example, assuming that the first prediction data set is Set A, the F prediction instances are respectively recorded as Set A#1, Set A#2, ..., Set A#F-1, and Set A#F, and assuming that the spatial filter is a transmit-receive beam pair (Tx-Rx beam pair) and the link quality information is L1-RSRP. Optionally, as shown in Table 1, the terminal device can send the prediction results of the F prediction instances to the network device through a channel state information (CSI) report, wherein the prediction results of the F prediction instances include 4 Tx-Rx beam pairs and the L1-RSRPs corresponding to the 4 Tx-Rx beam pairs. Optionally, as shown in Table 2, the terminal device can send the prediction results of the F prediction instances to the network device through a CSI report, wherein the prediction results of the F prediction instances include 4 Tx-Rx beam pairs, the L1-RSRPs corresponding to the 4 Tx-Rx beam pairs, and the beam dwelling times of the 4 Tx-Rx beam pairs. That is, in Table 1 and Table 2, K = 4. It should be understood that Table 1 and Table 2 are merely examples and do not limit the embodiments of the present application.

[0172] Optionally, the terminal device may report the prediction results of F prediction instances through one CSI report. That is, the terminal device may report all the contents in Table 1 or Table 2 through one CSI report.

[0173] Optionally, the terminal device may report the prediction results of F prediction instances through multiple CSI reports. For example, the terminal device reports the prediction result of one prediction instance in each CSI report. That is, the terminal device may report the prediction results of the F prediction instances in Table 1 through F CSI reports, or the terminal device may report the prediction results of the F prediction instances in Table 2 through F CSI reports.

[0174] Table 1 Beam pair ID and L1-RSRP performance of F prediction instances reported by UE

[0175]

[0176] It should be noted that in Table 1 above, in a prediction example, Differential L1-RSRP#2 is the difference relative to L1-RSRP#1, Differential L1-RSRP#3 is the difference relative to L1-RSRP#1, Differential L1-RSRP#4 is the difference relative to L1-RSRP#1, L1-RSRP#1 is the L1-RSRP corresponding to Tx-Rx beam pair#1, Differential L1-RSRP#2 is the L1-RSRP corresponding to Tx-Rx beam pair#2, Differential L1-RSRP#3 is the L1-RSRP corresponding to Tx-Rx beam pair#3, and Differential L1-RSRP#4 is the L1-RSRP corresponding to Tx-Rx beam pair#4.

[0177] Table 2 Beam pair ID, L1-RSRP performance, and beam duration for F prediction instances reported by the UE

[0178]

[0179] It should be noted that in Table 2 above, in a prediction example, Differential L1-RSRP#2 is the difference relative to L1-RSRP#1, Differential L1-RSRP#3 is the difference relative to L1-RSRP#1, and Differential L1-RSRP#4 is the difference relative to L1-RSRP#1; L1-RSRP#1 is the L1-RSRP corresponding to Tx-Rx beam pair#1, Differential L1-RSRP#2 is the L1-RSRP corresponding to Tx-Rx beam pair#2, Differential L1-RSRP#3 is the L1-RSRP corresponding to Tx-Rx beam pair#3, and Differential L1-RSRP#4 is the L1-RSRP corresponding to Tx-Rx beam pair#4; Beam dwelling time#1 is the beam duration of Tx-Rx beam pair#1, Beam dwelling time#2 is the beam duration of Tx-Rx beam pair#2, and Beam dwelling time#4 is the beam duration of Tx-Rx beam pair#1. time#3 is the beam dwelling time of Tx-Rx beam pair#3, and Beam dwelling time#4 is the beam dwelling time of Tx-Rx beam pair#4.

[0180] In some embodiments, the first communication device receives second information determined based on the first information;

[0181] The second information is used to indicate the identification information of the spatial filter used in each of the F prediction instances, or the second information is used to indicate the identification information of the spatial filter used in the i-th prediction instance; or the second information is used to indicate the identification information and duration of the spatial filter used in each of the F prediction instances, or the second information is used to indicate the identification information and duration of the spatial filter used in the i-th prediction instance;

[0182] Wherein, i is a positive integer, and 1≤i≤F.

[0183] That is, the first communication device may obtain the identification information of the spatial filter used in the prediction instance through the second information, or the first communication device may obtain the identification information and duration of the spatial filter used in the prediction instance through the second information.

[0184] Specifically, when the first prediction data set includes the durations of K spatial filters predicted in each of F prediction instances, the second information is used to indicate the identification information and duration of the spatial filter used in each of the F prediction instances, or, the second information is used to indicate the identification information and duration of the spatial filter used in the i-th prediction instance.

[0185] For example, a first communication device receives the second information sent by a second communication device, where the first communication device is a terminal device and the second communication device is a network device. That is, the network device can perform beam indication based on the prediction result of the first network model reported by the terminal device.

[0186] In some embodiments, the second information is carried by one of the following:

[0187] Downlink Control Information (DCI), MAC CE signaling.

[0188] Specifically, based on the prediction results of the F prediction instances reported by the terminal device, there are at least two ways for the network device to indicate the beam. The first way is that the network device performs beam indication in batches according to the report of the terminal device, such as performing F beam information indications under the beam prediction of F prediction instances, that is, indicating the beam used in each of the F prediction instances respectively; wherein, the beam information can be the index of the beam pair, or the index of the transmitted beam in the beam pair (in NR, the resource index of SSB or CSI-RS is generally used to refer to it). Another way is to perform a one-time beam indication, that is, the network device indicates the beam information of F prediction instances to the terminal device at one time, that is, indicates the beam used in each of the F prediction instances at one time. The advantage of a one-time beam indication is that the signaling overhead of the beam indication can be greatly reduced.

[0189] For example, if the UE's beam report does not include the beam duration, the NW indicates the beam information used in each of the F prediction instances at one time. If the beam information is a Tx-Rx beam pair index, then the MAC CE or DCI should include Tx-Rx beam pair index#1, Tx-Rx beam pair index#2, ..., Tx-Rx beam pair index#F. If the beam information is a Tx beam index, then the MAC CE or DCI should include Tx beam index(CSI-RS / SSB)#1, Tx beam index(CSI-RS / SSB)#2, ..., Tx beam index(CSI-RS / SSB)#F. In the time domain, this means that whenever a prediction instance arrives, the NW will switch to the indicated transmit beam, and the UE should also switch to the corresponding receive beam.

[0190] For example, if the UE's beam report includes the beam duration, the NW may choose to indicate the beam information and beam duration used in each of the F prediction instances to the UE at one time. If the beam information is a Tx-Rx beam pair index, then the MAC CE or DCI (i.e., the second information) should include (Tx-Rx beam pair index#1, Application time#1), (Tx-Rx beam pair index#2, Application time#2), ..., (Tx-Rx beam pair index#F, Application time#F). If the beam information is a Tx beam index, then the MAC CE or DCI (i.e., the second information) should include (Tx beam index(CSI-RS / SSB)#1, Application time#1), (Tx beam index(CSI-RS / SSB)#2, Application time#2), ..., (Tx beam index(CSI-RS / SSB)#F, Application time#F). In the time domain, it means that each indicated beam pair takes effect from the predicted instance, and its effective duration is the beam dwelling time indicated by the application time.

[0191] In some embodiments, the first communication device sends first capability information;

[0192] Among them, the first capability information includes at least one of the following: the maximum number of spatial filters supported by the first measurement data set, the maximum number of measurement instances supported by the first measurement data set, the maximum number of spatial filters supported by the first prediction data set, the maximum number of prediction instances supported by the first prediction data set, whether the prediction and reporting of the duration of the spatial filter are supported, and whether the identification information of the spatial filters in multiple prediction instances is supported at one time.

[0193] For example, a first communication device sends the first capability information to a second communication device, wherein the first communication device is a terminal device and the second communication device is a network device.

[0194] That is, the terminal device can report the first capability information, and the network device can configure, activate, or update M measurement instances and / or F prediction instances based on the first capability information, and / or the network device can indicate the identification information of the spatial filter used in each of the F prediction instances based on the first capability information.

[0195] In some embodiments, the first capability information may be carried by one of the following: UCI, MAC CE signaling, and RRC signaling.

[0196] For example, the first measurement data set is Set B, and the first prediction data set is Set A. The terminal device can report the following capability information:

[0197] Spatial domain capacity of Set B: the maximum number of beam pairs supported for measurement (at one time);

[0198] Time domain capacity of Set B: the maximum number of measurement instances supported;

[0199] Spatial domain capacity of Set A: the maximum number of beam pair predictions supported (at one time);

[0200] The time domain capacity of Set A: the maximum number of prediction instances supported;

[0201] Whether the prediction and reporting of beam duration is supported;

[0202] Whether to support one-time beam information indication.

[0203] In some embodiments, when the first communication device is a terminal device (UE), the signaling interaction process between the network device (NW) and the terminal device (UE) to complete time domain beam prediction can be as shown in Figure 13, which includes the UE's capability reporting, the NW's configuration and activation of the beam measurement set (Set B) and the beam prediction set (Set A), the UE's measurement of the beam measurement set, the UE's inference of the beam prediction set using the first network model, the UE's reporting of the prediction result, and the NW's indication of the beam used in the prediction instance. It should be noted that the beam measurement set (Set B) is the above-mentioned first measurement data set, and the beam prediction set (Set A) is the above-mentioned first prediction data set.

[0204] In some embodiments, the first communication device sends third information;

[0205] The third information is used to indicate the identification information of the spatial filter used in each of the F prediction instances, or the third information is used to indicate the identification information of the spatial filter used in the i-th prediction instance; or the third information is used to indicate the identification information and duration of the spatial filter used in each of the F prediction instances, or the third information is used to indicate the identification information and duration of the spatial filter used in the i-th prediction instance;

[0206] Wherein, i is a positive integer, and 1≤i≤F.

[0207] That is, the first communication device may indicate identification information of the spatial filter used in the prediction instance through the third information.

[0208] Specifically, when the first prediction data set includes the durations of K spatial filters predicted in each of F prediction instances, the third information is used to indicate the identification information and duration of the spatial filter used in each of the F prediction instances, or the third information is used to indicate the identification information and duration of the spatial filter used in the i-th prediction instance.

[0209] For example, a first communication device sends the third information to a second communication device, where the first communication device is a network device and the second communication device is a terminal device. That is, the network device can perform beam indication based on the prediction result of the first network model.

[0210] In some embodiments, the third information is carried via one of the following: DCI, MAC CE signaling.

[0211] Specifically, based on the prediction results of the F prediction instances predicted by the network device, there are at least two ways for the network device to indicate the beam. The first way is that the network device performs beam indication in batches according to the prediction results. For example, under the beam prediction of F prediction instances, F beam information indications are performed, that is, the beam used in each of the F prediction instances is indicated respectively; wherein, the beam information can be the index of the beam pair, or the index of the transmitted beam in the beam pair (in NR, the resource index of SSB or CSI-RS is generally used to refer to it). Another way is to perform a one-time beam indication, that is, the network device indicates the beam information of F prediction instances to the terminal device at one time, that is, the beam used in each of the F prediction instances is indicated at one time. The advantage of a one-time beam indication is that the signaling overhead of the beam indication can be greatly reduced.

[0212] For example, if the prediction result in the prediction instance predicted by the network device does not include the beam duration, the network device indicates the beam information used in each of the F prediction instances at one time. If the beam information is a Tx-Rx beam pair index, then the MAC CE or DCI (i.e., the third information) should include Tx-Rx beam pair index#1, Tx-Rx beam pair index#2,…, Tx-Rx beam pair index#F. If the beam information is a Tx beam index, then the MAC CE or DCI (i.e., the third information) should include Tx beam index(CSI-RS / SSB)#1, Tx beam index(CSI-RS / SSB)#2,…, Tx beam index(CSI-RS / SSB)#F. The meaning in the time domain is that whenever a prediction instance arrives, the NW will change to the indicated transmit beam, and the UE should also change the corresponding receive beam.

[0213] For example, if the prediction result in the prediction instance predicted by the network device includes the beam duration, the network device may choose to indicate the beam information and beam duration used in each of the F prediction instances to the UE at one time. If the beam information is a Tx-Rx beam pair index, then the MAC CE or DCI (i.e., the third information) should include (Tx-Rx beam pair index#1, Application time#1), (Tx-Rx beam pair index#2, Application time#2), ..., (Tx-Rx beam pair index#F, Application time#F). If the beam information is a Tx beam index, then the MAC CE or DCI (i.e., the third information) should include (Tx beam index(CSI-RS / SSB)#1, Application time#1), (Tx beam index(CSI-RS / SSB)#2, Application time#2), ..., (Tx beam index(CSI-RS / SSB)#F, Application time#F). In the time domain, it means that each indicated beam pair takes effect from the predicted instance, and its effective duration is the beam dwelling time indicated by the application time.

[0214] In some embodiments, the first communication device receives first capability information;

[0215] Among them, the first capability information includes at least one of the following: the maximum number of spatial filters supported by the first measurement data set, the maximum number of measurement instances supported by the first measurement data set, the maximum number of spatial filters supported by the first prediction data set, the maximum number of prediction instances supported by the first prediction data set, whether the prediction and reporting of the duration of the spatial filter are supported, and whether the identification information of the spatial filters in multiple prediction instances is supported at one time.

[0216] For example, a first communication device receives the first capability information sent by a second communication device, wherein the first communication device is a network device and the second communication device is a terminal device.

[0217] That is, the terminal device can report the first capability information, and the network device can configure, activate, or update M measurement instances and / or F prediction instances based on the first capability information, and / or the network device can indicate the identification information of the spatial filter used in each of the F prediction instances based on the first capability information.

[0218] In some embodiments, the first capability information may be carried by one of the following: UCI, MAC CE signaling, and RRC signaling.

[0219] For example, the first measurement data set is Set B, and the first prediction data set is Set A. The terminal device can report the following capability information:

[0220] Spatial domain capacity of Set B: the maximum number of beam pairs supported for measurement (at one time);

[0221] Time domain capacity of Set B: the maximum number of measurement instances supported;

[0222] Spatial domain capacity of Set A: the maximum number of beam pair predictions supported (at one time);

[0223] The time domain capacity of Set A: the maximum number of prediction instances supported;

[0224] Whether the prediction and reporting of beam duration is supported;

[0225] Whether to support one-time beam information indication.

[0226] In some embodiments, when the first communication device is a network device (NW), assuming the first measurement data set is Set B, the UE needs to measure M measurement instances of Set B. Then, all measurement results are reported to the model on the NW side. The specific reporting method is as described in Examples 1 and 2.

[0227] In Example 1, the reporting is performed in the time domain order in Set B m, 1 <= m <= M. In each measurement instance, the reporting is performed in the order of the configured transmit-receive beam pairs (Tx-Rx beam pairs), as shown in Table 3. Here, only the corresponding L1-RSRP or other performance indicators need to be reported.

[0228] Table 3 UE reports L1-RSRP values ​​according to the time domain order of Set B

[0229]

[0230] In Example 2, the performance of each transmit-receive beam pair (Tx-Rx beam pair) in Set B is sorted by their performance (e.g., L1-RSRP), generally from highest to lowest. As shown in Table 4, the Tx-Rx beam pair ID in Set B is listed first, followed by the absolute value of the highest beam pair performance, such as the absolute value of L1-RSRP. For beam pairs that do not perform at the highest performance, differential quantification can be used, reporting the difference in performance compared to the highest performance.

[0231] Table 4 UE reports L1-RSRP values ​​according to the time domain order of Set B

[0232]

[0233] It should be noted that in Table 4 above, in a prediction example, Differential L1-RSRP#2 is the difference relative to L1-RSRP#1, Differential L1-RSRP#3 is the difference relative to L1-RSRP#1, and Differential L1-RSRP#4 is the difference relative to L1-RSRP#1.

[0234] In some embodiments, when the first communication device is a network device (NW), the signaling interaction process between the network device (NW) and the terminal device (UE) to complete the time domain beam prediction can be as shown in Figure 14, which includes the UE's capability reporting, the NW's configuration and activation of the beam measurement set (Set B) and the beam prediction set (Set A), the UE's measurement of the beam measurement set, the UE's reporting of the measurement results for the beam measurement set, the network device's inference of the beam prediction set using the first network model, and the NW's indication of the beam used in the prediction instance. It should be noted that the beam measurement set (Set B) is the above-mentioned first measurement data set, and the beam prediction set (Set A) is the above-mentioned first prediction data set.

[0235] The technical solution of this application is described in detail below through Example 1 and Example 2.

[0236] In Example 1, the model is deployed on the UE side for transmission (Tx) beam prediction. The first measurement data set is Set B, and the M measurement instances are recorded as Set B#1, Set B#2, ..., Set B#M-1, and Set B#M. The first prediction data set is Set A, and the F prediction instances are recorded as Set A#1, Set A#2, ..., Set A#F-1, and Set A#F.

[0237] In general, the purpose of Tx beam prediction and the P2 process is the same: to find a suitable transmit beam using the optimal receive beam or a fixed receive beam during transmit beam scanning. During beam scanning, the NW scans the transmit beam, while the UE uses a fixed receive beam.

[0238] UE beam capability reporting

[0239] Optionally, the UE needs to report at least one of the following information:

[0240] Spatial domain capacity of Set B: the maximum number of Tx beams supported for measurement (at one time);

[0241] Time domain capacity of Set B: the maximum number of measurement instances supported;

[0242] Spatial domain capacity of Set A: the maximum number of Tx beams supported (at one time);

[0243] The time domain capacity of Set A: the maximum number of prediction instances supported;

[0244] Whether the prediction and reporting of Tx beam duration is supported;

[0245] Whether to support NW one-time Tx beam information indication.

[0246] NW configuration, activation, and update of Set A and Set B

[0247] The configuration of Set B can be fixed or variable across M measurement instances. For the variable configuration, Set B m, 1 <= m <= M can appear in a specific pattern, as shown in Figure 15 . Set B has different Tx beam configurations across different measurement instances.

[0248] For example, Set B#1 includes Tx beams from index#0 to index#3, Set B#2 includes Tx beams from index#4 to index#5, ..., and Set B#M includes Tx beams from index#6 to index#9. The above example shows non-overlapping Tx beams. It should be noted that the Tx beams included in different UE measurement instances can overlap to a certain extent, or even completely overlap. For example, Set B#1 and Set B#M can include the same Tx beam indexes#0 to #3.

[0249] The time domain intervals between different measurement instances in Set B can be equal, such as periodic measurements configured via RRC, or unequal, as shown by the different time gaps in Figure 15. For Set B measurements with uneven time domain intervals, from the perspective of NW configuration, for periodic and semi-static measurements, the NW can configure the same periodicity and different offsets for different measurement instances.

[0250] Another approach for aperiodic Set B measurements is for the NW to preconfigure some aperiodic Set B measurements and then use DCI to dynamically trigger the aperiodic measurements, thereby achieving unevenly spaced measurements. In the time-domain beam prediction use case, this solution designs a single DCI to trigger M (a sequence) of aperiodic CSI-RS, which can contain Set B m, where 1 <= m <= M, as shown in Figure 16. The advantage of this approach is that Set B measurements can be completed using only a single DCI (minimizing signaling overhead).

[0251] For the configuration of Set A, Set A can be fixed or non-fixed across F prediction instances. In the non-fixed case, Set A f,1<=f<=F can appear according to a pre-configured pattern. As shown in Figure 17, Set A has different Tx beam configurations for different prediction instances.

[0252] For example, Set A#1 includes Tx beams from index#0 to index#5, Set A#2 includes Tx beams from index#0 to index#3, ..., Set A#F includes Tx beams from index#0 to index#6. It should be noted that the above multiple Set A may or may not overlap.

[0253] The time intervals between different predictions in Set A can be evenly spaced, such as periodic predictions configured via RRC, or unevenly spaced, as shown in Figure 17. For unevenly spaced predictions in Set A, from a network worker (NW) configuration perspective, the NW can configure the same periodicity but different offsets for different prediction instances.

[0254] In addition to configuring Set B and Set A in multiple instances, the NW can configure multiple instances of Set B and Set A and then use MAC CE to activate one instance. Alternatively, MAC CE can be used to activate multiple instances of Set B and Set A, allowing them to be used in rotation. For example, use Set A's configuration X, followed by Set A's configuration Y, and then alternate between configurations X and Y, as shown in Figure 18.

[0255] UE measurements and inferences

[0256] After NW Set B and Set A are configured and activated, the UE performs measurements of Set B for M measurement instances. These are used as input to the model, and the measurement set is Set Bm, where 1<=m<=M.

[0257] Next, the model infers the optimal K Tx beams and their performance (e.g., L1-RSRP) from the F prediction instances in Set Af,1<=f<=F. The K optimal Tx beams are sorted from highest to lowest L1-RSRP. The value of K is typically 1, 2, 4, or 8. Alternatively, the model can infer multiple Tx beams, as long as their performance (e.g., L1-RSRP) exceeds a pre-defined threshold.

[0258] For each prediction instance, the model can also infer the duration of the optimal Tx beam (beam dwelling time). For example, there is a period of time between Set A#1 and Set A#2, which we define as time gap#1. When the model infers the optimal K Tx beams for the prediction instance of Set A#1, it can also predict the time for the L1-RSRP of the K Tx beams to remain stable. The stability here can be defined by relative quality, that is, the beam quality ranking, such as the time for the Top-1 Tx beam to remain in first place. It can also be defined by the absolute quality of L1-RSRP, such as the time for the L1-RSRP of the Top-1 Tx beam to drop below a certain threshold (such as 3dB). Specifically, the beam dwelling time in time-domain beam prediction can be shown in Figure 19.

[0259] UE beam information reporting

[0260] After the model completes the prediction of the time-domain beam pair, the UE needs to report the prediction results of the F prediction instances of the model to the NW. The specific format is shown in Table 5 or Table 6 below.

[0261] Table 5 Tx beam IDs of F prediction instances reported by UE and their L1-RSRP performance

[0262]

[0263] It should be noted that in Table 5 above, in a prediction example, Differential L1-RSRP#2 is the difference relative to L1-RSRP#1, Differential L1-RSRP#3 is the difference relative to L1-RSRP#1, and Differential L1-RSRP#4 is the difference relative to L1-RSRP#1; L1-RSRP#1 is the L1-RSRP corresponding to Tx beam pair#1, Differential L1-RSRP#2 is the L1-RSRP corresponding to Tx beam pair#2, Differential L1-RSRP#3 is the L1-RSRP corresponding to Tx beam pair#3, and Differential L1-RSRP#4 is the L1-RSRP corresponding to Tx beam pair#4.

[0264] Table 6 UE reports Tx beam ID, L1-RSRP performance and Tx beam duration for F prediction instances

[0265]

[0266] It should be noted that in Table 6 above, in a prediction example, Differential L1-RSRP#2 is the difference relative to L1-RSRP#1, Differential L1-RSRP#3 is the difference relative to L1-RSRP#1, and Differential L1-RSRP#4 is the difference relative to L1-RSRP#1; L1-RSRP#1 is the L1-RSRP corresponding to Tx beam pair#1, Differential L1-RSRP#2 is the L1-RSRP corresponding to Tx beam pair#2, Differential L1-RSRP#3 is the L1-RSRP corresponding to Tx beam pair#3, and Differential L1-RSRP#4 is the L1-RSRP corresponding to Tx beam pair#4; Beam dwelling time#1 is the beam duration of Tx beam pair#1, Beam dwelling time#2 is the beam duration of Tx beam pair#2, Beam dwelling time#3 is the beam duration of Tx beam pair#3, and Beam dwelling time#4 is the beam duration of Tx beam pair#4. Beam duration of pair #4.

[0267] In addition, in the format of reporting beam dwelling time, differential reporting can also be considered, that is, Beam dwelling time #1 is the absolute time, and Beam dwelling time #2 to Beam dwelling time #4 are the time differences relative to Beam dwelling time #1.

[0268] NW beam indication

[0269] Specifically, based on the prediction results of the F prediction instances reported by the terminal device, there are at least two ways for the network device to indicate the beam. The first way is that the network device performs beam indication in batches according to the report of the terminal device, such as performing F beam information indications under the beam prediction of F prediction instances, that is, indicating the beam used in each of the F prediction instances respectively; wherein, the beam information can be the index of the transmitted beam (in NR, it is generally referred to by the resource index of SSB or CSI-RS). Another way is to perform a one-time beam indication, that is, the network device indicates the beam information of F prediction instances to the terminal device at one time, that is, indicates the beam used in each of the F prediction instances at one time. The advantage of a one-time beam indication is that the signaling overhead of the beam indication can be greatly reduced.

[0270] For example, if the UE's beam reporting does not include beam duration, the NW will indicate the beam information used in each of F prediction instances at once. The MAC CE or DCI should include Tx beam index (CSI-RS / SSB) #1, Tx beam index (CSI-RS / SSB) #2, ..., Tx beam index (CSI-RS / SSB) #F. In the time domain, this means that for each prediction instance, the NW will switch to the indicated transmit beam, and the UE should also switch to the corresponding receive beam.

[0271] For example, if the UE's beam report includes the beam duration, the NW can choose to indicate the beam information and beam duration used in each of the F prediction instances to the UE at one time. The MAC CE or DCI (i.e., the second information) should include (Tx beam index (CSI-RS / SSB) #1, Application time #1), (Tx beam index (CSI-RS / SSB) #2, Application time #2), ..., (Tx beam index (CSI-RS / SSB) #F, Application time #F). The meaning in the time domain is that each indicated beam pair takes effect from the predicted instance, and its effective duration is the beam dwelling time indicated by the application time.

[0272] In Example 2, the model is deployed on the network (NW) side for transmission (Tx) beam prediction. The first measurement data set is Set B, and the M measurement instances are recorded as Set B#1, Set B#2, ..., Set B#M-1, and Set B#M. The first prediction data set is Set A, and the F prediction instances are recorded as Set A#1, Set A#2, ..., Set A#F-1, and Set A#F.

[0273] In general, the purpose of Tx beam prediction and the P2 process is the same: to find a suitable transmit beam using the optimal receive beam or a fixed receive beam during transmit beam scanning. During beam scanning, the NW scans the transmit beam, while the UE uses a fixed receive beam.

[0274] UE beam capability reporting

[0275] Optionally, the UE needs to report at least one of the following information:

[0276] Spatial domain capacity of Set B: the maximum number of Tx beams supported for measurement (at one time);

[0277] Time domain capacity of Set B: the maximum number of measurement instances supported;

[0278] Spatial domain capacity of Set A: the maximum number of Tx beams supported (at one time);

[0279] The time domain capacity of Set A: the maximum number of prediction instances supported;

[0280] Whether the prediction and reporting of Tx beam duration is supported;

[0281] Whether to support NW one-time Tx beam information indication.

[0282] NW configuration, activation, and update of Set A and Set B

[0283] The configuration of Set B can be fixed or variable across M measurement instances. For the variable configuration, Set B m, 1 <= m <= M can appear in a specific pattern, as shown in Figure 15 . Set B has different Tx beam configurations across different measurement instances.

[0284] For example, Set B#1 includes Tx beams from index#0 to index#3, Set B#2 includes Tx beams from index#4 to index#5, ..., and Set B#M includes Tx beams from index#6 to index#9. The above example shows non-overlapping Tx beams. It should be noted that the Tx beams included in different UE measurement instances can overlap to a certain extent, or even completely overlap. For example, Set B#1 and Set B#M can include the same Tx beam indexes#0 to #3.

[0285] The time domain intervals between different measurement instances in Set B can be equal, such as periodic measurements configured via RRC, or unequal, as shown by the different time gaps in Figure 15. For Set B measurements with uneven time domain intervals, from the perspective of NW configuration, for periodic and semi-static measurements, the NW can configure the same periodicity and different offsets for different measurement instances.

[0286] Another approach for aperiodic Set B measurements is for the NW to preconfigure some aperiodic Set B measurements and then use DCI to dynamically trigger the aperiodic measurements, thereby achieving unevenly spaced measurements. In the time-domain beam prediction use case, this solution designs a single DCI to trigger M (a sequence) of aperiodic CSI-RS, which can contain Set B m, where 1 <= m <= M, as shown in Figure 16. The advantage of this approach is that Set B measurements can be completed using only a single DCI (minimizing signaling overhead).

[0287] For the configuration of Set A, Set A can be fixed or non-fixed across F prediction instances. In the non-fixed case, Set A f,1<=f<=F can appear according to a pre-configured pattern. As shown in Figure 17, Set A has different Tx beam configurations for different prediction instances.

[0288] For example, Set A#1 includes Tx beams from index#0 to index#5, Set A#2 includes Tx beams from index#0 to index#3, ..., Set A#F includes Tx beams from index#0 to index#6. It should be noted that the above multiple Set A may or may not overlap.

[0289] The time intervals between different predictions in Set A can be evenly spaced, such as periodic predictions configured via RRC, or unevenly spaced, as shown in Figure 17. For unevenly spaced predictions in Set A, from a network worker (NW) configuration perspective, the NW can configure the same periodicity but different offsets for different prediction instances.

[0290] In addition to configuring Set B and Set A in multiple instances, the NW can configure multiple instances of Set B and Set A and then use MAC CE to activate one instance. Alternatively, MAC CE can be used to activate multiple instances of Set B and Set A, allowing them to be used in rotation. For example, use Set A's configuration X, followed by Set A's configuration Y, and then alternate between configurations X and Y, as shown in Figure 18.

[0291] UE measures and reports Set B

[0292] If the model is deployed on the NW side, the UE needs to measure M measurement instances of Set B and then report all measurement results to the NW-side model. Consider redesigning the beam reporting format. The specific reporting methods are described in Examples 3 and 4.

[0293] Example 3: Report in the time domain order in Set B m, 1 <= m <= M. In each measurement instance, report in the order of configured transmit beams (Tx beams), as shown in Table 7. Here, only the corresponding L1-RSRP or other performance indicators need to be reported.

[0294] Table 7 UE reports L1-RSRP values ​​according to the time domain order of Set B

[0295]

[0296] In Example 4, the transmit beams in Set B are sorted by their performance (e.g., L1-RSRP), generally from highest to lowest. As shown in Table 8, the Tx beam ID in Set B is listed first, followed by the absolute value of the highest Tx beam performance. For example, the absolute value of L1-RSRP is quantified. For beam pairs that do not perform the best, differential quantification can be used, reporting the performance difference compared to the best performance.

[0297] Table 8 UE reports Set B Tx beam ID and L1-RSRP value in time domain order

[0298]

[0299] It should be noted that in Table 8 above, in a prediction example, Differential L1-RSRP#2 is the difference relative to L1-RSRP#1, Differential L1-RSRP#3 is the difference relative to L1-RSRP#1, and Differential L1-RSRP#4 is the difference relative to L1-RSRP#1.

[0300] NW performs beam prediction

[0301] The NW uses the model to infer the optimal K Tx beams and their performance (e.g., L1-RSRP) from F prediction instances in Set Af,1<=f<=F. The K optimal Tx beams are sorted from highest to lowest L1-RSRP. The value of K is typically 1, 2, 4, or 8. Alternatively, the model can infer multiple Tx beams, as long as their performance (e.g., L1-RSRP) exceeds a predefined threshold.

[0302] For each prediction instance, the model can also infer the duration of the optimal Tx beam (beam dwelling time). For example, there is a period of time between Set A#1 and Set A#2, which we define as time gap#1. When the model infers the optimal K Tx beams for the prediction instance of Set A#1, it can also predict the time for the L1-RSRP of the K Tx beams to remain stable. The stability here can be defined based on relative quality, that is, the beam quality ranking, such as the time for the top-1 Tx beam to remain in first place. It can also be defined based on the absolute quality of L1-RSRP, such as the time for the L1-RSRP of the top-1 Tx beam to drop below a certain threshold (such as 3dB).

[0303] NW beam indication

[0304] Specifically, based on the prediction results of the F prediction instances predicted by the network device, there are at least two ways for the network device to indicate the beam. The first way is that the network device performs beam indication in batches according to the prediction results. For example, under the beam prediction of F prediction instances, F beam information indications are performed, that is, the beam used in each of the F prediction instances is indicated respectively; wherein, the beam information can be the index of the transmitted beam (in NR, the resource index of SSB or CSI-RS is generally used to refer to it). Another way is to perform a one-time beam indication, that is, the network device indicates the beam information of F prediction instances to the terminal device at one time, that is, indicates the beam used in each of the F prediction instances at one time. The advantage of a one-time beam indication is that the signaling overhead of the beam indication can be greatly reduced.

[0305] For example, if the prediction result of a prediction instance predicted by the network device does not include the beam duration, the network device will indicate the beam information used in each of the F prediction instances at one time. The MAC CE or DCI should include Tx beam index (CSI-RS / SSB) #1, Tx beam index (CSI-RS / SSB) #2, ..., Tx beam index (CSI-RS / SSB) #F. In the time domain, this means that whenever a prediction instance arrives, the network device will switch to the indicated transmit beam, and the UE should also switch to the corresponding receive beam.

[0306] For example, if the prediction result in the prediction instance predicted by the network device includes the beam duration, the network device can choose to indicate the beam information and beam duration used in each of the F prediction instances to the UE at one time. The MAC CE or DCI should include (Tx beam index (CSI-RS / SSB) #1, Application time #1), (Tx beam index (CSI-RS / SSB) #2, Application time #2), ..., (Tx beam index (CSI-RS / SSB) #F, Application time #F). In the time domain, it means that each indicated beam pair takes effect from the predicted instance, and its effective duration is the beam dwelling time indicated by the application time.

[0307] Therefore, in an embodiment of the present application, the first communication device can implement spatial filter prediction in the time domain based on the first network model, and the first communication device does not need to scan all deployed spatial filters, which is beneficial to reducing the overhead and delay caused by spatial filter scanning.

[0308] The above text, in combination with Figures 10 to 19, describes in detail the method embodiment of the present application. The following text, in combination with Figures 20 to 23, describes in detail the device embodiment of the present application. It should be understood that the device embodiment and the method embodiment correspond to each other, and similar descriptions can refer to the method embodiment.

[0309] FIG20 shows a schematic block diagram of a communication device 300 according to an embodiment of the present application. The communication device 300 is a first communication device. As shown in FIG20 , the communication device 300 includes:

[0310] The processing unit 310 is configured to obtain a first measurement data set; wherein the first measurement data set includes at least one of the following: identification information of the spatial filter in M ​​measurement instances, and link quality information corresponding to the spatial filter in the M measurement instances; wherein M is a positive integer;

[0311] The processing unit 310 is further configured to input the first measurement data set into a first network model and output a first prediction data set; wherein the first prediction data set includes at least one of the following: identification information of the K spatial filters predicted in each of the F prediction instances, link quality information corresponding to the K spatial filters predicted in each of the F prediction instances, and duration of the K spatial filters predicted in each of the F prediction instances; wherein F and K are both positive integers.

[0312] In some embodiments, the spatial filter in each of the M measurement instances is a partial spatial filter in all spatial filters.

[0313] In some embodiments, the configuration of the spatial filter in each of the M measurement instances is the same; or,

[0314] Configurations of spatial filters in at least some of the M measurement instances are different.

[0315] In some embodiments, some spatial filters in different measurement instances in the M measurement instances are the same.

[0316] In some embodiments, the M measurement instances are arranged at equal intervals in the time domain.

[0317] In some embodiments, the M measurement instances correspond to periodic spatial filter measurements within a first duration.

[0318] In some embodiments, the M measurement instances are arranged at unequal intervals in the time domain.

[0319] In some embodiments, the intervals between the M measurement instances in the time domain are determined based on the moving speed of the terminal device.

[0320] In some embodiments, different measurement instances in the M measurement instances correspond to the same period and different offsets.

[0321] In some embodiments, the M measurement instances are measurement instances activated by downlink control information DCI or a media access control layer control element MAC CE among multiple measurement instances of semi-persistent scheduling SPS; or,

[0322] The M measurement instances are measurement instances dynamically triggered by DCI among a plurality of pre-configured measurement instances.

[0323] In some embodiments, the M measurement instances are measurement instances corresponding to a set of measurement configurations activated by DCI or MAC CE in multiple measurement configurations, or the M measurement instances are measurement instances corresponding to a current measurement configuration in multiple measurement configurations activated by DCI or MAC CE and used in turn.

[0324] In some embodiments, the spatial filter in each measurement instance in the F prediction instances is a partial spatial filter in all spatial filters.

[0325] In some embodiments, the configuration of the spatial filter in each of the F prediction instances is the same; or,

[0326] Configurations of spatial filters in at least some of the F prediction instances are different.

[0327] In some embodiments, some or all of the spatial filters in different prediction instances among the F prediction instances are the same.

[0328] In some embodiments, the F prediction instances are arranged at equal intervals in the time domain.

[0329] In some embodiments, the F prediction instances correspond to periodic spatial filter predictions within a second duration.

[0330] In some embodiments, the F prediction instances are arranged at unequal intervals in the time domain.

[0331] In some embodiments, the intervals between the F prediction instances in the time domain are determined based on the moving speed of the terminal device.

[0332] In some embodiments, different prediction instances in the F prediction instances correspond to the same period and different offsets.

[0333] In some embodiments, the F prediction instances are prediction instances activated by DCI or MAC CE among multiple prediction instances of SPS; or, the F prediction instances are prediction instances dynamically triggered by DCI among multiple pre-configured prediction instances.

[0334] In some embodiments, the F prediction instances are prediction instances corresponding to a set of prediction configurations activated by DCI or MAC CE in multiple sets of prediction configurations, or the F prediction instances are prediction instances corresponding to the current prediction configuration in multiple sets of prediction configurations activated by DCI or MAC CE and used in turn.

[0335] In some embodiments, the value of K includes one of the following: 1, 2, 4, 8.

[0336] In some embodiments, the communication device 300 further includes: a communication unit 320;

[0337] The communication unit 320 is used to send the first information;

[0338] The first information includes at least one of the following: identification information of the K spatial filters predicted in each of the F prediction instances, link quality information corresponding to the K spatial filters predicted in each of the F prediction instances, and duration of the K spatial filters predicted in each of the F prediction instances.

[0339] In some embodiments, the link quality information corresponding to the K spatial filters is sent in a differential manner.

[0340] In some embodiments, the communication unit 320 is further configured to receive second information determined based on the first information;

[0341] The second information is used to indicate the identification information of the spatial filter used in each of the F prediction instances, or the second information is used to indicate the identification information of the spatial filter used in the i-th prediction instance; or the second information is used to indicate the identification information and duration of the spatial filter used in each of the F prediction instances, or the second information is used to indicate the identification information and duration of the spatial filter used in the i-th prediction instance;

[0342] Wherein, i is a positive integer, and 1≤i≤F.

[0343] In some embodiments, the communication unit 320 is further configured to send first capability information;

[0344] Among them, the first capability information includes at least one of the following: the maximum number of spatial filters supported by the first measurement data set, the maximum number of measurement instances supported by the first measurement data set, the maximum number of spatial filters supported by the first prediction data set, the maximum number of prediction instances supported by the first prediction data set, whether the prediction and reporting of the duration of the spatial filter are supported, and whether the identification information of the spatial filters in multiple prediction instances is supported at one time.

[0345] In some embodiments, the first communication device is a terminal device.

[0346] In some embodiments, the communication unit 320 is further configured to send third information;

[0347] The third information is used to indicate the identification information of the spatial filter used in each of the F prediction instances, or the third information is used to indicate the identification information of the spatial filter used in the i-th prediction instance; or the third information is used to indicate the identification information and duration of the spatial filter used in each of the F prediction instances, or the third information is used to indicate the identification information and duration of the spatial filter used in the i-th prediction instance;

[0348] Wherein, i is a positive integer, and 1≤i≤F.

[0349] In some embodiments, the communication unit 320 is further configured to receive first capability information;

[0350] Among them, the first capability information includes at least one of the following: the maximum number of spatial filters supported by the first measurement data set, the maximum number of measurement instances supported by the first measurement data set, the maximum number of spatial filters supported by the first prediction data set, the maximum number of prediction instances supported by the first prediction data set, whether the prediction and reporting of the duration of the spatial filter are supported, and whether the identification information of the spatial filters in multiple prediction instances is supported at one time.

[0351] In some embodiments, the first communication device is a network device.

[0352] In some embodiments, the spatial filter comprises a transmit spatial filter; or,

[0353] The spatial filter includes a transmitting spatial filter and a receiving spatial filter.

[0354] In some embodiments, the communication unit may be a communication interface or a transceiver, or an input / output interface of a communication chip or a system on chip. The processing unit may be one or more processors.

[0355] It should be understood that the communication device 300 according to the embodiment of the present application may correspond to the first communication device in the method embodiment of the present application, and the above-mentioned and other operations and / or functions of each unit in the communication device 300 are respectively for implementing the corresponding processes of the first communication device in the method 200 shown in Figure 10. For the sake of brevity, they will not be repeated here.

[0356] Figure 21 is a schematic structural diagram of a communication device 400 provided in an embodiment of the present application. The communication device 400 shown in Figure 21 includes a processor 410, which can call and run a computer program from a memory to implement the method in the embodiment of the present application.

[0357] In some embodiments, as shown in FIG21 , the communication device 400 may further include a memory 420. The processor 410 may call and execute a computer program from the memory 420 to implement the method in the embodiment of the present application.

[0358] The memory 420 may be a separate device independent of the processor 410 , or may be integrated into the processor 410 .

[0359] In some embodiments, as shown in FIG. 21 , the communication device 400 may further include a transceiver 430 , and the processor 410 may control the transceiver 430 to communicate with other devices. Specifically, the transceiver 430 may send information or data to other devices, or receive information or data sent by other devices.

[0360] The transceiver 430 may include a transmitter and a receiver. The transceiver 430 may further include an antenna, and the number of antennas may be one or more.

[0361] In some embodiments, the processor 410 may implement the functions of a processing unit in the communication device 300 , which will not be described in detail here for the sake of brevity.

[0362] In some embodiments, the transceiver 430 may implement the functionality of a communication unit in the communication device 300 , which will not be described in detail here for the sake of brevity.

[0363] In some embodiments, the communication device 400 may specifically be the communication device of the embodiment of the present application, and the communication device 400 may implement the corresponding processes implemented by the first communication device in each method of the embodiment of the present application. For the sake of brevity, they will not be repeated here.

[0364] Figure 22 is a schematic structural diagram of an apparatus according to an embodiment of the present application. The apparatus 500 shown in Figure 22 includes a processor 510, which can call and execute a computer program from a memory to implement the method according to the embodiment of the present application.

[0365] In some embodiments, as shown in FIG22 , the apparatus 500 may further include a memory 520. The processor 510 may call and execute a computer program from the memory 520 to implement the method in the embodiment of the present application.

[0366] The memory 520 may be a separate device independent of the processor 510 , or may be integrated into the processor 510 .

[0367] In some embodiments, the apparatus 500 may further include an input interface 530. The processor 510 may control the input interface 530 to communicate with other devices or chips, and specifically, may obtain information or data sent by other devices or chips. Optionally, the processor 510 may be located inside or outside the chip.

[0368] In some embodiments, the processor 510 may implement the functions of a processing unit in the communication device 300 , which will not be described in detail here for the sake of brevity.

[0369] In some embodiments, the input interface 530 may implement the functionality of a communication unit in the communication device 300 .

[0370] In some embodiments, the apparatus 500 may further include an output interface 540. The processor 510 may control the output interface 540 to communicate with other devices or chips, specifically, to output information or data to other devices or chips. Optionally, the processor 510 may be located inside or outside the chip.

[0371] In some embodiments, the output interface 540 may implement the functionality of a communication unit in the communication device 300 .

[0372] In some embodiments, the apparatus can be applied to the communication device in the embodiments of the present application, and the apparatus can implement the corresponding processes implemented by the first communication device in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0373] In some embodiments, the device mentioned in the embodiments of the present application may also be a chip, such as a system-on-chip, a system-on-chip, a chip system, or a system-on-chip chip.

[0374] FIG23 is a schematic block diagram of a communication system 600 provided in an embodiment of the present application. As shown in FIG23 , the communication system 600 includes a first communication device 610 and a second communication device 620 .

[0375] Among them, the first communication device 610 can be used to implement the corresponding functions implemented by the first communication device in the above method, and the second communication device 620 can be used to implement the corresponding functions implemented by the second communication device in the above method. For the sake of brevity, they are not repeated here.

[0376] It should be understood that the processor of the embodiments of the present application may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiment can be completed by hardware integrated logic circuits in the processor or software instructions. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The various methods, steps, and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0377] It is understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0378] It should be understood that the above-mentioned memories are exemplary but not restrictive. For example, the memories in the embodiments of the present application may also be static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM RAM (DR RAM), etc. In other words, the memories in the embodiments of the present application are intended to include, but are not limited to, these and any other suitable types of memories.

[0379] An embodiment of the present application also provides a computer-readable storage medium for storing a computer program.

[0380] In some embodiments, the computer-readable storage medium can be applied to the communication device in the embodiments of the present application, and the computer program enables the computer to execute the corresponding processes implemented by the first communication device in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0381] An embodiment of the present application also provides a computer program product, including computer program instructions.

[0382] In some embodiments, the computer program product can be applied to the communication device in the embodiments of the present application, and the computer program instructions enable the computer to execute the corresponding processes implemented by the first communication device in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0383] The embodiment of the present application also provides a computer program.

[0384] In some embodiments, the computer program can be applied to the communication device in the embodiments of the present application. When the computer program runs on a computer, the computer executes the corresponding processes implemented by the first communication device in the various methods of the embodiments of the present application. For the sake of brevity, they will not be repeated here.

[0385] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0386] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0387] In the several embodiments provided in this 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 merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, 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.

[0388] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0389] 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.

[0390] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. In view of this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0391] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A wireless communication method, characterized in that: include: The first communication device obtains a first measurement data set; wherein the first measurement data set includes at least one of the following: identification information of the spatial filter in M ​​measurement instances, and link quality information corresponding to the spatial filter in the M measurement instances; wherein M is a positive integer; The first communication device inputs the first measurement data set into a first network model and outputs a first prediction data set; wherein, the first prediction data set includes at least one of the following: identification information of the K spatial filters predicted in each of the F prediction instances, link quality information corresponding to the K spatial filters predicted in each of the F prediction instances, and duration of the K spatial filters predicted in each of the F prediction instances; wherein F and K are both positive integers.

2. The method according to claim 1, wherein The spatial filter in each measurement instance in the M measurement instances is a partial spatial filter in all spatial filters.

3. The method according to claim 2, wherein The configuration of the spatial filter in each of the M measurement instances is the same; or, Configurations of spatial filters in at least some of the M measurement instances are different.

4. The method according to claim 2 or 3, wherein: Some spatial filters in different measurement instances in the M measurement instances are the same.

5. The method according to any one of claims 1 to 4, characterized in that The M measurement instances are arranged at equal intervals in the time domain.

6. The method according to claim 5, wherein The M measurement instances correspond to periodic spatial filter measurements within a first duration.

7. The method according to any one of claims 1 to 4, characterized in that The M measurement instances are arranged at unequal intervals in the time domain.

8. The method according to claim 7, wherein The intervals between the M measurement instances in the time domain are determined based on the moving speed of the terminal device.

9. The method according to claim 7 or 8, wherein Different measurement instances in the M measurement instances correspond to the same period and different offsets.

10. The method according to any one of claims 7 to 9, characterized in that The M measurement instances are measurement instances activated by downlink control information DCI or media access control layer control element MAC CE among multiple measurement instances of semi-persistent scheduling SPS; or, The M measurement instances are measurement instances dynamically triggered by DCI among a plurality of pre-configured measurement instances.

11. The method according to any one of claims 1 to 9, characterized in that The M measurement instances are measurement instances corresponding to one set of measurement configurations activated by DCI or MAC CE in multiple measurement configurations, or the M measurement instances are measurement instances corresponding to a current measurement configuration in multiple sets of measurement configurations activated by DCI or MAC CE and used in turn.

12. The method according to any one of claims 1 to 11, characterized in that The spatial filter in each measurement instance in the F prediction instances is a partial spatial filter in all spatial filters.

13. The method according to claim 12, wherein: The configuration of the spatial filter in each of the F prediction instances is the same; or, Configurations of spatial filters in at least some of the F prediction instances are different.

14. The method according to claim 12 or 13, wherein: Part or all of the spatial filters in different prediction instances among the F prediction instances are the same.

15. The method according to any one of claims 1 to 14, characterized in that The F prediction instances are arranged at equal intervals in the time domain.

16. The method according to claim 15, wherein The F prediction instances correspond to periodic spatial filter predictions within a second duration.

17. The method according to any one of claims 1 to 14, characterized in that The F prediction instances are arranged at unequal intervals in the time domain.

18. The method according to claim 17, wherein The intervals between the F prediction instances in the time domain are determined based on the moving speed of the terminal device.

19. The method according to claim 17 or 18, wherein: Different prediction instances in the F prediction instances correspond to the same period and different offsets.

20. The method according to any one of claims 17 to 19, characterized in that The F prediction instances are prediction instances activated by DCI or MAC CE among multiple prediction instances of SPS; or, The F prediction instances are prediction instances dynamically triggered by DCI among a plurality of pre-configured prediction instances.

21. The method according to any one of claims 1 to 19, characterized in that The F prediction instances are prediction instances corresponding to a set of prediction configurations activated by DCI or MAC CE in multiple sets of prediction configurations, or the F prediction instances are prediction instances corresponding to the current prediction configuration in multiple sets of prediction configurations activated by DCI or MAC CE and used in turn.

22. The method according to any one of claims 1 to 21, characterized in that The value of K includes one of the following: 1, 2, 4, 8.

23. The method according to any one of claims 1 to 22, characterized in that The method further comprises: The first communication device sends first information; The first information includes at least one of the following: identification information of the K spatial filters predicted in each of the F prediction instances, link quality information corresponding to the K spatial filters predicted in each of the F prediction instances, and duration of the K spatial filters predicted in each of the F prediction instances.

24. The method according to claim 23, wherein The link quality information corresponding to the K spatial filters is sent in a differential manner.

25. The method according to claim 23 or 24, wherein: The method further comprises: The first communication device receives second information determined based on the first information; The second information is used to indicate identification information of the spatial filter used in each of the F prediction instances, or the second information is used to indicate identification information of the spatial filter used in the i-th prediction instance; or the second information is used to indicate identification information and duration of the spatial filter used in each of the F prediction instances, or the second information is used to indicate identification information and duration of the spatial filter used in the i-th prediction instance; Wherein, i is a positive integer, and 1≤i≤F.

26. The method according to any one of claims 1 to 25, characterized in that The method further comprises: The first communication device sends first capability information; The first capability information includes at least one of the following: the maximum number of spatial filters supported by the first measurement data set, the maximum number of measurement instances supported by the first measurement data set, the maximum number of spatial filters supported by the first prediction data set, the maximum number of prediction instances supported by the first prediction data set, whether prediction and reporting of the duration of the spatial filter are supported, and whether identification information indicating spatial filters in multiple prediction instances at one time is supported.

27. The method according to any one of claims 1 to 26, characterized in that The first communication device is a terminal device.

28. The method according to any one of claims 1 to 22, characterized in that The method further comprises: The first communication device sends third information; The third information is used to indicate the identification information of the spatial filter used in each of the F prediction instances, or the third information is used to indicate the identification information of the spatial filter used in the i-th prediction instance; or the third information is used to indicate the identification information and duration of the spatial filter used in each of the F prediction instances, or the third information is used to indicate the identification information and duration of the spatial filter used in the i-th prediction instance; Wherein, i is a positive integer, and 1≤i≤F.

29. The method according to any one of claims 1 to 22 and 28, characterized in that The method further comprises: The first communication device receives first capability information; The first capability information includes at least one of the following: the maximum number of spatial filters supported by the first measurement data set, the maximum number of measurement instances supported by the first measurement data set, the maximum number of spatial filters supported by the first prediction data set, the maximum number of prediction instances supported by the first prediction data set, whether prediction and reporting of the duration of the spatial filter are supported, and whether identification information indicating spatial filters in multiple prediction instances at one time is supported.

30. The method according to any one of claims 1 to 22, 28 and 29, wherein: The first communication device is a network device.

31. The method according to any one of claims 1 to 30, wherein The spatial filter comprises a transmit spatial filter; or, The spatial filter includes a transmitting spatial filter and a receiving spatial filter.

32. A communication device, characterized in that: The communication device is a first communication device, and the communication device includes: A processing unit, configured to obtain a first measurement data set; wherein the first measurement data set includes at least one of the following: identification information of the spatial filter in M ​​measurement instances, and link quality information corresponding to the spatial filter in the M measurement instances; wherein M is a positive integer; The processing unit is further used to input the first measurement data set into a first network model and output a first prediction data set; wherein the first prediction data set includes at least one of the following: identification information of the K spatial filters predicted in each of the F prediction instances, link quality information corresponding to the K spatial filters predicted in each of the F prediction instances, and duration of the K spatial filters predicted in each of the F prediction instances; wherein F and K are both positive integers.

33. A communication device, characterized in that: include: A processor and a memory, the memory being used to store a computer program, the processor being used to call and run the computer program stored in the memory, so that the communication device executes the method according to any one of claims 1 to 31.

34. A chip, characterized in that: include: A processor, configured to call and run a computer program from a memory, so that a device equipped with the chip executes the method according to any one of claims 1 to 31.

35. A computer-readable storage medium, characterized in that For storing a computer program, when the computer program is executed, the method according to any one of claims 1 to 31 is implemented.

36. A computer program product, characterized in that The method comprises computer program instructions, which, when executed, implement the method according to any one of claims 1 to 31.

37. A computer program, characterized in that When the computer program is executed, the method according to any one of claims 1 to 31 is implemented.

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