Wireless communication method, terminal device, and network device

Abstract

Provided are a wireless communication method, a terminal device, and a network device. The method comprises: a terminal device receives first information sent by a network device, wherein the first information is used for instructing the terminal device to perform RRM measurement and report a measurement result of the RRM measurement, the measurement result is used for monitoring a prediction model, and the prediction model is used for predicting the measurement result of the RRM measurement. Alternatively, the method comprises: a terminal device receives second information sent by a network device, wherein the second information comprises a predicted value of a measurement result of RRM measurement, the second information is used for instructing the terminal device to perform RRM measurement and monitor a prediction model on the basis of the measurement result of the RRM measurement and the predicted value, the second information is further used for instructing the terminal device to report third information related to a monitoring result of the prediction model, and the prediction model is used for predicting the measurement result of the RRM measurement. In this way, effective monitoring of prediction models is achieved.

Description

Wireless communication methods, terminal devices, and network devices Technical Field

[0001] This application relates to the field of communication technology, and more specifically, to a wireless communication method, terminal device, and network device. Background Technology

[0002] After performing radio resource management (RRM), the terminal device can report the measurement results and related information to the network device in the form of a measurement report. This allows the network device to make relevant decisions based on the reported information, such as cell handover decisions and beam switching decisions. If the network device has a model deployed, it can use this model to predict the RRM measurement results. The performance of this model directly affects the prediction results, and thus the network device's relevant decisions. Therefore, it is necessary to monitor this model.

[0003] Summary of the Invention

[0004] This application provides a wireless communication method, terminal device, and network device. The various aspects covered by this application are described below.

[0005] In a first aspect, a wireless communication method is provided, comprising: a terminal device receiving first information sent by a network device, the first information being used to instruct the terminal device to perform RRM measurement and report the measurement result of the RRM measurement, the measurement result being used for monitoring a prediction model, and the prediction model being used for predicting the measurement result of the RRM measurement.

[0006] In a second aspect, a wireless communication method is provided, comprising: a network device sending first information to a terminal device, the first information being used to instruct the terminal device to perform RRM measurement and report the measurement result of the RRM measurement, the measurement result being used for monitoring a prediction model, and the prediction model being used for predicting the measurement result of the RRM measurement.

[0007] Thirdly, a wireless communication method is provided, comprising: a terminal device receiving second information sent by a network device, the second information including a predicted value of the measurement result of an RRM measurement, the second information being used to instruct the terminal device to perform an RRM measurement and to monitor a prediction model based on the measurement result of the RRM measurement and the predicted value, the second information also being used to instruct the terminal device to report third information related to the monitoring result of the prediction model, the prediction model being used to predict the measurement result of the RRM measurement.

[0008] Fourthly, a wireless communication method is provided, comprising: a network device sending second information to a terminal device, the second information including a predicted value of the measurement result of an RRM measurement, the second information being used to instruct the terminal device to perform an RRM measurement and monitor a prediction model based on the measurement result of the RRM measurement and the predicted value, the second information also being used to instruct the terminal device to report third information related to the monitoring result of the prediction model, the prediction model being used to predict the measurement result of the RRM measurement.

[0009] Fifthly, a terminal device is provided, comprising: a transceiver unit, configured to receive first information sent by a network device, the first information being configured to instruct the terminal device to perform RRM measurement and report the measurement results, the measurement results being used for monitoring a prediction model, and the prediction model being used for predicting the measurement results of the RRM measurement.

[0010] In a sixth aspect, a network device is provided, comprising: a transceiver unit, configured to send first information to a terminal device, the first information being configured to instruct the terminal device to perform RRM measurement and report the measurement results, the measurement results being used for monitoring a prediction model, and the prediction model being used for predicting the measurement results of the RRM measurement.

[0011] A seventh aspect provides a terminal device, comprising: a transceiver unit, configured to receive second information sent by a network device, the second information including a predicted value of a measurement result of a radio resource management (RRM) measurement, the second information being configured to instruct the terminal device to perform RRM measurement and monitor a prediction model based on the measurement result and the predicted value, and to report third information related to the monitoring result, the prediction model being used to predict the measurement result of the RRM measurement.

[0012] Eighthly, a network device is provided, comprising: a transceiver unit, configured to send second information to a terminal device, the second information including a predicted value of a measurement result of a radio resource management (RRM) measurement, the second information being configured to instruct the terminal device to perform an RRM measurement and monitor a prediction model based on the measurement result and the predicted value, and to report third information related to the monitoring result, the prediction model being used to predict the measurement result of the RRM measurement.

[0013] A ninth aspect provides a terminal device including a transceiver, a memory, and a processor, wherein the memory is used to store a program, the processor is used to invoke the program in the memory, and to control the transceiver to receive or send signals, so that the terminal device performs the method as described in the first or third aspect.

[0014] In a tenth aspect, a network device is provided, including a transceiver, a memory, and a processor, wherein the memory is used to store a program, and the processor is used to invoke the program in the memory and control the transceiver to receive or transmit signals, so that the network device performs the method as described in the second or fourth aspect.

[0015] Eleventhly, an apparatus is provided, including a processor for calling a program from a memory to cause the apparatus to perform the method as described in the first, second, third, or fourth aspect.

[0016] In a twelfth aspect, a chip is provided, including a processor for calling a program from a memory to cause a device having the chip mounted to perform the methods described in the first, second, third, or fourth aspects.

[0017] In a thirteenth aspect, a computer-readable storage medium is provided having a program stored thereon that causes a computer to perform the methods described in the first, second, third, or fourth aspects.

[0018] Fourteenthly, a computer program product is provided, including a program that causes a computer to perform the methods described in the first, second, third, or fourth aspects.

[0019] In a fifteenth aspect, a computer program is provided that causes a computer to perform the methods described in the first, second, third, or fourth aspects.

[0020] In this embodiment, the terminal device can perform RRM measurement and report the measurement results via first information sent by the network device, so that the network device can monitor the prediction model of the RRM measurement based on the measurement results; or, the terminal device can perform RRM measurement via second information sent by the network device, monitor the prediction model of the RRM measurement based on the measurement results and the predicted value of the measurement results carried in the second information, and report third information related to the monitoring results. In this way, effective monitoring of the prediction model is achieved. Attached Figure Description

[0021] Figure 1 is a system architecture example diagram of a wireless communication system applicable to embodiments of this application.

[0022] Figures 2 and 3 are schematic diagrams of time-domain prediction scenarios that can be applied to the embodiments of this application.

[0023] Figure 4 is a schematic diagram of a spatial prediction scenario that can be applied to the embodiments of this application.

[0024] Figure 5 is a schematic diagram of a frequency domain prediction scenario that can be applied to the embodiments of this application.

[0025] Figure 6 is a schematic diagram of the functional framework of the AI / ML model that can be applied in the embodiments of this application.

[0026] Figure 7 is a schematic diagram of the inference process of the AI / ML model that can be applied to the embodiments of this application.

[0027] Figure 8 is a flowchart illustrating a wireless communication method provided in an embodiment of this application.

[0028] Figure 9 is a flowchart illustrating a wireless communication method provided in another embodiment of this application.

[0029] Figure 10 is a flowchart illustrating a wireless communication method provided in another embodiment of this application.

[0030] Figure 11 is a schematic diagram of the structure of the terminal device provided in the embodiment of this application.

[0031] Figure 12 is a schematic diagram of the structure of the network device provided in the embodiment of this application.

[0032] Figure 13 is a schematic diagram of the structure of the terminal device provided in the embodiment of this application.

[0033] Figure 14 is a schematic diagram of the network device provided in an embodiment of this application.

[0034] Figure 15 is a schematic diagram of a device applicable to embodiments of this application. Detailed Implementation

[0035] The technical solutions in this application will now be described with reference to the accompanying drawings.

[0036] Wireless communication system

[0037] Figure 1 is an example diagram of the system architecture of a wireless communication system 100 to which embodiments of this application can be applied. The wireless communication system 100 may include a network device 110 and a terminal device 120. The network device 110 may be a device that communicates with the terminal device 120. The network device 110 can provide network coverage for a specific geographical area and can communicate with the terminal device 120 located within that coverage area. The terminal device 120 can access a network, such as a wireless network, through the network device 110. Optionally, the wireless communication system 100 may also include other network entities such as a network controller and a mobility management entity; this embodiment of the application does not limit this.

[0038] It should be understood that the technical solutions of the embodiments of this application can be applied to various communication systems, such as: fifth generation (5G) systems, new radio (NR), long term evolution (LTE) systems, LTE frequency division duplex (FDD) systems, LTE time division duplex (TDD) systems, etc. The technical solutions provided in this application can also be applied to future communication systems, such as sixth generation mobile communication systems, satellite communication systems, etc.

[0039] The terminal device in this application embodiment can 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 this application embodiment can be a device that provides voice and / or data connectivity to a user, and can be used to connect people, objects, and machines, such as a handheld device with wireless connectivity, vehicle-mounted device, etc. The terminal devices in the embodiments of this application can be mobile phones, tablets, laptops, PDAs, mobile internet devices (MIDs), wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless terminals in industrial control, self-driving, remote medical surgery, smart grids, transportation safety, smart cities, and smart homes, etc. Optionally, the terminal device can act as a base station. For example, the terminal device can act as a scheduling entity, providing sidelink signals between terminal devices in vehicle-to-everything (V2X) or device-to-device (D2D) systems. For instance, cellular phones and cars communicate with each other using sidelink signals. Cellular phones and smart home devices communicate without relaying communication signals through base stations.

[0040] The network device in this application embodiment can be a device for communicating with terminal devices. This network device can be, for example, an access network device or a wireless access network device. For instance, the network device can be a base station. The term "base station" can broadly encompass various names, or be replaced by, the following: NodeB, evolved NodeB (eNB), next-generation NodeB (gNB), relay station, access point, transmitting and receiving point (TRP), transmitting point (TP), femtocell, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), positioning node, etc. A base station can be a macro base station, micro base station, relay node, donor node, or the like, or a combination thereof.

[0041] RRM measurement

[0042] In 3GPP cellular communication systems, terminal equipment typically needs to determine the strength or quality of the radio signals in the current serving cell and neighboring cells through measurements. For example, the terminal equipment can measure the cell's signal strength, i.e., reference signal receiving power (RSRP); it can also measure the cell's signal quality, i.e., reference signal receiving quality (RSRQ); and it can measure the cell's signal-to-interference noise ratio (SINR). These types of measurements are called RRM measurements. After performing RRM measurements, the terminal equipment can report the relevant information to the network equipment in the form of a measurement report, so that the network equipment can make relevant decisions based on the reported information, such as cell handover decisions and beam switching decisions.

[0043] The measurement report can be carried, for example, within a radio resource control (RRC) message. This report may include specific measurement events and / or measurement results. The cells reported by the terminal device in the measurement report may include the current serving cell and / or neighboring cells. For example, the terminal device may report the signal strength and signal quality of the current serving cell; or, the terminal device may report the signal strength and signal quality of both the current serving cell and neighboring cells. The measurement objects included in the measurement report can be various, such as frequencies from the same frequency, different frequencies, or different communication systems.

[0044] Artificial intelligence (AI) mobility project

[0045] In AI mobility projects, artificial intelligence (AI) / machine learning (ML) models can be used to predict RRM measurement results so that network devices can make decisions as early as possible based on the predicted values ​​of the measurement results, such as cell handover decisions and beam switching decisions.

[0046] In RRM measurement scenarios, when AI / ML models are applied, they can be deployed on terminal devices, network devices, or both. The technical solutions in this application embodiment address the case where the AI / ML model is deployed on a network device, i.e., the AI / ML model is a network-side model.

[0047] At the RAN2#126 meeting, the following four research scenarios were proposed for predicting the measurement results of RRM measurements.

[0048] Scenario 1: Time-domain prediction

[0049] Time-domain prediction includes two cases.

[0050] First, it predicts future measurement results based on historical measurement results, thus knowing in advance what will happen and preparing accordingly. As an example, as shown in Figure 2, black represents the actual measurement value, and white represents the predicted value obtained by using the model to predict the measurement results. Using the measurement values ​​at sampling times T1, T2, T3, and T4, the measurement results at sampling times T5 and T6 are predicted.

[0051] Secondly, interpolation prediction is used to fill in the missing measurement values ​​between adjacent measurements, thereby reducing the number of measurements required. As an example, as shown in Figure 3, black represents the actual measurement value, and white represents the predicted value obtained after using the model to predict the measurement results. Originally, measurements needed to be taken at sampling times T1, T2, T3, T4, T5, and T6. Now, measurements can be taken only at sampling times T1, T3, and T5, and the model can predict the measurement results at sampling times T2, T4, and T6. This effectively reduces the measurement overhead and energy consumption of the terminal equipment without compromising mobility performance.

[0052] Scenario 2: Airspace Prediction

[0053] Spatial prediction, for example, refers to using measurements of a subset of beams to predict the measurement results of the remaining unmeasured beams. For instance, as shown in Figure 4, black represents the measured beams, white represents the unmeasured beams, the horizontal axis represents the azimuth angle, and the vertical axis represents the zenith angle. Where 32 beams originally required measurement, now only 16 beams need to be measured, and the model can be used to predict the measurement results of the remaining 16 beams. Figure 4 uses a measurement reduction ratio (MRR) of 50% as an example, where MRR = 1 - 16 / 32 = 50%.

[0054] Scenario 3: Frequency Domain Prediction

[0055] Frequency domain prediction refers to using the measurement value of a certain frequency point to predict the measurement results of other frequencies and obtain the corresponding predicted values. For example, as shown in Figure 5, the black part represents the frequency point where cell A is located, and the white part represents the frequency point where cell B is located. The measurement value of the frequency point where cell A is located is used to predict the measurement result of the frequency point where cell B is located.

[0056] Functional framework of AI / ML models

[0057] The 3rd Generation Partnership Project (3GPP), in its Release 18 (Rel 18), investigated whether AI / ML models could improve physical layer performance. The findings are documented in Technical Report TR38.843. This report not only records the evaluation methods and results related to AI / ML models but also outlines the steps and content for managing them. This content forms the framework of lifecycle management (LCM). It's important to note that LCM is a broad term encompassing data collection, model training, model identification, model transfer, model inference, model selection, model activation, model deactivation, model replacement and fallback, model monitoring, model updates, and end-device capability reporting.

[0058] To facilitate understanding, the LCM of AI / ML models will be introduced below with reference to Figure 6.

[0059] As an example, Figure 6 illustrates the functional framework of an AI / ML model. After the model is trained and deployed on a communication device, it can be used for model inference. Model inference refers to the process of obtaining a corresponding output result after receiving a specific input, which is usually a prediction result. The AI / ML model used for model inference is periodically monitored, and model management is then performed based on the monitoring results. Model management includes, for example, activating the model or switching to a better-performing model when its performance is poor or it is unsuitable.

[0060] In this embodiment of the application, model monitoring can also be called model surveillance, which is used to monitor the accuracy, stability, robustness and efficiency of the model to ensure the reliability and effectiveness of the model in practical applications.

[0061] Referring to Figure 6, an AI / ML model typically includes functional modules such as data collection, model training, model storage, model inference, and model management. These functional modules are briefly described below.

[0062] The data collection module is used to collect data, such as training data, monitoring data, and inference data. The model training module can use the training data to train the model. The model storage module stores the trained model for later use as needed. The model inference module performs model inference; for example, it provides the model with specific inference data and obtains the model's output, which is usually a prediction, also known as an inference result or estimate. The model management module monitors and / or manages the model. For example, the model management module can monitor the model using monitoring data (or model labels) and provide feedback to the model training module when model performance is poor, facilitating model retraining. Alternatively, the model management module can send instructions to the model storage module to deploy a specific model. Furthermore, the model management module can send instructions to the model inference module to deactivate the model when model performance is poor or the model is unsuitable.

[0063] After training the AI / ML model using training data, the trained model is stored for subsequent deployment based on actual needs. Once deployed, the communication device with the model can use it for inference. For example, the model can be provided with RRM measurement values ​​and output predicted values ​​based on the measurement results. During this process, the model can be monitored, allowing for model management based on the monitoring results.

[0064] Measurement model

[0065] In 3GPP standard specification 38.331, the measurement results used for determining measurement events are those filtered through the radio resource control (RRC) layer, specifically layer 3 (L3). The initial measurement results within the terminal device are those of the physical layer, specifically layer 1 (L1), and are for a single beam. Section 5.5.3 of specification 38.331 describes how the terminal device performs intra-frequency / inter-frequency measurements, and how it proceeds from layer 1 beam-based measurement sampling to determining measurement events based on network configuration parameters. This can be described using the model diagram shown in section 5.5.3 of specification 38.331. The following is a brief description of the reference points designed for this process, with reference to Figure 7.

[0066] Reference point A: This is the physical layer measurement sampling step performed by the terminal device. The terminal device can perform physical layer measurement sampling according to the granularity of the beam. As an example, Figure 7 shows sampling for beams 1 to K. Here, the sampling can also be referred to as sampling or measurement.

[0067] Reference point A1: The terminal equipment performs Layer 1 filtering on the beam measurement results of the K beams. Typically, the protocol specifies the length of the measurement cycle under a specific RRC configuration. Within each measurement cycle, the terminal equipment must perform at least one sampling, and the beam measurement results after Layer 1 filtering must meet the performance requirements specified in specification 38.133. The number of samplings by the terminal equipment within each measurement cycle is predetermined; for example, the terminal equipment can perform 4 to 5 oversamplings within a test cycle.

[0068] Reference point B: The beam measurement results of K beams within a cell obtained at reference point A1 are merged to form a layer 1 cell-level measurement result. The layer 1 cell-level measurement result can typically represent the signal quality of the cell.

[0069] Reference point C: The cell-level layer 1 measurement results are sequentially filtered by layer 3 to obtain the cell-level layer 3 measurement results.

[0070] Reference point D: The measurement results of the serving cell and / or neighboring cells are used to determine whether a specific measurement event has occurred, based on predetermined decision conditions. For example, it may determine whether the measurement result of a neighboring cell is higher than the measurement result of the current serving cell by an offset value (i.e., an A3 event has occurred). These decision conditions can be configured by network devices, for example.

[0071] It should be noted that the specific details of the other contents shown in Figure 7 can be found in the relevant descriptions in the 3GPP protocol TS38.331. For the sake of brevity, they will not be repeated here.

[0072] For models deployed on network devices, measurement results can be predicted based on partial measurement data reported by terminal devices, as shown in the four prediction scenarios in Figures 2 to 5 above. To monitor this model, the predicted values ​​of the measurement results need to be compared with the actual measured values. For example, in the spatial domain prediction scenario, the terminal device measures 16 specified beams (or the measurement beam set) and obtains the corresponding measurement values. These measurement values ​​are then reported to the network device for model inference to obtain the measurement results for the remaining 16 unmeasured beams (or the prediction beam set). The monitoring process for this model is not necessarily continuous; for example, it can be performed at intervals, such as 10 seconds. When monitoring the model, the terminal device needs to perform actual measurements on the prediction beam set to obtain the actual measurement values, and then compare the predicted values ​​with the actual measurement values ​​to obtain the corresponding monitoring results. It should be noted that since the model in this embodiment is deployed on a network device, the inference result of the model, i.e. the prediction result, is located on the network device, while the actual measurement value is located on the terminal device. That is, the prediction value and the measurement value are distributed in different locations. If the prediction value and the measurement value are to be compared, the network device and the terminal device need to cooperate with each other.

[0073] To address this, this application provides two solutions for monitoring models deployed on network devices. One solution involves the network device monitoring the model. For example, the network device sends a first message to a terminal device, instructing the terminal device to perform RRM measurements and report the results. This allows the network device to monitor the model based on the measurement results and the model's predicted values. The other solution involves the terminal device monitoring the model. For example, the network device sends a second message carrying predicted values ​​to the terminal device. This allows the terminal device to monitor the model based on the predicted values ​​and the RRM measurements obtained from them.

[0074] The technical solutions of the embodiments of this application will be described in detail below with reference to Figures 8 to 10. Figure 8 shows a scheme for monitoring the model by a network device, while Figures 9 and 10 show schemes for monitoring the model by a terminal device. In this embodiment, the model is also referred to as a prediction model, which is used to predict the measurement results of RRM measurements.

[0075] Figure 8 is a flowchart illustrating a wireless communication method according to an embodiment of this application. The method 200 shown in Figure 8 can be executed by a terminal device and a network device.

[0076] Referring to Figure 8, in step 210, the network device sends the first information to the terminal device.

[0077] Accordingly, in step 220, the terminal device receives the first information sent by the network device.

[0078] The first information is used to instruct the terminal device to perform RRM measurement and report the measurement results of the RRM measurement, which are used for monitoring the predictive model.

[0079] This predictive model is used to predict the measurement results of RRM measurements, and can be, for example, the aforementioned AI / ML model. This predictive model is deployed on network devices. Therefore, the network device knows the predicted value output by the predictive model, and simultaneously instructs the terminal device to report the actual measurement value by sending a first message. The network device then compares the predicted value with the actual measurement value to obtain the monitoring result and performs corresponding operations on the predictive model based on this monitoring result.

[0080] The first piece of information will be described in detail below.

[0081] In some implementations, the first information can be carried in RRC signaling. For example, the first information can be carried in RRC reconfiguration signaling or RRC recovery signaling.

[0082] In some implementations, the first information includes one or more of the following: the reporting time of the measurement results; the prediction scenario to which the prediction model is applied; and the information of the measurement object of the RRM measurement.

[0083] The terminal device can perform RRM measurement based on the information of the predicted scenario and / or the information of the measurement object, and report the RRM measurement results to the network device based on the reporting time information of the measurement results. The following describes the reporting time information of the measurement results, the information of the predicted scenario to which the prediction model is applied, and the information of the measurement object of the RRM measurement that may be carried in the first information.

[0084] First, the reporting time information of the measurement results is described.

[0085] In some implementations, the reporting time information of the measurement results includes single reporting indication information and / or periodic reporting indication information. The single reporting indication information is used to instruct the terminal device to report the measurement results at a predetermined time, and the periodic reporting indication information is used to instruct the terminal device to report the measurement results based on a predetermined period.

[0086] If the first information includes a single-report instruction, the terminal device will report a measurement result once after receiving the single-report instruction. Afterward, the terminal device will only report the measurement result again if it receives another single-report instruction. Alternatively, the first information can carry multiple single-report instructions simultaneously. In this case, the terminal device can continuously report measurement results to the network device at multiple times indicated by the multiple single-report instructions.

[0087] After receiving a single-report instruction, the terminal device reports the measurement result to the network device at a predetermined time. This predetermined time can be an absolute time or a relative time, such as an offset relative to the moment the single-report instruction was received. Optionally, the predetermined time can be included in the first information; or, the first information may not include the predetermined time, i.e., the predetermined time is a default value. In this case, the predetermined time could be, for example, the current time, i.e., the moment the terminal device receives the single-report instruction.

[0088] If the first information includes periodic reporting indication information, the terminal device, upon receiving the periodic reporting indication information, reports the measurement results based on a predetermined period. Optionally, the predetermined period may be carried in the first information; or, the first information may not carry the predetermined period, i.e., the predetermined period is a default value. In this case, the predetermined period may be determined based on the prediction period of the prediction model. For example, the predetermined period may be equal to the prediction period of the prediction model, where the prediction period of the prediction model refers to the period during which the prediction model predicts the measurement results of the RRM measurement. In other implementations, the first information may also include a reporting start position, and the terminal device may report the measurement results to the network device based on the reporting start position and the predetermined period.

[0089] If the first information includes both single-reporting indication information and periodic reporting indication information, the terminal device can report the measurement results based on a predetermined period, and simultaneously report the measurement results based on the single-reporting indication information.

[0090] To conserve energy from air interface signaling and terminal equipment, network devices monitor the predictive model intermittently. Single-report indications provide flexibility in indicating monitoring timing, while periodic reporting further reduces air interface signaling transmission and terminal equipment energy consumption.

[0091] Secondly, the prediction scenarios in which the prediction model is applied are described.

[0092] In some implementations, the information of the prediction scenario applied by the prediction model includes one or more of the following: time-domain prediction; spatial-domain prediction; and frequency-domain prediction. Time-domain prediction refers to predicting future measurement results using historical measurement results, as shown in Figure 2 above; or, time-domain prediction refers to predicting the measurement results of sampling times that were not measured between two adjacent sampling times using measurement results from a subset of sampling times, as shown in Figure 3 above. Spatial-domain prediction refers to predicting the measurement results of the remaining unmeasured beams using measurement values ​​from a subset of beams, as shown in Figure 4 above. Frequency-domain prediction refers to predicting the measurement results of other frequencies using measurement values ​​from a specific frequency point, as shown in Figure 5 above. Optionally, if multiple prediction models are deployed for a certain prediction scenario, the first information may also carry corresponding model indication information to indicate which prediction model in the prediction scenario is currently being monitored.

[0093] In some implementations, if the prediction scenario includes time-domain prediction, the first information may also include one or more of the following: the start time of RRM measurement; the end time of RRM measurement; the duration of RRM measurement; the sampling time of RRM measurement; and the sampling period of RRM measurement.

[0094] For example, if the prediction scenario includes time-domain prediction, and specifically, predicts future measurement results using historical measurement results, then the first information may further include one or more of the following: the measurement start time, the measurement end time, and the measurement duration of the RRM measurement. The measurement start time and measurement end time can be absolute times or relative times, such as an offset relative to the moment the single reporting instruction information is received. If the first information does not carry a measurement end time, optionally, the measurement end time can be the moment when the time for reporting the measurement result, as indicated by the aforementioned single reporting instruction information or periodic reporting instruction information, ends.

[0095] For example, if the prediction scenario includes time-domain prediction, and specifically, uses the measurement results of some sampling times to predict the measurement results of sampling times that were not measured between two adjacent sampling times, then the first information may also include one or more of the following: the measurement start time of the RRM measurement, the measurement end time, the sampling time of the RRM measurement, and the sampling period of the RRM measurement. As an example, assuming the information carried in the first information is (measurement start time, sampling period) = {(measurement start time = 0, sampling period = 3), (measurement start time = 1, sampling period = 3)}, it means that a measurement is performed every 3 time slots starting from time slot 0, that is, measurements are performed at time slots 0, 3, 6, etc., and a measurement is also performed every 3 time slots starting from time slot 1, that is, measurements are performed at time slots 1, 4, 7, etc.

[0096] In this embodiment of the application, the measurement start time can also be referred to as the monitoring start time or the measurement monitoring start time; the measurement end time can also be referred to as the monitoring end time or the measurement monitoring end time; and the measurement duration can also be referred to as the monitoring duration or the measurement monitoring duration.

[0097] In some implementations, if the prediction scenario includes spatial prediction, the first information may also include one or more of the following: the start time of the RRM measurement; the end time of the RRM measurement; the duration of the RRM measurement; the sampling time of the RRM measurement; the sampling period of the RRM measurement; and the identification information of the monitored beam.

[0098] Here, the identity (ID) information of the monitored beams can be the individual beam identifiers of the multiple monitored beams, or it can be the beam set identifier of the beam set formed by these multiple beams. This beam set identifier can also be called a beam set mapping identifier, used to identify a specific group of beams. As an example, suppose the monitored beams include beam 1, beam 3, beam 5, and beam 7, with corresponding beam identifiers ID=1, ID=3, ID=5, and ID=7, respectively. In this case, the first information can carry the individual beam identifiers of beam 1, beam 3, beam 5, and beam 7, i.e., {1, 3, 5, 7}; or, the first information can also carry the identifier of the beam set formed by beam 1, beam 3, beam 5, and beam 7, i.e., {0}. The identifier of the beam set formed by beam 1, beam 3, beam 5, and beam 7 can be queried, for example, through the mapping relationship shown in Table 1.

[0099] Table 1

[0100] In some implementations, if the prediction scenario includes frequency domain prediction, the first information may also include one or more of the following: the start time of RRM measurement; the end time of RRM measurement; the duration of RRM measurement; the sampling time of RRM measurement; the sampling period of RRM measurement; the identification information of the monitored beam; the monitored frequency point; and the identification information of the monitored cell.

[0101] Finally, the measurement objects of RRM measurement are described.

[0102] In some implementations, the measurement objects of RRM measurements can include Layer 1 measurements and / or Layer 3 measurements. For example, the measurement objects can include one or more of the following: beam-level Layer 1 measurements; beam-level Layer 3 measurements; cell-level Layer 1 measurements; and cell-level Layer 3 measurements. Here, beam-level measurement results are obtained by measuring each beam individually, while cell-level measurement results can be considered as the result of merging the measurement results of each beam within the cell. Taking RSRP as an example, if the first information carries cell-level L3-RSRP, it indicates that it is a cell-level Layer 3 measurement for RSRP.

[0103] Based on the above description, since the prediction model is deployed on network devices, it is more convenient for the network devices to monitor the prediction model in method 200, which can reduce the application complexity of terminal devices.

[0104] Figure 9 is a flowchart illustrating a wireless communication method according to another embodiment of this application. The method 300 shown in Figure 9 can be executed by a terminal device and a network device.

[0105] Referring to Figure 9, in step 310, the network device sends the second information to the terminal device.

[0106] Accordingly, in step 320, the terminal device receives the second information sent by the network device.

[0107] The second information includes a predicted value of the RRM measurement result. This second information is used to instruct the terminal device to perform RRM measurement and to monitor the prediction model based on the RRM measurement result and the predicted value.

[0108] This predictive model is used to predict the measurement results of RRM measurements; for example, it can be the aforementioned AI / ML model. This predictive model is deployed on network devices. Therefore, the network devices know the predicted values ​​output by the predictive model and send these predicted values ​​to the terminal devices. The terminal devices then compare the predicted values ​​with the actual measured values ​​to obtain the monitoring results.

[0109] After the terminal device monitors the prediction model and obtains the monitoring results, as shown in Figure 10, method 300 may further include steps 330 and 340.

[0110] In step 330, the terminal device reports third information related to the monitoring results to the network device.

[0111] Accordingly, in step 340, the network device receives the third information reported by the terminal device.

[0112] The following is a message description of the third information.

[0113] In some implementations, the third information includes: monitoring results of the predictive model; and / or, operational recommendations for the predictive model based on the monitoring results.

[0114] In some implementations, the monitoring results of the prediction model in the third information include: the error between the predicted value and the actual measured value; and / or, error indication information to indicate whether the error is within a predetermined range.

[0115] The error refers to, for example, the difference between the actual measured value and the predicted value of RSRP, RSRQ, or SINR, which could be 3 dB. This error indication information indicates whether the error is within a predetermined range. As an example, the error indication information may include 0 or 1, where 0 indicates the error is not within the predetermined range and 1 indicates the error is within the predetermined range, or 0 indicates the error is within the predetermined range and 1 indicates the error is not within the predetermined range. The predetermined range may be pre-defined, or the predetermined information may be carried in the second information.

[0116] The terminal device can report the monitoring results of the prediction model to the network device, which can then obtain operational suggestions for the prediction model based on the monitoring results; alternatively, the terminal device can obtain operational suggestions for the prediction model based on the monitoring results of the prediction model and report these suggestions to the network device.

[0117] In some implementations, the operational suggestions in the third information include deactivating and / or switching the prediction model. For example, if the error exceeds a predetermined range, it indicates that the prediction model is performing poorly or is no longer applicable. In this case, certain operations can be performed on the prediction model, such as deactivating it or switching it to a better-performing model.

[0118] The terminal device monitors the prediction model and reports third information related to the detection results based on the second information it receives. The second information is described in detail below.

[0119] In some implementations, the second information can be carried in RRC signaling. For example, the second information can be carried in RRC reconfiguration signaling or RRC recovery signaling.

[0120] In some implementations, the second information includes one or more of the following: the reporting time information of the third information; the information of the prediction scenario to which the prediction model is applied; and the information of the measurement object of the RRM measurement.

[0121] The terminal device can perform RRM measurements based on the information of the predicted scenario and / or the information of the measurement object to obtain the actual measurement value. This measurement value, along with the predicted value carried in the second information, can then be used to monitor the prediction model. The following describes the reporting time information of the third information that may be carried in the second information, the information of the prediction scenario to which the prediction model is applied, and the information of the measurement object for the RRM measurement.

[0122] First, the reporting time information of the third information included in the second information is described.

[0123] In some implementations, the reporting time information for the third information includes one or more of the following: single-reporting indication information; periodic reporting indication information; and event-triggered indication information. Specifically, the single-reporting indication information instructs the terminal device to report measurement results at a predetermined time; the periodic reporting indication information instructs the terminal device to report measurement results based on a predetermined period; and the event-triggered indication information instructs the terminal device to report the third information when a predetermined event occurs.

[0124] The predetermined event includes, for example, the error between the predicted value and the actual measured value exceeding a predetermined range. This predetermined range may be pre-agreed upon, or the predetermined information may be carried within a second set of information.

[0125] If the second information includes a single-report instruction, the terminal device will report the third information once after receiving the single-report instruction. Afterward, the terminal device will only report the third information again if it receives another single-report instruction. Alternatively, the second information can carry multiple single-report instructions simultaneously. In this case, the terminal device can continuously report the third information to the network device at multiple times indicated by the multiple single-report instructions.

[0126] After receiving a single-report instruction, the terminal device reports third information to the network device at a predetermined time. This predetermined time can be an absolute time or a relative time, such as an offset relative to the moment the single-report instruction was received. Optionally, the predetermined time can be included in the second information; or, the second information may not include the predetermined time, i.e., the predetermined time is a default value. In this case, the predetermined time can be, for example, the current time, i.e., the moment the terminal device received the single-report instruction.

[0127] If the second information includes periodic reporting indication information, the terminal device, upon receiving the periodic reporting indication information, reports the third information based on a predetermined period. Optionally, the predetermined period may be carried in the second information; or, the second information may not carry the predetermined period, i.e., the predetermined period is a default value. In this case, the predetermined period may be determined based on the prediction period of the prediction model. For example, the predetermined period may be equal to the prediction period of the prediction model, which refers to the period during which the prediction model predicts the measurement results of the RRM measurement. In other implementations, the second information may also include a reporting start position, and the terminal device may report the third information to the network device based on the reporting start position and the predetermined period.

[0128] If the second information includes both single-reporting instruction information and periodic reporting instruction information, the terminal device can report the third information based on a predetermined period, and simultaneously report the third information based on the single-reporting instruction information.

[0129] To conserve energy from air interface signaling and terminal equipment, network devices monitor the predictive model intermittently. Single-report indications provide flexibility in indicating monitoring timing, while periodic reporting further reduces air interface signaling transmission and terminal equipment energy consumption.

[0130] Secondly, the prediction scenarios in which the prediction models included in the second set of information are applied are described.

[0131] In some implementations, the information of the prediction scenario applied by the prediction model includes one or more of the following: time-domain prediction; spatial-domain prediction; and frequency-domain prediction. Time-domain prediction refers to predicting future measurement results using historical measurement results, as shown in Figure 2 above; or, time-domain prediction refers to predicting the measurement results of sampling times that were not measured between two adjacent sampling times using measurement results from a subset of sampling times, as shown in Figure 3 above. Spatial-domain prediction refers to predicting the measurement results of the remaining unmeasured beams using measurement values ​​from a subset of beams, as shown in Figure 4 above. Frequency-domain prediction refers to predicting the measurement results of other frequencies using measurement values ​​from a specific frequency point, as shown in Figure 5 above. Optionally, if multiple prediction models are deployed for a certain prediction scenario, the second information may also carry corresponding model indication information to indicate which prediction model in that prediction scenario is currently being monitored.

[0132] In some implementations, if the prediction scenario includes time-domain prediction, the second information may also include one or more of the following: the start time of the RRM measurement; the end time of the RRM measurement; the duration of the RRM measurement; the sampling time of the RRM measurement; and the sampling period of the RRM measurement.

[0133] For example, if the prediction scenario includes time-domain prediction, and specifically, uses historical measurement results to predict future measurement results, then the second information may further include one or more of the following: the measurement start time, the measurement end time, and the measurement duration of the RRM measurement. The measurement start time and measurement end time can be absolute times or relative times, such as an offset relative to the moment the single reporting instruction information is received. If the second information does not carry a measurement end time, optionally, the measurement end time can be the moment when the time for reporting the measurement result, as indicated by the aforementioned single reporting instruction information or periodic reporting instruction information, ends.

[0134] For example, if the prediction scenario includes time-domain prediction, and specifically, using the measurement results of some sampling times to predict the measurement results of sampling times between two adjacent sampling times that were not measured, then the second information may also include one or more of the following: the measurement start time of the RRM measurement, the measurement end time, the sampling time of the RRM measurement, and the sampling period of the RRM measurement. As an example, assuming the information carried in the second information is (measurement start time, sampling period) = {(measurement start time = 0, sampling period = 3), (measurement start time = 1, sampling period = 3)}, it means that a measurement is performed every 3 time slots starting from time slot 0, that is, measurements are performed at time slots 0, 3, 6, etc., and a measurement is also performed every 3 time slots starting from time slot 1, that is, measurements are performed at time slots 1, 4, 7, etc.

[0135] In this embodiment of the application, the measurement start time can also be referred to as the monitoring start time or the measurement monitoring start time; the measurement end time can also be referred to as the monitoring end time or the measurement monitoring end time; and the measurement duration can also be referred to as the monitoring duration or the measurement monitoring duration.

[0136] In some implementations, if the prediction scenario includes spatial prediction, the second information may also include one or more of the following: the start time of the RRM measurement; the end time of the RRM measurement; the duration of the RRM measurement; the sampling time of the RRM measurement; the sampling period of the RRM measurement; and the identification information of the monitored beam.

[0137] Here, the identity (ID) information of the monitored beams can be the individual beam identifiers of the multiple monitored beams, or it can be the beam set identifier of the beam set formed by these multiple beams. This beam set identifier can also be called a beam set mapping identifier, used to identify a specific group of beams. As an example, suppose the monitored beams include beam 1, beam 3, beam 5, and beam 7, with corresponding beam identifiers ID=1, ID=3, ID=5, and ID=7, respectively. In this case, the second information can carry the individual beam identifiers of beam 1, beam 3, beam 5, and beam 7, i.e., {1, 3, 5, 7}; or, the second information can also carry the identifier of the beam set formed by beam 1, beam 3, beam 5, and beam 7, i.e., {0}. The identifier of the beam set formed by beam 1, beam 3, beam 5, and beam 7 can be queried, for example, through the mapping relationship shown in Table 1 above.

[0138] In some implementations, if the prediction scenario includes frequency domain prediction, the second information may also include one or more of the following: the start time of RRM measurement; the end time of RRM measurement; the duration of RRM measurement; the sampling time of RRM measurement; the sampling period of RRM measurement; the identification information of the monitored beam; the monitored frequency point; and the identification information of the monitored cell.

[0139] Finally, the measurement objects of the RRM measurement included in the second information are described.

[0140] In some implementations, the measurement objects of RRM measurements can include Layer 1 measurements and / or Layer 3 measurements. For example, the measurement objects can include one or more of the following: beam-level Layer 1 measurements; beam-level Layer 3 measurements; cell-level Layer 1 measurements; and cell-level Layer 3 measurements. Here, beam-level measurement results are obtained by measuring each beam individually, while cell-level measurement results can be considered as the result of merging the measurement results of each beam within the cell. Taking RSRP as an example, if the second information carries cell-level L3-RSRP, it indicates that it is a cell-level Layer 3 measurement for RSRP.

[0141] Based on the above description, in method 300, the terminal device monitors the prediction model, which can reduce the frequency of reporting of RRM measurement results and reduce the amount of uplink data that needs to be transmitted over the air interface.

[0142] In this embodiment of the application, a data transmission mechanism for model monitoring is designed for the situation where network devices have the inference results of the prediction model, i.e., the prediction results, while terminal devices have the corresponding actual measurement results. This solves the problem that the network devices and terminal devices have a relatively one-sided understanding of the model monitoring-related data, and enables the monitoring and management of the prediction model for RRM measurement.

[0143] The method embodiments of this application have been described in detail above with reference to Figures 8 to 10. The apparatus embodiments of this application will be described in detail below with reference to Figures 11 to 15. It should be understood that the descriptions of the method embodiments correspond to the descriptions of the apparatus embodiments; therefore, any parts not described in detail can be referred to the preceding method embodiments.

[0144] Figure 11 is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. The terminal device 400 shown in Figure 11 may include a transceiver unit 410. The transceiver unit 410 is used to receive first information sent by a network device. The first information is used to instruct the terminal device to perform RRM measurement and report the measurement result of the RRM measurement. The measurement result of the RRM measurement is used for monitoring the prediction model, and the prediction model is used for predicting the measurement result of the RRM measurement.

[0145] In some implementations, the first information includes one or more of the following: the reporting time information of the measurement result; the information of the prediction scenario to which the prediction model is applied; and the information of the measurement object of the RRM measurement.

[0146] In some implementations, the reporting time information of the measurement results includes: single reporting indication information, used to instruct the terminal device to report the measurement results at a predetermined time; and / or, periodic reporting indication information, used to instruct the terminal device to report the measurement results based on a predetermined period.

[0147] In some implementations, the predetermined time is the moment when the terminal device receives the single reporting instruction information; or, the predetermined time is carried in the first information.

[0148] In some implementations, the predetermined period is determined based on the prediction period of the prediction model; or, the predetermined period is carried in the first information.

[0149] In some implementations, the prediction scenario includes one or more of the following: time-domain prediction; spatial-domain prediction; frequency-domain prediction.

[0150] In some implementations, the time-domain prediction includes: predicting future measurement results using historical measurement results; and / or, predicting the measurement results of sampling times between two adjacent sampling times that were not measured, using measurement results from partial sampling times.

[0151] In some implementations, the prediction scenario includes time-domain prediction, and the first information further includes one or more of the following: the measurement start time of the RRM measurement; the measurement end time of the RRM measurement; the duration of the RRM measurement; the sampling time of the RRM measurement; and the sampling period of the RRM measurement.

[0152] In some implementations, the prediction scenario includes spatial prediction, and the first information further includes one or more of the following: the measurement start time of the RRM measurement; the measurement end time of the RRM measurement; the duration of the RRM measurement; the sampling time of the RRM measurement; the sampling period of the RRM measurement; and the identification information of the monitored beam.

[0153] In some implementations, the prediction scenario includes frequency domain prediction, and the first information further includes one or more of the following: the measurement start time of the RRM measurement; the measurement end time of the RRM measurement; the duration of the RRM measurement; the sampling time of the RRM measurement; the sampling period of the RRM measurement; the identification information of the monitored beam; the monitored frequency point; and the identification information of the monitored cell.

[0154] In some implementations, the RRM measurement targets include one or more of the following: beam-level layer 1 measurement; beam-level layer 3 measurement; cell-level layer 1 measurement; cell-level layer 3 measurement.

[0155] In some implementations, the first information is carried in RRC signaling.

[0156] It is understood that the transceiver unit 410 may be, for example, a transceiver 830. Additionally, the terminal device 400 may optionally include a processor 810 and a memory 820, as detailed in Figure 15.

[0157] Figure 12 is a schematic diagram of the structure of a network device provided in an embodiment of this application. The network device 500 shown in Figure 12 may include a transceiver unit 510. The transceiver unit 510 is used to send first information to a terminal device, the first information being used to instruct the terminal device to perform RRM measurement and report the measurement result of the RRM measurement, the measurement result of the RRM measurement being used for monitoring a prediction model, and the prediction model being used for predicting the measurement result of the RRM measurement.

[0158] In some implementations, the first information includes one or more of the following: the reporting time information of the measurement result; the information of the prediction scenario to which the prediction model is applied; and the information of the measurement object of the RRM measurement.

[0159] In some implementations, the reporting time information of the measurement results includes: single reporting indication information, used to instruct the terminal device to report the measurement results at a predetermined time; and / or, periodic reporting indication information, used to instruct the terminal device to report the measurement results based on a predetermined period.

[0160] In some implementations, the predetermined time is the moment when the terminal device receives the single reporting instruction information; or, the predetermined time is carried in the first information.

[0161] In some implementations, the predetermined period is determined based on the prediction period of the prediction model; or, the predetermined period is carried in the first information.

[0162] In some implementations, the prediction scenario includes one or more of the following: time-domain prediction; spatial-domain prediction; frequency-domain prediction.

[0163] In some implementations, the time-domain prediction includes: predicting future measurement results using historical measurement results; and / or, predicting the measurement results of sampling times between two adjacent sampling times that were not measured, using measurement results from partial sampling times.

[0164] In some implementations, the prediction scenario includes time-domain prediction, and the first information further includes one or more of the following: the measurement start time of the RRM measurement; the measurement end time of the RRM measurement; the duration of the RRM measurement; the sampling time of the RRM measurement; and the sampling period of the RRM measurement.

[0165] In some implementations, the prediction scenario includes spatial prediction, and the first information further includes one or more of the following: the measurement start time of the RRM measurement; the measurement end time of the RRM measurement; the duration of the RRM measurement; the sampling time of the RRM measurement; the sampling period of the RRM measurement; and the identification information of the monitored beam.

[0166] In some implementations, the prediction scenario includes frequency domain prediction, and the first information further includes one or more of the following: the measurement start time of the RRM measurement; the measurement end time of the RRM measurement; the duration of the RRM measurement; the sampling time of the RRM measurement; the sampling period of the RRM measurement; the identification information of the monitored beam; the monitored frequency point; and the identification information of the monitored cell.

[0167] In some implementations, the RRM measurement targets include one or more of the following: beam-level layer 1 measurement; beam-level layer 3 measurement; cell-level layer 1 measurement; cell-level layer 3 measurement.

[0168] In some implementations, the network device 500 further includes a processing unit 520 for monitoring the prediction model based on the measurement results reported by the terminal device and the predicted values ​​of the measurement results of the RRM measurement.

[0169] In some implementations, the first information is carried in RRC signaling.

[0170] It is understood that the transceiver unit 510 may be, for example, a transceiver 830, and the processing unit 520 may be, for example, a processor 810. Additionally, the network device 500 may optionally include a memory 820, as shown in Figure 15.

[0171] Figure 13 is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. The terminal device 600 shown in Figure 13 may include a transceiver unit 610. The transceiver unit 610 is used to receive second information sent by a network device. The second information includes a predicted value of the measurement result of an RRM measurement. The second information is used to instruct the terminal device to perform an RRM measurement and to monitor a prediction model based on the measurement result of the RRM measurement and the predicted value. The second information is also used to instruct the terminal device to report third information related to the monitoring result. The prediction model is used to predict the measurement result of the RRM measurement.

[0172] In some implementations, the third information includes: monitoring results of the prediction model; and / or, operational recommendations for the prediction model based on the monitoring results.

[0173] In some implementations, the monitoring results of the prediction model include: the error between the predicted value and the actual measured value; and / or error indication information indicating whether the error is within a predetermined range.

[0174] In some implementations, the operation suggestion includes: deactivating the prediction model; and / or switching the prediction model.

[0175] In some implementations, the second information may further include one or more of the following: the reporting time information of the third information; the information of the prediction scenario to which the prediction model is applied; and the information of the measurement object of the RRM measurement.

[0176] In some implementations, the reporting time information of the third information includes one or more of the following: single reporting indication information, used to instruct the terminal device to report the third information at a predetermined time; periodic reporting indication information, used to instruct the terminal device to report the third information based on a predetermined period; event triggering indication information, used to instruct the terminal device to report the third information when a predetermined event occurs.

[0177] In some implementations, the predetermined time is the moment when the terminal device receives the single reporting instruction information; or, the predetermined time is carried in the second information.

[0178] In some implementations, the predetermined period is determined based on the prediction period of the prediction model; or, the predetermined period is carried in the second information.

[0179] In some implementations, the predetermined event includes: the error between the predicted value and the actual measured value exceeds a predetermined range.

[0180] In some implementations, the predetermined range is carried within the second information.

[0181] In some implementations, the prediction scenario includes one or more of the following: time-domain prediction; spatial-domain prediction; frequency-domain prediction.

[0182] In some implementations, the time-domain prediction includes: predicting future measurement results using historical measurement results; and / or, predicting the measurement results of sampling times between two adjacent sampling times that were not measured, using measurement results from partial sampling times.

[0183] In some implementations, the prediction scenario includes time-domain prediction, and the second information further includes one or more of the following: the measurement start time of the RRM measurement; the measurement end time of the RRM measurement; the duration of the RRM measurement; the sampling time of the RRM measurement; and the sampling period of the RRM measurement.

[0184] In some implementations, the prediction scenario includes spatial prediction, and the second information further includes one or more of the following: the start time of the RRM measurement; the end time of the RRM measurement; the duration of the RRM measurement; the sampling time of the RRM measurement; the sampling period of the RRM measurement; and the identification information of the monitored beam.

[0185] In some implementations, the prediction scenario includes frequency domain prediction, and the second information further includes one or more of the following: the measurement start time of the RRM measurement; the measurement end time of the RRM measurement; the duration of the RRM measurement; the sampling time of the RRM measurement; the sampling period of the RRM measurement; the identification information of the monitored beam; the monitored frequency point; and the identification information of the monitored cell.

[0186] In some implementations, the RRM measurement targets include one or more of the following: beam-level layer 1 measurement; beam-level layer 3 measurement; cell-level layer 1 measurement; cell-level layer 3 measurement.

[0187] In some implementations, the second information is carried in RRC signaling.

[0188] It is understood that the transceiver unit 610 may be, for example, a transceiver 830. Additionally, the terminal device 600 may optionally include a processor 810 and a memory 820, as detailed in Figure 15.

[0189] Figure 14 is a schematic diagram of the network device provided in an embodiment of this application. The network device 700 shown in Figure 14 may include a transceiver unit 710. The transceiver unit 710 is used to send second information to a terminal device. The second information includes a predicted value of the measurement result of an RRM measurement. The second information is used to instruct the terminal device to perform an RRM measurement and monitor a prediction model based on the measurement result of the RRM measurement and the predicted value. The second information is also used to instruct the terminal device to report third information related to the monitoring result. The prediction model is used to predict the measurement result of the RRM measurement.

[0190] In some implementations, the third information includes: monitoring results of the prediction model; and / or, operational recommendations for the prediction model based on the monitoring results.

[0191] In some implementations, the monitoring results of the prediction model include: the error between the predicted value and the actual measured value; and / or error indication information indicating whether the error is within a predetermined range.

[0192] In some implementations, the operation suggestion includes: deactivating the prediction model; and / or switching the prediction model.

[0193] In some implementations, the second information may further include one or more of the following: the reporting time information of the third information; the information of the prediction scenario to which the prediction model is applied; and the information of the measurement object of the RRM measurement.

[0194] In some implementations, the reporting time information of the third information includes one or more of the following: single reporting indication information, used to instruct the terminal device to report the third information at a predetermined time; periodic reporting indication information, used to instruct the terminal device to report the third information based on a predetermined period; event triggering indication information, used to instruct the terminal device to report the third information when a predetermined event occurs.

[0195] In some implementations, the predetermined time is the moment when the terminal device receives the single reporting instruction information; or, the predetermined time is carried in the second information.

[0196] In some implementations, the predetermined period is determined based on the prediction period of the prediction model; or, the predetermined period is carried in the second information.

[0197] In some implementations, the predetermined event includes: the error between the predicted value and the actual measured value exceeds a predetermined range.

[0198] In some implementations, the predetermined range is carried within the second information.

[0199] In some implementations, the prediction scenario includes one or more of the following: time-domain prediction; spatial-domain prediction; frequency-domain prediction.

[0200] In some implementations, the time-domain prediction includes: predicting future measurement results using historical measurement results; and / or, predicting the measurement results of sampling times between two adjacent sampling times that were not measured, using measurement results from partial sampling times.

[0201] In some implementations, the prediction scenario includes time-domain prediction, and the second information further includes one or more of the following: the measurement start time of the RRM measurement; the measurement end time of the RRM measurement; the duration of the RRM measurement; the sampling time of the RRM measurement; and the sampling period of the RRM measurement.

[0202] In some implementations, the prediction scenario includes spatial prediction, and the second information further includes one or more of the following: the start time of the RRM measurement; the end time of the RRM measurement; the duration of the RRM measurement; the sampling time of the RRM measurement; the sampling period of the RRM measurement; and the identification information of the monitored beam.

[0203] In some implementations, the prediction scenario includes frequency domain prediction, and the second information further includes one or more of the following: the measurement start time of the RRM measurement; the measurement end time of the RRM measurement; the duration of the RRM measurement; the sampling time of the RRM measurement; the sampling period of the RRM measurement; the identification information of the monitored beam; the monitored frequency point; and the identification information of the monitored cell.

[0204] In some implementations, the RRM measurement targets include one or more of the following: beam-level layer 1 measurement; beam-level layer 3 measurement; cell-level layer 1 measurement; cell-level layer 3 measurement.

[0205] In some implementations, the second information is carried in RRC signaling.

[0206] It is understood that the transceiver unit 710 may be, for example, a transceiver 830. Additionally, the network device 700 may optionally include a processor 810 and a memory 820, as detailed in Figure 15.

[0207] Figure 15 is a schematic structural diagram of a communication device applicable to embodiments of this application. The dashed lines in Figure 15 indicate that the unit or module is optional. Device 800 can be used to implement the methods described in the foregoing method embodiments. Device 800 may be, for example, a chip, a terminal device, or a network device.

[0208] The apparatus 800 may include one or more processors 810. The processors 810 may support the apparatus 800 in implementing the methods described in the foregoing method embodiments. The processor 810 may be a general-purpose processor or a special-purpose processor. For example, the processor 800 may be a central processing unit (CPU). Alternatively, the processor 800 may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0209] The apparatus 800 may further include one or more memories 820. The memories 820 store a program that can be executed by the processor 810, causing the processor 810 to perform the methods described in the preceding method embodiments. The memories 820 may be independent of the processor 810 or integrated within the processor 810.

[0210] The device 800 may also include a transceiver 830. The processor 810 can communicate with other devices or chips via the transceiver 830. For example, the processor 810 can send and receive data with other devices or chips via the transceiver 830.

[0211] This application also provides a communication system. The system includes the terminal device and network device described above. In some implementations, the system further includes other devices that interact with the terminal device and network device.

[0212] This application also provides a computer-readable storage medium for storing a program. This computer-readable storage medium can be applied to a terminal device or network device provided in this application, and the program causes a computer to execute the methods performed by the terminal device or network device in various embodiments of this application.

[0213] This application also provides a computer program product. The computer program product includes a program. This computer program product can be applied to a terminal device or network device provided in this application embodiment, and the program causes a computer to execute the methods performed by the terminal device or network device in the various embodiments of this application.

[0214] This application also provides a computer program. This computer program can be applied to the terminal device or network device provided in this application, and the computer program causes the computer to execute the methods performed by the terminal device or network device in the various embodiments of this application.

[0215] It should be understood that the terms "system" and "network" in this application can be used interchangeably. Furthermore, the terminology used in this application is only for explaining specific embodiments of the application and is not intended to limit the application. The terms "first," "second," "third," and "fourth," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. In addition, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0216] In the embodiments of this application, the term "instruction" can be a direct instruction, an indirect instruction, or an indication of a relationship. For example, A instructing B can mean that A directly instructs B, such as B being able to obtain information through A; it can also mean that A indirectly instructs B, such as A instructing C, so B can obtain information through C; or it can mean that there is a relationship between A and B.

[0217] In the embodiments of this application, "B corresponding to A" means that B is associated with A, and B can be determined based on A. However, it should also be understood that determining B based on A does not mean that B is determined solely based on A; B can also be determined based on A and / or other information.

[0218] In the embodiments of this application, the term "correspondence" can indicate a direct or indirect correspondence between two things, or an association between two things, or a relationship such as instruction and being instructed, configuration and being configured.

[0219] In this application embodiment, "predefined" or "preconfigured" can be implemented by pre-storing corresponding codes, tables, or other means that can be used to indicate relevant information in the device (e.g., including terminal devices and network devices). This application does not limit the specific implementation method. For example, predefined can refer to what is defined in the protocol.

[0220] In this application embodiment, the "protocol" may refer to a standard protocol in the field of communication, such as the LTE protocol, the NR protocol, and related protocols applied to future communication systems. This application does not limit this.

[0221] In the embodiments of this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0222] In the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0223] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0224] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0225] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0226] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially 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, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can read or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0227] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method of wireless communication, comprising: Comprising: The terminal device receives first information sent by the network device, the first information being used for instructing the terminal device to perform a radio resource management (RRM) measurement and report a measurement result of the RRM measurement, the measurement result of the RRM measurement being used for monitoring of a prediction model, the prediction model being used for prediction of the measurement result of the RRM measurement.

2. The method of claim 1, wherein, The first information comprises one or more of the following: reporting time information of the measurement result; information of a prediction scenario to which the prediction model is applied; information of a measurement object of the RRM measurement.

3. The method of claim 2, wherein, The reporting time information of the measurement result comprises: single-reporting indication information, used for instructing the terminal device to report the measurement result at a predetermined time; and / or periodic-reporting indication information, used for instructing the terminal device to report the measurement result based on a predetermined period.

4. The method of claim 3, wherein: the predetermined time is a time at which the terminal device receives the single-reporting indication information; or the predetermined time is carried in the first information.

5. The method of claim 3 or 4, wherein: the predetermined period is determined based on a prediction period of the prediction model; or the predetermined period is carried in the first information.

6. The method according to any one of claims 2 to 5, characterized in that, The prediction scenario comprises one or more of the following: time-domain prediction; space-domain prediction; frequency-domain prediction.

7. The method of claim 6, wherein, The time-domain prediction comprises: prediction of a future measurement result by using a historical measurement result; and / or prediction of a measurement result of a sampling time that is not measured between two adjacent sampling times in the sampling time by using measurement results of the two adjacent sampling times.

8. The method according to claim 6 or 7, characterized in that, The prediction scenario comprises time-domain prediction, and the first information further comprises one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling time of the RRM measurement; a sampling period of the RRM measurement.

9. The method according to any one of claims 6 to 8, characterized in that, The prediction scenario comprises space-domain prediction, and the first information further comprises one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling time of the RRM measurement; a sampling period of the RRM measurement; identification information of a monitored beam.

10. The method according to any one of claims 6 to 9, characterized in that, The prediction scenario comprises frequency-domain prediction, and the first information further comprises one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling time of the RRM measurement; a sampling period of the RRM measurement; identification information of a monitored beam; a monitored frequency point; identification information of a monitored cell.

11. The method according to any one of claims 2 to 10, characterized in that, The measurement object of the RRM measurement comprises one or more of the following: beam-level layer 1 measurement; beam-level layer 3 measurement; cell-level layer 1 measurement; cell-level layer 3 measurement.

12. The method according to any one of claims 1 to 11, characterized in that, The first information is carried in radio resource control (RRC) signaling.

13. A method of wireless communication, comprising: Comprising: The network device sends first information to the terminal device, the first information being used to instruct the terminal device to perform a radio resource management (RRM) measurement and report a measurement result of the RRM measurement, the measurement result of the RRM measurement being used for monitoring of a prediction model, the prediction model being used for prediction of the measurement result of the RRM measurement.

14. The method of claim 13, wherein, The first information comprises one or more of the following: reporting time information of the measurement result; information of a prediction scenario to which the prediction model is applied; information of a measurement object of the RRM measurement.

15. The method of claim 14, wherein, The reporting time information of the measurement result comprises: single-reporting indication information, used to instruct the terminal device to report the measurement result at a predetermined time; and / or periodic-reporting indication information, used to instruct the terminal device to report the measurement result based on a predetermined period.

16. The method of claim 15, wherein: the predetermined time is a time at which the terminal device receives the single-reporting indication information; or the predetermined time is carried in the first information.

17. The method of claim 15 or 16, wherein: the predetermined period is determined based on a prediction period of the prediction model; or the predetermined period is carried in the first information.

18. The method according to any one of claims 14 to 17, characterized in that, The prediction scenario comprises one or more of the following: time-domain prediction; space-domain prediction; and frequency-domain prediction.

19. The method of claim 18, wherein, The time-domain prediction comprises: prediction of a future measurement result by using a historical measurement result; and / or prediction of a measurement result of a sampling time that is not measured between two adjacent sampling times by using measurement results of the two adjacent sampling times.

20. The method of claim 18 or 19, wherein, The prediction scenario comprises time-domain prediction, and the first information further comprises one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling time of the RRM measurement; and a sampling period of the RRM measurement.

21. The method of any one of claims 18-20, wherein, The prediction scenario comprises space-domain prediction, and the first information further comprises one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling time of the RRM measurement; a sampling period of the RRM measurement; and identification information of a monitored beam.

22. The method of any one of claims 18-21, wherein, The prediction scenario comprises frequency-domain prediction, and the first information further comprises one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling time of the RRM measurement; a sampling period of the RRM measurement; identification information of a monitored beam; a monitored frequency point; and identification information of a monitored cell.

23. The method of any one of claims 14 to 22, wherein, The measurement object of the RRM measurement comprises one or more of the following: beam-level layer 1 measurement; beam-level layer 3 measurement; cell-level layer 1 measurement; and cell-level layer 3 measurement.

24. The method of any one of claims 13-23, wherein, The method further comprises: monitoring, by the network device, the prediction model based on the measurement result reported by the terminal device and a predicted value of the measurement result of the RRM measurement.

25. The method of any one of claims 13-24, wherein, The first information is carried in radio resource control (RRC) signaling.

26. A method of wireless communication, comprising: The method comprises: The terminal device receives second information sent by the network device, the second information comprising a predicted value of a measurement result of a radio resource management (RRM) measurement, the second information being used to instruct the terminal device to perform the RRM measurement and to perform monitoring of a prediction model based on the measurement result of the RRM measurement and the predicted value, the second information further being used to instruct the terminal device to report third information related to a monitoring result of the prediction model, the prediction model being used for prediction of the measurement result of the RRM measurement.

27. The method of claim 26, wherein, The third information comprises: The monitoring result of the prediction model; and / or, an operation suggestion for the prediction model based on the monitoring result.

28. The method of claim 27, wherein, The monitoring result of the prediction model comprises: an error between the predicted value and an actual measurement value; and / or, error indication information used to indicate whether the error is within a predetermined range.

29. The method of claim 27 or 28, wherein, The operation suggestion comprises: deactivating the prediction model; and / or, switching the prediction model.

30. The method of any one of claims 26-29, wherein, The second information further comprises one or more of the following: reporting time information of the third information; information of a prediction scenario to which the prediction model is applied; information of a measurement object of the RRM measurement.

31. The method of claim 30, wherein, The reporting time information of the third information comprises one or more of the following: one-time reporting indication information used to instruct the terminal device to report the third information at a predetermined time; periodic reporting indication information used to instruct the terminal device to report the third information based on a predetermined period; event-triggered indication information used to instruct the terminal device to report the third information when a predetermined event occurs.

32. The method of claim 31, wherein: the predetermined time is a time at which the terminal device receives the one-time reporting indication information; or the predetermined time is carried in the second information.

33. The method of claim 31 or 32, wherein: the predetermined period is determined based on a prediction period of the prediction model; or the predetermined period is carried in the second information.

34. The method of any one of claims 31-33, wherein, The predetermined event comprises an error between the predicted value and an actual measurement value exceeding a predetermined range.

35. The method of claim 28 or 34, wherein, The predetermined range is carried in the second information.

36. The method of any one of claims 30-35, wherein, The prediction scenario comprises one or more of the following: time-domain prediction; space-domain prediction; frequency-domain prediction.

37. The method of claim 36, wherein, The time-domain prediction comprises: predicting a future measurement result using a historical measurement result; and / or predicting a measurement result of a sampling time at which no measurement is performed between two adjacent sampling times using measurement results of the two adjacent sampling times.

38. The method of claim 36 or 37, wherein, The prediction scenario comprises time-domain prediction, and the second information further comprises one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling time of the RRM measurement; and / or a sampling period of the RRM measurement.

39. The method of any one of claims 35-38, wherein, The prediction scenario comprises space-domain prediction, and the second information further comprises one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; and / or a measurement object of the RRM measurement. a duration of the RRM measurement; a sampling time of the RRM measurement; a sampling period of the RRM measurement; identification information of a monitored beam.

40. The method of any one of claims 36-39, wherein, the prediction scenario comprises a frequency domain prediction, and the second information further comprises one or more of the following: a start time of the RRM measurement; an end time of the RRM measurement; a duration of the RRM measurement; a sampling time of the RRM measurement; a sampling period of the RRM measurement; identification information of a monitored beam; a monitored frequency point; identification information of a monitored cell.

41. The method of any one of claims 30-40, wherein, the measurement object of the RRM measurement comprises one or more of the following: a beam-level layer 1 measurement; a beam-level layer 3 measurement; a cell-level layer 1 measurement; a cell-level layer 3 measurement.

42. The method of any one of claims 26-41, wherein, the second information is carried in radio resource control (RRC) signaling.

43. A method of wireless communication, the method comprising: comprises: a network device sends second information to a terminal device, the second information comprising a predicted value of a measurement result of a radio resource management (RRM) measurement, the second information being used to instruct the terminal device to perform the RRM measurement and monitor a prediction model based on the measurement result of the RRM measurement and the predicted value, and the second information further being used to instruct the terminal device to report third information related to a monitoring result of the prediction model, the prediction model being used for prediction of the measurement result of the RRM measurement.

44. The method of claim 43, wherein, the third information comprises: the monitoring result of the prediction model; and / or an operation suggestion for the prediction model based on the monitoring result.

45. The method of claim 44, wherein, the monitoring result of the prediction model comprises: an error between the predicted value and an actual measurement value; and / or error indication information used to indicate whether the error is within a predetermined range.

46. The method of claim 44 or 45, wherein, the operation suggestion comprises: deactivation of the prediction model; and / or switching of the prediction model.

47. The method of any one of claims 43-46, wherein, the second information further comprises one or more of the following: reporting time information of the third information; information of a prediction scenario to which the prediction model is applied; information of a measurement object of the RRM measurement.

48. The method of claim 47, wherein, the reporting time information of the third information comprises one or more of the following: one-time reporting indication information used to instruct the terminal device to report the third information at a predetermined time; periodic reporting indication information used to instruct the terminal device to report the third information based on a predetermined period; event-triggered indication information used to instruct the terminal device to report the third information when a predetermined event occurs.

49. The method of claim 48, wherein the predetermined time is a time at which the terminal device receives the one-time reporting indication information; or the predetermined time is carried in the second information.

50. The method of claim 48 or 49, wherein the predetermined period is determined based on a prediction period of the prediction model; or the predetermined period is carried in the second information.

51. The method of any one of claims 48-50, wherein, the predetermined event comprises an error between the predicted value and an actual measurement value exceeding a predetermined range.

52. The method of claim 45 or 51, wherein, the predetermined range is carried in the second information.

53. The method of any one of claims 47-52, wherein, the prediction scenario comprises one or more of the following: time domain prediction; space domain prediction; and frequency domain prediction.

54. The method of claim 53, wherein, The time domain prediction comprises: predicting future measurement results by using historical measurement results; and / or, predicting measurement results of sampling instants that are not measured between two adjacent sampling instants among the partial sampling instants by using measurement results of the partial sampling instants.

55. The method of claim 53 or 54, wherein, The prediction scenario comprises a time domain prediction, and the second information further comprises one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling instant of the RRM measurement; a sampling period of the RRM measurement.

56. The method of any one of claims 52-55, wherein, The prediction scenario comprises a spatial domain prediction, and the second information further comprises one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling instant of the RRM measurement; a sampling period of the RRM measurement; identification information of a monitored beam.

57. The method of any one of claims 53-56, wherein, The prediction scenario comprises a frequency domain prediction, and the second information further comprises one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling instant of the RRM measurement; a sampling period of the RRM measurement; identification information of a monitored beam; a monitored frequency point; identification information of a monitored cell.

58. The method of any one of claims 47-57, wherein, The measurement object of the RRM measurement comprises one or more of the following: a beam-level Layer 1 measurement; a beam-level Layer 3 measurement; a cell-level Layer 1 measurement; a cell-level Layer 3 measurement.

59. The method of any one of claims 43-58, wherein, The second information is carried in radio resource control (RRC) signaling.

60. A terminal device, comprising: comprises: a transceiver, configured to receive first information sent by a network device, the first information being used to instruct a terminal device to perform radio resource management (RRM) measurement and report measurement results of the RRM measurement, the measurement results of the RRM measurement being used for monitoring of a prediction model, the prediction model being used for prediction of measurement results of the RRM measurement.

61. The terminal device of claim 60, wherein, The first information comprises one or more of the following: reporting time information of the measurement results; information of a prediction scenario to which the prediction model is applied; information of a measurement object of the RRM measurement.

62. The terminal device of claim 61, wherein, The reporting time information of the measurement results comprises: one-time reporting indication information, used to instruct the terminal device to report the measurement results at a predetermined time; and / or, periodic reporting indication information, used to instruct the terminal device to report the measurement results based on a predetermined period.

63. The terminal device according to claim 62, wherein: the predetermined time is a time at which the terminal device receives the one-time reporting indication information; or the predetermined time is carried in the first information.

64. The terminal device according to claim 62 or 63, wherein: the predetermined period is determined based on a prediction period of the prediction model; or the predetermined period is carried in the first information.

65. The terminal device of any one of claims 61 to 64, wherein, The prediction scenario comprises one or more of the following: a time domain prediction; a spatial domain prediction; a frequency domain prediction.

66. The terminal device of claim 65, wherein, The time domain prediction comprises: predicting future measurement results by using historical measurement results; and / or, The measurement result of a sampling moment between two adjacent sampling moments in the partial sampling moments is predicted by using the measurement result of the partial sampling moments.

67. The terminal device of claim 65 or 66, wherein, The prediction scenario includes time domain prediction, and the first information further includes one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling moment of the RRM measurement; a sampling period of the RRM measurement.

68. The terminal device of any one of claims 65 to 67, wherein, The prediction scenario includes spatial domain prediction, and the first information further includes one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling moment of the RRM measurement; a sampling period of the RRM measurement; identification information of a monitored beam.

69. The terminal device of any one of claims 63 to 68, wherein, The prediction scenario includes frequency domain prediction, and the first information further includes one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling moment of the RRM measurement; a sampling period of the RRM measurement; identification information of a monitored beam; a monitored frequency point; identification information of a monitored cell.

70. The terminal device of any one of claims 61 to 69, wherein, The measurement object of the RRM measurement includes one or more of the following: a beam level layer 1 measurement; a beam level layer 3 measurement; a cell level layer 1 measurement; a cell level layer 3 measurement.

71. The terminal device of any one of claims 60 to 70, wherein, The first information is carried in radio resource control (RRC) signaling.

72. A network device, comprising: The first information includes one or more of the following: reporting time information of the measurement result; 73. The network device of claim 72, wherein, information of a prediction scenario applied by the prediction model; information of a measurement object of the RRM measurement. The reporting time information of the measurement result includes: single reporting indication information, used to instruct the terminal device to report the measurement result at a predetermined time; and / or 74. The network device of claim 73, wherein, periodic reporting indication information, used to instruct the terminal device to report the measurement result based on a predetermined period.

75. The network device of claim 74, wherein: the predetermined time is a time when the terminal device receives the single reporting indication information; or the predetermined time is carried in the first information.

76. The network device of claim 74 or 75, wherein: the predetermined period is determined based on a prediction period of the prediction model; or the predetermined period is carried in the first information. The prediction scenario includes one or more of the following: time domain prediction; spatial domain prediction; and frequency domain prediction. The time domain prediction includes: 77.The network device according to any one of claims 73-76, characterized by, prediction of future measurement results by using historical measurement results; and / or 78. The network device of claim 77, wherein, ​ ​ The measurement result of a sampling moment between two adjacent sampling moments in the partial sampling moments is predicted by using the measurement results of the partial sampling moments.

79. The network device of claim 77 or 78, wherein, The prediction scenario includes time domain prediction, and the first information further includes one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling moment of the RRM measurement; a sampling period of the RRM measurement. 80.The network device according to any one of claims 77-79, characterized by, The prediction scenario includes spatial domain prediction, and the first information further includes one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling moment of the RRM measurement; a sampling period of the RRM measurement; identification information of a monitored beam. 81.The network device according to any one of claims 77-80, characterized by, The prediction scenario includes frequency domain prediction, and the first information further includes one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling moment of the RRM measurement; a sampling period of the RRM measurement; identification information of a monitored beam; a monitored frequency point; identification information of a monitored cell.

82. The network device of any of claims 73-81, wherein, The measurement object of the RRM measurement includes one or more of the following: a beam level layer 1 measurement; a beam level layer 3 measurement; a cell level layer 1 measurement; a cell level layer 3 measurement. 83.The network device according to any one of claims 72 to 82, characterized in that, Further comprising: a processing unit configured to monitor the prediction model based on the measurement result reported by the terminal device and the predicted value of the measurement result of the RRM measurement. 84.The network device according to any one of claims 72-83, wherein, The first information is carried in radio resource control (RRC) signaling.

85. A terminal device, comprising: Comprising: a terminal device receiving second information sent by a network device, the second information including a predicted value of a measurement result of a radio resource management (RRM) measurement, the second information being used to instruct the terminal device to perform the RRM measurement and monitor a prediction model based on the measurement result of the RRM measurement and the predicted value, the second information further being used to instruct the terminal device to report third information related to a monitoring result of the prediction model, the prediction model being used for prediction of the measurement result of the RRM measurement.

86. The terminal device of claim 85, wherein, The third information includes: the monitoring result of the prediction model; and / or an operation suggestion for the prediction model based on the monitoring result.

87. The terminal device of claim 86, wherein, The monitoring result of the prediction model includes: an error between the predicted value and an actual measurement value; and / or error indication information used to indicate whether the error is within a predetermined range.

88. The terminal device of claim 86 or 87, wherein, The operation suggestion includes: deactivating the prediction model; and / or switching the prediction model.

89. The terminal device of any one of claims 85 to 88, wherein, The second information further includes one or more of the following: reporting time information of the third information; information of a prediction scenario to which the prediction model is applied; information of a measurement object of the RRM measurement.

90. The terminal device of claim 89, wherein, The reporting time information of the third information includes one or more of the following: single reporting indication information used to instruct the terminal device to report the third information at a predetermined time; Periodic reporting indication information, used to instruct the terminal device to report the third information based on a predetermined period; Event-triggered indication information, used to instruct the terminal device to report the third information when a predetermined event occurs.

91. The terminal device of claim 90, wherein, the predetermined time is a time when the terminal device receives the single-time reporting indication information; or the predetermined time is carried in the second information.

92. The terminal device of claim 90 or 91, wherein, the predetermined period is determined based on a prediction period of the prediction model; or the predetermined period is carried in the second information.

93. The terminal device of any one of claims 90-92, wherein, the predetermined event includes that an error between the prediction value and an actual measurement value exceeds a predetermined range.

94. The terminal device of claim 87 or 93, wherein, the predetermined range is carried in the second information.

95. The terminal device of any one of claims 89 to 94, wherein, the prediction scenario includes one or more of the following: time-domain prediction; space-domain prediction; frequency-domain prediction.

96. The terminal device of claim 95, wherein, the time-domain prediction includes: predicting future measurement results using historical measurement results; and / or predicting measurement results of sampling instants that are not measured between two adjacent sampling instants of the partial sampling instants using measurement results of the partial sampling instants.

97. The terminal device of claim 95 or 96, wherein, the prediction scenario includes time-domain prediction, and the second information further includes one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling instant of the RRM measurement; a sampling period of the RRM measurement.

98. The terminal device of any one of claims 94 to 97, wherein, the prediction scenario includes space-domain prediction, and the second information further includes one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling instant of the RRM measurement; a sampling period of the RRM measurement; identification information of a monitored beam.

99. The terminal device of any one of claims 95 to 98, wherein, the prediction scenario includes frequency-domain prediction, and the second information further includes one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling instant of the RRM measurement; a sampling period of the RRM measurement; identification information of a monitored beam; a monitored frequency point; identification information of a monitored cell.

100. The terminal device of any one of claims 89 to 99, wherein, the measurement object of the RRM measurement includes one or more of the following: beam-level layer 1 measurement; beam-level layer 3 measurement; cell-level layer 1 measurement; cell-level layer 3 measurement.

101. The terminal device of any one of claims 85 to 100, wherein, the second information is carried in radio resource control (RRC) signaling. 102.A network device, characterized by, includes: a transceiver, configured to send second information to a terminal device, the second information including a prediction value of a measurement result of a radio resource management (RRM) measurement, the second information being used to instruct the terminal device to perform the RRM measurement and monitor a prediction model based on the measurement result of the RRM measurement and the prediction value, the second information further being used to instruct the terminal device to report third information related to a monitoring result of the prediction model, the prediction model being used for prediction of the measurement result of the RRM measurement.

103. The network device of claim 102, wherein, the third information includes: a monitoring result of the prediction model; and / or, an operation suggestion for the prediction model based on the monitoring result.

104. The network device of claim 103, wherein, The monitoring result of the prediction model comprises: an error between the prediction value and an actual measurement value; and / or, error indication information indicating whether the error is within a predetermined range.

105. The network device of claim 103 or 104, wherein, The operation suggestion comprises: deactivating the prediction model; and / or, switching the prediction model.

106. The network device of any of claims 102 to 105, wherein, The second information further comprises one or more of the following information: reporting time information of the third information; information of a prediction scenario to which the prediction model is applied; information of a measurement object of the RRM measurement.

107. The network device of claim 106, wherein, The reporting time information of the third information comprises one or more of the following: one-time reporting indication information indicating that the terminal device reports the third information at a predetermined time; periodic reporting indication information indicating that the terminal device reports the third information based on a predetermined period; event-triggered indication information indicating that the terminal device reports the third information when a predetermined event occurs. 108.The network device of claim 107, wherein the predetermined time is a time when the terminal device receives the one-time reporting indication information; or the predetermined time is carried in the second information. 109.The network device of claim 107 or 108, wherein the predetermined period is determined based on a prediction period of the prediction model; or the predetermined period is carried in the second information.

110. The network device of any of claims 107-109, wherein, The predetermined event comprises that an error between the prediction value and an actual measurement value exceeds a predetermined range.

111. The network device of claim 104 or 110, wherein, The predetermined range is carried in the second information.

112. The network device of any of claims 106-111, wherein, The prediction scenario comprises one or more of the following: time-domain prediction; space-domain prediction; and frequency-domain prediction.

113. The network device of claim 112, wherein, The time-domain prediction comprises: predicting future measurement results using historical measurement results; and / or predicting measurement results of sampling instants that are not measured between two adjacent sampling instants among the sampling instants using measurement results of the sampling instants.

114. The network device of claim 112 or 113, wherein, The prediction scenario comprises time-domain prediction, and the second information further comprises one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling instant of the RRM measurement; a sampling period of the RRM measurement.

115. The network device of any of claims 111 to 114, wherein, The prediction scenario comprises space-domain prediction, and the second information further comprises one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling instant of the RRM measurement; a sampling period of the RRM measurement; identification information of a monitored beam.

116. The network device of any of claims 112 to 115, wherein, The prediction scenario comprises frequency-domain prediction, and the second information further comprises one or more of the following: a measurement start time of the RRM measurement; a measurement end time of the RRM measurement; a duration of the RRM measurement; a sampling instant of the RRM measurement; a sampling period of the RRM measurement; identification information of a monitored beam; a monitored frequency point; identification information of a monitored cell. 117.The network device according to any one of claims 106 to 116, characterized in that, The measurement object of the RRM measurement comprises one or more of the following: a beam level layer 1 measurement; a beam level layer 3 measurement; a cell level layer 1 measurement; a cell level layer 3 measurement.

118. The network device of any of claims 102-117, wherein, The second information is carried in radio resource control (RRC) signaling.

119. A terminal device, comprising: A terminal device comprising a transceiver, a memory and a processor, wherein the memory is configured to store a program, and the processor is configured to invoke the program in the memory and control the transceiver to receive or send a signal, so that the terminal device performs the method according to any one of claims 1-12, or the method according to any one of claims 26-42.

120. A network device, comprising: A network device comprising a transceiver, a memory and a processor, wherein the memory is configured to store a program, and the processor is configured to invoke the program in the memory and control the transceiver to receive or send a signal, so that the network device performs the method according to any one of claims 13-25, or the method according to any one of claims 43-59.

121. An apparatus, comprising: An apparatus comprising a processor configured to invoke a program from a memory, so that the apparatus performs the method according to any one of claims 1-12, or the method according to any one of claims 26-42, or the method according to any one of claims 13-25, or the method according to any one of claims 43-59.

122. A chip, comprising: An apparatus comprising a processor configured to invoke a program from a memory, so that the apparatus performs the method according to any one of claims 1-12, or the method according to any one of claims 26-42, or the method according to any one of claims 13-25, or the method according to any one of claims 43-59.

123. A computer-readable storage medium, characterized in that, A computer program product comprising a program, which causes a computer to perform the method according to any one of claims 1-12, or the method according to any one of claims 26-42, or the method according to any one of claims 13-25, or the method according to any one of claims 43-59.

124. A computer program product, characterized in that, A computer program product comprising a program, which causes a computer to perform the method according to any one of claims 1-12, or the method according to any one of claims 26-42, or the method according to any one of claims 13-25, or the method according to any one of claims 43-59.

125. A computer program characterised in that, A computer program product comprising a program, which causes a computer to perform the method according to any one of claims 1-12, or the method according to any one of claims 26-42, or the method according to any one of claims 13-25, or the method according to any one of claims 43-59.

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