Method and apparatus in a node for wireless communication

Abstract

The invention provides a method and an apparatus in a node for wireless communication. The method comprises: receiving first information, the first information being used for indicating a plurality of adaptive thresholds, the plurality of adaptive thresholds being used for selecting beam information, and the beam information being used for beam prediction based on a first model. Compared with a traditional beam reporting mechanism based on a first model, the first node can autonomously select the threshold value suitable for different channel conditions and user requirements based on the first information, and reports the beam based on the selected threshold value, thereby being beneficial to improving the flexibility and adaptive capability of the beam reporting mechanism.

Description

Technical Field

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

[0002] In a communication system, a first model (e.g., an artificial intelligence / machine learning (AI / ML) model) can be used for beam management. However, in the traditional beam management process based on the first model, the reporting mechanism of the terminal device lacks flexibility and may not be able to adapt to different channel conditions and user needs. Summary of the invention

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

[0004] In a first aspect, a method in a first node for wireless communication is provided, comprising: receiving first information, wherein the first information is used to indicate multiple adaptive thresholds, the multiple adaptive thresholds are used to select beam information, and the beam information is used for beam prediction based on a first model.

[0005] In a second aspect, a method in a second node for wireless communication is provided, comprising: sending first information, wherein the first information is used to indicate multiple adaptive thresholds, the multiple adaptive thresholds are used to select beam information, and the beam information is used for beam prediction based on a first model.

[0006] According to a third aspect, a first node for wireless communication is provided, comprising: a first transceiver, configured to receive first information, wherein the first information is used to indicate a plurality of adaptive thresholds, wherein the plurality of adaptive thresholds are used to select beam information, and the beam information is used for beam prediction based on a first model.

[0007] In a fourth aspect, a second node for wireless communication is provided, comprising: a second transceiver, used to send first information, wherein the first information is used to indicate multiple adaptive thresholds, the multiple adaptive thresholds are used to select beam information, and the beam information is used for beam prediction based on a first model.

[0008] In a fifth aspect, a first node used for wireless communication is provided, comprising a transceiver, a memory and a processor, wherein the memory is used to store programs, the processor is used to call the programs in the memory and control the transceiver to receive or send signals so that the first node executes the method described in the first aspect.

[0009] In a sixth aspect, a second node used for wireless communication is provided, comprising a transceiver, a memory and a processor, wherein the memory is used to store programs, the processor is used to call the programs in the memory and control the transceiver to receive or send signals so that the second node executes the method described in the second aspect.

[0010] In a seventh aspect, an embodiment of the present application provides a communication system, which includes the first node and / or the second node described above. In another possible design, the system may also include other devices that interact with the first node or the second node in the solution provided in the embodiment of the present application.

[0011] In an eighth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program enables a computer to execute part or all of the steps in the methods of the above aspects.

[0012] In a ninth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps in the above-mentioned various aspects of the method. In some implementations, the computer program product can be a software installation package.

[0013] In the tenth aspect, an embodiment of the present application provides a chip, which includes a memory and a processor. The processor can call and run a computer program from the memory to implement some or all of the steps described in the methods of the above aspects.

[0014] In an embodiment of the present application, the first node may receive first information, and the first information may be used to indicate multiple adaptive thresholds for the first node to select beam information, wherein the beam information is used for beam prediction based on the first model. Compared with the traditional beam reporting mechanism based on the first model, the first node may autonomously select a threshold suitable for different channel conditions and user needs based on the first information, and report the beam information based on the selected threshold, which helps to improve the flexibility and adaptability of the beam reporting mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a wireless communication system 100 applied in an embodiment of the present application.

[0016] Figure 2 It is a schematic flowchart of a network (NW) side model scenario applicable to an embodiment of the present application.

[0017] Figure 3 This is an example diagram of the specific workflow of the NW side model scenario.

[0018] Figure 4 It is a schematic flowchart of the terminal side model scenario applicable to the embodiments of the present application.

[0019] Figure 5 It is a schematic flowchart of a method in a first node for wireless communication provided in an embodiment of the present application.

[0020] Figure 6 It is an example diagram of a solution combining Example 1 and Example 2 of the present application.

[0021] Figure 7 It is a schematic diagram of the adaptive threshold generation model.

[0022] Figure 8 It is an example diagram of a solution that combines the solutions of Example 1, Example 2 and Example 3 of the present application.

[0023] Fig. 9 It is an example diagram of a solution that combines the solutions of another embodiment 1, embodiment 2 and embodiment 3 of the present application.

[0024] Fig.10 It is a structural diagram of a first node for wireless communication provided in an embodiment of the present application.

[0025] Fig.11 It is a structural diagram of a second node for wireless communication provided in an embodiment of the present application.

[0026] Fig.12 It is a schematic structural diagram of the device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0027] The technical solution in this application will be described below in conjunction with the accompanying drawings.

[0028] Figure 1 The wireless communication system 100 used in the embodiment of the present application may include a network device 110 and a terminal device 120. The network device 110 may be a device that communicates with the terminal device 120. The network device 110 may provide communication coverage for a specific geographical area and may communicate with the terminal device 120 located in the coverage area.

[0029] Figure 1 One network device and two terminals are shown exemplarily. Optionally, the wireless 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.

[0030] Optionally, the wireless communication system 100 may also 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.

[0031] It should be understood that the technical solutions of the embodiments of the present application can be applied to various communication systems, such as: the fifth generation (5th generation, 5G) system or new radio (new radio, NR), long term evolution (long term evolution, LTE) system, LTE frequency division duplex (frequency division duplex, FDD) system, LTE time division duplex (time division duplex, TDD), etc. The technical solutions provided by the present application can also be applied to future communication systems, such as the sixth generation mobile communication system, satellite communication system, and so on.

[0032] The terminal device in the embodiment of the present application may also be referred to as user equipment (UE), access terminal, user unit, user station, mobile station, mobile station (MS), mobile terminal (MT), remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent or user device. The terminal device in the embodiment of the present application may be a device that provides voice and / or data connectivity to a user, and may be used to connect people, objects and machines, such as a handheld device with wireless connection function, a vehicle-mounted device, etc. The terminal device in the embodiment of the present application can be a mobile phone, a tablet computer, a laptop computer, a PDA, a mobile internet device (MID), a wearable device, a virtual reality (VR) device, an augmented reality (AR) device, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical surgery, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, etc. Optionally, the UE can be used to act as a base station. For example, the UE can act as a scheduling entity that provides sidelink signals between UEs in V2X or D2D, etc. For example, a cellular phone and a car communicate with each other using a sidelink signal. The cellular phone and the smart home device communicate with each other without relaying the communication signal through the base station.

[0033] The network device in the embodiment of the present application may be a device for communicating with a terminal device, and the network device may also be referred to as an access network device or a wireless access network device, such as a base station. The network device in the embodiment of the present application may refer to a wireless access network (RAN) node (or device) that connects a terminal device to a wireless network. Base station can broadly cover various names as follows, or replace with the following names, such as: NodeB, evolved NodeB (eNB), next generation NodeB (gNB), relay station, access point, transmission point (TRP), transmission point (TP), master station MeNB, auxiliary station SeNB, multi-standard radio (MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, base band unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), positioning node, etc. The base station can be a macro base station, a micro base station, a relay node, a donor node or the like, or a combination thereof. The base station can also refer to a communication module, a modem or a chip used to be arranged in the aforementioned device or apparatus. The base station can also be a mobile switching center and a device that performs base station functions in device-to-device D2D, vehicle-to-everything (V2X), machine-to-machine (M2M) communications, a network-side device in a 6G network, and a device that performs base station functions in future communication systems. The base station can support networks with the same or different access technologies. The embodiments of the present application do not limit the specific technology and specific device form used by the network equipment.

[0034] Base stations can be fixed or mobile. For example, a helicopter or drone can be configured to act as a mobile base station, and one or more cells can move based on the location of the mobile base station. In other examples, a helicopter or drone can be configured to act as a device that communicates with another base station.

[0035] In some deployments, the network device in the embodiments of the present application may refer to a CU or a DU, or the network device includes a CU and a DU. The gNB may also include an AAU.

[0036] The network equipment and terminal equipment can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on the water surface; they can also be deployed on aircraft, balloons and satellites in the air. The embodiments of the present application do not limit the scenarios in which the network equipment and terminal equipment are located.

[0037] It should be understood that all or part of the functions of the communication device in the present application may also be implemented by software functions running on hardware, or by virtualization functions instantiated on a platform (eg, a cloud platform).

[0038] Beam management

[0039] In modern wireless communication systems, beam management is one of the key technologies to ensure signal quality and user experience. For example, in 5G NR networks, beam management improves signal coverage and communication performance by optimizing the direction of radio beams. With the advancement of technology, related mobile communication technologies (e.g., the evolution and enhancement of 5G (5G-Advanced, 5G-A)) have introduced beam prediction technology based on AI / ML models in relevant protocols (e.g., TR 38.843 of Release 18) to further improve network performance.

[0040] In mobile communication technology, beam management based on AI / ML models, channel state information (CSI) feedback enhancement, and positioning accuracy enhancement have gradually entered the standards and become mainstream. Specifically, the general framework based on AI / ML models controls data collection, model training, function / model specification, model delivery / transfer, model reasoning operations, function / model selection / activation / deactivation / switching and fallback operations, function / model monitoring, model updates, and terminal device capabilities of AI / ML models through life cycle management (LCM). For beam management, compared with traditional solutions, through AI / ML models, 5G network equipment (e.g., gNB) can more accurately predict and adjust beam directions, optimize resource utilization and user experience. The 3rd Generation Partnership Project (3GPP) proposed two AI / ML-based beam management (BM) use cases in relevant protocols (e.g., TR 38.843), BM Use Case 1 (BM-Case1) and BM Use Case 2 (BM-Case2).

[0041] BM-Case1 is the spatial domain prediction of the downlink set A (Set A) beam based on the beam measurement results in set B (Set B). According to the current measurement results, the prediction result of the optimal beam is inferred, and beam management is performed in real time based on the prediction result; BM-Case2 is the time domain prediction of the downlink set A beam based on the historical measurement results of the beam in set B. According to the historical measurement results, the optimal beam prediction results of the subsequent multiple time slots are inferred, and beam management is performed according to the prediction result corresponding to the current time slot.

[0042] Among them, set B corresponds to the input of the AI / ML model, and set A corresponds to the output of the AI / ML model. At the same time, depending on the deployment location of the AI / ML model, each sub-use case is divided into network (NW) side model and terminal side model scenarios.

[0043] As an example, for BM-Case1, set B can be a subset of set A, or it can be different from set A. If there is an intersection between set B and set A, then set A can be the full set or subset of CSI reference signal (CSI-reference signal, CSI-RS) resources, where if it is the full set, it corresponds to the initial access stage of beam management, and if it is a subset, it corresponds to the beam optimization stage. The physical meaning of the beam prediction based on the AI / ML model is to predict the overall space through feature extraction of partial spatial information. If there is no intersection between set A and set B, set B is usually a wide beam corresponding to the synchronization signal broadcast channel block (synchronization signal block / physical broadcast channel block, SS / PBCH block, SSB), and set A is a narrow beam under the wide beam angular coverage range. The physical meaning of the beam prediction based on the AI / ML model is to perform high-precision prediction through feature extraction of a small number of low-precision spatial information.

[0044] As an example, for BM-Case2, its model input is the measurement results of the latest K (K≥1) measurement instances, and the model output is F prediction results of F (F≥1) future time slots. Except for the possible additional timestamp information, each measurement instance is consistent with the reporting rules under the BM-Case1 sub-use case. Therefore, in the embodiment of the present application, the two sub-use cases BM-Case1 and BM-Case2 are not distinguished, and both are applicable to the embodiment of the present application.

[0045] NW side model scene

[0046] The embodiment of the present application is applicable to the LCM stages of data collection, model reasoning and performance monitoring of the NW side model scenario, wherein the schematic flow chart of data collection is shown in Figure 2 As shown, Figure 2 Includes steps S210 to S240.

[0047] In step S210, the terminal device sends a capability report to the network device.

[0048] The terminal device reports its capabilities under LCM scheduling and requests the data collection phase to begin. The network device side will perform LCM operations such as model or function selection based on the capability report.

[0049] In step S220, the network device sends a measurement resource set (RS) and a report configuration to the terminal device.

[0050] The network equipment side collects data for model training under LCM scheduling. The network equipment side configures CSI-ResourceConfig and CSI-ReportConfig to configure the measurement resource set and terminal device report. This configuration includes the configuration of the CSI-RS / SSB measurement beam resource set of the current set A, which is used for beam scanning. At the same time, it also includes the configuration information of the report reported by the terminal device, such as report trigger type, report settings, measurement object, report content, etc.

[0051] In step S230, the network device transmits set A and / or set B to the terminal device.

[0052] During the data collection phase of training, set A is used as the measurement set to provide ground-truth for AI / ML model training. When there is no intersection between set A and set B, set B needs to be included in the measurement set for measurement to provide input for the model.

[0053] In step S240, the terminal device reports data to the network device.

[0054] The network device side performs beam scanning of the current resource set according to the configuration, and the terminal device measures the CSI-RS / SSB signal. The terminal device will report beam-related information according to the report configuration, which includes the reference signal received power (layer 1 reference signal received power, L1-RSRP) measured at the physical layer and / or the corresponding resource index. As an example, the specific workflow of the NW side model scenario can be found in Figure 3As shown in the figure, the network device side collects enough data for model training. When the model converges and the predicted performance indicators meet the requirements, the model inference stage is entered. At this time, the network device side can use set B as the measurement set to configure CSI resources and reports. The network device scans the beams in set B according to the resource configuration. The terminal device measures the current resource set and reports the measurement results to the network device. The NW side model uses the measurement results fed back by the terminal device as the AI / ML model input, and the trained model performs inference based on the input. The model outputs the predicted value of the beam in set A, such as the L1-RSRP predicted value of each beam or the predicted probability of each beam as a Top-K beam. The network device side performs beam management based on the prediction results. At the same time, in the inference stage, LCM will periodically start model performance monitoring, configure the predicted Top-K beams as the measurement set, and perform beam scanning. The terminal device measures and reports, and the network device side receives the measurement results, calculates the performance indicators, and adjusts the LCM operation.

[0055] Terminal-side model scenario

[0056] The present application embodiment is applicable to the LCM stages of data collection, model reasoning and performance monitoring of the terminal side model scenario. The data collection process is shown in Figure 4 As shown, Figure 4 Includes steps S410 to S440.

[0057] In step S410, the terminal device sends a capability report to the network device.

[0058] In step S420, the data collection process is triggered or initialized due to the capability report sent by the terminal device.

[0059] In step S430, the network device side performs corresponding resource set and report configuration (eg, data collection identity (ID)), and sends it to the terminal device.

[0060] In step S440, the network device performs beam scanning according to the configuration of the resource set, the terminal device side performs beam measurement, and uses the measurement results as training data for training the AI / ML model on the terminal device side.

[0061] As an example, when set B is a subset of set A, the network device needs to scan all beams in set A. The terminal device inputs the L1-RSRP measurement value corresponding to set B in the measurement result into the model, and uses all measurement results as true values ​​to train the model parameters. When set B is not a subset of set A, the network device needs to scan the beams of set B in addition to set A to provide true values ​​for the model.

[0062] After the model training is completed, it enters the model inference stage. The network device scans the set B beam, the terminal device performs measurement, and the measurement results are input into the AI / ML model. After model inference, the L1-RSRP prediction value / Top-K beam prediction probability and / or confidence corresponding to the set A beam can be obtained. The terminal device will feedback the Top-K beam related information according to the report configuration, which may include the beam index corresponding to the Top-K beam, L1-RSRP prediction value / measurement value, beam prediction probability, and beam prediction result confidence.

[0063] During the model inference process, performance monitoring will be inserted to perform real-time monitoring of the model prediction accuracy. There are two types of performance monitoring in the relevant protocols (for example, TR 38.843): Type 1 and Type 2. In Type 1, the network device directly configures the resources and reports for performance monitoring, and the terminal device can report beam-related information used for performance indicator calculation on the network device side; the terminal device can also independently calculate indicators and report indicators or events to assist in performance monitoring. In Type 2, performance monitoring is actively triggered by the terminal device. Based on the performance monitoring results, LCM will perform corresponding operations and perform model maintenance.

[0064] The use of AI / ML models for beam management first requires a large amount of high-quality data as training samples, which has a potential impact on the current CSI framework. In relevant meetings of 3GPP (for example, the #116, #116 second (bis) and #117 meetings of the radio access network (RAN) 1 working group), there have been many discussions on the embedding of AI / ML beam prediction models based on the traditional CSI framework, including the maintenance and modification of CSI resources and report configuration, report format and other contents. The current large-scale array of network equipment can provide a large number of CSI resources, that is, the number of beams available is very large, which will directly lead to the data collection and even model training stage. The AI / ML model requires a huge amount of data samples, requiring a large amount of signaling interaction between the network equipment side and the terminal equipment side. In the current CSI framework, the upper limit of the number of beam-related information for each report is 4, and the corresponding beam report format is shown in Table 1. Table 1 is a table in the relevant protocol (for example, Table 6.3.1.1.2-8 in protocol TR 38.212), and its content is the mapping order of the CSI field reported by CSI-RS resource index (CSI-RS resource indicator, CRI / ) reference signal received power (reference signal received power, RSRP) or SSB resource index (SSB resource indicator, SSBRI) / RSPR or CRI / capability index (capabilityindex) or SSBRI / RSRP / capability index, or the mapping order of the CSI field reported by SSBRI / RSRP between cells (inter-cell). It should be noted that the content of the relevant table in Table 1 (for example, Table 6.3.1.1.2-8 in protocol TR 38.212) is the CRI / SSBRI / RSRP code stream length. The large amount of signaling interaction between network equipment and terminal equipment will undoubtedly challenge the load of the uplink control link. Taking the configuration of 256 beams in set A as an example, during the training phase of the NW side model, if feedback of all measurement results is required, at least 64 reports need to be submitted based on the upper limit of the number of reported beams in the current CSI framework. At the same time, during the performance monitoring phase of the terminal side model, the terminal device side also needs to report a large amount of beam-related information. Therefore, subsequent versions of the communication protocol will undoubtedly need to adjust the content, quantity, signaling, and reporting mechanism of the reported beam information.

[0065] Table 1

[0066]

[0067]

[0068] In addition, not all data samples have a positive effect on model training. When the terminal device is static and the channel coherence time is large, there will inevitably be a large amount of duplicate data during multiple beam scans. The reporting of these data not only occupies a large amount of uplink load, but also has no significant effect on model performance. On the contrary, when the link quality undergoes drastic changes, the measurement results are of poor quality, which has a negative impact on the model. The relevant meetings are also discussing the screening and deletion of measurement results on the terminal side to ensure the efficiency of beam management and the robustness of AI / ML prediction models. At present, there is no consensus on the reporting mechanism for the data collection phase of the BM-Case1 NW side model, that is, the agreement in the meeting minutes (Chairnotes). However, in the proposal in the FL summary of the subsequent meeting, all companies agree that "the terminal device reports the relevant information of the Top-M beams of the signal strength in the current test set", and how to specify M remains to be studied in the future; all companies also agree that "the terminal device reports the relevant information of the beam with a difference of X dB between the current L1-RSRP and the best beam", but further research is needed. At the same time, the CSI reporting framework needs to be improved not only for the BM-Case1 NW-side model, but also for the BM-Case2 and terminal-side model scenarios to meet the large amount of data reporting requirements at different stages of LCM.

[0069] As described above, in a communication system, a first model (e.g., an AI / ML model) can be used for beam management. However, in the traditional beam management process based on the first model, the reporting mechanism of the terminal device lacks flexibility and may not be able to adapt to different channel conditions and user needs.

[0070] Taking BM-Case1 and BM-Case2 in the above text as examples, the first model is the AI / ML model, and relevant meetings (for example, Rel-19 RAN1#117 meeting) discussed the report content reported by the terminal device in the reasoning phase. For a single time slot of BM-Case1 and BM-Case2, the beam report content of a single layer 1 (layer 1, L1) signaling should support the L1-RSRP and corresponding beam information of the Top-M beam in a single resource set, and support two options:

[0071] Option 1: Top-M beams are the M beams with the largest L1-RSRP measurement values, where M is specified by the access network device (e.g., gNB).

[0072] Option 2: Up to M beams within X dB of the strongest L1-RSRP measurement, where X and M are specified by the access network equipment.

[0073] Option 1 can be understood as fixed feedback of the best beams in M ​​(M≤4) measurement sets, while Option 2 is a measurement beam reporting mechanism based on the L1-RSRP differential threshold (X dB in the above text).

[0074] There are many problems with the above-mentioned reporting mechanism based on a single number of beams or a single L1-RSRP differential threshold. Regardless of the channel conditions, these two schemes report the same number of beams or beams that meet the same differential threshold conditions. The method is simple but lacks flexibility and cannot adapt to different channel conditions and user needs. For example, the current channel conditions are poor, the L1-RSRP of different beams are low and the difference is small. The report of a single number of beams will lose a lot of channel space information, resulting in a decrease in prediction performance; and a single differential threshold may cause a surge in the number of reported beams, resulting in a large amount of uplink control information (IoT) resources or uplink signaling overhead. In addition to the waste of resources, due to the high latency requirements of the beam prediction of the AI / ML model in 5G-A, it is necessary to ensure the timeliness of training, reasoning and monitoring. The use of a single differential threshold may cause more backhaul CSI signaling, slow model response, and affect the overall performance of the system. In many Internet of Things (IoT) scenarios, it is necessary to consider the energy consumption control of terminal devices. The low flexibility of the single number of beams and the single differential threshold scheme may lead to low energy efficiency of terminal devices.

[0075] In response to the above problems, an embodiment of the present application provides a method in a node for wireless communication. In the method, a first node can receive first information, and the first information can be used to indicate multiple adaptive thresholds for the first node to select beam information. Among them, the beam information is used for beam prediction based on the first model. Compared with the traditional beam reporting mechanism based on the first model, the first node can autonomously select a threshold suitable for different channel conditions and user needs based on the first information, and report the beam information based on the selected threshold, which helps to improve the flexibility and adaptability of the beam reporting mechanism.

[0076] On the other hand, the adaptive threshold used for reporting beam information can be autonomously selected by the first node (for example, the first node actively selects the adaptive threshold based on a statistical set of historical beam measurement results), thereby ensuring that the adaptive threshold currently used for reporting meets the scenario and channel environment requirements, helping to reduce unnecessary reporting of beam information, thereby reducing system overhead and improving system performance.

[0077] Combined with the following Figure 5 A method in a first node for wireless communication according to an embodiment of the present application is introduced. Figure 5 is a schematic flow chart of a method in a first node for wireless communication according to an embodiment of the present application. Figure 5 As shown, the method is executed by the first node.

[0078] As an embodiment, the first node may be a network-controlled repeater (NCR).

[0079] As an embodiment, the first node may be a terminal device. Figure 1 The terminal device 120 is shown.

[0080] As an embodiment, the first node may be a relay, such as a relay terminal.

[0081] Figure 5 The method shown includes step S510, which is described below.

[0082] In step S510, the first node receives first information. Exemplarily, the first node may receive the first information sent by the second node.

[0083] As an embodiment, the second node may be a network device. For example, the second node may be an access network device. For another example, the second node may be a gNB.

[0084] In some embodiments, the first information is used to indicate multiple adaptive thresholds, the multiple adaptive thresholds are used to select beam information, and the beam information is used for beam prediction based on the first model. Accordingly, the first node receives the first information and can select beam information based on multiple adaptive thresholds. For example, the first node can actively select an adaptive threshold from multiple adaptive thresholds based on the current environment and scenario, its capabilities and beam measurement results, and report beam information based on the threshold.

[0085] As an embodiment, the first information is used to indicate multiple adaptive thresholds, which can be understood as the first information is used to indicate at least two adaptive thresholds. For example, the first node is a terminal device, the second node is an access network device, and the access network device generates at least two adaptive thresholds [Th1, Th2, ...] according to the access network device manufacturer or network operator policy, and sends them to the terminal device through the first information.

[0086] In the embodiment of the present application, the adaptive threshold is not limited. For example, the adaptive threshold can be used to indicate a threshold corresponding to an L1-RSRP measurement value or prediction value, a measurement or prediction result confidence, a measurement or prediction probability, etc.

[0087] As an embodiment, the adaptive threshold is an absolute threshold. For example, the adaptive threshold is an absolute threshold of the L1-RSRP measurement value, the measurement result confidence, or the measurement probability. For another example, the adaptive threshold is an absolute threshold of the L1-RSRP measurement value. If the adaptive threshold is used as a threshold for beam reporting, the beam information corresponding to the L1-RSRP measurement value that indicates that is higher than the threshold will be reported.

[0088] As an embodiment, the adaptive threshold is a differential threshold. For example, the adaptive threshold is a differential threshold of the predicted value of L1-RSRP, the confidence of the prediction result, or the predicted probability. For another example, the adaptive threshold is a differential threshold of the predicted value of L1-RSRP. If the adaptive threshold is used as the threshold for beam reporting, the predicted value of the strongest L1-RSRP in the current measurement set is subtracted from the adaptive threshold to obtain an absolute threshold, then the beam information corresponding to the measured value of L1-RSRP higher than the absolute threshold will be reported.

[0089] In the embodiment of the present application, the first model is not limited. For example, the first model may be an AI / ML model.

[0090] As an embodiment, the AI ​​model may include a deep learning model, for example, deep neural networks (DNN), convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM), generative adversarial networks (GAN), variational autoencoder (VAE), transformer, etc.

[0091] As an example, the ML model may include a model generated by machine learning or device self-learning, such as deep reinforcement learning (DRL).

[0092] In the embodiments of the present application, the deployment method of the first model is also not limited. In some embodiments, the first model is deployed on the first node side. For example, taking the first model as an AI / ML model and the first node as a terminal device as an example, refer to the terminal side model scenario above, and the first model is deployed on the terminal device side. In other embodiments, the first model is deployed on the second node side. For example, taking the first model as an AI / ML model and the second node as a network device as an example, refer to the NW side model scenario above, and the first model is deployed on the network device side.

[0093] As an embodiment, the first information may also be used to indicate an upper limit of the number of beams, that is, a maximum number of beams reported by the first node. For example, the first information indicates an upper limit N of the number of beams, where N is a positive integer greater than or equal to 1.

[0094] As an embodiment, multiple adaptive thresholds are used to select beam information, which can be understood as that the beam information can be determined based on multiple adaptive thresholds. For example, the adaptive threshold is an absolute threshold of L1-RSRP, and the first node determines the relevant beam information of the beam corresponding to the L1-RSRP measurement value of one or more beams based on the absolute thresholds of the multiple L1-RSRPs.

[0095] As a sub-embodiment of the above embodiment, the beam information may be a beam measurement value and / or a corresponding beam index. For example, the beam information is an L1-RSRP measurement value of a beam and its corresponding beam index. For another example, the beam information is a beam index corresponding to an L1-RSRP measurement value of a beam.

[0096] As a sub-embodiment of the above embodiment, the beam information may be a beam prediction value and / or a corresponding beam index. For example, the beam information is an L1-RSRP prediction value of a beam and its corresponding beam index. For another example, the beam information is a beam index corresponding to an L1-RSRP prediction value of a beam.

[0097] As an embodiment, the beam information is used for beam prediction based on the first model, and it can be understood that the beam information can be used as the output of the beam prediction process based on the first model. For example, taking the first model as an AI / ML model, the first node as a terminal device, and the second node as a network device as an example, referring to the terminal side model above, the first model is deployed on the terminal device side, and the output of the first model is the L1-RSRP prediction value of the beam. The L1-RSRP prediction values ​​of some beams are selected from the L1-RSRP prediction values ​​of the beams based on multiple adaptive thresholds, and the corresponding beam information is used as the final output of the beam prediction process based on the first model, which helps to reduce the number of reported beams.

[0098] As an embodiment, the beam information is used for beam prediction based on the first model, which can be understood as the beam information can be used as input for the beam prediction process based on the first model. For example, taking the first model as an AI / ML model, the first node as a terminal device, and the second node as a network device as an example, referring to the NW side model above, the first model is deployed on the network device side, and the beam information can be the L1-RSRP measurement value of the beam determined by the terminal device based on multiple adaptive thresholds. The terminal device reports the determined L1-RSRP measurement value of the beam to the network device, and the network device uses the L1-RSRP measurement value of the beam as input to the first model and performs beam prediction, that is, determines the L1-RSRP prediction value of the beam.

[0099] As an embodiment, the first information may be carried in a radio resource control (RRC) message. For example, the first information may be carried in RRC messages such as RRC connection establish, RRC reconfiguration, RRC connection re-establishment, RRC connection release, and RRC connection resume, and may be carried in information elements (IE) such as measConfig, BeamFailureRecoveryConfig, CSI-MeasConfig, and CSI-ReportConfig of the relevant RRC messages.

[0100] As a sub-embodiment of the above embodiment, a first field may be added to the IE carrying the first information, and the first field is used to indicate support for the issuance of a single threshold (i.e., the traditional solution) or multiple adaptive thresholds (i.e., the first information). For example, the first field is a thresholds field, and a thresholds field is added to the high-level parameter reportQuantity of the CSI-ReportConfig IE in the RRC Reconfiguration to add an array corresponding to multiple adaptive thresholds. In addition, the maxQuantity parameter may be added to change the upper limit of the number of beams.

[0101] In some embodiments, multiple adaptive thresholds are used to select beam information, which can be understood as determining a first threshold based on multiple adaptive thresholds, and the first threshold is used to determine the beam information. For example, the first node is a terminal device, and the terminal device selects a first threshold from multiple adaptive thresholds indicated by the first information during or after the current resource set measurement. After selecting the first threshold, the terminal device can determine the above beam information in the report reported to the network device based on the first threshold. The following introduces a related scheme for determining the first threshold based on multiple adaptive thresholds through Example 1.

[0102] Embodiment 1: Determine a first threshold based on multiple adaptive thresholds.

[0103] Implementation method 1-1: Based on multiple adaptive thresholds, the first threshold is determined using the second model.

[0104] In some embodiments, determining the first threshold based on multiple adaptive thresholds can be understood as determining the first threshold using a second model, the multiple adaptive thresholds being inputs of the second model, and the first threshold being output of the second model.

[0105] It should be noted that the second model and the first model mentioned above can be the same model or different models. The description of the first model mentioned above is also applicable to the second model and will not be repeated here.

[0106] As an embodiment, the second model is deployed on the first node side.

[0107] As a sub-embodiment of the above-mentioned embodiment, the second model is the same model as the first model, that is, the first model can be used for both beam prediction and determination of the first threshold. For example, the first node is a terminal device, and the first model may include a first threshold generation model and a beam prediction model, that is, the first model can be used for both beam prediction and determination of the first threshold. The terminal device uses the first model to receive multiple adaptive thresholds indicated by the first information, the capabilities of the terminal device (such as the current LCM stage and the current specified function), the current measurement resource set measurement results, the current beam prediction results, the mobility of the terminal device, the uplink (UL) resource allocation, and the beam prediction performance indicators as input, and the first threshold as output; the first threshold generation model training phase will be carried out simultaneously with the beam prediction model training phase, and during this process the first threshold generation model will extract the features of the first threshold that is the most comprehensive and optimal until convergence; its reasoning phase will perform model reasoning at the end of each measurement resource set measurement, and generate a first threshold that fits the current scenario, and apply it to determine the beam information in the beam report.

[0108] As an embodiment, the performance indicator of the second model is determined based on the type of the second model, the performance indicator of the second model is used to comprehensively judge the suitability of the first threshold for beam management, and the second model can be adjusted based on the performance indicator.

[0109] As a sub-embodiment of the above embodiment, the type of the second model may include a regression model or a classification model.

[0110] As a sub-embodiment of the above embodiment, the type of the second model is a regression model, and the performance indicators of the second model can be one or more of the following: mean square error, root mean square error, mean absolute error, and determination coefficient.

[0111] As a sub-embodiment of the above embodiment, the type of the second model is a classification model, and the performance indicators of the second model can be one or more of the following: accuracy, recall rate, F1 score (F1 score) and area under the ROC curve (area under the curve-receiver operating characteristic, AUC-ROC).

[0112] It should be noted that the specific definition of the performance indicators of the second model depends on the specific application.

[0113] As an example, the second model is the same model as the first model, and the performance indicator of the second model can be a weighted accuracy-resource efficiency ratio (WARER). For example, if the first model and the second model are the same model, the first model includes a first threshold generation model and a beam prediction model. Since performance indicators such as Top-K beam prediction accuracy Acc are generated during the performance monitoring stage of the beam prediction model, Acc is taken as an example here to define the weighted accuracy-resource efficiency ratio. The weighted accuracy-resource efficiency ratio can be calculated by the formula Determine. Among them, α is the accuracy weight, which reflects the priority of prediction accuracy. It is recommended to set it to a higher value to ensure that the prediction accuracy of the model dominates the indicators; β is the resource overhead weight, which reflects the importance of uplink resource utilization. If resources are very limited, it is recommended to increase this weight; γ is the delay weight, which reflects the impact of delay on the performance and user experience of the terminal device side. For delay-sensitive applications, this weight should be higher; ULRO is the UL resource overhead, that is, the uplink resources (such as UCI) used when reporting; ULRA is the UL resource allocation, which refers to the total amount of uplink resources allocated for reporting; D is the delay from the completion of the measurement to the generation of the threshold and reporting; TRTL is the time length for the model to calculate the threshold (threshold predicting time length).

[0114] Implementation method 2-1: Determine the first threshold based on multiple adaptive thresholds and / or historical data.

[0115] As an embodiment, determining the first threshold based on multiple adaptive thresholds and historical data may be referred to as “hard decision with memory”.

[0116] In some embodiments, the historical data is used to indicate one or more of the following: a set of statistics of historical beam measurement results; a set of first thresholds corresponding to historical beam measurements; an update frequency of the first information; and a changing trend of the first threshold.

[0117] As an embodiment, the beam measurement result may be a measurement result of the first reference signal. For example, the beam measurement result may be L1-RSRP, reference signal received quality (RSRQ), received signal strength indication (RSSI), signal interference noise ratio (SINR), etc.

[0118] As a sub-embodiment of the above embodiment, the first reference signal may be a CSI-RS.

[0119] As a sub-embodiment of the above embodiment, the first reference signal may be SSB.

[0120] As a sub-embodiment of the above embodiment, the first reference signal is configured by the CSI-ResourceConfig IE on the network device side.

[0121] As a sub-embodiment of the above embodiment, the statistic of the beam measurement result may be a first-order mean, a second-order variance, etc. of the beam measurement result. For example, the beam measurement result is L1-RSRP, and the statistic of the beam measurement result is the second-order variance of L1-RSRP of the M beams in the measurement set.

[0122] In some embodiments, determining the first threshold based on multiple adaptive thresholds and / or historical data can be understood as determining the first threshold corresponding to the i-th round of beam measurement based on multiple adaptive thresholds and / or historical data.

[0123] As an embodiment, the first threshold corresponding to the i-th round of beam measurement can be understood as the first threshold determined based on multiple adaptive thresholds and / or historical data during the i-th round of beam measurement, or in other words, the first threshold corresponding to the i-th round of beam measurement can be used to select the above beam information from the i-th round of beam measurement results.

[0124] As an embodiment, the statistical set of historical beam measurement results can be understood as the statistical set of historical beam measurement results cached by the first node. For example, the first node is a terminal device, the cache depth available for use by the terminal device is C, and the terminal device can store a set of statistical sets of beam measurement results at C moments. In other words, the terminal device can store a set of statistics of C rounds of beam measurement results The cache data of the terminal device is first in first out.

[0125] As a sub-embodiment of the above embodiment, if the round i of beam measurement is less than or equal to the cache depth of the first node, the statistical set of historical beam measurement results includes the statistical set of the beam measurement results of the previous i-1 rounds. For example, the first node is a terminal device, and the cache depth available for use by the terminal device is C. The terminal device can store the statistical set of C rounds of beam measurement results. When i≤C, the statistical set of historical beam measurement results is S i―1 is the statistic of the i-1th round of beam measurement results.

[0126] As a sub-embodiment of the above embodiment, if the round i of beam measurement is greater than the cache depth of the first node, the statistical set of historical beam measurement results includes the statistical set of beam measurement results from rounds i-C to i-1. For example, the first node is a terminal device, and the cache depth available for use by the terminal device is C. The terminal device can store the statistical set of C rounds of beam measurement results. When i>C, the statistical set of historical beam measurement results is S i―1 is the statistic of the i-1th round of beam measurement results.

[0127] As an embodiment, the first threshold set corresponding to the historical beam measurement can be understood as the first threshold set corresponding to the historical beam measurement cached by the first node. For example, the first node is a terminal device, and the cache depth available for use by the terminal device is C. The terminal device can store the first threshold set corresponding to the beam measurement at C moments. In other words, the terminal device may store the first threshold set corresponding to the C-round beam measurement Cache data is first-in, first-out.

[0128] As a sub-embodiment of the above embodiment, if the round i of beam measurement is less than or equal to the cache depth of the first node, the first threshold set corresponding to the historical beam measurement includes the first threshold set corresponding to the previous i-1 rounds of beam measurement. For example, the first node is a terminal device, and the cache depth available for use by the terminal device is C. The terminal device can store the first threshold set corresponding to C beam measurements. When i≤C, the first threshold set corresponding to the historical beam measurement is Th i―1 is the first threshold corresponding to the i-1th round of beam measurement.

[0129] As a sub-embodiment of the above embodiment, if the round i of beam measurement is greater than the statistical set of beam measurement results, the first threshold set corresponding to the historical beam measurement includes the first threshold set corresponding to the i-Cth round to the i-1th round of beam measurement. For example, the first node is a terminal device, and the cache depth available for use by the terminal device is C. The terminal device can store the first threshold set corresponding to the Cth round of beam measurement. When i>C, the first threshold set corresponding to the historical beam measurement Th i―1 is the first threshold corresponding to the i-1th round of beam measurement.

[0130] As an embodiment, the update frequency of the first information can be understood as the update frequency of the historical first information. For example, the time interval between the reception times of the two first information closest to the current moment is compared with a time threshold. If the time interval is greater than the time threshold, the update frequency of the first information is high, and if it is less than the time threshold, the update frequency of the first information is low.

[0131] As an embodiment, the change trend of the first threshold value can be understood as the change trend of the first threshold value corresponding to the historical beam measurement. For example, the first threshold value set corresponding to the historical beam measurement is When determining the first threshold corresponding to the i-th round of beam measurement, the value change trend of the first threshold corresponding to two adjacent rounds of beam measurement is determined, that is, the first threshold Th corresponding to the i-1th round of beam measurement i―1The value of is compared with the first threshold Th corresponding to the i-2th round of beam measurement i―2 The value of is increasing or decreasing.

[0132] In some embodiments, the first threshold corresponding to the first round of beam measurement is an adaptive threshold that can screen out more beams among multiple adaptive thresholds. For example, the multiple adaptive thresholds include -102dBm and -106dBm. The first threshold corresponding to the first round of beam measurement can be a lower -106dBm to screen out more beams. At this time, Th1 = -106dBm. The first node can cache the statistics S1 and Th1 of the measurement results corresponding to the first round of beam measurement. Then the cached data of the first node is and

[0133] As an embodiment, the first round of beam measurement can be understood as the initialization round of beam measurement. For example, the first model is deployed on the second node side, the LCM of the first model is initialized, the first node and the second node enter the data collection phase, the second node collects a large amount of data for model training of the first model, and the first round of beam measurement provides the first data for the model training phase.

[0134] In some embodiments, if the absolute value of the difference between the statistic of the i-th round of beam measurement results and the weighted sum of the statistic set of historical beam measurement results is less than or equal to a second threshold, then the first threshold corresponding to the i-th round of beam measurement is an adaptive threshold that is closest to the weighted sum of the first threshold set corresponding to the historical beam measurement among multiple adaptive thresholds.

[0135] As an embodiment, the second threshold may be preset by a terminal equipment manufacturer or a network operator.

[0136] As an embodiment, the weighted sum of the statistical set of historical beam measurement results may be the product of the transposition of the vector corresponding to the preset memory factor and the vector corresponding to the statistical set of historical beam measurement results; the weighted sum of the first threshold set corresponding to the historical beam measurement may be the product of the transposition of the vector corresponding to the preset memory factor and the vector corresponding to the first threshold set corresponding to the historical beam measurement. For example, the statistic of the beam measurement result of the i-th round is S i , the statistical set of historical beam measurement results is The first threshold set corresponding to the historical beam measurement is The second threshold is δ, and the preset memory factor is and The weighted sum of the statistical set of historical beam measurement results can be expressed as and The weighted sum of the first threshold set corresponding to the historical beam measurement is and The product of . When the first threshold corresponding to the i-th round of beam measurement is the closest to the multiple adaptive thresholds The adaptive threshold.

[0137] As a sub-embodiment of the above embodiment, the above preset memory factor can be set by the manufacturer of the terminal device or the network operator, and the influence of historical data at different times on the first threshold judgment can be controlled by adjusting the preset memory factor.

[0138] In some embodiments, if the absolute value of the difference between the statistic of the i-th round beam measurement result and the weighted sum of the statistic set of historical beam measurement results is greater than the second threshold, the first threshold corresponding to the i-th round beam measurement is determined based on the update frequency of the first information and / or the change trend of the first threshold. For example, the statistic of the i-th round beam measurement result is S i , the statistical set of historical beam measurement results is The first threshold set corresponding to the historical beam measurement is The second threshold is δ, and the preset factor is and The weighted sum of the statistical set of historical beam measurement results can be expressed as and The weighted sum of the first threshold set corresponding to the historical beam measurement is and The product of . When the first threshold value corresponding to the i-th round of beam measurement is determined based on the update frequency of the first information and / or the change trend of the first threshold value.

[0139] In some embodiments, the first threshold corresponding to the i-th round of beam measurement is determined based on the update frequency of the first information. It can be understood that if the time interval between the reception time of the first information and the reception time of the previous first information is less than the first threshold, then the first threshold corresponding to the i-th round of beam measurement is an adaptive threshold that can screen out fewer beams from multiple adaptive thresholds. For example, the first threshold is T up , the first node records the reception time of the first information, and the time interval between the reception time of the most recent first information and the reception time of the previous first information is ΔT up ,like And ΔT up <T up , indicating that the channel changes greatly at this time, the quality of the current i-th round beam measurement result is poor, and the first threshold corresponding to the i-th round beam measurement is an adaptive threshold that can screen out fewer beams among multiple adaptive thresholds.

[0140] As an embodiment, the first threshold may be preset by a terminal equipment manufacturer or a network operator and is used to determine the degree of channel variation.

[0141] In some embodiments, the first threshold corresponding to the i-th round of beam measurement is determined based on the change trend of the first threshold and the update frequency of the first information. It can be understood that if the time interval between the reception time of the first information and the reception time of the previous first information is greater than or equal to the first threshold, the first threshold corresponding to the i-th round of beam measurement is determined based on the change trend of the first threshold. For example, the first threshold is T up , the first node records the reception time of the first information, and the time interval between the reception time of the most recent first information and the reception time of the previous first information is ΔT up ,like And ΔT up ≥T up , the first threshold corresponding to the i-th round of beam measurement is determined based on the changing trend of the first threshold.

[0142] In some embodiments, the first threshold corresponding to the i-th round of beam measurement is determined based on the change trend of the first threshold. It can be understood that if the first threshold corresponding to the i-1th round of beam measurement is greater than or equal to the first threshold corresponding to the i-2nd round of beam measurement, then the first threshold corresponding to the i-th round of beam measurement is an adaptive threshold greater than the first threshold corresponding to the i-1th round of beam measurement among multiple adaptive thresholds. For example, the first threshold corresponding to the i-1th round of beam measurement is Th i―1 , the first threshold corresponding to the i-2th round of beam measurement is Th i―2 , if Th i―1 ≥Th i―2 , that is, Th i―1 ―Th i―2 is a positive value, and the first threshold corresponding to the i-th round of beam measurement is greater than Th among multiple adaptive thresholds. i―1 The adaptive threshold.

[0143] In some embodiments, the first threshold corresponding to the i-th round of beam measurement is determined based on the change trend of the first threshold. It can be understood that if the first threshold corresponding to the i-1th round of beam measurement is less than the first threshold corresponding to the i-2nd round of beam measurement, then the first threshold corresponding to the i-th round of beam measurement is an adaptive threshold that is less than the first threshold corresponding to the i-1th round of beam measurement among multiple adaptive thresholds. For example, the first threshold corresponding to the i-1th round of beam measurement is Th i―1 , the first threshold corresponding to the i-2th round of beam measurement is Th i―2 , if Th i―1 <Th i―2 , that is, Th i―1 ―Th i―2is a negative value, the first threshold corresponding to the i-th round of beam measurement is smaller than Th among multiple adaptive thresholds i―1 The adaptive threshold.

[0144] In some embodiments, when determining the first threshold corresponding to the i-th round of beam measurement based on the changing trend of the first threshold, when i=2, since only the threshold corresponding to the first round of beam measurement is cached, the adaptive threshold that can filter out fewer beams from multiple adaptive thresholds is selected as the first threshold corresponding to the second round of beam measurement.

[0145] Implementation 1-3: Determine the first threshold based on multiple adaptive thresholds and the first event.

[0146] In some embodiments, the first event comprises an existing event type.

[0147] As an embodiment, the first event is an event related to the signal quality of a neighboring cell.

[0148] As an embodiment, the first event includes one or more of the following: event A1; event A2; event I2; condition-based event T1 (CondEvent T1).

[0149] As an embodiment, the first event includes event A1, that is, the first event includes that the signal quality of the serving cell of the first node is higher than a specified threshold.

[0150] As an embodiment, the first event includes event A2, that is, the first event includes that the signal quality of the serving cell of the first node is lower than a specified threshold.

[0151] As an embodiment, the first event includes event I2, that is, the first event includes interference of a serving cell of the first node being higher than a specified threshold.

[0152] As an embodiment, the first event includes a condition-based event T1, that is, the first event includes that the measurement time of the serving cell of the first node is within a duration of a specified threshold.

[0153] In other embodiments, the first event includes a newly defined event type.

[0154] As an embodiment, the newly defined event type may be channel degradation event R1, and the triggering strategy of channel degradation event R1 is that the difference between two adjacent channel quality measurement values ​​of the first node is lower than a specified threshold, that is, the first event includes the difference between two adjacent channel quality measurement values ​​of the first node being lower than a specified threshold.

[0155] In some embodiments, determining the first threshold based on multiple adaptive thresholds and a first event can be understood as determining the first threshold based on one or more of the following: multiple adaptive thresholds; hysteresis parameters of the first event; event threshold of the first event: measurement results of the service cell of the first node; measurement results of the channel quality of the first node.

[0156] Since the stopping or triggering of the first event can be determined based on one or more of the first event hysteresis parameter, the event threshold of the first event, the measurement result of the service cell of the first node, and the measurement result of the channel quality of the first node, the above-mentioned embodiment can also be understood as selecting the first threshold from multiple adaptive thresholds based on the triggering and / or stopping of the first event.

[0157] As an embodiment, the delay parameter of the first event and the event threshold of the first event may be sent to the first node via the reportConfigNR IE or an IE including the reportConfigNR field.

[0158] As an embodiment, the measurement result of the serving cell of the first node may be L1-RSRP / RSRQ / SINR of the serving cell of the first node.

[0159] As an embodiment, the measurement result of the channel quality of the first node may be L1-RSRP / RSRQ / SINR of the channel where the first node is located.

[0160] As an embodiment, the first event includes event A1. If event A1 is triggered, that is, the difference between the measurement result of the service cell of the first node and the hysteresis parameter of event A1 is greater than the event threshold of event A1, the first threshold is an adaptive threshold that can screen out more beams from multiple adaptive thresholds. For example, the measurement result of the service cell of the first node is Ms, the hysteresis parameter of event A1 is Hys1, and the event threshold of event A1 is Thresh1. If ms-Hys1>Thresh1, that is, event A1 is triggered, the current link channel quality is good, and the first node can select an adaptive threshold that can screen out more beams from multiple adaptive thresholds as the first threshold.

[0161] As an embodiment, the first event includes event A1. If event A1 is stopped, that is, the sum of the measurement result of the serving cell of the first node and the hysteresis parameter of event A1 is less than the event threshold of event A1, the first threshold is the adaptive threshold among multiple adaptive thresholds that can screen out a moderate (relatively large) number of beams. For example, the measurement result of the serving cell of the first node is Ms, the hysteresis parameter of event A1 is Hys1, and the event threshold of event A1 is Thresh1. If Ms + Hys1 < Thresh1, that is, event A1 is stopped, then the first node can select the adaptive threshold that can screen out a moderate (relatively large) number of beams from multiple adaptive thresholds as the first threshold.

[0162] As an embodiment, the first event includes event A2. If event A2 is stopped, that is, the difference between the measurement result of the serving cell of the first node and the hysteresis parameter of event A2 is greater than the event threshold of event A2, the first threshold is the adaptive threshold among multiple adaptive thresholds that can screen out a moderate (relatively small) number of beams. For example, the measurement result of the serving cell of the first node is Ms, the hysteresis parameter of event A2 is Hys2, and the event threshold of event A2 is Thresh2. If Ms - Hys2 > Thresh2, that is, event A2 is stopped, then the first node can select the adaptive threshold that can screen out a moderate (relatively small) number of beams from multiple adaptive thresholds as the first threshold.

[0163] As an embodiment, the first event includes event A2. If event A2 is triggered, that is, the sum of the measurement result of the serving cell of the first node and the hysteresis parameter of event A2 is less than the event threshold of event A2, the first threshold is the adaptive threshold among multiple adaptive thresholds that can screen out a smaller number of beams. For example, the measurement result of the serving cell of the first node is Ms, the hysteresis parameter of event A2 is Hys2, and the event threshold of event A2 is Thresh2. If Ms + Hys2 < Thresh2, that is, event A2 is triggered, then the first node can select the adaptive threshold that can screen out a smaller number of beams from multiple adaptive thresholds as the first threshold. Another example is that the multiple adaptive thresholds include absolute thresholds of -102 dBm and -106 dBm, and the preset first threshold is -96 dB. The first node detects that the channel quality deteriorates but not severely, that is, the sum of the measurement result of the serving cell of the first node and the hysteresis parameter of event A2 is less than event A2, triggering event A2. Then the first node selects the lower absolute threshold of -102 dBm to ensure that less beam information is reported to help the second node side better optimize resource allocation.

[0164] As an example, the first event includes a channel degradation event R1. If the channel degradation event R1 is triggered, that is, the sum of the difference between the maximum values of the measurement results of the channel quality of the first node in two adjacent times and the hysteresis parameter of the channel degradation event R1 is less than the event threshold of the channel degradation event R1, the first threshold is the adaptive threshold among multiple adaptive thresholds that can filter out fewer beams. For example, the difference between the maximum values of the measurement results of the channel quality of the first node in two adjacent times is Diff, the hysteresis parameter of the channel degradation event R1 is Hys3, and the event threshold of the channel degradation event R1 is Thresh3. If Diff + Hys3 < Thresh3, that is, the channel degradation event R1 is triggered, the first node can select the adaptive threshold that can filter out fewer beams from multiple adaptive thresholds as the first threshold. Another example is that multiple adaptive thresholds include absolute thresholds of -102 dBm and -106 dBm, and the preset first threshold is -96 dB. When the first node detects that the channel change is weak and triggers event R1, the terminal device selects the moderate absolute threshold of -102 dBm to maintain moderate beam information reporting; when the first node discovers according to its own rules that the link quality is higher than expected but the uplink pressure is large, it selects the higher absolute threshold of -96 dBm to reduce the reported beam information and maintain good link quality.

[0165] As a sub - embodiment of the above - mentioned embodiment, the hysteresis parameter and the event threshold of the channel degradation event R1 can be defined in reportConfigNR.

[0166] As a sub - embodiment of the above - mentioned embodiment, the event threshold of the channel degradation event R1 can be the differential threshold of L1 - RSRP / SINR / RSRQ (usually negative).

[0167] The above introduces the scheme for determining the first threshold based on multiple adaptive thresholds through implementation methods 1 - 1, 1 - 2, and 1 - 3. After the first node determines the first threshold, it can select beam information based on the first threshold and report it through the second information. The following introduces the scheme for the first node to send the second information through Embodiment 2.

[0168] Embodiment 2: The first node sends the second information.

[0169] Exemplarily, the second node can receive the second information sent by the first node.

[0170] In some embodiments, the second information includes one or more of the following: beam information corresponding to the beam measurement result that satisfies the first condition; beam information corresponding to the beam prediction result that satisfies the first condition; the first threshold.

[0171] In some embodiments, the first condition is associated with the first threshold.

[0172] As an embodiment, the beam information corresponding to the beam measurement result that meets the first condition can be understood as the beam information corresponding to the beam measurement result that has been screened by the first threshold.

[0173] As an embodiment, the beam information corresponding to the beam prediction result that meets the first condition can be understood as the beam information corresponding to the beam prediction result that has been screened by the first threshold.

[0174] As an embodiment, the first condition may also be associated with an upper limit on the number of beams. For example, the upper limit on the number of beams is N, the first condition is greater than a first threshold, and the number of beams does not exceed N, if the number of beams corresponding to the beam prediction results greater than the first threshold is greater than N, then the beam information corresponding to the N beams with the highest beam measurement results is selected for transmission.

[0175] As an embodiment, if the first model is deployed on the first node side, the second information includes beam information corresponding to the beam prediction result that meets the first condition and the first threshold.

[0176] As an embodiment, if the first model is deployed on the second node side, the second information includes beam information corresponding to the beam measurement result that meets the first condition and the first threshold.

[0177] As an embodiment, the second information is carried in an L1 message.

[0178] As an embodiment, the second information is carried in an RRC message. For example, the second information is carried in a MeasResults IE in a MeasurmentReport message, and the MeasurmentReport message is an RRC message.

[0179] As an embodiment, the second information carries a medium access control element (MAC CE).

[0180] As an embodiment, the second information is carried in a beam report.

[0181] As an embodiment, the second node receives the second information and can perform beam prediction based on the second information. For example, the beam information included in the second information is the L1-RSRP measurement value of the beam. After receiving the second information, the second node completes and inputs the Default Value of the lowest L1-RSRP deleted data into the first model, trains the first model, and performs beam prediction in the inference stage of the first model.

[0182] As an embodiment, the second node receives the second information and can perform beam management based on the second information. For example, the beam information included in the second information is the L1-RSRP prediction value of the beam. After the second node receives the second information, it can perform beam management based on the L1-RSRP prediction values ​​of different beams.

[0183] As an embodiment, the second node receives the second information and can adjust multiple adaptive thresholds based on the second information. For example, the second information includes the first threshold. After receiving the second information, the second node can adjust the multiple adaptive thresholds corresponding to the first node according to the first threshold to send the first information again.

[0184] The above describes the related schemes of determining the first threshold based on multiple adaptive thresholds and the first node sending the second information through Embodiment 1 and Embodiment 2. In some scenarios, the two schemes can be used independently. In other scenarios, the two schemes can be used in combination with each other.

[0185] Combined with the following Figure 6 An example is given to illustrate a solution that combines the two solutions. Figure 6 The method includes steps S610 to S640. Assume that the first node is a terminal device and the second node is a network device.

[0186] In step S610, the network device sends first information to the terminal device.

[0187] The first information sent by the network device to the terminal device indicates multiple adaptive thresholds.

[0188] In step S620, the terminal device determines a first threshold.

[0189] The terminal device receives the first information sent by the network device, and determines the first threshold based on the multiple adaptive thresholds indicated by the first information.

[0190] In step S630, the terminal device determines the second information.

[0191] The terminal device selects beam information based on the first threshold determined in step S620, and determines the beam information corresponding to the beam measurement result that meets the first condition, where the first condition is associated with the first threshold, and the second information includes the beam information and the first threshold.

[0192] In step S640, the terminal device sends second information to the network device.

[0193] The terminal device sends the second information determined in step S630 to the network device.

[0194] The first information received by the first node above indicates multiple adaptive thresholds, and the first information may be sent by the second node. Before the second node sends, the second node needs to determine multiple adaptive thresholds. The following introduces a solution for the second node to determine multiple adaptive thresholds through Example 3.

[0195] Embodiment 3: The second node determines multiple adaptive thresholds.

[0196] In some embodiments, the second node determines multiple adaptive thresholds based on one or more of the following: first node status information; first model information; network status information; link quality information. In the process of determining multiple adaptive thresholds, the first node does not need to provide redundant auxiliary information to protect user privacy and security, and the first node can make timely and autonomous selection of thresholds based on available information such as the first node privacy information and measurement results, thereby ensuring the high quality of the returned data and the completeness of the spatial information contained therein. In addition, the second node uses its own computing advantages and information advantages to generate adaptive reporting thresholds, comprehensively considering the first model information, network status information, first node status information, etc., which helps to significantly reduce UCI overhead under the premise that system performance is controllable.

[0197] As an embodiment, the state information of the first node may include one or more of the following: mobility of the first node; location information of the first node; demand of the first node; behavior pattern of the first node.

[0198] As a sub-embodiment of the above embodiment, the state information of the first node may be carried in the capability report of the first node. For example, before the second node decides to start training of the first model, the first node may send the capability report of the first node to the second node for reference by the LCM of the first model of the second node. The capability report of the first node carries the state information of the first node, which may be used to determine multiple adaptive thresholds.

[0199] As an embodiment, the first model information includes one or more of the following: an LCM indication of the first model; a beam prediction result output by the first model; and a prediction accuracy monitoring indicator of the first model.

[0200] As an embodiment, the network status information includes one or more of the following: network load information; interference information; network strategy.

[0201] As an embodiment, the link quality information includes one or more of the following: historical CSI; current CSI; environmental information.

[0202] As an embodiment, the second node determines multiple adaptive thresholds based on the first node state information, the first model information, the network state information and the link quality information. For example, since the first nodes are dense, the channel interference is high, the network load is large, and the mobility of the first node is weak, the second node decides to generate multiple lower adaptive thresholds, such as -102dBm, -106dBm, based on the LCM indication of the first model, the location information of the first node, the historical CSI data, etc.

[0203] As an embodiment, one or more of the above-mentioned determination of the multiple adaptive thresholds may be determined by policies of network operators and equipment manufacturers.

[0204] As an embodiment, multiple adaptive thresholds may be generated by traditional condition judgment, optimization problem modeling and optimization methods.

[0205] As an embodiment, multiple adaptive thresholds may be generated by a third model, which may also be referred to as an "adaptive threshold generation model", and one or more of the above-mentioned determinations of multiple adaptive thresholds may be used as inputs to the adaptive threshold generation model. Figure 7 The adaptive threshold generation model, the first node state information, the first model information, the network state information and the link quality information are all inputs of the adaptive threshold generation model.

[0206] As a sub-embodiment of the above embodiment, the third model may be an AI / ML model.

[0207] As a sub-embodiment of the above embodiment, the third model may be a general model deployed on the second node side. For example, the third model may be the same model as the first model.

[0208] As a sub-embodiment of the above embodiment, the third model may be a dedicated model deployed on the second node side. For example, the third model adopts AI / ML methods such as random forest.

[0209] As an embodiment, the second node may also generate multiple adaptive thresholds based on the typical threshold and one or more of the multiple adaptive thresholds determined above.

[0210] As a sub-embodiment of the above embodiment, the typical threshold may be specified by the network operator and the equipment manufacturer.

[0211] In some embodiments, the first node sends first indication information to the second node, and the first indication information is used to indicate the type of the adaptive threshold. For example, if the output of the first model is confidence or probability, the first node sends the first indication information to the second node, and the first indication information is carried in the newly added output type field in the capability report of the first node, so that the second node side can know that the required adaptive threshold type is not the L1-RSRP absolute value or differential value, and the second node generates the corresponding type of adaptive threshold based on the first indication information for the first node side to select.

[0212] In some embodiments, the second node may send updated first information, where the updated first information indicates the adjusted multiple adaptive thresholds.

[0213] As an embodiment, the adjustment of multiple adaptive thresholds is triggered by the performance monitoring result. For example, taking the first node as a terminal device and the second node as a network device as an example, when the first model is deployed on the terminal device side and the performance monitoring adopts Type 1, or when the first model is deployed on the network device side, the adjustment of multiple adaptive thresholds is triggered by the network device according to the performance monitoring result. When the performance monitoring result is generated on the terminal device side, the terminal device can feedback the monitoring result, and the adjustment of multiple adaptive thresholds is still triggered by the network device side, otherwise it is triggered by the terminal device side.

[0214] The above describes, through Example 1, Example 2, and Example 3, the related schemes of determining the first threshold based on multiple adaptive thresholds, the first node sending the second information, and the second node determining multiple adaptive thresholds. In some scenarios, the three schemes can be used independently. In other scenarios, the three schemes can be used in combination with each other.

[0215] Combined with the following Figure 8 and Fig. 9 The following examples are given to illustrate the combination of the three solutions.

[0216] Figure 8 The method includes steps S810 to S860, and takes the example of a terminal device using the second model to determine the first threshold. Assume that the first node is a terminal device, the second node is a network device, the second model is the same as the first model, and the first model is deployed on the terminal device side.

[0217] In step S810 , the network device determines a plurality of adaptive thresholds.

[0218] The network device determines a plurality of adaptive thresholds according to the status information of the terminal device and the historical link quality information.

[0219] In step S820, the network device sends first information to the terminal device.

[0220] The network device sends the multiple adaptive thresholds to the terminal device through the first information. In addition, the network device evaluates the capabilities of the received terminal device and configures the LCM indication, resource configuration and beam report.

[0221] In step S830, the terminal device uses the first model to perform beam prediction and determine a first threshold.

[0222] The network device scans the beam according to the resource configuration, the terminal device performs beam measurement on the corresponding beam based on the received first reference signal, and uses the first model to predict the beam and determine the first threshold. Multiple adaptive thresholds and beam measurement results serve as inputs of the first model, and the first threshold and beam prediction results serve as outputs of the first model.

[0223] In step S840, the terminal device determines the second information.

[0224] The second information includes the first threshold and the beam information corresponding to the beam prediction result that meets the first condition. The first condition is associated with the first threshold. The terminal device determines the second information based on the beam prediction result output by the first model in step S830 and the first threshold.

[0225] In step S850, the terminal device sends second information to the network device.

[0226] The terminal device sends the second information determined in step S840 to the network device.

[0227] In step S860 , the network device adjusts multiple adaptive thresholds.

[0228] The network device receives the second information sent by the network device, and adjusts the multiple adaptive thresholds corresponding to the terminal device based on the first threshold included in the second information, so as to send the first information again.

[0229] Fig. 9 The method includes steps S910 to S960, and takes the example of a terminal device determining a first threshold based on multiple adaptive thresholds and / or historical data as an example. Assume that the first node is a terminal device, the second node is a network device, and the first model is deployed on the network device side.

[0230] In step S910 , the network device determines a plurality of adaptive thresholds.

[0231] The network device determines a plurality of adaptive thresholds according to the status information of the terminal device and the historical link quality information.

[0232] In step S920, the network device sends first information to the terminal device.

[0233] The network device sends the multiple adaptive thresholds to the terminal device through the first information. In addition, the network device evaluates the capabilities of the received terminal device and configures the LCM indication, resource configuration and beam report.

[0234] In step S930, the terminal device determines a first threshold based on multiple adaptive thresholds and / or historical data.

[0235] The network device scans the beam according to the resource configuration, the terminal device performs beam measurement on the corresponding beam based on the received first reference signal, and determines the first threshold based on multiple adaptive thresholds and / or historical data, the historical data indicating a statistical set of historical beam measurement results, a first threshold set corresponding to the historical beam measurement, the update frequency of the first information, and a changing trend of the first threshold.

[0236] In step S940, the terminal device determines the second information.

[0237] The second information includes the first threshold and the beam information corresponding to the beam measurement result that meets the first condition. The first condition is associated with the first threshold. The terminal device determines the second information based on the beam measurement result determined in step S930 and the first threshold.

[0238] In step S950, the terminal device sends second information to the network device.

[0239] The terminal device sends the second information determined in step S940 to the network device.

[0240] In step S960, the network device performs beam prediction.

[0241] The network device receives the second information, uses the beam information corresponding to the beam measurement result that meets the first condition and is included in the second information as input to the first model, and performs beam prediction using the first model.

[0242] In step S970 , the network device adjusts multiple adaptive thresholds.

[0243] The network device receives the second information sent by the network device, and adjusts the multiple adaptive thresholds corresponding to the terminal device based on the first threshold included in the second information, so as to send the first information again.

[0244] Combination of the above Figures 1 to 9 , describes the method embodiment of the present application in detail, and the following is combined with Figures 10 to 12 , describes the device embodiment of the present application in detail. It should be understood that the description of the method embodiment corresponds to the description of the device embodiment, so the parts not described in detail can refer to the previous method embodiment.

[0245] Fig.10A first node for wireless communication provided in an embodiment of the present application. Fig.10 As shown, the first node 1000 includes a first transceiver 1010 .

[0246] The first transceiver 1010 is used to receive first information, where the first information is used to indicate multiple adaptive thresholds, and the multiple adaptive thresholds are used to select beam information, and the beam information is used for beam prediction based on a first model.

[0247] As an embodiment, the multiple adaptive thresholds are used to select beam information, including: determining a first threshold based on the multiple adaptive thresholds, and the first threshold is used to determine the beam information.

[0248] As an embodiment, determining the first threshold based on multiple adaptive thresholds includes: determining the first threshold using a second model, the multiple adaptive thresholds are inputs of the second model, and the first threshold is an output of the second model.

[0249] As an embodiment, the determining of the first threshold based on multiple adaptive thresholds includes: determining the first threshold based on the multiple adaptive thresholds and / or historical data, wherein the historical data is used to indicate one or more of the following: a set of statistics of historical beam measurement results; a set of first thresholds corresponding to historical beam measurements; an update frequency of the first information; and a changing trend of the first threshold.

[0250] As an embodiment, determining the first threshold based on the multiple adaptive thresholds includes: the first threshold corresponding to the first round of beam measurement is an adaptive threshold that can screen out more beams from the multiple adaptive thresholds.

[0251] As an embodiment, determining the first threshold based on the multiple adaptive thresholds and historical data includes: if the absolute value of the difference between the statistic of the i-th round of beam measurement results and the weighted sum of the statistical set of historical beam measurement results is less than or equal to a second threshold, then the first threshold corresponding to the i-th round of beam measurement is the adaptive threshold of the weighted sum of the first threshold set corresponding to the historical beam measurement that is closest to the multiple adaptive thresholds.

[0252] As an embodiment, determining the first threshold based on the multiple adaptive thresholds and historical data includes: if the absolute value of the difference between the statistic of the i-th round of beam measurement results and the weighted sum of the statistical set of historical beam measurement results is greater than a second threshold, then determining the first threshold corresponding to the i-th round of beam measurement based on the update frequency of the first information and / or the changing trend of the first threshold.

[0253] As an embodiment, the first threshold is determined based on the multiple adaptive thresholds and historical data, including: if the time interval between the receiving time of the first information and the receiving time of the previous first information is less than a first threshold, then the first threshold corresponding to the i-th round of beam measurement is the adaptive threshold that can screen out fewer beams among the multiple adaptive thresholds.

[0254] As an embodiment, the method of determining the first threshold corresponding to the i-th round of beam measurement based on the changing trend of the first threshold includes: if the first threshold corresponding to the i-1th round of beam measurement is greater than or equal to the first threshold corresponding to the i-2nd round of beam measurement, then the first threshold corresponding to the i-th round of beam measurement is an adaptive threshold among the multiple adaptive thresholds that is greater than the first threshold corresponding to the i-1th round of beam measurement; if the first threshold corresponding to the i-1th round of beam measurement is less than the first threshold corresponding to the i-2nd round of beam measurement, then the first threshold corresponding to the i-th round of beam measurement is an adaptive threshold among the multiple adaptive thresholds that is less than the first threshold corresponding to the i-1th round of beam measurement; the first threshold corresponding to the second round of beam measurement is an adaptive threshold among the multiple adaptive thresholds that can screen out fewer beams.

[0255] As an embodiment, determining the first threshold based on the multiple adaptive thresholds includes: determining the first threshold based on the multiple adaptive thresholds and a first event, wherein the first event includes one or more of the following: the service cell signal quality of the first node is higher than a specified threshold; the service cell signal quality of the first node is lower than a specified threshold; the difference between two adjacent channel quality measurement values ​​of the first node is lower than a specified threshold.

[0256] As an embodiment, determining the first threshold based on the multiple adaptive thresholds and the first event includes: determining the first threshold based on one or more of the following: the multiple adaptive thresholds; the hysteresis parameter of the first event; the event threshold of the first event: the measurement result of the service cell of the first node; the measurement result of the channel quality of the first node.

[0257] As an embodiment, the first node also includes: the first transceiver 1010 is also used to send second information, and the second information includes one or more of the following: beam information corresponding to the beam measurement result that meets the first condition, the first condition is associated with the first threshold; beam information corresponding to the beam prediction result that meets the first condition; the first threshold.

[0258] Fig.11 A second node for wireless communication provided in an embodiment of the present application. Fig.11 As shown, the second node 1100 includes a second transceiver 1110 .

[0259] The second transceiver 1110 is used to send first information, where the first information is used to indicate multiple adaptive thresholds, and the multiple adaptive thresholds are used to select beam information, and the beam information is used for beam prediction based on the first model.

[0260] As an embodiment, the multiple adaptive thresholds are used to select beam information, including: determining a first threshold based on the multiple adaptive thresholds, and the first threshold is used to determine the beam information.

[0261] As an embodiment, determining the first threshold based on multiple adaptive thresholds includes: determining the first threshold using a second model, the multiple adaptive thresholds are inputs of the second model, and the first threshold is an output of the second model.

[0262] As an embodiment, the determining of the first threshold based on multiple adaptive thresholds includes: determining the first threshold based on the multiple adaptive thresholds and / or historical data, wherein the historical data is used to indicate one or more of the following: a set of statistics of historical beam measurement results; a set of first thresholds corresponding to historical beam measurements; an update frequency of the first information; and a changing trend of the first threshold.

[0263] As an embodiment, determining the first threshold based on the multiple adaptive thresholds includes: the first threshold corresponding to the first round of beam measurement is an adaptive threshold that can screen out more beams from the multiple adaptive thresholds.

[0264] As an embodiment, determining the first threshold based on the multiple adaptive thresholds and historical data includes: if the absolute value of the difference between the statistic of the i-th round of beam measurement results and the weighted sum of the statistical set of historical beam measurement results is less than or equal to a second threshold, then the first threshold corresponding to the i-th round of beam measurement is the adaptive threshold of the weighted sum of the first threshold set corresponding to the historical beam measurement that is closest to the multiple adaptive thresholds.

[0265] As an embodiment, determining the first threshold based on the multiple adaptive thresholds and historical data includes: if the absolute value of the difference between the statistic of the i-th round of beam measurement results and the weighted sum of the statistical set of historical beam measurement results is greater than a second threshold, then determining the first threshold corresponding to the i-th round of beam measurement based on the update frequency of the first information and / or the changing trend of the first threshold.

[0266] As an embodiment, the first threshold is determined based on the multiple adaptive thresholds and historical data, including: if the time interval between the receiving time of the first information and the receiving time of the previous first information is less than a first threshold, then the first threshold corresponding to the i-th round of beam measurement is the adaptive threshold that can screen out fewer beams among the multiple adaptive thresholds.

[0267] As an embodiment, the method of determining the first threshold corresponding to the i-th round of beam measurement based on the changing trend of the first threshold includes: if the first threshold corresponding to the i-1th round of beam measurement is greater than or equal to the first threshold corresponding to the i-2nd round of beam measurement, then the first threshold corresponding to the i-th round of beam measurement is an adaptive threshold among the multiple adaptive thresholds that is greater than the first threshold corresponding to the i-1th round of beam measurement; if the first threshold corresponding to the i-1th round of beam measurement is less than the first threshold corresponding to the i-2nd round of beam measurement, then the first threshold corresponding to the i-th round of beam measurement is an adaptive threshold among the multiple adaptive thresholds that is less than the first threshold corresponding to the i-1th round of beam measurement; the first threshold corresponding to the second round of beam measurement is an adaptive threshold among the multiple adaptive thresholds that can screen out fewer beams.

[0268] As an embodiment, determining the first threshold based on the multiple adaptive thresholds includes: determining the first threshold based on the multiple adaptive thresholds and a first event, wherein the first event includes one or more of the following: the service cell signal quality of the first node is higher than a specified threshold; the service cell signal quality of the first node is lower than a specified threshold; the difference between two adjacent channel quality measurement values ​​of the first node is lower than a specified threshold.

[0269] As an embodiment, determining the first threshold based on the multiple adaptive thresholds and the first event includes: determining the first threshold based on one or more of the following: the multiple adaptive thresholds; the hysteresis parameter of the first event; the event threshold of the first event: the measurement result of the service cell of the first node; the measurement result of the channel quality of the first node.

[0270] As an embodiment, the second node also includes: the second transceiver 1110 is also used to receive second information, and the second information includes one or more of the following: beam information corresponding to the beam measurement result that meets the first condition, the first condition is associated with the first threshold; beam information corresponding to the beam prediction result that meets the first condition; the first threshold.

[0271] As an embodiment, the second node also includes: a first processor, used to determine the multiple adaptive thresholds based on one or more of the following: the first node status information; the first model information; network status information; link quality information.

[0272] In an optional embodiment, the first transceiver 1010 may be a transceiver 1230. The first node 1000 may also include a processor 1210 and a memory 1220. Fig.12 shown.

[0273] In an optional embodiment, the second transceiver 1110 may be a transceiver 1230. The second node 1100 may also include a processor 1210 and a memory 1220. Fig.12 shown.

[0274] Fig.12 It is a schematic structural diagram of a communication device according to an embodiment of the present application. Fig.12 The dotted line in the figure indicates that the unit or module is optional. The device 1200 can be used to implement the method described in the above method embodiment. The device 1200 can be a chip, a terminal device or a network device.

[0275] The device 1200 may include one or more processors 1210. The processor 1210 may support the device 1200 to implement the method described in the method embodiment above. The processor 1210 may be a general-purpose processor or a special-purpose processor. For example, the processor may be a central processing unit (CPU). Alternatively, the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0276] The apparatus 1200 may further include one or more memories 1220. The memory 1220 stores a program, which can be executed by the processor 1210, so that the processor 1210 executes the method described in the above method embodiment. The memory 1220 may be independent of the processor 1210 or integrated in the processor 1210.

[0277] The apparatus 1200 may further include a transceiver 1230. The processor 1210 may communicate with other devices or chips through the transceiver 1230. For example, the processor 1210 may transmit and receive data with other devices or chips through the transceiver 1230.

[0278] The present application also provides a computer-readable storage medium for storing a program. The computer-readable storage medium can be applied to a terminal or network device provided in the present application, and the program enables a computer to execute the method performed by the terminal or network device in each embodiment of the present application.

[0279] The embodiment of the present application also provides a computer program product. The computer program product includes a program. The computer program product can be applied to the terminal or network device provided in the embodiment of the present application, and the program enables the computer to execute the method performed by the terminal or network device in each embodiment of the present application.

[0280] The embodiment of the present application also provides a computer program. The computer program can be applied to the terminal or network device provided in the embodiment of the present application, and the computer program enables a computer to execute the method executed by the terminal or network device in each embodiment of the present application.

[0281] It should be understood that the terms "system" and "network" in this application can be used interchangeably. In addition, the terms used in this application are only used to explain the specific embodiments of the present application, and are not intended to limit the present application. The terms "first", "second", "third" and "fourth" in the specification and claims of this application and the accompanying drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions.

[0282] In the embodiments of the present application, the "indication" mentioned can be a direct indication, an indirect indication, or an indication of an association relationship. For example, A indicates B, which can mean that A directly indicates B, for example, B can be obtained through A; it can also mean that A indirectly indicates B, for example, A indicates C, and B can be obtained through C; it can also mean that there is an association relationship between A and B.

[0283] In the embodiments of the present application, the term "corresponding" may indicate that there is a direct or indirect correspondence between the two, or an association relationship between the two, or a relationship of indication and being indicated, configuration and being configured, etc.

[0284] In the embodiments of the present application, the “protocol” may refer to a standard protocol in the communication field, for example, it may include an LTE protocol, an NR protocol, and related protocols used in future communication systems, and the present application does not limit this.

[0285] In the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0286] In various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0287] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0288] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0289] 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.

[0290] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that can be read by a computer or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0291] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method in a first node for wireless communication, characterized in that include: First information is received, where the first information is used to indicate a plurality of adaptive thresholds, where the plurality of adaptive thresholds are used to select beam information, where the beam information is used for beam prediction based on a first model.

2. The method according to claim 1, characterized in that The multiple adaptive thresholds are used to select beam information, including: A first threshold is determined based on the multiple adaptive thresholds, where the first threshold is used to determine the beam information.

3. The method according to claim 2, characterized in that The determining the first threshold based on a plurality of adaptive thresholds comprises: The first threshold is determined by using a second model, the multiple adaptive thresholds are inputs of the second model, and the first threshold is output of the second model.

4. The method according to claim 2, characterized in that The determining the first threshold based on a plurality of adaptive thresholds comprises: Based on the plurality of adaptive thresholds and / or the first threshold of historical data, the historical data is used to indicate one or more of the following: A statistical collection of historical beam measurement results; a first threshold set corresponding to historical beam measurements; an update frequency of the first information; The variation trend of the first threshold value.

5. The method according to claim 4, characterized in that The determining the first threshold based on the multiple adaptive thresholds comprises: The first threshold corresponding to the first round of beam measurement is an adaptive threshold that can screen out more beams among the multiple adaptive thresholds.

6. The method according to claim 4 or 5, characterized in that The determining the first threshold based on the multiple adaptive thresholds and historical data comprises: If the absolute value of the difference between the statistic of the i-th round of beam measurement results and the weighted sum of the statistic set of historical beam measurement results is less than or equal to the second threshold, then the first threshold corresponding to the i-th round of beam measurement is the adaptive threshold of the weighted sum of the first threshold set corresponding to the historical beam measurement among the multiple adaptive thresholds.

7. The method according to claim 4 or 5, characterized in that The determining the first threshold based on the multiple adaptive thresholds and historical data comprises: If the absolute value of the difference between the statistic of the i-th round of beam measurement results and the weighted sum of the statistical set of historical beam measurement results is greater than the second threshold, the first threshold corresponding to the i-th round of beam measurement is determined based on the update frequency of the first information and / or the changing trend of the first threshold.

8. The method according to claim 7, characterized in that The determining the first threshold based on the multiple adaptive thresholds and historical data comprises: If the time interval between the reception time of the first information and the reception time of the previous first information is less than the first threshold, the first threshold corresponding to the i-th round of beam measurement is the adaptive threshold that can screen out fewer beams from the multiple adaptive thresholds.

9. The method according to claim 7, characterized in that The determining the first threshold corresponding to the i-th round of beam measurement based on the change trend of the first threshold includes: If the first threshold corresponding to the (i-1)th round of beam measurement is greater than or equal to the first threshold corresponding to the (i-2)th round of beam measurement, the first threshold corresponding to the (i-1)th round of beam measurement is an adaptive threshold among the multiple adaptive thresholds that is greater than the first threshold corresponding to the (i-1)th round of beam measurement; If the first threshold corresponding to the (i-1)th round of beam measurement is smaller than the first threshold corresponding to the (i-2)th round of beam measurement, the first threshold corresponding to the (i-1)th round of beam measurement is an adaptive threshold among the multiple adaptive thresholds that is smaller than the first threshold corresponding to the (i-1)th round of beam measurement; The first threshold corresponding to the second round of beam measurement is an adaptive threshold that can screen out fewer beams among the multiple adaptive thresholds.

10. The method according to claim 2, characterized in that The determining the first threshold based on the multiple adaptive thresholds comprises: The first threshold is determined based on the multiple adaptive thresholds and a first event, where the first event includes one or more of the following: The serving cell signal quality of the first node is higher than a specified threshold; The serving cell signal quality of the first node is lower than a specified threshold; A difference between two adjacent channel quality measurement values ​​of the first node is lower than a specified threshold.

11. The method according to claim 10, characterized in that The determining the first threshold based on the plurality of adaptive thresholds and the first event comprises: The first threshold is determined based on one or more of the following: the plurality of adaptive thresholds; a hysteresis parameter of the first event; The event threshold of the first event is: a measurement result of a serving cell of the first node; A measurement result of the channel quality of the first node.

12. The method according to any one of claims 1 to 11, characterized in that The method further comprises: Sending second information, where the second information includes one or more of the following: beam information corresponding to a beam measurement result that satisfies a first condition, wherein the first condition is associated with the first threshold; Beam information corresponding to the beam prediction result that meets the first condition; The first threshold.

13. A method in a second node for wireless communication, characterized in that: include: First information is sent, where the first information is used to indicate multiple adaptive thresholds, where the multiple adaptive thresholds are used to select beam information, and the beam information is used for beam prediction based on a first model.

14. The method according to claim 13, characterized in that The multiple adaptive thresholds are used to select beam information, including: A first threshold is determined based on the multiple adaptive thresholds, where the first threshold is used to determine the beam information.

15. The method according to claim 14, characterized in that The determining the first threshold based on a plurality of adaptive thresholds comprises: The first threshold is determined by using a second model, the multiple adaptive thresholds are inputs of the second model, and the first threshold is output of the second model.

16. The method according to claim 14, characterized in that The determining the first threshold based on a plurality of adaptive thresholds comprises: Based on the plurality of adaptive thresholds and / or the first threshold of historical data, the historical data is used to indicate one or more of the following: A statistical collection of historical beam measurement results; a first threshold set corresponding to historical beam measurements; an update frequency of the first information; The variation trend of the first threshold value.

17. The method according to claim 16, characterized in that The determining the first threshold based on the multiple adaptive thresholds comprises: The first threshold corresponding to the first round of beam measurement is an adaptive threshold that can screen out more beams among the multiple adaptive thresholds.

18. The method according to claim 16 or 17, characterized in that The determining the first threshold based on the multiple adaptive thresholds and historical data comprises: If the absolute value of the difference between the statistic of the i-th round of beam measurement results and the weighted sum of the statistic set of historical beam measurement results is less than or equal to the second threshold, then the first threshold corresponding to the i-th round of beam measurement is the adaptive threshold of the weighted sum of the first threshold set corresponding to the historical beam measurement among the multiple adaptive thresholds.

19. The method according to claim 16 or 17, characterized in that The determining the first threshold based on the multiple adaptive thresholds and historical data comprises: If the absolute value of the difference between the statistic of the i-th round of beam measurement results and the weighted sum of the statistical set of historical beam measurement results is greater than the second threshold, the first threshold corresponding to the i-th round of beam measurement is determined based on the update frequency of the first information and / or the changing trend of the first threshold.

20. The method of claim 19, wherein: The determining the first threshold based on the multiple adaptive thresholds and historical data comprises: If the time interval between the reception time of the first information and the reception time of the previous first information is less than the first threshold, the first threshold corresponding to the i-th round of beam measurement is the adaptive threshold that can screen out fewer beams from the multiple adaptive thresholds.

21. The method of claim 19, wherein: The determining the first threshold corresponding to the i-th round of beam measurement based on the change trend of the first threshold includes: If the first threshold corresponding to the (i-1)th round of beam measurement is greater than or equal to the first threshold corresponding to the (i-2)th round of beam measurement, the first threshold corresponding to the (i-1)th round of beam measurement is an adaptive threshold among the multiple adaptive thresholds that is greater than the first threshold corresponding to the (i-1)th round of beam measurement; If the first threshold corresponding to the (i-1)th round of beam measurement is smaller than the first threshold corresponding to the (i-2)th round of beam measurement, the first threshold corresponding to the (i-1)th round of beam measurement is an adaptive threshold among the multiple adaptive thresholds that is smaller than the first threshold corresponding to the (i-1)th round of beam measurement; The first threshold corresponding to the second round of beam measurement is an adaptive threshold that can screen out fewer beams among the multiple adaptive thresholds.

22. The method of claim 14, wherein: The determining the first threshold based on the multiple adaptive thresholds comprises: The first threshold is determined based on the multiple adaptive thresholds and a first event, where the first event includes one or more of the following: The serving cell signal quality of the first node is higher than a specified threshold; The serving cell signal quality of the first node is lower than a specified threshold; A difference between two adjacent channel quality measurement values ​​of the first node is lower than a specified threshold.

23. The method of claim 22, wherein: The determining the first threshold based on the plurality of adaptive thresholds and the first event comprises: The first threshold is determined based on one or more of the following: the plurality of adaptive thresholds; a hysteresis parameter of the first event; The event threshold of the first event is: a measurement result of a serving cell of the first node; A measurement result of the channel quality of the first node.

24. The method according to any one of claims 13 to 23, characterized in that The method further comprises: Receive second information, where the second information includes one or more of the following: beam information corresponding to a beam measurement result that satisfies a first condition, wherein the first condition is associated with the first threshold; Beam information corresponding to the beam prediction result that meets the first condition; The first threshold.

25. The method according to any one of claims 13 to 24, characterized in that The method further comprises: The plurality of adaptive thresholds are determined based on one or more of the following: The first node status information; the first model information; Network status information; Link quality information.

26. A first node for wireless communication, characterized in that: include: The first transceiver is used to receive first information, where the first information is used to indicate multiple adaptive thresholds, the multiple adaptive thresholds are used to select beam information, and the beam information is used for beam prediction based on a first model.

27. The first node according to claim 26, characterized in that The multiple adaptive thresholds are used to select beam information, including: A first threshold is determined based on the multiple adaptive thresholds, where the first threshold is used to determine the beam information.

28. The first node according to claim 27, characterized in that The determining the first threshold based on a plurality of adaptive thresholds comprises: The first threshold is determined by using a second model, the multiple adaptive thresholds are inputs of the second model, and the first threshold is output of the second model.

29. The first node according to claim 27, characterized in that The determining the first threshold based on a plurality of adaptive thresholds comprises: Based on the plurality of adaptive thresholds and / or the first threshold of historical data, the historical data is used to indicate one or more of the following: A statistical collection of historical beam measurement results; a first threshold set corresponding to historical beam measurements; an update frequency of the first information; The variation trend of the first threshold value.

30. The first node according to claim 29, characterized in that The determining the first threshold based on the multiple adaptive thresholds comprises: The first threshold corresponding to the first round of beam measurement is an adaptive threshold that can screen out more beams among the multiple adaptive thresholds.

31. The first node according to claim 29 or 30, characterized in that The determining the first threshold based on the multiple adaptive thresholds and historical data comprises: If the absolute value of the difference between the statistic of the i-th round of beam measurement results and the weighted sum of the statistic set of historical beam measurement results is less than or equal to the second threshold, then the first threshold corresponding to the i-th round of beam measurement is the adaptive threshold of the weighted sum of the first threshold set corresponding to the historical beam measurement among the multiple adaptive thresholds.

32. The first node according to claim 29 or 30, characterized in that The determining the first threshold based on the multiple adaptive thresholds and historical data comprises: If the absolute value of the difference between the statistic of the i-th round of beam measurement results and the weighted sum of the statistical set of historical beam measurement results is greater than the second threshold, the first threshold corresponding to the i-th round of beam measurement is determined based on the update frequency of the first information and / or the changing trend of the first threshold.

33. The first node according to claim 32, characterized in that The determining the first threshold based on the multiple adaptive thresholds and historical data comprises: If the time interval between the reception time of the first information and the reception time of the previous first information is less than the first threshold, the first threshold corresponding to the i-th round of beam measurement is the adaptive threshold that can screen out fewer beams from the multiple adaptive thresholds.

34. The first node according to claim 32, characterized in that The determining the first threshold corresponding to the i-th round of beam measurement based on the change trend of the first threshold includes: If the first threshold corresponding to the (i-1)th round of beam measurement is greater than or equal to the first threshold corresponding to the (i-2)th round of beam measurement, the first threshold corresponding to the (i-1)th round of beam measurement is an adaptive threshold among the multiple adaptive thresholds that is greater than the first threshold corresponding to the (i-1)th round of beam measurement; If the first threshold corresponding to the (i-1)th round of beam measurement is smaller than the first threshold corresponding to the (i-2)th round of beam measurement, the first threshold corresponding to the (i-1)th round of beam measurement is an adaptive threshold among the multiple adaptive thresholds that is smaller than the first threshold corresponding to the (i-1)th round of beam measurement; The first threshold corresponding to the second round of beam measurement is an adaptive threshold that can screen out fewer beams among the multiple adaptive thresholds.

35. The first node according to claim 27, characterized in that The determining the first threshold based on the multiple adaptive thresholds comprises: The first threshold is determined based on the multiple adaptive thresholds and a first event, where the first event includes one or more of the following: The serving cell signal quality of the first node is higher than a specified threshold; The serving cell signal quality of the first node is lower than a specified threshold; A difference between two adjacent channel quality measurement values ​​of the first node is lower than a specified threshold.

36. The first node according to claim 35, characterized in that The determining the first threshold based on the plurality of adaptive thresholds and the first event comprises: The first threshold is determined based on one or more of the following: the plurality of adaptive thresholds; a hysteresis parameter of the first event; The event threshold of the first event is: a measurement result of a serving cell of the first node; A measurement result of the channel quality of the first node.

37. The first node according to any one of claims 26 to 36, characterized in that: The first node also includes: The first transceiver is further configured to send second information, where the second information includes one or more of the following: beam information corresponding to a beam measurement result that satisfies a first condition, wherein the first condition is associated with the first threshold; Beam information corresponding to the beam prediction result that meets the first condition; The first threshold.

38. A second node for wireless communication, characterized in that: include: The second transceiver is used to send first information, where the first information is used to indicate multiple adaptive thresholds, the multiple adaptive thresholds are used to select beam information, and the beam information is used for beam prediction based on the first model.

39. The second node according to claim 38, characterized in that The multiple adaptive thresholds are used to select beam information, including: A first threshold is determined based on the multiple adaptive thresholds, where the first threshold is used to determine the beam information.

40. The second node according to claim 39, characterized in that The determining the first threshold based on a plurality of adaptive thresholds comprises: The first threshold is determined by using a second model, the multiple adaptive thresholds are inputs of the second model, and the first threshold is output of the second model.

41. The second node according to claim 39, characterized in that The determining the first threshold based on a plurality of adaptive thresholds comprises: Based on the plurality of adaptive thresholds and / or the first threshold of historical data, the historical data is used to indicate one or more of the following: A statistical collection of historical beam measurement results; a first threshold set corresponding to historical beam measurements; an update frequency of the first information; The variation trend of the first threshold value.

42. The second node according to claim 41, characterized in that The determining the first threshold based on the multiple adaptive thresholds comprises: The first threshold corresponding to the first round of beam measurement is an adaptive threshold that can screen out more beams among the multiple adaptive thresholds.

43. The second node according to claim 41 or 42, characterized in that: The determining the first threshold based on the multiple adaptive thresholds and historical data comprises: If the absolute value of the difference between the statistic of the i-th round of beam measurement results and the weighted sum of the statistic set of historical beam measurement results is less than or equal to the second threshold, then the first threshold corresponding to the i-th round of beam measurement is the adaptive threshold of the weighted sum of the first threshold set corresponding to the historical beam measurement among the multiple adaptive thresholds.

44. The second node according to claim 41 or 42, characterized in that: The determining the first threshold based on the multiple adaptive thresholds and historical data comprises: If the absolute value of the difference between the statistic of the i-th round of beam measurement results and the weighted sum of the statistical set of historical beam measurement results is greater than the second threshold, the first threshold corresponding to the i-th round of beam measurement is determined based on the update frequency of the first information and / or the changing trend of the first threshold.

45. The second node according to claim 44, characterized in that The determining the first threshold based on the multiple adaptive thresholds and historical data comprises: If the time interval between the reception time of the first information and the reception time of the previous first information is less than the first threshold, the first threshold corresponding to the i-th round of beam measurement is the adaptive threshold that can screen out fewer beams from the multiple adaptive thresholds.

46. ​​The second node according to claim 44, characterized in that The determining the first threshold corresponding to the i-th round of beam measurement based on the change trend of the first threshold includes: If the first threshold corresponding to the (i-1)th round of beam measurement is greater than or equal to the first threshold corresponding to the (i-2)th round of beam measurement, the first threshold corresponding to the (i-1)th round of beam measurement is an adaptive threshold among the multiple adaptive thresholds that is greater than the first threshold corresponding to the (i-1)th round of beam measurement; If the first threshold corresponding to the (i-1)th round of beam measurement is smaller than the first threshold corresponding to the (i-2)th round of beam measurement, the first threshold corresponding to the (i-1)th round of beam measurement is an adaptive threshold among the multiple adaptive thresholds that is smaller than the first threshold corresponding to the (i-1)th round of beam measurement; The first threshold corresponding to the second round of beam measurement is an adaptive threshold that can screen out fewer beams among the multiple adaptive thresholds.

47. The second node according to claim 39, characterized in that The determining the first threshold based on the multiple adaptive thresholds comprises: The first threshold is determined based on the multiple adaptive thresholds and a first event, where the first event includes one or more of the following: The serving cell signal quality of the first node is higher than a specified threshold; The serving cell signal quality of the first node is lower than a specified threshold; A difference between two adjacent channel quality measurement values ​​of the first node is lower than a specified threshold.

48. The second node according to claim 47, characterized in that The determining the first threshold based on the plurality of adaptive thresholds and the first event comprises: The first threshold is determined based on one or more of the following: the plurality of adaptive thresholds; a hysteresis parameter of the first event; The event threshold of the first event is: a measurement result of a serving cell of the first node; A measurement result of the channel quality of the first node.

49. The second node according to any one of claims 38 to 48, characterized in that: The second node also includes: The second transceiver is further configured to receive second information, where the second information includes one or more of the following: beam information corresponding to a beam measurement result that satisfies a first condition, wherein the first condition is associated with the first threshold; Beam information corresponding to the beam prediction result that meets the first condition; The first threshold.

50. The second node according to any one of claims 38 to 49, characterized in that: The second node also includes: The first processor is configured to determine the plurality of adaptive thresholds based on one or more of the following: The first node status information; the first model information; Network status information; Link quality information.

51. A terminal device, characterized in that: The terminal comprises a transceiver, a memory and a processor, wherein the memory is used to store programs, and the processor is used to call the programs in the memory and control the transceiver to receive or send signals, so that the terminal executes the method as described in any one of claims 1 to 12.

52. A network device, characterized in that: It comprises a transceiver, a memory and a processor, wherein the memory is used to store programs, and the processor is used to call the programs in the memory and control the transceiver to receive or send signals so that the network device executes the method as described in any one of claims 13-25.

53. A device, characterized in that: The device comprises a processor, configured to call a program from a memory so as to enable the device to execute the method according to any one of claims 1 to 25.

54. A chip, characterized in that: It comprises a processor, which is used to call a program from a memory, so that a device equipped with the chip executes a method as claimed in any one of claims 1 to 25.

55. A computer-readable storage medium, characterized in that: A program is stored thereon, the program causing a computer to execute the method according to any one of claims 1 to 25.

56. A computer program product, characterized in that The method comprises a program which causes a computer to execute the method according to any one of claims 1 to 25.

57. A computer program, characterized in that The computer program enables a computer to execute the method according to any one of claims 1 to 25.

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