Measurement processing method, measurement configuration method, apparatus, device, and readable storage medium

Abstract

The present application discloses a measurement processing method, a measurement configuration method, an apparatus, a device, and a readable storage medium. The method comprises: a terminal acquires a first predicted value of a beam in a first beam set of a first cell on the basis of an AI model; and the terminal generates a first predicted measurement value of the first cell on the basis of the first predicted value, wherein an input of the AI model comprises a first actual measurement value of a beam in a second beam set of the first cell.

Description

Measurement processing method, measurement configuration method, device, equipment and readable storage medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Chinese Patent Application No. 202410250531.X filed in China on March 5, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present application belongs to the field of communication technology, and specifically relates to a measurement processing method, a measurement configuration method, an apparatus, a device, and a readable storage medium. Background Art

[0004] In related technologies, actual cell measurements are often performed by terminals or base stations. After obtaining the cell measurement values, the base station can make handover decisions and other operations based on the cell measurement values. Accurate measurement of cell measurements is crucial for communication. However, in actual measurement, limitations such as link conditions and measurement accuracy often lead to inaccurate measurement results. Therefore, obtaining cell measurement values ​​is an urgent issue. Summary of the Invention

[0005] The embodiments of the present application provide a measurement processing method, a measurement configuration method, an apparatus, a device, and a readable storage medium to solve the problem of how to accurately obtain measurement values ​​of a cell.

[0006] In a first aspect, a measurement processing method is provided, comprising:

[0007] The terminal obtains, based on the AI ​​model, a first predicted value of a beam in the first beam focus of the first cell;

[0008] generating, by the terminal, a first predicted measurement value of the first cell according to the first predicted value;

[0009] The input of the AI ​​model includes a first actual measurement value of a beam in the second beam set of the first cell.

[0010] In a second aspect, a measurement processing method is provided, comprising:

[0011] The terminal obtains a first cell measurement value of the first cell;

[0012] The terminal performs layer 3 filtering based on the first cell measurement value and the last layer 3 filtered cell measurement value of the first cell to obtain the layer 3 filtered cell measurement value of the first cell;

[0013] The terminal performing layer 3 filtering based on the first cell measurement value and the last layer 3 filtered cell measurement value of the first cell includes:

[0014] When the first cell measurement value is obtained based on the AI ​​model, the terminal performs layer 3 filtering based on the first layer 3 filtering parameter, the first cell measurement value, and the last layer 3 filtered cell measurement value of the first cell;

[0015] In a case where the first cell measurement value is obtained based on actual measurement, the terminal performs layer 3 filtering based on a second layer 3 filtering parameter, the first cell measurement value, and a last layer 3 filtered cell measurement value of the first cell.

[0016] In a third aspect, a measurement configuration method is provided, including:

[0017] The network-side device sends a first configuration, where the first configuration includes a second configuration, where the second configuration is used to indicate at least one of the following:

[0018] a configuration for obtaining a first predicted value for a beam of a first beam set for a first cell;

[0019] a configuration for generating a first predicted measurement value of the first cell based on a first predicted value of a beam of the first beam set;

[0020] Three filter parameters for the first layer and three filter parameters for the second layer;

[0021] The first layer 3 filtering parameter is used to perform layer 3 filtering on the cell measurement value obtained based on the AI ​​model, and the second layer 3 filtering parameter is used to perform layer 3 filtering on the cell measurement value obtained based on actual measurement.

[0022] In a fourth aspect, a measurement processing device is provided, comprising: a first processing unit and a first transceiver unit;

[0023] The first processing unit is configured to obtain a first predicted value of a beam in a first beam set of the first cell based on the AI ​​model;

[0024] The first processing unit is further configured to generate a first predicted measurement value of the first cell according to the first predicted value;

[0025] The input of the AI ​​model includes a first actual measurement value of a beam in the second beam set of the first cell.

[0026] In a fifth aspect, a measurement processing device is provided, comprising: a second processing unit and a second transceiver unit;

[0027] The second processing unit is configured to obtain a first cell measurement value of the first cell;

[0028] The second processing unit is further configured to perform layer 3 filtering based on the first cell measurement value and the last layer 3 filtered cell measurement value of the first cell to obtain the layer 3 filtered cell measurement value of the first cell;

[0029] The performing layer 3 filtering based on the first cell measurement value and the last layer 3 filtered cell measurement value of the first cell includes:

[0030] When the first cell measurement value is obtained based on the AI ​​model, performing layer 3 filtering based on the first layer 3 filtering parameter, the first cell measurement value, and the last layer 3 filtered cell measurement value of the first cell;

[0031] In a case where the first cell measurement value is obtained based on actual measurement, layer 3 filtering is performed based on a second layer 3 filtering parameter, the first cell measurement value, and a last layer 3 filtered cell measurement value of the first cell.

[0032] In a sixth aspect, a measurement configuration device is provided, comprising: a third transceiver unit and a third processing unit;

[0033] The third transceiver unit is configured to send a first configuration, where the first configuration includes a second configuration, and the second configuration is configured to indicate at least one of the following:

[0034] a configuration for obtaining a first predicted value for a beam of a first beam set for a first cell;

[0035] a configuration for generating a first predicted measurement value of the first cell based on a first predicted value of a beam of the first beam set;

[0036] Three filter parameters for the first layer and three filter parameters for the second layer;

[0037] The first layer 3 filtering parameter is used to perform layer 3 filtering on the cell measurement value obtained based on the AI ​​model, and the second layer 3 filtering parameter is used to perform layer 3 filtering on the cell measurement value obtained based on actual measurement.

[0038] In the seventh aspect, a terminal is provided, comprising: a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method described in the first aspect or the second aspect.

[0039] In the eighth aspect, a network side device is provided, comprising: a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method described in the third aspect.

[0040] In the ninth aspect, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor of a terminal, the steps of the method described in the first aspect, the second aspect, or the third aspect are implemented.

[0041] In the tenth aspect, a chip is provided, which includes a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run programs or instructions to implement the steps of the method described in the first aspect, the second aspect, or the third aspect.

[0042] In the eleventh aspect, a computer program / program product is provided, which is stored in a non-volatile storage medium and is executed by at least one processor to implement the steps of the method described in the first aspect, the second aspect, or the third aspect.

[0043] In the twelfth aspect, a communication system is provided, which includes a terminal and a network side device, wherein the terminal is used to execute the steps of the method described in the first aspect or the second aspect, or the network side device is used to execute the steps of the method described in the third aspect.

[0044] In an embodiment of the present application, the terminal obtains a first predicted value of the beam in the first beam focus of the first cell based on the AI ​​model; then the terminal can generate a first predicted measurement value of the first cell based on the first predicted value, and since the actual measurement value of the beam in the second beam focus of the first cell is taken into account in the generation of the first predicted value, that is, the base station simultaneously considers the actual measurement value and the predicted value of the first cell through the AI ​​model, thereby accurately obtaining the measurement value of the first cell through the prediction of the AI ​​model, thereby improving the mobility performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a schematic diagram of a neural network;

[0046] Figure 2 is a schematic diagram of a neuron;

[0047] FIG3 is a schematic diagram of a measurement model based on measurement;

[0048] FIG4 a is a schematic diagram of beam-level measurement prediction;

[0049] FIG4 b is a schematic diagram of cell-level measurement prediction;

[0050] FIG5 is a schematic diagram of the architecture of a wireless communication system provided in an embodiment of the present application;

[0051] FIG6 is a flow chart of a measurement processing method according to an embodiment of the present application;

[0052] FIG7 is a second flowchart of the measurement processing method provided in an embodiment of the present application;

[0053] FIG8 is a flowchart of a measurement configuration method provided in an embodiment of the present application;

[0054] FIG9a is a schematic diagram of a first prediction method provided in an embodiment of the present application;

[0055] FIG9 b is a schematic diagram of a second prediction method provided in an embodiment of the present application;

[0056] FIG9c is a schematic diagram of prediction method 3 provided in an embodiment of the present application;

[0057] FIG10 is a schematic diagram of measurement prediction in Solution 1 provided in an embodiment of the present application;

[0058] FIG11 is a schematic diagram of measurement prediction in Solution 2 provided in an embodiment of the present application;

[0059] FIG12 is a schematic diagram of generating cell measurement values ​​by alternating actual measurement and prediction according to an embodiment of the present application;

[0060] FIG13 is a schematic diagram of configuring filter coefficients for a base station according to an embodiment of the present application;

[0061] FIG14 is a schematic diagram of reporting cell measurement value information provided in an embodiment of the present application;

[0062] FIG15 is a structural diagram of a measurement processing device according to an embodiment of the present application;

[0063] FIG16 is a second structural diagram of the measurement processing device provided in an embodiment of the present application;

[0064] FIG17 is a structural diagram of a measurement configuration device provided in an embodiment of the present application;

[0065] FIG18 is a structural diagram of a terminal provided in an embodiment of the present application;

[0066] FIG19 is a structural diagram of a network-side device provided in an embodiment of the present application;

[0067] FIG20 is a structural diagram of a communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0068] The following will be combined with the accompanying drawings in the embodiments of this application to clearly describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0069] The terms "first", "second", etc. in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same type, and do not limit the number of objects, for example, the first object can be one or more. In addition, "or" in this application represents at least one of the connected objects. For example, "A or B" covers three options, namely, Option 1: including A but not including B; Option 2: including B but not including A; Option 3: including both A and B. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0070] It is worth noting that the technology described in the embodiments of the present application is not limited to the Long Term Evolution (LTE) / LTE-Advanced (LTE-A) system, but can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency Division Multiple Access (SC-FDMA) or other systems. The terms "system" and "network" in this application are often used interchangeably, and the technology described can be used for the systems and radio technologies mentioned above, as well as for other systems and radio technologies. The following description describes a New Radio (NR) system for example purposes, and NR terminology is used in most of the following description, but these technologies can also be applied to systems other than NR system applications, such as 6th generation (6G) systems. th Generation, 6G) communication system.

[0071] To facilitate understanding of the embodiments of this application, the following technical points are first introduced:

[0072] 1. Introduction to artificial intelligence.

[0073] Artificial intelligence (AI) is currently being widely used in various fields. There are many ways to implement AI modules, such as neural networks, decision trees, support vector machines, and Bayesian classifiers.

[0074] This application uses a neural network as an example for illustration, but does not limit the specific type of AI module. The structure of the neural network is shown in Figure 1.

[0075] The neural network is composed of neurons, and the schematic diagram of neurons is shown in Figure 2. Among them, a1, a2, ...a K is the input, w is the weight (multiplicative coefficient), b is the bias (additive coefficient), σ(.) is the activation function, z=a1w1+…+a k w k +…+a K w K +b. Common activation functions include Sigmoid function, tanh function, Rectified Linear Unit (ReLU), etc.

[0076] Neural network parameters can be optimized using optimization algorithms. An optimization algorithm is a type of algorithm that minimizes or maximizes an objective function (sometimes called a loss function). The objective function is often a mathematical combination of model parameters and data. For example, given data X and its corresponding label Y, a neural network model f(.) is constructed. With the model, the predicted output f(x) can be obtained based on the input x. The difference between the predicted value and the true value (f(x) - Y) can be calculated. This is the loss function. Finding the appropriate values ​​W and b minimizes the loss function. The smaller the loss value, the closer the model is to the true value.

[0077] Currently, most common optimization algorithms are based on the back propagation (BP) algorithm. The basic concept of the BP algorithm is that the learning process consists of two steps: forward propagation of signals and back propagation of errors. During forward propagation, input samples are passed from the input layer, processed layer by layer through each hidden layer, and then transmitted to the output layer. If the actual output of the output layer does not match the expected output, the error begins to propagate backward. Back propagation involves propagating the output error back through the hidden layers to the input layer layer by layer in some form, distributing the error to all units in each layer. This error signal is then generated, which serves as the basis for adjusting the weights of each unit. This process of adjusting the weights of each layer, through forward propagation of signals and back propagation of errors, is repeated over and over again. This process of continuous weight adjustment is the network's learning and training process. This process continues until the error in the network output is reduced to an acceptable level, or until a pre-set number of learning cycles is reached.

[0078] Common optimization algorithms include gradient descent, stochastic gradient descent (SGD), mini-batch gradient descent, momentum, stochastic gradient descent with momentum, adaptive gradient descent (Adagrad), adaptive delta (Adadelta), root mean square prop (RMSprop), and adaptive momentum estimation (Adam). Adadelta is an optimization algorithm with adaptive learning rate adjustment.

[0079] When these optimization algorithms backpropagate errors, they all calculate the derivative or partial derivative of the current neuron based on the error or loss obtained by the loss function, add the influence of the learning rate, previous gradient, derivative or partial derivative, obtain the gradient, and pass the gradient to the previous layer.

[0080] 2. Introduction to AI models.

[0081] In this application, the AI ​​model may also be referred to as an AI unit, a machine learning (ML) model, an ML unit, an AI structure, an AI function, an AI feature, a machine learning model, a neural network, a neural network function, a neural network function, etc., or the AI ​​model may also refer to a processing unit that can implement specific algorithms, formulas, processing procedures, capabilities, etc. related to AI, or the AI ​​model may be a processing method, algorithm, function, module or unit for a specific data set, or the AI ​​model may be a processing method, algorithm, function, module or unit running on AI or machine learning (ML) related hardware such as a graphics processing unit (GPU), a neural network processor (NPU), a tensor processing unit (TPU), an application specific integrated circuit (ASIC), etc., and this application does not make specific limitations on this. Optionally, the specific data set includes the input or output of the AI ​​model.

[0082] Optionally, the identifier of the AI ​​model can also be called an AI unit identifier, an AI structure identifier, an AI algorithm identifier, or an identifier of a specific data set associated with the AI ​​model, or an identifier of a specific scenario, environment, channel feature, or device related to AI or ML, or an identifier of a function, feature, capability, or module related to AI or ML. This application does not make any specific limitations on this.

[0083] The above-mentioned AI functionality can be understood as an AI algorithm function. For a terminal (for example, user equipment (UE)), the AI ​​functionality may include one or more AI models.

[0084] 3. Introduction to the derivation of cell measurement values ​​based on measurements.

[0085] Beamforming is a wireless communication technology used to focus wireless signals in a specific direction to improve coverage and quality. In fifth-generation (5G) mobile communication technology, New Radio (NR) systems, beamforming is a key technology that can help cells improve signal coverage and quality.

[0086] 5G systems in related technologies use beamforming technology. Within a cell, there can be multiple synchronization signal blocks (SSBs), each of which is a beam that can point in different directions. The measurement model for 5G systems is shown in Figure 3.

[0087] Specifically, after receiving the measurement configuration sent by the base station, the UE performs measurements on the cell at the relevant frequency. First, the UE's physical layer performs measurements to obtain samples of the measured signal quality of K beams in the cell. The measured signal quality includes one of the following:

[0088] 1) Reference Signal Received Power (RSRP) is an indicator to measure signal strength.

[0089] 2) Reference Signal Received Quality (RSRQ) is an indicator to measure signal quality.

[0090] 3) Signal to Interference plus Noise Ratio (SINR) is an indicator to measure signal interference.

[0091] The UE's physical layer performs Layer 1 (L1) filtering on each beam. Specifically, it filters the samples of each beam at different time points (different sampling periods). For example, it averages the values ​​of five consecutive samples to obtain the beam measurement value. The UE's physical layer then reports each filtered beam measurement value to the UE's Layer 3 (L3).

[0092] The UE's Layer 3 selects and combines the measurement values ​​of multiple beams of a cell based on the parameters configured by the base station to generate the cell measurement value. Specifically, if the relevant parameters (the number of synchronization signal blocks to average (nrofSS-BlocksToAverage) and the absolute threshold for synchronization signal block integration (absThreshSS-BlocksConsolidation)) are configured, the measurement values ​​of up to N beams above the threshold are selected and linearly averaged to generate the cell measurement value. If the measurement value of the best beam is less than the threshold, the measurement value of the best beam is used as the cell measurement value. If none of the above parameters are configured, the measurement value of the best beam is used as the cell measurement value.

[0093] Since the signal is fluctuating, in order to avoid inaccurate cell measurement values ​​caused by signal fluctuations. Layer 3 can also perform layer 3 filtering (smoothing) on ​​the cell measurement values. Specifically, based on the weighted average filtering method, the latest cell measurement value and the previous smoothed value are weighted averaged to obtain the smoothed cell measurement value. The smoothed cell measurement value is used to evaluate the measurement event and report the cell measurement value in the measurement report. The base station can control the degree of smoothing by adjusting the weighting coefficient. The formula is as follows: F n =(1-a)×F n-1 +a×M n

[0094] Among them, M n It is the cell measurement value generated by selecting and combining the measurement values ​​of the most recent beam based on physical layer measurements;

[0095] F n It is the measurement value of the cell after L3 filtering, which is used to evaluate the reporting criteria of the measurement report;

[0096] F n-1 It is the measurement value of the cell after the last L3 filtering;

[0097] a=1 / 2 (k / 4) is the filter coefficient, where k is a parameter configured by the base station.

[0098] If the eNB is configured to report beam measurements, the beam measurements must also be filtered using the same L3 filtering described above. The measurement report then reports the beam indices of up to X beams above the threshold, along with the corresponding L3-filtered beam measurements.

[0099] It should be noted that the cell measurement value in the related art is generated based on the beam measurement value and the derived parameters configured by the base station, that is, the cell measurement value is affected by the derived parameters configured by the base station. The base station may configure different derived parameters for cells with different frequencies, and different base stations may also configure different parameters. For cell-level measurement prediction based on AI, deviations in the cell measurement prediction value may occur due to different derived parameters (for example, different frequencies correspond to different derived parameters, or different base stations are configured with different derived parameters, or the base station changes the derived parameters). Therefore, this application can apply the method corresponding to the above formula in combination with the AI ​​model to the measurement of cell measurement values.

[0100] 4. Introduction to AI for mobility.

[0101] Related technologies propose improving mobility performance by introducing AI. One important use case is radio resource management (RRM) measurement prediction, including cell-level measurement prediction and beam-level measurement prediction, as shown in Figures 4a and 4b.

[0102] The AI ​​model can be seen as a black box. Its internal implementation is invisible. What can be seen is that each time the AI ​​model reasoning is executed, some parameters (input) need to be input and some results (output) are output.

[0103] FIG5 shows a block diagram of a wireless communication system applicable to embodiments of the present application. The wireless communication system includes a terminal 51 and a network-side device 52. The wireless communication system may be a 5G-Advanced or 6G communication system with wireless AI capabilities.

[0104] The terminal 51 may be a mobile phone, a tablet personal computer, a laptop computer or a notebook computer, a personal digital assistant (PDA), a handheld computer, a netbook, an ultra-mobile personal computer (UMPC), a mobile internet device (MID), an augmented reality (AR) or virtual reality (VR) device, a robot, a wearable device (Wearable Device), a vehicle user equipment (VUE), a pedestrian user equipment (Pedestrian User Equipment, PUE), a smart home (home appliances with wireless communication capabilities, such as refrigerators, televisions, washing machines, or furniture), a game console, a personal computer (PC), an ATM, or a self-service machine, etc. The wearable devices include: smart watches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc. In addition to the above-mentioned terminal devices, the terminal involved in this application can also be a chip inside the terminal, such as a modem chip or a system-on-chip (SoC). It should be noted that the specific type of the terminal 51 is not limited in this embodiment of the application.

[0105] The network side device 52 may include an access network device or a core network device, wherein the access network device may also be referred to as a radio access network device, a radio access network (RAN), a radio access network function, or a radio access network unit. The access network device may include a base station, a wireless local area network (WLAN) access point, or a wireless fidelity (WiFi) node, etc. The base station may be referred to as a node B, an evolved node B (eNB), an access point, a base transceiver station (BTS), a radio base station, a radio transceiver, a basic service set (BSS), an extended service set (ESS), a home node B, a home evolved node B, a transmitting and receiving point (TRP), or other appropriate terms in the field. As long as the same technical effect is achieved, the base station is not limited to a specific technical vocabulary. It should be noted that in the embodiment of the present application, only the base station in the NR system is used as an example for introduction, and the specific type of the base station is not limited.

[0106] The core network equipment may include but is not limited to at least one of the following: core network node, core network function, mobility management entity (MME), access and mobility management function (AMF), session management function (SMF), user plane function (UPF), policy control function (PCF), policy and charging rules function unit (PCRF), edge application service discovery function (EASDF), unified data management (UDM), unified data storage (UDR), home subscriber server (HSS), centralized network configuration (CNC), network storage function (NRF), network exposure function (NEF), local NEF (L-NEF), binding support function (BSF), application function ( Function, AF), etc. It should be noted that in the embodiments of the present application, only the core network device in the NR system is introduced as an example, and the specific type of the core network device is not limited.

[0107] The following, in conjunction with the accompanying drawings, describes in detail the measurement processing method, measurement configuration method, apparatus, device, and readable storage medium provided in the embodiments of the present application through some embodiments and their application scenarios.

[0108] 6 , an embodiment of the present application provides a measurement processing method, the specific steps of which include:

[0109] Step 601: The terminal obtains a first predicted value of a beam in a first beam set of a first cell based on an AI model;

[0110] Step 602: The terminal generates a first predicted measurement value of the first cell according to the first predicted value;

[0111] The input of the AI ​​model includes a first actual measurement value of a beam in the second beam set of the first cell.

[0112] It can be understood that the first beam set is a set of partial beams of the first cell, and the first beam set may include one or more beams (prediction beams); the second beam set is a set of partial beams of the first cell, and the second beam set includes one or more beams (measurement beams), wherein the second beam set does not overlap with the first beam set.

[0113] Optionally, the second beam set includes all beams in the first cell except the beams in the first beam set.

[0114] In the present application, the terminal can perform inference based on the AI ​​model to output a beam-level prediction value (first prediction value), and then generate a predicted measurement value of the first cell based on the beam-level prediction value.

[0115] The aforementioned "beam-level prediction value output based on AI model inference" is equivalent to beam measurement value prediction. There are three prediction methods for beam measurement value prediction:

[0116] Prediction method 1: spatial domain prediction, that is, predicting the predicted values ​​of other (or all) beams of the cell based on the current actual measurement values ​​of some beams of the cell, see Figure 9a.

[0117] Optionally, a partial beam of a cell may include one or more beams.

[0118] Prediction method 2: time domain prediction, that is, predicting the predicted value of part of the beam at one or more time points in the future based on the current actual measurement value of the part of the beam in the first cell, see Figure 9b.

[0119] Prediction method three: frequency domain prediction, that is, predicting the predicted value of the beam of one or some frequencies / cells based on the current actual measurement value of the beam of one or some frequencies / cells, see Figure 9c.

[0120] For beam-level measurement prediction, it can be any one of the three prediction methods mentioned above, or it can be any combination of the three prediction methods mentioned above, such as a combination of spatial domain prediction and time domain prediction, that is, based on the current measurement values ​​of part of the beams of the cell, the predicted values ​​of other (or all) beams at the current and one or more future time points are predicted; another example is spatial domain prediction and frequency domain prediction, that is, based on the current measurement values ​​of part of the beams of a cell, the predicted values ​​of all current beams of the cell and the neighboring cells (different frequencies) are predicted.

[0121] For prediction method 1 or a combined prediction method related to prediction method 1, the measurement operations of the UE can be reduced, thereby achieving effects such as terminal power saving and reduced measurement delay.

[0122] In one embodiment of the present application, prediction method one can be used to generate a first predicted measurement value of the first cell based on beam merging of the first predicted value of the beam in the first beam of the first cell and the first actual measurement value of the beam in the second beam set of the first cell. This can reduce the problem that the predicted value of the beam may be biased, resulting in inaccurate measurement values ​​of the first cell, wherein the first predicted measurement value of the first cell can be used to evaluate measurement events or report to a base station.

[0123] In one implementation manner of the present application, the terminal generates, according to the first predicted value, a first predicted measurement value of the first cell, including:

[0124] The terminal generates the first predicted measurement value according to the first predicted value and the first actual measurement value.

[0125] In one embodiment of the present application, the terminal generates the first predicted measurement value according to the first predicted value and the first actual measurement value, including:

[0126] The terminal performs beam selection on the second beam set to obtain a third beam set;

[0127] The terminal performs beam selection on the first beam set to obtain a fourth beam set;

[0128] The terminal generates the first predicted measurement value according to the first actual measurement value of the beam in the third beam set and the first predicted value of the beam in the fourth beam set.

[0129] In one implementation manner of the present application, the terminal generates, according to the first predicted value of the first cell, a first predicted measurement value of the first cell, including:

[0130] The terminal performs beam selection on a combination of the first beam set and the second beam set to obtain a fifth beam set, where the fifth beam set is composed of a sixth beam set and a seventh beam set, the sixth beam set being a set composed of selected beams in the second beam set, and the seventh beam set being a set composed of selected beams in the first beam set;

[0131] The terminal generates a first predicted measurement value of the first cell according to the first actual measurement value of the beam in the sixth beam focus and the first predicted value of the beam in the seventh beam focus.

[0132] In this embodiment, when generating the first predicted measurement value of the first cell, the terminal uses both the first actual measurement value and the first predicted value, which can reduce the problem that the first predicted measurement value of the first cell may be inaccurate due to deviation of the first predicted value.

[0133] In one embodiment of the present application, the method further includes:

[0134] The terminal performs a first operation on the first beam set, the fourth beam set, or the seventh beam set;

[0135] The first operation is used to adjust first prediction values ​​of at least part of the beams in the first beam set, the fourth beam set, or the seventh beam set.

[0136] In one embodiment of the present application, the first operation includes subtracting a first offset from the first predicted value of at least some beams in the first beam set, the fourth beam set, or the seventh beam set, or multiplying the first predicted value of at least some beams in the first beam set, the fourth beam set, or the seventh beam set by a first coefficient; wherein the first coefficient is greater than 0 and less than 1. In this embodiment, the first offset is not specifically limited.

[0137] It can be understood that the terminal may perform the first operation on the first beam set before performing beam selection on the first beam set of the first cell to obtain the fourth beam set, or the terminal may perform the first operation on the fourth beam set after performing beam selection on the first beam set to obtain the fourth beam set, or the terminal may perform the first operation on the seventh beam set in the fifth beam set after performing beam selection on the combination of the first beam set and the second beam set to obtain the fifth beam set. This can reduce the problem that the first predicted value may be biased, resulting in inaccurate first predicted measurement value of the first cell.

[0138] In one embodiment of the present application, the terminal performs beam selection on the first beam set of the first cell to obtain a fourth beam set, including at least one of the following:

[0139] 1) The terminal selects, from the first beam set, beams whose first prediction values ​​are higher than a first threshold to form the fourth beam set;

[0140] 2) The terminal selects, from the first beam set, beams whose first prediction values ​​are not less than a first threshold to form the fourth beam set;

[0141] 3) The terminal selects a maximum first number of beams in the first beam set whose best first prediction values ​​are higher than or equal to the first threshold to form the fourth beam set.

[0142] For example, there are 8 beams in the first beam set whose first prediction values ​​are higher than or equal to the first threshold, and the first number is 4. Then the fourth beam set includes the first 4 beams among the 8 beams in the first beam set whose best first prediction values ​​are higher than or equal to the first threshold, where the best first 4 beams refer to the first 4 beams with the largest first prediction values ​​among the 8 beams.

[0143] It can be understood that, in this embodiment, there is no specific limitation on the first threshold and the first number.

[0144] In one embodiment of the present application, the terminal performs beam selection on the second beam set to obtain a third beam set, including at least one of the following:

[0145] 1) The terminal selects beams in the second beam set whose first actual measurement value is higher than or equal to a second threshold to form the third beam set;

[0146] 2) The terminal selects a maximum of a second number of beams in the second beam set whose best first actual measurement values ​​are higher than or equal to the second threshold to form the third beam set.

[0147] For example, the number of beams in the second beam set whose first actual measurement values ​​are higher than or equal to the second threshold is 8, and the second number is 4. Then the third beam set includes the first 4 beams of the 8 best beams in the second beam set whose first actual measurement values ​​are higher than or equal to the second threshold.

[0148] It can be understood that, in this embodiment, the second threshold and the second number are not specifically limited.

[0149] In one embodiment of the present application, the terminal generates a first predicted measurement value of the first cell based on the first actual measurement value of the beam in the third beam focus and the first predicted value of the beam in the fourth beam focus, including at least one of the following:

[0150] 1) performing weighted averaging on the first actual measurement value of the beam in the third beam concentration and the first predicted value of the beam in the fourth beam concentration to obtain a first predicted measurement value of the first cell;

[0151] 2) averaging the first actual measurement value of the beam in the third beam concentration and the first predicted value of the beam in the fourth beam concentration to obtain a first predicted measurement value of the first cell;

[0152] 3) when the fourth beam set is an empty set and the third beam set is a non-empty set, averaging the first actual measurement values ​​of the beams in the third beam set to obtain a first predicted measurement value of the first cell;

[0153] 4) when the third beam set is an empty set and the fourth beam set is a non-empty set, averaging the first predicted values ​​of the beams in the fourth beam set to obtain a first predicted measurement value of the first cell;

[0154] 5) When the fourth beam set and the third beam set are both empty sets, the first actual measurement value or the first predicted value of the best beam among all beams in the second beam set and the first beam set is used as the first predicted measurement value of the first cell, or the first actual measurement value of the best beam in the second beam set is used as the first predicted measurement value of the first cell.

[0155] The first actual measurement value of the best beam refers to the maximum or highest first actual measurement value of the beam, and the first predicted value of the best beam refers to the maximum or highest first predicted value of the beam. In one embodiment of the present application, the first predicted measurement value of the first cell is obtained by weighted averaging the first actual measurement value of the beam in the third beam set and the second predicted value of the beam in the fourth beam set, including:

[0156] Averaging the first actual measurement values ​​of the beams in the third beam set to obtain a second actual measurement value;

[0157] Averaging the first predicted values ​​of the beams in the fourth beam set to obtain a second predicted value;

[0158] A weighted average of the second actual measurement value and the second predicted value is performed to obtain a first predicted measurement value of the first cell.

[0159] For example, the first predicted measurement value of the first cell can be calculated using the following weighted average formula:

[0160] The first predicted measurement value = (1-a1) × the second actual measurement value + a1 × the second predicted value, or the first predicted measurement value = a1 × the second actual measurement value + (1-a1) × the second predicted value, where a1 is a number greater than 0 and less than 1.

[0161] It should be noted that, in the present application, "weighted average" may refer to: the weighted average of the first value M and the second value N is: (1-a)×M+a×N, or a×M+(1-a)×N, where a is a number greater than 0 and less than 1. "Average" may refer to a linear average. It will not be repeated elsewhere. In one embodiment of the present application, the terminal generates a first predicted measurement value of the first cell based on the first actual measurement value of the beam in the sixth beam concentration and the first predicted value of the beam in the seventh beam concentration, including at least one of the following:

[0162] 1) performing weighted averaging on the first actual measurement value of the beam in the sixth beam set and the first predicted value of the beam in the seventh beam set to obtain a first predicted measurement value of the first cell;

[0163] 2) averaging the first actual measurement value of the beam in the sixth beam set and the first predicted value of the beam in the seventh beam set to obtain a first predicted measurement value of the first cell;

[0164] 3) when the seventh beam set is an empty set and the sixth beam set is a non-empty set, averaging the first actual measurement values ​​of the beams in the sixth beam set to obtain a first predicted measurement value of the first cell;

[0165] 4) when the sixth beam set is an empty set and the seventh beam set is a non-empty set, averaging the first predicted values ​​of the beams in the seventh beam set to obtain a first predicted measurement value of the first cell;

[0166] 5) When the seventh beam set and the sixth beam set are both empty sets, the first actual measurement value or the first predicted value of the best beam among all beams in the second beam set and the first beam set is used as the first predicted measurement value of the first cell, or the first actual measurement value of the best beam in the second beam set is used as the first predicted measurement value of the first cell.

[0167] In one embodiment of the present application, performing weighted averaging on the first actual measurement value of the beam in the sixth beam focus and the first predicted value of the beam in the seventh beam focus to obtain the first predicted measurement value of the first cell includes:

[0168] Averaging the first actual measurement values ​​of the beams in the sixth beam set to obtain a third actual measurement value;

[0169] Averaging the first predicted values ​​of the beams in the seventh beam set to obtain a third predicted value;

[0170] A first predicted measurement value of the first cell is obtained based on a weighted average of the third actual measurement value and the third predicted value.

[0171] For example, the first predicted measurement value of the first cell can be calculated using the following weighted average formula:

[0172] The first predicted measurement value = (1-a2) × the third actual measurement value + a2 × the third predicted value, or the first predicted measurement value = a2 × the third actual measurement value + (1-a2) × the third predicted value, where a2 is a number greater than 0 and less than 1.

[0173] In one embodiment of the present application, the method further includes:

[0174] The terminal receives a first configuration, where the first configuration includes a second configuration, where the second configuration is used to indicate at least one of the following: a configuration for obtaining a first predicted value of a beam of a first beam set of a first cell, and a configuration for the terminal to generate a first predicted measurement value of the first cell based on the first predicted value of the beam of the first beam set.

[0175] In one embodiment of the present application, the second configuration includes at least one of the following:

[0176] 1) A first parameter set, where the first parameter set is used by the terminal to perform beam selection on the first beam set of the first cell to obtain a fourth beam set; wherein the first parameter set may include a first threshold or a first number, and the terminal selects, based on the first parameter set, beams in the first beam set whose first prediction values ​​are higher than or not lower than the first threshold to form the fourth beam set, or the terminal selects a maximum first number of beams in the first beam set whose best first prediction values ​​are higher than or higher than or equal to the first threshold to form the fourth beam set.

[0177] 2) a second parameter set, wherein the second parameter set is used by the terminal to perform beam selection on the second beam set of the first cell to obtain a third beam set; wherein the second parameter set may include a second threshold or a second number, and the terminal selects, based on the second parameter set, beams in the second beam set whose first actual measurement value is higher than or higher than or equal to the second threshold to form the third beam set, or the terminal selects a maximum of a second number of beams in the second beam set whose best first actual measurement value is higher than or higher than or equal to the second threshold to form the third beam set.

[0178] Optionally, the second parameter set is different from the first parameter set.

[0179] 3) a third parameter set, wherein the third parameter set is used by the terminal to perform a first operation on the first beam set, the fourth beam set, or the seventh beam set; wherein the third parameter set may include a first offset or a first coefficient, and the terminal subtracts the first offset or multiplies the first predicted value of at least part of the beams in the first beam set, the fourth beam set, or the seventh beam set by the first coefficient based on the third parameter set.

[0180] 4) a fourth parameter set, the fourth parameter set is used by the terminal to perform beam merging on the third beam set and the fourth beam set to generate the first predicted measurement value; wherein the fourth parameter set includes a first weight coefficient, and the first weight coefficient can be used to control the respective weights of the first actual measurement value of the beam in the third beam set and the first predicted value of the beam in the fourth beam set. For example, if the first weight coefficient is a3, then the first predicted measurement value = a3 × the average value of the first actual measurement value of the beam in the third beam set + (1-a3) × the average value of the first predicted value of the beam in the fourth beam set, or the first predicted measurement value = (1-a3) × the average value of the first actual measurement value of the beam in the third beam set + a3 × the average value of the first predicted value of the beam in the fourth beam set, and a3 is greater than 0 and less than 1, thereby reducing the inaccuracy of the predicted value on the first predicted measurement value.

[0181] 5) A fifth parameter set, wherein the fifth parameter set is used by the terminal to perform beam selection on the combination of the first beam set and the second beam set to obtain a fifth beam set; wherein the fifth parameter set may include a fourth threshold or a third number, and the terminal selects, based on the fifth parameter set, beams in the combination whose first actual measurement value or first predicted value is higher than or higher than or equal to the fourth threshold to form the fifth beam set, or the terminal selects the best first actual measurement value or first predicted value in the combination whose first actual measurement value is higher than or higher than or equal to the fourth threshold and whose maximum third number of beams form the fifth beam set.

[0182] 6) A sixth parameter set, wherein the sixth parameter set is used by the terminal to perform beam merging on the sixth beam set and the seventh beam set to generate the first predicted measurement value; wherein the sixth parameter set includes a second weight coefficient, and the second weight coefficient can be used to control the respective weights of the first actual measurement value of the beam in the sixth beam set and the first predicted value of the beam in the seventh beam set. For example, if the second weight coefficient is a4, then the first predicted measurement value = a4 × the average value of the first actual measurement value of the beam in the sixth beam set + (1-a4) × the average value of the first predicted value of the beam in the seventh beam set, or the first predicted measurement value = (1-a4) × the average value of the first actual measurement value of the beam in the sixth beam set + a4 × the average value of the first predicted value of the beam in the seventh beam set, and a4 is greater than 0 and less than 1, thereby reducing the inaccuracy of the predicted value on the first predicted measurement value.

[0183] It should be noted that, in the present application, each of the first parameter set to the sixth parameter set may include one or more parameters.

[0184] In one embodiment of the present application, the second configuration further includes at least one of the following:

[0185] 1) An identifier or index of the beam of the first beam set; wherein the identifier or index of the beam of the first beam set can be used by the terminal to know which beams need to obtain prediction values ​​through the AI ​​model.

[0186] 2) The identifier or index of the beam of the second beam set; wherein, the identifier or index of the beam of the second beam set can be used by the terminal to know which beams need to be actually measured to obtain actual measurement values.

[0187] 3) The number of beams in the second beam set; wherein, the number of beams in the second beam set can be used by the terminal to know the actual measurement values ​​of how many beams need to be obtained through actual measurement.

[0188] In one embodiment of the present application, the first configuration further includes a third configuration, and the third configuration includes at least one of the following:

[0189] 1) First indication information, where the first indication information is used to indicate the type of the measurement value of the reported cell;

[0190] 2) Second indication information, where the second indication information is used to indicate reporting of information based on a predicted beam, or to indicate reporting of information based on a predicted beam that is higher than or equal to a third threshold.

[0191] In one embodiment of the present application, the method further includes:

[0192] The terminal sends information about a first predicted measurement value of the first cell to a network side device;

[0193] The information about the first predicted measurement value of the first cell includes at least one of the following:

[0194] 1) a type of the first predicted measurement value of the first cell;

[0195] 2) an actual measured value or predicted value of the first beam of the first cell;

[0196] 3) Third indication information, where the third indication information is used to indicate whether the first beam is an actual measurement beam or a predicted beam.

[0197] Among them, the type of the first predicted measurement value or the third indication information can be used by the network device to obtain the reliability of the reported measurement value of the first cell or the measurement value of the first beam, which can be used as a reference when making a switching decision.

[0198] In one embodiment of the present application, the type of the first predicted measurement value includes at least one of the following:

[0199] 1) Cell measurement values ​​based on actual measurements;

[0200] 2) based on predicted cell measurements;

[0201] 3) Cell measurement values ​​generated based on actual measurement values ​​of some beams and predicted values ​​of another part of beams.

[0202] In an embodiment of the present application, the terminal obtains a first predicted value of a beam in the first beam focus of the first cell based on an AI model; then the terminal can generate a first predicted measurement value of the first cell based on the first predicted value, so that the measurement value of the first cell can be obtained by prediction by the AI ​​model, thereby improving mobility performance.

[0203] 7 , an embodiment of the present application provides a measurement processing method, the specific steps of which include:

[0204] Step 701: The terminal obtains a first cell measurement value of a first cell;

[0205] The introduction of AI models may enable a method to reduce actual measurements: in the time dimension, actual measurements and predictions based on the AI ​​model are performed alternately to obtain a first cell measurement value for the first cell. Here, the first cell measurement value refers to the cell measurement value that has not been filtered through Layer 3. In other words, the first cell measurement value may be a predicted value obtained based on the AI ​​model or an actual measurement value obtained based on actual measurements.

[0206] Step 702: The terminal performs layer 3 filtering based on the first cell measurement value and the last layer 3 filtered cell measurement value of the first cell to obtain the layer 3 filtered cell measurement value of the first cell.

[0207] The terminal performing layer 3 filtering based on the first cell measurement value and the last layer 3 filtered cell measurement value of the first cell includes:

[0208] When the first cell measurement value is obtained based on the AI ​​model, the terminal performs layer 3 filtering based on the first layer 3 filtering parameter, the first cell measurement value, and the last layer 3 filtered cell measurement value of the first cell;

[0209] In a case where the first cell measurement value is obtained based on actual measurement, the terminal performs layer 3 filtering based on a second layer 3 filtering parameter, the first cell measurement value, and a last layer 3 filtered cell measurement value of the first cell.

[0210] In one embodiment of the present application, the first layer three filtering parameters are different from the second layer three filtering parameters.

[0211] In one embodiment of the present application, the first cell measurement value is obtained based on an AI model, and includes at least one of the following:

[0212] 1) The first cell measurement value is a cell measurement value predicted based on the AI ​​model;

[0213] 2) The first cell measurement value is a cell measurement value generated based on the beam value predicted by the AI ​​model;

[0214] 3) The first cell measurement value is a cell measurement value generated based on the predicted value of part of the beams and the measurement value of another part of the beams.

[0215] In one embodiment of the present application, before the terminal obtains the first cell measurement value of the first cell, the method further includes:

[0216] The terminal receives the first layer 3 filtering parameter and the second layer 3 filtering parameter from a network-side device.

[0217] In an embodiment of the present application, the terminal uses the first layer three filtering parameters and the second layer three filtering parameters respectively based on whether the first cell measurement value is a predicted value obtained based on the AI ​​model, or an actual measurement value obtained based on actual measurement, so as to adjust the weighting coefficient of the predicted value to reduce the impact of the inaccurate predicted value on the smoothed cell measurement value. For example, the k value corresponding to the predicted value is larger than the k value corresponding to the actual measurement value (according to the existing layer three filtering method, the larger the k value, the smaller the weighting coefficient).

[0218] 8 , an embodiment of the present application provides a measurement configuration method, which is applied to a network-side device, such as a base station, and includes the following steps:

[0219] Step 801: A network-side device sends a first configuration, where the first configuration includes a second configuration, and the second configuration is used to indicate at least one of the following:

[0220] 1) A configuration for obtaining a first predicted value for a beam of a first beam set for a first cell;

[0221] 2) a configuration for generating a first predicted measurement value of the first cell based on a first predicted value of a beam of the first beam set;

[0222] 3) First layer three filter parameters and second layer three filter parameters;

[0223] The first layer 3 filtering parameter is used to perform layer 3 filtering on the cell measurement value obtained based on the AI ​​model, and the second layer 3 filtering parameter is used to perform layer 3 filtering on the cell measurement value obtained based on actual measurement.

[0224] In one embodiment of the present application, the first configuration includes at least one of the following:

[0225] 1) A first parameter set, where the first parameter set is used by the terminal to perform beam selection on the first beam set of the first cell to obtain a fourth beam set; the first parameter set may include a first threshold or a first number, and the terminal selects, based on the first parameter set, beams in the first beam set whose first predicted values ​​are higher than or not lower than the first threshold to form the fourth beam set, or the terminal selects a maximum first number of beams in the first beam set whose best first predicted values ​​are higher than or equal to the first threshold to form the fourth beam set;

[0226] 2) A second parameter set, where the second parameter set is used by the terminal to perform beam selection on the second beam set of the first cell to obtain a third beam set; the second parameter set may include a second threshold or a second number, and based on the second parameter set, the terminal selects beams in the second beam set whose first actual measurement values ​​are higher than or equal to the second threshold to form the third beam set, or the terminal selects a maximum of a second number of beams in the second beam set whose best first actual measurement values ​​are higher than or equal to the second threshold to form the third beam set. Optionally, the second parameter set is different from the first parameter set.

[0227] 3) a third parameter set, wherein the third parameter set is used by the terminal to perform a first operation on the first beam set, the fourth beam set, or the seventh beam set; wherein the third parameter set may include a first offset or a first coefficient, and the terminal subtracts the first offset or multiplies the first predicted value of at least part of the beams in the first beam set, the fourth beam set, or the seventh beam set by the first coefficient based on the third parameter set.

[0228] 4) a fourth parameter set, the fourth parameter set is used by the terminal to perform beam merging on the third beam set and the fourth beam set to generate the first predicted measurement value; wherein the fourth parameter set includes a first weight coefficient, and the first weight coefficient can be used to control the respective weights of the first actual measurement value of the beam in the third beam set and the first predicted value of the beam in the fourth beam set. For example, if the first weight coefficient is a3, then the first predicted measurement value = a3 × the average value of the first actual measurement value of the beam in the third beam set + (1-a3) × the average value of the first predicted value of the beam in the fourth beam set, or the first predicted measurement value = (1-a3) × the average value of the first actual measurement value of the beam in the third beam set + a3 × the average value of the first predicted value of the beam in the fourth beam set, and a3 is greater than 0 and less than 1, thereby reducing the inaccuracy of the predicted value on the first predicted measurement value.

[0229] 5) A fifth parameter set, wherein the fifth parameter set is used by the terminal to perform beam selection on the combination of the first beam set and the second beam set to obtain a fifth beam set; wherein the fifth parameter set may include a fourth threshold or a third number, and the terminal selects, based on the fifth parameter set, beams in the combination whose first actual measurement value or first predicted value is higher than or higher than or equal to the fourth threshold to form the fifth beam set, or the terminal selects the best first actual measurement value or first predicted value in the combination whose first actual measurement value is higher than or higher than or equal to the fourth threshold and whose maximum third number of beams form the fifth beam set.

[0230] 6) A sixth parameter set, wherein the sixth parameter set is used by the terminal to perform beam merging on the sixth beam set and the seventh beam set to generate the first predicted measurement value. The sixth parameter set includes a second weight coefficient, which is used to control the respective weights of the first actual measurement value of the beam in the sixth beam set and the first predicted value of the beam in the seventh beam set. For example, if the second weight coefficient is a4, then the first predicted measurement value = a4 × the average value of the first actual measurement value of the beam in the sixth beam set + (1-a4) × the average value of the first predicted value of the beam in the seventh beam set, or the first predicted measurement value = (1-a4) × the average value of the first actual measurement value of the beam in the sixth beam set + a4 × the average value of the first predicted value of the beam in the seventh beam set. The a4 is greater than 0 and less than 1, thereby reducing the inaccuracy of the predicted value on the first predicted measurement value.

[0231] In one embodiment of the present application, the second configuration further includes at least one of the following:

[0232] 1) An identifier or index of the beam of the first beam set; wherein the identifier or index of the beam of the first beam set can be used by the terminal to know which beams need to obtain prediction values ​​through the AI ​​model.

[0233] 2) The identifier or index of the beam of the second beam set; wherein, the identifier or index of the beam of the second beam set can be used by the terminal to know which beams need to be actually measured to obtain actual measurement values.

[0234] 3) The number of beams in the second beam set; wherein, the number of beams in the second beam set can be used by the terminal to know the actual measurement values ​​of how many beams need to be obtained through actual measurement.

[0235] In one embodiment of the present application, the first configuration further includes a third configuration, and the third configuration includes at least one of the following:

[0236] 1) First indication information, where the first indication information is used to indicate the type of the measurement value of the reported cell;

[0237] 2) Second indication information, where the second indication information is used to indicate reporting of information based on a predicted beam, or to indicate reporting of information based on a predicted beam that is higher than or equal to a third threshold.

[0238] In one embodiment of the present application, the method further includes:

[0239] The network side device receives information about a first predicted measurement value of the first cell;

[0240] The information about the first predicted measurement value of the first cell includes at least one of the following:

[0241] 1) a type of the first predicted measurement value of the first cell;

[0242] 2) an actual measured value or predicted value of the first beam of the first cell;

[0243] 3) Third indication information, where the third indication information is used to indicate whether the first beam is an actual measurement beam or a predicted beam.

[0244] In this embodiment, the network side device can configure the terminal with relevant parameters for obtaining the cell measurement value based on the predicted value of the predicted beam or the actual measurement value of the measured beam, so that the terminal can obtain the cell measurement value based on the predicted value of the predicted beam or the actual measurement value of the measured beam.

[0245] The following describes the implementation of the present application in conjunction with Example 1, Example 2 and Example 3.

[0246] Example 1

[0247] 10 and 11, the flow of the measurement and processing method is introduced. The specific steps are as follows:

[0248] Step 1: The UE performs measurement to obtain a first actual measurement value of each beam in the second beam set.

[0249] Optionally, the second beam set may be a set of some beams in the first cell. For example, the second beam set may include one or more beams. Assume that the beams in the second beam set are respectively called measurement beam 1 (measured beam 1), measurement beam 2 (measured beam 2) to measurement beam X (measured beam X).

[0250] Optionally, the first actual measurement value of each beam in the second beam set may be a measurement value received by layer 3 (Radio Resource Control (RRC) layer) of the UE from layer 1 (physical layer).

[0251] Step 2: The UE performs AI inference based on the first actual measurement value of the beam in the second beam set to obtain a first predicted value for each beam in the first beam set of the first cell.

[0252] Optionally, the first beam set may be a set of partial beams in the first cell, and the first beam set may not overlap with the second beam set. For example, the first beam set may include one or more beams. Assume that the beams in the first beam set are respectively called predicted beam 1, predicted beam 2 to predicted beam K.

[0253] Optionally, the first beam set may include all beams of the first cell except the beams in the second beam set.

[0254] Optionally, each time layer 3 of the UE obtains a first actual measurement value of all beams in a set of second beam sets from layer 1, layer 3 of the UE can trigger an AI inference to obtain a first predicted value of each beam in the first beam set.

[0255] Step 3: The UE generates a first predicted measurement value of the first cell based on the first actual measurement value and the first predicted value.

[0256] Specifically, corresponding to the solutions shown in FIG. 9 a to FIG. 9 c , step 3 may include the following steps:

[0257] Step 3a-1: The UE performs beam selection on the second beam set based on the second parameter set to obtain a third beam set.

[0258] Optionally, the second parameter set may include a second threshold or a second number, and the UE selects beams in the second beam set whose first actual measurement value is higher than or equal to the second threshold to form a third beam set according to the second parameter set, or selects a maximum of a second number of beams in the second beam set whose best first actual measurement value is higher than or equal to the second threshold to form a third beam set.

[0259] Optionally, if the first actual measurement value of no beam in the second beam set is higher than or higher than equal to the second threshold, or the first actual measurement value of the best beam in the second beam set is lower than or lower than equal to the second threshold, a beam with the best first actual measurement value is selected to form a third beam set.

[0260] Step 3a-2: Optionally, considering that the predicted value is inaccurate, the UE performs a first operation on the first beam set based on the third parameter set to adjust the first predicted value of the beam in the first beam set.

[0261] Optionally, step 3a-2 may also be performed after step 3a-3, that is, beam selection may be performed on the first beam set to obtain a fourth beam set, and then the first operation may be performed on the fourth beam set based on the third parameter set to adjust the first predicted value of the beam in the fourth beam set.

[0262] Optionally, the third parameter set may include a first offset or a first coefficient, and the terminal subtracts the first offset or multiplies the first predicted value of at least part of the beams in the first beam set or the fourth beam set by the first coefficient based on the third parameter set.

[0263] Optionally, the first operation includes subtracting a first offset (or adding a negative first offset) from the first predicted value of the beam in the corresponding beam set (the first beam set or the fourth beam set), for example, subtracting 2 dBm, or multiplying by a first coefficient less than 1, for example, multiplying by 0.9, to reduce the impact of an inaccurate first predicted value. In other words, the first predicted value of the beam in the first beam set or the fourth beam set may be inaccurate. By lowering the first predicted value of the beam in the first beam set or the fourth beam set, the probability of switching to a cell with a poor signal is reduced. In addition, after lowering the first predicted value of the beam in the first beam set or the fourth beam set, more predicted beams can be filtered out during beam selection, thereby increasing the proportion of the first actually measured beam.

[0264] Step 3a-3: The UE performs beam selection on the first beam set based on the first parameter set to obtain a fourth beam set.

[0265] Optionally, the first parameter set may include a first threshold or a first number, and the UE selects beams in the first beam set whose first prediction values ​​are higher than or higher than or equal to the first threshold to form a fourth beam set, or the UE selects a maximum second number of beams in the first beam set whose best first prediction values ​​are higher than or higher than or equal to the second threshold to form a fourth beam set, or the UE selects the beam with the best first prediction value in the first beam set to form a fourth beam set (since the prediction value is inaccurate, only one beam with the best first prediction value is selected, and in this case, the second parameter set is empty).

[0266] Optionally, the first parameter set may be the same as or different from the second parameter set.

[0267] For example, when the first parameter set is different from the second parameter set, for example, because the first prediction value may be inaccurate, the first threshold may be greater than the second threshold to reduce the number of beams selected in the first beam set;

[0268] For example, when the first parameter set and the second parameter set are the same, the base station may only configure the second parameter set, and the UE performs beam selection on the first beam set based on the second parameter set to obtain the fourth beam set. Furthermore, optionally, when the UE performs beam selection on the first beam set based on the second parameter set, an offset is applied (for example, the offset is a positive value), and the threshold used for beam selection is the second threshold plus the offset.

[0269] Optionally, if no beam in the first beam set has a first prediction value higher than or equal to the first threshold, or if the first prediction value of the highest beam in the first beam set is lower than or equal to the first threshold, a beam with the highest first prediction value is selected to form a fourth beam set.

[0270] Steps 3a-4: The UE performs beam combining on the third beam set and the fourth beam set based on the fourth parameter set to generate a predicted measurement value of the first cell. Specifically:

[0271] Method 1: Perform weighted averaging on the first actual measurement value of the beam in the third beam set and the first predicted value of the beam in the fourth beam set to obtain the first predicted measurement value of the first cell.

[0272] Due to the inaccuracy of the first predicted value, different weights may be considered during merging, for example, the weight of the beam of the first predicted value is lower than the weight of the beam of the measured value.

[0273] A specific method may be to first average the beams in the third beam set and the fourth beam set to obtain the second measurement value (V Meas ) and the second predicted value (V predic ), and then use the weight coefficient a1 (for example, a coefficient less than 1) to merge, for example: the first predicted measurement value of the first cell = (1-a1)×V Meas +a1×V predic , where a1 can be less than 0.5, so that the weight of the measurement value is high.

[0274] Method 2: directly average all beams in the third beam set and the fourth beam set to obtain the predicted measurement value of the first cell.

[0275] It is possible that the fourth beam set or the third beam set is an empty set. When the fourth beam set is an empty set and the third beam set is a non-empty set, all beams in the third beam set are averaged to obtain the first predicted measurement value of the first cell; when the third beam set is an empty set and the fourth beam set is a non-empty set, all beams in the fourth beam set are averaged to obtain the first predicted measurement value of the first cell (in this case, it can also be called the cell predicted value); when both the fourth beam set and the third beam set are empty sets, the first actual measurement value or the first predicted value of the best beam among all beams in the second beam set and the first beam set is used as the first predicted measurement value of the first cell, or (the first beam set is not considered because it is the first predicted value and is unreliable) the first actual measurement value of the best beam in the second beam set is used as the predicted measurement value of the first cell.

[0276] Corresponding to the solution shown in FIG11 , step 3 may include the following steps:

[0277] Step 3b-1: Optionally, considering that the predicted value is inaccurate, the UE performs a first operation on the first beam set based on the third parameter set to adjust the first predicted value of the beam in the first beam set.

[0278] Step 3b-2: The UE performs beam selection on the union of the second beam set and the first beam set based on the fifth parameter set to obtain a fifth beam set, wherein the fifth beam set is composed of a sixth beam set and a seventh beam set, the sixth beam set is a set composed of selected beams in the second beam set, and the seventh beam set is a set composed of selected beams in the first beam set.

[0279] Optionally, before step 3b-3, the terminal may perform a first operation on the seventh beam set based on a third parameter set; wherein the third parameter set may include a first offset or a first coefficient, and the terminal subtracts the first offset or multiplies the first predicted value of at least part of the beams in the seventh beam set by the first coefficient based on the third parameter set.

[0280] Step 3b-3: The UE performs beam combining on the sixth beam set and the seventh beam set based on the sixth parameter set to generate a first predicted measurement value of the first cell.

[0281] Among them, the sixth parameter set includes a second weight coefficient, which is used to control the respective weights of the first actual measurement value of the beam in the sixth beam set and the first predicted value of the beam in the seventh beam set, thereby reducing the inaccuracy problem caused by the predicted value to the first predicted measurement value.

[0282] Method 1: Perform weighted averaging on the first actual measurement value of the beam in the sixth beam concentration and the first predicted value of the beam in the seventh beam concentration to obtain the first predicted measurement value of the first cell.

[0283] Method 2: Directly average all beams in the sixth beam set and the seventh beam set to obtain the predicted measurement value of the first cell.

[0284] It is possible that the sixth beam set or the seventh beam set is an empty set. When the sixth beam set is an empty set and the seventh beam set is a non-empty set, all beams in the seventh beam set are averaged to obtain the first predicted measurement value of the first cell; when the seventh beam set is an empty set and the sixth beam set is a non-empty set, all beams in the sixth beam set are averaged to obtain the first predicted measurement value of the first cell (in this case, it can also be called the cell predicted value); when the sixth beam set and the seventh beam set are both empty sets, the first actual measurement value or the first predicted value of the best beam among all beams in the second beam set and the first beam set is used as the first predicted measurement value of the first cell, or (the first beam set is not considered because it is the first predicted value and is unreliable) the first actual measurement value of the best beam in the second beam set is used as the predicted measurement value of the first cell.

[0285] It should be noted that the solution shown in FIG10 performs beam selection on the beams in the first and second beam sets separately, while the solution shown in FIG11 combines the beams in the first and second beam sets before performing beam selection. For other details, refer to the solution shown in FIG10 and will not be repeated here.

[0286] It should be noted that the above-mentioned first parameter set to sixth parameter set may be configured by the base station to the UE.

[0287] Step 4: Optionally, the UE performs layer 3 (L3) filtering on the measurement value of the first cell to obtain the L3 filtered measurement value of the first cell. The filtered measurement value can be used for measurement event evaluation and cell measurement value reporting.

[0288] Example 2

[0289] Embodiment 1 introduces a solution for merging and generating a first predicted measurement value for the first cell when part of the beams in the first cell are actually measured and the other part of the beams are predicted.

[0290] In order to cope with the impact of signal fluctuations, Layer 3 needs to perform Layer 3 smoothing (filtering) on ​​the cell measurement value, that is, to perform a weighted average of the latest cell measurement value and the previous smoothed value to obtain the smoothed cell measurement value. The smoothed cell measurement value is used to evaluate the measurement event and report the cell measurement value in the measurement report. The base station can control the degree of smoothing (base station configuration) by adjusting the weighting coefficient (base station configuration k value, based on the k value to obtain the filter coefficient a). There may be a method to reduce actual measurements: in the time dimension, the cell measurement value obtained by actual measurement and the cell measurement value obtained by prediction (AI model) are alternately generated to generate cell measurement values ​​(here refers to the cell measurement value that has not been smoothed by L3), see Figure 12 for an example.

[0291] Furthermore, considering that the predicted cell measurement value may not be so accurate, different smoothing weighting coefficients (i.e., filtering coefficients) can be used for the cell measurement value obtained by actual measurement and the cell measurement value obtained by prediction. That is, when performing layer 3 smoothing, the latest cell measurement value (M n ) is an actual measurement value or a predicted value to use different filter coefficients. Correspondingly, the base station configures a filter coefficient k1 corresponding to the cell measurement value obtained by actual measurement, and a filter coefficient k2 corresponding to the cell measurement value obtained by prediction. For example, the filter coefficient k2 corresponding to the predicted value is larger than the filter coefficient k1 corresponding to the actual measurement value, thereby reducing the weighting coefficient of the predicted value and reducing the impact of the inaccurate predicted value on the smoothed cell measurement value.

[0292] Referring to Figure 13, the specific steps are as follows:

[0293] Step 1: The base station (i.e., the network-side device) sends the filter coefficient k1 (i.e., the second layer three filter parameter) corresponding to the actual measured cell measurement value and the filter coefficient k2 (i.e., the first layer three filter parameter) corresponding to the predicted cell measurement value to the UE;

[0294] Step 2: If the latest cell measurement value (M n ) is the actual measurement value, then use the filter coefficient k1 to perform layer 3 filtering; if the latest cell measurement value (M n ) is the predicted value, the filter coefficient k2 is used for layer 3 filtering.

[0295] Example 3

[0296] Example 1 introduces generating a first predicted measurement value of the first cell based on a first actual measurement value of a beam in the second beam concentration of the first cell and a first predicted value of a beam in the first beam concentration of the first cell. This embodiment introduces reporting of the first predicted measurement value of the first cell.

[0297] Referring to Figure 14, the specific steps are as follows:

[0298] Step 1: The base station (ie, the network-side device) sends a first configuration to the UE.

[0299] Optionally, the first configuration includes a second configuration (i.e., a prediction configuration), which is used to indicate at least one of the following: a configuration for obtaining a first predicted value of a beam of a first beam set of a first cell, and a configuration for the terminal to generate a first predicted measurement value of the first cell based on the first predicted value of the beam of the first beam set.

[0300] Optionally, the second configuration may include any one or any combination of the first to sixth parameter sets in the first embodiment.

[0301] Optionally, the second configuration may further include any one or any combination of the following:

[0302] 1) The ID or index of the beams that make up the second beam set (the second beam is the beam set composed of the beams that the UE needs to perform measurements on, or the beam set composed of the beams that serve as the AI ​​model input). This information is used to indicate which beams the UE needs to measure. This information can be cell-level (i.e., different cells can correspond to different second beam sets), frequency-level (i.e., cells at the same frequency correspond to the same second beam set, and different cells can correspond to different second beam sets), or UE-level (i.e., all cells correspond to the same second beam set). The base station indicates the beams included in the first beam set, which can improve the performance of model inference.

[0303] 2) The ID or index of the beams constituting the first beam set (ie, the beam set consisting of the beams output by the model), which corresponds to the first beam set.

[0304] 3) the number of beams in the second beam set;

[0305] Optionally, the number of beams in the second beam set may include a minimum number or a maximum number, and the number of beams in the second beam set is used to indicate the number of beams that the UE needs to measure, the minimum number of the second beam set is used to indicate the minimum number of beams that the UE needs to measure, and the maximum number of the second beam set is used to indicate the maximum number of beams that the UE needs to measure, wherein the indicated number or the minimum number may be to ensure that the performance of the AI ​​model inference is met, and the indicated maximum number may be to reduce the measurement operations of the UE.

[0306] Optionally, the first configuration also includes a third configuration (i.e., a reporting configuration), where the third configuration is used to instruct the UE how to report the first predicted measurement value of the first cell. The third configuration may include any one or any combination of the following:

[0307] 1) First indication information, used to indicate the type of reported cell measurement value;

[0308] Optionally, the type includes at least one of the following: a) a cell measurement value based on actual measurement (all obtained based on measurement), b) a cell measurement value based on prediction (all obtained based on prediction), c) a cell measurement value generated based on actual measurement values ​​of some beams and predicted values ​​of another part of the beams;

[0309] 2) second indication information, used to indicate whether to report information based on the predicted beam, or to indicate whether to report information based on the predicted beam that is greater than or equal to a third threshold;

[0310] 3) Information indicating the measured or predicted value of the reported beam;

[0311] 4) Information indicating the maximum number of reported beams in a cell, or the maximum number of reported prediction-based beams in a cell.

[0312] Step 2: The UE performs measurement to obtain a second beam set under the first cell and a first actual measurement value of a beam in the second beam set;

[0313] Optionally, the UE performs measurement based on the second configuration in step 1, for example, based on the ID or index of the beam constituting the second beam set indicated in the second configuration, performs measurement on the beam of the second beam set to obtain a first actual measurement value of the beam of the second beam set.

[0314] Step 3: The UE performs AI inference based on the first actual measurement value to obtain a first predicted value for each beam in the first beam set in the first cell.

[0315] Step 4: The UE generates a first predicted measurement value of the first cell based on the first actual measurement value and the first predicted value. For the specific method, please refer to step 3 of embodiment 1 and will not be repeated here.

[0316] Step 5: Optional step: The UE sends information about the first predicted measurement value of the first cell to the base station;

[0317] Optionally, when the measurement report is triggered, the UE sends information about the first predicted measurement value of the first cell to the base station.

[0318] Optionally, the information about the first predicted measurement value of the first cell includes at least one of the following:

[0319] 1) a first predicted measurement value of a first cell;

[0320] 2) a type of the first predicted measurement value of the first cell;

[0321] Optionally, the type includes at least one of the following: a) a cell measurement value based on actual measurement (all obtained based on measurement), b) a cell measurement value based on prediction (all obtained based on prediction), c) a cell measurement value generated based on actual measurement values ​​of some beams and predicted values ​​of another part of the beams;

[0322] 3) The actual measurement value or predicted value of the first beam in the first cell. Optionally, the information of the first predicted measurement value may also include: third indication information indicating whether the first beam is based on an actual measurement or a predicted beam.

[0323] Optionally, the UE sends information about the first predicted measurement value of the first cell to the base station based on the third configuration in step 1, for example, reporting information based on the predicted beam, or reporting information based on the predicted beam in the first cell that is higher than or equal to the third threshold, or reporting information based on the predicted beam in the first cell that does not exceed the specified maximum number.

[0324] Optionally, the base station may perform switching decisions and other operations based on information about the first predicted measurement value of the first cell, for example, considering information such as the strength of the first predicted measurement value of the first cell and the type of the first predicted measurement value of the first cell.

[0325] 15 , an embodiment of the present application provides a measurement processing device, the device 1500 including: a first processing unit 1501 and a first transceiver unit 1502;

[0326] The first processing unit 1501 is configured to obtain a first predicted value of a beam in a first beam set of a first cell based on the AI ​​model;

[0327] The first processing unit 1501 is further configured to generate a first predicted measurement value of the first cell according to the first predicted value;

[0328] The input of the AI ​​model includes a first actual measurement value of a beam in the second beam set of the first cell.

[0329] In one embodiment of the present application, the first processing unit 1501 is further configured to generate the first predicted measurement value according to the first predicted value and the first actual measurement value.

[0330] In one embodiment of the present application, the first processing unit 1501 is further used to: perform beam selection on the second beam set to obtain a third beam set; perform beam selection on the first beam set to obtain a fourth beam set; and generate the first predicted measurement value based on the first actual measurement value of the beam in the third beam set and the first predicted value of the beam in the fourth beam set.

[0331] In one embodiment of the present application, the first processing unit 1501 is further used to: perform beam selection on the union of the first beam set and the second beam set to obtain a fifth beam set, wherein the fifth beam set is composed of a sixth beam set and a seventh beam set, the sixth beam set is a set composed of selected beams in the second beam set, and the seventh beam set is a set composed of selected beams in the first beam set; and generate a first predicted measurement value of the first cell according to the first actual measurement value of the beam in the sixth beam set and the first predicted value of the beam in the seventh beam set.

[0332] In one embodiment of the present application, the first processing unit 1501 is further used to: perform a first operation on the first beam set or the fourth beam set or the seventh beam set; wherein the first operation is used to adjust the first prediction value of at least part of the beams in the first beam set or the fourth beam set or the seventh beam set.

[0333] In one embodiment of the present application, the first operation includes subtracting a first offset from the first predicted value of at least some beams in the first beam set, the fourth beam set, or the seventh beam set, or multiplying the first predicted value of at least some beams in the first beam set, the fourth beam set, or the seventh beam set by a first coefficient; wherein the first coefficient is greater than 0 and less than 1.

[0334] In one embodiment of the present application, the first processing unit 1501 is further configured to perform at least one of the following:

[0335] 1) Selecting beams in the first beam set whose first prediction values ​​are higher than a first threshold to form the fourth beam set;

[0336] 2) selecting beams in the first beam set whose first prediction values ​​are not less than a first threshold to form the fourth beam set;

[0337] 3) Selecting a maximum first number of beams in the first beam set whose best first prediction values ​​are higher than or equal to the first threshold to form the fourth beam set.

[0338] In one embodiment of the present application, the first processing unit 1501 is further configured to perform at least one of the following:

[0339] 1) Selecting beams in the second beam set whose first actual measurement values ​​are higher than or equal to a second threshold to form the third beam set;

[0340] 2) Selecting a maximum of a second number of beams in the second beam set whose best first actual measurement values ​​are higher than or equal to the second threshold to form the third beam set.

[0341] In one embodiment of the present application, the first processing unit 1501 is further configured to perform at least one of the following:

[0342] 1) performing weighted averaging on the first actual measurement value of the beam in the third beam concentration and the first predicted value of the beam in the fourth beam concentration to obtain a first predicted measurement value of the first cell;

[0343] 2) averaging the first actual measurement value of the beam in the third beam concentration and the first predicted value of the beam in the fourth beam concentration to obtain a first predicted measurement value of the first cell;

[0344] 3) when the fourth beam set is an empty set and the third beam set is a non-empty set, averaging the first actual measurement values ​​of the beams in the third beam set to obtain a first predicted measurement value of the first cell;

[0345] 4) when the third beam set is an empty set and the fourth beam set is a non-empty set, averaging the first predicted values ​​of the beams in the fourth beam set to obtain a first predicted measurement value of the first cell;

[0346] 5) When the fourth beam set and the third beam set are both empty sets, the first actual measurement value or the first predicted value of the best beam among all beams in the second beam set and the first beam set is used as the first predicted measurement value of the first cell, or the first actual measurement value of the best beam in the second beam set is used as the first predicted measurement value of the first cell.

[0347] In one embodiment of the present application, the first processing unit 1501 is further configured to:

[0348] Averaging the first actual measurement values ​​of the beams in the third beam set to obtain a second actual measurement value;

[0349] Averaging the first predicted values ​​of the beams in the fourth beam set to obtain a second predicted value;

[0350] A weighted average of the second actual measurement value and the second predicted value is performed to obtain a first predicted measurement value of the first cell.

[0351] In one embodiment of the present application, the first processing unit 1501 is further configured to perform at least one of the following:

[0352] 1) performing weighted averaging on the first actual measurement value of the beam in the sixth beam set and the first predicted value of the beam in the seventh beam set to obtain a first predicted measurement value of the first cell;

[0353] 2) averaging the first actual measurement value of the beam in the sixth beam set and the first predicted value of the beam in the seventh beam set to obtain a first predicted measurement value of the first cell;

[0354] 3) when the seventh beam set is an empty set and the sixth beam set is a non-empty set, averaging the first actual measurement values ​​of the beams in the sixth beam set to obtain a first predicted measurement value of the first cell;

[0355] 4) when the sixth beam set is an empty set and the seventh beam set is a non-empty set, averaging the first predicted values ​​of the beams in the seventh beam set to obtain a first predicted measurement value of the first cell;

[0356] 5) When the seventh beam set and the sixth beam set are both empty sets, the first actual measurement value or the first predicted value of the best beam among all beams in the second beam set and the first beam set is used as the first predicted measurement value of the first cell, or the first actual measurement value of the best beam in the second beam set is used as the first predicted measurement value of the first cell.

[0357] In one embodiment of the present application, the first processing unit 1501 is further configured to:

[0358] Averaging the first actual measurement values ​​of the beams in the sixth beam set to obtain a third actual measurement value;

[0359] Averaging the first predicted values ​​of the beams in the seventh beam set to obtain a third predicted value;

[0360] A first predicted measurement value of the first cell is obtained based on a weighted average of the third actual measurement value and the third predicted value.

[0361] In one embodiment of the present application, the first transceiver unit 1502 is used to receive a first configuration, where the first configuration includes a second configuration, and the second configuration is used to indicate at least one of the following: a configuration for obtaining a first predicted value of a beam of a first beam set of a first cell, and a configuration for the terminal to generate a first predicted measurement value of the first cell based on the first predicted value of the beam of the first beam set.

[0362] In one embodiment of the present application, the second configuration includes at least one of the following:

[0363] 1) A first parameter set, where the first parameter set is used by the terminal to perform beam selection on the first beam set of the first cell to obtain a fourth beam set; wherein the first parameter set may include a first threshold or a first number, and the terminal selects, based on the first parameter set, beams in the first beam set whose first prediction values ​​are higher than or not lower than the first threshold to form the fourth beam set, or the terminal selects a maximum first number of beams in the first beam set whose best first prediction values ​​are higher than or higher than or equal to the first threshold to form the fourth beam set.

[0364] 2) A second parameter set, where the second parameter set is used by the terminal to perform beam selection on the second beam set of the first cell to obtain a third beam set; the second parameter set may include a second threshold or a second number, and based on the second parameter set, the terminal selects beams in the second beam set whose first actual measurement values ​​are higher than or equal to the second threshold to form the third beam set, or the terminal selects a maximum of a second number of beams in the second beam set whose best first actual measurement values ​​are higher than or equal to the second threshold to form the third beam set. Optionally, the second parameter set is different from the first parameter set.

[0365] 3) a third parameter set, wherein the third parameter set is used by the terminal to perform a first operation on the first beam set, the fourth beam set, or the seventh beam set; wherein the third parameter set may include a first offset or a first coefficient, and the terminal subtracts the first offset or multiplies the first predicted value of at least part of the beams in the first beam set, the fourth beam set, or the seventh beam set by the first coefficient based on the third parameter set.

[0366] 4) A fourth parameter set, wherein the fourth parameter set is used by the terminal to perform beam merging on the third beam set and the fourth beam set to generate the first predicted measurement value; wherein the fourth parameter set includes a first weight coefficient, and the first weight coefficient can be used to control the respective weights of the first actual measurement value of the beam in the third beam set and the first predicted value of the beam in the fourth beam set. For example, if the first weight coefficient is a3, then the first predicted measurement value a3×the average value of the first actual measurement value of the beam in the third beam set+(1-a3)×the average value of the first predicted value of the beam in the fourth beam set, or the first predicted measurement value=(1-a3)×the average value of the first actual measurement value of the beam in the third beam set+a3×the average value of the first predicted value of the beam in the fourth beam set, and a3 is greater than 0 and less than 1, thereby reducing the inaccuracy of the predicted value on the first predicted measurement value.

[0367] 5) A fifth parameter set, wherein the fifth parameter set is used by the terminal to perform beam selection on the combination of the first beam set and the second beam set to obtain a fifth beam set; wherein the fifth parameter set may include a fourth threshold or a third number, and the terminal selects, based on the fifth parameter set, beams in the combination whose first actual measurement value or first predicted value is higher than or higher than or equal to the fourth threshold to form the fifth beam set, or the terminal selects the best first actual measurement value or first predicted value in the combination whose first actual measurement value is higher than or higher than or equal to the fourth threshold and whose maximum third number of beams form the fifth beam set.

[0368] 6) A sixth parameter set, wherein the sixth parameter set is used by the terminal to perform beam merging on the sixth beam set and the seventh beam set to generate the first predicted measurement value; wherein the sixth parameter set includes a second weight coefficient, and the second weight coefficient can be used to control the respective weights of the first actual measurement value of the beam in the sixth beam set and the first predicted value of the beam in the seventh beam set. For example, if the second weight coefficient is a4, then the first predicted measurement value = a4 × the average value of the first actual measurement value of the beam in the sixth beam set + (1-a4) × the average value of the first predicted value of the beam in the seventh beam set, or the first predicted measurement value = (1-a4) × the average value of the first actual measurement value of the beam in the sixth beam set + a4 × the average value of the first predicted value of the beam in the seventh beam set, and a4 is greater than 0 and less than 1, thereby reducing the inaccuracy of the predicted value on the first predicted measurement value.

[0369] In one embodiment of the present application, the second configuration further includes at least one of the following:

[0370] 1) An identifier or index of the beam of the first beam set; wherein the identifier or index of the beam of the first beam set can be used by the terminal to know which beams need to obtain prediction values ​​through the AI ​​model.

[0371] 2) The identifier or index of the beam of the second beam set; wherein, the identifier or index of the beam of the second beam set can be used by the terminal to know which beams need to be actually measured to obtain actual measurement values.

[0372] 3) The number of beams in the second beam set; wherein, the number of beams in the second beam set can be used by the terminal to know the actual measurement values ​​of how many beams need to be obtained through actual measurement.

[0373] In one embodiment of the present application, the first configuration further includes a third configuration, and the third configuration includes at least one of the following:

[0374] 1) First indication information, where the first indication information is used to indicate the type of the measurement value of the reported cell;

[0375] 2) Second indication information, where the second indication information is used to indicate reporting of information based on a predicted beam, or to indicate reporting of information based on a predicted beam that is higher than or equal to a third threshold.

[0376] In one embodiment of the present application, the first transceiver unit 1502 is further configured to: send information about the measurement value of the first cell to a network side device;

[0377] Wherein, information of the first predicted measurement value of the first cell;

[0378] The information about the first predicted measurement value of the first cell includes at least one of the following:

[0379] 1) a type of the first predicted measurement value of the first cell;

[0380] 2) an actual measured value or predicted value of the first beam of the first cell;

[0381] 3) Third indication information, where the third indication information is used to indicate whether the first beam is an actual measurement beam or a predicted beam.

[0382] In one embodiment of the present application, the type of the first predicted measurement value includes at least one of the following:

[0383] 1) Cell measurement values ​​based on actual measurements;

[0384] 2) based on predicted cell measurements;

[0385] 3) Cell measurement values ​​generated based on actual measurement values ​​of some beams and predicted values ​​of another part of beams.

[0386] The device provided in the embodiment of the present application can implement each process implemented in the method embodiment of Figure 6 and achieve the same technical effect. To avoid repetition, it will not be described here.

[0387] 16 , an embodiment of the present application provides a measurement processing device, the device 1600 including: a second processing unit 1601 and a second transceiver unit 1602 ;

[0388] The second processing unit 1601 is configured to obtain a first cell measurement value of a first cell;

[0389] The second processing unit 1601 is further configured to perform layer 3 filtering based on the first cell measurement value and the last layer 3 filtered cell measurement value of the first cell, to obtain the layer 3 filtered cell measurement value of the first cell;

[0390] The terminal performing layer 3 filtering based on the first cell measurement value and the last layer 3 filtered cell measurement value of the first cell includes:

[0391] When the first cell measurement value is obtained based on the AI ​​model, the terminal performs layer 3 filtering based on the first layer 3 filtering parameter, the first cell measurement value, and the last layer 3 filtered cell measurement value of the first cell;

[0392] In a case where the first cell measurement value is obtained based on actual measurement, the terminal performs layer 3 filtering based on a second layer 3 filtering parameter, the first cell measurement value, and a last layer 3 filtered cell measurement value of the first cell.

[0393] In one embodiment of the present application, the first layer three filtering parameters are different from the second layer three filtering parameters.

[0394] In one embodiment of the present application, the first cell measurement value is obtained based on an AI model, including at least one of the following:

[0395] 1) The first cell measurement value is a cell measurement value predicted based on the AI ​​model;

[0396] 2) The first cell measurement value is a cell measurement value generated based on the beam value predicted by the AI ​​model;

[0397] 3) The first cell measurement value is a cell measurement value generated based on the predicted value of part of the beams and the measurement value of another part of the beams.

[0398] In one embodiment of the present application, the second transceiver unit 1602 is configured to receive the first layer 3 filtering parameter and the second layer 3 filtering parameter from a network-side device.

[0399] The device provided in the embodiment of the present application can implement each process implemented in the method embodiment of Figure 7 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0400] 17 , an embodiment of the present application provides a measurement configuration device, wherein the device 1700 includes: a third transceiver unit 1701 and a third processing unit 1702 ;

[0401] The third transceiver unit 1701 is configured to send a first configuration, where the first configuration includes a second configuration, and the second configuration is configured to indicate at least one of the following:

[0402] 1) A configuration for obtaining a first predicted value for a beam of a first beam set for a first cell;

[0403] 2) a configuration for generating a first predicted measurement value of the first cell based on a first predicted value of a beam of the first beam set;

[0404] 3) First layer three filter parameters and second layer three filter parameters;

[0405] The first layer 3 filtering parameter is used to perform layer 3 filtering on the cell measurement value obtained based on the AI ​​model, and the second layer 3 filtering parameter is used to perform layer 3 filtering on the cell measurement value obtained based on actual measurement.

[0406] In one embodiment of the present application, the first configuration includes at least one of the following:

[0407] 1) a first parameter set, where the first parameter set is used by the terminal to perform beam selection on the first beam set of the first cell to obtain a fourth beam set;

[0408] 2) a second parameter set, where the second parameter set is used by the terminal to perform beam selection on the second beam set of the first cell to obtain a third beam set;

[0409] 3) a third parameter set, where the third parameter set is used by the terminal to perform a first operation on the first beam set, the fourth beam set, or the seventh beam set;

[0410] 4) a fourth parameter set, where the fourth parameter set is used by the terminal to perform beam combining on the third beam set and the fourth beam set to generate the first predicted measurement value;

[0411] 5) a fifth parameter set, where the fifth parameter set is used by the terminal to perform beam selection on a combination of the first beam set and the second beam set to obtain a fifth beam set;

[0412] 6) A sixth parameter set, where the sixth parameter set is used by the terminal to perform beam combining on the sixth beam set and the seventh beam set to generate the first predicted measurement value.

[0413] In one embodiment of the present application, the second configuration further includes at least one of the following:

[0414] 1) an identifier or index of a beam of the first beam set;

[0415] 2) an identifier or index of a beam of the second beam set;

[0416] 3) The number of beams in the second beam set.

[0417] In one embodiment of the present application, the first configuration further includes a third configuration, and the third configuration includes at least one of the following:

[0418] 1) First indication information, where the first indication information is used to indicate the type of the measurement value of the reported cell;

[0419] 2) Second indication information, where the second indication information is used to indicate reporting of information based on a predicted beam, or to indicate reporting of information based on a predicted beam that is higher than or equal to a third threshold.

[0420] In one embodiment of the present application, the third transceiver unit 1701 is further configured to receive information about the measurement value of the first cell;

[0421] Wherein, information of the first predicted measurement value of the first cell;

[0422] The information about the first predicted measurement value of the first cell includes at least one of the following:

[0423] 1) a type of the first predicted measurement value of the first cell;

[0424] 2) an actual measured value or predicted value of the first beam of the first cell;

[0425] 3) Third indication information, where the third indication information is used to indicate whether the first beam is an actual measurement beam or a predicted beam.

[0426] The device provided in the embodiment of the present application can implement each process implemented in the method embodiment of Figure 8 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0427] FIG18 is a schematic diagram of the hardware structure of a terminal implementing an embodiment of the present application. The terminal 1800 includes, but is not limited to, at least some of the components including a radio frequency unit 1801, a network module 1802, an audio output unit 1803, an input unit 1804, a sensor 1805, a display unit 1806, a user input unit 1807, an interface unit 1808, a memory 1809, and a processor 1810.

[0428] Those skilled in the art will appreciate that the terminal 1800 may also include a power supply (such as a battery) to power various components. The power supply may be logically connected to the processor 1810 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The terminal structure shown in FIG18 does not limit the terminal. The terminal may include more or fewer components than shown, or may combine certain components, or have different component arrangements, which will not be described in detail here.

[0429] It should be understood that in an embodiment of the present application, the input unit 1804 may include a graphics processing unit (GPU) 18041 and a microphone 18042, and the graphics processor 18041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The display unit 1806 may include a display panel 18061, and the display panel 18061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 1807 includes a touch panel 18071 and at least one of other input devices 18072. The touch panel 18071 is also called a touch screen. The touch panel 18071 may include two parts: a touch detection device and a touch controller. Other input devices 18072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.

[0430] In the embodiment of the present application, after receiving downlink data from a network-side device, the RF unit 1801 may transmit the data to the processor 1810 for processing. Furthermore, the RF unit 1801 may send uplink data to the network-side device. Typically, the RF unit 1801 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, and the like.

[0431] The memory 1809 can be used to store software programs or instructions and various data. The memory 1809 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, applications or instructions required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory 1809 may include a volatile memory or a non-volatile memory, or the memory 1809 may include a non-transient memory. Among them, the non-volatile memory or non-transient memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DRRAM). The memory 1809 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0432] Processor 1810 may include one or more processing units. Optionally, processor 1810 integrates an application processor and a modem processor. The application processor primarily handles operations related to the operating system, user interface, and application programs, while the modem processor primarily processes wireless communication signals, such as a baseband processor. It is understood that the modem processor may not be integrated into processor 1810.

[0433] The terminal provided in the embodiment of the present application can implement each process implemented in the method embodiment of Figure 6 or Figure 7 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0434] Please refer to Figure 19, which is a structural diagram of the network side device used in the embodiment of the present application.

[0435] As shown in Figure 19, the network side device 1900 includes: a processor 1901, a transceiver 1902, a memory 1903 and a bus interface, wherein the processor 1901 may be responsible for managing the bus architecture and general processing. The memory 1903 may store data used by the processor 1901 when performing operations.

[0436] In one embodiment of the present application, the network side device 1900 further includes: a program stored in the memory 1903 and executable on the processor 1901 , which implements the steps of the method shown in FIG. 8 above when executed by the processor 1901 .

[0437] In Figure 19 , the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits such as one or more processors represented by processor 1901 and memory represented by memory 1903. The bus architecture can also link various other circuits such as peripherals, voltage regulators, and power management circuits, all of which are well known in the art and are therefore not further described herein. The bus interface provides an interface. Transceiver 1902 can be multiple components, including a transmitter and a receiver, providing a means for communicating with various other devices over a transmission medium.

[0438] As shown in Figure 20, an embodiment of the present application also provides a communication device 2000, which can be a terminal or a network side device. The communication device includes a processor 2001 and a memory 2002. The memory 2002 stores a program or instruction that can be run on the processor 2001. When the program or instruction is executed by the processor 2001, it implements the various steps of the method embodiments of Figure 6 or Figure 7 or Figure 8 above and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0439] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the method of Figure 6 or Figure 7 or Figure 8 and the various processes of the above-mentioned embodiments are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0440] The processor is the processor in the terminal or network-side device described in the above embodiments. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk. In some examples, the readable storage medium may be a non-transitory readable storage medium.

[0441] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes shown in Figure 6 or Figure 7 or Figure 8 and the various method embodiments mentioned above, and can achieve the same technical effect. To avoid repetition, they will not be repeated here.

[0442] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0443] An embodiment of the present application further provides a computer program / program product, which is stored in a storage medium. The computer program / program product is executed by at least one processor to implement the various processes shown in Figure 6 or Figure 7 or Figure 8 and the various method embodiments described above, and can achieve the same technical effects. To avoid repetition, they are not described here.

[0444] An embodiment of the present application also provides a communication system, which includes a terminal and a network-side device. The terminal is used to execute the various processes shown in Figure 6 or Figure 7 and the various method embodiments described above, or the network-side device is used to execute the various processes shown in Figure 8 and the various method embodiments described above, and can achieve the same technical effects. To avoid repetition, they will not be repeated here.

[0445] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the statement "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be noted that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0446] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of a computer software product plus a necessary general-purpose hardware platform, or of course, by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.) and includes a number of instructions for enabling a terminal or network-side device to execute the methods described in each embodiment of the present application.

[0447] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms of implementation methods without departing from the purpose of this application and the scope of protection of the claims. These implementation methods are all within the protection of this application.

Claims

1. A measurement processing method, comprising: The terminal obtains, based on the artificial intelligence (AI) model, a first predicted value of a beam in the first beam focus of the first cell; generating, by the terminal, a first predicted measurement value of the first cell according to the first predicted value; The input of the AI ​​model includes a first actual measurement value of a beam in the second beam set of the first cell.

2. The method according to claim 1, wherein The terminal generating, according to the first predicted value, a first predicted measurement value of the first cell, including: The terminal generates the first predicted measurement value according to the first predicted value and the first actual measurement value.

3. The method according to claim 2, wherein: The terminal generates the first predicted measurement value according to the first predicted value and the first actual measurement value, including: The terminal performs beam selection on the second beam set to obtain a third beam set; The terminal performs beam selection on the first beam set to obtain a fourth beam set; The terminal generates the first predicted measurement value according to the first actual measurement value of the beam in the third beam set and the first predicted value of the beam in the fourth beam set.

4. The method according to claim 2, wherein: The terminal generating, according to the first predicted value, a first predicted measurement value of the first cell, including: The terminal performs beam selection on a combination of the first beam set and the second beam set to obtain a fifth beam set, wherein the fifth beam set is composed of a sixth beam set and a seventh beam set, the sixth beam set is a set composed of selected beams in the second beam set, and the seventh beam set is a set composed of selected beams in the first beam set. The terminal generates a first predicted measurement value of the first cell according to the first actual measurement value of the beam in the sixth beam focus and the first predicted value of the beam in the seventh beam focus.

5. The method according to claim 3 or 4, further comprising: The terminal performs a first operation on the first beam set, the fourth beam set, or the seventh beam set; The first operation is used to adjust first prediction values ​​of at least part of the beams in the first beam set, the fourth beam set, or the seventh beam set.

6. The method according to claim 5, wherein: The first operation includes subtracting a first offset from a first predicted value of at least some beams in the first beam set, the fourth beam set, or the seventh beam set, or multiplying the first predicted value of at least some beams in the first beam set, the fourth beam set, or the seventh beam set by a first coefficient; The first coefficient is greater than 0 and less than 1.

7. The method according to claim 3, wherein: The terminal performs beam selection on the first beam set to obtain a fourth beam set, including at least one of the following: The terminal selects, from the first beam set, beams whose first prediction values ​​are higher than a first threshold to form the fourth beam set; The terminal selects, from the first beam set, beams whose first prediction values ​​are not less than a first threshold to form the fourth beam set; The terminal selects, from the first beam set, a maximum first number of beams whose best first prediction values ​​are higher than or equal to the first threshold to form the fourth beam set.

8. The method according to claim 3, wherein: The terminal performs beam selection on the second beam set to obtain a third beam set, including at least one of the following: The terminal selects, in the second beam set, beams whose first actual measurement values ​​are higher than or equal to a second threshold to form the third beam set; The terminal selects, in the second beam set, a maximum second number of beams whose best first actual measurement values ​​are higher than or equal to the second threshold to form the third beam set.

9. The method according to claim 3, wherein: Generating, by the terminal, a first predicted measurement value of the first cell based on the first actual measurement value of the beam in the third beam focus and the first predicted value of the beam in the fourth beam focus, including at least one of the following: Taking a weighted average of the first actual measurement value of the beam in the third beam concentration and the first predicted value of the beam in the fourth beam concentration to obtain a first predicted measurement value of the first cell; Averaging the first actual measurement value of the beam in the third beam concentration and the first predicted value of the beam in the fourth beam concentration to obtain a first predicted measurement value of the first cell; When the fourth beam set is an empty set and the third beam set is a non-empty set, averaging first actual measurement values ​​of beams in the third beam set to obtain a first predicted measurement value of the first cell; When the third beam set is an empty set and the fourth beam set is a non-empty set, averaging the first predicted values ​​of the beams in the fourth beam set to obtain a first predicted measurement value of the first cell; In the case that both the fourth beam set and the third beam set are empty sets, the first actual measurement value or the first predicted value of the best beam among all beams in the second beam set and the third beam set is used as the first predicted measurement value of the first cell, or the first actual measurement value of the best beam in the second beam set is used as the first predicted measurement value of the first cell.

10. The method according to claim 9, wherein: The method of performing weighted averaging on the first actual measurement value of the beam in the third beam concentration and the second predicted value of the beam in the fourth beam concentration to obtain the first predicted measurement value of the first cell includes: Averaging the first actual measurement values ​​of the beams in the third beam set to obtain a second actual measurement value; Averaging the first predicted values ​​of the beams in the fourth beam set to obtain a second predicted value; A weighted average of the second actual measurement value and the second predicted value is performed to obtain a first predicted measurement value of the first cell.

11. The method according to claim 4, wherein Generating, by the terminal, a first predicted measurement value of the first cell based on the first actual measurement value of the beam in the sixth beam focus and the first predicted value of the beam in the seventh beam focus, including at least one of the following: Taking a weighted average of the first actual measurement value of the beam in the sixth beam concentration and the first predicted value of the beam in the seventh beam concentration to obtain a first predicted measurement value of the first cell; Averaging the first actual measurement value of the beam in the sixth beam concentration and the first predicted value of the beam in the seventh beam concentration to obtain a first predicted measurement value of the first cell; When the seventh beam set is an empty set and the sixth beam set is a non-empty set, averaging first actual measurement values ​​of beams in the sixth beam set to obtain a first predicted measurement value of the first cell; When the sixth beam set is an empty set and the seventh beam set is a non-empty set, averaging the first predicted values ​​of the beams in the seventh beam set to obtain a first predicted measurement value of the first cell; In the case that both the seventh beam set and the sixth beam set are empty sets, the first actual measurement value or the first predicted value of the best beam among all beams in the second beam set and the first beam set is used as the first predicted measurement value of the first cell, or the first actual measurement value of the best beam in the second beam set is used as the first predicted measurement value of the first cell.

12. The method according to claim 11, wherein The method of performing weighted averaging on the first actual measurement value of the beam in the sixth beam focus and the first predicted value of the beam in the seventh beam focus to obtain the first predicted measurement value of the first cell includes: Averaging the first actual measurement values ​​of the beams in the sixth beam set to obtain a third actual measurement value; averaging the third predicted values ​​of the beams in the seventh beam set to obtain a third predicted value; A first predicted measurement value of the first cell is obtained based on a weighted average of the third actual measurement value and the third predicted value.

13. The method according to claim 1, 3 or 4, further comprising: The terminal receives a first configuration, where the first configuration includes a second configuration, where the second configuration is used to indicate at least one of the following: a configuration for obtaining a first predicted value of a beam of a first beam set of a first cell, and a configuration for the terminal to generate a first predicted measurement value of the first cell based on the first predicted value of the beam of the first beam set.

14. The method according to claim 13, wherein The second configuration includes at least one of the following: a first parameter set, where the first parameter set is used by the terminal to perform beam selection on the first beam set of the first cell to obtain a fourth beam set; a second parameter set, where the second parameter set is used by the terminal to perform beam selection on the second beam set of the first cell to obtain a third beam set; a third parameter set, where the third parameter set is used by the terminal to perform a first operation on the first beam set, the fourth beam set, or the seventh beam set; a fourth parameter set, where the fourth parameter set is used by the terminal to perform beam combining on the third beam set and the fourth beam set to generate the first predicted measurement value; a fifth parameter set, where the fifth parameter set is used by the terminal to perform beam selection on a combination of the first beam set and the second beam set to obtain a fifth beam set; A sixth parameter set is used by the terminal to perform beam combining on the sixth beam set and the seventh beam set to generate the first predicted measurement value.

15. The method according to claim 14, wherein The second configuration further includes at least one of the following: an identifier or index of a beam of the first beam set; an identifier or index of a beam of the second beam set; The number of beams in the second beam set.

16. The method according to claim 13, wherein: The first configuration further includes a third configuration, wherein the third configuration includes at least one of the following: First indication information, where the first indication information is used to indicate a type of a measurement value of a reported cell; Second indication information, where the second indication information is used to indicate reporting of information based on a predicted beam, or to indicate reporting of information based on a predicted beam that is higher than or equal to a third threshold.

17. The method according to claim 1, further comprising: The terminal sends information about a first predicted measurement value of the first cell to a network side device; The information about the first predicted measurement value of the first cell includes at least one of the following: a type of the first predicted measurement value of the first cell; an actual measured value or a predicted value of the first beam of the first cell; Third indication information, where the third indication information is used to indicate whether the first beam is an actual measurement beam or a predicted beam.

18. The method according to claim 17, wherein: The type of the first predicted measurement value includes at least one of the following: Cell measurement values ​​based on actual measurements; Based on predicted cell measurements; The cell measurement values ​​are generated based on the actual measurement values ​​of some beams and the predicted values ​​of another part of the beams.

19. A measurement processing method, comprising: The terminal obtains a first cell measurement value of the first cell; The terminal performs layer 3 filtering based on the first cell measurement value and the last layer 3 filtered cell measurement value of the first cell to obtain the layer 3 filtered cell measurement value of the first cell; The terminal performing layer 3 filtering based on the first cell measurement value and the last layer 3 filtered cell measurement value of the first cell includes: When the first cell measurement value is obtained based on the AI ​​model, the terminal performs layer 3 filtering based on the first layer 3 filtering parameter, the first cell measurement value, and the last layer 3 filtered cell measurement value of the first cell; In a case where the first cell measurement value is obtained based on actual measurement, the terminal performs layer 3 filtering based on a second layer 3 filtering parameter, the first cell measurement value, and a last layer 3 filtered cell measurement value of the first cell.

20. The method of claim 19, wherein: The first layer three filtering parameters are different from the second layer three filtering parameters.

21. The method of claim 19, wherein: The first cell measurement value is obtained based on the AI ​​model, including at least one of the following: The first cell measurement value is a cell measurement value predicted based on the AI ​​model; The first cell measurement value is a cell measurement value generated based on the beam value predicted by the AI ​​model; The first cell measurement value is a cell measurement value generated based on the predicted value of some beams and the measurement value of another part of beams.

22. The method according to claim 19, wherein Before the terminal obtains the first cell measurement value of the first cell, the method further includes: The terminal receives the first layer 3 filtering parameter and the second layer 3 filtering parameter from a network-side device.

23. A measurement configuration method, comprising: The network-side device sends a first configuration, where the first configuration includes a second configuration, where the second configuration is used to indicate at least one of the following: a configuration for obtaining a first predicted value for a beam of a first beam set for a first cell; a configuration for generating a first predicted measurement value of the first cell based on a first predicted value of a beam of the first beam set; Three filter parameters for the first layer and three filter parameters for the second layer; The first layer 3 filtering parameter is used to perform layer 3 filtering on the cell measurement value obtained based on the AI ​​model, and the second layer 3 filtering parameter is used to perform layer 3 filtering on the cell measurement value obtained based on actual measurement.

24. The method according to claim 23, wherein The second configuration includes at least one of the following: a first parameter set, where the first parameter set is used by the terminal to perform beam selection on the first beam set of the first cell to obtain a fourth beam set; a second parameter set, where the second parameter set is used by the terminal to perform beam selection on the second beam set of the first cell to obtain a third beam set; a third parameter set, where the third parameter set is used by the terminal to perform a first operation on the first beam set, the fourth beam set, or the seventh beam set; a fourth parameter set, where the fourth parameter set is used by the terminal to perform beam combining on the third beam set and the fourth beam set to generate the first predicted measurement value; a fifth parameter set, where the fifth parameter set is used by the terminal to perform beam selection on a combination of the first beam set and the second beam set to obtain a fifth beam set; A sixth parameter set is used by the terminal to perform beam combining on the sixth beam set and the seventh beam set to generate the first predicted measurement value.

25. The method according to claim 24, wherein The second configuration further includes at least one of the following: an identifier or index of a beam of the first beam set; an identifier or index of a beam of the second beam set; The number of beams in the second beam set.

26. The method according to claim 23, wherein The first configuration further includes a third configuration, wherein the third configuration includes at least one of the following: First indication information, where the first indication information is used to indicate a type of a measurement value of a reported cell; Second indication information, where the second indication information is used to indicate reporting of information based on a predicted beam, or to indicate reporting of information based on a predicted beam that is higher than or equal to a third threshold.

27. The method of claim 23, further comprising: The network side device receives information about a first predicted measurement value of the first cell; The information about the first predicted measurement value of the first cell includes at least one of the following: a type of the first predicted measurement value of the first cell; an actual measured value or a predicted value of the first beam of the first cell; Third indication information, where the third indication information is used to indicate whether the first beam is an actual measurement beam or a predicted beam.

28. A measurement processing device comprising: a first processing unit and a first transceiver unit; The first processing unit is configured to obtain a first predicted value of a beam in a first beam set of the first cell based on the AI ​​model; The first processing unit is further configured to generate a first predicted measurement value of the first cell according to the first predicted value; The input of the AI ​​model includes a first actual measurement value of a beam in the second beam set of the first cell.

29. The apparatus according to claim 28, wherein The first processing unit is further configured to generate the first predicted measurement value according to the first predicted value and the first actual measurement value.

30. The apparatus according to claim 29, wherein The first processing unit is further used to: perform beam selection on the second beam set to obtain a third beam set; perform beam selection on the first beam set to obtain a fourth beam set; and generate the first predicted measurement value based on the first actual measurement value of the beam in the third beam set and the second predicted value of the beam in the fourth beam set.

31. The apparatus according to claim 29, wherein The first processing unit is further used to: perform beam selection on the union of the first beam set and the second beam set to obtain a fifth beam set, wherein the fifth beam set is composed of a sixth beam set and a seventh beam set, the sixth beam set is a set composed of selected beams in the second beam set, and the seventh beam set is a set composed of selected beams in the first beam set; and generate a first predicted measurement value of the first cell according to the first actual measurement value of the beam in the sixth beam set and the first predicted value of the beam in the seventh beam set.

32. A measurement processing device comprising: a second processing unit and a second transceiver unit; The second processing unit is configured to obtain a first cell measurement value of the first cell; The second processing unit is further configured to perform layer 3 filtering based on the first cell measurement value and the last layer 3 filtered cell measurement value of the first cell to obtain the layer 3 filtered cell measurement value of the first cell; The performing layer 3 filtering based on the first cell measurement value and the last layer 3 filtered cell measurement value of the first cell includes: When the first cell measurement value is obtained based on the AI ​​model, performing layer 3 filtering based on the first layer 3 filtering parameter, the first cell measurement value, and the last layer 3 filtered cell measurement value of the first cell; In a case where the first cell measurement value is obtained based on actual measurement, layer 3 filtering is performed based on a second layer 3 filtering parameter, the first cell measurement value, and a last layer 3 filtered cell measurement value of the first cell.

33. A measurement configuration device comprising: a third transceiver unit and a third processing unit; The third transceiver unit is configured to send a first configuration, where the first configuration includes a second configuration, and the second configuration is configured to indicate at least one of the following: a configuration for obtaining a first predicted value for a beam of a first beam set for a first cell; a configuration for generating a first predicted measurement value of the first cell based on a first predicted value of a beam of the first beam set; Three filter parameters for the first layer and three filter parameters for the second layer; The first layer 3 filtering parameter is used to perform layer 3 filtering on the cell measurement value obtained based on the AI ​​model, and the second layer 3 filtering parameter is used to perform layer 3 filtering on the cell measurement value obtained based on actual measurement.

34. The apparatus according to claim 33, wherein The first configuration includes at least one of the following: a first parameter set, where the first parameter set is used by the terminal to perform beam selection on the first beam set of the first cell to obtain a fourth beam set; a second parameter set, where the second parameter set is used by the terminal to perform beam selection on the second beam set of the first cell to obtain a third beam set; a third parameter set, where the third parameter set is used by the terminal to perform a first operation on the first beam set, the fourth beam set, or the seventh beam set; a fourth parameter set, where the fourth parameter set is used by the terminal to perform beam combining on the third beam set and the fourth beam set to generate the first predicted measurement value; a fifth parameter set, where the fifth parameter set is used by the terminal to perform beam selection on a combination of the first beam set and the second beam set to obtain a fifth beam set; A sixth parameter set is used by the terminal to perform beam combining on the sixth beam set and the seventh beam set to generate the first predicted measurement value.

35. A terminal comprising a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method according to any one of claims 1 to 22.

36. A network-side device, comprising a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the method according to any one of claims 23 to 27.

37. A readable storage medium storing a program or instruction, wherein the program or instruction, when executed by a processor of a terminal, implements the steps of the method according to any one of claims 1 to 27.

38. A computer program product comprising computer instructions, which, when executed by a processor, implement the steps of the method according to any one of claims 1 to 27.

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