Monitoring method and apparatus, device, medium and program product

Abstract

The present disclosure provides a monitoring method and apparatus, a device, a medium and a program product, which aim to solve the problem in the related art whereby terminal switching performance cannot be ensured. The monitoring method is applied to a network device. The method comprises: performing function and / or model performance monitoring on first information, wherein the first information comprises at least one of radio resource management (RRM) measurement prediction information, failure event prediction information and measurement event prediction information.

Description

Monitoring methods, devices, equipment, media and procedures products

[0001] Cross-references to related applications

[0002] This disclosure claims priority to Chinese Patent Application No. 202411515907.1, filed in China on October 29, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to the field of communication technology, and in particular to a monitoring method, apparatus, device, medium, and program product. Background Technology

[0004] Mobility optimization is a typical scenario combining 5G technology and artificial intelligence (AI). Specific prediction scenarios can be divided into two main categories: predicting the movement trajectory of user equipment (UE); and predicting UE radio resource management (RRM) measurements, handover failures (HOF) / radio link failures (RLF), or measurement events. Analysis of these two AI-based mobility prediction scenarios reveals that the interaction between prediction and feedback information is more complex in scenarios involving RRM measurements, HOF / RLF, or measurement events. Especially when the AI / machine learning (ML) model is located on the terminal side, the UE can use the AI / ML model to predict its own RRM measurements, HOF / RLF, and measurement events. The prediction information reported by the UE to the network equipment varies depending on the type of prediction. However, the network devices did not evaluate the prediction accuracy for each type of prediction information, which resulted in the inability to guarantee terminal handover performance. Summary of the Invention

[0005] This disclosure provides a monitoring method, apparatus, device, medium, and program product to address the problem of inability to guarantee terminal switching performance in related technologies.

[0006] To solve the above-mentioned technical problems, this disclosure is implemented as follows:

[0007] In a first aspect, embodiments of this disclosure provide a monitoring method applied to a network device, the method comprising:

[0008] The first information is used for functional and / or model performance monitoring, and the first information includes at least one of Radio Resource Management (RRM) measurement prediction information, failure event prediction information, and measurement event prediction information.

[0009] Optionally, the method further includes:

[0010] The receiving terminal sends a first message, which includes the first information.

[0011] Optionally, the functional and / or model performance monitoring of the first information includes:

[0012] Statistical performance index information is collected, and based on the performance index information, it is determined whether the prediction accuracy meets the performance requirements;

[0013] If it is determined that the prediction accuracy does not meet the preset requirements, a second message is sent to the terminal, the second message being used to instruct the terminal to adjust the mobility prediction function and / or model.

[0014] Optionally, the method further includes:

[0015] A first mobility decision is generated based on the first information, and the first mobility decision is used to determine the target handover cell of the terminal.

[0016] A third message is sent to the terminal, the third message being used to instruct the target cell to be switched.

[0017] Optionally, generating a first mobility decision based on the first information includes:

[0018] Based on the RRM measurement prediction information in the first information, compare the RRM measurement prediction results of the serving cell and the candidate target cell within the first preset time period;

[0019] If the RRM measurement prediction result of the candidate target cell is greater than or equal to the RRM measurement prediction result of the serving cell within the first preset time period, or if the RRM measurement prediction result of the candidate target cell is greater than or equal to a first preset value within the first preset time period, a first mobility decision is determined. The first mobility decision is used to determine the candidate target cell as the target handover cell.

[0020] Optionally, generating a first mobility decision based on the first information includes:

[0021] Based on the failure event prediction information in the first information, calculate the time elapsed until the expected occurrence of the failure event;

[0022] If the duration is greater than the cell handover preparation time, a first mobility decision is generated.

[0023] Optionally, generating a first mobility decision based on the first information includes:

[0024] Based on the measurement event prediction information in the first information, determine whether the terminal will experience a failure event;

[0025] If it is determined that a failure event will occur at the terminal, a first mobility decision is generated.

[0026] Optionally, the statistical performance index information, and determining whether the prediction accuracy meets the performance requirements based on the performance index information, includes:

[0027] The performance indicators within the second preset time period are statistically analyzed to obtain the first performance indicator value, and the performance indicator information includes the first performance indicator value.

[0028] If the first performance index value is less than the second preset value, and the first mobility decision is based on the predicted value, it is determined that the prediction accuracy does not meet the performance requirements.

[0029] Optionally, the statistical performance index information, and determining whether the prediction accuracy meets the performance requirements based on the performance index information, includes:

[0030] A second mobility decision is generated based on the predicted values ​​in the RRM measurement and prediction information;

[0031] A third mobility decision is generated based on the measured values ​​in the RRM measurement prediction information;

[0032] The second performance index value corresponding to the second mobility decision and the third performance index value corresponding to the third mobility decision are statistically analyzed. The performance index information includes the second performance index value and the third performance index value.

[0033] If the difference between the second performance index value and the third performance index value is greater than a third preset value, it is determined that the prediction accuracy does not meet the performance requirements.

[0034] Optionally, the second message includes at least one of the following:

[0035] The third indication information is used to indicate the type of prediction information;

[0036] The fourth indication information is used to instruct the terminal to improve the accuracy of mobility prediction;

[0037] The fifth indication information is used to indicate the type of prediction information that the terminal uses to improve prediction accuracy;

[0038] The sixth instruction information is used to instruct the terminal to perform at least one of stopping mobility prediction, suspending mobility prediction, and deactivating mobility prediction;

[0039] The seventh indication information is used to indicate at least one of the prediction information types of the terminal: stopping mobility prediction, pausing mobility prediction, and deactivating mobility prediction.

[0040] The eighth indication information is used to indicate the predicted configuration information.

[0041] Optionally, the RRM measurement prediction information includes at least one of the following:

[0042] Wireless information quality index information, which includes measured values ​​and predicted values;

[0043] The first indication information is used to indicate that the wireless information quality index information is a measured value or a predicted value;

[0044] The second indication information is used to indicate the reduction of the corresponding time-domain, frequency-domain, or spatial-domain pattern in the measurement.

[0045] The terminal's prediction-related information;

[0046] Prediction confidence, prediction accuracy, or prediction precision;

[0047] The time or period in which the predicted value in the wireless information quality index is generated;

[0048] The predicted time or time period corresponding to the predicted value in the wireless information quality index information.

[0049] Optionally, the failure event prediction information includes at least one of the following:

[0050] The expected time or period of occurrence of the failure event; the event type of the failure event; the location information of the failure event; the RRM measurement information corresponding to the failure event; and the time or period of generation of the failure event prediction information.

[0051] Optionally, the measurement event prediction information includes at least one of the following:

[0052] The expected time or period for the measurement event to be met; the type of the measurement event; the time or period for the generation of the measurement event prediction information.

[0053] Secondly, embodiments of this disclosure provide a monitoring method applied to a terminal, the method comprising:

[0054] Send a first message to the network device, the first message including at least one of Radio Resource Management (RRM) measurement prediction information, failure event prediction information, and measurement event prediction information;

[0055] The first message is used by the network device to perform functional and / or model performance monitoring.

[0056] Optionally, the method further includes:

[0057] The terminal receives a second message sent by the network device, the second message being used to instruct the terminal to adjust its mobility prediction function and / or model.

[0058] Optionally, the second message includes at least one of the following:

[0059] The third indication information is used to indicate the type of prediction information;

[0060] The fourth indication information is used to instruct the terminal to improve the prediction accuracy;

[0061] The fifth indication information is used to indicate the type of prediction information that the terminal uses to improve prediction accuracy;

[0062] The sixth instruction information is used to instruct the terminal to perform at least one of stopping mobility prediction, suspending mobility prediction, and deactivating mobility prediction;

[0063] The seventh indication information is used to indicate at least one of the prediction information types of the terminal: stopping mobility prediction, pausing mobility prediction, and deactivating mobility prediction.

[0064] The eighth indication information is used to indicate the predicted configuration information.

[0065] Optionally, the method further includes:

[0066] Adjust the configuration information and / or model input information for mobility prediction based on the second message;

[0067] Alternatively, the type of prediction information that improves prediction accuracy can be determined based on the second message;

[0068] Alternatively, the type of prediction information to stop, pause, or deactivate prediction can be determined based on the second message.

[0069] Optionally, the method further includes:

[0070] The terminal receives a third message sent by the network device, the third message being used to indicate the target handover cell.

[0071] Optionally, the RRM measurement prediction information includes at least one of the following:

[0072] Wireless information quality index information, which includes measured values ​​and predicted values;

[0073] The first indication information is used to indicate that the wireless information quality index information is a measured value or a predicted value;

[0074] The second indication information is used to indicate the reduction of the corresponding time-domain, frequency-domain, or spatial-domain pattern in the measurement.

[0075] The terminal's prediction-related information;

[0076] Prediction confidence, prediction accuracy, or prediction precision;

[0077] The time or period in which the predicted value in the wireless information quality index is generated;

[0078] The predicted time or time period corresponding to the predicted value in the wireless information quality index information.

[0079] Optionally, the failure event prediction information includes at least one of the following:

[0080] The expected time or period of occurrence of the failure event; the event type of the failure event; the location information of the failure event; the RRM measurement information corresponding to the failure event; and the time or period of generation of the failure event prediction information.

[0081] Optionally, the measurement event prediction information includes at least one of the following:

[0082] The expected time or period for the measurement event to be met; the type of the measurement event; the time or period for the generation of the measurement event prediction information.

[0083] Thirdly, embodiments of this disclosure provide a monitoring device, including:

[0084] A monitoring module is used to perform functional and / or model performance monitoring on first information, the first information including at least one of radio resource management (RRM) measurement prediction information, failure event prediction information, and measurement event prediction information.

[0085] Optionally, the device further includes:

[0086] The first receiving module is used to receive a first message sent by the terminal, the first message including the first information.

[0087] Optionally, the monitoring module includes:

[0088] A statistical unit is used to collect statistical performance index information and determine whether the prediction accuracy meets the performance requirements based on the performance index information.

[0089] The sending unit is configured to send a second message to the terminal when it is determined that the prediction accuracy does not meet the preset requirements. The second message is used to instruct the terminal to adjust the mobility prediction function and / or model.

[0090] Optionally, the device further includes:

[0091] A generation module is used to generate a first mobility decision based on the first information, wherein the first mobility decision is used to determine the target handover cell of the terminal;

[0092] The sending module is used to send a third message to the terminal, the third message being used to instruct the target cell to switch.

[0093] Optionally, the generation module includes:

[0094] The comparison unit is used to compare the RRM measurement prediction results of the serving cell and the candidate target cell within a first preset time period based on the RRM measurement prediction information in the first information.

[0095] The determining unit is configured to determine a first mobility decision when the RRM measurement prediction result of the candidate target cell is greater than or equal to the RRM measurement prediction result of the serving cell within the first preset time period, or when the RRM measurement prediction result of the candidate target cell is greater than or equal to a first preset value within the first preset time period. The first mobility decision is used to determine the candidate target cell as the target handover cell.

[0096] Optionally, the generation module includes:

[0097] The calculation unit is used to calculate the time remaining until the expected occurrence of the failure event based on the failure event prediction information in the first information;

[0098] The first generation unit is configured to generate a first mobility decision when the duration is greater than the cell handover preparation time.

[0099] Optionally, the generation module includes:

[0100] The judgment unit is used to determine whether the terminal will experience a failure event based on the measurement event prediction information in the first information;

[0101] The second generation unit is used to generate a first mobility decision when it is determined that a failure event will occur in the terminal.

[0102] Optionally, the statistical unit is specifically used for:

[0103] The performance indicators within the second preset time period are statistically analyzed to obtain the first performance indicator value, and the performance indicator information includes the first performance indicator value.

[0104] If the first performance index value is less than the second preset value, and the first mobility decision is based on the predicted value, it is determined that the prediction accuracy does not meet the performance requirements.

[0105] Optionally, the statistical unit is specifically used for:

[0106] A second mobility decision is generated based on the predicted values ​​in the RRM measurement and prediction information;

[0107] A third mobility decision is generated based on the measured values ​​in the RRM measurement prediction information;

[0108] The second performance index value corresponding to the second mobility decision and the third performance index value corresponding to the third mobility decision are statistically analyzed. The performance index information includes the second performance index value and the third performance index value.

[0109] If the difference between the second performance index value and the third performance index value is greater than a third preset value, it is determined that the prediction accuracy does not meet the performance requirements.

[0110] Optionally, the second message includes at least one of the following:

[0111] The third indication information is used to indicate the type of prediction information;

[0112] The fourth indication information is used to instruct the terminal to improve the accuracy of mobility prediction;

[0113] The fifth indication information is used to indicate the type of prediction information that the terminal uses to improve prediction accuracy;

[0114] The sixth instruction information is used to instruct the terminal to perform at least one of stopping mobility prediction, suspending mobility prediction, and deactivating mobility prediction;

[0115] The seventh indication information is used to indicate at least one of the prediction information types of the terminal: stopping mobility prediction, pausing mobility prediction, and deactivating mobility prediction.

[0116] The eighth indication information is used to indicate the predicted configuration information.

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

[0118] Wireless information quality index information, which includes measured values ​​and predicted values;

[0119] The first indication information is used to indicate that the wireless information quality index information is a measured value or a predicted value;

[0120] The second indication information is used to indicate the reduction of the corresponding time-domain, frequency-domain, or spatial-domain pattern in the measurement.

[0121] The terminal's prediction-related information;

[0122] Prediction confidence, prediction accuracy, or prediction precision;

[0123] The time or period in which the predicted value in the wireless information quality index is generated;

[0124] The predicted time or time period corresponding to the predicted value in the wireless information quality index information.

[0125] Optionally, the failure event prediction information includes at least one of the following:

[0126] The expected time or period of occurrence of the failure event; the event type of the failure event; the location information of the failure event; the RRM measurement information corresponding to the failure event; and the time or period of generation of the failure event prediction information.

[0127] Optionally, the measurement event prediction information includes at least one of the following:

[0128] The expected time or period for the measurement event to be met; the type of the measurement event; the time or period for the generation of the measurement event prediction information.

[0129] Fourthly, embodiments of this disclosure provide a network device, including a processor, the processor being used for:

[0130] The first information is used for functional and / or model performance monitoring, and the first information includes at least one of Radio Resource Management (RRM) measurement prediction information, failure event prediction information, and measurement event prediction information.

[0131] Optionally, the network device further includes a transceiver, the transceiver being used for:

[0132] The receiving terminal sends a first message, which includes the first information.

[0133] Optionally, the processor is specifically used for:

[0134] Statistical performance index information is collected, and based on the performance index information, it is determined whether the prediction accuracy meets the performance requirements;

[0135] If it is determined that the prediction accuracy does not meet the preset requirements, a second message is sent to the terminal, the second message being used to instruct the terminal to adjust the mobility prediction function and / or model.

[0136] Optionally, the processor is further configured to:

[0137] A first mobility decision is generated based on the first information, and the first mobility decision is used to determine the target handover cell of the terminal.

[0138] The transceiver is also used to send a third message to the terminal, the third message being used to instruct the target cell to switch.

[0139] Optionally, the processor is specifically used for:

[0140] Based on the RRM measurement prediction information in the first information, compare the RRM measurement prediction results of the serving cell and the candidate target cell within the first preset time period;

[0141] If the RRM measurement prediction result of the candidate target cell is greater than or equal to the RRM measurement prediction result of the serving cell within the first preset time period, or if the RRM measurement prediction result of the candidate target cell is greater than or equal to a first preset value within the first preset time period, a first mobility decision is determined. The first mobility decision is used to determine the candidate target cell as the target handover cell.

[0142] Optionally, the processor is specifically used for:

[0143] Based on the failure event prediction information in the first information, calculate the time elapsed until the expected occurrence of the failure event;

[0144] If the duration is greater than the cell handover preparation time, a first mobility decision is generated.

[0145] Optionally, the processor is specifically used for:

[0146] Based on the measurement event prediction information in the first information, determine whether the terminal will experience a failure event;

[0147] If it is determined that a failure event will occur at the terminal, a first mobility decision is generated.

[0148] Optionally, the processor is specifically used for:

[0149] The performance indicators within the second preset time period are statistically analyzed to obtain the first performance indicator value, and the performance indicator information includes the first performance indicator value.

[0150] If the first performance index value is less than the second preset value, and the first mobility decision is based on the predicted value, it is determined that the prediction accuracy does not meet the performance requirements.

[0151] Optionally, the processor is specifically used for:

[0152] A second mobility decision is generated based on the predicted values ​​in the RRM measurement and prediction information;

[0153] A third mobility decision is generated based on the measured values ​​in the RRM measurement prediction information;

[0154] The second performance index value corresponding to the second mobility decision and the third performance index value corresponding to the third mobility decision are statistically analyzed. The performance index information includes the second performance index value and the third performance index value.

[0155] If the difference between the second performance index value and the third performance index value is greater than a third preset value, it is determined that the prediction accuracy does not meet the performance requirements.

[0156] Optionally, the second message includes at least one of the following:

[0157] The third indication information is used to indicate the type of prediction information;

[0158] The fourth indication information is used to instruct the terminal to improve the accuracy of mobility prediction;

[0159] The fifth indication information is used to indicate the type of prediction information that the terminal uses to improve prediction accuracy;

[0160] The sixth instruction information is used to instruct the terminal to perform at least one of stopping mobility prediction, suspending mobility prediction, and deactivating mobility prediction;

[0161] The seventh indication information is used to indicate at least one of the prediction information types of the terminal: stopping mobility prediction, pausing mobility prediction, and deactivating mobility prediction.

[0162] The eighth indication information is used to indicate prediction configuration information. Optionally, the RRM measurement prediction information includes at least one of the following:

[0163] Wireless information quality index information, which includes measured values ​​and predicted values;

[0164] The first indication information is used to indicate that the wireless information quality index information is a measured value or a predicted value;

[0165] The second indication information is used to indicate the reduction of the corresponding time-domain, frequency-domain, or spatial-domain pattern in the measurement.

[0166] The terminal's prediction-related information;

[0167] Prediction confidence, prediction accuracy, or prediction precision;

[0168] The time or period in which the predicted value in the wireless information quality index is generated;

[0169] The predicted time or time period corresponding to the predicted value in the wireless information quality index information.

[0170] Optionally, the failure event prediction information includes at least one of the following:

[0171] The expected time or period of occurrence of the failure event; the event type of the failure event; the location information of the failure event; the RRM measurement information corresponding to the failure event; and the time or period of generation of the failure event prediction information.

[0172] Optionally, the measurement event prediction information includes at least one of the following:

[0173] The expected time or period for the measurement event to be met; the type of the measurement event; the time or period for the generation of the measurement event prediction information.

[0174] Fifthly, embodiments of this disclosure provide a monitoring device, comprising:

[0175] A sending module is configured to send a first message to a network device, the first message including at least one of Radio Resource Management (RRM) measurement prediction information, failure event prediction information, and measurement event prediction information;

[0176] The first message is used by the network device to perform functional and / or model performance monitoring.

[0177] Optionally, the device further includes:

[0178] The second receiving module is used to receive a second message sent by the network device, the second message being used to instruct the terminal to adjust the mobility prediction function and / or model.

[0179] Optionally, the second message includes at least one of the following:

[0180] The third indication information is used to indicate the type of prediction information;

[0181] The fourth indication information is used to instruct the terminal to improve the prediction accuracy;

[0182] The fifth indication information is used to indicate the type of prediction information that the terminal uses to improve prediction accuracy;

[0183] The sixth instruction information is used to instruct the terminal to perform at least one of stopping mobility prediction, suspending mobility prediction, and deactivating mobility prediction;

[0184] The seventh indication information is used to indicate at least one of the prediction information types of the terminal: stopping mobility prediction, pausing mobility prediction, and deactivating mobility prediction.

[0185] The eighth indication information is used to indicate the predicted configuration information. Optionally, the device further includes:

[0186] The adjustment module is used to adjust the configuration information and / or model input information of mobility prediction based on the second message;

[0187] Alternatively, the first determining module is used to determine the type of prediction information that improves prediction accuracy based on the second message;

[0188] Alternatively, the second determining module is used to determine the type of prediction information—stop prediction, pause prediction, or deactivate prediction—based on the second message.

[0189] Optionally, the method further includes:

[0190] The third receiving module is used to receive a third message sent by the network device, the third message being used to indicate the target handover cell of the terminal.

[0191] Optionally, the RRM measurement prediction information includes at least one of the following:

[0192] Wireless information quality index information, which includes measured values ​​and predicted values;

[0193] The first indication information is used to indicate that the wireless information quality index information is a measured value or a predicted value;

[0194] The second indication information is used to indicate the reduction of the corresponding time-domain, frequency-domain, or spatial-domain pattern in the measurement.

[0195] The terminal's prediction-related information;

[0196] Prediction confidence, prediction accuracy, or prediction precision;

[0197] The time or period in which the predicted value in the wireless information quality index is generated;

[0198] The predicted time or time period corresponding to the predicted value in the wireless information quality index information.

[0199] Optionally, the failure event prediction information includes at least one of the following:

[0200] The expected time or period of occurrence of the failure event; the event type of the failure event; the location information of the failure event; the RRM measurement information corresponding to the failure event; and the time or period of generation of the failure event prediction information.

[0201] Optionally, the measurement event prediction information includes at least one of the following:

[0202] The expected time or period for the measurement event to be met; the type of the measurement event; the time or period for the generation of the measurement event prediction information.

[0203] Sixthly, embodiments of this disclosure provide a terminal, including a transceiver, the transceiver being used for:

[0204] Send a first message to the network device, the first message including at least one of Radio Resource Management (RRM) measurement prediction information, failure event prediction information, and measurement event prediction information;

[0205] The first message is used by the network device to perform functional and / or model performance monitoring.

[0206] Optionally, the transceiver is further used for:

[0207] The terminal receives a second message sent by the network device, the second message being used to instruct the terminal to adjust its mobility prediction function and / or model.

[0208] Optionally, the second message includes at least one of the following:

[0209] The third indication information is used to indicate the type of prediction information;

[0210] The fourth indication information is used to instruct the terminal to improve the prediction accuracy;

[0211] The fifth indication information is used to indicate the type of prediction information that the terminal uses to improve prediction accuracy;

[0212] The sixth instruction information is used to instruct the terminal to perform at least one of stopping mobility prediction, suspending mobility prediction, and deactivating mobility prediction;

[0213] The seventh indication information is used to indicate at least one of the prediction information types of the terminal: stopping mobility prediction, pausing mobility prediction, and deactivating mobility prediction.

[0214] The eighth indication information is used to indicate predicted configuration information. Optionally, the terminal further includes a processor, the processor being used to:

[0215] Adjust the configuration information and / or model input information for mobility prediction based on the second message;

[0216] Alternatively, the type of prediction information that improves prediction accuracy can be determined based on the second message;

[0217] Alternatively, the type of prediction information to stop, pause, or deactivate prediction can be determined based on the second message.

[0218] Optionally, the transceiver is further used for:

[0219] The terminal receives a third message sent by the network device, the third message being used to indicate the target handover cell.

[0220] Optionally, the RRM measurement prediction information includes at least one of the following:

[0221] Wireless information quality index information, which includes measured values ​​and predicted values;

[0222] The first indication information is used to indicate that the wireless information quality index information is a measured value or a predicted value;

[0223] The second indication information is used to indicate the reduction of the corresponding time-domain, frequency-domain, or spatial-domain pattern in the measurement.

[0224] The terminal's prediction-related information;

[0225] Prediction confidence, prediction accuracy, or prediction precision;

[0226] The time or period in which the predicted value in the wireless information quality index is generated;

[0227] The predicted time or time period corresponding to the predicted value in the wireless information quality index information.

[0228] Optionally, the failure event prediction information includes at least one of the following:

[0229] The expected time or period of occurrence of the failure event; the event type of the failure event; the location information of the failure event; the RRM measurement information corresponding to the failure event; and the time or period of generation of the failure event prediction information.

[0230] Optionally, the measurement event prediction information includes at least one of the following:

[0231] The expected time or period for the measurement event to be met; the type of the measurement event; the time or period for the generation of the measurement event prediction information.

[0232] In a seventh aspect, embodiments of this disclosure provide an electronic device, including: a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, it implements the steps of the monitoring method as described in the first aspect above; or, when the program is executed by the processor, it implements the steps of the monitoring method as described in the second aspect above.

[0233] Eighthly, embodiments of this disclosure provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the monitoring method as described in the first aspect above; or, when executed by a processor, the computer program implements the steps of the monitoring method as described in the second aspect above.

[0234] In a ninth aspect, embodiments of this disclosure provide a computer program product, including computer instructions that, when executed by a processor, implement the steps of the monitoring method as described in the first aspect above; or, when executed by a processor, the computer instructions implement the steps of the monitoring method as described in the second aspect above.

[0235] In this embodiment of the disclosure, the above-mentioned monitoring method is applied to a network device. Based on at least one of the first information, including Radio Resource Management (RRM) measurement prediction information, failure event prediction information, and measurement event prediction information, functional and / or model performance monitoring is performed. This allows the predictive performance of the terminal to be monitored in real time based on the first information, so as to avoid problems such as the decline in terminal handover performance due to insufficient prediction accuracy, thereby improving the cell handover performance of the terminal and the user experience. Attached Figure Description

[0236] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments of this disclosure will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0237] Figure 1 is a schematic diagram of an AI / ML model functional framework provided in an embodiment of this disclosure;

[0238] Figure 2 is a flowchart of one of the monitoring methods provided in the embodiments of this disclosure;

[0239] Figure 3 is a second flowchart of a monitoring method provided in an embodiment of this disclosure;

[0240] Figure 4 is a schematic diagram of one of the structures of a monitoring device provided in an embodiment of this disclosure;

[0241] Figure 5 is a schematic diagram of the structure of a network device provided in an embodiment of this disclosure;

[0242] Figure 6 is a second schematic diagram of the structure of a monitoring device provided in an embodiment of this disclosure;

[0243] Figure 7 is a schematic diagram of the structure of a terminal provided in an embodiment of this disclosure. Detailed Implementation

[0244] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0245] For ease of understanding, the following describes some aspects related to the embodiments of this disclosure:

[0246] AI technology can extract patterns and features from massive amounts of data in complex scenarios, and then train and deduce models to achieve prediction and optimize decision-making. Therefore, it is increasingly gaining attention and application across various industries. 5G mobile communication systems are also actively exploring the use of AI technology to build intelligent wireless access networks, addressing the problems of traditional methods such as error-proneness, high complexity, and inability to model complex issues, thereby accelerating network automation and intelligence and improving user experience.

[0247] The 5G access network introduces AI / ML models, and the general functional framework is shown in Figure 1. Data Collection: provides input data for model training and model inference; Model Training: responsible for training, testing, and validating the AI / ML model, and generating performance metrics for the model; Model Inference: provides the inference output (prediction or decision) of the AI / ML model, and can provide Model Performance Feedback to the Model Training module; Actor: initiates corresponding actions after receiving the inference output (prediction or decision) from the Model Inference module.

[0248] Mobility optimization is one of the typical scenarios combining 5G and AI technologies. Specific prediction scenarios can be divided into the following two main categories:

[0249] (1) Predicting UE movement trajectory: The source node uses the AI / ML model inference module to predict the movement trajectory of a specific UE and sends the predicted UE movement trajectory to the target node. The target node can directly use the prediction information and reserve transmission resources in advance. On the other hand, the source node also needs to obtain the actual movement trajectory or UE performance of the UE from the target node as feedback information to monitor the performance of the AI / ML model output, retrain the AI / ML model in a timely manner, and continuously improve the prediction and decision-making performance of the AI / ML model.

[0250] (2) Predicting UE RRM measurements, HOF / RLF, or measurement events: The AI / ML model can be located on the network side or the terminal side, and the prediction information can assist network nodes in making decisions on UE cell handover. For example, the network node can use the AI / ML model Inference module to predict the UE's RRM measurements based on the measurement results reported by the UE; or, the UE can use the AI / ML model to predict its own RRM measurements, HOF / RLF, measurement events, etc., based on the measurement results and terminal location data. On the one hand, prediction can reduce the measurements that need to be performed on the terminal side, and on the other hand, the prediction information reported to the network can also assist the network node in making decisions on UE cell handover, thereby improving the UE's handover performance.

[0251] Analysis of the two AI-based mobility prediction scenarios reveals that the interaction between prediction and feedback information is more complex in scenarios involving predictions of RRM measurements, HOF / RLF, or measurement events. Particularly when the AI / ML model resides on the terminal side, the UE can use this model to predict its own RRM measurements, HOF / RLF, and measurement events. However, the prediction information reported by the UE to the network device differs depending on the type of prediction. Furthermore, for each type of prediction, the network cannot directly feed back the same type of real information to the UE for training and performance optimization of the terminal-side AI / ML model. For example, for RRM measurements, the UE typically measures the downlink reference signal and reports the RRM measurement results to the network side for mobility management and network resource management; the network side cannot directly obtain the RRM measurement results. Similarly, for RRM measurement predictions where the AI / ML model resides on the terminal, the network side cannot directly obtain the actual RRM measurement results to feed back to the terminal. On the other hand, from the current working framework of radio access networks, the behavior of terminals needs to be managed and controlled by the network side. Even if the AI / ML model is located on the terminal side, network equipment (such as Next Generation Radio Access Network (NG-RAN) nodes, i.e. base stations) still needs corresponding means to evaluate whether the prediction accuracy of the AI / ML model meets the needs of mobility management such as handover decisions, and promptly notify the terminal to adjust the AI / ML model prediction function or suspend / deactivate / de-enable the terminal's prediction behavior when the prediction accuracy does not meet the requirements, so as to ensure that the terminal handover performance is not affected.

[0252] Currently, there is no solution in the relevant technologies to support the monitoring of the mobility prediction performance of the AI / ML model on the terminal side. The lack of relevant mechanisms may lead to the following: the network side or the terminal side cannot timely assess whether the prediction accuracy of the model meets the needs of mobility management such as handover decisions made by the network side, especially whether the prediction made to reduce terminal measurements can ensure that the handover performance of the UE is not affected; in the absence of assessment of prediction accuracy, the network or the terminal cannot take timely measures to adjust the AI / ML model prediction function or suspend / deactivate / disable the prediction behavior of the terminal, thus failing to guarantee the handover performance of the terminal and affecting the user experience.

[0253] In this disclosure, the term "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0254] This disclosure presents a monitoring method, apparatus, device, medium, and program product to address the problem of inability to guarantee terminal switching performance in related technologies.

[0255] Referring to Figure 2, which is a flowchart of one of the monitoring methods provided in this disclosure embodiment, applied to a network device, the method includes the following steps:

[0256] Step 201: Perform functional and / or model performance monitoring on the first information, wherein the first information includes at least one of Radio Resource Management (RRM) measurement prediction information, failure event prediction information, and measurement event prediction information.

[0257] Specifically, the aforementioned Radio Resource Management (RRM) measurement prediction information may include radio information quality indicators, prediction accuracy, and prediction-related information of the terminal obtained by measurement or prediction. The aforementioned failure event prediction information may be related information obtained by predicting failure events, including the occurrence time, event type, and location information of the failure event. Specifically, the failure events may include Radio Link Failure (RLF) and Handover Failure (HOF) events; that is, the failure event prediction information may include RLF / HOF prediction information.

[0258] The aforementioned measurement event prediction information can be related information obtained by predicting measurement events. The aforementioned measurement events can be conditional events that trigger specific reports or operations, such as event A3: the signal quality of the candidate target cell is higher than the signal quality of the serving cell minus an offset, and event A5: the signal quality of the serving cell is lower than a certain threshold, while the signal quality of the candidate cell exceeds another different threshold.

[0259] It is understandable that mobility prediction on the terminal side can be a specific function or an independent model execution, such as a mobility prediction scenario where the AI / ML model is located on the terminal side (UE-sided model). Therefore, detecting the mobility prediction performance of the terminal can be either functional performance monitoring or model performance monitoring.

[0260] The network device involved in this disclosure can be a base station, which may include multiple cells providing services to terminals. Depending on the specific application, the base station may also be called an access point, or a device in the access network that communicates with wireless terminal devices through one or more sectors on the air interface, or other names. The network device can be used to exchange received air frames with Internet Protocol (IP) packets, acting as a router between the wireless terminal device and the rest of the access network, where the rest of the access network may include an Internet Protocol (IP) communication network. The network device can also coordinate the attribute management of the air interface. For example, the network device involved in this disclosure can be an evolved Node B (eNB or e-NodeB) in a long term evolution (LTE) system, a 5G base station (gNB) in a next generation system, or a Home evolved Node B (HeNB), relay node, femto, pico, network testing equipment, etc., and is not limited in this disclosure. In some network architectures, network devices may include centralized unit (CU) nodes and distributed unit (DU) nodes, which may also be geographically separated.

[0261] The terminal involved in the embodiments of this disclosure may be a device that provides voice and / or data connectivity to a user, a handheld device with wireless connectivity, or other processing devices connected to a wireless modem. The name of the terminal device may differ in different systems; for example, in a 5G system or a 6th Generation Mobile Communication Technology (6G) system, the terminal device may be called User Equipment (UE). The wireless terminal device may be a Universal Serial Bus (USB) storage device, other personal computer memory devices, and a dongle. It may also communicate with one or more core networks (CNs) via a Radio Access Network (RAN). The wireless terminal device may be a mobile terminal device, such as a mobile phone (or "cellular" phone) and a computer with a mobile terminal device. For example, it may be a portable, pocket-sized, handheld, computer-embedded, or vehicle-mounted mobile device that exchanges voice and / or data with the radio access network. Examples of such devices include Personal Communication Service (PCS) telephones, cordless phones, Session Initiated Protocol (SIP) phones, Wireless Local Loop (WLL) stations, Personal Digital Assistants (PDAs), personal computers, tablets, and Machine-type Communication (MTC) terminal devices. Wireless terminal devices can also be referred to as systems, subscriber units, subscriber stations, mobile stations, mobile devices, remote stations, access points, remote terminals, access terminals, user terminals, user agents, user devices, and wireless access devices and routers / modems that meet the limitations of this definition, but are not limited to these in the embodiments of this disclosure.

[0262] In this embodiment of the disclosure, the above-mentioned monitoring method is applied to a network device. Based on at least one of the first information, including Radio Resource Management (RRM) measurement prediction information, failure event prediction information, and measurement event prediction information, functional and / or model performance monitoring is performed. This allows the predictive performance of the terminal to be monitored in real time based on the first information, so as to avoid problems such as the decline in terminal handover performance due to insufficient prediction accuracy, thereby improving the cell handover performance of the terminal and the user experience.

[0263] Optionally, the method further includes:

[0264] The receiving terminal sends a first message, which includes the first information.

[0265] It should be noted that the first message mentioned above can be one or more messages. That is, the terminal can send at least one of the following to the network device through one or more messages: Radio Resource Management (RRM) measurement prediction information, failure event prediction information, and measurement event prediction information.

[0266] Optionally, the functional and / or model performance monitoring of the first information includes:

[0267] Statistical performance index information is collected, and based on the performance index information, it is determined whether the prediction accuracy meets the performance requirements;

[0268] If it is determined that the prediction accuracy does not meet the preset requirements, a second message is sent to the terminal, the second message being used to instruct the terminal to adjust the mobility prediction function and / or model.

[0269] Specifically, the aforementioned performance metrics can be statistically derived from cell handover performance and / or terminal performance and / or system performance, and can be used to monitor prediction accuracy. Specifically, the aforementioned cell handover performance can include at least one of the following: handover failure rate, for example, the ratio of the number of failed handovers to the total number of handovers; radio link failure performance metrics, for example, the average number of RLFs per second for a single UE; ping-pong handover rate, for example, the ratio of the number of ping-pongs to the number of successful handovers; unnecessary handover rates such as excessively short dwell times, for example, the ratio of the number of times a UE's dwell time in a cell is less than a certain threshold to the number of successful handovers. The aforementioned terminal performance can include, but is not limited to, UE downlink throughput, UE uplink throughput, packet latency, and packet loss rate. The aforementioned system performance can include, but is not limited to, system throughput and resource utilization.

[0270] It should be noted that the above-mentioned monitoring of functional and / or model performance can be carried out statistically after the terminal uses the predictive information to make mobility decisions.

[0271] In this embodiment, the above monitoring method uses statistical performance index information to determine whether the prediction accuracy meets the performance requirements. If the prediction accuracy does not meet the preset requirements, a second message is sent to the terminal. The second message is used to instruct the terminal to adjust the mobility prediction function and / or model, so that when the terminal prediction accuracy does not meet the preset requirements, it can be adjusted in time, thereby improving the terminal prediction performance and further ensuring cell handover performance.

[0272] Optionally, the method further includes:

[0273] A first mobility decision is generated based on the first information, and the first mobility decision is used to determine the target handover cell of the terminal.

[0274] A third message is sent to the terminal, the third message being used to instruct the target cell to be switched.

[0275] Specifically, the first mobility decision mentioned above may be the cell handover decision of the terminal, and the third message mentioned above may be a Radio Resource Control (RRC) reconfiguration message, that is, the network device may use relevant mechanisms to send an RRC reconfiguration message to the UE to indicate the target cell for handover.

[0276] In this embodiment, the monitoring method generates a first mobility decision based on the first information. The first mobility decision is used to determine the target handover cell of the terminal and send a third message to the terminal. The third message is used to indicate the target handover cell, so that the network device can determine the target handover cell of the terminal according to the first message, thereby further improving the cell handover performance of the terminal.

[0277] Optionally, generating a first mobility decision based on the first information includes:

[0278] Based on the RRM measurement prediction information in the first information, compare the RRM measurement prediction results of the serving cell and the candidate target cell within the first preset time period;

[0279] If the RRM measurement prediction result of the candidate target cell is greater than or equal to the RRM measurement prediction result of the serving cell within the first preset time period, or if the RRM measurement prediction result of the candidate target cell is greater than or equal to a first preset value within the first preset time period, a first mobility decision is determined. The first mobility decision is used to determine the candidate target cell as the target handover cell.

[0280] Specifically, the first preset time period can be a future moment or a certain period of time. For example, the observation window length can be preset, and the first preset time period can be a period of time within the observation window length. For example, the trigger time (Time-to-Trigger, TTT) duration in the measurement event configured for the UE by the current network can be used.

[0281] The aforementioned serving cell is the current serving cell of the terminal, and the aforementioned candidate target cell can be a candidate target cell determined for the terminal based on a handover decision algorithm in related technologies. It is understood that there can be one or more candidate target cells. The aforementioned first preset value can be a preset threshold value.

[0282] In this embodiment, the monitoring method determines a first mobility decision when the RRM measurement prediction result of the candidate target cell is greater than or equal to the RRM measurement prediction result of the serving cell within the first preset time period, or when the RRM measurement prediction result of the candidate target cell is greater than or equal to a first preset value within the first preset time period. The first mobility decision is used to determine the candidate target cell as the target handover cell. By using RRM measurement prediction information to optimize the handover decision, ping-pong handover can be effectively avoided, thereby reducing unnecessary handovers, reducing signaling overhead, and optimizing network resource utilization.

[0283] Optionally, generating a first mobility decision based on the first information includes:

[0284] Based on the failure event prediction information in the first information, calculate the time elapsed until the expected occurrence of the failure event;

[0285] If the duration is greater than the cell handover preparation time, a first mobility decision is generated.

[0286] It is understood that the aforementioned estimated time from the occurrence of the failure event is the time from the current moment to the estimated occurrence of the failure event, and the aforementioned cell handover preparation time can be the time required for the UE to smoothly hand over from one cell to another. If the aforementioned time is greater than the cell handover preparation time, it is determined that the UE will perform a handover operation, and the relevant handover mechanism will be used to perform the handover. Furthermore, the target cell for cell handover can be selected for the UE based on the RRM measurement prediction information of neighboring cells.

[0287] In this embodiment, the monitoring method performs terminal cell handover when the time elapsed between the predicted occurrence of the failure event and the cell handover preparation time is longer than the predicted failure event. This ensures that the cell handover is completed before the predicted failure event occurs, thereby reducing connection interruptions or data loss caused by the failure event and improving network robustness.

[0288] Optionally, generating a first mobility decision based on the first information includes:

[0289] Based on the measurement event prediction information in the first information, determine whether the terminal will experience a failure event;

[0290] If it is determined that a failure event will occur at the terminal, a first mobility decision is generated.

[0291] Specifically, based on the expected time or moment of the measurement event and other relevant information, it can be determined whether the UE will experience failure events such as RLF / HOF. If a failure event is predicted, the UE is deemed to be performing a handover operation ahead of schedule. It should be noted that the handover preparation execution time must ensure that the cell handover is completed before the failure event occurs. Furthermore, the target cell for cell handover can be selected for the UE based on the RRM measurement prediction information of neighboring cells.

[0292] In this embodiment, the monitoring method performs a terminal cell handover in advance when it is determined that a failure event will occur in the terminal, so as to ensure that the cell handover is completed before the predicted failure event occurs, thereby reducing connection interruption or data loss caused by the failure event and further improving the robustness of the network.

[0293] Optionally, the statistical performance index information, and determining whether the prediction accuracy meets the performance requirements based on the performance index information, includes:

[0294] The performance indicators within the second preset time period are statistically analyzed to obtain the first performance indicator value, and the performance indicator information includes the first performance indicator value.

[0295] If the first performance index value is less than the second preset value, and the first mobility decision is based on the predicted value, it is determined that the prediction accuracy does not meet the performance requirements.

[0296] Specifically, the aforementioned second preset time period can be a pre-set window length, which can be a fixed window or a sliding window. The aforementioned second preset value can be a preset threshold value, or it can be the difference between the statistical indicator value within the previous time window and the preset threshold value.

[0297] It should be noted that the first indication information in the RRM measurement prediction information reported by the UE can be used to indicate the measured value and the predicted value, and the RLF / HOF information and measurement event information in the failure event prediction information reported by the UE can be directly identified as the predicted value.

[0298] For example, the network uses a fixed window or a sliding window (with a pre-set window length) to statistically analyze performance metrics. If the statistical metrics within a certain time window differ significantly from those within previous time windows, for example, exceeding a certain threshold, the network combines the information reported by the UE to determine whether the handover decision is based on prediction information. If it is based on prediction information, the network determines that the prediction accuracy does not meet performance requirements. Alternatively, if a threshold is pre-set and the statistical metrics within a certain time window are below that threshold, the network combines the information reported by the UE to determine whether the handover decision is based on prediction information. If it is based on prediction information, the network determines that the prediction accuracy does not meet performance requirements.

[0299] In this embodiment, the above-mentioned monitoring method statistically analyzes the performance indicators within a second preset time period to obtain a first performance indicator value. The performance indicator information includes the first performance indicator value. When the first performance indicator value is less than a second preset value and the first mobility decision is based on the predicted value, it is determined that the prediction accuracy does not meet the performance requirements. In this case, the terminal can be instructed to adjust the mobility prediction function and / or model, thereby avoiding problems such as the decline in terminal handover performance due to insufficient prediction accuracy and ensuring cell handover performance.

[0300] Optionally, the statistical performance index information, and determining whether the prediction accuracy meets the performance requirements based on the performance index information, includes:

[0301] A second mobility decision is generated based on the predicted values ​​in the RRM measurement and prediction information;

[0302] A third mobility decision is generated based on the measured values ​​in the RRM measurement prediction information;

[0303] The second performance index value corresponding to the second mobility decision and the third performance index value corresponding to the third mobility decision are statistically analyzed. The performance index information includes the second performance index value and the third performance index value.

[0304] If the difference between the second performance index value and the third performance index value is greater than a third preset value, it is determined that the prediction accuracy does not meet the performance requirements.

[0305] Specifically, the network device statistically analyzes the performance indicators of handover decisions based on measured values ​​and predicted values ​​according to the information reported by the UE. If the statistical indicators of handover decisions based on predicted values ​​differ too much from those based on measured values ​​(e.g., exceeding a certain threshold), it is determined that the prediction accuracy does not meet the performance requirements.

[0306] It should be noted that the first indication information in the RRM measurement prediction information reported by the UE can be used to indicate the measured value and the predicted value, and the RLF / HOF information and measurement event information in the failure event prediction information reported by the UE can be directly identified as the predicted value.

[0307] In this embodiment, the above monitoring method uses network devices to statistically analyze the performance indicators of handover decisions based on measured and predicted values ​​according to the information reported by the UE, and determines whether the prediction accuracy meets the performance requirements. If the performance requirements are not met, the method can instruct the terminal to adjust its mobility prediction function and / or model, thereby avoiding problems such as decreased terminal handover performance due to insufficient prediction accuracy and ensuring cell handover performance. Optionally, the second message includes at least one of the following:

[0308] The third indication information is used to indicate the type of prediction information;

[0309] The fourth indication information is used to instruct the terminal to improve the accuracy of mobility prediction;

[0310] The fifth indication information is used to indicate the type of prediction information that the terminal uses to improve prediction accuracy;

[0311] The sixth instruction information is used to instruct the terminal to perform at least one of stopping mobility prediction, suspending mobility prediction, and deactivating mobility prediction;

[0312] The seventh indication information is used to indicate at least one of the prediction information types of the terminal: stopping mobility prediction, pausing mobility prediction, and deactivating mobility prediction.

[0313] The eighth indication information is used to indicate prediction configuration information. Specifically, the aforementioned third indication information can be used to indicate prediction information types with insufficient prediction accuracy or prediction information types that require improved accuracy, such as one or more of RRM measurement prediction information, failure event prediction information, and measurement event prediction information.

[0314] The aforementioned prediction configuration information can be prediction-related configuration information for cases where the prediction accuracy is insufficient. One approach is to specify the generation time or moment of the prediction information. This information can serve as an index to help the UE find the relevant configuration of the AI / ML model that generated the prediction information, as well as the relevant input information used for inference, facilitating the adjustment of the AI / ML model configuration to improve prediction accuracy. Another approach is to specify whether the prediction result uses measurement reduction indication information and / or the actual measurement reduction rate (e.g., Measurement Reduction Rate of Time (MRRT), Measurement Reduction Rate of Frequency (MRRF), Measurement Reduction Rate of Signal (MRRS)) and / or the time-frequency, frequency-domain, or spatial-domain pattern used for prediction. For example, the time-domain pattern can be actual measurement or sample points or time information used as input; the frequency-domain pattern can be actual measurement or frequency band or frequency point information used as input; and the spatial-domain pattern can be actual measurement or beam information used as input. A third approach is prediction-related configuration information, such as the observation / observation window duration and the prediction window. The information includes the window duration, L1 sampling period, measurement period, and filtering coefficients used in the prediction. This information can assist the UE in adjusting the configuration of the AI / ML model and the configuration of measurement reduction, thereby improving prediction accuracy.

[0315] Optionally, the RRM measurement prediction information includes at least one of the following:

[0316] Wireless information quality index information, which includes measured values ​​and predicted values;

[0317] The first indication information is used to indicate that the wireless information quality index information is a measured value or a predicted value;

[0318] The second indication information is used to indicate the reduction of the corresponding time-domain, frequency-domain, or spatial-domain pattern in the measurement.

[0319] The terminal's prediction-related information;

[0320] Prediction confidence, prediction accuracy, or prediction precision;

[0321] The time or period in which the predicted value in the wireless information quality index is generated;

[0322] The predicted time or time period corresponding to the predicted value in the wireless information quality index information.

[0323] Specifically, the aforementioned wireless information quality indicators can be various parameters of signal transmission quality in a wireless communication system, such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal to Interference plus Noise Ratio (SINR). Specifically, they can include measured and / or predicted values ​​of RSRP / RSRQ / SINR at the cell level or beam level. The measured values ​​refer to the actual measurement results of RSRP / RSRQ / SINR at the cell level or beam level for the current serving cell and / or neighboring cells; the predicted information refers to the predicted results of RSRP / RSRQ / SINR at the cell level or beam level for the current serving cell and / or neighboring cells obtained through time-frequency, frequency-domain, or spatial-domain prediction.

[0324] In this embodiment, the wireless information quality index information can be used to determine candidate target cells and avoid unnecessary handover processes such as ping-pong handover.

[0325] The aforementioned first indication information can be used to indicate whether the wireless information quality index information is a measured value or a predicted value. This indication information can take two forms: explicit and implicit. Explicit indication can use explicit bits, bitmaps, or information elements (IEs) to indicate which wireless information quality index information is obtained through measurement or prediction. Implicit indication can distinguish between measured and predicted results by including them in different IEs / fields or different messages.

[0326] In this embodiment, the purpose of the first indication information is that the network can know which data (measured value or predicted value) is used to make mobility decisions, and can separately count indicators such as handover performance to evaluate prediction accuracy.

[0327] The aforementioned second indication information can be used to indicate whether the predicted value of the wireless information quality index information is used for the purpose of measuring reduction and / or the actual amount of reduction and / or the time-frequency, frequency-domain, or spatial-domain pattern used to measure reduction.

[0328] In this embodiment, the purpose of providing the second indication information to the network device is that, when the accuracy of the prediction result can meet the mobility management requirements, the network can comprehensively consider the prediction situation of multiple terminals and consider whether to reduce the transmission of some measurement reference signals or transmit data on the original terminal measurement resources; when the accuracy of the prediction result cannot meet the mobility management requirements, the actual measurement reduction situation can help the network determine whether to notify the UE to adjust the measurement reduction configuration or directly suspend / deactivate / disable the prediction behavior of the terminal.

[0329] Specifically, the prediction-related information of the aforementioned terminal may include information related to the prediction method itself, such as UE power consumption and mobile speed, as well as auxiliary information.

[0330] Optionally, the failure event prediction information includes at least one of the following:

[0331] The expected time or period of occurrence of the failure event; the event type of the failure event; the location information of the failure event; the RRM measurement information corresponding to the failure event; and the time or period of generation of the failure event prediction information.

[0332] Specifically, the predicted time or period of occurrence of the aforementioned failure events can be in absolute time, such as Coordinated Universal Time (UTC), or in relative time, such as a period of time in the future from the time this prediction was generated. The event types of the aforementioned failure events can include HOF, RLF, etc.

[0333] The location information of the aforementioned failure event can be the cell ID of the predicted failure event or specific latitude and longitude information. In this embodiment, the location information of the failure event can assist the network in determining the scenario in which the failure occurred and adjusting the configuration of mobility-related parameters.

[0334] The RRM measurement information corresponding to the aforementioned failure event can refer to the predicted RRM measurement information at the expected time of the UE failure event, and can be represented by measurements such as RSRP / RSRQ / SINR. In this embodiment, the RRM measurement information corresponding to the aforementioned failure event can assist the network in determining the scenario in which the failure occurred and adjusting the configuration of mobility-related parameters.

[0335] Optionally, the measurement event prediction information includes at least one of the following:

[0336] The expected time or period for the measurement event to be met; the type of the measurement event; the time or period for the generation of the measurement event prediction information.

[0337] Specifically, the expected time or period for the aforementioned measurement event can be in absolute time, such as Coordinated Universal Time (UTC), or in relative time, such as a period of time in the future from the time this prediction was generated. The types of the aforementioned measurement events can include A3, A5, etc.

[0338] Referring to Figure 3, which is a second flowchart of a monitoring method provided in this embodiment of the present disclosure, applied to a terminal device, the method includes the following steps:

[0339] Step 301: Send a first message to the network device, the first message including at least one of Radio Resource Management (RRM) measurement prediction information, failure event prediction information, and measurement event prediction information;

[0340] The first message is used by the network device to perform functional and / or model performance monitoring.

[0341] It should be noted that this embodiment is an implementation of the terminal device corresponding to the embodiment shown in Figure 2. For the specific implementation, please refer to the relevant description in the embodiment shown in Figure 2. To avoid repetition, this embodiment will not be described again.

[0342] Optionally, the method further includes:

[0343] The terminal receives a second message sent by the network device, the second message being used to instruct the terminal to adjust its mobility prediction function and / or model.

[0344] Optionally, the second message includes at least one of the following:

[0345] The third indication information is used to indicate the type of prediction information;

[0346] The fourth indication information is used to instruct the terminal to improve the prediction accuracy;

[0347] The fifth indication information is used to indicate the type of prediction information that the terminal uses to improve prediction accuracy;

[0348] The sixth instruction information is used to instruct the terminal to perform at least one of stopping mobility prediction, suspending mobility prediction, and deactivating mobility prediction;

[0349] The seventh indication information is used to indicate at least one of the prediction information types of the terminal: stopping mobility prediction, pausing mobility prediction, and deactivating mobility prediction.

[0350] The eighth indication information is used to indicate the predicted configuration information. Optionally, the method further includes:

[0351] Adjust the configuration information and / or model input information for mobility prediction based on the second message;

[0352] Alternatively, the type of prediction information that improves prediction accuracy can be determined based on the second message;

[0353] Alternatively, the type of prediction information to stop, pause, or deactivate prediction can be determined based on the second message.

[0354] Specifically, the above-mentioned adjustment of mobility prediction configuration information and / or model input information based on the second message, or the determination of prediction information type to improve prediction accuracy based on the second message, can be that the terminal determines the prediction type that needs to improve prediction accuracy based on the third and / or fourth indication information in the second message, or based on the fifth indication information in the second message. Furthermore, the terminal can determine the relevant configuration parameters of the prediction information that needs to improve accuracy based on the time or period of prediction information generation or its own stored information, and make adjustments accordingly to improve prediction accuracy.

[0355] For example, the terminal-side AI / ML model can obtain the relevant configuration information used for prediction based on the generation time or time period index of the prediction information contained in the second message, or use the prediction-related configuration information contained in the second message as input information for the AI / ML model to retrain / iterate the AI / ML model and improve the prediction performance of the AI / ML model.

[0356] Furthermore, for cases where the type of prediction information that needs to improve prediction accuracy is RRM measurement prediction information, if it is a temporal domain prediction and is based on measurement reduction (specifically, it can be determined based on the measurement reduction information or stored information in the second message), then it is determined to reduce the number of prediction sampling points, that is, reduce the measurement reduction rate, and replace the predicted value with more measured values.

[0357] If it is a time-domain prediction but not a prediction based on measurement reduction, then it is determined to increase the observation window length, that is, increase the number of measured sampling points used for prediction, and / or shorten the prediction window length, that is, reduce the number of sampling points for prediction output, to avoid predicting measurement results for a longer period of time in the future;

[0358] If it is a spatial domain prediction, the previous prediction-related configuration and measurement reduction pattern can be determined based on the measurement reduction information or stored information in the second message, and the number of beams to be reduced can be determined, that is, the number of measured beams can be increased to replace the number of beams to be predicted.

[0359] If it is frequency domain prediction, determine whether to increase the amount of measured data for the different frequency points required for prediction, such as the number of neighboring cells measured, or adjust the different frequency points used for prediction, such as adjusting them to frequencies that have a better correlation with the frequency point to be predicted.

[0360] Furthermore, for cases where the prediction information type requiring improved prediction accuracy is failure event prediction information, the default is time-domain prediction. It can be determined to increase the observation window length or increase the amount of input data used for prediction, and / or shorten the prediction window length or reduce the time granularity of failure event prediction to avoid predicting failure events occurring over a longer period in the future.

[0361] Furthermore, for the type of prediction information that needs to improve prediction accuracy, which is measurement event prediction information, the default is time domain prediction. It is determined to increase the observation window length or increase the amount of input data used for prediction, and / or shorten the prediction window length or reduce the time granularity of measurement event prediction, and avoid predicting measurement events that meet the conditions for a longer period of time in the future.

[0362] Specifically, the prediction information type for determining whether to stop, pause, or deactivate prediction based on the second message can be determined by the terminal based on the third and / or sixth indication information in the second message, or based on the seventh indication information in the second message. The terminal then reverts to the actual measurement method and reports the RRM measurement prediction information, or stops the prediction failure event prediction information or the measurement event prediction information.

[0363] Optionally, the method further includes:

[0364] The terminal receives a third message sent by the network device, the third message being used to indicate the target handover cell.

[0365] Optionally, the RRM measurement prediction information includes at least one of the following:

[0366] Wireless information quality index information, which includes measured values ​​and predicted values;

[0367] The first indication information is used to indicate that the wireless information quality index information is a measured value or a predicted value;

[0368] The second indication information is used to indicate the reduction of the corresponding time-domain, frequency-domain, or spatial-domain pattern in the measurement.

[0369] The terminal's prediction-related information;

[0370] Prediction confidence, prediction accuracy, or prediction precision;

[0371] The time or period in which the predicted value in the wireless information quality index is generated;

[0372] The predicted time or time period corresponding to the predicted value in the wireless information quality index information.

[0373] Optionally, the failure event prediction information includes at least one of the following:

[0374] The expected time or period of occurrence of the failure event; the event type of the failure event; the location information of the failure event; the RRM measurement information corresponding to the failure event; and the time or period of generation of the failure event prediction information.

[0375] Optionally, the measurement event prediction information includes at least one of the following:

[0376] The expected time or period for the measurement event to be met; the type of the measurement event; the time or period for the generation of the measurement event prediction information.

[0377] The above optional implementation methods can be referred to the relevant descriptions in the embodiments shown in Figure 2. To avoid repetition, these descriptions will not be repeated in this embodiment.

[0378] Referring to Figure 4, which is a schematic diagram of one of the structures of a monitoring device provided in this disclosure, as shown in Figure 4, the monitoring device 400 includes:

[0379] The monitoring module 401 is used to perform functional and / or model performance monitoring on the first information, the first information including at least one of radio resource management (RRM) measurement prediction information, failure event prediction information, and measurement event prediction information.

[0380] Optionally, the device further includes:

[0381] The first receiving module is used to receive a first message sent by the terminal, the first message including the first information.

[0382] Optionally, the monitoring module 402 includes:

[0383] A statistical unit is used to collect statistical performance index information and determine whether the prediction accuracy meets the performance requirements based on the performance index information.

[0384] The sending unit is configured to send a second message to the terminal when it is determined that the prediction accuracy does not meet the preset requirements. The second message is used to instruct the terminal to adjust the mobility prediction function and / or model.

[0385] Optionally, the device further includes:

[0386] A generation module is used to generate a first mobility decision based on the first information, wherein the first mobility decision is used to determine the target handover cell of the terminal;

[0387] The sending module is used to send a third message to the terminal, the third message being used to instruct the target cell to switch.

[0388] Optionally, the generation module includes:

[0389] The comparison unit is used to compare the RRM measurement prediction results of the serving cell and the candidate target cell within a first preset time period based on the RRM measurement prediction information in the first information.

[0390] The determining unit is configured to determine a first mobility decision when the RRM measurement prediction result of the candidate target cell is greater than or equal to the RRM measurement prediction result of the serving cell within the first preset time period, or when the RRM measurement prediction result of the candidate target cell is greater than or equal to a first preset value within the first preset time period. The first mobility decision is used to determine the candidate target cell as the target handover cell.

[0391] Optionally, the generation module includes:

[0392] The calculation unit is used to calculate the time remaining until the expected occurrence of the failure event based on the failure event prediction information in the first information;

[0393] The first generation unit is configured to generate a first mobility decision when the duration is greater than the cell handover preparation time.

[0394] Optionally, the generation module includes:

[0395] The judgment unit is used to determine whether the terminal will experience a failure event based on the measurement event prediction information in the first information;

[0396] The second generation unit is used to generate a first mobility decision when it is determined that a failure event will occur in the terminal.

[0397] Optionally, the statistical unit is specifically used for:

[0398] The performance indicators within the second preset time period are statistically analyzed to obtain the first performance indicator value, and the performance indicator information includes the first performance indicator value.

[0399] If the first performance index value is less than the second preset value, and the first mobility decision is based on the predicted value, it is determined that the prediction accuracy does not meet the performance requirements.

[0400] Optionally, the statistical unit is specifically used for:

[0401] A second mobility decision is generated based on the predicted values ​​in the RRM measurement and prediction information;

[0402] A third mobility decision is generated based on the measured values ​​in the RRM measurement prediction information;

[0403] The second performance index value corresponding to the second mobility decision and the third performance index value corresponding to the third mobility decision are statistically analyzed. The performance index information includes the second performance index value and the third performance index value.

[0404] If the difference between the second performance index value and the third performance index value is greater than a third preset value, it is determined that the prediction accuracy does not meet the performance requirements.

[0405] Optionally, the second message includes at least one of the following:

[0406] The third indication information is used to indicate the type of prediction information;

[0407] The fourth indication information is used to instruct the terminal to improve the accuracy of mobility prediction;

[0408] The fifth indication information is used to indicate the type of prediction information that the terminal uses to improve prediction accuracy;

[0409] The sixth instruction information is used to instruct the terminal to perform at least one of stopping mobility prediction, suspending mobility prediction, and deactivating mobility prediction;

[0410] The seventh indication information is used to indicate at least one of the prediction information types of the terminal: stopping mobility prediction, pausing mobility prediction, and deactivating mobility prediction.

[0411] The eighth indication information is used to indicate prediction configuration information. Optionally, the RRM measurement prediction information includes at least one of the following:

[0412] Wireless information quality index information, which includes measured values ​​and predicted values;

[0413] The first indication information is used to indicate that the wireless information quality index information is a measured value or a predicted value;

[0414] The second indication information is used to indicate the reduction of the corresponding time-domain, frequency-domain, or spatial-domain pattern in the measurement.

[0415] The terminal's prediction-related information;

[0416] Prediction confidence, prediction accuracy, or prediction precision;

[0417] The time or period in which the predicted value in the wireless information quality index is generated;

[0418] The predicted time or time period corresponding to the predicted value in the wireless information quality index information.

[0419] Optionally, the failure event prediction information includes at least one of the following:

[0420] The expected time or period of occurrence of the failure event; the event type of the failure event; the location information of the failure event; the RRM measurement information corresponding to the failure event; and the time or period of generation of the failure event prediction information.

[0421] Optionally, the measurement event prediction information includes at least one of the following:

[0422] The expected time or period for the measurement event to be met; the type of the measurement event; the time or period for the generation of the measurement event prediction information.

[0423] It should be noted that the monitoring device provided in this embodiment is capable of executing the above-described monitoring method. Therefore, all implementation methods in the above-described monitoring method embodiments are applicable to this device and can achieve the same or similar beneficial effects. To avoid repetition, this embodiment will not elaborate further.

[0424] Specifically, as shown in Figure 5, this embodiment of the present disclosure also provides a network device, including a bus 501, a transceiver 502, an antenna 503, a bus interface 504, a processor 505, and a memory 506.

[0425] The processor 505 is used to perform functional and / or model performance monitoring on the first information, the first information including at least one of radio resource management (RRM) measurement prediction information, failure event prediction information, and measurement event prediction information.

[0426] In Figure 5, a bus architecture (represented by bus 501) is shown. Bus 501 may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 505 and memory represented by memory 506. Bus 501 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 504 provides an interface between bus 501 and transceiver 502. Transceiver 502 may be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 505 is transmitted over a wireless medium via antenna 503, which further receives data and transmits it to processor 505.

[0427] Processor 505 manages bus 501 and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory 506 can be used to store data used by processor 505 during operation.

[0428] Optionally, the processor 505 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a complex programmable logic device (CPLD).

[0429] Optionally, the network device further includes a transceiver 502, the transceiver 502 being used for:

[0430] The receiving terminal sends a first message, which includes the first information.

[0431] Optionally, the processor 505 is specifically used for:

[0432] Statistical performance index information is collected, and based on the performance index information, it is determined whether the prediction accuracy meets the performance requirements;

[0433] If it is determined that the prediction accuracy does not meet the preset requirements, a second message is sent to the terminal, the second message being used to instruct the terminal to adjust the mobility prediction function and / or model.

[0434] Optionally, the processor 505 is further configured to:

[0435] A first mobility decision is generated based on the first information, and the first mobility decision is used to determine the target handover cell of the terminal.

[0436] The transceiver is also used to send a third message to the terminal, the third message being used to instruct the target cell to switch.

[0437] Optionally, the processor 505 is specifically used for:

[0438] Based on the RRM measurement prediction information in the first information, compare the RRM measurement prediction results of the serving cell and the candidate target cell within the first preset time period;

[0439] If the RRM measurement prediction result of the candidate target cell is greater than or equal to the RRM measurement prediction result of the serving cell within the first preset time period, or if the RRM measurement prediction result of the candidate target cell is greater than or equal to a first preset value within the first preset time period, a first mobility decision is determined. The first mobility decision is used to determine the candidate target cell as the target handover cell.

[0440] Optionally, the processor 505 is specifically used for:

[0441] Based on the failure event prediction information in the first information, calculate the time elapsed until the expected occurrence of the failure event;

[0442] If the duration is greater than the cell handover preparation time, a first mobility decision is generated.

[0443] Optionally, the processor 505 is specifically used for:

[0444] Based on the measurement event prediction information in the first information, determine whether the terminal will experience a failure event;

[0445] If it is determined that a failure event will occur at the terminal, a first mobility decision is generated.

[0446] Optionally, the processor 505 is specifically used for:

[0447] The performance indicators within the second preset time period are statistically analyzed to obtain the first performance indicator value, and the performance indicator information includes the first performance indicator value.

[0448] If the first performance index value is less than the second preset value, and the first mobility decision is based on the predicted value, it is determined that the prediction accuracy does not meet the performance requirements.

[0449] Optionally, the processor 505 is specifically used for:

[0450] A second mobility decision is generated based on the predicted values ​​in the RRM measurement and prediction information;

[0451] A third mobility decision is generated based on the measured values ​​in the RRM measurement prediction information;

[0452] The second performance index value corresponding to the second mobility decision and the third performance index value corresponding to the third mobility decision are statistically analyzed. The performance index information includes the second performance index value and the third performance index value.

[0453] If the difference between the second performance index value and the third performance index value is greater than a third preset value, it is determined that the prediction accuracy does not meet the performance requirements.

[0454] Optionally, the second message includes at least one of the following:

[0455] The third indication information is used to indicate the type of prediction information;

[0456] The fourth indication information is used to instruct the terminal to improve the accuracy of mobility prediction;

[0457] The fifth indication information is used to indicate the type of prediction information that the terminal uses to improve prediction accuracy;

[0458] The sixth instruction information is used to instruct the terminal to perform at least one of stopping mobility prediction, suspending mobility prediction, and deactivating mobility prediction;

[0459] The seventh indication information is used to indicate at least one of the prediction information types of the terminal: stopping mobility prediction, pausing mobility prediction, and deactivating mobility prediction.

[0460] The eighth indication information is used to indicate prediction configuration information. Optionally, the RRM measurement prediction information includes at least one of the following:

[0461] Wireless information quality index information, which includes measured values ​​and predicted values;

[0462] The first indication information is used to indicate that the wireless information quality index information is a measured value or a predicted value;

[0463] The second indication information is used to indicate the reduction of the corresponding time-domain, frequency-domain, or spatial-domain pattern in the measurement.

[0464] The terminal's prediction-related information;

[0465] Prediction confidence, prediction accuracy, or prediction precision;

[0466] The time or period in which the predicted value in the wireless information quality index is generated;

[0467] The predicted time or time period corresponding to the predicted value in the wireless information quality index information.

[0468] Optionally, the failure event prediction information includes at least one of the following:

[0469] The expected time or period of occurrence of the failure event; the event type of the failure event; the location information of the failure event; the RRM measurement information corresponding to the failure event; and the time or period of generation of the failure event prediction information.

[0470] Optionally, the measurement event prediction information includes at least one of the following:

[0471] The expected time or period for the measurement event to be met; the type of the measurement event; the time or period for the generation of the measurement event prediction information.

[0472] It should be noted that the electronic device provided in this embodiment is a device capable of executing the above monitoring method. Therefore, all implementation methods in the above monitoring method embodiments are applicable to this electronic device and can achieve the same or similar beneficial effects. To avoid repetition, this embodiment will not elaborate further.

[0473] Referring to Figure 6, which is a second structural schematic diagram of a monitoring device provided in this embodiment of the present disclosure, as shown in Figure 6, the monitoring device 600 includes:

[0474] The sending module 601 is configured to send a first message to the network device, the first message including at least one of Radio Resource Management (RRM) measurement prediction information, failure event prediction information and measurement event prediction information;

[0475] The first message is used by the network device to perform functional and / or model performance monitoring.

[0476] Optionally, the device further includes:

[0477] The second receiving module is used to receive a second message sent by the network device, the second message being used to instruct the terminal to adjust the mobility prediction function and / or model.

[0478] Optionally, the second message includes at least one of the following:

[0479] The third indication information is used to indicate the type of prediction information;

[0480] The fourth indication information is used to instruct the terminal to improve the prediction accuracy;

[0481] The fifth indication information is used to indicate the type of prediction information that the terminal uses to improve prediction accuracy;

[0482] The sixth instruction information is used to instruct the terminal to perform at least one of stopping mobility prediction, suspending mobility prediction, and deactivating mobility prediction;

[0483] The seventh indication information is used to indicate at least one of the prediction information types of the terminal: stopping mobility prediction, pausing mobility prediction, and deactivating mobility prediction.

[0484] The eighth indication information is used to indicate the predicted configuration information. Optionally, the device further includes:

[0485] The adjustment module is used to adjust the configuration information and / or model input information of mobility prediction based on the second message;

[0486] Alternatively, the first determining module is used to determine the type of prediction information that improves prediction accuracy based on the second message;

[0487] Alternatively, the second determining module is used to determine the type of prediction information—stop prediction, pause prediction, or deactivate prediction—based on the second message.

[0488] Optionally, the method further includes:

[0489] The third receiving module is used to receive a third message sent by the network device, the third message being used to indicate the target handover cell of the terminal.

[0490] Optionally, the RRM measurement prediction information includes at least one of the following:

[0491] Wireless information quality index information, which includes measured values ​​and predicted values;

[0492] The first indication information is used to indicate that the wireless information quality index information is a measured value or a predicted value;

[0493] The second indication information is used to indicate the reduction of the corresponding time-domain, frequency-domain, or spatial-domain pattern in the measurement.

[0494] The terminal's prediction-related information;

[0495] Prediction confidence, prediction accuracy, or prediction precision;

[0496] The time or period in which the predicted value in the wireless information quality index is generated;

[0497] The predicted time or time period corresponding to the predicted value in the wireless information quality index information.

[0498] Optionally, the failure event prediction information includes at least one of the following:

[0499] The expected time or period of occurrence of the failure event; the event type of the failure event; the location information of the failure event; the RRM measurement information corresponding to the failure event; and the time or period of generation of the failure event prediction information.

[0500] Optionally, the measurement event prediction information includes at least one of the following:

[0501] The expected time or period for the measurement event to be met; the type of the measurement event; the time or period for the generation of the measurement event prediction information.

[0502] It should be noted that the monitoring device provided in this embodiment is capable of executing the above-described monitoring method. Therefore, all implementation methods in the above-described monitoring method embodiments are applicable to this device and can achieve the same or similar beneficial effects. To avoid repetition, this embodiment will not elaborate further.

[0503] Specifically, as shown in Figure 7, this embodiment of the present disclosure also provides a terminal, including a bus 701, a transceiver 702, an antenna 703, a bus interface 704, a processor 705, and a memory 706.

[0504] Transceiver 702 is used for:

[0505] Send a first message to the network device, the first message including at least one of Radio Resource Management (RRM) measurement prediction information, failure event prediction information, and measurement event prediction information;

[0506] The first message is used by the network device to perform functional and / or model performance monitoring.

[0507] In Figure 7, a bus architecture (represented by bus 701) is shown. Bus 701 may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 705 and memory represented by memory 706. Bus 701 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 704 provides an interface between bus 701 and transceiver 702. Transceiver 702 may be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 705 is transmitted over a wireless medium via antenna 703, which further receives data and transmits it to processor 705.

[0508] Processor 705 manages bus 701 and general processing, and also provides various functions, including timing, peripheral interface, voltage regulation, power management, and other control functions. Memory 706 can be used to store data used by processor 705 during operation.

[0509] Optionally, the processor 705 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a complex programmable logic device (CPLD).

[0510] Optionally, the transceiver 702 is further configured to:

[0511] The terminal receives a second message sent by the network device, the second message being used to instruct the terminal to adjust its mobility prediction function and / or model.

[0512] Optionally, the second message includes at least one of the following:

[0513] The third indication information is used to indicate the type of prediction information;

[0514] The fourth indication information is used to instruct the terminal to improve the prediction accuracy;

[0515] The fifth indication information is used to indicate the type of prediction information that the terminal uses to improve prediction accuracy;

[0516] The sixth instruction information is used to instruct the terminal to perform at least one of stopping mobility prediction, suspending mobility prediction, and deactivating mobility prediction;

[0517] The seventh indication information is used to indicate at least one of the prediction information types of the terminal: stopping mobility prediction, pausing mobility prediction, and deactivating mobility prediction.

[0518] The eighth indication information is used to indicate the predicted configuration information. Optionally, the processor 705 is further configured to:

[0519] Adjust the configuration information and / or model input information for mobility prediction based on the second message;

[0520] Alternatively, the type of prediction information that improves prediction accuracy can be determined based on the second message;

[0521] Alternatively, the type of prediction information to stop, pause, or deactivate prediction can be determined based on the second message.

[0522] Optionally, the transceiver 702 is further configured to:

[0523] The terminal receives a third message sent by the network device, the third message being used to indicate the target handover cell.

[0524] Optionally, the RRM measurement prediction information includes at least one of the following:

[0525] Wireless information quality index information, which includes measured values ​​and predicted values;

[0526] The first indication information is used to indicate that the wireless information quality index information is a measured value or a predicted value;

[0527] The second indication information is used to indicate the reduction of the corresponding time-domain, frequency-domain, or spatial-domain pattern in the measurement.

[0528] The terminal's prediction-related information;

[0529] Prediction confidence, prediction accuracy, or prediction precision;

[0530] The time or period in which the predicted value in the wireless information quality index is generated;

[0531] The predicted time or time period corresponding to the predicted value in the wireless information quality index information.

[0532] Optionally, the failure event prediction information includes at least one of the following:

[0533] The expected time or period of occurrence of the failure event; the event type of the failure event; the location information of the failure event; the RRM measurement information corresponding to the failure event; and the time or period of generation of the failure event prediction information.

[0534] Optionally, the measurement event prediction information includes at least one of the following:

[0535] The expected time or period for the measurement event to be met; the type of the measurement event; the time or period for the generation of the measurement event prediction information.

[0536] It should be noted that the electronic device provided in this embodiment is a device capable of executing the above monitoring method. Therefore, all implementation methods in the above monitoring method embodiments are applicable to this electronic device and can achieve the same or similar beneficial effects. To avoid repetition, this embodiment will not elaborate further.

[0537] This disclosure also provides an electronic device, including: a processor, a memory, and a program stored in the memory and executable on the processor. When the program is executed by the processor, it implements the various processes of the above-described monitoring method embodiments and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0538] This disclosure also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the various processes of the above-described monitoring method embodiments and achieves the same technical effects. To avoid repetition, further details are omitted here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0539] This disclosure also provides a computer program product, including computer instructions. When executed by a processor, these computer instructions implement the various processes of the above-described monitoring method embodiments and achieve the same technical effects. To avoid repetition, further details are omitted here.

[0540] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0541] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this disclosure.

[0542] The embodiments of this disclosure have been described above with reference to the accompanying drawings. However, this disclosure is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this disclosure without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this disclosure.

Claims

1. A monitoring method applied to a network device, the method comprising: The first information is used for functional and / or model performance monitoring, and the first information includes at least one of Radio Resource Management (RRM) measurement prediction information, failure event prediction information, and measurement event prediction information.

2. The method according to claim 1, further comprising: The receiving terminal sends a first message, which includes the first information.

3. The method according to claim 1, wherein, The monitoring of the first information's functionality and / or model performance includes: Statistical performance index information is collected, and based on the performance index information, it is determined whether the prediction accuracy meets the performance requirements; If it is determined that the prediction accuracy does not meet the preset requirements, a second message is sent to the terminal, the second message being used to instruct the terminal to adjust the mobility prediction function and / or model.

4. The method according to claim 1, further comprising: A first mobility decision is generated based on the first information, and the first mobility decision is used to determine the target handover cell of the terminal. A third message is sent to the terminal, the third message being used to instruct the target cell to be switched.

5. The method according to claim 4, wherein, The step of generating a first mobility decision based on the first information includes: Based on the RRM measurement prediction information in the first information, compare the RRM measurement prediction results of the serving cell and the candidate target cell within the first preset time period; If the RRM measurement prediction result of the candidate target cell is greater than or equal to the RRM measurement prediction result of the serving cell within the first preset time period, or if the RRM measurement prediction result of the candidate target cell is greater than or equal to a first preset value within the first preset time period, a first mobility decision is determined. The first mobility decision is used to determine the candidate target cell as the target handover cell.

6. The method according to claim 4, wherein, The step of generating a first mobility decision based on the first information includes: Based on the failure event prediction information in the first information, calculate the time elapsed until the expected occurrence of the failure event; If the duration is greater than the cell handover preparation time, a first mobility decision is generated.

7. The method according to claim 4, wherein, The step of generating a first mobility decision based on the first information includes: Based on the measurement event prediction information in the first information, determine whether the terminal will experience a failure event; If it is determined that a failure event will occur at the terminal, a first mobility decision is generated.

8. The method according to claim 3, wherein, The statistical performance index information, and determining whether the prediction accuracy meets the performance requirements based on the performance index information, includes: The performance indicators within the second preset time period are statistically analyzed to obtain the first performance indicator value, and the performance indicator information includes the first performance indicator value. If the first performance index value is less than the second preset value, and the first mobility decision is based on the predicted value, it is determined that the prediction accuracy does not meet the performance requirements.

9. The method according to claim 3, wherein, The statistical performance index information, and determining whether the prediction accuracy meets the performance requirements based on the performance index information, includes: A second mobility decision is generated based on the predicted values ​​in the RRM measurement and prediction information; A third mobility decision is generated based on the measured values ​​in the RRM measurement prediction information; The second performance index value corresponding to the second mobility decision and the third performance index value corresponding to the third mobility decision are statistically analyzed. The performance index information includes the second performance index value and the third performance index value. If the difference between the second performance index value and the third performance index value is greater than a third preset value, it is determined that the prediction accuracy does not meet the performance requirements.

10. The method according to claim 3, wherein, The second message includes at least one of the following: The third indication information is used to indicate the type of prediction information; The fourth indication information is used to instruct the terminal to improve the accuracy of mobility prediction; The fifth indication information is used to indicate the type of prediction information that the terminal uses to improve prediction accuracy; The sixth instruction information is used to instruct the terminal to perform at least one of stopping mobility prediction, suspending mobility prediction, and deactivating mobility prediction; The seventh indication information is used to indicate at least one of the prediction information types of the terminal: stopping mobility prediction, pausing mobility prediction, and deactivating mobility prediction. The eighth indication information is used to indicate the predicted configuration information.

11. The method according to any one of claims 1-10, wherein, The RRM measurement prediction information includes at least one of the following: Wireless information quality index information, which includes measured values ​​and predicted values; The first indication information is used to indicate that the wireless information quality index information is a measured value or a predicted value; The second indication information is used to indicate the reduction of the corresponding time-domain, frequency-domain, or spatial-domain pattern in the measurement. Terminal-related prediction information; Prediction confidence, prediction accuracy, or prediction precision; The time or period in which the predicted value in the wireless information quality index is generated; The predicted time or time period corresponding to the predicted value in the wireless information quality index information.

12. The method according to any one of claims 1-11, wherein, The failure event prediction information includes at least one of the following: The expected time or period of occurrence of the failure event; the event type of the failure event; the location information of the failure event; the RRM measurement information corresponding to the failure event; and the time or period of generation of the failure event prediction information.

13. The method according to any one of claims 1-12, wherein, The measurement event prediction information includes at least one of the following: The expected time or period for the measurement event to be met; the type of the measurement event; the time or period for the generation of the measurement event prediction information.

14. A monitoring method applied to a terminal, the method comprising: Send a first message to the network device, the first message including at least one of Radio Resource Management (RRM) measurement prediction information, failure event prediction information, and measurement event prediction information; The first message is used by the network device to perform functional and / or model performance monitoring.

15. The method according to claim 14, further comprising: The terminal receives a second message sent by the network device, the second message being used to instruct the terminal to adjust its mobility prediction function and / or model.

16. The method according to claim 15, wherein, The second message includes at least one of the following: The third indication information is used to indicate the type of prediction information; The fourth indication information is used to instruct the terminal to improve the prediction accuracy; The fifth indication information is used to indicate the type of prediction information that the terminal uses to improve prediction accuracy; The sixth instruction information is used to instruct the terminal to perform at least one of stopping mobility prediction, suspending mobility prediction, and deactivating mobility prediction; The seventh indication information is used to indicate at least one of the prediction information types of the terminal: stopping mobility prediction, pausing mobility prediction, and deactivating mobility prediction. The eighth indication information is used to indicate the predicted configuration information.

17. The method according to claim 15, further comprising: Adjust the configuration information and / or model input information for mobility prediction based on the second message; Alternatively, the type of prediction information that improves prediction accuracy can be determined based on the second message; Alternatively, the type of prediction information to stop, pause, or deactivate prediction can be determined based on the second message.

18. The method according to claim 14, further comprising: The terminal receives a third message sent by the network device, the third message being used to indicate the target handover cell.

19. The method according to any one of claims 14-18, wherein, The RRM measurement prediction information includes at least one of the following: Wireless information quality index information, which includes measured values ​​and predicted values; The first indication information is used to indicate that the wireless information quality index information is a measured value or a predicted value; The second indication information is used to indicate the reduction of the corresponding time-domain, frequency-domain, or spatial-domain pattern in the measurement. The prediction-related information of the terminal; Prediction confidence, prediction accuracy, or prediction precision; The time or period in which the predicted value in the wireless information quality index is generated; The predicted time or time period corresponding to the predicted value in the wireless information quality index information.

20. The method according to any one of claims 14-19, wherein, The failure event prediction information includes at least one of the following: The expected time or period of occurrence of the failure event; the event type of the failure event; the location information of the failure event; the RRM measurement information corresponding to the failure event; and the time or period of generation of the failure event prediction information.

21. The method according to any one of claims 14-20, wherein, The measurement event prediction information includes at least one of the following: The expected time or period for the measurement event to be met; the type of the measurement event; the time or period for the generation of the measurement event prediction information.

22. A monitoring device, comprising: A monitoring module is used to perform functional and / or model performance monitoring on first information, the first information including at least one of radio resource management (RRM) measurement prediction information, failure event prediction information, and measurement event prediction information.

23. A network device, comprising a processor, the processor being configured to: The first information is used for functional and / or model performance monitoring, and the first information includes at least one of Radio Resource Management (RRM) measurement prediction information, failure event prediction information, and measurement event prediction information.

24. A monitoring device, comprising: A sending module is configured to send a first message to a network device, the first message including at least one of Radio Resource Management (RRM) measurement prediction information, failure event prediction information, and measurement event prediction information; The first message is used by the network device to perform functional and / or model performance monitoring.

25. A terminal, comprising a transceiver, the transceiver being used for: Send a first message to the network device, the first message including at least one of Radio Resource Management (RRM) measurement prediction information, failure event prediction information, and measurement event prediction information; in, The first message is used by the network device to perform functional and / or model performance monitoring.

26. An electronic device, comprising: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the steps of the monitoring method as described in any one of claims 1 to 13; or, the program, when executed by the processor, implements the steps of the monitoring method as described in any one of claims 14 to 21.

27. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the monitoring method as described in any one of claims 1 to 13; or, when executed by a processor, the computer program implements the steps of the monitoring method as described in any one of claims 14 to 21.

28. A computer program product comprising computer instructions that, when executed by a processor, implement the steps of the monitoring method as described in any one of claims 1 to 13; or, when executed by a processor, the computer instructions implement the steps of the monitoring method as described in any one of claims 14 to 21.

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