Prediction methods, terminal and network-side device

Abstract

The present application belongs to the technical field of communications. Disclosed are prediction methods, a terminal and a network-side device. A prediction method in the embodiments of the present application comprises: a terminal acquiring a first parameter; and on the basis of the first parameter, the terminal determining whether to predict cell signal quality, so as to obtain a predicted value.

Description

Prediction method, terminal and network side device

[0001] Cross-reference

[0002] The present disclosure claims priority to Chinese Patent Application No. 202410443463.9, filed on April 12, 2024, entitled “Prediction method, terminal and network side device”, the entire contents of which are incorporated herein by reference in its entirety. TECHNICAL FIELD

[0003] The present application belongs to the field of communication technology, and specifically relates to a prediction method, a terminal and a network side device. BACKGROUND

[0004] Currently, a terminal needs to perform periodic measurement to monitor the signal quality of a camping cell, a serving cell and / or a neighbor cell, so as to perform mobility management. However, frequent measurement introduces additional overhead, especially when the terminal in a connected state performs inter-frequency or inter-system measurement, the network also needs to configure a measurement gap for the terminal, in the measurement gap, the terminal will not send and receive any data, and the receiver of the terminal is tuned to the frequency point of the target cell to perform inter-frequency measurement.

[0005] Therefore, how to reduce the measurement overhead of frequent measurement of the terminal is a technical problem to be solved. SUMMARY

[0006] Embodiments of the present application provide a prediction method, a terminal and a network side device, which can solve the problem of how to reduce the measurement overhead of frequent measurement of the terminal.

[0007] In a first aspect, a prediction method is provided, which is executed by a terminal, and the method comprises:

[0008] The terminal acquires a first parameter;

[0009] The terminal determines whether to predict the cell signal quality based on the first parameter, to obtain a predicted value.

[0010] In a second aspect, a prediction method is provided, which is executed by a network side device, and the method comprises:

[0011] 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 according to the first parameter, to obtain a predicted value.

[0012] In a third aspect, a prediction device is provided, which comprises:

[0013] An acquisition module is configured to acquire a first parameter;

[0014] The execution module is configured to determine whether to predict the cell signal quality based on the first parameter, and obtain a predicted value.

[0015] In a fourth aspect, a prediction device is provided, which comprises:

[0016] The configuration module is configured to configure a terminal with a first parameter, wherein 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.

[0017] In a fifth aspect, a terminal is provided, which comprises a processor and a memory, wherein the memory stores programs or instructions executable on the processor, and the programs or instructions are executed by the processor to implement the steps of the method according to the first aspect.

[0018] In a sixth aspect, a terminal is provided, which comprises a processor and a communication interface, wherein the processor is configured to obtain a first parameter, and determine whether to predict the cell signal quality based on the first parameter, and obtain a predicted value.

[0019] In a seventh aspect, a network-side device is provided, which comprises a processor and a memory, wherein the memory stores programs or instructions executable on the processor, and the programs or instructions are executed by the processor to implement the steps of the method according to the second aspect.

[0020] In an eighth aspect, a network-side device is provided, which comprises a processor and a communication interface, wherein the communication interface is configured to configure a terminal with a first parameter, wherein 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.

[0021] In a ninth aspect, a communication system is provided, which comprises a terminal and a network-side device, wherein the terminal is configured to implement the steps of the method according to the first aspect, and the network-side device is configured to implement the steps of the method according to the second aspect.

[0022] In a tenth aspect, a readable storage medium is provided, which stores programs or instructions, wherein the programs or instructions are executed by a processor to implement the steps of the method according to the first aspect, or implement the steps of the method according to the second aspect.

[0023] In an eleventh aspect, a chip is provided, which comprises a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to run programs or instructions to implement the method according to the first aspect, or implement the method according to the second aspect.

[0024] 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 according to the first aspect, or to implement the steps of the method according to the second aspect.

[0025] In the embodiments of the present application, the terminal acquires the first parameter, and determines whether to predict the cell signal quality based on the first parameter, and obtains a predicted value. Since the terminal can predict the cell signal quality, the terminal can reduce the measurement of the cell signal quality, thereby reducing the measurement overhead of frequent measurement of the terminal. BRIEF DESCRIPTION OF DRAWINGS

[0026] FIG. 1 is a schematic diagram of a wireless communication system to which the embodiments of the present application can be applied;

[0027] FIG. 2 is a flow diagram of a prediction method according to an embodiment of the present application;

[0028] FIG. 3 is a flow diagram of a prediction method according to another embodiment of the present application;

[0029] FIG. 4 is a schematic diagram of interaction between a terminal and a network-side device according to an embodiment of the present application;

[0030] FIG. 5 is a schematic diagram of interaction between a terminal and a network-side device according to another embodiment of the present application;

[0031] FIG. 6 is a schematic diagram of a prediction apparatus according to an embodiment of the present application;

[0032] FIG. 7 is a schematic diagram of a prediction apparatus according to another embodiment of the present application;

[0033] FIG. 8 is a schematic diagram of a communication device according to an embodiment of the present application;

[0034] FIG. 9 is a schematic diagram of a terminal according to an embodiment of the present application;

[0035] FIG. 10 is a schematic diagram of a network-side device according to an embodiment of the present application. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present application will be described clearly below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present application.

[0037] The terms "first", "second", and the like in the specification and claims of this application are used as identifiers for convenience and are not intended to convey an importance or a chronological sequence among or between the identified objects. It is to be understood that the terms so used are interchangeable under appropriate circumstances and that the embodiments of this application are capable of functioning in other sequences than those described herein. The terms "first", "second", and the like are used to distinguish between similar objects, and are not used to describe a particular order or sequence. It is to be understood that such terms so used are interchangeable under appropriate circumstances and that the embodiments of this application are capable of functioning in other sequences than those described herein, and that the terms "first", "second", etc. are used to distinguish between similar objects, and are not used to describe a particular order or sequence, for example, unless otherwise specified. Furthermore, the term "or" as used in this application is to be interpreted as inclusive, i.e., the term "A or B" covers all possibilities, i.e., A, B, or both A and B. The character " / " is generally used to represent an "or" relationship between the associated objects.

[0038] The term "indicate" in this application can be a direct indication (or explicit indication) or an indirect indication (or implicit indication). The direct indication can be understood as that the sender explicitly informs the receiver of the specific information, the operation to be performed or the request result, etc. in the sent indication. The indirect indication can be understood as that the receiver determines the corresponding information according to the indication sent by the sender, or judges and determines the operation to be performed or the request result, etc. according to the judgment result.

[0039] It is worth noting that the techniques described in the embodiments of 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 the embodiments of this application are often used interchangeably, and the described techniques can be used in the above mentioned systems and radio technologies, as well as in other systems and radio technologies. The following description describes a New Radio (NR) system for example purposes, and NR terminology is used in most of the following description, but these techniques can also be applied to systems other than NR systems, such as 6 thA communication system (5G, 6G) communication system.

[0040] FIG. 1 shows a block diagram of a wireless communication system to which embodiments of the present application can be applied. The wireless communication system includes a terminal 11 and a network-side device 12. The terminal 11 can be a terminal-side device such as a mobile phone, a Tablet Personal Computer, a Laptop Computer, a notebook computer, a Personal Digital Assistant (PDA), a palmtop computer, a netbook, an Ultra-mobile Personal Computer (UMPC), a Mobile Internet Device (MID), an Augmented Reality (AR) device, a Virtual Reality (VR) device, a robot, a wearable device, a flight vehicle, a Vehicle User Equipment (VUE), a shipboard device, a Pedestrian User Equipment (PUE), a smart home (a home device with a wireless communication function such as a refrigerator, a television, a washing machine, or furniture), a game console, a Personal Computer (PC), a kiosk, or a self-service machine. The wearable device includes a smart watch, a smart bracelet, a smart earphone, smart glasses, smart jewelry (a smart bracelet, a smart necklace, a smart ring, a smart necklace, a smart anklet, a smart necklace, etc.), a smart wristband, smart clothing, etc. The vehicle-mounted device can also be referred to as a vehicle-mounted terminal, a vehicle-mounted controller, a vehicle-mounted module, a vehicle-mounted component, a vehicle-mounted chip, or a vehicle-mounted unit, etc. It should be noted that the specific type of the terminal 11 is not limited in the embodiments of the present application. The network-side device 12 can include an access network device or a core network device. The access network device can also be referred to as a Radio Access Network (RAN) device, a radio access network function, or a radio access network unit. The access network device can include a base station, a Wireless Local Area Network (WLAN) Access Point (AP), or a Wireless Fidelity (WiFi) node, etc.The base station can be referred to as a Node B (NB), an evolved Node B (eNB), a next generation Node B (gNB), a New Radio Node B (NR Node B), an access point, a relay station (RBS), a serving base station (SBS), a base transceiver station (BTS), a radio base station, a radio transceiver, a basic service set (BSS), an extended service set (ESS), a home Node B (HNB), a home evolved Node B, a transmission reception point (TRP), an on-board access network device, a centralized unit (CU) of an access network device, a distributed unit (DU) of an access network device, a CU of an on-board access network device, a DU of an on-board access network device, or some other suitable terminology in the art, so long as the same technical effect is achieved. The base station is not limited to a specific technical term, and it is noted that the base station is only taken as an example in the NR system in the embodiments of the present application, and the specific type of the base station is not limited.

[0041] The core network device can include, but is not limited to, at least one of the following: a core network node, a core network function, a mobility management entity (MME), an access and mobility management function (AMF), a session management function (SMF), a user plane function (UPF), a policy control function (PCF), a policy and charging rules function (PCRF), an edge application server discovery function (EASDF), a unified data management (UDM), a unified data repository (UDR), a home subscriber server (HSS), a centralized network configuration (CNC), a network repository function (NRF), a network exposure function (NEF), a local NEF (L-NEF), a binding support function (BSF), an application function (AF), and the like. It should be noted that, in the embodiments of the present application, only the core network device in the NR system is taken as an example for introduction, and the specific type of the core network device is not limited.

[0042] In order to more clearly understand the embodiments of the present application, first, some related background knowledge is introduced as follows.

[0043] I. Non-connected state measurement relaxation

[0044] According to the location and movement state, the non-connected state (RRC_IDLE / INACTIVE) terminal relaxes the measurement according to certain decision criteria. The decision criteria mainly include whether the user equipment (UE) is low mobility and whether the UE is at the cell edge, and the related configuration is broadcast in the system information block 2 (SIB2).

[0045] The measurement relaxation criterion of low mobility: the reference signal received power (RSRP) drop is less than the threshold within a certain time, or even rises, and then the measurement relaxation is selected.

[0046] (SrxlevRef-Srxlev)<S SearchDeltaP for a period of T SearchDeltaP

[0047] Wherein, Srxlev represents the S value of the current serving cell, and the specific calculation can refer to the cell selection and reselection process;

[0048] Srxlev Ref The reference S value of the current serving cell is represented, and the specific setting method of the reference S value is: at the moment when the UE completes the cell selection or reselection, that is, at the moment when the UE completes the camping in the new cell, the reference S value is assigned to the current S value; then in the subsequent measurement process, 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 it cannot be maintained for T SearchDeltaP for a long time, the reference S value is also updated to the current S value;

[0049] S SearchDeltaP The configured RSRP drop threshold value is represented;

[0050] T SearchDeltaP The configured specific time length is represented.

[0051] The measurement relaxation criterion of not being at the cell edge: when the terminal is not at the cell edge (still in the good coverage area of the cell), the measurement relaxation is selected.

[0052] Srxlev>S SearchThresholdP , and Squal>S SearchThresholdQ (if S SearchThresholdQ is configured)

[0053] When the above criteria are met, the corresponding measurement is relaxed. For example, for intra-frequency measurement, the terminal will multiply the measurement period by a relaxation factor K1 = 3, that is, the measurement period is increased to 3 times. For inter-frequency measurement, the measurement period is enlarged by a factor M2 relative to the Discontinuous Reception (DRX) cycle, which is fixed at 1.5 (originally 1.5 or 1).

[0054] II. Redcap UE connected state radio resource management (RRM) measurement relaxation

[0055] Similar to the low mobility criterion in the non-connected state, the difference is:

[0056] (1) The base station sends the falling threshold value and a specific time length to the UE through dedicated signaling;

[0057] (2) If the serving cell RSRP drop value is lower than the threshold and the duration exceeds the threshold, the terminal does not directly relax the measurement, but sends UE Assistance Information (UAI) feedback to indicate that the measurement relaxation condition is met, that is, rrm-MeasRelaxationFulfilment is true.

[0058] III. Measurement gap

[0059] If the UE needs to perform inter-frequency measurement (including inter-standard measurement), a simple way is to install 2 kinds of radio frequency receivers in the UE device to measure the frequency points of the current cell and the target cell respectively, but this will bring the problem of cost increase and mutual interference between different frequency points. Therefore, 3GPP proposes a measurement gap (GAP) method, that is, a part of time (i.e. measurement GAP time) is reserved, during which the UE will not send and receive any data, and the receiver will be adjusted to the target cell frequency point to perform inter-frequency measurement, and then switch back to the current cell after the GAP time ends.

[0060] The measurement gap configuration parameters mainly include length (such as 1ms), period (such as 20ms), and the UE can perform periodic measurement within the measurement gap.

[0061] The terminal measurement provided by the embodiments of the present application will be described in detail in combination with some embodiments and their application scenarios.

[0062] The prediction method provided in the embodiments of the present application can be applied to a terminal measurement scenario. A terminal acquires a first parameter. The terminal can determine whether to predict a cell signal quality based on the first parameter, and obtain a predicted value. Since the terminal can predict the cell signal quality, the terminal measurement of the cell signal quality is reduced, and the measurement overhead of the terminal measurement is reduced.

[0063] FIG. 2 is a flowchart of a prediction method provided in the embodiments of the present application. As shown in FIG. 2, the method includes steps 201-202.

[0064] In step 201, a terminal acquires a first parameter.

[0065] It should be noted that the embodiments of the present application can be applied to a terminal measurement scenario. The terminal includes, but is not limited to, the types of the terminal 11 listed above. The network side device includes, but is not limited to, the types of the network side device 12 listed above. The embodiments of the present application are not limited in this regard. The terminal supports a prediction-based measurement relaxation function. The terminal can report to the network side device that the terminal supports the prediction-based measurement relaxation function.

[0066] The terminal can acquire the first parameter according to a network side device broadcast. The terminal can also acquire the first parameter by receiving the first parameter sent by the network side device through an RRC message. The first parameter is related to terminal measurement.

[0067] In step 202, the terminal determines whether to predict a cell signal quality based on the first parameter, and obtains a predicted value.

[0068] The cell is a camping cell, a serving cell and / or a neighbor cell. The cell signal quality is, for example, a reference signal received power (RSRP), a signal to interference plus noise ratio (SINR) or a reference signal received quality (RSRQ).

[0069] After the terminal acquires the first parameter, the terminal can determine whether to predict the cell signal quality at a measurement gap corresponding to a measurement gap period or a time point corresponding to a measurement period according to the first parameter, and obtain a predicted value. That is, when the terminal determines that the cell signal quality can be predicted, the terminal does not perform actual measurement of the cell signal quality at the measurement gap corresponding to the measurement gap period or the time point corresponding to the measurement period. The terminal measurement of the cell signal quality is reduced, and the measurement overhead of the terminal measurement is reduced.

[0070] Optionally, the predicted value is obtained by the terminal based on an Artificial Intelligence (AI) model.

[0071] Optionally, since the terminal can determine to predict the cell signal quality at a time point corresponding to a measurement gap period or a measurement period, the network side device can reduce the configuration of the corresponding measurement gap and improve the time domain length of scheduling the terminal, thereby improving the rate of the terminal.

[0072] In the prediction method provided by the embodiments of the present application, the terminal obtains the first parameter, and the terminal 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 the cell signal quality, thereby reducing the measurement overhead of frequent measurement of the terminal.

[0073] Optionally, the first parameter comprises at least one of the following:

[0074] (1) first indication information, used for whether to predict the cell signal quality, or used for indicating whether to use a first rule to predict the cell signal quality, or used for indicating 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 the AI model to relax the measurement based on the first indication information, that is, the terminal can use the predicted value of the AI model prediction reasoning as the actual measurement value of the cell signal quality, thereby reducing the measurement of the cell signal quality of the terminal.

[0075] (2) a cell signal quality measurement value threshold. The cell signal quality measurement value threshold is used to determine whether to predict the cell signal quality, for example, to use the AI model to relax the measurement reasoning of the cell signal quality, for example, when the RSRP measurement value actually measured by the terminal is greater than or equal to the cell signal quality measurement value threshold, the terminal starts the AI model and uses the AI model to relax the measurement reasoning of the cell signal quality, uses the predicted value of the AI model prediction reasoning as the actual measurement value of the cell signal quality, and reduces the measurement of the cell signal quality of the terminal.

[0076] (3) A cell signal quality change amount threshold value, used to determine whether to predict the cell signal quality, for example, to use an AI model to relax the inference of the cell signal quality measurement. For example, when the actual RSRP measurement of the terminal in the first time length decreases by less than or equal to the cell signal quality change amount threshold value, the terminal starts the AI model and uses the AI model to relax the inference of the cell signal quality measurement, and uses the predicted value of the AI model prediction inference as the actual measurement value of the cell signal quality, to reduce the measurement of the terminal. It should be noted that the actual RSRP measurement of the terminal decreases in the first time length, indicating that the terminal is moving away from the network side device, and the terminal can start the AI model to relax the inference of the measurement.

[0077] (4) The first time length, indicating the time length corresponding to the change amount of the cell signal quality measurement, that is, the time length of the actual cell signal quality measurement of the terminal.

[0078] (5) Model parameters, indicating the parameters of the artificial intelligence AI model used to predict the cell signal quality. Optionally, the model parameters include at least one of the following: model identifier, model structure information, and model parameter information.

[0079] (6) A cell signal quality prediction value, used to determine whether to predict the cell signal quality, for example, to use an AI model to relax the inference of the cell signal quality measurement. For example, when the RSRP prediction value of the AI model prediction inference is greater than the cell signal quality prediction value threshold value, the terminal determines to use the AI model to predict the cell signal quality.

[0080] (7) A cell signal quality change amount threshold value, used to determine whether to predict the cell signal quality, for example, to use an AI model to relax the inference of the cell signal quality measurement. For example, when the decrease amount of the RSRP prediction value of the AI model inference in the second time length is less than the cell signal quality prediction, the terminal determines to use the AI model to predict the cell signal quality.

[0081] (8) The second time length, indicating the time length corresponding to the change amount of the cell signal quality prediction.

[0082] (9) A prediction accuracy threshold. For example, the terminal determines whether to select the measurement relaxation based on the AI model based on the prediction accuracy threshold. For example, in the non-connected state, the terminal can select the AI based measurement relaxation or the legacy measurement relaxation scheme according to the prediction accuracy threshold. The terminal can determine to select the AI based measurement relaxation based on the actual measured RSRP measurement value and the AI model inferred RSRP prediction value, or the terminal can determine to select the AI based measurement relaxation based on the actual measured RSRQ measurement value and the AI model inferred RSRQ prediction value. For example, the prediction accuracy threshold is a root mean square error (RMSE) threshold, the terminal can determine a target value based on the actual measured RSRP measurement value and the AI model inferred RSRP prediction value, the target value is the RMSE, when the target value is less than the prediction accuracy threshold, the terminal selects the AI based measurement relaxation, or when the target value is not less than the prediction accuracy threshold, the terminal selects the AI based measurement relaxation.

[0083] It should be noted that the above prediction of the cell signal quality according to the AI model can be based on the historical measurement of the cell signal quality and the like. The method of predicting the cell signal quality is not limited to using the AI model, and other prediction methods are also within the scope of the present application.

[0084] Optionally, the first parameter can be configured in the measurement reporting configuration ReportConfig or OtherConfig, and the terminal obtains the first parameter in the form of broadcasting or sending the configuration information by the network side device.

[0085] Optionally, the specific implementation of the step 202 includes at least one of the following:

[0086] (a) The terminal determines whether to predict the cell signal quality according to the first indication information in the first parameter.

[0087] The terminal can determine whether to predict the cell signal quality according to the first indication information in the first parameter.

[0088] (b) The terminal predicts the cell signal quality when the first parameter meets the preset condition.

[0089] The preset condition is configured by the network side device to the terminal based on the first parameter. The terminal determines whether the first parameter satisfies the preset condition. In the case that the first parameter satisfies the preset condition, the terminal can predict the cell signal quality. The terminal does not need to perform actual measurement on the cell signal quality at the time point of predicting the cell signal quality or the measurement gap, thereby reducing the measurement of the terminal on the cell signal quality, and reducing the measurement overhead of the terminal. Meanwhile, the network side device can schedule the terminal without performing actual measurement on the cell signal quality at the time point of predicting the cell signal quality or the measurement gap, thereby improving the rate of the terminal.

[0090] (c) The terminal measures the cell signal quality when the first parameter does not satisfy the preset condition.

[0091] When the first parameter does not satisfy the preset condition, the terminal needs to perform actual measurement on the cell signal quality, and the terminal performs measurement according to the conventional measurement mode.

[0092] Optionally, the preset condition comprises at least one of the following:

[0093] (a) The cell signal quality measurement value measured by the terminal is greater than or equal to the cell signal quality measurement value threshold.

[0094] When the actual measurement value of the cell signal quality measured by the terminal is greater than or equal to the cell signal quality measurement value threshold, the terminal can predict the cell signal quality. For example, the terminal can start the AI model, and use the AI model to perform relaxed inference on the cell signal quality, take the predicted value of the AI model prediction inference as the actual measurement value of the cell signal quality, or the terminal can use the first rule to perform relaxed inference on the cell signal quality, thereby reducing the measurement of the terminal.

[0095] (b) The decrease amount of the cell signal quality measurement value measured by the terminal within the first time length is less than or equal to the cell signal quality change amount threshold.

[0096] When the decrease amount of the actual measurement value of the cell signal quality measured by the terminal within the first time length is less than or equal to the cell signal quality change amount threshold, the terminal can predict the cell signal quality. For example, the terminal can start the AI model, and use the AI model to perform relaxed inference on the cell signal quality, take the predicted value of the AI model prediction inference as the actual measurement value of the cell signal quality, or the terminal can use the first rule to perform relaxed inference on the cell signal quality, thereby reducing the measurement of the terminal.

[0097] (c) The predicted value is greater than or equal to the cell signal quality prediction value threshold.

[0098] The terminal can predict the cell signal quality when the predicted value obtained by predicting the cell signal quality is greater than or equal to a cell signal quality prediction value threshold. For example, the terminal can perform AI model-based measurement relaxation, i.e., taking the predicted value of the AI model prediction inference as the actual measurement value of the cell signal quality, or the terminal can use the first rule to perform measurement relaxation inference on the cell signal quality to reduce the measurement of the terminal, when the RSRP predicted value of the AI model prediction inference is greater than or equal to the cell signal quality prediction value threshold.

[0099] (d) the amount of decrease in the cell signal quality prediction value within the second time length is less than or equal to the cell signal quality prediction change amount threshold.

[0100] The terminal can predict the cell signal quality when the amount of decrease in the cell signal quality prediction value of the AI model inference within the second time length is less than or equal to the cell signal quality prediction change amount threshold. For example, the terminal can perform AI model-based measurement relaxation, i.e., taking the predicted value of the AI model prediction inference as the actual measurement value of the cell signal quality, or the terminal can use the first rule to perform measurement relaxation inference on the cell signal quality to reduce the measurement of the terminal, when the RSRP predicted value of the AI model prediction inference within the second time length is less than or equal to the cell signal quality prediction change amount threshold.

[0101] (e) the target value determined based on the cell signal quality measurement value measured by the terminal and the cell signal quality prediction value is less than or equal to the prediction accuracy threshold.

[0102] The target value can be RMSE, and when the target value determined based on the cell signal quality measurement value measured by the terminal and the cell signal quality prediction value is less than the prediction accuracy threshold, the terminal can select AI model-based measurement relaxation, i.e., taking the predicted value of the AI model prediction inference as the actual measurement value of the cell signal quality, or the terminal can use the first rule to perform measurement relaxation inference on the cell signal quality to reduce the measurement of the terminal.

[0103] (f) the target value determined based on the cell signal quality measurement value measured by the terminal and the cell signal quality prediction value is greater than the prediction accuracy threshold.

[0104] When the target value determined based on the cell signal quality measurement value measured by the terminal and the cell signal quality prediction value is greater than the prediction accuracy threshold, the terminal can select AI model-based measurement relaxation, i.e., taking the predicted value of the AI model prediction inference as the actual measurement value of the cell signal quality, or the terminal can use the first rule to perform measurement relaxation inference on the cell signal quality to reduce the measurement of the terminal.

[0105] Optionally, in the case of predicting the cell signal quality, the method further comprises any one of the following:

[0106] When the target value determined based on the cell signal quality measurement value measured by the terminal and the cell signal quality prediction value is greater than the prediction accuracy threshold, the terminal sends prediction failure information to the network side device; when the target value determined based on the cell signal quality measurement value measured by the terminal and the cell signal quality prediction value is less than or equal to the prediction accuracy threshold, the terminal sends prediction failure information to the network side device.

[0107] As a possible implementation, the target value can be RMSE. In the case of predicting the cell signal quality, when the target value determined based on the cell signal quality measurement value measured by the terminal and the cell signal quality prediction value predicted by the AI model inference is greater than the prediction accuracy threshold, it indicates that the cell signal quality prediction value predicted by the AI model inference is inaccurate, and then the terminal fallbacks to the conventional legacy measurement relaxation, the terminal records this event, and generates a first report; the first report includes the prediction failure information. The terminal sends the prediction failure information to the network side device, and the network side device can obtain the prediction failure information and optimize the AI model based on the prediction failure information or not allow the terminal to relax the measurement based on the AI model. Or, when the target value determined based on the cell signal quality measurement value measured by the terminal and the cell signal quality prediction value predicted by the AI model inference is less than or equal to the prediction accuracy threshold, it indicates that the cell signal quality prediction value predicted by the AI model inference is inaccurate, and then the terminal fallbacks to the conventional legacy measurement relaxation, the terminal records this event, and generates a first report; the first report includes the prediction failure information. The terminal sends the prediction failure information to the network side device, and the network side device can obtain the prediction failure information and optimize the AI model based on the prediction failure information or not allow the terminal to relax the measurement based on the AI model.

[0108] For example, the prediction accuracy threshold is 0.8, the target value determined based on the cell signal quality measurement value measured by the terminal and the cell signal quality prediction value is 0.5, the target value determined based on the cell signal quality measurement value measured by the terminal and the cell signal quality prediction value is less than the prediction accuracy threshold, which indicates that the cell signal quality prediction value predicted by the AI model inference is inaccurate, and then the terminal fallbacks to the conventional legacy measurement relaxation, the terminal records this event, and generates a first report; the first report includes the prediction failure information. The terminal sends the prediction failure information to the network side device, and the network side device can obtain the prediction failure information and optimize the AI model based on the prediction failure information or not allow the terminal to relax the measurement based on the AI model.

[0109] Optionally, the prediction failure information includes at least one of the following:

[0110] an identification of an AI model used for predicting the cell signal quality; specifically, an identification of an AI model that fails to make a prediction.

[0111] an identification of a cell; the identification of the cell is used to indicate a serving cell in which the terminal resides when the AI model inference fails;

[0112] a prediction failure cause, i.e., a prediction accuracy of the inference does not meet a prediction accuracy threshold requirement, such as a root mean square error (RMSE) not meeting the prediction accuracy threshold requirement.

[0113] Optionally, the terminal receives first configuration information, and the first configuration information includes at least one of the following:

[0114] (a) an identification of a measurement gap configuration; the identification of the measurement gap configuration is used to identify a measurement gap for measuring the cell signal quality and / or a measurement gap for predicting the cell signal quality by the terminal.

[0115] (b) a target frequency point; the target frequency point indicates at least one frequency point for measuring the cell signal quality and / or at least one frequency point for predicting the cell signal quality by the terminal.

[0116] (c) a target cell; the target cell indicates at least one cell for measuring the cell signal quality and / or at least one cell for predicting the cell signal quality by the terminal.

[0117] (d) a target beam; the target beam indicates at least one beam for measuring the cell signal quality and / or at least one beam for predicting the cell signal quality by the terminal.

[0118] (e) a preset ratio; the preset ratio can be a ratio of measuring and predicting the cell signal quality within a measurement gap period, or a ratio between a number of reported measurement values and a number of reported AI model prediction values, i.e., the preset ratio can be a number of reported measurement values / total number of reported values, a number of reported AI model prediction values / total number of reported values, or a number of reported measurement values / a number of reported AI model prediction values, all of which are used to represent a proportion between the number of reported measurement values and the number of reported prediction values. The preset ratio can be indicated in a reporting configuration ReportConfig, and represented by a fraction, for example, the preset ratio is 1 / 3, indicating that the number of reported AI model prediction values accounts for 1 / 3 of the total number of reported values, i.e., 2 actual measurement values are reported within the measurement gap period, and 1 prediction value is reported.

[0119] (f) a first rule; the first rule is used for the terminal to predict the cell signal quality.

[0120] (g) Pattern period; the pattern period is used for the terminal to predict the cell signal quality, and the pattern period can be an integer multiple of the original measurement gap period or a specific length, for example, ms.

[0121] (h) Pattern bitmap bitmap; for example, 1 indicates that a measurement gap is configured in the original measurement gap period, and 0 indicates that no measurement gap is configured in the original measurement gap period.

[0122] Optionally, the prediction of the cell signal quality or the first rule comprises at least one of the following:

[0123] 1) The terminal predicts the cell signal quality at a target frequency point.

[0124] 2) The terminal measures the cell signal quality at a part of the target frequency point and predicts the cell signal quality at another part of the target frequency point.

[0125] When the number of target frequency points is multiple, the terminal can actually measure the cell signal quality at a part of the target frequency points and predict the cell signal quality at another part of the target frequency points, for example, using an AI model to predict the cell signal quality at another part of the target frequency points, and taking the predicted value obtained by the AI model as the predicted value of another part of the frequency points. Among them, another part of the frequency points can be indicated in MeasObjectNR in the measurement target configured by the network side device.

[0126] 3) The terminal predicts the cell signal quality at a target cell.

[0127] 4) The terminal measures the cell signal quality of a part of the target cells and predicts the cell signal quality of another part of the target cells.

[0128] When the number of target cells is multiple, the terminal can actually measure the cell signal quality of a part of the target cells and predict the cell signal quality of another part of the target cells, for example, using an AI model to predict the cell signal quality of another part of the target cells, and taking the predicted value obtained by the AI model as the predicted value of another part of the cells; among them, another part of the cells is indicated by the way of cell list in the measurement target MeasObjectNR configured by the network side device, that is, which cells in the cell list perform AI model prediction.

[0129] 5) The terminal predicts the cell signal quality at a target beam.

[0130] 6) The terminal measures the cell signal quality in one part of the target beams and predicts the cell signal quality in another part of the target beams.

[0131] When the number of target beams is multiple beams, the terminal can actually measure the cell signal quality in one part of the target beams and predict the cell signal quality in another part of the target beams, for example, using an AI model to predict the cell signal quality in another part of the target beams, and taking the predicted value obtained by the AI model as the predicted value of another part of the beams; wherein the other part of the beams is indicated in the measurement target MeasObjectNR, and the other part of the beams can be associated with the target cell.

[0132] 7) The terminal predicts the cell signal quality in the corresponding measurement gap configuration according to the pattern period and / or the pattern bitmap in the measurement gap configuration identifier.

[0133] The terminal measures the cell signal quality in one part of the measurement gap according to the pattern period and / or the pattern bitmap in the measurement gap configuration identifier, and predicts the cell signal quality in another part of the measurement gap.

[0134] 8) The terminal predicts the cell signal quality based on the preset proportion.

[0135] The terminal measures the cell signal quality in the measurement gap period corresponding to the measurement gap by a preset proportion, wherein the preset proportion can be the proportion of measurement and prediction of the cell signal quality in the measurement gap period, or the proportion between the reporting number of measurement values and the reporting number of AI model predicted values, that is, the reporting number of measurement values / total reporting number, the reporting number of AI model predicted values / total reporting number, or the reporting number of measurement values / the reporting number of AI model predicted values, all of which are used to represent the proportion between the reporting number of measurement values and the reporting number of predicted values. The preset proportion can be indicated in the reporting configuration ReportConfig by a fraction, for example, the preset proportion is 1 / 3, which means that the reporting number of AI model predicted values reported by the terminal accounts for 1 / 3 of the total reporting number, that is, 2 times of actual measurement value is reported in the measurement gap period, and 1 time of predicted value is reported.

[0136] Optionally, the method further comprises:

[0137] The terminal sends a first request to the network side device according to the first parameter; the first request is used to request the first configuration information.

[0138] The network-side device does not pre-configure the first configuration information to the terminal, and the terminal judges, according to the first parameter, that the first parameter meets a preset condition, and the terminal can send a first request to the network-side device, the first request being used to request the first configuration information; after the network-side device receives 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 the terminal predicts the cell signal quality based on the first configuration information, thereby reducing the measurement overhead of the terminal measurement.

[0139] Optionally, the first request comprises at least one of the following: second indication information used to indicate that the condition for the terminal to perform measurement relaxation according to the first rule is met; and third indication information used to indicate the measurement gap required by the terminal measurement.

[0140] Optionally, the third indication information comprises at least one of the following:

[0141] A measurement gap configuration identification ID, which is used to identify the measurement gap actually required by the terminal measurement.

[0142] A pattern period, which can be an integer multiple of the original measurement gap MG period, or a specific length, such as ms.

[0143] A bitmap, for example, 1 indicates that the MG is configured in the original MG period, and 0 indicates that the MG is not configured in the original MG period.

[0144] Optionally, the method further comprises at least one of the following: the terminal performs measurement relaxation according to the predicted value; and the terminal reports the predicted value.

[0145] Optionally, the relaxation multiple of the terminal performing measurement relaxation according to the predicted value is determined based on at least one of the following: a maximum relaxation multiple configured by the network-side device; and an inference value of an AI model used for predicting the cell signal quality.

[0146] The relaxation multiple can be a fixed value, i.e., the maximum relaxation multiple configured by the network-side device; or the relaxation multiple can be an inference output value, i.e., the predicted value of the AI model used for predicting the cell signal quality. The terminal can take the minimum value between the maximum relaxation multiple configured by the network-side device and the predicted value obtained by the inference of the AI model used for predicting the cell signal quality as the relaxation multiple.

[0147] The terminal can multiply the measurement period and the relaxation multiple based on the relaxation multiple and the measurement period to obtain a new measurement period, the new measurement period being greater than the measurement period before multiplication, so that the measurement period is increased, actual measurement is performed at a target time point corresponding to the measurement period, and prediction of the cell signal quality is performed at other time points corresponding to the measurement period except the target time point; the target time point is a measurement time point after the measurement period is increased. For example, the measurement period is 2 seconds (s), the relaxation multiple is 2, the measurement period is increased to 4 s, the target time point is a time point corresponding to 4 s, for example, 1 s, 5 s, and 9 s, and the other time points are 3 s, 7 s, and 11 s.

[0148] The terminal can also report the predicted value obtained by predicting the cell signal quality, and the network-side device configures a time point corresponding to a new measurement period or a measurement gap corresponding to a new measurement gap period for the terminal according to the predicted value reported by the terminal, so that the terminal measures the cell signal quality at the time point corresponding to the new measurement period or the measurement gap corresponding to the new measurement gap period, and predicts the cell signal quality at other time points or other measurement gaps.

[0149] It should be noted that the network-side device can configure second configuration information for the terminal according to 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 the first configuration information, or can be different from the first configuration information, that is, the network-side device can refer to the first configuration information when configuring the second configuration information for the terminal, or can not refer to the first configuration information, which is determined by the network-side device itself.

[0150] FIG. 3 is a flowchart of a prediction method according to an embodiment of the present application, as shown in FIG. 3, the method comprises step 301; wherein:

[0151] In step 301, the network-side device configures a first parameter for the terminal; the first parameter is used by the terminal to determine whether to predict the cell signal quality to obtain a predicted value according to the first parameter.

[0152] The cell is a camping cell, a serving cell, and / or a neighbor cell, and the cell signal quality is, for example, RSRP, SINR, or RSRQ.

[0153] The network-side device configures the first parameter for the terminal. The network-side device configures the first parameter for the terminal through broadcasting or an RRC message, so that the terminal obtains the first parameter. The first parameter is related to terminal measurement. After receiving the first parameter, the terminal can determine whether to predict the cell signal quality in the measurement gap corresponding to the measurement gap period or the time point corresponding to the measurement period according to the first parameter, to obtain a predicted value. That is, in the case where the terminal determines that the cell signal quality can be predicted, the terminal can not actually measure the cell signal quality at the measurement gap corresponding to the measurement gap period or the time point corresponding to the measurement period, thereby reducing the measurement of the terminal on the cell signal quality and reducing the measurement overhead of the terminal measurement.

[0154] Optionally, the predicted value is obtained based on an artificial intelligence (AI) model.

[0155] Optionally, since the terminal can determine to predict the cell signal quality in the measurement gap corresponding to the measurement gap period or the time point corresponding to the measurement period, the network-side device can reduce the configuration of the corresponding measurement gap, improve the time domain length of scheduling the terminal, and improve the rate of the terminal.

[0156] In the prediction method provided by the embodiments of the present application, the network-side device configures the first parameter for the terminal. The first parameter is used for the terminal to 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 measurement of the terminal on the cell signal quality is reduced, thereby reducing the measurement overhead of frequent measurement of the terminal.

[0157] Optionally, the first parameter comprises at least one of the following:

[0158] (1) first indication information, used for whether to predict the cell signal quality, or used for indicating whether to use a first rule to predict the cell signal quality, or used for indicating 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 to relax measurement based on the first indication information. That is, the terminal can use the predicted value of AI model prediction reasoning as the actual measurement value of the cell signal quality, thereby reducing the measurement of the terminal on the cell signal quality.

[0159] (2) A cell signal quality measurement value threshold. The cell signal quality measurement value threshold is used to determine whether to use an AI model to predict the cell signal quality, that is, to use the AI model to relax the inference of the cell signal quality measurement. For example, when the RSRP measurement value actually measured by the terminal is greater than or equal to the cell signal quality measurement value threshold, the terminal starts the AI model and uses the AI model to relax the inference of the cell signal quality measurement, and the predicted value of the AI model prediction inference is taken as the actual measurement value of the cell signal quality, thereby reducing the measurement of the terminal.

[0160] (3) A cell signal quality change threshold. The cell signal quality change threshold is used to determine whether to use an AI model to predict the cell signal quality, that is, to use the AI model to relax the inference of the cell signal quality measurement. For example, when the decrease amount of the RSRP actually measured by the terminal in the first time length is less than or equal to the cell signal quality change threshold, the terminal starts the AI model and uses the AI model to relax the inference of the cell signal quality measurement, and the predicted value of the AI model prediction inference is taken as the actual measurement value of the cell signal quality, thereby reducing the measurement of the terminal. It should be noted that the decrease of the RSRP actually measured by the terminal in the first time length indicates that the terminal is moving away from the network side device, and the terminal can start the AI model to relax the inference of the measurement.

[0161] (4) A first time length, indicating a time length corresponding to a cell signal quality measurement change amount, that is, a time length of actually measuring the cell signal quality by the terminal.

[0162] (5) Model parameters, indicating parameters of an artificial intelligence AI model used to predict the cell signal quality. Optionally, the model parameters include at least one of the following: a model identifier, model structure information, and model parameter information.

[0163] (6) A cell signal quality prediction value, used to determine whether to use an AI model to predict the cell signal quality, that is, to use the AI model to relax the inference of the cell signal quality measurement. For example, when the RSRP prediction value of the AI model prediction inference is greater than the cell signal quality prediction value threshold, the terminal determines to use the AI model to predict the cell signal quality.

[0164] (7) A cell signal quality change threshold, used to determine whether to use an AI model to predict the cell signal quality, that is, to use the AI model to relax the inference of the cell signal quality measurement. For example, when the decrease amount of the RSRP prediction value of the AI model inference in the second time length is less than the cell signal quality prediction, the terminal determines to use the AI model to predict the cell signal quality.

[0165] (8) A second time length, indicating a time length corresponding to a cell signal quality prediction change amount.

[0166] (9) a prediction accuracy threshold. The prediction accuracy threshold is used to indicate whether the terminal selects the measurement relaxation based on the AI model, i.e., in the non-connected state, the terminal can select the AI based measurement relaxation or the legacy measurement relaxation scheme according to the prediction accuracy threshold. The terminal can determine to select the AI based measurement relaxation based on the actual measured RSRP measurement value and the AI model inferred RSRP prediction value, or the terminal can determine to select the AI based measurement relaxation based on the actual measured RSRQ measurement value and the AI model inferred RSRQ prediction value. For example, the prediction accuracy threshold is a root mean square error (RMSE threshold), the terminal can determine a target value based on the actual measured RSRP measurement value and the AI model inferred RSRP prediction value, the target value is the RMSE, when the target value is less than the prediction accuracy threshold, the terminal selects the AI based measurement relaxation, or when the target value is not less than the prediction accuracy threshold, the terminal selects the AI based measurement relaxation.

[0167] Optionally, the first parameter can be configured in a measurement reporting configuration ReportConfig or OtherConfig, in the form of network side device broadcasting or sending configuration information, so that the terminal obtains the first parameter.

[0168] Optionally, the network side device sends the first configuration information to the terminal, and the first configuration information includes at least one of the following:

[0169] (a) measurement gap configuration identifier; the measurement gap configuration identifier is used to identify the measurement gap for the terminal to measure the cell signal quality and / or the measurement gap for the terminal to predict the cell signal quality.

[0170] (b) target frequency point; the target frequency point represents at least one frequency point for the terminal to measure the cell signal quality and / or at least one frequency point for the terminal to predict the cell signal quality.

[0171] (c) target cell; the target cell represents at least one cell for the terminal to measure the cell signal quality and / or at least one cell for the terminal to predict the cell signal quality.

[0172] (d) target beam; the target beam represents at least one beam for the terminal to measure the cell signal quality and / or at least one beam for the terminal to predict the cell signal quality.

[0173] (e) a preset proportion; the preset proportion can be a proportion of measuring and predicting the cell signal quality in a measurement gap period, or a proportion between the reporting number of measurement values and the reporting number of AI model predicted values, i.e., the preset proportion can be the reporting number of measurement values / total reporting number, the reporting number of AI model predicted values / total reporting number, or the reporting number of measurement values / the reporting number of AI model predicted values, all of which are used to represent the proportion between the reporting number of measurement values and the reporting number of predicted values. The preset proportion can be indicated in the reporting configuration ReportConfig, and represented by a fraction, for example, the preset proportion is 1 / 3, which means that the reporting number of AI model predicted values accounts for 1 / 3 of the total reporting number, i.e., 2 times of actual measurement values are reported in the measurement gap period, and 1 time of predicted values is reported.

[0174] (f) a first rule; the first rule is used for the terminal to predict the cell signal quality.

[0175] (g) a pattern period; the pattern period is used for the terminal to predict the cell signal quality, and the pattern period can be an integer multiple of the original measurement gap period, or a specific length, for example, ms.

[0176] (h) a bitmap; for example, 1 represents that a measurement gap is configured in the original measurement gap period, and 0 represents that no measurement gap is configured in the original measurement gap period.

[0177] Optionally, the first rule includes at least one of the following:

[0178] 1) the terminal predicts the cell signal quality at a target frequency point.

[0179] 2) the terminal measures the cell signal quality at a part of the target frequency point, and predicts the cell signal quality at another part of the target frequency point.

[0180] When the number of target frequency points is multiple, the terminal can measure the cell signal quality at a part of the target frequency points, and predict the cell signal quality at another part of the target frequency points, for example, using an AI model to predict the cell signal quality at another part of the target frequency points, and taking the predicted value obtained by the AI model as the predicted value of another part of the frequency points. The another part of the frequency points can be indicated in a measurement target MeasObjectNR configured by a network side device.

[0181] 3) the terminal predicts the cell signal quality at a target cell.

[0182] 4) The terminal measures the cell signal quality of a part of the target cells and predicts the cell signal quality of another part of the target cells.

[0183] When the number of target cells is multiple, the terminal can actually measure the cell signal quality of a part of the target cells and predict the cell signal quality of another part of the target cells, for example, using an AI model to predict the cell signal quality of another part of the target cells, and taking the predicted value of the AI model prediction as the predicted value of another part of the cells; wherein another part of the cells is indicated by a cell list in the measurement target MeasObjectNR configured by the network side device, that is, which cells in the cell list perform AI model prediction.

[0184] 5) The terminal predicts the cell signal quality in the target beam.

[0185] 6) The terminal measures the cell signal quality in a part of the target beams and predicts the cell signal quality in another part of the target beams.

[0186] When the number of target beams is multiple, the terminal can actually measure the cell signal quality in a part of the target beams and predict the cell signal quality in another part of the target beams, for example, using an AI model to predict the cell signal quality in another part of the target beams, and taking the predicted value of the AI model prediction as the predicted value of another part of the beams; wherein another part of the beams is indicated in the measurement target MeasObjectNR, and another part of the beams can be associated with the target cells.

[0187] 7) The terminal predicts the cell signal quality in the corresponding measurement gap configuration identified by the measurement gap configuration identifier according to the pattern period and / or the pattern bitmap.

[0188] The terminal measures the cell signal quality in a part of the measurement gaps corresponding to the pattern period and / or the pattern bitmap and predicts the cell signal quality in another part of the measurement gaps in the corresponding measurement gap configuration identified by the measurement gap configuration identifier.

[0189] 8) The terminal predicts the cell signal quality based on the preset proportion.

[0190] The cell signal quality is measured by a preset proportion in a measurement gap period corresponding to the measurement gap, wherein the preset proportion can be a proportion of measurement of the cell signal quality and prediction in the measurement gap period, or a proportion between the number of reported measurement values and the number of reported AI model prediction values, that is, the preset proportion can be the number of reported measurement values / total number of reported values, the number of reported AI model prediction values / total number of reported values, or the number of reported measurement values / the number of reported AI model prediction values, which are all used to represent the proportion between the number of reported measurement values and the number of reported prediction values. The preset proportion can be indicated in the reporting configuration ReportConfig by a fraction, for example, the preset proportion is 1 / 3, which means that the number of reported AI model prediction values accounts for 1 / 3 of the total number of reported values, that is, 2 times of actual measurement values are reported in the measurement gap period, and 1 time of prediction value is reported.

[0191] Optionally, the method further comprises:

[0192] The network side device receives the first request sent by the terminal; the first request is used to request the first configuration information.

[0193] The network side device does not pre-configure the first configuration information to the terminal, and the terminal judges that the first parameter satisfies the preset condition according to the first parameter. In this case, the terminal can send a first request to the network side device, and the first request is used to request the first configuration information. After the network side device receives 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 the terminal predicts the cell signal quality based on the first configuration information, thereby reducing the measurement overhead of the terminal.

[0194] Optionally, the first request comprises at least one of the following: second indication information used to indicate that the condition for the terminal to adopt the first rule for measurement relaxation is satisfied; and third indication information used to indicate the measurement gap required by the terminal for measurement.

[0195] Optionally, the third indication information comprises at least one of the following:

[0196] A measurement gap configuration identifier ID, which is used to identify the measurement gap actually required by the terminal for measurement.

[0197] A pattern period, which can be an integer multiple of the original measurement gap MG period, or a specific length, such as ms.

[0198] A bitmap, for example, 1 represents that the MG is configured in the original measurement gap MG period, and 0 represents that the MG is not configured in the original MG period.

[0199] Optionally, the network-side device can further configure the terminal with second information according to the predicted value reported by the terminal, the first rule, or third indication information reported by the terminal. The second configuration information can be the same as or different from the first configuration information, i.e., the network-side device can or can not refer to the first configuration information when configuring the terminal with the second configuration information, which is determined by the network-side device itself.

[0200] FIG. 4 is one of the interaction schematic diagrams between the terminal and the network-side device provided by the embodiments of the present application, as shown in FIG. 4, which includes the following steps:

[0201] Step 401: The network-side device sends the terminal with first parameters.

[0202] Step 402: The terminal determines whether to predict the cell signal quality based on the first parameters, and obtains a predicted value.

[0203] FIG. 5 is another of the interaction schematic diagrams between the terminal and the network-side device provided by the embodiments of the present application, as shown in FIG. 5, which includes the following steps:

[0204] Step 501: The terminal sends the network-side device with a first request; the first request is used to request first configuration information.

[0205] Step 502: The terminal receives the first configuration information sent by the network-side device, and the first configuration information includes at least one of the following: measurement gap configuration identification; target frequency point; target cell; target beam; preset ratio; first rule; pattern period; pattern bitmap.

[0206] Step 503: The terminal predicts the cell signal quality based on the first configuration information.

[0207] Next, the prediction method provided by the embodiments of the present application is further described through specific embodiments.

[0208] Embodiment one: measurement relaxation of the non-connected state terminal

[0209] Step 1: The network-side device broadcasts first parameters, and the first parameters include at least one of the following:

[0210] First indication information, used to indicate whether to predict the cell signal quality, or to indicate whether to predict the cell signal quality by using the first rule, or to indicate whether the network-side device allows the terminal to request configuration information for predicting the cell signal quality;

[0211] A cell signal quality measurement value threshold value is used to determine whether the AI model is used to predict the cell signal quality, i.e., the AI model is used to relax the measurement inference of the cell signal quality.

[0212] A cell signal quality change threshold value is used to determine whether the AI model is used to predict the cell signal quality, i.e., the AI model is used to relax the measurement inference of the cell signal quality.

[0213] A first time length indicates a time length corresponding to the cell signal quality measurement change. For example, when the RSRP drop measured by the terminal within the first time length is less than or equal to the cell signal quality change threshold value, the AI model is started, and the AI model is used to relax the measurement inference of the cell signal quality.

[0214] A model parameter indicates a parameter of an artificial intelligence AI model used to predict the cell signal quality. The model parameter includes a model identifier, model structure information, and model parameter information.

[0215] A cell signal quality measurement value threshold value is used to determine whether the AI model is used to predict the cell signal quality, i.e., the AI model is used to relax the measurement inference of the cell signal quality. For example, when the RSRP prediction value inferred by the AI model is greater than or equal to the cell signal quality measurement value threshold value, the terminal determines to use the AI model to predict the cell signal quality.

[0216] A cell signal quality change threshold value is used to determine whether the AI model is used to predict the cell signal quality, i.e., the AI model is used to relax the measurement inference of the cell signal quality.

[0217] A second time length indicates a time length corresponding to the cell signal quality prediction change. For example, when the drop of the RSRP prediction value inferred by the AI model within the second time length is less than or equal to the cell signal quality change threshold value, the terminal determines to use the AI model to predict the cell signal quality.

[0218] A prediction accuracy threshold value is used to indicate whether the terminal selects the AI based measurement relaxation. If the AI model inference includes the RSRP or RSRQ prediction value, the terminal simultaneously performs actual measurement and AI model prediction in the measurement gap, determines a target value according to the actual measurement value and the prediction value, the target value is the RMSE, and selects the AI based or legacy measurement relaxation scheme according to the prediction accuracy threshold value.

[0219] Step 2, the terminal performs model inference, and performs measurement relaxation based on the first parameter.

[0220] The relaxation multiple of the measurement relaxation can be a fixed value or an AI model prediction value. In particular, the network side device can configure a maximum relaxation multiple, and the terminal performs measurement relaxation based on the minimum value of the prediction value obtained by the AI model inference on the cell signal quality and the maximum relaxation multiple configured by the network side device.

[0221] Step 3, if the network side device configures a prediction accuracy threshold, and the terminal performs model inference, and determines that the target value based on the RSRP measurement value measured by the terminal and the RSRP prediction value of the AI model inference is less than or equal to, or greater than the prediction accuracy threshold, i.e. the prediction accuracy threshold is not satisfied, the terminal fallbacks to legacy measurement relaxation, and the terminal records this event and generates a first report, the first report includes prediction failure information, and the prediction failure information includes at least one of the following:

[0222] AI model identification for predicting the cell signal quality;

[0223] Cell identification, i.e. the serving cell in which the terminal resides when the AI model prediction fails;

[0224] Prediction failure reason, for example, the accuracy of the AI model prediction does not meet the prediction accuracy threshold requirement, such as the RMSE does not meet the prediction accuracy threshold requirement.

[0225] Step 4: After the terminal enters the connected state, the network side device obtains the first report (i.e. inference failure report), optimizes the AI model or does not allow the terminal to perform AI model based measurement relaxation.

[0226] Embodiment two, connected state terminal measurement reduction (network side device configures a first rule)

[0227] Step 0, the terminal reports support for prediction based connected state measurement reduction function.

[0228] Step 1, the network side device sends a first parameter, the first parameter includes at least one of the following:

[0229] Cell signal quality measurement value threshold, used to determine whether to use an AI model to predict the cell signal quality, i.e. to use an AI model to perform measurement relaxation inference on the cell signal quality. For example, when the RSRP measurement value measured by the terminal in the serving cell is greater than or equal to the cell signal quality measurement value threshold, the terminal can perform measurement relaxation inference on the cell signal quality using the first rule to reduce the terminal's measurement.

[0230] a cell signal quality change threshold, used to determine whether to use the AI model to predict the cell signal quality, i.e., to use the AI model to relax the inference of the cell signal quality measurement.

[0231] a first time length, representing the time length corresponding to the cell signal quality measurement change. For example, if the amount of decrease in the RSRP measurement value measured by the terminal in the serving cell within the first time length is less than or equal to the cell signal quality change threshold, the terminal can use the first rule to relax the inference of the cell signal quality measurement, and reduce the measurement of the terminal.

[0232] a prediction accuracy threshold, used by the terminal to determine whether to select the AI model-based measurement relaxation. If the model inference includes the RSRP or RSRQ prediction value, the terminal simultaneously performs actual measurement and AI model prediction in the measurement gap, determines the target value according to the actual measurement value and the prediction value, the target value is the RMSE, and determines whether the terminal can use the first rule to relax the inference of the cell signal quality measurement according to the prediction accuracy threshold, and reduce the measurement of the terminal.

[0233] In particular, the above parameters can be indicated in the measurement reporting configuration ReportConfig.

[0234] The first rule includes at least one of the following:

[0235] 1) The terminal predicts the cell signal quality at the target frequency point.

[0236] 2) The terminal measures the cell signal quality at a part of the target frequency point, and predicts the cell signal quality at another part of the target frequency point.

[0237] When the number of target frequency points is multiple, the terminal can actually measure the cell signal quality at a part of the target frequency points, and predict the cell signal quality at another part of the target frequency points, for example, use the AI model to predict the cell signal quality at another part of the target frequency points, and use the prediction value obtained by the AI model as the prediction value of another part of the frequency points. Among them, another part of the frequency points can be indicated in the measurement target MeasObjectNR configured by the network side device.

[0238] 3) The terminal predicts the cell signal quality at the target cell.

[0239] 4) The terminal measures the cell signal quality of a part of the target cells, and predicts the cell signal quality of another part of the target cells.

[0240] When the number of target cells is multiple cells, the terminal can actually measure the cell signal quality of a part of the target cells, and predict the cell signal quality of another part of the target cells, for example, use an AI model to predict the cell signal quality of another part of the target cells, and take the predicted value obtained by the AI model as the predicted value of another part of the cells; wherein another part of the cells is indicated by a cell list in the measurement target MeasObjectNR configured by the network side device, that is, which cells in the cell list perform AI model prediction.

[0241] 5) The terminal predicts the cell signal quality in the target beam pair.

[0242] 6) The terminal measures the cell signal quality in a part of the target beams, and predicts the cell signal quality in another part of the target beams.

[0243] When the number of target beams is multiple beams, the terminal can actually measure the cell signal quality in a part of the target beams, and predict the cell signal quality in another part of the target beams, for example, use an AI model to predict the cell signal quality of another part of the target beams, and take the predicted value obtained by the AI model as the predicted value of another part of the beams; wherein another part of the beams is indicated in the measurement target MeasObjectNR, and another part of the beams can be associated with the target cell.

[0244] 7) The terminal predicts the cell signal quality in the corresponding measurement gap configuration identified by the measurement gap configuration identifier according to the pattern period and / or the pattern bitmap.

[0245] The terminal measures the cell signal quality in a part of the measurement gap corresponding to the pattern period and / or the pattern bitmap, and predicts the cell signal quality in another part of the measurement gap in the corresponding measurement gap configuration identified by the measurement gap configuration identifier.

[0246] 8) The terminal predicts the cell signal quality based on the preset proportion.

[0247] The cell signal quality is measured by a preset proportion in a measurement gap period corresponding to the measurement gap, wherein the preset proportion can be a proportion of measurement and prediction of the cell signal quality in the measurement gap period, or a proportion between the reporting number of the measurement value and the reporting number of the AI model prediction value, i.e., the reporting number of the measurement value / total reporting number, the reporting number of the AI model prediction value / total reporting number, or the reporting number of the measurement value / the reporting number of the AI model prediction value, which are all used to represent the proportion between the reporting number of the measurement value and the reporting number of the prediction value. The preset proportion can be indicated in the reporting configuration ReportConfig by a fraction, for example, the preset proportion is 1 / 3, which means that the reporting number of the AI model prediction value of the terminal accounts for 1 / 3 of the total reporting number, i.e., 2 times of the actual measurement value is reported in the measurement gap period and 1 time of the prediction value is reported.

[0248] Step 2: In the case that the first parameter meets the preset condition, the terminal predicts the cell signal quality based on the first rule and reports.

[0249] Step 3: The network can obtain the actual measurement requirement of the terminal according to the first rule, and reduce the MG time domain configuration. The reduction of the MG time domain configuration can be realized by a specific MG pattern.

[0250] Measurement gap configuration identifier;

[0251] Pattern period, which can be an integer multiple of the original measurement gap MG period, or a specific length, such as ms;

[0252] Pattern bitmap, 1 represents that the MG is configured in the original MG period, and 0 represents that the MG is not configured in the original MG period.

[0253] Embodiment three, measurement reduction of the connected terminal (the terminal requests the first rule and the first configuration information from the network side device)

[0254] Step 0, the terminal reports support for the prediction-based connected measurement reduction function.

[0255] Step 1, the network side device sends a first parameter, and the first parameter includes at least one of the following:

[0256] A cell signal quality measurement value threshold, which is used to indicate whether the network side device allows the terminal to request to use the first rule for measurement relaxation. For example, if the RSRP measurement value measured by the terminal in the serving cell is greater than a fourth RSRP measurement value threshold, the network allows the terminal to request to use the first rule for measurement reduction.

[0257] A cell signal quality measurement change threshold for determining whether to use an AI model to predict the cell signal quality or instructing the network-side device whether to allow the terminal to request to use the first rule to predict the cell signal quality.

[0258] A first time length representing a time length corresponding to the cell signal quality measurement change. For example, if a decrease in the RSRP measurement value measured by the terminal in the serving cell within the first time length is less than or equal to the cell signal quality measurement change threshold, the network allows the terminal to request to use the first rule to predict the cell signal quality.

[0259] In particular, the above parameters can be indicated in OtherConfig.

[0260] Step 2: If the first parameter in step 1 meets a preset condition, the terminal sends a first request to the network-side device, and the first request is used to request the network-side device to send first configuration information so that the terminal predicts the cell signal quality based on the first configuration information. The first request includes at least one of the following:

[0261] Second indication information for indicating that the terminal meets the condition for measuring and relaxing using the first rule;

[0262] Third indication information for indicating the measurement gap required by the terminal to measure, and the third indication information can be implemented by a specific MG pattern. The third indication information includes at least one of the following:

[0263] Measurement gap MG configuration identification ID;

[0264] Pattern period; the pattern period can be an integer multiple of the original measurement gap MG period, or a specific length such as ms;

[0265] Pattern bitmap bitmap; for example, 1 indicates that the MG is configured in the original measurement gap MG period, and 0 indicates that the MG is not configured in the original measurement gap MG period.

[0266] The prediction method provided by the embodiments of the present application can be executed by a prediction device. In the embodiments of the present application, the prediction method executed by the prediction device is taken as an example to illustrate the prediction device provided by the embodiments of the present application.

[0267] FIG. 6 is a structural schematic diagram of a prediction device provided by the embodiments of the present application, as shown in FIG. 6, the prediction device 600 includes an acquisition module 601 and a determination module 602; wherein,

[0268] The acquisition module 601 is configured to acquire a first parameter.

[0269] The determination module 602 is configured to determine whether to predict the cell signal quality based on the first parameter, and obtain a prediction value.

[0270] The prediction device provided by the embodiments of the present application can determine whether to predict the cell signal quality based on the first parameter, obtain a prediction value, and reduce the measurement of the cell signal quality, thereby reducing the measurement overhead of frequent measurement.

[0271] Optionally, the first parameter comprises at least one of the following:

[0272] The first indication information is used to indicate whether to predict the cell signal quality, or to indicate whether to predict the cell signal quality by using the first rule, or to indicate whether the network-side device allows the terminal to request configuration information for predicting the cell signal quality.

[0273] The cell signal quality measurement value threshold;

[0274] The cell signal quality measurement change threshold;

[0275] The first time length, representing a time length corresponding to the cell signal quality measurement change;

[0276] The model parameter, representing a parameter of an artificial intelligence (AI) model used for predicting the cell signal quality;

[0277] The cell signal quality prediction value threshold;

[0278] The cell signal quality prediction change threshold;

[0279] The second time length, representing a time length corresponding to the cell signal quality prediction change;

[0280] The prediction accuracy threshold.

[0281] Optionally, the determination module 602 is specifically configured to perform at least one of the following:

[0282] Determine whether to predict the cell signal quality according to the first indication information;

[0283] Predict the cell signal quality when the first parameter meets a preset condition;

[0284] Measure the cell signal quality when the first parameter does not meet the preset condition.

[0285] Optionally, the preset condition comprises at least one of the following:

[0286] The cell signal quality measurement value measured by the terminal is greater than or equal to the cell signal quality measurement value threshold;

[0287] a decrease in the cell signal quality measurement value measured by the terminal within the first time length is less than or equal to the cell signal quality change threshold;

[0288] the prediction value is greater than or equal to the cell signal quality prediction threshold;

[0289] a decrease in the cell signal quality prediction value inferred by the AI model within the second time length is less than or equal to the cell signal quality prediction change threshold;

[0290] a 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;

[0291] a 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.

[0292] Optionally, the prediction value is obtained by the terminal based on the AI model.

[0293] Optionally, in the case of predicting the cell signal quality, the prediction device 600 further comprises at least one of the following:

[0294] a second sending module configured to send prediction failure information to a network side device when a 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;

[0295] a third sending module configured to send prediction failure information to a network side device when a 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.

[0296] Optionally, the prediction failure information comprises at least one of the following:

[0297] an identifier of an AI model used to predict the cell signal quality;

[0298] a cell identifier;

[0299] a prediction failure reason.

[0300] Optionally, the prediction device 600 further comprises:

[0301] a first receiving module configured to receive first configuration information, the first configuration information comprising at least one of the following:

[0302] a measurement gap configuration identifier;

[0303] a target frequency point;

[0304] Target cell;

[0305] Target beam;

[0306] Pre-set proportion;

[0307] First rule;

[0308] Pattern period;

[0309] Pattern bitmap.

[0310] Optionally, the predicting the cell signal quality or the first rule comprises at least one of:

[0311] The terminal predicts the cell signal quality at a target frequency point;

[0312] The terminal measures the cell signal quality at one part of the target frequency point and predicts the cell signal quality at another part of the target frequency point;

[0313] The terminal predicts the cell signal quality at a target cell;

[0314] The terminal measures the cell signal quality of one part of the target cell and predicts the cell signal quality of another part of the target cell;

[0315] The terminal predicts the cell signal quality at a target beam;

[0316] The terminal measures the cell signal quality at one part of the target beam and predicts the cell signal quality at another part of the target beam;

[0317] The terminal predicts the cell signal quality according to the pattern period and / or the pattern bitmap in the corresponding measurement gap configuration identified by the measurement gap configuration identifier;

[0318] The terminal predicts the cell signal quality based on the pre-set proportion.

[0319] Optionally, the prediction device 600 further comprises:

[0320] A fourth sending module configured to send a first request to a network side device according to the first parameter, the first request being used to request the first configuration information.

[0321] Optionally, the prediction device 600 further comprises at least one of:

[0322] A measurement relaxation module configured to perform measurement relaxation according to the prediction value;

[0323] The reporting module is configured to report the predicted value.

[0324] Optionally, the measurement relaxation multiple of the terminal performing measurement relaxation according to the predicted value is determined based on at least one of the following: a maximum relaxation multiple configured by the network-side device; and inference of an AI model used for predicting the cell signal quality.

[0325] Optionally, the first request comprises at least one of the following: second indication information indicating that a condition for the terminal to perform measurement relaxation according to the first rule is met; and third indication information indicating a measurement gap required by the terminal for measurement.

[0326] FIG. 7 is a second structural schematic diagram of a prediction device according to an embodiment of the present application. As shown in FIG. 7, the prediction device 700 comprises a configuration module 701; and

[0327] The configuration module 701 is configured to configure a first parameter for a terminal; and the first parameter is used by the terminal to determine whether to predict a cell signal quality based on the first parameter to obtain a predicted value.

[0328] In the prediction device provided by the embodiments of the present application, a first parameter is configured for a terminal, and the first parameter is used by the terminal to determine whether to predict a cell signal quality based on the first parameter to obtain a predicted value. Since the terminal can predict the cell signal quality, the measurement of the cell signal quality by the terminal is reduced, and thus the measurement overhead of frequent measurement by the terminal is reduced.

[0329] Optionally, the first parameter comprises at least one of the following:

[0330] First indication information indicating whether to predict the cell signal quality, or indicating whether to predict the cell signal quality according to a first rule, or indicating whether the network-side device allows the terminal to request configuration information for predicting the cell signal quality;

[0331] A cell signal quality measurement value threshold;

[0332] A cell signal quality measurement change threshold;

[0333] A first time length representing a time length corresponding to the cell signal quality measurement change;

[0334] A model parameter representing a parameter of an artificial intelligence (AI) model used for predicting the cell signal quality;

[0335] A cell signal quality predicted value threshold;

[0336] A cell signal quality predicted change threshold;

[0337] A second time length, representing a time length corresponding to the cell signal quality prediction change amount;

[0338] A prediction accuracy threshold.

[0339] Optionally, the prediction value is obtained by the terminal based on an AI model.

[0340] Optionally, the prediction device 700 further comprises:

[0341] A first sending module, configured to send first configuration information to the terminal, the first configuration information comprising at least one of the following:

[0342] A measurement gap configuration identifier;

[0343] A target frequency point;

[0344] A target cell;

[0345] A target beam;

[0346] A preset ratio;

[0347] A first rule;

[0348] A pattern period;

[0349] A pattern bitmap.

[0350] Optionally, the first rule comprises at least one of the following:

[0351] The terminal predicts the cell signal quality at a target frequency point;

[0352] The terminal measures the cell signal quality at a part of the target frequency point and predicts the cell signal quality at another part of the target frequency point;

[0353] The terminal predicts the cell signal quality in a target cell;

[0354] The terminal measures the cell signal quality in a part of the target cell and predicts the cell signal quality in another part of the target cell;

[0355] The terminal predicts the cell signal quality in a target beam;

[0356] The terminal measures the cell signal quality in a part of the target beam and predicts the cell signal quality in another part of the target beam;

[0357] The terminal predicts the cell signal quality in the measurement gap configuration corresponding to the measurement gap configuration identifier, according to the pattern period and / or the pattern bitmap.

[0358] The terminal predicts the cell signal quality based on the preset proportion.

[0359] Optionally, the prediction device 700 further includes:

[0360] The second receiving module is configured to receive a first request sent by the terminal, and the first request is used to request the first configuration information.

[0361] Optionally, the first request includes at least one of the following: second indication information used to indicate that a condition for the terminal to perform measurement relaxation according to the first rule is met; and third indication information used to indicate a measurement gap required by the terminal for measurement.

[0362] The prediction device in the embodiments of the present application can be an electronic device, for example, an electronic device with an operating system, or a component in an electronic device, for example, an integrated circuit or a chip. The electronic device can be a terminal or other device. For example, the terminal can include, but is not limited to, the types of the terminal 11 listed above, and the other device can be a server, a network attached storage (NAS), etc., which are not limited in the embodiments of the present application.

[0363] The prediction device provided in the embodiments of the present application can implement each process of the method embodiments of FIG. 2 to FIG. 5 and achieve the same technical effects. To avoid repetition, details are not described herein.

[0364] FIG. 8 is a structural schematic diagram of a communication device provided in the embodiments of the present application. As shown in FIG. 8, the communication device 800 includes a processor 801 and a memory 802, and the memory 802 stores programs or instructions executable on the processor 801. For example, when the communication device 800 is a terminal, the programs or instructions are executed by the processor 801 to implement each step of the prediction method embodiments described above and achieve the same technical effects. When the communication device 800 is a network side device, the programs or instructions are executed by the processor 801 to implement each step of the prediction method embodiments described above and achieve the same technical effects. To avoid repetition, details are not described herein.

[0365] The embodiments of the present application further provide a terminal including a processor and a communication interface. The processor is configured to acquire a first parameter, determine whether to predict a cell signal quality based on the first parameter, and obtain a prediction value. The terminal embodiment corresponds to the terminal side method embodiment described above. Each implementation process and implementation manner of the method embodiment described above can be applied to the terminal embodiment and achieve the same technical effects.

[0366] Fig. 9 is a structural schematic diagram of a terminal provided in an embodiment of the present application. As shown in Fig. 9, the terminal 900 includes, but is not limited to, at least part of the following components: a radio frequency unit 901, a network module 902, an audio output unit 903, an input unit 904, a sensor 905, a display unit 906, a user input unit 907, an interface unit 908, a memory 909, and a processor 910, etc.

[0367] Those skilled in the art can understand that the terminal 900 can further include a power supply (such as a battery) for supplying power to each component, and the power supply can be logically connected to the processor 910 through a power management system, so as to realize functions such as management of charging, discharging, and power consumption management through the power management system. The terminal structure shown in Fig. 9 does not constitute a limitation on the terminal, and the terminal can include more or fewer components than those shown, or combine certain components, or different component arrangements, which are not described here again.

[0368] It should be understood that in the embodiments of the present application, the input unit 904 can include a graphics processor (GPU) 9041 and a microphone 9042. The graphics processor 9041 processes image data of a still picture or a video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The display unit 906 can include a display panel 9061, which can be configured in the form of a liquid crystal display, an organic light-emitting diode, etc. 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 can include two parts of a touch detection device and a touch controller. The other input devices 9072 can include, but are not limited to, a physical keyboard, function keys (such as volume control keys, on-off keys, etc.), trackballs, mice, joysticks, etc., which are not described here again.

[0369] In the embodiments of the present application, the radio frequency unit 901 can transmit the downlink data received from the network side device to the processor 910 for processing. In addition, the radio frequency unit 901 can send uplink data to the network side device. Generally, the radio frequency unit 901 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc.

[0370] The memory 909 can be used to store software programs or instructions and various data. The memory 909 can mainly include a first storage area storing programs or instructions and a second storage area storing data, wherein the first storage area can store an operating system, application programs or instructions required by at least one function (such as a sound playing function, an image playing function, etc.), and the like. In addition, the memory 909 can include a volatile memory or a non-volatile memory. The non-volatile memory can be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a Random Access Memory (RAM), a Static RAM (SRAM), a Dynamic RAM (DRAM), a Synchronous DRAM (SDRAM), a Double Data Rate SDRAM (DDR SDRAM), an Enhanced SDRAM (ESDRAM), a Synch link DRAM (SLDRAM), and a Direct Rambus RAM (DRRAM). The memory 909 in the embodiments of the present application includes but is not limited to these and any other suitable types of memory.

[0371] The processor 910 can include one or more processing units; optionally, the processor 910 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and an application program, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It can be understood that the above-mentioned modem processor can also not be integrated into the processor 910.

[0372] The processor 910 is configured to obtain a first parameter; determine whether to predict the cell signal quality based on the first parameter, and obtain a predicted value.

[0373] Based on the terminal provided in the embodiments of the present application, the terminal obtains a first parameter, and 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 terminal can reduce the measurement of the cell signal quality, thereby reducing the measurement overhead of frequent measurement of the terminal.

[0374] The embodiment of the present application further provides a network side device, comprising a processor and a communication interface, the communication interface being used for configuring a first parameter for a terminal; the first parameter being used for the terminal to determine whether to predict a cell signal quality based on the first parameter and obtain a predicted value. The network side device embodiment corresponds to the network side device method embodiment, each implementation process and implementation manner of the method embodiment can be applied to the network side device embodiment and the same technical effects can be achieved.

[0375] FIG. 10 is a structural schematic diagram of the network side device provided by the embodiment of the present application. As shown in FIG. 10, the network side device 1000 comprises an antenna 1001, a radio frequency device 1002, a baseband device 1003, a processor 1004 and a memory 1005. The antenna 1001 is connected with the radio frequency device 1002. In the uplink direction, the radio frequency device 1002 receives information through the antenna 1001 and sends the received information to the baseband device 1003 for processing. In the downlink direction, the baseband device 1003 processes the information to be sent and sends it to the radio frequency device 1002, and the radio frequency device 1002 processes the received information and sends it out through the antenna 1001.

[0376] The method performed by the network side device in the above embodiment can be implemented in the baseband device 1003, which comprises a baseband processor.

[0377] The baseband device 1003 may, for example, comprise at least one baseband board, and a plurality of chips are arranged on the baseband board, as shown in FIG. 10. One of the chips is, for example, a baseband processor, which is connected with the memory 1005 through a bus interface to call the program in the memory 1005 and perform the network device operation shown in the above method embodiment.

[0378] The network side device may further comprise a network interface 1006, which is, for example, a Common Public Radio Interface (CPRI).

[0379] The network side device 1000 of the embodiment of the present application further comprises instructions or programs stored in the memory 1005 and executable on the processor 1004. The processor 1004 calls the instructions or programs in the memory 1005 to execute the method shown in FIG. 3 and achieve the same technical effects. To avoid repetition, the details are not described herein.

[0380] The embodiment of the present application further provides a communication system, comprising a terminal and a network side device. The terminal can be used to execute the steps of the prediction method on the terminal side as described above. The network side device can be used to execute the steps of the prediction method on the network side device side as described above.

[0381] The embodiment of the present application further provides a readable storage medium, which stores a program or instructions, and the program or instructions are executed by a processor to realize the processes of the above-mentioned prediction method embodiments and achieve the same technical effects. To avoid repetition, details are not described herein.

[0382] The processor is the processor in the terminal in the above-mentioned embodiments. The readable storage medium includes a computer readable storage medium, such as a computer readable only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc. In some examples, the readable storage medium can be a non-transitory readable storage medium.

[0383] The embodiment of the present application further provides a chip, which includes a processor and a communication interface, the communication interface is coupled with the processor, and the processor is used to run a program or instructions to realize the processes of the above-mentioned prediction method embodiments and achieve the same technical effects. To avoid repetition, details are not described herein.

[0384] It should be understood that the chip mentioned in the embodiment of the present application can also be referred to as a system chip, a system chip, a chip system or a system on chip, etc.

[0385] The embodiment of the present application further provides a computer program / program product, which is stored in a storage medium and is executed by at least one processor to realize the processes of the above-mentioned prediction method embodiments and achieve the same technical effects. To avoid repetition, details are not described herein.

[0386] It should be noted that in this document, the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that processes, methods, articles or devices including a series of elements not only include those elements, but also include other elements not explicitly listed, or include elements inherent to such processes, methods, articles or devices. Without more limitations, the element defined by the statement "including a" does not exclude the presence of additional identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to the order of performing the functions as shown or discussed, but can also include performing the functions in a substantially simultaneous manner or in a reverse order, for example, the described method can be performed in an order different from the described order, and various steps can be added, omitted or combined. In addition, the features described with reference to some examples can be combined in other examples.

[0387] Through the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned example methods can be realized by means of a computer software product and a necessary general hardware platform, and of course can also be realized by hardware. The computer software product is stored in a storage medium (such as a ROM / RAM, a magnetic disc, an optical disc, etc.), and includes a plurality of instructions for enabling a terminal or a network side device to execute the method described in each embodiment of the present application.

[0388] The embodiments of the present application are described above in combination with the drawings, but the present application is not limited to the specific embodiments described above, and the specific embodiments described above are merely illustrative rather than limiting, and those of ordinary skill in the art can make many forms of real-time manners under the inspiration of the present application without departing from the scope of the present application and the scope protected by the claims.

Claims

1. A prediction method comprising: The terminal obtains the first parameter; The terminal determines whether to predict the cell signal quality based on the first parameter to obtain a predicted value.

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

3. The prediction method according to claim 2, wherein: The terminal determines, according to the first parameter, whether to predict the cell signal quality, including at least one of the following: Determining, by the terminal, whether to predict the cell signal quality according to the first indication information; When the first parameter meets a preset condition, the terminal predicts the cell signal quality; When the first parameter does not meet a preset condition, the terminal measures the cell signal quality.

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

5. The prediction method according to any one of claims 1 to 4, wherein: The predicted value is obtained by the terminal based on the AI ​​model.

6. The prediction method according to any one of claims 3 to 5, wherein: In the case of predicting the cell signal quality, the method further includes any one of the following: When a target value determined based on a cell signal quality measurement value and a cell signal quality prediction value measured by the terminal is greater than the prediction accuracy threshold, the terminal sends prediction failure information to a network side device; When a 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 prediction failure information to a network-side device.

7. The prediction method according to claim 6, wherein: The prediction failure information includes at least one of the following: An identifier of an AI model used to predict the cell signal quality; cell identification; Causes of prediction failure.

8. The prediction method according to any one of claims 1 to 7, wherein: The method further comprises: The terminal receives first configuration information, where 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 cycle; Pattern bitmap.

9. The prediction method according to claim 8, wherein: The prediction of the cell signal quality or the first rule includes at least one of the following: The terminal predicts the cell signal quality at a target frequency; The terminal measures the cell signal quality at a portion of the target frequency points, and predicts the cell signal quality at another portion of the target frequency points; The terminal predicts the cell signal quality in 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 cell signal quality in the target beam; The terminal measures the cell signal quality in one part of the target beam and predicts the cell signal quality in another part of the target beam; Predicting, by the terminal, 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 cell signal quality based on the preset ratio.

10. The prediction method according to claim 8, wherein: The method further comprises: The terminal sends a first request to a network-side device according to the first parameter, where the first request is used to request the first configuration information.

11. The prediction method according to any one of claims 2 to 10, wherein: The method further comprises at least one of the following: The terminal performs measurement relaxation according to the predicted value; The terminal reports the predicted value.

12. The prediction method according to claim 11, wherein: The relaxation multiple of the measurement relaxation performed by the terminal according to the predicted value is determined based on at least one of the following: the maximum relaxation multiple configured by the network side device; and is obtained by reasoning with the AI ​​model used to predict the cell signal quality.

13. The prediction method according to claim 10, wherein: The first request includes at least one of the following: second indication information, used to indicate that a condition for the terminal to adopt the first rule to perform measurement relaxation is met; and third indication information, used to indicate a measurement gap required for measurement by the terminal.

14. A prediction method comprising: 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 based on the first parameter to obtain a predicted value.

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

16. The prediction method according to claim 14 or 15, wherein: The predicted value is obtained by the terminal based on the AI ​​model.

17. The prediction method according to any one of claims 14 to 16, wherein: The method further comprises: The network-side device sends first configuration information to the terminal, where 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 cycle; Pattern bitmap.

18. The prediction method according to claim 17, wherein: The first rule includes at least one of the following: The terminal predicts the cell signal quality at a target frequency; The terminal measures the cell signal quality at a portion of the target frequency points, and predicts the cell signal quality at another portion of the target frequency points; The terminal predicts the cell signal quality in 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 cell signal quality in the target beam; The terminal measures the cell signal quality in one part of the target beam and predicts the cell signal quality in another part of the target beam; Predicting, by the terminal, 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 cell signal quality based on the preset ratio.

19. The prediction method according to claim 17, wherein: The method further comprises: 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, wherein: The first request includes at least one of the following: second indication information, used to indicate that a condition for the terminal to adopt the first rule to perform measurement relaxation is met; and third indication information, used to indicate a measurement gap required for measurement by the terminal.

21. A prediction device comprising: An acquisition module, used for acquiring a first parameter; The determination module is used to determine whether to predict the cell signal quality based on the first parameter to obtain a predicted value.

22. The prediction device according to claim 21, wherein: The first parameter includes at least one of the following: 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 a network-side device allows the terminal to request configuration information for predicting the cell signal quality; Cell signal quality measurement value threshold; Cell signal quality measurement change threshold; The first duration represents the time length corresponding to the change in the cell signal quality measurement; Model parameters, representing parameters of an artificial intelligence (AI) model used to predict the cell signal quality; Cell signal quality prediction value threshold; Cell signal quality prediction change threshold; The second duration represents the time length corresponding to the predicted change in cell signal quality; Prediction accuracy threshold.

23. The prediction device according to claim 22, wherein: The determining module is specifically configured to: determining, according to the first indication information, whether to predict the cell signal quality; When the first parameter meets a preset condition, predicting the cell signal quality; When the first parameter does not meet a preset condition, measuring the cell signal quality.

24. The prediction device according to claim 23, wherein: The preset conditions include at least one of the following: A cell signal quality measurement value measured by the terminal is greater than or equal to the cell signal quality measurement value threshold; A decrease in a cell signal quality measurement value measured by the terminal within the first duration 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; The decrease in the cell signal quality prediction value inferred by the AI ​​model within the second time period is less than or equal to the cell signal quality prediction change threshold; A target value determined based on a cell signal quality measurement value measured by the terminal and a cell signal quality prediction value inferred by the AI ​​model is less than or equal to the prediction accuracy threshold; A 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 comprising: A configuration module, configured to configure a first parameter for the terminal; The first parameter is used by the terminal to determine whether to predict the cell signal quality based on the first parameter to obtain a predicted value.

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

27. The prediction device according to claim 25 or 26, wherein: The predicted value is obtained by the terminal based on the AI ​​model.

28. The prediction device according to any one of claims 25 to 27, wherein: The prediction device further comprises: A first sending module is configured to send first configuration information to the terminal, where 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 cycle; Pattern bitmap.

29. A terminal comprising a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the prediction method according to any one of claims 1 to 13 are implemented.

30. A network-side device comprising a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the prediction method according to any one of claims 14 to 20 are implemented.

31. A chip comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to execute a program or instruction to implement the prediction method according to any one of claims 1 to 13, or to implement the steps of the prediction method according to any one of claims 14 to 20.

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