Prediction method, terminal and network side equipment

Abstract

The invention discloses a prediction method, a terminal and network side equipment, and belongs to the technical field of communication, and the prediction method comprises the steps that the terminal obtains a first parameter; and the terminal determines whether to predict the signal quality of the cell based on the first parameter to obtain a predicted value.

Description

Technical Field

[0001] This application belongs to the field of communication technology, specifically relating to a prediction method, a terminal, and a network-side device. Background Technology

[0002] Currently, terminals need to perform periodic measurements to monitor the signal quality of the stationary cell, serving cell, and / or neighboring cells for mobility management. However, frequent measurements introduce additional overhead, especially when terminals in connected state perform inter-frequency or inter-system measurements. The network also needs to configure measurement gaps for the terminals. During measurement gaps, the terminals do not send or receive any data, but instead tune their receivers to the frequency of the target cell to perform inter-frequency measurements.

[0003] Therefore, how to reduce the measurement overhead of frequent measurements at the terminal is a technical problem that urgently needs to be solved. Summary of the Invention

[0004] This application provides a prediction method, a terminal, and a network-side device, which can solve the problem of how to reduce the measurement overhead of frequent terminal measurements.

[0005] Firstly, a prediction method is provided, executed by a terminal, the method comprising:

[0006] The terminal obtains the first parameter;

[0007] The terminal determines whether to predict the cell signal quality based on the first parameter, and obtains the predicted value.

[0008] Secondly, a prediction method is provided, executed by a network-side device, the method comprising:

[0009] The network-side device configures a first parameter to the terminal; the first parameter is used by the terminal to determine whether to predict the cell signal quality and obtain a predicted value.

[0010] Thirdly, a prediction device is provided, the device comprising:

[0011] The acquisition module is used to obtain the first parameter;

[0012] The execution module is used to determine whether to predict the cell signal quality based on the first parameter and obtain the predicted value.

[0013] Fourthly, a prediction device is provided, the device comprising:

[0014] The configuration module is used to configure a first parameter to the terminal; the first parameter is used by the terminal to determine whether to predict the cell signal quality based on the first parameter, and to obtain a predicted value.

[0015] Fifthly, a terminal is provided, the terminal including a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the method as described in the first aspect.

[0016] In a sixth aspect, a terminal is provided, including a processor and a communication interface; wherein the processor is used to acquire a first parameter; and based on the first parameter, to determine whether to predict the cell signal quality and obtain a predicted value.

[0017] In a seventh aspect, a network-side device is provided, the network-side device including a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the method as described in the second aspect.

[0018] Eighthly, a network-side device is provided, including a processor and a communication interface; wherein the communication interface is used to configure a first parameter to a terminal; the first parameter is used by the terminal to determine whether to predict the cell signal quality based on the first parameter, and to obtain a predicted value.

[0019] A ninth aspect provides a communication system comprising: a terminal and a network-side device, wherein the terminal is configured to perform the steps of the method described in the first aspect, and the network-side device is configured to perform the steps of the method described in the second aspect.

[0020] In a tenth aspect, a readable storage medium is provided, on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect, or implement the steps of the method described in the second aspect.

[0021] Eleventhly, a chip is provided, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect, or to implement the method as described in the second aspect.

[0022] In a twelfth aspect, a computer program / program product is provided, which is stored in a storage medium and is executed by at least one processor to implement the steps of the method as described in the first aspect, or to implement the steps of the method as described in the second aspect.

[0023] In this embodiment of the application, the terminal obtains a first parameter, and determines whether to predict the cell signal quality based on the first parameter to obtain a predicted value. Since the terminal can predict the cell signal quality, the terminal reduces the measurement of cell signal quality, thereby reducing the measurement overhead of frequent measurements by the terminal. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of a wireless communication system to which embodiments of this application can be applied;

[0025] Figure 2 This is one of the flowcharts illustrating the prediction method provided in the embodiments of this application;

[0026] Figure 3 This is a second schematic flowchart of the prediction method provided in the embodiments of this application;

[0027] Figure 4 This is one of the interactive diagrams between the terminal and the network-side device provided in the embodiments of this application;

[0028] Figure 5 This is the second schematic diagram of the interaction between the terminal and the network-side device provided in the embodiments of this application;

[0029] Figure 6 This is one of the structural schematic diagrams of the prediction device provided in the embodiments of this application;

[0030] Figure 7 This is a second schematic diagram of the predictive device provided in the embodiments of this application;

[0031] Figure 8 This is a schematic diagram of the structure of the communication device provided in the embodiments of this application;

[0032] Figure 9 This is a schematic diagram of the terminal structure provided in the embodiments of this application;

[0033] Figure 10 This is a schematic diagram of the network-side device provided in the embodiments of this application. Detailed Implementation

[0034] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0035] The terms "first," "second," etc., used in this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same class, without limiting the number of objects; for example, the first object can be one or more. Furthermore, "or" in this application indicates at least one of the connected objects. For example, "A or B" covers three scenarios: Scenario 1: including A but not B; Scenario 2: including B but not A; Scenario 3: including both A and B. The character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0036] The term "instruction" in this application can be either a direct instruction (or explicit instruction) or an indirect instruction (or implicit instruction). A direct instruction can be understood as one in which the sender explicitly informs the receiver of specific information, the operation to be performed, or the requested result, etc., in the instruction sent. An indirect instruction can be understood as one in which the receiver determines the corresponding information based on the instruction sent by the sender, or makes a judgment and determines the operation to be performed or the requested result, etc., based on the judgment result.

[0037] It is worth noting that the technologies described in this application are not limited to Long Term Evolution (LTE) / LTE-Advanced (LTE-A) systems, 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 described technologies can be used with the systems and radio technologies mentioned above, as well as with other systems and radio technologies. The following description describes New Radio (NR) systems for illustrative purposes, and the term NR is used in most of the following description; however, these technologies can also be applied to systems other than NR systems, such as 6th generation (6G) radio systems. thGeneration 6G communication system.

[0038] Figure 1This diagram illustrates a block diagram of a wireless communication system applicable to embodiments of this application. The wireless communication system includes a terminal 11 and a network-side device 12. The terminal 11 can be a mobile phone, tablet computer, laptop computer, notebook computer, personal digital assistant (PDA), handheld computer, netbook, ultra-mobile personal computer (UMPC), mobile internet device (MID), augmented reality (AR), virtual reality (VR) device, robot, wearable device, flight vehicle, vehicle user equipment (VUE), shipboard equipment, pedestrian user equipment (PUE), smart home (home devices with wireless communication capabilities, such as refrigerators, televisions, washing machines, or furniture), game console, personal computer (PC), ATM, or self-service machine, etc. Wearable devices include: smartwatches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart chains, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc. Among these, in-vehicle devices can also be referred to as in-vehicle terminals, in-vehicle controllers, in-vehicle modules, in-vehicle components, in-vehicle chips, or in-vehicle units, etc. It should be noted that the specific type of terminal 11 is not limited in this application embodiment. Network-side equipment 12 may include access network equipment or core network equipment, wherein access network equipment may also be referred to as Radio Access Network (RAN) equipment, radio access network function, or radio access network unit. Access network equipment may include base stations, Wireless Local Area Network (WLAN) access points (APs), or Wireless Fidelity (WiFi) nodes, etc.Among them, base stations can be referred to as Node B (NB), Evolved Node B (eNB), Next Generation Node B (gNB), New Radio Node B (NR Node B), Access Point, Relay Base Station (RBS), Serving Base Station (SBS), Base Transceiver Station (BTS), Radio Base Station, Radio Transceiver, Basic Service Set (BSS), Extended Service Set (ESS), Home Node B (HNB), Home Evolved Node B, Transmission Reception Point (TRP), Onboard Access Network Equipment, Onboard Centralized Unit (CU) of Access Network Equipment, and Onboard Distributed Unit of Access Network Equipment. The term "base station" is not limited to any specific technical term, as long as it achieves the same technical effect. It should be noted that the base station in this application embodiment is only used as an example of a base station in an NR system and is not limited to any specific type of base station.

[0039] 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 (PCRF), Edge Application Server Discovery Function (EASDF), Unified Data Management (UDM), Unified Data Repository (UDR), Home Subscriber Server (HSS), Centralized network configuration (CNC), Network Repository Function (NRF), Network Exposure Function (NEF), Local NEF (or L-NEF), Binding Support Function (BSF), and Application Function. Function (AF), etc. It should be noted that the embodiments of this application only use the core network equipment in the NR system as an example for introduction, and do not limit the specific type of core network equipment.

[0040] To facilitate a clearer understanding of the various embodiments of this application, some relevant background technical knowledge will be introduced as follows.

[0041] I. Non-connected state measurement relaxation

[0042] Depending on their location and mobility status, disconnected (RRC_IDLE / INACTIVE) terminals will relax measurements according to certain decision criteria. The decision criteria mainly include whether the User Equipment (UE) is in a low-mobility state and whether the UE is at the cell edge. The relevant configuration is broadcast in System Information Block 2 (SIB2).

[0043]

[0044] Measurement relaxation criteria for low mobility: If the decrease in Reference Signal Received Power (RSRP) is less than a threshold or even increases within a specific time period, measurement relaxation can be performed.

[0045] (SrxlevRef–Srxlev) SearchDeltaP for a period of T SearchDeltaP

[0046] Wherein, Srxlev represents the S value of the current serving cell, and the specific calculation can be found in the cell selection and reselection process;

[0047] Srxlev Ref This represents the reference S-value of the current serving cell. The specific setting method for the reference S-value is as follows: at the moment the UE completes cell selection or reselection, that is, whenever the UE completes camping in a new cell, the reference S-value is assigned the current S-value; then, during subsequent measurements, if the S-value is greater than the reference S-value, the reference S-value is immediately updated to the larger current S-value; if the above criterion is met, but T is not maintained... SearchDeltaP If the time is long, the reference S value should also be updated to the current S value;

[0048] S SearchDeltaP This indicates the configured RSRP fall threshold value;

[0049] T SearchDeltaP Indicates the specific time length configured.

[0050] Measurement relaxation criteria for terminals not located at the cell edge: Measurement relaxation can be performed when the terminal is not at the cell edge (but still within the cell's good coverage area).

[0051] Srxlev>S SearchThresholdP And Squal>S SearchThresholdQ (If equipped with S) SearchThresholdQ )

[0052] Once the above criteria are met, the measurement is relaxed accordingly. For example, for same-frequency measurements, the terminal multiplies the measurement period by a relaxation factor K1 = 3, meaning the measurement period becomes 3 times larger. For different-frequency measurements, the amplification factor M2 of the measurement period relative to the discontinuous reception (DRX) period is fixed at 1.5 (originally 1.5 or 1).

[0053] ​II. Reduced Capability (Redcap) UE Connected-State Radio Resource Management (RRM) Measurement Relaxation

[0054] Similar to the low mobility criterion for disconnected states, the difference is:

[0055] (1) The base station sends a fall threshold and a specific time length to the UE via dedicated signaling;

[0056] (2) If the RSRP drop value of the serving cell is lower than the threshold and the duration exceeds the threshold, the terminal will not directly relax the measurement, but will send UE Assistance Information (UAI) to report that the measurement relaxation condition is met, i.e., rrm-MeasRelaxationFulfilment is true.

[0057] RRM-MeasRelaxationReportingConfig-r17::=SEQUENCE{

[0058] s-SearchDeltaP-Stationary-r17 ENUMERATED{dB2,dB3,dB6,dB9,dB12,dB15,spare2,spare1},

[0059] t-SearchDeltaP-Stationary-r17 ENUMERATED{s5,s10,s20,s30,s60,s120,s180,s240,s300,spare7,spare6,spare5,spare4,spare3,spare2,spare1}

[0060] }

[0061] III. Measurement Gap

[0062] If a UE needs to perform inter-frequency measurements (including inter-system measurements), a simple approach is to install two types of radio frequency receivers in the UE device to measure the frequency of its own cell and the frequency of the target cell, respectively. However, this increases costs and introduces the problem of interference between different frequencies. Therefore, 3GPP proposed the Measurement Gap (GAP) method, which reserves a period of time (i.e., the measurement GAP time) during which the UE does not send or receive any data but instead tunes its receiver to the target cell frequency to perform inter-frequency measurements. When the GAP time ends, it switches back to its current cell.

[0063] The measurement gap configuration parameters mainly include length (e.g., 1ms) and period (e.g., 20ms), allowing the UE to perform measurements periodically within the measurement gap.

[0064] GapConfig::=SEQUENCE{

[0065] gapOffset INTEGER(0..159),

[0066] mgl ENUMERATED{ms1dot5,ms3,ms3dot5,ms4,ms5dot5,ms6},

[0067] mgrp ENUMERATED{ms20,ms40,ms80,ms160},

[0068] mgta ENUMERATED{ms0,ms0dot25,ms0dot5}

[0069] The terminal measurement provided in this application will be described in detail below with reference to the accompanying drawings, through some embodiments and application scenarios.

[0070] The prediction method provided in this application can be applied to terminal measurement scenarios. By obtaining a first parameter through the terminal, the terminal can determine whether to predict the cell signal quality based on the first parameter and obtain a predicted value. Since the terminal can predict the cell signal quality, the measurement of cell signal quality by the terminal is reduced, thereby reducing the measurement overhead of frequent terminal measurements.

[0071] Figure 2 This is one of the flowcharts illustrating the prediction method provided in the embodiments of this application, such as... Figure 2 As shown, the method includes steps 201-202; wherein:

[0072] Step 201: The terminal obtains the first parameter.

[0073] It should be noted that the embodiments of this application can be applied to scenarios involving terminal measurements. The terminal includes, but is not limited to, the types of terminal 11 listed above, and the network-side device includes, but is not limited to, the types of network-side device 12 listed above. The embodiments of this application do not limit this. The terminal supports prediction-based measurement relaxation functionality, and the terminal can report to the network-side device that it supports prediction-based measurement relaxation functionality.

[0074] The terminal can obtain the first parameter based on the broadcast from the network-side device; the terminal can also obtain the first parameter by receiving the first parameter sent by the network-side device through an RRC message; wherein, the first parameter is related to the terminal measurement.

[0075] Step 202: The terminal determines whether to predict the cell signal quality based on the first parameter and obtains the predicted value.

[0076] The cell can be the cell where the terminal is camped, the serving cell, and / or the neighboring cell. The cell signal quality can be, for example, the Reference Signal Received Power (RSRP), the Signal to Interference plus Noise Ratio (SINR), or the Reference Signal Received Quality (RSRQ).

[0077] After obtaining the first parameter, the terminal can determine whether to predict the cell signal quality at the measurement gap or the time point corresponding to the measurement gap period based on the first parameter, and obtain the predicted value. That is, if the terminal determines that it can predict the cell signal quality, it can choose not to actually measure the cell signal quality at the measurement gap or the time point corresponding to the measurement gap period, thereby reducing the terminal's measurement of the cell signal quality and reducing the measurement overhead of the terminal.

[0078] Optionally, the predicted value is obtained by the terminal based on an artificial intelligence (AI) model.

[0079] Optionally, since the terminal can predict the cell signal quality at the measurement gap or the time point corresponding to the measurement gap period, the network-side equipment can reduce the configuration of the corresponding measurement gap and increase the time domain length of the scheduling terminal, thereby improving the terminal's speed.

[0080] In the prediction method provided in this application embodiment, a first parameter is obtained through the terminal. The terminal can determine whether to predict the cell signal quality based on the first parameter and obtain a predicted value. Since the terminal can predict the cell signal quality, the measurement of cell signal quality by the terminal is reduced, thereby reducing the measurement overhead of frequent measurements by the terminal.

[0081] Optionally, the first parameter includes at least one of the following:

[0082] (1) First indication information, used to indicate whether to predict the cell signal quality, or to indicate whether to use a first rule to predict the cell signal quality, or to indicate whether the network-side device allows the terminal to request configuration information for predicting the cell signal quality. The terminal can predict the cell signal quality based on the first indication information. For example, the terminal can use an AI model for measurement relaxation based on the first indication information, that is, the terminal can use the predicted value inferred by the AI ​​model as the actual measured value of the cell signal quality, thereby reducing the terminal's measurement of the cell signal quality.

[0083] (2) Cell signal quality measurement threshold. The cell signal quality measurement threshold is used to determine whether to predict the cell signal quality, such as using an AI model to perform measurement relaxation inference on the cell signal quality. For example, when the actual RSRP measurement value measured by the terminal is greater than or equal to the cell signal quality measurement threshold, the terminal starts the AI ​​model and uses the AI ​​model to perform measurement relaxation inference on the cell signal quality. The predicted value of the AI ​​model is used as the actual measurement value of the cell signal quality, thereby reducing the terminal's measurement of cell signal quality.

[0084] (3) Cell signal quality change threshold. The cell signal quality change threshold is used to determine whether to predict cell signal quality, such as using an AI model to perform measurement relaxation inference on cell signal quality. For example, if the decrease in RSRP actually measured by the terminal within the first time period is less than or equal to the cell signal quality change threshold, the terminal activates the AI ​​model and uses the AI ​​model to perform measurement relaxation inference on cell signal quality. The predicted value of the AI ​​model is used as the actual measured value of cell signal quality, reducing the terminal's measurement. It should be noted that if the RSRP actually measured by the terminal decreases within the first time period, it indicates that the terminal is far away from the network-side equipment, and the terminal can activate the AI ​​model to perform measurement relaxation inference.

[0085] (4) First duration, which represents the time length corresponding to the change in cell signal quality measurement, that is, the actual time length of the terminal measuring cell signal quality.

[0086] (5) Model parameters, representing the parameters of the artificial intelligence (AI) model used to predict the signal quality of the cell. Optionally, the model parameters include at least one of the following: model identifier, model structure information, and model parameter information.

[0087] (6) Cell signal quality prediction value, used to determine whether to predict the cell signal quality, such as using an AI model to perform measurement relaxation inference on the cell signal quality. For example, if the RSRP prediction value of the AI ​​model is greater than the cell signal quality prediction value threshold, the terminal determines to use the AI ​​model to predict the cell signal quality.

[0088] (7) Cell signal quality change threshold, used to determine whether to predict cell signal quality, such as using an AI model to measure and relax inference on cell signal quality. For example, if the decrease in the RSRP prediction value inferred by the AI ​​model is less than the cell signal quality prediction within the second time period, the terminal determines to use the AI ​​model to predict cell signal quality.

[0089] (8) Second duration, which represents the time length corresponding to the predicted change in the cell signal quality.

[0090] (9) Prediction accuracy threshold. For example, the terminal determines whether to select measurement relaxation based on the AI ​​model based on the prediction accuracy threshold. For example, in a disconnected state, the terminal can select between AI-based measurement relaxation and a legacy measurement relaxation scheme based on the prediction accuracy threshold. The terminal can determine whether to select AI-based measurement relaxation based on the actual measured RSRP value and the predicted RSRP value inferred by the AI ​​model, or the terminal can determine whether to select AI-based measurement relaxation based on the actual measured RSRQ value and the predicted RSRQ value inferred by the AI ​​model. For example, if the prediction accuracy threshold is the root mean square error (RMSE) threshold, the terminal can determine a target value based on the actual measured RSRP value and the predicted RSRP value inferred by the AI ​​model. The target value is RMSE. If the target value is less than the prediction accuracy threshold, the terminal selects AI-based measurement relaxation; if the target value is not less than the prediction accuracy threshold, the terminal selects AI-based measurement relaxation.

[0091] It should be noted that the above-mentioned prediction of cell signal quality based on AI models can be based on parameters such as historically measured cell signal quality. The method for predicting cell signal quality is not limited to using AI models; other prediction methods are also within the scope of this application.

[0092] Optionally, the first parameter can be configured in the measurement reporting configuration ReportConfig or OtherConfig, and the terminal can obtain the first parameter by broadcasting or sending configuration information through the network-side device.

[0093] Optionally, step 202 above may be implemented in at least one of the following ways:

[0094] (a) The terminal determines whether to predict the cell signal quality based on the first indication information.

[0095] Based on the first indication information in the first parameter, the terminal can determine whether to predict the cell signal quality.

[0096] (b) When the first parameter meets the preset conditions, the terminal predicts the signal quality of the cell.

[0097] The preset conditions are configured to the terminal by the network-side equipment based on a first parameter. The terminal determines whether the first parameter meets the preset conditions. If the first parameter meets the preset conditions, the terminal can predict the cell signal quality. The terminal does not need to perform actual measurements of the cell signal quality at the time point or measurement interval when predicting the cell signal quality, reducing the terminal's measurement overhead. Simultaneously, this allows the network-side equipment to schedule the terminal at the time point or measurement interval when actual cell signal quality measurements are not required, improving the terminal's speed.

[0098] (c) When the first parameter does not meet the preset conditions, the terminal measures the signal quality of the cell.

[0099] When the first parameter does not meet the preset conditions, the terminal needs to perform actual measurement of the cell signal quality, and the terminal performs the measurement in accordance with the conventional measurement method.

[0100] Optionally, the preset conditions include at least one of the following:

[0101] (a) The cell signal quality measurement value measured by the terminal is greater than or equal to the cell signal quality measurement value threshold.

[0102] When the actual measured value of cell signal quality by the terminal is greater than or equal to the cell signal quality measurement threshold, the terminal can predict the cell signal quality. For example, the terminal can activate an AI model and use the AI ​​model to perform measurement relaxation inference on the cell signal quality, using the predicted value of the AI ​​model as the actual measured value of the cell signal quality. Alternatively, the terminal can adopt a first rule to perform measurement relaxation inference on the cell signal quality, reducing the terminal's measurements.

[0103] (b) The decrease in the cell signal quality measurement value measured by the terminal within the first time period is less than or equal to the cell signal quality change threshold.

[0104] If the decrease in the cell signal quality measurement value actually measured by the terminal within the first time period is less than or equal to the cell signal quality change threshold, the terminal can predict the cell signal quality. For example, the terminal can activate an AI model and use the AI ​​model to perform measurement relaxation inference on the cell signal quality, using the predicted value of the AI ​​model as the actual measured value of the cell signal quality. Alternatively, the terminal can adopt a first rule to perform measurement relaxation inference on the cell signal quality, reducing the terminal's measurements.

[0105] (c) The predicted value is greater than or equal to the cell signal quality predicted value threshold.

[0106] When the predicted value obtained from predicting cell signal quality is greater than or equal to the cell signal quality prediction threshold, the terminal can predict the cell signal quality. For example, when the RSRP predicted by the AI ​​model is greater than or equal to the cell signal quality prediction threshold, the terminal can perform measurement relaxation based on the AI ​​model, that is, use the predicted value of the AI ​​model as the actual measurement value of the cell signal quality. Alternatively, the terminal can adopt the first rule to perform measurement relaxation inference on the cell signal quality, reducing the terminal's measurements.

[0107] (d) The decrease in the predicted value of cell signal quality during the second time period is less than or equal to the threshold value of the predicted change in cell signal quality.

[0108] When the decrease in the predicted cell signal quality value inferred by the AI ​​model within the second time period is less than or equal to the predicted change threshold for cell signal quality, the terminal can predict the cell signal quality. For example, when the predicted RSRP value inferred by the AI ​​model within the second time period is less than or equal to the predicted change threshold for cell signal quality, the terminal can perform measurement relaxation based on the AI ​​model, that is, use the predicted value inferred by the AI ​​model as the actual measured value of cell signal quality. Alternatively, the terminal can adopt the first rule to perform measurement relaxation inference on cell signal quality, reducing the terminal's measurements.

[0109] (e) The target value determined based on the cell signal quality measurement value and the cell signal quality prediction value measured by the terminal is less than or equal to the prediction accuracy threshold.

[0110] The target value can be RMSE. When the target value determined by the measured value of cell signal quality based on the terminal and the predicted value of cell signal quality is less than the prediction accuracy threshold, the terminal can choose to relax the measurement based on the AI ​​model, that is, use the predicted value of the AI ​​model prediction inference as the actual measured value of cell signal quality. Alternatively, the terminal can adopt the first rule to perform measurement relaxation inference on cell signal quality to reduce the terminal's measurement.

[0111] (f) The target value determined based on the cell signal quality measurement value and the cell signal quality prediction value measured by the terminal is greater than the prediction accuracy threshold.

[0112] When the target value determined by the measured value of cell signal quality based on the terminal and the predicted value of cell signal quality is greater than the prediction accuracy threshold, the terminal can choose to relax the measurement based on the AI ​​model, that is, use the predicted value of the AI ​​model prediction inference as the actual measured value of cell signal quality. Alternatively, the terminal can adopt the first rule to perform measurement relaxation inference on cell signal quality to reduce the terminal's measurement.

[0113] Optionally, when predicting the cell signal quality, the method further includes any one of the following:

[0114] When the target value determined based on the cell signal quality measurement value and the cell signal quality prediction value measured by the terminal is greater than the prediction accuracy threshold, the terminal sends a prediction failure message to the network-side device; when the target value determined based on the cell signal quality measurement value and the cell signal quality prediction value measured by the terminal is less than or equal to the prediction accuracy threshold, the terminal sends a prediction failure message to the network-side device.

[0115] As one possible implementation, the target value can be RMSE. When predicting cell signal quality, if the target value determined by the cell signal quality measurement based on the terminal and the predicted cell signal quality based on the AI ​​model is greater than the prediction accuracy threshold, it indicates that the predicted cell signal quality by the AI ​​model is inaccurate. In this case, the terminal falls back to the regular legacy measurement relaxation, records the event, and generates a first report; the first report includes prediction failure information. The terminal sends the prediction failure information to the network-side device, which can obtain this information and optimize the AI ​​model based on it or disallow the terminal's measurement relaxation based on the AI ​​model. Alternatively, if the target value determined by the cell signal quality measurement based on the terminal and the predicted cell signal quality based on the AI ​​model is less than or equal to the prediction accuracy threshold, it indicates that the predicted cell signal quality by the AI ​​model is inaccurate. In this case, the terminal falls back to the regular legacy measurement relaxation, records the event, and generates a first report; the first report includes prediction failure information. The terminal sends prediction failure information to the network-side device. The network-side device can obtain the prediction failure information and optimize the AI ​​model based on the prediction failure information or prevent the terminal from relaxing the measurement based on the AI ​​model.

[0116] For example, if the prediction accuracy threshold is 0.8, and the target value determined based on the cell signal quality measurement and prediction values ​​from the terminal is 0.5, and this target value is less than the prediction accuracy threshold, it indicates that the cell signal quality prediction value inferred by the AI ​​model is inaccurate. In this case, the terminal falls back to the conventional legacy measurement relaxation, records the event, and generates a first report. The first report includes prediction failure information. The terminal sends the prediction failure information to the network-side device, which can obtain this information and optimize the AI ​​model based on it, or disallow the terminal from performing measurement relaxation based on the AI ​​model.

[0117] Optionally, the prediction failure information includes at least one of the following:

[0118] The identifier of the AI ​​model used to predict the signal quality of the cell; specifically, the identifier of the AI ​​model that failed to predict.

[0119] Cell identifier; The cell identifier is used to indicate the serving cell where the terminal resides when the AI ​​model inference fails;

[0120] The reason for prediction failure is that the accuracy of the inference prediction does not meet the prediction accuracy threshold requirement, such as the RMSE not meeting the prediction accuracy threshold requirement.

[0121] Optionally, the terminal receives first configuration information, which includes at least one of the following:

[0122] (a) Measurement gap configuration identifier; The measurement gap configuration identifier is used to identify the measurement gap in which the terminal measures cell signal quality and / or predicts cell signal quality.

[0123] (b) Target frequency point; the target frequency point represents at least one frequency point on which the terminal measures the cell signal quality and / or predicts the cell signal quality.

[0124] (c) Target cell; the target cell refers to at least one cell in which the terminal measures the cell signal quality and / or predicts the cell signal quality.

[0125] (d) Target beam; the target beam represents at least one beam used by the terminal to measure cell signal quality and / or at least one beam used to predict cell signal quality.

[0126] (e) Preset Ratio; The preset ratio can be the proportion of cell signal quality measurement and prediction within the measurement interval period. It can also be the ratio between the number of reported measured values ​​and the number of reported AI model predicted values. Specifically, the preset ratio can be the ratio of the number of reported measured values ​​to the total number of reported values, the number of reported AI model predicted values ​​to the total number of reported values, or the number of reported measured values ​​to the number of reported AI model predicted values. All of these represent the percentage between the number of reported measured values ​​and the number of reported predicted values. The preset ratio can be indicated in the ReportConfig configuration and expressed as a fraction. For example, a preset ratio of 1 / 3 means that the number of times the terminal reports AI model predicted values ​​accounts for 1 / 3 of the total number of reports, i.e., 2 actual measured values ​​and 1 predicted value are reported within the measurement interval period.

[0127] (f) First rule; the first rule is used by the terminal to predict the cell signal quality.

[0128] (g) Pattern period; The pattern period is used by the terminal to predict the cell signal quality. The pattern period can be an integer multiple of the original measurement gap period or a specific length, such as ms.

[0129] (h) Bitmap; for example, 1 indicates that a measurement gap is configured within the original measurement gap period, and 0 indicates that no measurement gap is configured within the original measurement gap period.

[0130] Optionally, the prediction of cell signal quality or the first rule includes at least one of the following:

[0131] 1) The terminal predicts the signal quality of the cell at the target frequency.

[0132] 2) The terminal measures the cell signal quality in one part of the target frequency and predicts the cell signal quality in the other part of the target frequency.

[0133] When there are multiple target frequency points, the terminal can perform actual measurements of cell signal quality on a portion of the target frequency points and predict cell signal quality on another portion. For example, an AI model can be used to predict cell signal quality on the other portion of the target frequency points, and the predicted value obtained by the AI ​​model can be used as the predicted value for the other portion of the frequency points. The other portion of the frequency points can be indicated in the MeasObjectNR of the measurement targets configured by the network-side equipment.

[0134] 3) The terminal predicts the signal quality of the target cell.

[0135] 4) The terminal measures the cell signal quality of a portion of the target cells and predicts the cell signal quality of another portion of the target cells.

[0136] When there are multiple target cells, the terminal can actually measure the cell signal quality of a portion of the target cells and predict the cell signal quality of another portion of the target cells. For example, it can use an AI model to predict the cell signal quality of another portion of the target cells and use the predicted value obtained by the AI ​​model as the predicted value of the other portion of cells. The other portion of cells is indicated by a cell list in the measurement target MeasObjectNR configured on the network side device, that is, the cell list indicates which cells perform AI model prediction.

[0137] 5) The terminal predicts the signal quality of the cell using the target beam.

[0138] 6) The terminal measures the cell signal quality in one part of the target beam and predicts the cell signal quality in the other part of the target beam.

[0139] When there are multiple target beams, the terminal can perform actual measurements of cell signal quality on one part of the target beams and predict cell signal quality on the other part of the target beams. For example, an AI model can be used to predict cell signal quality on the other part of the target beams, and the predicted value obtained by the AI ​​model can be used as the predicted value of the other part of the beams. The other part of the beams is indicated in the measurement target MeasObjectNR and can be associated with the target cell.

[0140] 7) Within the measurement gap configuration corresponding to the measurement gap configuration identifier, the terminal predicts the cell signal quality according to the pattern period and / or the pattern bitmap.

[0141] Within the measurement gap configuration corresponding to the measurement gap configuration identifier, the terminal measures the cell signal quality according to a portion of the measurement gaps corresponding to the pattern period and / or the pattern bitmap, and predicts the cell signal quality in another portion of the measurement gaps.

[0142] 8) The terminal predicts the signal quality of the cell based on the preset ratio.

[0143] Within the measurement interval period corresponding to the measurement gap, the terminal measures the cell signal quality according to a preset ratio. This preset ratio can be the proportion of cell signal quality measurement and prediction within the measurement interval period, or it can be the ratio between the number of reported measured values ​​and the number of reported AI model predicted values. Specifically, the preset ratio can be the ratio of the number of reported measured values ​​to the total number of reported values, the number of reported AI model predicted values ​​to the total number of reported values, or the ratio of the number of reported measured values ​​to the number of reported AI model predicted values. The preset ratio can be indicated in the ReportConfig configuration and expressed as a fraction. For example, a preset ratio of 1 / 3 means that the number of times the terminal reports AI model predicted values ​​accounts for 1 / 3 of the total number of reports, i.e., 2 actual measured values ​​and 1 predicted value are reported within the measurement interval period.

[0144] Optionally, the method further includes:

[0145] The terminal sends a first request to the network-side device based on the first parameter; the first request is used to request the first configuration information.

[0146] The network-side device does not pre-configure the first configuration information to the terminal. Based on the first parameter, if the terminal determines that the first parameter meets the preset conditions, the terminal can send a first request to the network-side device. The first request is used to request the first configuration information. After receiving the first request sent by the terminal, the network-side device sends the first configuration information to the terminal. The terminal receives the first configuration information sent by the network-side device and predicts the cell signal quality based on the first configuration information, thereby reducing the measurement overhead of the terminal measurement.

[0147] Optionally, the first request includes at least one of the following: a second indication information for indicating that the conditions for the terminal to perform measurement relaxation according to the first rule are met; and a third indication information for indicating the measurement interval required by the terminal.

[0148] Optionally, the third instruction information includes at least one of the following:

[0149] Measurement gap configuration identifier ID; The measurement gap configuration identifier ID is used to identify the measurement gap actually required by the terminal for measurement.

[0150] The pattern period can be an integer multiple of the original measurement gap MG period, or it can be a specific length, such as ms.

[0151] The pattern bitmap, for example, 1 indicates that MG is configured within the original measurement gap MG cycle, and 0 indicates that MG is not configured within the original MG cycle.

[0152] Optionally, the method further includes at least one of the following: the terminal performs a measurement relaxation based on the predicted value; the terminal reports the predicted value.

[0153] Optionally, the relaxation factor measured by the terminal based on the predicted value is determined based on at least one of the following: the maximum relaxation factor configured by the network-side equipment; or obtained through inference using an AI model used to predict the cell signal quality.

[0154] The relaxation factor can be a fixed value, i.e., the maximum relaxation factor configured by the network-side equipment; or it can be an inference output value, i.e., the predicted value inferred by the AI ​​model used to predict the cell signal quality. The terminal can use the minimum value between the maximum relaxation factor configured by the network-side equipment and the predicted value obtained by the AI ​​model used to predict the cell signal quality as the relaxation factor.

[0155] The terminal can multiply the measurement period and the relaxation factor based on the relaxation factor to obtain a new measurement period. This new measurement period is larger than the original measurement period, thus increasing the measurement period. Actual measurements are then taken at the target time point corresponding to this new measurement period. Cell signal quality is predicted at other time points corresponding to this measurement period, excluding the target time point. The target time point is the measurement time point after the measurement period has been increased. For example, if the measurement period is 2 seconds (s) and the relaxation factor is 2, the measurement period increases to 4 seconds. The target time points are the time points corresponding to 4 seconds, such as 1 second, 5 seconds, and 9 seconds, while other time points are 3 seconds, 7 seconds, and 11 seconds.

[0156] The terminal can also report the predicted values ​​obtained from predicting the cell signal quality. The network-side equipment configures the time point corresponding to the new measurement cycle or the measurement interval corresponding to the new measurement interval cycle based on the predicted values ​​reported by the terminal. This allows the terminal to measure the cell signal quality at the time point corresponding to the new measurement cycle or the measurement interval corresponding to the new measurement interval cycle, and predict the cell signal quality at other time points or other measurement intervals.

[0157] It should be noted that the network-side device can configure the second configuration information to the terminal based on the predicted value, the first rule, or the third indication information reported by the terminal. The second configuration information can be the same as or different from the first configuration information. That is, when configuring the second configuration information to the terminal, the network-side device can refer to the first configuration information or not, which is determined by the network-side device itself.

[0158] Figure 3 This is a second schematic flowchart of the prediction method provided in the embodiments of this application, as shown below. Figure 3 As shown, the method includes step 301; wherein:

[0159] Step 301: The network-side device configures a first parameter to the terminal; the first parameter is used by the terminal to determine whether to predict the cell signal quality and obtain a predicted value.

[0160] The cell is the terminal's camp cell, serving cell, and / or neighboring cell, and the cell signal quality is, for example, RSRP, SINR, or RSRQ.

[0161] The network-side device configures a first parameter to the terminal via broadcast or RRC message, enabling the terminal to acquire the first parameter. This first parameter is related to the terminal's measurements. After receiving the first parameter, the terminal can determine whether to predict the cell signal quality during the measurement interval or at the corresponding time point of the measurement interval period, obtaining a predicted value. In other words, if the terminal determines that it can predict the cell signal quality, it can refrain from actually measuring the cell signal quality during the measurement interval or at the corresponding time point of the measurement interval period, reducing the terminal's measurement overhead.

[0162] Optionally, the predicted value is obtained by the terminal based on an artificial intelligence (AI) model.

[0163] Optionally, since the terminal can predict the cell signal quality at the measurement gap or the time point corresponding to the measurement gap period, the network-side equipment can reduce the configuration of the corresponding measurement gap and increase the time domain length of the scheduling terminal, thereby improving the terminal's speed.

[0164] In the prediction method provided in this application embodiment, a first parameter is configured to the terminal through a network-side device. The first parameter is used by the terminal to determine whether to predict the cell signal quality based on the first parameter and obtain a predicted value. Since the terminal can predict the cell signal quality, the measurement of cell signal quality by the terminal is reduced, thereby reducing the measurement overhead of frequent measurements by the terminal.

[0165] Optionally, the first parameter includes at least one of the following:

[0166] (1) First indication information, used to indicate whether to predict the cell signal quality, or to indicate whether to use a first rule to predict the cell signal quality, or to indicate whether the network-side device allows the terminal to request configuration information for predicting the cell signal quality. The terminal can predict the cell signal quality based on the first indication information. For example, the terminal can use an AI model for measurement relaxation based on the first indication information, that is, the terminal can use the predicted value inferred by the AI ​​model as the actual measured value of the cell signal quality, thereby reducing the terminal's measurement of the cell signal quality.

[0167] (2) Cell signal quality measurement threshold. The cell signal quality measurement threshold is used to determine whether to use the AI ​​model to predict cell signal quality, that is, to use the AI ​​model to perform measurement relaxation inference on cell signal quality. For example, when the actual RSRP measurement value measured by the terminal is greater than or equal to the cell signal quality measurement threshold, the terminal starts the AI ​​model and uses the AI ​​model to perform measurement relaxation inference on cell signal quality. The predicted value of the AI ​​model is used as the actual measurement value of cell signal quality, thereby reducing the terminal's measurement of cell signal quality.

[0168] (3) Cell signal quality change threshold. The cell signal quality change threshold is used to determine whether to use the AI ​​model to predict cell signal quality, i.e., to use the AI ​​model for measurement relaxation inference of cell signal quality. For example, if the decrease in RSRP actually measured by the terminal within the first time period is less than or equal to the cell signal quality change threshold, the terminal activates the AI ​​model and uses the AI ​​model to perform measurement relaxation inference of cell signal quality. The predicted value of the AI ​​model is used as the actual measured value of cell signal quality, reducing the terminal's measurement. It should be noted that if the RSRP actually measured by the terminal decreases within the first time period, it means that the terminal is far away from the network-side equipment, and the terminal can activate the AI ​​model for measurement relaxation inference.

[0169] (4) First duration, which represents the time length corresponding to the change in cell signal quality measurement, that is, the actual time length of the terminal measuring cell signal quality.

[0170] (5) Model parameters, representing the parameters of the artificial intelligence (AI) model used to predict the signal quality of the cell. Optionally, the model parameters include at least one of the following: model identifier, model structure information, and model parameter information.

[0171] (6) Cell signal quality prediction value, used to determine whether to use the AI ​​model to predict the cell signal quality, that is, to use the AI ​​model to measure and relax inference on the cell signal quality. For example, if the RSRP prediction value of the AI ​​model is greater than the cell signal quality prediction value threshold, the terminal determines to use the AI ​​model to predict the cell signal quality.

[0172] (7) Cell signal quality change threshold, used to determine whether to use the AI ​​model to predict cell signal quality, that is, to use the AI ​​model to measure and relax inference on cell signal quality. For example, if the decrease in the RSRP prediction value inferred by the AI ​​model is less than the cell signal quality prediction in the second time period, the terminal determines to use the AI ​​model to predict cell signal quality.

[0173] (8) Second duration, which represents the time length corresponding to the predicted change in the cell signal quality.

[0174] (9) Prediction Accuracy Threshold. The prediction accuracy threshold is used to indicate whether the terminal selects measurement relaxation based on the AI ​​model. That is, in the disconnected state, the terminal can select between AI-based measurement relaxation and the conventional (legacy) measurement relaxation scheme according to the prediction accuracy threshold. The terminal can determine whether to select AI-based measurement relaxation based on the actual measured RSRP value and the RSRP predicted value inferred by the AI ​​model, or the terminal can determine whether to select AI-based measurement relaxation based on the actual measured RSRQ value and the RSRQ predicted value inferred by the AI ​​model. For example, the prediction accuracy threshold is the root mean square error (RMSE) threshold. The terminal can determine a target value based on the actual measured RSRP value and the RSRP predicted value inferred by the AI ​​model. The target value is RMSE. If the target value is less than the prediction accuracy threshold, the terminal selects AI-based measurement relaxation; if the target value is not less than the prediction accuracy threshold, the terminal selects AI-based measurement relaxation.

[0175] Optionally, the first parameter can be configured in the measurement reporting configuration ReportConfig or OtherConfig, and the terminal can obtain the first parameter by broadcasting or sending configuration information through the network-side device.

[0176] Optionally, the network-side device sends first configuration information to the terminal, the first configuration information including at least one of the following:

[0177] (a) Measurement gap configuration identifier; The measurement gap configuration identifier is used to identify the measurement gap in which the terminal measures cell signal quality and / or predicts cell signal quality.

[0178] (b) Target frequency point; the target frequency point represents at least one frequency point on which the terminal measures the cell signal quality and / or predicts the cell signal quality.

[0179] (c) Target cell; the target cell refers to at least one cell in which the terminal measures the cell signal quality and / or predicts the cell signal quality.

[0180] (d) Target beam; the target beam represents at least one beam used by the terminal to measure cell signal quality and / or at least one beam used to predict cell signal quality.

[0181] (e) Preset Ratio; The preset ratio can be the proportion of cell signal quality measurement and prediction within the measurement interval period. It can also be the ratio between the number of reported measured values ​​and the number of reported AI model predicted values. Specifically, the preset ratio can be the ratio of the number of reported measured values ​​to the total number of reported values, the number of reported AI model predicted values ​​to the total number of reported values, or the number of reported measured values ​​to the number of reported AI model predicted values. All of these represent the percentage between the number of reported measured values ​​and the number of reported predicted values. The preset ratio can be indicated in the ReportConfig configuration and expressed as a fraction. For example, a preset ratio of 1 / 3 means that the number of times the terminal reports AI model predicted values ​​accounts for 1 / 3 of the total number of reports, i.e., 2 actual measured values ​​and 1 predicted value are reported within the measurement interval period.

[0182] (f) First rule; the first rule is used by the terminal to predict the cell signal quality.

[0183] (g) Pattern period; The pattern period is used by the terminal to predict the cell signal quality. The pattern period can be an integer multiple of the original measurement gap period or a specific length, such as ms.

[0184] (h) Bitmap; for example, 1 indicates that a measurement gap is configured within the original measurement gap period, and 0 indicates that no measurement gap is configured within the original measurement gap period.

[0185] Optionally, the first rule includes at least one of the following:

[0186] 1) The terminal predicts the signal quality of the cell at the target frequency.

[0187] 2) The terminal measures the cell signal quality in one part of the target frequency and predicts the cell signal quality in the other part of the target frequency.

[0188] When there are multiple target frequency points, the terminal can perform actual measurements of cell signal quality on a portion of the target frequency points and predict cell signal quality on another portion. For example, an AI model can be used to predict cell signal quality on the other portion of the target frequency points, and the predicted value obtained by the AI ​​model can be used as the predicted value for the other portion of the frequency points. The other portion of the frequency points can be indicated in the MeasObjectNR of the measurement targets configured by the network-side equipment.

[0189] 3) The terminal predicts the signal quality of the target cell.

[0190] 4) The terminal measures the cell signal quality of a portion of the target cells and predicts the cell signal quality of another portion of the target cells.

[0191] When there are multiple target cells, the terminal can actually measure the cell signal quality of a portion of the target cells and predict the cell signal quality of another portion of the target cells. For example, it can use an AI model to predict the cell signal quality of another portion of the target cells and use the predicted value obtained by the AI ​​model as the predicted value of the other portion of cells. The other portion of cells is indicated by a cell list in the measurement target MeasObjectNR configured on the network side device, that is, the cell list indicates which cells perform AI model prediction.

[0192] 5) The terminal predicts the signal quality of the cell using the target beam.

[0193] 6) The terminal measures the cell signal quality in one part of the target beam and predicts the cell signal quality in the other part of the target beam.

[0194] When there are multiple target beams, the terminal can perform actual measurements of cell signal quality on one part of the target beams and predict cell signal quality on the other part of the target beams. For example, an AI model can be used to predict cell signal quality on the other part of the target beams, and the predicted value obtained by the AI ​​model can be used as the predicted value of the other part of the beams. The other part of the beams is indicated in the measurement target MeasObjectNR and can be associated with the target cell.

[0195] 7) Within the measurement gap configuration corresponding to the measurement gap configuration identifier, the terminal predicts the cell signal quality according to the pattern period and / or the pattern bitmap.

[0196] Within the measurement gap configuration corresponding to the measurement gap configuration identifier, the terminal measures the cell signal quality according to a portion of the measurement gaps corresponding to the pattern period and / or the pattern bitmap, and predicts the cell signal quality in another portion of the measurement gaps.

[0197] 8) The terminal predicts the signal quality of the cell based on the preset ratio.

[0198] Within the measurement gap period corresponding to the measurement gap, the cell signal quality is measured using a preset ratio. This preset ratio can be the proportion of cell signal quality measurement and prediction within the measurement gap period, or it can be the ratio between the number of reported measured values ​​and the number of reported AI model predicted values. Specifically, the preset ratio can be the ratio of the number of reported measured values ​​to the total number of reported values, the number of reported AI model predicted values ​​to the total number of reported values, or the number of reported measured values ​​to the number of reported AI model predicted values—all representing the percentage between the number of reported measured values ​​and the number of reported predicted values. The preset ratio can be indicated in the ReportConfig configuration as a fraction. For example, a preset ratio of 1 / 3 indicates that the number of times the terminal reports AI model predicted values ​​accounts for 1 / 3 of the total number of reports, meaning that within the measurement gap period, two actual measured values ​​are reported and one predicted value is reported.

[0199] Optionally, the method further includes:

[0200] The network-side device receives a first request sent by the terminal; the first request is used to request the first configuration information.

[0201] The network-side device does not pre-configure the first configuration information to the terminal. Based on the first parameter, if the terminal determines that the first parameter meets the preset conditions, the terminal can send a first request to the network-side device. The first request is used to request the first configuration information. After receiving the first request sent by the terminal, the network-side device sends the first configuration information to the terminal. The terminal receives the first configuration information sent by the network-side device and predicts the cell signal quality based on the first configuration information, thereby reducing the measurement overhead of the terminal measurement.

[0202] Optionally, the first request includes at least one of the following: a second indication information for indicating that the conditions for the terminal to perform measurement relaxation according to the first rule are met; and a third indication information for indicating the measurement interval required by the terminal.

[0203] Optionally, the third indication information includes at least one of the following:

[0204] Measurement gap configuration identifier ID; The measurement gap configuration identifier ID is used to identify the measurement gap actually required by the terminal for measurement.

[0205] The pattern period can be an integer multiple of the original measurement gap MG period, or it can be a specific length, such as ms.

[0206] The pattern bitmap, for example, 1 indicates that MG is configured within the original measurement gap MG cycle, and 0 indicates that MG is not configured within the original MG cycle.

[0207] Optionally, the network-side device can also configure second information to the terminal based on the predicted value reported by the terminal, the first rule, or the third indication information reported by the terminal. The second configuration information can be the same as or different from the first configuration information. That is, when configuring the second configuration information to the terminal, the network-side device can refer to the first configuration information or not, which is determined by the network-side device itself.

[0208] Figure 4 This is one of the interactive diagrams between the terminal and the network-side device provided in the embodiments of this application, such as... Figure 4 As shown, it includes:

[0209] Step 401: The network-side device sends the first parameter to the terminal.

[0210] Step 402: The terminal determines whether to predict the cell signal quality based on the first parameter and obtains the predicted value.

[0211] Figure 5 This is the second schematic diagram of the interaction between the terminal and the network-side device provided in the embodiments of this application, such as... Figure 5 As shown, it includes:

[0212] Step 501: The terminal sends a first request to the network-side device; the first request is used to request first configuration information.

[0213] Step 502: The terminal receives the first configuration information sent by the network-side device. The first configuration information includes at least one of the following: measurement gap configuration identifier; target frequency; target cell; target beam; preset ratio; first rule; pattern period; pattern bitmap.

[0214] Step 503: The terminal predicts the cell signal quality based on the first configuration information.

[0215] The prediction method provided in this application will be further explained through specific embodiments.

[0216] Example 1: Relaxation of non-connected terminal measurement

[0217] Step 1: The network-side device broadcasts a first parameter, which includes at least one of the following:

[0218] The first indication information is used to indicate whether to predict the cell signal quality, or to indicate whether to use a first rule to predict the cell signal quality, or to indicate whether the network-side device allows the terminal to request configuration information for predicting the cell signal quality.

[0219] The cell signal quality measurement threshold is used by the terminal to determine whether to use the AI ​​model to predict cell signal quality. For example, when the actual RSRP measurement value measured by the terminal is greater than or equal to the cell signal quality measurement threshold, the AI ​​model is activated, and the AI ​​model is used to perform measurement relaxation inference on the cell signal quality. The predicted value of the AI ​​model is then used as the actual measurement value of the cell signal quality, reducing the number of cell signal quality measurements required by the terminal.

[0220] The threshold for changes in cell signal quality is used to determine whether to use an AI model to predict cell signal quality, i.e., to use an AI model to measure and relax inference of cell signal quality.

[0221] The first duration refers to the length of time corresponding to the change in cell signal quality measurement. For example, if the actual RSRP decrease measured by the terminal within the first duration is less than or equal to the cell signal quality change threshold, the AI ​​model is activated, and the AI ​​model is used to perform measurement relaxation inference on the cell signal quality.

[0222] Model parameters refer to the parameters of the artificial intelligence (AI) model used to predict the signal quality of the cell. Model parameters include model identifier, model structure information, and model parameter information.

[0223] The cell signal quality measurement threshold is used to determine whether to use an AI model to predict cell signal quality, i.e., to use an AI model to perform measurement relaxation inference on cell signal quality. For example, if the RSRP prediction value inferred by the AI ​​model is greater than or equal to the cell signal quality measurement threshold, the terminal determines to use the AI ​​model to predict cell signal quality.

[0224] The threshold for changes in cell signal quality is used to determine whether to use an AI model to predict cell signal quality, i.e., to use an AI model to measure and relax inference of cell signal quality.

[0225] The second duration corresponds to the predicted change in cell signal quality. For example, if the decrease in the RSRP predicted value inferred by the AI ​​model is less than or equal to the cell signal quality change threshold within the second duration, the terminal determines to use the AI ​​model to predict the cell signal quality.

[0226] The prediction accuracy threshold is used to indicate whether the terminal should select measurement relaxation based on the AI ​​model. If the AI ​​model inference includes RSRP or RSRQ predictions, the terminal performs both actual measurements and AI model predictions during the measurement intervals, determines the target value (RMSE) based on the actual measurements and predictions, and selects between AI-based or legacy measurement relaxation schemes based on the prediction accuracy threshold.

[0227] Step 2: The terminal performs model inference and measures relaxation based on the first parameter.

[0228] The relaxation factor for measurement relaxation can be a fixed value or a value predicted by the AI ​​model. In particular, the network-side device can be configured with a maximum relaxation factor. The terminal will measure relaxation by the minimum value of the predicted value obtained by the AI ​​model inference used to predict the cell signal quality and the maximum relaxation factor configured by the network-side device.

[0229] Step 3: If the network-side device is configured with a prediction accuracy threshold, and after the terminal performs model inference, the target value determined by the RSRP measurement value measured by the terminal and the RSRP prediction value inferred by the AI ​​model is less than or equal to, or greater than, the prediction accuracy threshold is not met. In this case, the terminal falls back to legacy measurement relaxation. The terminal records this event and generates a first report. The first report includes prediction failure information, which includes at least one of the following:

[0230] AI model identifier used to predict the signal quality of the cell;

[0231] Cell identifier, which is the serving cell where the terminal resides when the AI ​​model fails to predict;

[0232] Reasons for prediction failure include, for example, the accuracy of the AI ​​model's prediction does not meet the prediction accuracy threshold, such as the RMSE not meeting the prediction accuracy threshold.

[0233] Step 4: After the terminal enters the connected state, the network-side device obtains the first report (i.e., the inference failure report), optimizes the AI ​​model, or does not allow the terminal to perform measurement relaxation based on the AI ​​model.

[0234] Example 2: Reduced Measurements of Connected Terminals (Network-Side Device Configuration Rule 1)

[0235] Step 0: The terminal reports support for the prediction-based connection state measurement reduction function.

[0236] Step 1: The network-side device sends a first parameter, which includes at least one of the following:

[0237] The cell signal quality measurement threshold is used to determine whether to use an AI model to predict cell signal quality, i.e., to use an AI model to perform measurement relaxation inference on cell signal quality. For example, if the RSRP measurement value of the terminal in the serving cell is greater than or equal to the cell signal quality measurement threshold, the terminal can use the first rule to perform measurement relaxation inference on cell signal quality, reducing the terminal's measurements.

[0238] The threshold for changes in cell signal quality is used to determine whether to use an AI model to predict cell signal quality, i.e., to use an AI model to measure and relax inference of cell signal quality.

[0239] The first duration refers to the length of time corresponding to the change in cell signal quality measurement. For example, if the decrease in the RSRP measurement value of the terminal in the serving cell is less than or equal to the cell signal quality change threshold within the first duration, the terminal can use the first rule to relax the inference of cell signal quality measurement and reduce the terminal's measurement.

[0240] The prediction accuracy threshold is used by the terminal to determine whether to select measurement relaxation based on the AI ​​model. If the model inference includes RSRP or RSRQ prediction values, the terminal performs both actual measurements and AI model predictions during the measurement interval, and determines the target value based on the actual measured value and the predicted value. The target value is RMSE. Based on the prediction accuracy threshold, it is determined whether the terminal can use the first rule to perform measurement relaxation inference for cell signal quality, thereby reducing the terminal's measurements.

[0241] Specifically, the above parameters can be indicated in the measurement reporting configuration ReportConfig.

[0242] The first rule includes at least one of the following:

[0243] 1) The terminal predicts the signal quality of the cell at the target frequency.

[0244] 2) The terminal measures the cell signal quality in one part of the target frequency and predicts the cell signal quality in the other part of the target frequency.

[0245] When there are multiple target frequency points, the terminal can perform actual measurements of cell signal quality on a portion of the target frequency points and predict cell signal quality on another portion. For example, an AI model can be used to predict cell signal quality on the other portion of the target frequency points, and the predicted value obtained by the AI ​​model can be used as the predicted value for the other portion of the frequency points. The other portion of the frequency points can be indicated in the MeasObjectNR of the measurement targets configured by the network-side equipment.

[0246] 3) The terminal predicts the signal quality of the target cell.

[0247] 4) The terminal measures the cell signal quality of a portion of the target cells and predicts the cell signal quality of another portion of the target cells.

[0248] When there are multiple target cells, the terminal can actually measure the cell signal quality of a portion of the target cells and predict the cell signal quality of another portion of the target cells. For example, it can use an AI model to predict the cell signal quality of another portion of the target cells and use the predicted value obtained by the AI ​​model as the predicted value of the other portion of cells. The other portion of cells is indicated by a cell list in the measurement target MeasObjectNR configured on the network side device, that is, the cell list indicates which cells perform AI model prediction.

[0249] 5) The terminal predicts the signal quality of the cell using the target beam.

[0250] 6) The terminal measures the cell signal quality in one part of the target beam and predicts the cell signal quality in the other part of the target beam.

[0251] When there are multiple target beams, the terminal can perform actual measurements of cell signal quality on one part of the target beams and predict cell signal quality on the other part of the target beams. For example, an AI model can be used to predict cell signal quality on the other part of the target beams, and the predicted value obtained by the AI ​​model can be used as the predicted value of the other part of the beams. The other part of the beams is indicated in the measurement target MeasObjectNR and can be associated with the target cell.

[0252] 7) Within the measurement gap configuration corresponding to the measurement gap configuration identifier, the terminal predicts the cell signal quality according to the pattern period and / or the pattern bitmap.

[0253] Within the measurement gap configuration corresponding to the measurement gap configuration identifier, the terminal measures the cell signal quality according to a portion of the measurement gaps corresponding to the pattern period and / or the pattern bitmap, and predicts the cell signal quality in another portion of the measurement gaps.

[0254] 8) The terminal predicts the signal quality of the cell based on the preset ratio.

[0255] Within the measurement gap period corresponding to the measurement gap, the cell signal quality is measured using a preset ratio. This preset ratio can be the proportion of cell signal quality measurement and prediction within the measurement gap period, or it can be the ratio between the number of reported measured values ​​and the number of reported AI model predicted values. Specifically, the preset ratio can be the ratio of the number of reported measured values ​​to the total number of reported values, the number of reported AI model predicted values ​​to the total number of reported values, or the number of reported measured values ​​to the number of reported AI model predicted values—all representing the percentage between the number of reported measured values ​​and the number of reported predicted values. The preset ratio can be indicated in the ReportConfig configuration as a fraction. For example, a preset ratio of 1 / 3 indicates that the number of times the terminal reports AI model predicted values ​​accounts for 1 / 3 of the total number of reports, meaning that within the measurement gap period, two actual measured values ​​are reported and one predicted value is reported.

[0256] Step 2: When the first parameter meets the preset conditions, the terminal predicts the cell signal quality based on the first rule and reports it.

[0257] Step 3: According to the first rule, the network can obtain the actual measurement requirements of the terminal and reduce the time domain configuration of MG. The reduction of the time domain configuration of MG can be achieved through a specific MG pattern.

[0258] Measurement gap configuration markings;

[0259] The pattern period can be an integer multiple of the original measurement gap MG period, or it can be a specific length, such as ms;

[0260] The bitmap pattern has a value of 1, which indicates that MG is configured within the original MG cycle, and a value of 0, which indicates that MG is not configured within the original MG cycle.

[0261] Example 3: Reduced Measurements by Connected Terminals (Terminal requests first rule and first configuration information from network-side device)

[0262] Step 0: The terminal reports support for the prediction-based connection state measurement reduction function.

[0263] Step 1: The network-side device sends a first parameter, which includes at least one of the following:

[0264] The cell signal quality measurement threshold is used to indicate whether the network-side equipment allows the terminal to request measurement relaxation using the first rule. For example, if the RSRP measurement value measured by the terminal in the serving cell is greater than the fourth RSRP measurement threshold, the network allows the terminal to request measurement reduction using the first rule.

[0265] The threshold for changes in cell signal quality measurement is used to determine whether to use an AI model to predict cell signal quality, or to instruct network-side devices whether to allow terminals to request the use of the first rule to predict cell signal quality.

[0266] The first duration refers to the length of time corresponding to the change in cell signal quality measurement. For example, if the decrease in the RSRP measurement value of the terminal in the serving cell is less than or equal to the threshold of cell signal quality measurement change within the first duration, the network allows the terminal to request the use of the first rule to predict the cell signal quality.

[0267] Specifically, the above parameters can be indicated in OtherConfig.

[0268] Step 2: If the first parameter in Step 1 meets the preset conditions, the terminal sends a first request to the network-side device. The first request requests the network-side device to send first configuration information, enabling the terminal to predict the cell signal quality based on the first configuration information. The first request includes at least one of the following:

[0269] The second instruction information is used to indicate that the conditions for the terminal to perform measurement relaxation according to the first rule have been met;

[0270] The third indication information is used to indicate the measurement gap required for the terminal measurement. This third indication information can be implemented using a specific MG pattern. The third indication information includes at least one of the following:

[0271] Measurement gap MG configuration identifier ID;

[0272] Pattern period; the pattern period can be an integer multiple of the original measurement gap MG period, or it can be a specific length, such as ms;

[0273] The pattern is a bitmap; for example, 1 indicates that MG is configured within the original measurement gap MG cycle, and 0 indicates that MG is not configured within the original measurement gap MG cycle.

[0274] The prediction method provided in this application can be executed by a prediction device. This application uses the example of a prediction device executing the prediction method to illustrate the prediction device provided in this application.

[0275] Figure 6 This is one of the structural schematic diagrams of the prediction device provided in the embodiments of this application, such as... Figure 6 As shown, the prediction device 600 includes: an acquisition module 601 and a determination module 602; wherein,

[0276] Module 601 is used to obtain the first parameter;

[0277] The determination module 602 is used to determine whether to predict the cell signal quality based on the first parameter, and obtain the predicted value.

[0278] In the prediction device provided in this application embodiment, by obtaining a first parameter, it is possible to determine whether to predict the cell signal quality based on the first parameter and obtain a predicted value. Since the cell signal quality can be predicted, the measurement of cell signal quality is reduced, thereby reducing the measurement overhead of frequent measurements.

[0279] Optionally, the first parameter includes at least one of the following:

[0280] The first indication information is used to indicate whether to predict the cell signal quality, or to indicate whether to use a first rule to predict the cell signal quality, or to indicate whether the network-side device allows the terminal to request configuration information for predicting the cell signal quality.

[0281] Cell signal quality measurement thresholds;

[0282] Threshold for changes in cell signal quality measurement;

[0283] The first duration represents the time length corresponding to the change in the cell signal quality measurement;

[0284] Model parameters represent the parameters of the artificial intelligence (AI) model used to predict the signal quality of the cell.

[0285] Cell signal quality prediction threshold;

[0286] Threshold for predicted change in cell signal quality;

[0287] The second duration represents the time length corresponding to the predicted change in the cell signal quality.

[0288] Prediction accuracy threshold.

[0289] Optionally, the determining module 602 is specifically used for at least one of the following:

[0290] Based on the first indication information, determine whether to predict the cell signal quality;

[0291] When the first parameter meets the preset conditions, the signal quality of the cell is predicted;

[0292] When the first parameter does not meet the preset conditions, the signal quality of the cell is measured.

[0293] Optionally, the preset conditions include at least one of the following:

[0294] The cell signal quality measurement value measured by the terminal is greater than or equal to the cell signal quality measurement value threshold;

[0295] Within the first time period, the decrease in the cell signal quality measurement value measured by the terminal is less than or equal to the cell signal quality change threshold.

[0296] The predicted value is greater than or equal to the cell signal quality predicted value threshold;

[0297] Within the second time period, the decrease in the predicted cell signal quality value inferred by the AI ​​model is less than or equal to the threshold of the predicted change in cell signal quality.

[0298] The target value determined based on the cell signal quality measurement value and the cell signal quality prediction value measured by the terminal is less than or equal to the prediction accuracy threshold.

[0299] The target value determined based on the cell signal quality measurement value and the cell signal quality prediction value measured by the terminal is greater than the prediction accuracy threshold.

[0300] Optionally, the predicted value is obtained by the terminal based on an AI model.

[0301] Optionally, when predicting the cell signal quality, the prediction device 600 further includes at least one of the following:

[0302] The second sending module is used to send prediction failure information to the network-side device when the target value determined based on the cell signal quality measurement value and the cell signal quality prediction value measured by the terminal is greater than the prediction accuracy threshold.

[0303] The third sending module is used to send prediction failure information to the network-side device when the target value determined based on the cell signal quality measurement value and the cell signal quality prediction value measured by the terminal is less than or equal to the prediction accuracy threshold.

[0304] Optionally, the prediction failure information includes at least one of the following:

[0305] Identifier of the AI ​​model used to predict the signal quality of the cell;

[0306] Community signage;

[0307] Predict the reasons for failure.

[0308] Optionally, the prediction device 600 further includes:

[0309] A first receiving module is configured to receive first configuration information, wherein the first configuration information includes at least one of the following:

[0310] Measurement gap configuration markings;

[0311] Target frequency;

[0312] Target residential area;

[0313] Target beam;

[0314] Preset ratio;

[0315] First rule;

[0316] Pattern cycle;

[0317] Pattern bitmap.

[0318] Optionally, the prediction of cell signal quality or the first rule includes at least one of the following:

[0319] The terminal predicts the signal quality of the cell at the target frequency.

[0320] The terminal measures the cell signal quality at one part of the target frequency and predicts the cell signal quality at the other part of the target frequency.

[0321] The terminal predicts the signal quality of the target cell.

[0322] The terminal measures the cell signal quality of a portion of the target cells and predicts the cell signal quality of another portion of the target cells.

[0323] The terminal predicts the signal quality of the cell using the target beam;

[0324] The terminal measures the cell signal quality in one part of the target beam and predicts the cell signal quality in the other part of the target beam.

[0325] The terminal predicts the cell signal quality according to the pattern period and / or the pattern bitmap within the measurement gap configuration corresponding to the measurement gap configuration identifier;

[0326] The terminal predicts the signal quality of the cell based on the preset ratio.

[0327] Optionally, the prediction device 600 further includes:

[0328] The fourth sending module is used to send a first request to the network-side device according to the first parameter, wherein the first request is used to request the first configuration information.

[0329] Optionally, the prediction device 600 further includes at least one of the following:

[0330] A relaxation measurement module is used to measure relaxation based on the predicted value;

[0331] The reporting module is used to report the predicted values.

[0332] Optionally, the relaxation factor measured by the terminal based on the predicted value is determined based on at least one of the following: the maximum relaxation factor configured by the network-side equipment; or obtained through inference using an AI model used to predict the cell signal quality.

[0333] Optionally, the first request includes at least one of the following: a second indication information for indicating that the conditions for the terminal to perform measurement relaxation according to the first rule are met; and a third indication information for indicating the measurement interval required by the terminal.

[0334] Figure 7 This is a second schematic diagram of the predictive device provided in the embodiments of this application, as shown below. Figure 7 As shown, the prediction device 700 includes: a configuration module 701; wherein,

[0335] The configuration module 701 is used to configure a first parameter to the terminal; the first parameter is used by the terminal to determine whether to predict the cell signal quality based on the first parameter, and obtain a predicted value.

[0336] In the prediction device provided in this application embodiment, a first parameter is configured to the terminal. The first parameter is used by the terminal to determine whether to predict the cell signal quality based on the first parameter and obtain a predicted value. Since the terminal can predict the cell signal quality, the measurement of cell signal quality by the terminal is reduced, thereby reducing the measurement overhead of frequent measurements by the terminal.

[0337] Optionally, the first parameter includes at least one of the following:

[0338] The first indication information is used to indicate whether to predict the cell signal quality, or to indicate whether to use a first rule to predict the cell signal quality, or to indicate whether the network-side device allows the terminal to request configuration information for predicting the cell signal quality.

[0339] Cell signal quality measurement thresholds;

[0340] Threshold for changes in cell signal quality measurement;

[0341] The first duration represents the time length corresponding to the change in the cell signal quality measurement;

[0342] Model parameters represent the parameters of the artificial intelligence (AI) model used to predict the signal quality of the cell.

[0343] Cell signal quality prediction threshold;

[0344] Threshold for predicted change in cell signal quality;

[0345] The second duration represents the time length corresponding to the predicted change in the cell signal quality.

[0346] Prediction accuracy threshold.

[0347] Optionally, the predicted value is obtained by the terminal based on an AI model.

[0348] Optionally, the prediction device 700 also includes:

[0349] A first sending module is configured to send first configuration information to the terminal, the first configuration information including at least one of the following:

[0350] Measurement gap configuration markings;

[0351] Target frequency;

[0352] Target residential area;

[0353] Target beam;

[0354] Preset ratio;

[0355] First rule;

[0356] Pattern cycle;

[0357] Pattern bitmap.

[0358] Optionally, the first rule includes at least one of the following:

[0359] The terminal predicts the signal quality of the cell at the target frequency.

[0360] The terminal measures the cell signal quality at one part of the target frequency and predicts the cell signal quality at the other part of the target frequency.

[0361] The terminal predicts the signal quality of the target cell.

[0362] The terminal measures the cell signal quality of a portion of the target cells and predicts the cell signal quality of another portion of the target cells.

[0363] The terminal predicts the signal quality of the cell using the target beam;

[0364] The terminal measures the cell signal quality in one part of the target beam and predicts the cell signal quality in the other part of the target beam.

[0365] The terminal predicts the cell signal quality according to the pattern period and / or the pattern bitmap within the measurement gap configuration corresponding to the measurement gap configuration identifier;

[0366] The terminal predicts the signal quality of the cell based on the preset ratio.

[0367] Optionally, the prediction device 700 further includes:

[0368] The second receiving module is used to receive a first request sent by the terminal; the first request is used to request the first configuration information.

[0369] Optionally, the first request includes at least one of the following: a second indication information for indicating that the conditions for the terminal to perform measurement relaxation according to the first rule are met; and a third indication information for indicating the measurement interval required by the terminal.

[0370] The prediction device in this application embodiment can be an electronic device, such as an electronic device with an operating system, or a component in an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal, or other devices besides a terminal. For example, the terminal can include, but is not limited to, the type of terminal 11 listed above; other devices can be servers, network attached storage (NAS), etc., and this application embodiment does not specifically limit the type.

[0371] The prediction device provided in this application embodiment can achieve... Figures 2 to 5 The various processes implemented in the method embodiments achieve the same technical effect, and will not be described again here to avoid repetition.

[0372] Figure 8 This is a schematic diagram of the structure of the communication device provided in the embodiments of this application, such as... Figure 8 As shown, the communication device 800 includes a processor 801 and a memory 802. The memory 802 stores programs or instructions that can run on the processor 801. For example, when the communication device 800 is a terminal, the program or instructions executed by the processor 801 implement the various steps of the above-described prediction method embodiment and achieve the same technical effect. When the communication device 800 is a network-side device, the program or instructions executed by the processor 801 implement the various steps of the above-described prediction method embodiment and achieve the same technical effect. To avoid repetition, this will not be described again here.

[0373] This application also provides a terminal, including a processor and a communication interface. The processor is used to acquire a first parameter; and based on the first parameter, to determine whether to predict the cell signal quality to obtain a predicted value. This terminal embodiment corresponds to the above-described terminal-side method embodiment. All implementation processes and methods of the above-described method embodiments can be applied to this terminal embodiment and achieve the same technical effect.

[0374] Figure 9 This is a schematic diagram of the terminal structure provided in the embodiments of this application, such as... Figure 9 As shown, the terminal 900 includes, but is not limited to, at least some of the following components: radio frequency unit 901, network module 902, audio output unit 903, input unit 904, sensor 905, display unit 906, user input unit 907, interface unit 908, memory 909, and processor 910.

[0375] Those skilled in the art will understand that the terminal 900 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 910 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 9 The terminal structure shown does not constitute a limitation on the terminal. The terminal may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0376] It should be understood that, in this embodiment, the input unit 904 may include a graphics processing unit (GPU) 9041 and a microphone 9042. The GPU 9041 processes image data of still images or videos obtained by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 906 may include a display panel 9061, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like. The user input unit 907 includes at least one of a touch panel 9071 and other input devices 9072. The touch panel 9071 is also called a touch screen. The touch panel 9071 may include a touch detection device and a touch controller. Other input devices 9072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be described in detail here.

[0377] In this embodiment, after receiving downlink data from the network-side device, the radio frequency unit 901 can transmit it to the processor 910 for processing; in addition, the radio frequency unit 901 can send uplink data to the network-side device. Typically, the radio frequency unit 901 includes, but is not limited to, antennas, amplifiers, transceivers, couplers, low-noise amplifiers, duplexers, etc.

[0378] The memory 909 can be used to store software programs or instructions, as well as various data. The memory 909 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 909 may include volatile memory or non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or 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 memory bus RAM (DRRAM). The memory 909 in the embodiments of this application includes, but is not limited to, these and any other suitable types of memory.

[0379] Processor 910 may include one or more processing units; optionally, processor 910 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 910.

[0380] The processor 910 is used to acquire a first parameter; and based on the first parameter, to determine whether to predict the cell signal quality and obtain a predicted value.

[0381] Based on the terminal provided in the embodiments of this application, the terminal obtains a first parameter and can determine whether to predict the cell signal quality based on the first parameter to obtain a predicted value. Since the terminal can predict the cell signal quality, the terminal can reduce the measurement of cell signal quality, thereby reducing the measurement overhead of frequent measurements by the terminal.

[0382] This application also provides a network-side device, including a processor and a communication interface. The communication interface is used to configure a first parameter to a terminal. The first parameter is used by the terminal to determine whether to predict the cell signal quality based on the first parameter, thereby obtaining a predicted value. This network-side device embodiment corresponds to the above-described network-side device method embodiment. All implementation processes and methods of the above-described method embodiments can be applied to this network-side device embodiment and achieve the same technical effects.

[0383] Figure 10 This is a schematic diagram of the network-side device provided in the embodiments of this application, such as... Figure 10 As shown, the network-side device 1000 includes: an antenna 1001, a radio frequency (RF) device 1002, a baseband device 1003, a processor 1004, and a memory 1005. The antenna 1001 is connected to the RF device 1002. In the uplink direction, the RF device 1002 receives information through the antenna 1001 and transmits the received information to the baseband device 1003 for processing. In the downlink direction, the baseband device 1003 processes the information to be transmitted and sends it to the RF device 1002. The RF device 1002 processes the received information and transmits it through the antenna 1001.

[0384] The method executed by the network-side device in the above embodiments can be implemented in the baseband device 1003, which includes a baseband processor.

[0385] The baseband device 1003 may include, for example, at least one baseband board on which multiple chips are disposed, such as... Figure 10 As shown, one of the chips is, for example, a baseband processor, which is connected to the memory 1005 via a bus interface to call the program in the memory 1005 and execute the network device operation shown in the above method embodiment.

[0386] The network-side device may also include a network interface 1006, such as a Common Public Radio Interface (CPRI).

[0387] The network-side device 1000 in this embodiment further includes: instructions or programs stored in memory 1005 and executable on processor 1004, wherein processor 1004 calls the instructions or programs in memory 1005 to execute as described above. Figure 3The method shown achieves the same technical effect, and will not be elaborated here to avoid repetition.

[0388] This application also provides a communication system, including: a terminal and a network-side device, wherein the terminal can be used to perform the steps of the terminal-side prediction method as described above, and the network-side device can be used to perform the steps of the network-side device-side prediction method as described above.

[0389] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described prediction method embodiments and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0390] The processor mentioned above is the processor in the terminal described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk. In some examples, the readable storage medium may be a non-transient readable storage medium.

[0391] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the above prediction method embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0392] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0393] This application also provides a computer program / program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the above-described prediction method embodiments, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0394] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0395] From the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of computer software products plus necessary general-purpose hardware platforms, and of course, they can also be implemented by hardware. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk, etc.) and includes several instructions to cause the terminal or network-side device to execute the methods described in the various embodiments of this application.

[0396] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many different implementations under the guidance of this application without departing from the spirit and scope of the claims. All such implementations are within the protection scope of this application.

Claims

1. A prediction method, characterized in that, include: The terminal obtains the first parameter; The terminal determines whether to predict the cell signal quality based on the first parameter and obtains a predicted value.

2. The prediction method according to claim 1, characterized in that, The first parameter includes at least one of the following: The first indication information is used to indicate whether to predict the cell signal quality, or to indicate whether to use a first rule to predict the cell signal quality, or to indicate whether the network-side device allows the terminal to request configuration information for predicting the cell signal quality. Cell signal quality measurement thresholds; Threshold for changes in cell signal quality measurement; The first duration represents the time length corresponding to the change in the cell signal quality measurement; Model parameters represent the parameters of the artificial intelligence (AI) model used to predict the signal quality of the cell. Cell signal quality prediction threshold; Threshold for predicted change in cell signal quality; The second duration represents the time length corresponding to the predicted change in the cell signal quality. Prediction accuracy threshold.

3. The prediction method according to claim 2, characterized in that, The terminal determines whether to predict the cell signal quality based on the first parameter, including at least one of the following: The terminal determines whether to predict the cell signal quality based on the first indication information; When the first parameter meets the preset conditions, the terminal predicts the signal quality of the cell. When the first parameter does not meet the preset conditions, the terminal measures the signal quality of the cell.

4. The prediction method according to claim 3, characterized in that, The preset conditions include at least one of the following: The cell signal quality measurement value measured by the terminal is greater than or equal to the cell signal quality measurement value threshold; Within the first time period, the decrease in the cell signal quality measurement value measured by the terminal is less than or equal to the cell signal quality change threshold. The predicted value is greater than or equal to the cell signal quality predicted value threshold; Within the second time period, the decrease in the predicted cell signal quality value inferred by the AI ​​model is less than or equal to the threshold of the predicted change in cell signal quality. The target value determined based on the cell signal quality measurement value and the cell signal quality prediction value measured by the terminal is less than or equal to the prediction accuracy threshold. The target value determined based on the cell signal quality measurement value and the cell signal quality prediction value measured by the terminal is greater than the prediction accuracy threshold.

5. The prediction method according to any one of claims 1 to 4, characterized in that, The predicted value is obtained by the terminal based on an AI model.

6. The prediction method according to any one of claims 3 to 5, characterized in that, When predicting the signal quality of the cell, the method further includes any one of the following: When the target value determined based on the cell signal quality measurement value and the cell signal quality prediction value measured by the terminal is greater than the prediction accuracy threshold, the terminal sends a prediction failure message to the network-side device. When the target value determined based on the cell signal quality measurement value and the cell signal quality prediction value measured by the terminal is less than or equal to the prediction accuracy threshold, the terminal sends a prediction failure message to the network-side device.

7. The prediction method according to claim 6, characterized in that, The prediction failure information includes at least one of the following: Identifier of the AI ​​model used to predict the signal quality of the cell; Community signage; Predicting the reasons for failure.

8. The prediction method according to any one of claims 1 to 7, characterized in that, The method further includes: The terminal receives first configuration information, which includes at least one of the following: Measurement gap configuration markings; Target frequency; Target residential area; Target beam; Preset ratio; First rule; Pattern cycle; Pattern bitmap.

9. The prediction method according to claim 8, characterized in that, The prediction of cell signal quality or the first rule includes at least one of the following: The terminal predicts the signal quality of the cell at the target frequency. The terminal measures the cell signal quality at one part of the target frequency and predicts the cell signal quality at the other part of the target frequency. The terminal predicts the signal quality of the target cell. The terminal measures the cell signal quality of a portion of the target cells and predicts the cell signal quality of another portion of the target cells. The terminal predicts the signal quality of the cell using the target beam; The terminal measures the cell signal quality in one part of the target beam and predicts the cell signal quality in the other part of the target beam. The terminal predicts the cell signal quality according to the pattern period and / or the pattern bitmap within the measurement gap configuration corresponding to the measurement gap configuration identifier; The terminal predicts the signal quality of the cell based on the preset ratio.

10. The prediction method according to claim 8, characterized in that, The method further includes: The terminal sends a first request to the network-side device based on the first parameter, and the first request is used to request the first configuration information.

11. The prediction method according to any one of claims 2 to 10, characterized in that, The method further includes at least one of the following: The terminal measures relaxation based on the predicted value; The terminal reports the predicted value.

12. The prediction method according to claim 11, characterized in that, The relaxation factor measured by the terminal based on the predicted value is determined based on at least one of the following: the maximum relaxation factor configured by the network-side equipment; or obtained through inference using an AI model used to predict the cell signal quality.

13. The prediction method according to claim 10, characterized in that, The first request includes at least one of the following: a second indication message for indicating that the conditions for the terminal to perform measurement relaxation according to the first rule are met; and a third indication message for indicating the measurement interval required by the terminal.

14. A prediction method, characterized in that, include: The network-side device configures a first parameter to the terminal; the first parameter is used by the terminal to determine whether to predict the cell signal quality and obtain a predicted value.

15. The prediction method according to claim 14, characterized in that, The first parameter includes at least one of the following: The first indication information is used to indicate whether to predict the cell signal quality, or to indicate whether to use a first rule to predict the cell signal quality, or to indicate whether the network-side device allows the terminal to request configuration information for predicting the cell signal quality. Cell signal quality measurement thresholds; Threshold for changes in cell signal quality measurement; The first duration represents the time length corresponding to the change in the cell signal quality measurement; Model parameters represent the parameters of the artificial intelligence (AI) model used to predict the signal quality of the cell. Cell signal quality prediction threshold; Threshold for predicted change in cell signal quality; The second duration represents the time length corresponding to the predicted change in the cell signal quality. Prediction accuracy threshold.

16. The prediction method according to claim 14 or 15, characterized in that, The predicted value is obtained by the terminal based on an AI model.

17. The prediction method according to any one of claims 14 to 16, characterized in that, The method further includes: The network-side device sends first configuration information to the terminal, the first configuration information including at least one of the following: Measurement gap configuration markings; Target frequency; Target residential area; Target beam; Preset ratio; First rule; Pattern cycle; Pattern bitmap.

18. The prediction method according to claim 17, characterized in that, The first rule includes at least one of the following: The terminal predicts the signal quality of the cell at the target frequency. The terminal measures the cell signal quality at one part of the target frequency and predicts the cell signal quality at the other part of the target frequency. The terminal predicts the signal quality of the target cell. The terminal measures the cell signal quality of a portion of the target cells and predicts the cell signal quality of another portion of the target cells. The terminal predicts the signal quality of the cell using the target beam; The terminal measures the cell signal quality in one part of the target beam and predicts the cell signal quality in the other part of the target beam. The terminal predicts the cell signal quality according to the pattern period and / or the pattern bitmap within the measurement gap configuration corresponding to the measurement gap configuration identifier; The terminal predicts the signal quality of the cell based on the preset ratio.

19. The prediction method according to claim 17, characterized in that, The method further includes: The network-side device receives a first request sent by the terminal; the first request is used to request the first configuration information.

20. The prediction method according to claim 19, characterized in that, The first request includes at least one of the following: a second indication message for indicating that the conditions for the terminal to perform measurement relaxation according to the first rule are met; and a third indication message for indicating the measurement interval required by the terminal.

21. A prediction device, characterized in that, include: The acquisition module is used to obtain the first parameter; The determination module is used to determine whether to predict the cell signal quality based on the first parameter, and to obtain the predicted value.

22. The prediction device according to claim 21, characterized in that, The first parameter includes at least one of the following: The first indication information is used to indicate whether to predict the cell signal quality, or to indicate whether to use a first rule to predict the cell signal quality, or to indicate whether the network-side device allows the terminal to request configuration information for predicting the cell signal quality. Cell signal quality measurement thresholds; Threshold for changes in cell signal quality measurement; The first duration represents the time length corresponding to the change in the cell signal quality measurement; Model parameters represent the parameters of the artificial intelligence (AI) model used to predict the signal quality of the cell. Cell signal quality prediction threshold; Threshold for predicted change in cell signal quality; The second duration represents the time length corresponding to the predicted change in the cell signal quality. Prediction accuracy threshold.

23. The prediction device according to claim 22, characterized in that, The determining module is specifically used for at least one of the following: Based on the first indication information, determine whether to predict the cell signal quality; When the first parameter meets the preset conditions, the signal quality of the cell is predicted; When the first parameter does not meet the preset conditions, the signal quality of the cell is measured.

24. The prediction device according to claim 23, characterized in that, The preset conditions include at least one of the following: The cell signal quality measurement value measured by the terminal is greater than or equal to the cell signal quality measurement value threshold; Within the first time period, the decrease in the cell signal quality measurement value measured by the terminal is less than or equal to the cell signal quality change threshold. The predicted value is greater than or equal to the cell signal quality predicted value threshold; Within the second time period, the decrease in the predicted cell signal quality value inferred by the AI ​​model is less than or equal to the threshold of the predicted change in cell signal quality. The target value determined based on the cell signal quality measurement value measured by the terminal and the cell signal quality prediction value inferred by the AI ​​model is less than or equal to the prediction accuracy threshold. The target value determined based on the cell signal quality measurement value measured by the terminal and the cell signal quality prediction value inferred by the AI ​​model is greater than the prediction accuracy threshold.

25. A prediction device, characterized in that, include: The configuration module is used to configure a first parameter to the terminal; the first parameter is used by the terminal to determine whether to predict the cell signal quality and obtain a predicted value.

26. The prediction device according to claim 25, characterized in that, The first parameter includes at least one of the following: The first indication information is used to indicate whether to predict the cell signal quality, or to indicate whether to use a first rule to predict the cell signal quality, or to indicate whether the network-side device allows the terminal to request configuration information for predicting the cell signal quality. Cell signal quality measurement thresholds; Threshold for changes in cell signal quality measurement; The first duration represents the time length corresponding to the change in the cell signal quality measurement; Model parameters represent the parameters of the artificial intelligence (AI) model used to predict the signal quality of the cell. Cell signal quality prediction threshold; Threshold for predicted change in cell signal quality; The second duration represents the time length corresponding to the predicted change in the cell signal quality. Prediction accuracy threshold.

27. The prediction device according to claim 25 or 26, characterized in that, The predicted value is obtained by the terminal based on an AI model.

28. The prediction device according to any one of claims 25 to 27, characterized in that, The prediction device further includes: A first sending module is configured to send first configuration information to the terminal, the first configuration information including at least one of the following: Measurement gap configuration markings; Target frequency; Target residential area; Target beam; Preset ratio; First rule; Pattern cycle; Pattern bitmap.

29. A terminal, characterized in that, It includes a processor and a memory, the memory storing a program or instructions that can run on the processor, the program or instructions being executed by the processor to implement the steps of the prediction method as described in any one of claims 1 to 13.

30. A network-side device, characterized in that, It includes a processor and a memory, the memory storing a program or instructions that can run on the processor, the program or instructions being executed by the processor to implement the steps of the prediction method as described in any one of claims 14 to 20.

31. A readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the prediction method as described in any one of claims 1 to 13, or implement the steps of the prediction method as described in any one of claims 14 to 20.

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