Information reporting method, information transmission method, apparatus, device, and medium

Abstract

The present disclosure provides an information reporting method, an information transmission method, an apparatus, a device, and a medium. The method of the present disclosure comprises: obtaining performance monitoring related information for a first object, wherein the first object is an artificial intelligence model or an artificial intelligence function; reporting the performance monitoring related information to a network side device, wherein the performance monitoring related information comprises one or more of the following information: feature values of performance monitoring indexes of N moments; performance monitoring related information of each moment among the N moments; moment related information corresponding to the feature values of the performance monitoring indexes of the N moments, N being a positive integer greater than 1; feature values of performance monitoring indexes corresponding to multiple model inferences; and moment related information corresponding to the feature values of the performance monitoring indexes corresponding to the multiple model inferences.

Description

Information reporting method, information transmission method, device, equipment and medium

[0001] The present disclosure claims priority to the Chinese patent application No. 202410477480.4, filed on April 19, 2024, and entitled "Information reporting method, information transmission method, device, equipment and medium", the whole content of which is incorporated herein by reference. TECHNICAL FIELD

[0002] The present disclosure relates to the technical field of communication, and in particular to an information reporting method, an information transmission method, a device, an equipment and a medium. BACKGROUND

[0003] In new radio (NR) beam management, the related technology is to determine the optimal transmission beam by measuring all beam information transmitted by a transmission end Tx, which needs to measure the reference signal (RS) of all beams, causing a large resource overhead. In artificial intelligence (AI) based beam management, only part of the beam information needs to be measured, and all beam information can be obtained through training of an AI model, greatly reducing the RS resource overhead.

[0004] As a data-driven algorithm, the generalization ability of an AI / machine learning (ML) model is limited, and different models may be deployed for different scenarios, i.e., each model is applicable to a limited scenario or configuration, and therefore the performance of the model needs to be monitored to ensure that the AI model used is matched with the current scenario or configuration, so as to ensure the system performance.

[0005] For a terminal device side model, performance monitoring may be at a base station side or at a terminal device side. When performance monitoring is at the base station side, the terminal device side needs to report information to enable the network side to perform performance monitoring, but there is no specific method for reporting what information. SUMMARY

[0006] The present disclosure aims to provide an information reporting method, an information transmission method, a device, an equipment and a medium to solve the problem of what information a terminal device reports to enable the network side to perform performance monitoring when performance monitoring is at the network side.

[0007] To achieve the above-mentioned purpose, in a first aspect, an embodiment of the present disclosure provides an information reporting method applied to a terminal device, comprising:

[0008] obtaining performance monitoring related information for a first object, the first object being an artificial intelligence model or an artificial intelligence function;

[0009] reporting the performance monitoring related information to the network side device, wherein the performance monitoring related information comprises one or more of the following information:

[0010] characteristic values of performance monitoring indicators at N time instants;

[0011] performance monitoring related information at each of the N time instants;

[0012] time instant related information corresponding to the characteristic values of the performance monitoring indicators at the N time instants, N being a positive integer greater than 1;

[0013] characteristic values of performance monitoring indicators corresponding to multiple model inferences;

[0014] time instant related information corresponding to the characteristic values of the performance monitoring indicators corresponding to the multiple model inferences.

[0015] In some embodiments, the characteristic values comprise one or more of a mean value, a maximum value, and a minimum value.

[0016] In some embodiments, the obtaining the performance monitoring related information for the first object comprises:

[0017] receiving first reference signals corresponding to M time instants configured by the network side device, one or more first reference signals corresponding to each time instant;

[0018] performing measurement on the first reference signals corresponding to the M time instants, M being a positive integer;

[0019] obtaining predicted results at each of the N time instants according to measurement results of the first reference signals corresponding to the M time instants;

[0020] receiving second reference signals corresponding to N time instants configured by the network side device, one or more second reference signals corresponding to each time instant;

[0021] performing measurement on the second reference signals corresponding to the N time instants;

[0022] obtaining the performance monitoring related information for the first object according to the predicted results at each of the N time instants and measurement results at each of the N time instants.

[0023] In some embodiments, the reporting the performance monitoring related information to the network side device comprises:

[0024] reporting, to the network side device, a number of time instants or a correct prediction rate among the N time instants, wherein the characteristic values of the performance monitoring indicators at the N time instants comprise the number of time instants or the correct prediction rate among the N time instants; and / or,

[0025] reporting first indication information to the network side device, the first indication information being used to indicate whether the model inference of each of the N time instants is correct, the performance monitoring related information of each of the N time instants including the first indication information.

[0026] In some embodiments, the prediction result of each of the N time instants includes K indexes of the N time instants predicted, K being a positive integer; and the measurement result of each of the N time instants includes indexes of the maximum or strongest K reference signals of each of the N time instants measured.

[0027] The performance monitoring related information of the first object is obtained according to the prediction result of each of the N time instants and the measurement result of each of the N time instants, including:

[0028] The number of time instants in the N time instants that are predicted correctly or the prediction accuracy is obtained according to the K indexes of each of the N time instants predicted and the indexes of the maximum or strongest K reference signals of each of the N time instants measured; and / or,

[0029] The first indication information is obtained according to the K indexes of each of the N time instants predicted and the indexes of the maximum or strongest K reference signals of each of the N time instants measured, the first indication information being used to indicate whether the model inference of each of the N time instants is correct.

[0030] In some embodiments, the first indication information is an N-bit bitmap.

[0031] In some embodiments, the measurement of the second reference signals corresponding to the N time instants includes:

[0032] The second reference signals corresponding to the N time instants are measured to obtain first RSRP measurement values of each of the N time instants.

[0033] The first RSRP measurement values of each of the N time instants are sorted to obtain indexes of the maximum or strongest K reference signals of each of the N time instants.

[0034] In some embodiments, the reporting of the performance monitoring related information to the network side device includes:

[0035] The following one or more information is reported to the network side device:

[0036] A first average value, the first average value being an average value of differences between RSRP prediction values and RSRP measurement values of target reference signals corresponding to the N time instants;

[0037] a maximum value of a difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to each of the N time instants;

[0038] time information corresponding to a maximum value of a difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to each of the N time instants;

[0039] a minimum value of a difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to each of the N time instants;

[0040] time information corresponding to a minimum value of a difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to each of the N time instants.

[0041] In some embodiments, the prediction result of each of the N time instants comprises a RSRP prediction value of the target reference signal corresponding to each of the N time instants; and the measurement result of each of the N time instants comprises a RSRP measurement value of the target reference signal corresponding to each of the N time instants.

[0042] The performance monitoring related information of the first object is obtained according to the prediction result of each of the N time instants and the measurement result of each of the N time instants, comprising:

[0043] a difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to each of the N time instants is calculated according to the RSRP prediction value of the target reference signal corresponding to each of the N time instants and the RSRP measurement value of the target reference signal corresponding to each of the N time instants.

[0044] one or more of the following information is obtained according to the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to each of the N time instants:

[0045] a first mean value, the first mean value being a mean value of a difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to each of the N time instants;

[0046] a maximum value of a difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to each of the N time instants;

[0047] time information corresponding to a maximum value of a difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to each of the N time instants;

[0048] a minimum value of a difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to each of the N time instants;

[0049] an instant corresponding to a minimum value of a difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N instants.

[0050] In some embodiments, the obtaining of the prediction result of each of the N instants according to the measurement result of the first reference signal corresponding to the M instants comprises:

[0051] inputting the measurement result of the first reference signal corresponding to the M instants into an artificial intelligence model to obtain the RSRP prediction value of the second reference signal corresponding to each of the N instants;

[0052] obtaining the RSRP prediction value of the target reference signal corresponding to each of the N instants according to the RSRP prediction value of the second reference signal corresponding to each of the N instants.

[0053] In some embodiments, the reporting of the performance monitoring related information to the network side device comprises:

[0054] reporting one or more of the following information to the network side device:

[0055] a maximum value of a difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within a first time period, P being a positive integer greater than 1, for the target reference signal;

[0056] an instant related information or an index value of measurement corresponding to the maximum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within a first time period, for the target reference signal;

[0057] a minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within a first time period, for the target reference signal;

[0058] an instant related information or an index value of measurement corresponding to the minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within a first time period, for the target reference signal.

[0059] The feature value of the performance monitoring indicator corresponding to the multiple times of model inference comprises: the maximum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within a first time period, and / or the minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within a first time period, for the target reference signal.

[0060] In some embodiments, the obtaining of the performance monitoring related information for the first object comprises:

[0061] For a measurement, receiving a first reference signal configured by the network-side device;

[0062] measuring the first reference signal;

[0063] Obtaining an RSRP prediction value for a target reference signal according to a measurement result of the first reference signal;

[0064] Calculate, based on the RSRP predicted value for the target reference signal and the RSRP measured value for the target reference signal, a difference between the RSRP predicted value and the RSRP measured value for the target reference signal;

[0065] After performing P measurements or measurements within the first time period, obtaining one or more of the following information based on a difference between a predicted RSRP value and a measured RSRP value for the target reference signal corresponding to each measurement:

[0066] The maximum value of the difference between the RSRP predicted value and the RSRP measured value for the target reference signal corresponding to P measurements or measurements within the first time period;

[0067] Time-related information or a measurement index value corresponding to the maximum value of the difference between the RSRP predicted value and the RSRP measured value for the target reference signal corresponding to P measurements or measurements within the first time period;

[0068] The minimum value of the difference between the RSRP predicted value and the RSRP measured value for the target reference signal corresponding to P measurements or measurements within the first time period;

[0069] The time information or the measurement index value corresponding to the minimum value of the difference between the RSRP prediction value and the RSRP measurement value for the target reference signal corresponding to the P measurements or the measurements within the first time period.

[0070] In some embodiments, the target reference signal is determined by a first reference signal or a prediction result.

[0071] In some embodiments, the method further comprises:

[0072] Receive configuration information sent by a network-side device, where the configuration information is used to instruct the terminal device to report content related to performance monitoring of the first object.

[0073] In a second aspect, an embodiment of the present disclosure further provides an information transmission method, applied to a network-side device, comprising:

[0074] Receive performance monitoring related information reported by the terminal device, where the performance monitoring related information includes one or more of the following information:

[0075] Characteristic values ​​of performance monitoring indicators at N moments;

[0076] Performance monitoring information at each of N moments;

[0077] The characteristics of the performance monitoring indicators at N moments refer to the corresponding moment-related information, where N is a positive integer greater than 1;

[0078] The characteristic values ​​of performance monitoring indicators corresponding to multiple model inferences;

[0079] Time-related information corresponding to the characteristic values ​​of performance monitoring indicators corresponding to multiple model inferences.

[0080] In some embodiments, the characteristic value includes one or more of a mean value, a maximum value, and a minimum value.

[0081] In some embodiments, the method further comprises:

[0082] Configuring a first reference signal corresponding to M time moments for the terminal device, where each time moment corresponds to one or more first reference signals, the first reference signals corresponding to the M time moments are used to obtain a prediction result for each of N time moments, where M is a positive integer;

[0083] The terminal device is configured with second reference signals corresponding to N moments, each moment corresponds to one or more second reference signals, and the second reference signals corresponding to the N moments are used to obtain measurement results at each of the N moments.

[0084] In some embodiments, the method further comprises:

[0085] For each measurement in P measurements or within a first time period, a first reference signal is configured for the terminal device, where the first reference signal is used to obtain an RSRP prediction value for a target reference signal, where the target reference signal is determined by the first reference signal or the prediction result, and P is a positive integer greater than 1.

[0086] In some embodiments, the method further comprises:

[0087] Configuration information is sent to the terminal device, where the configuration information is used to instruct the terminal device to report content related to performance monitoring of the first object.

[0088] In a third aspect, an embodiment of the present disclosure further provides a terminal device, comprising: a memory, a transceiver, and a processor: the memory is used to store a computer program; the transceiver is used to send and receive data under the control of the processor; and the processor is used to read program instructions in the memory and perform the following operations:

[0089] obtaining performance monitoring related information for a first object, the first object being an artificial intelligence model or an artificial intelligence function;

[0090] reporting the performance monitoring related information to a network side device, wherein the performance monitoring related information comprises one or more of the following information:

[0091] characteristic values of performance monitoring indicators at N time instants;

[0092] performance monitoring related information at each of the N time instants;

[0093] time instant related information corresponding to the characteristic values of the performance monitoring indicators at the N time instants, N being a positive integer greater than 1;

[0094] characteristic values of performance monitoring indicators corresponding to multiple model inferences;

[0095] time instant related information corresponding to the characteristic values of the performance monitoring indicators corresponding to the multiple model inferences.

[0096] In some embodiments, the characteristic values comprise one or more of a mean value, a maximum value, and a minimum value.

[0097] In some embodiments, the operations further comprise:

[0098] receiving first reference signals corresponding to M time instants configured by the network side device, one or more first reference signals corresponding to each time instant;

[0099] measuring the first reference signals corresponding to the M time instants; wherein M is a positive integer;

[0100] obtaining predicted results at each of the N time instants according to measurement results of the first reference signals corresponding to the M time instants;

[0101] receiving second reference signals corresponding to N time instants configured by the network side device, one or more second reference signals corresponding to each time instant;

[0102] measuring the second reference signals corresponding to the N time instants;

[0103] obtaining performance monitoring related information for the first object according to the predicted results at each of the N time instants and measurement results at each of the N time instants.

[0104] In some embodiments, the operations further comprise:

[0105] reporting, to the network side device, a number of time instants correctly predicted or a correct prediction rate among the N time instants, the characteristic values of the performance monitoring indicators at the N time instants comprising the number of time instants correctly predicted or the correct prediction rate among the N time instants; and / or,

[0106] reporting first indication information to the network side device, the first indication information being used to indicate whether the model inference of each of the N time instants is correct, the performance monitoring related information of each of the N time instants including the first indication information.

[0107] In some embodiments, the prediction result of each of the N time instants includes K indexes of the N time instants predicted, the measurement result of each of the N time instants includes indexes of the maximum or strongest K reference signals of each of the N time instants measured, K being a positive integer, and the operations further include:

[0108] obtaining the number of correctly predicted time instants or the correct prediction rate in the N time instants according to the K indexes of the N time instants predicted and the indexes of the maximum or strongest K reference signals of the N time instants measured; and / or,

[0109] obtaining the first indication information according to the K indexes of the N time instants predicted and the indexes of the maximum or strongest K reference signals of the N time instants measured, the first indication information being used to indicate whether the model inference of each of the N time instants is correct.

[0110] In some embodiments, the first indication information is an N-bit bitmap.

[0111] In some embodiments, the operations further include:

[0112] measuring the second reference signals corresponding to the N time instants to obtain first RSRP measurement values of each of the N time instants;

[0113] sorting the first RSRP measurement values of each of the N time instants to obtain indexes of the maximum or strongest K reference signals of each of the N time instants.

[0114] In some embodiments, the operations further include:

[0115] reporting one or more of the following information to the network side device:

[0116] a first average value, the first average value being an average value of differences between RSRP prediction values and RSRP measurement values of target reference signals corresponding to the N time instants;

[0117] a maximum value of differences between RSRP prediction values and RSRP measurement values of target reference signals corresponding to the N time instants;

[0118] information of a time corresponding to a maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants;

[0119] a minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants;

[0120] information of a time corresponding to a minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants.

[0121] In some embodiments, the prediction result of each of the N time instants includes a RSRP prediction value of the target reference signal corresponding to the each of the N time instants; the measurement result of each of the N time instants includes a RSRP measurement value of the target reference signal corresponding to the each of the N time instants; and the operations further include:

[0122] a difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to each of the N time instants is calculated according to the RSRP prediction value of the target reference signal corresponding to each of the N time instants and the RSRP measurement value of the target reference signal corresponding to each of the N time instants;

[0123] one or more of the following information is obtained according to the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to each of the N time instants:

[0124] a first mean value, the first mean value being a mean value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants;

[0125] a maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants;

[0126] information of a time corresponding to a maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants;

[0127] a minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants;

[0128] information of a time corresponding to a minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants.

[0129] In some embodiments, the operations further include:

[0130] inputting a measurement result of the first reference signal corresponding to the M time points into an artificial intelligence model to obtain an RSRP prediction value of the second reference signal corresponding to each of the N time points;

[0131] obtaining, according to the RSRP prediction value of the second reference signal corresponding to each of the N time points, an RSRP prediction value of the target reference signal corresponding to each of the N time points.

[0132] In some embodiments, the operations further include:

[0133] reporting one or more of the following information to the network side device:

[0134] a maximum value of a difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within the first time period, P being a positive integer greater than 1;

[0135] time related information or an index value of the measurement corresponding to the maximum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within the first time period;

[0136] a minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within the first time period;

[0137] time related information or an index value of the measurement corresponding to the minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within the first time period;

[0138] The feature value of the performance monitoring index corresponding to the multiple model inferences includes: the maximum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within the first time period, and / or the minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within the first time period.

[0139] In some embodiments, the operations further include:

[0140] receiving, for one measurement, a first reference signal configured by the network side device;

[0141] measuring the first reference signal;

[0142] obtaining, according to a measurement result of the first reference signal, an RSRP prediction value of a target reference signal;

[0143] According to the RSRP prediction value for the target reference signal and the RSRP measurement value for the target reference signal, a difference between the RSRP prediction value and the RSRP measurement value for the target reference signal is calculated;

[0144] After the P measurements or the measurements in the first time period are performed, according to the difference between the RSRP prediction value and the RSRP measurement value for the target reference signal corresponding to each measurement, one or more of the following information is obtained:

[0145] The maximum value of the difference between the RSRP prediction value and the RSRP measurement value for the target reference signal corresponding to the P measurements or the measurements in the first time period;

[0146] The time point related information or the index value of the measurement corresponding to the maximum value of the difference between the RSRP prediction value and the RSRP measurement value for the target reference signal corresponding to the P measurements or the measurements in the first time period;

[0147] The minimum value of the difference between the RSRP prediction value and the RSRP measurement value for the target reference signal corresponding to the P measurements or the measurements in the first time period;

[0148] The time point related information or the index value of the measurement corresponding to the minimum value of the difference between the RSRP prediction value and the RSRP measurement value for the target reference signal corresponding to the P measurements or the measurements in the first time period.

[0149] In some embodiments, the target reference signal is determined by the first reference signal or the prediction result.

[0150] In some embodiments, the operations further include:

[0151] Receiving configuration information sent by the network side device, the configuration information being used to indicate the content of the performance monitoring related information for the first object reported by the terminal device.

[0152] In a fourth aspect, the embodiments of the present disclosure further provide an information reporting apparatus, including:

[0153] An obtaining unit is configured to obtain performance monitoring related information for a first object, the first object being an artificial intelligence model or an artificial intelligence function;

[0154] A reporting unit is configured to report the performance monitoring related information to a network side device, wherein the performance monitoring related information includes one or more of the following information:

[0155] Characteristic values of performance monitoring indicators at N time points;

[0156] Performance monitoring related information at each of the N time points;

[0157] characteristic values of the performance monitoring indicators corresponding to the N times of model inference, N being a positive integer greater than 1;

[0158] characteristic values of the performance monitoring indicators corresponding to the N times of model inference, N being a positive integer greater than 1;

[0159] characteristic values of the performance monitoring indicators corresponding to the N times of model inference, N being a positive integer greater than 1.

[0160] In a fifth aspect, the embodiments of the present disclosure further provide a network side device, comprising: a memory, a transceiver, and a processor: the memory is configured to store a computer program; the transceiver is configured to transceive data under the control of the processor; and the processor is configured to read program instructions in the memory and perform the following operations:

[0161] receiving performance monitoring related information reported by a terminal device, wherein the performance monitoring related information comprises one or more of the following information:

[0162] characteristic values of the performance monitoring indicators corresponding to the N times of model inference, N being a positive integer greater than 1;

[0163] performance monitoring related information of each of the N times;

[0164] characteristic values of the performance monitoring indicators corresponding to the N times of model inference, N being a positive integer greater than 1;

[0165] characteristic values of the performance monitoring indicators corresponding to the N times of model inference, N being a positive integer greater than 1;

[0166] characteristic values of the performance monitoring indicators corresponding to the N times of model inference, N being a positive integer greater than 1.

[0167] In some embodiments, the characteristic values include one or more of a mean value, a maximum value, and a minimum value.

[0168] In some embodiments, the operations further comprise:

[0169] configuring the terminal device with first reference signals corresponding to M times, one or more first reference signals corresponding to each time, the first reference signals corresponding to the M times being used to obtain predicted results of each of the N times, M being a positive integer;

[0170] configuring the terminal device with second reference signals corresponding to the N times, one or more second reference signals corresponding to each time, the second reference signals corresponding to the N times being used to obtain measurement results of each of the N times.

[0171] In some embodiments, the operations further comprise:

[0172] For each measurement in P measurements or in a first time period, a first reference signal is configured for the terminal device, the first reference signal being used to obtain a RSRP prediction value for a target reference signal, the target reference signal being determined by the first reference signal or a prediction result, P being a positive integer greater than 1.

[0173] In some embodiments, the operations further include:

[0174] The configuration information is sent to the terminal device, the configuration information being used to indicate content of the performance monitoring related information reported by the terminal device for the first object.

[0175] In a sixth aspect, the embodiments of the present disclosure further provide an information transmission device, comprising:

[0176] A first receiving unit is configured to receive performance monitoring related information reported by a terminal device, wherein the performance monitoring related information comprises one or more of the following information:

[0177] Characteristic values of performance monitoring indicators at N time points;

[0178] Performance monitoring related information at each of N time points;

[0179] Characteristic values of performance monitoring indicators at N time points and corresponding time related information, N being a positive integer greater than 1;

[0180] Characteristic values of performance monitoring indicators corresponding to multiple model inferences;

[0181] Characteristic values of performance monitoring indicators corresponding to multiple model inferences and corresponding time related information.

[0182] In a seventh aspect, the embodiments of the present disclosure further provide a processor readable storage medium, which stores a computer program, the computer program being used to make the processor execute steps of the information reporting method in the first aspect or steps of the information transmission method in the second aspect.

[0183] In an eighth aspect, the embodiments of the present disclosure further provide a computer program product, which comprises computer instructions, the computer instructions being executed by a processor to implement steps in the information reporting method in the first aspect or steps in the information transmission method in the second aspect.

[0184] The above technical solutions of the present disclosure have at least the following beneficial effects:

[0185] In the above-mentioned technical solution of the embodiment of the present disclosure, by obtaining performance monitoring related information for the first object, the first object is an artificial intelligence model or an artificial intelligence function; the performance monitoring related information is reported to the network side device, wherein the performance monitoring related information includes one or more of the following information: characteristic values ​​of performance monitoring indicators at N moments; performance monitoring related information at each of the N moments; moment related information corresponding to the characteristic values ​​of performance monitoring indicators at N moments, N is a positive integer greater than 1; characteristic values ​​of performance monitoring indicators corresponding to multiple model inferences; moment related information corresponding to the characteristic values ​​of performance monitoring indicators corresponding to multiple model inferences. In this way, by reporting the above-mentioned performance monitoring related information to the network side device, the network side can realize performance monitoring of the artificial intelligence model or artificial intelligence function on the terminal device side. BRIEF DESCRIPTION OF THE DRAWINGS

[0186] Figure 1 is a schematic diagram of the basic reporting process of UE-side model performance monitoring;

[0187] FIG2 is a flow chart of an information reporting method according to an embodiment of the present disclosure;

[0188] FIG3 is a schematic diagram of a bitmap of the present disclosure;

[0189] FIG4 is a schematic diagram of a flow chart of an information transmission method according to an embodiment of the present disclosure;

[0190] FIG5 is a structural block diagram of a terminal device according to an embodiment of the present disclosure;

[0191] FIG6 is a schematic diagram of modules of an information reporting device according to an embodiment of the present disclosure;

[0192] FIG7 is a structural block diagram of a network side device according to an embodiment of the present disclosure;

[0193] FIG8 is a schematic diagram of a module of an information transmission device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0194] In the embodiments of the present disclosure, the term "and / or" describes the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0195] In the embodiments of the present disclosure, the term "plurality" refers to two or more than two, and other quantifiers are similar thereto.

[0196] With reference to the drawings of the embodiments of the present disclosure, the technical solutions in the embodiments of the present disclosure will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, and not all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present disclosure.

[0197] In order to facilitate the understanding of the solutions of the present disclosure, the related contents involved in the present disclosure are introduced first.

[0198] There are two sub-use cases of AI beam management in the 3rd Generation Partnership Project (3GPP), which are spatial beam prediction and temporal beam prediction. In spatial beam prediction, the input of the model is the reference signal received power (RSRP) of SetB beams, and the output is the optimal beam ID or RSRP of SetA beams. SetA is the set of all beams, and SetB is a subset of SetA. Alternatively, SetA is a set of narrow beams, and SetB is a set of wide beams. In temporal beam prediction, the input of the model is the RSRP of SetB beams at the past M time instants, and the output is the optimal beam ID or RSRP of SetA beams at the future N time instants.

[0199] The performance monitoring indicators can be beam prediction accuracy related key performance indicators (KPIs), such as Top-1 beam prediction accuracy and Top-K / 1 beam prediction accuracy. The Top-1 beam prediction accuracy refers to the probability that the predicted Top-1 beam is the actual Top-1 beam, and the Top-K / 1 beam prediction accuracy refers to the probability that the actual Top-1 beam is included in the predicted Top-K beams. The performance monitoring indicators can also be the difference between the measured value and the predicted value of the layer 1 reference signal received power (L1-RSRP) of the predicted beam output by the model, such as the difference between the actual L1-RSRP and the predicted L1-RSRP of the predicted Top-K beams, or the difference between the actual L1-RSRP and the predicted L1-RSRP of Set B beams.

[0200] The basic performance monitoring process of the AI / ML model deployed on the UE side is shown in FIG. 1. On the one hand, in performance monitoring, the performance monitoring results of the AI model or AI function in a period of time need to be reported. If the existing UE reporting process is followed, the UE needs to report the results of a certain time or report the average value. In order to save overhead, the UE may not need to report the measurement results of all beams at all times, but in order to ensure monitoring performance, it cannot report only the measurement and inference results of the Top-K beams at a certain time. On the other hand, in time-domain beam prediction, the AI model or AI function can predict the optimal beam at multiple time points at a time. For one model monitoring, there is no method for the UE to report performance monitoring indicators related to multiple prediction time points.

[0201] In summary, for the performance monitoring technology of the models of the related AI beam management BM-Case1 (spatial domain beam prediction) and BM-Case2 (time domain beam prediction), when the model is deployed on the UE side but needs to be monitored by the network side, there is no specific method for the specific content reported by the UE.

[0202] To solve the above technical problems, the embodiments of the present disclosure provide an information reporting method, an information transmission method, a device, equipment and a medium, wherein the method and the device are based on the same application concept. Since the principles of the method and the device for solving the problem are similar, the implementation of the device and the method can be mutually referred to, and the repeated parts will not be described again.

[0203] As shown in FIG. 2, it is a flowchart of the information reporting method provided by the embodiments of the present disclosure, and the method is applied to a terminal device, that is, the method is executed by the terminal device. The method specifically includes the following steps:

[0204] Step 201, obtaining performance monitoring related information for a first object, wherein the first object is an artificial intelligence model or an artificial intelligence function;

[0205] In some embodiments, the artificial intelligence model is an AI beam management model, such as a spatial domain beam prediction model or a time domain beam prediction model.

[0206] Step 202, reporting the performance monitoring related information to a network side device, wherein the performance monitoring related information includes one or more of the following information:

[0207] Characteristic values of performance monitoring indicators at N time points; here, the characteristic values of the performance monitoring indicators at the N time points can be characteristic values of the performance monitoring indicators at the N time points for each monitoring.

[0208] Performance monitoring related information at each of the N time points;

[0209] The characteristic value of the performance monitoring index corresponding to the time information of the N times; N is a positive integer greater than 1; here, the performance monitoring related information of each time of the N times can be the performance monitoring related information of each time of the N times of each monitoring.

[0210] The characteristic value of the performance monitoring index corresponding to the time information of the N times; N is a positive integer greater than 1; here, the performance monitoring related information of each time of the N times can be the performance monitoring related information of each time of the N times of each monitoring.

[0211] The characteristic value of the performance monitoring index corresponding to the time information of the N times; N is a positive integer greater than 1; here, the performance monitoring related information of each time of the N times can be the performance monitoring related information of each time of the N times of each monitoring.

[0212] It should be noted that the performance monitoring index can be a beam prediction accuracy related KPI, or the difference between the measured value and the predicted value of the L1-RSRP of the predicted beam output by the model.

[0213] In some embodiments, the characteristic value includes one or more of the mean value, the maximum value, and the minimum value.

[0214] In some embodiments, the performance monitoring related information includes one or more of the following information:

[0215] The mean value of the performance monitoring index of the N times;

[0216] The performance monitoring related information of each time of the N times;

[0217] The maximum value of the performance monitoring index of the N times;

[0218] The time information corresponding to the maximum value of the performance monitoring index of the N times;

[0219] The minimum value of the performance monitoring index of the N times;

[0220] The time information corresponding to the minimum value of the performance monitoring index of the N times;

[0221] The maximum value of the performance monitoring index corresponding to the multiple model inferences;

[0222] The time information corresponding to the maximum value of the performance monitoring index corresponding to the multiple model inferences;

[0223] The minimum value of the performance monitoring index corresponding to the multiple model inferences;

[0224] The time information corresponding to the minimum value of the performance monitoring index corresponding to the multiple model inferences.

[0225] The information reporting method of the embodiments of the present disclosure, by obtaining performance monitoring related information for a first object, the first object being an artificial intelligence model or an artificial intelligence function; reporting the performance monitoring related information to a network side device, wherein the performance monitoring related information includes one or more of the following information: characteristic values of performance monitoring indicators at N time instants; performance monitoring related information at each of the N time instants; time instant related information corresponding to the characteristic values of the performance monitoring indicators at the N time instants, N being a positive integer greater than 1; characteristic values of performance monitoring indicators corresponding to multiple model inferences; time instant related information corresponding to the characteristic values of the performance monitoring indicators corresponding to the multiple model inferences, so that by reporting the above performance monitoring related information to the network side device, the performance monitoring of the artificial intelligence model or the artificial intelligence function on the terminal device side by the network side can be realized.

[0226] In some embodiments, the step 201 of obtaining performance monitoring related information for a first object includes:

[0227] Step 2011a, receiving M time instants corresponding to the first reference signals configured by the network side device, one or more first reference signals corresponding to each time instant;

[0228] In some embodiments, the first reference signal is a reference signal for a first beam set. Wherein, the first beam set can be SetB beam, here, SetA is a set of all beams, and SetB is a subset of SetA; or, SetA is a set of narrow beams, and SetB is a set of wide beams.

[0229] Step 2012a, measuring the M time instants corresponding to the first reference signals; wherein M is a positive integer;

[0230] Here, the measurement results of the M time instants corresponding to the first reference signals correspond to the input of the AI model.

[0231] Step 2013a, obtaining the prediction results at each of the N time instants according to the measurement results of the M time instants corresponding to the first reference signals;

[0232] Specifically, the measurement results of the M time instants corresponding to the first reference signals are input into the AI model to obtain the prediction results at each of the N time instants. Wherein, the prediction results at each of the N time instants can include K indexes predicted at each of the N time instants, can also include RSRP prediction values of target reference signals corresponding to each of the N time instants, and can also include RSRP prediction values for target reference signals.

[0233] Here, the K indices of each of the N time instants predicted can refer to the indices of the top or strongest K reference signals of each of the N time instants predicted. It should be understood that the top or strongest K reference signals refer to the K reference signals with the largest or strongest signal strength (such as RSRP).

[0234] In the case where the AI model is an AI beam management model, the reference signals are transmitted in a certain beam direction, i.e., the reference signals have a corresponding relationship with the beams, and then the indices of the top or strongest K reference signals of each of the N time instants predicted can also be understood as the indices of the top or strongest K beams (Top-K beams) of each of the N time instants predicted.

[0235] In some embodiments, the target reference signal is determined by the first reference signal or the prediction result. In the case where the AI model is an AI beam management model, the target reference signal determined by the first reference signal or the prediction result can be understood as including reference signals for the first beam set (SetB beams) or including reference signals corresponding to the top or strongest K beams determined by the prediction result.

[0236] Step 2014a, receiving the second reference signals corresponding to the N time instants configured by the network side device, one or more second reference signals corresponding to each time instant;

[0237] In some embodiments, the second reference signals are reference signals for the second beam set, reference signals for the first beam set, or reference signals for the top or strongest K beams of each of the N time instants. The second beam set can be SetA beams, and the first beam set can be SetB beams. Here, SetA is a set of all beams, and SetB is a subset of SetA. Alternatively, SetA is a set of narrow beams, and SetB is a set of wide beams.

[0238] Step 2015a, measuring the second reference signals corresponding to the N time instants;

[0239] Step 2016a, obtaining performance monitoring related information for the first object according to the prediction result of each of the N time instants and the measurement result of each of the N time instants.

[0240] It should be noted that the measurement result of each of the N time instants is a result obtained by performing the above step 2015a.

[0241] Here, the above embodiments are applicable to the scenario where the AI model is a time domain beam prediction model.

[0242] As an optional implementation manner one, the step 202 of reporting the performance monitoring related information to the network side device comprises:

[0243] (1) reporting the number of times or the number of times of correct prediction or the correct prediction rate of the N time points to the network side device, wherein the characteristic value of the performance monitoring index of the N time points comprises the number of times of correct prediction or the correct prediction rate of the N time points; and the number of times of correct prediction or the correct prediction rate of the N time points is contained in the mean value of the performance monitoring index of the N time points. It can be understood that the number of times of correct prediction and the number of times of the same meaning.

[0244] (2) reporting first indication information to the network side device, wherein the first indication information is used to indicate whether the model inference of each time point of the N time points is correct, and the performance monitoring related information of each time point of the N time points comprises the first indication information. The relationship between (1) and (2) is "and / or".

[0245] In some embodiments, the first indication information is a bitmap of N bits.

[0246] In this implementation manner, the performance monitoring index is a beam prediction accuracy related KPI.

[0247] Based on this, in an optional embodiment, the prediction result of each time point of the N time points comprises K indexes of each time point of the N time points predicted; that is, the AI model outputs the predicted indexes. Here, the K indexes of each time point of the N time points predicted can refer to the indexes of the maximum or strongest K reference signals of each time point of the N time points predicted. In the case that the AI model is an AI beam management model, the reference signal is transmitted in a certain beam direction, that is, the reference signal has a corresponding relationship with the beam, and then the indexes of the maximum or strongest K reference signals of each time point of the N time points predicted can also be understood as the indexes of the maximum or strongest K beams (Top-K beams) of each time point of the N time points predicted.

[0248] The measurement result of each time point of the N time points comprises the indexes of the maximum or strongest K reference signals of each time point of the N time points measured, and K is a positive integer;

[0249] Correspondingly, the step 2016a of obtaining the performance monitoring related information of the first object according to the prediction result of each time point of the N time points and the measurement result of each time point of the N time points comprises:

[0250] I) obtaining the number of times or the accuracy of prediction of the N times according to the K indexes of each time of the N times obtained by prediction and the indexes of the maximum or strongest K reference signals of each time of the N times obtained by measurement;

[0251] Specifically, the number of times or the accuracy of prediction of the N times is obtained by comparing the K indexes obtained by prediction and the K indexes obtained by measurement.

[0252] II) obtaining the first indication information according to the K indexes of each time of the N times obtained by prediction and the indexes of the maximum or strongest K reference signals of each time of the N times obtained by measurement, the first indication information being used to indicate whether the model inference of each time of the N times is correct.

[0253] Specifically, whether the model inference of each time of the N times is correct is determined by comparing the K indexes obtained by prediction and the K indexes obtained by measurement, and then indicated by the first indication information.

[0254] The relationship between I) and II) is "and / or".

[0255] It should be noted that the reporting overhead can be reduced by reporting the number of times or the accuracy of prediction of the N times or the first indication information.

[0256] In order to obtain the indexes of the maximum or strongest K reference signals of each time of the N times obtained by measurement, the second reference signals corresponding to the N times are measured in step 2015a, including:

[0257] The first RSRP measurement value of each time of the N times is obtained by measuring the second reference signals corresponding to the N times.

[0258] Here, since each time corresponds to one or more second reference signals, it should be understood that the first RSRP measurement value of each time of the N times obtained by measurement, wherein the first RSRP measurement value of each time refers to the RSRP measurement value of the one or more second reference signals corresponding to each time.

[0259] The first RSRP measurement value of each time of the N times is sorted to obtain the indexes of the maximum or strongest K reference signals of each time of the N times.

[0260] The number of times of reporting the prediction of the N times is described below by way of an embodiment.

[0261] Embodiment one

[0262] In this embodiment, the UE-side AI / ML model is a time-domain beam prediction model, the input of the model is the measurement results of M time SetB beams, and the output is the index of the optimal beam (Top-K beam) in N time SetA beams.

[0263] 1. The base station configures the UE to report the number of correctly predicted time points in the predicted N time points, for example, the base station instructs the UE to report I bits to indicate the number of correctly predicted time points, wherein,

[0264] 2. The base station sends the reference signals of M time SetB beams;

[0265] 3. The UE measures the received reference signals of M time SetB beams to obtain the L1-RSRP of SetB beams, which is used as the input of the AI model for inference to obtain the index of the optimal beam in the predicted N time SetA beams.

[0266] Here, SetB beams are a set of beams. It should be understood that the reference signals of SetB beams refer to the reference signals of a set of beams, wherein one beam corresponds to one reference signal, and the reference signals of SetB beams are a group of reference signals. The reference signals of M time SetB beams should be understood as a group of reference signals corresponding to each time, i.e., one or more reference signals corresponding to each time.

[0267] The UE measures the reference signals of M time SetB beams to obtain L1-RSRP, specifically M groups of L1-RSRP, each time corresponding to a group of L1-RSRP.

[0268] 4. The base station sends the reference signals of N time SetA beams predicted by the UE to the UE, and the UE measures the reference signals of N time SetA beams to obtain the RSRP measurement values of N time SetA beams, and sorts the RSRP measurement values of N time SetA to obtain the index of the optimal beam in N time SetA beams.

[0269] It should be noted that the understanding of the reference signals of SetA beams can be referred to the understanding of the reference signals of SetB beams in step 2 above; the understanding of the reference signals of N time SetA beams can be referred to the understanding of the reference signals of M time SetB beams in step 2 above.

[0270] 5. The UE compares the prediction result in step 3 and the measurement result in step 4 to determine whether the optimal beam in N time is correctly predicted, and obtains the number of correctly predicted time points in the predicted N time points. Of course, further, the UE can obtain the prediction accuracy based on the number of correctly predicted time points and N.

[0271] If N=4, the UE reports 3 bits to represent the number of correct prediction time points, as shown in Table 1 below:

[0272] Table 1

[0273] Assuming that the number of correct prediction time points is 3 in N=4 time points, the UE reports 011.

[0274] 6、UE reports the number of correct prediction time points in the predicted N time points.

[0275] 7、After receiving the UE's reporting information, the base station can calculate the beam prediction accuracy of this monitoring to be 75%, and the base station can also average the UE's multiple reporting results to calculate the beam prediction accuracy. If the accuracy is low, the base station can instruct the UE to deactivate the AI model or AI function.

[0276] The implementation process of reporting the first indication information is described below through an embodiment.

[0277] Embodiment Two

[0278] In this embodiment, the UE-side AI / ML model is a time-domain beam prediction model, and the input and output of the AI / ML model are the same as in Embodiment One.

[0279] 1、The base station configures the UE to report N bits, and the i-th bit represents the prediction result of the i-th time point in the predicted N time points, where 0 represents a prediction error and 1 represents a correct prediction.

[0280] 2、The base station sends the reference signals of the M time points of SetB beams;

[0281] 3、The UE measures the L1-RSRP of the SetB beams based on the received reference signals of the M time points of SetB beams, and the L1-RSRP of the SetB beams is used as the AI model input for inference to obtain the index of the optimal beam in the N time points of SetA beams.

[0282] 4、The base station sends the reference signals of the N time points of SetA beams predicted by the UE to the UE, and the UE measures the RSRP of the N time points of SetA beams, sorts the RSRP measurement values of the N time points of SetA, and obtains the index of the optimal beam in the N time points of SetA beams.

[0283] 5、The UE compares the prediction result in step 3 with the measurement result in step 4 to determine whether the optimal beam in the N time points is predicted correctly, and obtains whether the model inference of each time point in the predicted N time points is correct.

[0284] Assuming that the number of correct prediction times among the N = 4 times is the 1st, 3rd, and 4th times, the UE reports 1011.

[0285] 6、UE reports a 4-bit bitmap (first indication information) indicating whether the model inference is accurate at each of the N times. As shown in FIG. 3, 0 represents a prediction error, and 1 represents a correct prediction.

[0286] 7、After receiving the UE's report information, the base station can calculate that the beam prediction accuracy of this monitoring is 75%, and the beam prediction of the 2nd time is incorrect. The base station can average the UE's multiple reporting results to calculate the beam prediction accuracy. If the accuracy is low, the base station can instruct the UE to deactivate the AI model or AI function.

[0287] As an optional implementation, the step 202 of reporting the performance monitoring related information to the network side device includes:

[0288] Reporting one or more of the following information to the network side device:

[0289] a first mean value, the first mean value being a mean value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N times;

[0290] a maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N times;

[0291] time related information corresponding to the maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N times;

[0292] a minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N times;

[0293] time related information corresponding to the minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N times.

[0294] Here, the characteristic values of the performance monitoring indicators of the N times include one or more of the first mean value, the maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N times, and the minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N times.

[0295] The time-related information corresponding to the characteristic value of the performance monitoring index at the N time points includes time-related information corresponding to a maximum value of a difference between a predicted value and a measured value of a reference signal received power (RSRP) of the target reference signal at the N time points, and / or time-related information corresponding to a minimum value of the difference between the predicted value and the measured value of the RSRP of the target reference signal at the N time points.

[0296] In this implementation, the performance monitoring index is a difference between a predicted value and a measured value of a reference signal received power (RSRP) of a predicted beam output by the model.

[0297] To obtain the reporting information, as an optional implementation three, the prediction result at each of the N time points includes a predicted value of a reference signal received power (RSRP) of the target reference signal at the time point, and the measurement result at each of the N time points includes a measured value of the RSRP of the target reference signal at the time point.

[0298] Correspondingly, the performance monitoring related information for the first object is obtained according to the prediction result at each of the N time points and the measurement result at each of the N time points in the step 2016a, and includes:

[0299] A difference between a predicted value and a measured value of a reference signal received power (RSRP) of the target reference signal at each of the N time points is calculated according to the predicted value of the RSRP of the target reference signal at each of the N time points and the measured value of the RSRP of the target reference signal at each of the N time points.

[0300] One or more of the following information is obtained according to the difference between the predicted value and the measured value of the RSRP of the target reference signal at each of the N time points:

[0301] A first mean value, which is a mean value of the difference between the predicted value and the measured value of the RSRP of the target reference signal at the N time points;

[0302] A maximum value of the difference between the predicted value and the measured value of the RSRP of the target reference signal at the N time points;

[0303] Time-related information corresponding to the maximum value of the difference between the predicted value and the measured value of the RSRP of the target reference signal at the N time points;

[0304] A minimum value of the difference between the predicted value and the measured value of the RSRP of the target reference signal at the N time points;

[0305] Time-related information corresponding to the minimum value of the difference between the predicted value and the measured value of the RSRP of the target reference signal at the N time points.

[0306] To achieve the above-mentioned implementation mode two and implementation mode three, the prediction result of each time point of N time points needs to be obtained, that is, the RSRP prediction value of the target reference signal corresponding to each time point of N time points needs to be obtained.

[0307] As an optional implementation mode four, the step 2013a of obtaining the prediction result of each time point of N time points according to the measurement result of the first reference signal corresponding to the M time points comprises:

[0308] inputting the measurement result of the first reference signal corresponding to the M time points into an artificial intelligence model to obtain the RSRP prediction value of the second reference signal corresponding to each time point of N time points;

[0309] obtaining the RSRP prediction value of the target reference signal corresponding to each time point of N time points according to the RSRP prediction value of the second reference signal corresponding to each time point of N time points.

[0310] It should be noted that the above-mentioned implementation mode two, implementation mode three and implementation mode four, the terminal device first executes the implementation mode four, that is, the step of obtaining the prediction result of each time point of N time points, then executes the implementation mode three, that is, the step of obtaining the reporting information, and finally executes the implementation mode two, that is, the step of reporting the obtained reporting information to the network side device.

[0311] In some embodiments, the target reference signal is determined by the first reference signal or the prediction result. In the case of an AI model being an AI beam management model, the target reference signal determined by the first reference signal or the prediction result can be understood as the target reference signal including the reference signal for the first beam set (SetB beam), or the target reference signal including the reference signal corresponding to the maximum or strongest K beams determined by the prediction result. Therefore, there are two execution processes including the above-mentioned implementation mode two, implementation mode three and implementation mode four, which are as follows:

[0312] Process one:

[0313] Step 1-1, in the case of the target reference signal being the first reference signal, inputting the measurement result of the first reference signal corresponding to the M time points into an artificial intelligence model to obtain the RSRP prediction value of the second reference signal corresponding to each time point of N time points;

[0314] Step 1-2, obtaining the RSRP prediction value of the first reference signal corresponding to each time point of N time points according to the RSRP prediction value of the second reference signal corresponding to each time point of N time points;

[0315] For example, the first reference signal is a reference signal for a SetB beam, and the second reference signal is a reference signal for a SetA beam. The measurement results of the reference signals of the SetB beams corresponding to the M time points are input into the AI model to obtain the RSRP prediction values of the reference signals of the SetA beams corresponding to the N time points. Since the SetB beams are a subset of the SetA beams, the RSRP prediction values of the reference signals of the SetB beams corresponding to the N time points can be obtained from the RSRP prediction values of the reference signals of the SetA beams corresponding to the N time points.

[0316] Step 1-3, in the case that the prediction results of the N time points each include the RSRP prediction values of the first reference signals corresponding to the N time points each, and the measurement results of the N time points each include the RSRP measurement values of the first reference signals corresponding to the N time points each, the difference between the RSRP prediction value and the RSRP measurement value of the first reference signal corresponding to each time point is calculated according to the RSRP prediction value of the first reference signal corresponding to each time point and the RSRP measurement value of the first reference signal corresponding to each time point.

[0317] Step 1-4, according to the difference between the RSRP prediction value and the RSRP measurement value of the first reference signal corresponding to each time point, one or more of the following information is obtained:

[0318] a first mean value, the first mean value being a mean value of the differences between the RSRP prediction values and the RSRP measurement values of the first reference signals corresponding to the N time points;

[0319] a maximum value of the differences between the RSRP prediction values and the RSRP measurement values of the first reference signals corresponding to the N time points;

[0320] time point related information corresponding to the maximum value of the differences between the RSRP prediction values and the RSRP measurement values of the first reference signals corresponding to the N time points;

[0321] a minimum value of the differences between the RSRP prediction values and the RSRP measurement values of the first reference signals corresponding to the N time points;

[0322] time point related information corresponding to the minimum value of the differences between the RSRP prediction values and the RSRP measurement values of the first reference signals corresponding to the N time points.

[0323] Step 1-5, one or more of the following information is reported to the network side device:

[0324] a first mean value of differences between RSRP prediction values and RSRP measurement values of the first reference signal corresponding to the N time instants;

[0325] a maximum value of differences between RSRP prediction values and RSRP measurement values of the first reference signal corresponding to the N time instants;

[0326] time information corresponding to the maximum value of differences between RSRP prediction values and RSRP measurement values of the first reference signal corresponding to the N time instants;

[0327] a minimum value of differences between RSRP prediction values and RSRP measurement values of the first reference signal corresponding to the N time instants;

[0328] time information corresponding to the minimum value of differences between RSRP prediction values and RSRP measurement values of the first reference signal corresponding to the N time instants.

[0329] Flow II:

[0330] Step 2-1, in the case that the target reference signal includes the maximum or strongest K reference signals, input the measurement results of the first reference signal corresponding to the M time instants into the artificial intelligence model to obtain the RSRP prediction value of the second reference signal corresponding to each of the N time instants;

[0331] Step 2-2, according to the RSRP prediction value of the second reference signal corresponding to each of the N time instants, obtain the RSRP prediction value of the maximum or strongest K reference signals corresponding to each of the N time instants;

[0332] For example, the first reference signal is a reference signal for SetB beam, and the second reference signal is a reference signal for SetA beam. The measurement results of the reference signal of SetB beam corresponding to the M time instants are input into the AI model to obtain the RSRP prediction value of the reference signal of SetA beam corresponding to each of the N time instants. The RSRP prediction values of the reference signal of SetA beam corresponding to each of the N time instants are sorted to obtain the RSRP prediction value of the maximum or strongest K reference signals corresponding to each of the N time instants.

[0333] Step 2-3, in the case that the prediction result at each of the N time instants comprises RSRP prediction values of the maximum or strongest K reference signals corresponding to the each of the N time instants, and the measurement result at each of the N time instants comprises RSRP measurement values of the maximum or strongest K reference signals corresponding to the each of the N time instants, the difference between the RSRP prediction value and the RSRP measurement value of the maximum or strongest K reference signals corresponding to each of the N time instants is calculated according to the RSRP prediction value and the RSRP measurement value of the maximum or strongest K reference signals corresponding to each of the N time instants;

[0334] Step 2-4, one or more of the following information is obtained according to the difference between the RSRP prediction value and the RSRP measurement value of the maximum or strongest K reference signals corresponding to each of the N time instants:

[0335] a first average value, the first average value being an average value of the difference between the RSRP prediction value and the RSRP measurement value of the maximum or strongest K reference signals corresponding to the N time instants;

[0336] a maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the maximum or strongest K reference signals corresponding to the N time instants;

[0337] time related information corresponding to the maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the maximum or strongest K reference signals corresponding to the N time instants;

[0338] a minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the maximum or strongest K reference signals corresponding to the N time instants;

[0339] time related information corresponding to the minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the maximum or strongest K reference signals corresponding to the N time instants.

[0340] Step 2-5, one or more of the following information is reported to the network side device:

[0341] a first average value, the first average value being an average value of the difference between the RSRP prediction value and the RSRP measurement value of the maximum or strongest K reference signals corresponding to the N time instants;

[0342] a maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the maximum or strongest K reference signals corresponding to the N time instants;

[0343] time related information corresponding to the maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the maximum or strongest K reference signals corresponding to the N time instants;

[0344] a minimum value of a difference between the RSRP measurement value and the RSRP prediction value of the maximum or strongest K reference signals corresponding to the N moments;

[0345] a minimum value of a difference between the RSRP measurement value and the RSRP prediction value of the maximum or strongest K reference signals corresponding to the N moments;

[0346] The following examples illustrate the implementation process of the UE performing information reporting when the target reference signal is the first reference signal.

[0347] Embodiment Three

[0348] In this embodiment, the UE-side AI / ML model is a time-domain beam prediction model, and the input of the model is the measurement result of the M moments of SetB beams, and the output is the L1-RSRP prediction value of the N moments of SetA beams.

[0349] 1. The base station configures the UE to report the average value of the difference between the L1-RSRP prediction value and the L1-RSRP measurement value of the N moments of SetB beams.

[0350] 2. The base station sends the reference signals of the M moments of SetB beams;

[0351] 3. The UE measures the received reference signals of the M moments of SetB beams to obtain the L1-RSRP of SetB beams, which is used as the input of the AI model for inference to obtain the predicted L1-RSRP prediction value of the corresponding SetA beams at the N moments. Assuming that the SetB beams are a subset of the SetA beams, the L1-RSRP prediction value of the corresponding SetB beams at the N moments can be obtained.

[0352] 4. The base station sends the reference signals of the N moments of SetB beams predicted by the UE, and the UE measures the reference signals of the N moments of SetB beams to obtain the L1-RSRP measurement value of SetB at the N moments (corresponding to the RSRP measurement value of the target reference signal at the N moments, and the target reference signal is the first reference signal, i.e., the reference signal for SetB beams);

[0353] 5. The UE calculates the difference between the L1-RSRP measurement value and the prediction value of SetB at the N moments, and obtains the average value of the difference between the L1-RSRP measurement value and the prediction value of SetB at the N moments after averaging the N moments.

[0354] 6、UE reports the average of the difference between the L1-RSRP measurement value and the predicted value of SetB at N time points to the base station.

[0355] 7、The base station can average the reporting results of the UE multiple times as a monitoring index. If the absolute value of the average is greater than 1dB, it is considered that the performance of the AI model or AI function is poor, and the UE can be instructed to deactivate the AI model or AI function.

[0356] Embodiment Four

[0357] In this embodiment, the UE-side AI / ML model is a time-domain beam prediction model, and the input and output of the AI / ML model are the same as in Embodiment Three.

[0358] 1、The base station configures the UE to report the maximum and minimum values of the difference between the L1-RSRP prediction value and the L1-RSRP measurement value of SetB beams at N time points, as well as the corresponding time information.

[0359] 2、The base station sends reference signals of SetB beams at M time points.

[0360] 3、The UE measures the received reference signals of SetB beams at M time points to obtain the L1-RSRP of SetB beams. The L1-RSRP of SetB beams is used as the input of the AI model for inference to obtain the predicted L1-RSRP prediction value of SetA beams corresponding to each time point at N time points. Assuming that SetB beams are a subset of SetA beams, the L1-RSRP prediction value of SetB beams corresponding to each time point at N time points can be obtained.

[0361] 4、The base station sends the reference signals of SetB beams predicted by the UE at N time points. The UE measures the reference signals of SetB beams at N time points to obtain the L1-RSRP measurement value of SetB corresponding to each time point at N time points (corresponding to the RSRP measurement value of the target reference signal at each time point at N time points, the target reference signal being the first reference signal, i.e., the reference signal for SetB beams).

[0362] 5、The UE calculates the difference between the L1-RSRP measurement value and the predicted value of SetB corresponding to each time point at N time points. The calculation result is that the difference at the first time point is the maximum, and the difference at the third time point is the minimum. Assuming that 4 bits are used to represent the difference between the L1-RSRP measurement value and the predicted value of SetB, and 2 bits are used to represent which time point the reported difference corresponds to.

[0363] The UE reports the content as shown in Table 2 below:

[0364] Table 2

[0365] 6、The base station can know the accuracy of the predicted optimal beam at the N time points according to the reporting result of the UE. If it is considered that the difference is greater than a certain threshold, the prediction is incorrect. The base station can average the reporting results of the UE multiple times to serve as a monitoring index, so as to judge the performance of the AI model or AI function on the UE side.

[0366] The implementation process of the UE performing information reporting in the case where the target reference signal includes the maximum or strongest K reference signals is described below through an embodiment.

[0367] Embodiment Five

[0368] In this embodiment, the AI / ML model on the UE side is a time-domain beam prediction model, and the input and output of the AI / ML model are the same as in Embodiment Three.

[0369] 1、The base station configures the UE to report the average of the difference between the predicted L1-RSRP value and the measured L1-RSRP value of the Top-2 beams at the predicted N time points.

[0370] 2、The base station sends the reference signals of the SetB beams at the M time points;

[0371] 3、The UE measures the L1-RSRP of the SetB beams based on the received reference signals of the SetB beams at the M time points, and the L1-RSRP of the SetB beams is used as the input of the AI model for inference to obtain the predicted L1-RSRP value of the SetA corresponding to each time point at the predicted N time points. The predicted L1-RSRP values of the SetA corresponding to each time point at the predicted N time points are sorted to obtain the indexes of the Top-2 beams at each time point at the predicted N time points.

[0372] 4、The UE reports the indexes of the Top-2 beams at each time point at the predicted N time points to the base station.

[0373] 5、The base station sends the reference signals of the Top-2 beams at each time point at the predicted N time points, and the UE measures the L1-RSRP of the Top-2 beams at each time point at the N time points to obtain the L1-RSRP measurement values of the Top-2 beams at each time point at the N time points (corresponding to the RSRP measurement values of the target reference signals corresponding to each time point at the N time points, and the target reference signal includes the maximum or strongest K reference signals).

[0374] 6、The UE calculates the difference between the L1-RSRP measurement value and the predicted value of the Top-2 beams at each time point, averages the differences at the N time points to obtain the average of the differences between the L1-RSRP measurement value and the predicted value of the Top-2 beams at the N time points, and reports it to the base station.

[0375] 7、The base station can average the multiple reporting results of the UE and use the average as a monitoring indicator. If the absolute value of the average is greater than 1 dB, it is considered that the performance of the AI model or AI function is poor, and the UE can be instructed to deactivate the AI model or AI function.

[0376] Embodiment six

[0377] In this embodiment, the UE-side AI / ML model is a time-domain beam prediction model, and the input and output of the AI / ML model are the same as in Embodiment Three.

[0378] 1、The base station configures the UE to report the maximum value of the difference between the predicted L1-RSRP value and the measured L1-RSRP value of the Top-2 beams in the predicted N time instants.

[0379] 2、The base station sends reference signals of the SetB beams at M time instants;

[0380] 3、The UE measures the L1-RSRP of the SetB beams based on the received reference signals of the SetB beams at M time instants, and uses the L1-RSRP of the SetB beams as AI model input for inference to obtain the predicted L1-RSRP value of the SetA corresponding to each time instant in the predicted N time instants. The predicted L1-RSRP values of the SetA corresponding to each time instant in the predicted N time instants are sorted to obtain the index of the Top-2 beams at each time instant in the predicted N time instants.

[0381] 4、The UE reports the index of the Top-2 beams at each time instant in the predicted N time instants to the base station.

[0382] 5、The base station sends reference signals of the Top-2 beams at each time instant in the predicted N time instants, and the UE measures the L1-RSRP of the Top-2 beams at each time instant in the N time instants to obtain the L1-RSRP measurement value of the Top-2 beams at each time instant in the N time instants (corresponding to the RSRP measurement value of the target reference signal corresponding to each time instant in the N time instants, and the target reference signal includes the maximum or strongest K reference signals);

[0383] 6、The UE calculates the difference between the L1-RSRP measurement value and the predicted value of the Top-2 beams at each time instant, and the calculation result is that the difference at the second time instant is the largest.

[0384] The UE reports the following Table 3:

[0385] Table 3

[0386] 7、The base station can know the accuracy of the predicted optimal beam at the N time points according to the reporting result of the UE. If it is considered that the difference is greater than a certain threshold, the prediction is incorrect. The base station can average the reporting results of the UE multiple times as a monitoring index, so as to judge the performance of the AI model or AI function on the UE side.

[0387] As an optional implementation mode five, the step 202 of reporting the performance monitoring related information to the network side device comprises:

[0388] The following one or more information is reported to the network side device:

[0389] The maximum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to the target reference signal in P times of measurement or measurement within a first time period, P being a positive integer greater than 1; here, the first time period is indicated by the network side device, indicating a period of time.

[0390] The maximum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to the target reference signal in P times of measurement or measurement within a first time period, P being a positive integer greater than 1; here, the first time period is indicated by the network side device, indicating a period of time.

[0391] The minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to the target reference signal in P times of measurement or measurement within a first time period.

[0392] The minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to the target reference signal in P times of measurement or measurement within a first time period.

[0393] The feature value of the performance monitoring index corresponding to the multiple model inferences comprises: the maximum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to the target reference signal in P times of measurement or measurement within a first time period, and / or the minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to the target reference signal in P times of measurement or measurement within a first time period.

[0394] Here, the above implementation mode is applicable to the scenario where the AI model is a spatial beam prediction model.

[0395] Based on this, as an optional implementation mode six, the step 201 of obtaining the performance monitoring related information for the first object comprises:

[0396] Step 2011b, for one measurement, receiving the first reference signal configured by the network side device;

[0397] In some embodiments, the first reference signal is a reference signal for a first beam set. Wherein, the first beam set can be SetB beams, where SetA is a set of all beams, and SetB is a subset of SetA; or SetA is a set of narrow beams, and SetB is a set of wide beams.

[0398] Step 2012b, measuring the first reference signal;

[0399] Step 2013b, obtaining a RSRP prediction value for a target reference signal according to the measurement result of the first reference signal;

[0400] Step 2014b, calculating a difference between the RSRP prediction value for the target reference signal and a RSRP measurement value for the target reference signal according to the RSRP prediction value for the target reference signal and the RSRP measurement value for the target reference signal;

[0401] Step 2015b, after performing P times of measurements or measurements within a first time period, obtaining one or more of the following information according to the difference between the RSRP prediction value for the target reference signal and the RSRP measurement value for the target reference signal corresponding to each measurement:

[0402] A maximum value of the difference between the RSRP prediction value for the target reference signal and the RSRP measurement value for the target reference signal corresponding to the P times of measurements or the measurements within the first time period;

[0403] Time related information or an index value of the measurement corresponding to the maximum value of the difference between the RSRP prediction value for the target reference signal and the RSRP measurement value for the target reference signal corresponding to the P times of measurements or the measurements within the first time period;

[0404] A minimum value of the difference between the RSRP prediction value for the target reference signal and the RSRP measurement value for the target reference signal corresponding to the P times of measurements or the measurements within the first time period;

[0405] Time related information or an index value of the measurement corresponding to the minimum value of the difference between the RSRP prediction value for the target reference signal and the RSRP measurement value for the target reference signal corresponding to the P times of measurements or the measurements within the first time period.

[0406] It should be noted that in the actual execution steps of the method, the terminal device first executes the implementation mode six, i.e., the step of obtaining the reported information, and then executes the implementation mode five, i.e., the step of reporting the obtained reported information to the network side device.

[0407] In some embodiments, the target reference signal is determined by the first reference signal or the prediction result. In the case that the AI model is an AI beam management model, the target reference signal determined by the first reference signal or the prediction result can be understood as that the target reference signal includes the reference signal for the first beam set (SetB beam), or the target reference signal includes the reference signal corresponding to the maximum or strongest K beams determined by the prediction result. Therefore, there are two execution flows including the above-mentioned implementation mode five and implementation mode six, which are as follows:

[0408] Flow one:

[0409] Step 1a, in the case that the target reference signal is the first reference signal, for one measurement, the first reference signal configured by the network side device is received;

[0410] Step 2a, the first reference signal is measured;

[0411] Step 3a, according to the measurement result of the first reference signal, the RSRP prediction value for the first reference signal is obtained;

[0412] Here, the measurement result of the first reference signal is input into the AI model to obtain the RSRP prediction value for the first reference signal.

[0413] For example, the first reference signal is the reference signal for SetB beam, the measurement result of the reference signal for SetB beam is taken as the input of the AI model to obtain the RSRP prediction value for the reference signal of SetA beam; it is known that SetB beam is a subset of SetA beam, and based on the predicted RSRP prediction value for the reference signal of SetA beam, the RSRP prediction value for the reference signal of SetB beam can be obtained.

[0414] Step 4a, according to the RSRP prediction value for the first reference signal and the RSRP measurement value for the first reference signal, the difference between the RSRP prediction value and the RSRP measurement value for the first reference signal is calculated;

[0415] Step 5a, after performing P times of measurements or measurements within a first time period, according to the difference between the RSRP prediction value and the RSRP measurement value for the first reference signal corresponding to each measurement, one or more of the following information is obtained:

[0416] The maximum value of the difference between the RSRP prediction value and the RSRP measurement value for the first reference signal corresponding to P times of measurements or measurements within a first time period;

[0417] a time point related information or an index value of a measurement corresponding to a maximum value of a difference between the RSRP prediction value for the first reference signal and the RSRP measurement value corresponding to the P measurements or the measurements within the first time period;

[0418] a minimum value of a difference between the RSRP prediction value for the first reference signal and the RSRP measurement value corresponding to the P measurements or the measurements within the first time period;

[0419] a time point related information or an index value of a measurement corresponding to a minimum value of a difference between the RSRP prediction value for the first reference signal and the RSRP measurement value corresponding to the P measurements or the measurements within the first time period.

[0420] Flow II:

[0421] Step 1b, in the case that the target reference signal includes the maximum or strongest K reference signals, for one measurement, receiving the first reference signal configured by the network side device;

[0422] Step 2b, measuring the first reference signal;

[0423] Step 3b, obtaining the RSRP prediction value for the maximum or strongest K reference signals according to the measurement result of the first reference signal;

[0424] Here, the measurement result of the first reference signal is input to the AI model to obtain the RSRP prediction value for the maximum or strongest K reference signals.

[0425] For example, the first reference signal is the reference signal for SetB beam, the measurement result of the reference signal for SetB beam is taken as the input of the AI model to obtain the RSRP prediction value for the reference signal for SetA beam; based on the predicted RSRP prediction value for the reference signal for SetA beam, the RSRP prediction value for the maximum or strongest K beams (corresponding reference signals) of SetA beam is obtained.

[0426] Step 4b, calculating the difference between the RSRP prediction value for the maximum or strongest K reference signals and the RSRP measurement value for the maximum or strongest K reference signals according to the RSRP prediction value for the maximum or strongest K reference signals and the RSRP measurement value for the maximum or strongest K reference signals;

[0427] Here, the RSRP measurement value for the maximum or strongest K reference signals can be obtained by the following steps:

[0428] The UE reports the indexes of the maximum or strongest K beams of the SetA beams to the network side device; then receives the maximum or strongest K reference signals corresponding to the SetA beams (i.e., the reference signals of the maximum or strongest K beams of the SetA beams) sent by the network side device, and then the UE performs measurement on the maximum or strongest K reference signals to obtain the RSRP measurement values of the maximum or strongest K reference signals.

[0429] Step 5a, after performing P times of measurement or measurement within the first time period, according to the difference between the RSRP prediction value and the RSRP measurement value corresponding to each measurement for the maximum or strongest K reference signals, one or more of the following information is obtained:

[0430] The maximum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within the first time period for the maximum or strongest K reference signals;

[0431] The maximum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within the first time period for the maximum or strongest K reference signals;

[0432] The minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within the first time period for the maximum or strongest K reference signals;

[0433] The minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within the first time period for the maximum or strongest K reference signals.

[0434] The following describes the implementation process of reporting the characteristic values of the performance monitoring indicators of the multiple times of model inference through an embodiment.

[0435] Embodiment Seven

[0436] In this embodiment, the AI / ML model on the UE side is a spatial beam prediction model, the input of the model is the measurement result of the SetB beam, and the output is the RSRP prediction value of the SetA beam. By sorting the RSRP prediction value of the SetA beam, the index corresponding to the optimal beam at each time in the predicted N times is obtained.

[0437] 1. The base station configures the UE to periodically report the maximum and minimum values of the difference between the RSRP prediction value and the RSRP measurement value of the SetB beam within the performance monitoring time T. T is the reporting period, i.e., the UE reports the maximum and minimum values of the difference between the RSRP prediction value and the RSRP measurement value of the SetB beam within the reporting interval.

[0438] 2. The base station transmits the reference signal of the SetB beam;

[0439] 3. The UE measures the received reference signal of the SetB beam to obtain the RSRP of the SetB beam, and the RSRP of the SetB beam is used as the input of the AI model for inference to obtain the predicted RSRP of the SetA beam. Assuming that the SetB is a subset of the SetA, the predicted RSRP of the SetB can be obtained; the UE calculates the difference between the predicted RSRP and the measured RSRP of the Set B;

[0440] 4. Steps 2 and 3 are repeated until the measurement within the time T indicated by the base station is performed;

[0441] 5. Assuming that the base station transmits the reference signal corresponding to the SetB beam P times within the time T, the UE respectively uses the RSRP of the reference signal corresponding to the SetB beam P times as the input of the model for inference to obtain P groups of predicted RSRP of the SetA beam. Assuming that the SetB is a subset of the SetA, the predicted RSRP of the SetB beam can be obtained from the inference result of the UE. According to the measurement and the inference result, the UE obtains the maximum value and the minimum value of the difference between the predicted RSRP and the measured RSRP of the P SetB beams, and reports the maximum value and the minimum value to the base station. It should be noted that in addition to reporting the maximum value and the minimum value of the difference between the predicted RSRP and the measured RSRP of the P SetB beams, the time information corresponding to the maximum value and the minimum value or the corresponding monitoring times can also be reported.

[0442] 6. The base station calculates the monitoring index according to the reporting result of the UE, such as the average of the multiple reporting results of the UE, so as to judge the performance of the AI model or the AI function. If the performance is poor, the UE can be instructed to deactivate the AI model or the AI function.

[0443] In some embodiments, the method of the present disclosure further comprises:

[0444] receiving configuration information sent by the network side device, the configuration information being used to indicate the content of the performance monitoring related information reported by the terminal device for the first object.

[0445] Here, the terminal device reports the content required by the network side device according to the indication of the configuration information sent by the network side device, so as to meet the performance monitoring requirement of the network side device.

[0446] The information reporting method of the embodiments of the present disclosure, by obtaining performance monitoring related information for a first object, the first object being an artificial intelligence model or an artificial intelligence function; reporting the performance monitoring related information to a network side device, wherein the performance monitoring related information includes one or more of the following information: characteristic values of performance monitoring indicators at N time instants; performance monitoring related information at each of the N time instants; time instant related information corresponding to the characteristic values of the performance monitoring indicators at the N time instants, N being a positive integer greater than 1; characteristic values of performance monitoring indicators corresponding to multiple model inferences; time instant related information corresponding to the characteristic values of the performance monitoring indicators corresponding to the multiple model inferences, so that by reporting the above performance monitoring related information to the network side device, the performance monitoring of the artificial intelligence model or the artificial intelligence function on the terminal device side by the network side can be realized.

[0447] As shown in FIG. 4, a flowchart of an information transmission method provided by the embodiments of the present disclosure is shown, and the method is applied to a network side device, that is, the method is executed by the network side device. The method specifically includes:

[0448] Step 401, receiving performance monitoring related information reported by a terminal device, wherein the performance monitoring related information includes one or more of the following information:

[0449] Characteristic values of performance monitoring indicators at N time instants;

[0450] Performance monitoring related information at each of the N time instants;

[0451] Time instant related information corresponding to the characteristic values of the performance monitoring indicators at the N time instants, N being a positive integer greater than 1;

[0452] Characteristic values of performance monitoring indicators corresponding to multiple model inferences;

[0453] Time instant related information corresponding to the characteristic values of the performance monitoring indicators corresponding to the multiple model inferences.

[0454] It should be noted that the method executed by the network side device corresponds to the method executed by the terminal device side described above, and the understanding of related concepts and steps can refer to the terminal device side, which will not be repeated here.

[0455] In some embodiments, the characteristic values include one or more of the mean value, the maximum value, and the minimum value.

[0456] In some embodiments, the method of the present disclosure further includes:

[0457] Configuring the terminal device with first reference signals corresponding to M time instants, one or more first reference signals corresponding to each time instant, the first reference signals corresponding to the M time instants being used to obtain predicted results at each of the N time instants, M being a positive integer.

[0458] configure the terminal device with second reference signals corresponding to N time instants, each time instant corresponding to one or more second reference signals, the second reference signals corresponding to the N time instants being used to obtain measurement results at each of the N time instants.

[0459] In some embodiments, the method of the present disclosure further comprises:

[0460] configuring the terminal device with a first reference signal for each of P measurements or within a first time period, the first reference signal being used to obtain an RSRP prediction value for a target reference signal, the target reference signal being determined by the first reference signal or the prediction result, P being a positive integer greater than 1.

[0461] In some embodiments, the method of the present disclosure further comprises:

[0462] sending configuration information to the terminal device, the configuration information being used to indicate the content of the performance monitoring related information reported by the terminal device for the first object.

[0463] Here, the network side device sends configuration information to the terminal device, so that the terminal device reports the content required by the network side device according to the indication of the configuration information, thereby meeting the performance monitoring requirements of the network side device.

[0464] The information transmission method of the embodiments of the present disclosure receives the performance monitoring related information reported by the terminal device, wherein the performance monitoring related information includes one or more of the following information: characteristic values of performance monitoring indicators at N time instants; performance monitoring related information at each of the N time instants; time related information corresponding to the characteristic values of the performance monitoring indicators at the N time instants, N being a positive integer greater than 1; characteristic values of performance monitoring indicators corresponding to multiple model inferences; time related information corresponding to the characteristic values of the performance monitoring indicators corresponding to the multiple model inferences, so that the network side can realize performance monitoring of the artificial intelligence model or artificial intelligence function on the terminal device side by receiving the above performance monitoring related information reported by the terminal device.

[0465] As shown in FIG. 5, the embodiments of the present disclosure also provide a terminal device, comprising: a memory 520, a transceiver 500, and a processor 510; the memory 520 is used to store program instructions; the transceiver 500 is used to transceive data under the control of the processor 510; the processor 510 is used to read the program instructions in the memory 520 and perform the following operations:

[0466] obtain performance monitoring related information for a first object, the first object being an artificial intelligence model or an artificial intelligence function;

[0467] reporting the performance monitoring related information to the network side device, wherein the performance monitoring related information comprises one or more of the following information:

[0468] characteristic values of the performance monitoring indicators at the N time instants;

[0469] performance monitoring related information at each of the N time instants;

[0470] time instant related information corresponding to the characteristic values of the performance monitoring indicators at the N time instants, N being a positive integer greater than 1;

[0471] characteristic values of the performance monitoring indicators corresponding to the multiple model inferences;

[0472] time instant related information corresponding to the characteristic values of the performance monitoring indicators corresponding to the multiple model inferences.

[0473] In FIG. 5, the bus architecture can include any number of interconnecting buses and bridges, depending on the specific application of the processor 510 and the memory 520 that are linked together by the various circuits representing one or more processors and memories. The bus architecture can also include various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are all well known in the art, and therefore, not described further. The bus interface provides an interface to the transceiver 500, which can be a number of elements, including a transmitter and a receiver, that provides means for communicating with various other apparatus over a transmission medium, including a wireless channel, a wired channel, optical cable, and the like. The user interface 530 can also be an interface to other means that can be external or internal to the device, including but not limited to a keypad, a display, a speaker, a microphone, a joystick, and the like.

[0474] The processor 510 is responsible for managing the bus architecture and general processing, and the memory 520 can store data used by the processor 510 in executing operations.

[0475] In some embodiments, the processor 510 can 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), and the processor 510 can also adopt a multi-core architecture.

[0476] The processor 510 invokes program instructions stored in the memory 520 to perform any of the methods provided by the embodiments of the present disclosure according to the executable instructions obtained. The processor 510 and the memory 520 can also be physically arranged separately.

[0477] In some embodiments, the characteristic value includes one or more of a mean value, a maximum value, and a minimum value.

[0478] In some embodiments, the operations further include:

[0479] receiving first reference signals corresponding to the M time instants configured by the network-side device, each time instant corresponding to one or more first reference signals;

[0480] measuring the first reference signals corresponding to the M time instants; wherein M is a positive integer;

[0481] obtaining a prediction result of each time instant of the N time instants according to the measurement results of the first reference signals corresponding to the M time instants;

[0482] receiving second reference signals corresponding to the N time instants configured by the network-side device, each time instant corresponding to one or more second reference signals;

[0483] measuring the second reference signals corresponding to the N time instants;

[0484] obtaining performance monitoring related information for the first object according to the prediction result of each time instant of the N time instants and the measurement result of each time instant of the N time instants.

[0485] In some embodiments, the operations further include:

[0486] reporting, to the network-side device, a number of time instants correctly predicted or a correct prediction rate in the N time instants, and a characteristic value of a performance monitoring indicator of the N time instants includes the number of time instants correctly predicted or the correct prediction rate in the N time instants; and / or,

[0487] reporting, to the network-side device, first indication information, the first indication information being used to indicate whether a model inference of each time instant of the N time instants is correct, and performance monitoring related information of each time instant of the N time instants includes the first indication information.

[0488] In some embodiments, the prediction result of each time instant of the N time instants includes K indexes of the time instants of the N time instants predicted, and the measurement result of each time instant of the N time instants includes indexes of the maximum or strongest K reference signals of each time instant of the N time instants measured, K being a positive integer; the operations further include:

[0489] obtaining, according to the K indexes of each of the N moments obtained by prediction and the indexes of the maximum or strongest K reference signals of each of the N moments obtained by measurement, a number of correctly predicted moments or a correct prediction rate in the N moments; and / or,

[0490] obtaining, according to the K indexes of each of the N moments obtained by prediction and the indexes of the maximum or strongest K reference signals of each of the N moments obtained by measurement, first indication information, the first indication information being used to indicate whether the model inference of each of the N moments is correct.

[0491] In some embodiments, the first indication information is an N-bit bitmap.

[0492] In some embodiments, the operations further include:

[0493] measuring the second reference signals corresponding to the N moments to obtain first RSRP measurement values of each of the N moments;

[0494] sorting the first RSRP measurement values of each of the N moments to obtain indexes of the maximum or strongest K reference signals of each of the N moments.

[0495] In some embodiments, the operations further include:

[0496] reporting one or more of the following information to a network side device:

[0497] a first average value, the first average value being an average value of differences between RSRP prediction values and RSRP measurement values of target reference signals corresponding to the N moments;

[0498] a maximum value of the differences between the RSRP prediction values and the RSRP measurement values of the target reference signals corresponding to the N moments;

[0499] moment related information corresponding to the maximum value of the differences between the RSRP prediction values and the RSRP measurement values of the target reference signals corresponding to the N moments;

[0500] a minimum value of the differences between the RSRP prediction values and the RSRP measurement values of the target reference signals corresponding to the N moments;

[0501] moment related information corresponding to the minimum value of the differences between the RSRP prediction values and the RSRP measurement values of the target reference signals corresponding to the N moments.

[0502] In some embodiments, the prediction result of each of the N time instants comprises a RSRP predicted value of the target reference signal corresponding to the each of the N time instants; the measurement result of each of the N time instants comprises a RSRP measured value of the target reference signal corresponding to the each of the N time instants; and the operations further comprise:

[0503] According to the RSRP predicted value of the target reference signal corresponding to each of the N time instants and the RSRP measured value of the target reference signal corresponding to each of the N time instants, a difference value between the RSRP predicted value and the RSRP measured value of the target reference signal corresponding to each of the N time instants is calculated.

[0504] According to the difference value between the RSRP predicted value and the RSRP measured value of the target reference signal corresponding to each of the N time instants, one or more of the following information is obtained:

[0505] a first mean value, which is a mean value of the difference values between the RSRP predicted value and the RSRP measured value of the target reference signal corresponding to the N time instants;

[0506] a maximum value of the difference values between the RSRP predicted value and the RSRP measured value of the target reference signal corresponding to the N time instants;

[0507] time related information corresponding to the maximum value of the difference values between the RSRP predicted value and the RSRP measured value of the target reference signal corresponding to the N time instants;

[0508] a minimum value of the difference values between the RSRP predicted value and the RSRP measured value of the target reference signal corresponding to the N time instants;

[0509] time related information corresponding to the minimum value of the difference values between the RSRP predicted value and the RSRP measured value of the target reference signal corresponding to the N time instants.

[0510] In some embodiments, the operations further comprise:

[0511] inputting the measurement result of the first reference signal corresponding to the M time instants into an artificial intelligence model to obtain a RSRP predicted value of a second reference signal corresponding to each of the N time instants;

[0512] obtaining the RSRP predicted value of the target reference signal corresponding to each of the N time instants according to the RSRP predicted value of the second reference signal corresponding to each of the N time instants.

[0513] In some embodiments, the operations further comprise:

[0514] reporting one or more of the following information to a network side device:

[0515] a maximum value of a difference between the RSRP prediction value and the RSRP measurement value corresponding to P measurements or measurements within the first time period, P being a positive integer greater than 1;

[0516] time-related information or an index value corresponding to a maximum value of a difference between the RSRP prediction value and the RSRP measurement value corresponding to P measurements or measurements within the first time period;

[0517] a minimum value of a difference between the RSRP prediction value and the RSRP measurement value corresponding to P measurements or measurements within the first time period;

[0518] time-related information or an index value corresponding to a minimum value of a difference between the RSRP prediction value and the RSRP measurement value corresponding to P measurements or measurements within the first time period;

[0519] The feature values of the performance monitoring indicators corresponding to the multiple model inferences include: a maximum value of a difference between the RSRP prediction value and the RSRP measurement value corresponding to P measurements or measurements within the first time period, and / or a minimum value of a difference between the RSRP prediction value and the RSRP measurement value corresponding to P measurements or measurements within the first time period.

[0520] In some embodiments, the operations further include:

[0521] receiving, for one measurement, a first reference signal configured by the network-side device;

[0522] performing measurement on the first reference signal;

[0523] obtaining, according to a measurement result of the first reference signal, a RSRP prediction value for a target reference signal;

[0524] calculating, according to the RSRP prediction value for the target reference signal and a RSRP measurement value for the target reference signal, a difference between the RSRP prediction value and the RSRP measurement value for the target reference signal;

[0525] After performing P measurements or measurements within the first time period, according to the difference between the RSRP prediction value and the RSRP measurement value for the target reference signal corresponding to each measurement, one or more of the following information is obtained:

[0526] a maximum value of a difference between the RSRP prediction value and the RSRP measurement value corresponding to P measurements or measurements within the first time period;

[0527] a maximum value of a difference between the RSRP prediction value and the RSRP measurement value corresponding to the P measurements or the measurements within the first time period, and an index value of the measurement corresponding to the maximum value of the difference;

[0528] a minimum value of a difference between the RSRP prediction value and the RSRP measurement value corresponding to the P measurements or the measurements within the first time period;

[0529] an index value of the measurement corresponding to the minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to the P measurements or the measurements within the first time period.

[0530] In some embodiments, the target reference signal is determined by the first reference signal or the prediction result.

[0531] In some embodiments, the operations further include:

[0532] receiving configuration information sent by the network-side device, the configuration information being used to indicate content of the performance monitoring related information for the first object reported by the terminal device.

[0533] The terminal device of the embodiments of the present disclosure, by obtaining the performance monitoring related information for the first object, the first object being an artificial intelligence model or an artificial intelligence function, reports the performance monitoring related information to the network-side device, wherein the performance monitoring related information includes one or more of the following information: characteristic values of performance monitoring indicators at N time points; performance monitoring related information at each of the N time points; time point related information corresponding to the characteristic values of the performance monitoring indicators at the N time points, N being a positive integer greater than 1; characteristic values of performance monitoring indicators corresponding to multiple model inferences; and time point related information corresponding to the characteristic values of the performance monitoring indicators corresponding to the multiple model inferences. In this way, by reporting the above performance monitoring related information to the network-side device, the network-side can perform performance monitoring on the artificial intelligence model or the artificial intelligence function of the terminal device side.

[0534] As shown in FIG. 6, the embodiments of the present disclosure also provide an information reporting apparatus, which includes:

[0535] an obtaining unit 601, configured to obtain performance monitoring related information for a first object, the first object being an artificial intelligence model or an artificial intelligence function;

[0536] a reporting unit 602, configured to report the performance monitoring related information to a network-side device, wherein the performance monitoring related information includes one or more of the following information:

[0537] characteristic values of performance monitoring indicators at N time points;

[0538] performance monitoring related information of each of the N time points;

[0539] time point related information corresponding to the feature value of the performance monitoring index of the N time points, N being a positive integer greater than 1;

[0540] the feature value of the performance monitoring index corresponding to the multiple times of model inference;

[0541] time point related information corresponding to the feature value of the performance monitoring index corresponding to the multiple times of model inference.

[0542] In some embodiments, the feature value includes one or more of a mean value, a maximum value, and a minimum value.

[0543] In some embodiments, the obtaining unit 601 is specifically configured to:

[0544] receive first reference signals corresponding to M time points configured by the network side device, one or more first reference signals corresponding to each time point;

[0545] measure the first reference signals corresponding to the M time points, M being a positive integer;

[0546] obtain a prediction result of each of the N time points according to a measurement result of the first reference signals corresponding to the M time points;

[0547] receive second reference signals corresponding to the N time points configured by the network side device, one or more second reference signals corresponding to each time point;

[0548] measure the second reference signals corresponding to the N time points;

[0549] obtain performance monitoring related information for the first object according to the prediction result of each of the N time points and a measurement result of each of the N time points.

[0550] In some embodiments, the reporting unit 602 is specifically configured to:

[0551] report, to the network side device, a number of time points correctly predicted or a correct prediction rate in the N time points, the feature value of the performance monitoring index of the N time points including the number of time points correctly predicted or the correct prediction rate in the N time points; and / or,

[0552] report, to the network side device, first indication information, the first indication information being used to indicate whether model inference of each of the N time points is correct, the performance monitoring related information of each of the N time points including the first indication information.

[0553] In some embodiments, the prediction result of each of the N time points comprises K indexes predicted at each of the N time points; the measurement result of each of the N time points comprises indexes of the maximum or strongest K reference signals measured at each of the N time points, K being a positive integer; accordingly, the obtaining unit 601 is specifically configured to:

[0554] According to the K indexes predicted at each of the N time points and the indexes of the maximum or strongest K reference signals measured at each of the N time points, the number of time points predicted correctly or the prediction accuracy rate in the N time points is obtained; and / or,

[0555] According to the K indexes predicted at each of the N time points and the indexes of the maximum or strongest K reference signals measured at each of the N time points, first indication information is obtained, the first indication information being used to indicate whether the model inference at each of the N time points is correct.

[0556] In some embodiments, the first indication information is an N-bit bitmap.

[0557] In some embodiments, the obtaining unit 601 is specifically configured to:

[0558] measure the second reference signals corresponding to the N time points to obtain first RSRP measurement values of each of the N time points;

[0559] sort the first RSRP measurement values of each of the N time points to obtain indexes of the maximum or strongest K reference signals at each of the N time points.

[0560] In some embodiments, the reporting unit 602 is specifically configured to:

[0561] report one or more of the following information to the network side device:

[0562] a first average value, the first average value being an average value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time points;

[0563] a maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time points;

[0564] time point related information corresponding to the maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time points;

[0565] a minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time points.

[0566] a time corresponding to a minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants.

[0567] In some embodiments, the prediction result of each of the N time instants includes the RSRP prediction value of the target reference signal corresponding to each of the N time instants; the measurement result of each of the N time instants includes the RSRP measurement value of the target reference signal corresponding to each of the N time instants; accordingly, the obtaining unit 601 is specifically configured to:

[0568] According to the RSRP prediction value of the target reference signal corresponding to each of the N time instants and the RSRP measurement value of the target reference signal corresponding to each of the N time instants, the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to each time instant is calculated.

[0569] According to the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to each time instant, one or more of the following information is obtained:

[0570] a first mean value, the first mean value being a mean value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants;

[0571] a maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants;

[0572] time information corresponding to the maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants;

[0573] a minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants;

[0574] time information corresponding to the minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants.

[0575] In some embodiments, the obtaining unit 601 is specifically configured to:

[0576] input the measurement result of the first reference signal corresponding to the M time instants into an artificial intelligence model to obtain the RSRP prediction value of the second reference signal corresponding to each of the N time instants;

[0577] obtain the RSRP prediction value of the target reference signal corresponding to each of the N time instants according to the RSRP prediction value of the second reference signal corresponding to each of the N time instants.

[0578] In some embodiments, the reporting unit 602 is specifically configured to:

[0579] report one or more of the following information to the network side device:

[0580] a maximum value of a difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within a first time period, P being a positive integer greater than 1, for the target reference signal;

[0581] time related information or an index value of the measurement corresponding to the maximum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within a first time period, for the target reference signal;

[0582] a minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within a first time period, for the target reference signal;

[0583] time related information or an index value of the measurement corresponding to the minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within a first time period, for the target reference signal;

[0584] In some embodiments, the performance monitoring index corresponding to the multiple model inferences includes: the maximum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within a first time period, and / or the minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to P times of measurement or measurement within a first time period, for the target reference signal.

[0585] In some embodiments, the obtaining unit 601 is specifically configured to:

[0586] for one measurement, receiving a first reference signal configured by the network side device;

[0587] measuring the first reference signal;

[0588] obtaining a RSRP prediction value for a target reference signal according to a measurement result of the first reference signal;

[0589] calculating a difference between the RSRP prediction value and a RSRP measurement value for the target reference signal according to the RSRP prediction value for the target reference signal and the RSRP measurement value for the target reference signal;

[0590] After the P measurements or the measurements in the first time period are performed, one or more of the following information is obtained according to the difference between the RSRP prediction value and the RSRP measurement value corresponding to each measurement for the target reference signal:

[0591] The maximum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to the P measurements or the measurements in the first time period for the target reference signal;

[0592] The time point related information or the index value of the measurement corresponding to the maximum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to the P measurements or the measurements in the first time period for the target reference signal;

[0593] The minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to the P measurements or the measurements in the first time period for the target reference signal;

[0594] The time point related information or the index value of the measurement corresponding to the minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to the P measurements or the measurements in the first time period for the target reference signal.

[0595] In some embodiments, the target reference signal is determined by the first reference signal or the prediction result.

[0596] In some embodiments, the apparatus of the present disclosure further comprises:

[0597] The second receiving unit is configured to receive configuration information sent by the network side device, and the configuration information is used to indicate the content of the performance monitoring related information for the first object reported by the terminal device.

[0598] The information reporting apparatus of the embodiments of the present disclosure obtains the performance monitoring related information for the first object, the first object being an artificial intelligence model or an artificial intelligence function; and reports the performance monitoring related information to the network side device, wherein the performance monitoring related information comprises one or more of the following information: characteristic values of performance monitoring indicators at N time points; performance monitoring related information at each time point at N time points; time point related information corresponding to the characteristic values of the performance monitoring indicators at N time points, N being a positive integer greater than 1; characteristic values of performance monitoring indicators corresponding to multiple model inferences; and time point related information corresponding to the characteristic values of the performance monitoring indicators corresponding to the multiple model inferences. In this way, by reporting the above performance monitoring related information to the network side device, the performance monitoring of the artificial intelligence model or the artificial intelligence function on the terminal device side by the network side can be realized.

[0599] It should be noted that the division of the units in the embodiments of the present disclosure is illustrative, and is only a logical function division. In actual implementation, another division manner can be used. In addition, each functional unit in each embodiment of the present disclosure can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0600] When the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a processor-readable storage medium. Based on this understanding, the technical solutions of the present disclosure essentially or the parts that make contributions to the related art or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in the various embodiments of the present disclosure. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0601] It should be noted that the above device provided by the embodiments of the present disclosure can realize all method steps achieved by the above method embodiments, and can achieve the same technical effects. Here, the same parts and beneficial effects of the method embodiments in this embodiment will not be described in detail.

[0602] In some embodiments of the present disclosure, a processor-readable storage medium is also provided, which stores program instructions for causing the processor to execute the following steps:

[0603] Obtaining performance monitoring related information for a first object, the first object being an artificial intelligence model or an artificial intelligence function;

[0604] Reporting the performance monitoring related information to a network side device, wherein the performance monitoring related information includes one or more of the following information:

[0605] Characteristic values of performance monitoring indicators at N time points;

[0606] Performance monitoring related information at each of the N time points;

[0607] Time point related information corresponding to the characteristic values of the performance monitoring indicators at the N time points, N being a positive integer greater than 1.

[0608] the characteristic value of the performance monitoring index corresponding to the multiple model inferences;

[0609] the time-related information corresponding to the characteristic value of the performance monitoring index corresponding to the multiple model inferences.

[0610] The program is executed by the processor to realize all the implementation manners in the above-described method embodiment applied to the terminal device side as shown in FIG. 2. To avoid repetition, details are not described herein again.

[0611] As shown in FIG. 7, the embodiment of the disclosure further provides a network side device, comprising: a memory 720, a transceiver 700, and a processor 710. The memory 720 is configured to store a computer program. The transceiver 700 is configured to transceive data under the control of the processor 710. The processor 710 is configured to read program instructions in the memory 720 and perform the following operations:

[0612] receiving the performance monitoring related information reported by the terminal device, wherein the performance monitoring related information comprises one or more of the following information:

[0613] the characteristic value of the performance monitoring index at the N time points;

[0614] the performance monitoring related information at each of the N time points;

[0615] the time-related information corresponding to the characteristic value of the performance monitoring index at the N time points, N being a positive integer greater than 1;

[0616] the characteristic value of the performance monitoring index corresponding to the multiple model inferences;

[0617] the time-related information corresponding to the characteristic value of the performance monitoring index corresponding to the multiple model inferences.

[0618] In FIG. 7, the bus architecture can comprise any number of interconnected buses and bridges, which are linked together by various circuits of the processor 710 representing one or more processors and the memory 720 representing a memory. The bus architecture can also link various other circuits such as peripheral devices, voltage stabilizers, and power management circuits, which are well known in the art, and thus, further description thereof is not provided herein. The bus interface provides an interface. The transceiver 700 can be a plurality of elements, i.e., comprising a transmitter and a receiver, which provide units for communicating with various other devices on transmission media, including wireless channels, wired channels, optical cables, and the like transmission media.

[0619] The processor 710 is responsible for managing the bus architecture and general processing, and the memory 720 can store data used by the processor 710 when performing operations.

[0620] In some embodiments, the processor 710 can 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), and the processor 710 can also adopt a multi-core architecture.

[0621] The processor 710 is configured to execute any of the methods provided by the embodiments of the present disclosure by invoking stored program instructions of the memory. The processor 710 and the memory 720 can also be physically arranged separately.

[0622] In some embodiments, the characteristic value includes one or more of a mean value, a maximum value, and a minimum value.

[0623] In some embodiments, the operations further include:

[0624] The terminal device is configured with first reference signals corresponding to M time instants, one or more first reference signals corresponding to each time instant, and the first reference signals corresponding to the M time instants are used to obtain a prediction result at each of N time instants, M being a positive integer;

[0625] The terminal device is configured with second reference signals corresponding to N time instants, one or more second reference signals corresponding to each time instant, and the second reference signals corresponding to the N time instants are used to obtain a measurement result at each of the N time instants.

[0626] In some embodiments, the operations further include:

[0627] For each measurement in P measurements or within a first time period, the terminal device is configured with a first reference signal, and the first reference signal is used to obtain an RSRP prediction value for a target reference signal, the target reference signal being determined by the first reference signal or the prediction result, P being a positive integer greater than 1.

[0628] In some embodiments, the operations further include:

[0629] The configuration information is sent to the terminal device, and the configuration information is used to indicate the content of the performance monitoring related information reported by the terminal device for the first object.

[0630] The network side device of the embodiments of the present disclosure receives the performance monitoring related information reported by the terminal device, wherein the performance monitoring related information includes one or more of the following information: characteristic values of performance monitoring indicators at N time points; performance monitoring related information at each of the N time points; time point related information corresponding to the characteristic values of the performance monitoring indicators at the N time points, N being a positive integer greater than 1; characteristic values of performance monitoring indicators corresponding to multiple model inferences; time point related information corresponding to the characteristic values of the performance monitoring indicators corresponding to the multiple model inferences. In this way, by receiving the above performance monitoring related information reported by the terminal device, the network side can realize performance monitoring of the artificial intelligence model or artificial intelligence function on the terminal device side.

[0631] As shown in FIG. 8, the embodiments of the present disclosure also provide an information transmission device, which comprises:

[0632] The first receiving unit 801 is configured to receive performance monitoring related information reported by a terminal device, wherein the performance monitoring related information includes one or more of the following information:

[0633] Characteristic values of performance monitoring indicators at N time points;

[0634] Performance monitoring related information at each of the N time points;

[0635] Time point related information corresponding to the characteristic values of the performance monitoring indicators at the N time points, N being a positive integer greater than 1;

[0636] Characteristic values of performance monitoring indicators corresponding to multiple model inferences;

[0637] Time point related information corresponding to the characteristic values of the performance monitoring indicators corresponding to the multiple model inferences.

[0638] In some embodiments, the characteristic values include one or more of the mean value, the maximum value, and the minimum value.

[0639] In some embodiments, the device of the present disclosure further comprises:

[0640] The first processing unit is configured to configure the terminal device with first reference signals corresponding to M time points, one or more first reference signals corresponding to each time point, and the first reference signals corresponding to the M time points being used to obtain predicted results at each of the N time points, M being a positive integer;

[0641] The second processing unit is configured to configure the terminal device with second reference signals corresponding to the N time points, one or more second reference signals corresponding to each time point, and the second reference signals corresponding to the N time points being used to obtain measurement results at each of the N time points.

[0642] In some embodiments, the device of the present disclosure further comprises:

[0643] The third processing unit configures the terminal device with a first reference signal for each measurement in P measurements or in a first time period, the first reference signal being used to obtain a RSRP prediction value for a target reference signal, the target reference signal being determined by the first reference signal or a prediction result, P being a positive integer greater than 1.

[0644] In some embodiments, the apparatus of the present disclosure further comprises:

[0645] The first sending unit is configured to send configuration information to the terminal device, the configuration information being used to indicate content of the performance monitoring related information reported by the terminal device for the first object.

[0646] The information transmission apparatus of the embodiments of the present disclosure receives the performance monitoring related information reported by the terminal device, wherein the performance monitoring related information comprises one or more of the following information: characteristic values of performance monitoring indicators at N time points; performance monitoring related information at each of the N time points; time point related information corresponding to characteristic values of performance monitoring indicators at the N time points, N being a positive integer greater than 1; characteristic values of performance monitoring indicators corresponding to multiple model inferences; time point related information corresponding to characteristic values of performance monitoring indicators corresponding to the multiple model inferences. In this way, by receiving the performance monitoring related information reported by the terminal device, the network side can perform performance monitoring on the artificial intelligence model or artificial intelligence function of the terminal device side.

[0647] It should be noted that the division of units in the embodiments of the present disclosure is illustrative, and is only a logical function division. In actual implementation, another division mode can be used. In addition, each functional unit in each embodiment of the present disclosure can be integrated in one processing unit, or each unit can be physically present alone, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0648] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a processor-readable storage medium. Based on such an understanding, the technical solutions of the present disclosure, essentially or in other words, the part that contributes to the related art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform all or part of the steps of the methods described in the various embodiments of the present disclosure. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0649] It should be noted that the above-mentioned device provided by the embodiments of the present disclosure can realize all method steps realized by the method embodiments and achieve the same technical effects. Therefore, the same parts and beneficial effects of the method embodiments will not be described in detail.

[0650] In some embodiments of the present disclosure, a processor-readable storage medium is also provided, which stores program instructions for causing the processor to perform the following steps:

[0651] Receiving performance monitoring related information reported by a terminal device, wherein the performance monitoring related information includes one or more of the following information:

[0652] Characteristic values of performance monitoring indicators at N time points;

[0653] Performance monitoring related information at each of the N time points;

[0654] Characteristic values of performance monitoring indicators at N time points;

[0655] Characteristic values of performance monitoring indicators corresponding to multiple model inferences;

[0656] Time point related information corresponding to the characteristic values of the performance monitoring indicators corresponding to the multiple model inferences.

[0657] The program is executed by the processor to implement all implementation manners in the above method embodiments applied to the network side device, and thus repeated descriptions are omitted here.

[0658] In some embodiments of the present disclosure, a computer program product is also provided, which comprises computer instructions, which, when executed by a processor, implement each process of the method embodiments shown in FIG. 2 or FIG. 4, and can achieve the same technical effects. To avoid repetition, details are not described here.

[0659] The technical solutions provided by the embodiments of the present disclosure can be applied to various systems, such as a 5th-Generation (5G) system and above. For example, the applicable systems can be a Global System of Mobile communication (GSM) system, a Code Division Multiple Access (CDMA) system, a Wideband Code Division Multiple Access (WCDMA) General Packet Radio Service (GPRS) system, a Long Term Evolution (LTE) system, an LTE Frequency Division Duplex (FDD) system, an LTE Time Division Duplex (TDD) system, a Long Term Evolution Advanced (LTE-A) system, a Universal Mobile Telecommunication System (UMTS), a Worldwide interoperability for Microwave Access (WiMAX) system, a 5G New Radio (NR) system, and the like. The various systems all include terminal devices and network devices. The system can also include a core network part, such as an Evolved Packet System (EPS), a 5G system (5GS), and the like.

[0660] The terminal device to which the embodiments of the present disclosure relate can refer to a device that provides voice and / or data connectivity to a user, a handheld device with a wireless connection function, or other processing devices connected to a wireless modem, etc. In different systems, the name of the terminal device can also be different, for example, in the 5G system, the terminal device can be called a user equipment (UE). The wireless terminal device can communicate with one or more core networks (CN) through a radio access network (RAN). The wireless terminal device can be a mobile terminal device, such as a mobile phone (or called a "cellular" phone) and a computer with a mobile terminal device, for example, it can be a portable, pocket, handheld, computer built-in or vehicle-mounted mobile device, which exchanges language and / or data with the radio access network. For example, personal communication service (PCS) phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), etc. The wireless terminal device can also be called a system, a subscriber unit, a subscriber station, a mobile station, a mobile, a remote station, an access point, a remote terminal, an access terminal, a user terminal, a user agent, a user device, which is not limited in the embodiments of the present disclosure.

[0661] The network device related to the embodiments of the present disclosure can be a base station, which can include a plurality of cells serving terminals. According to different application scenarios, the base station can also be referred to as an access point, or can be a device in an access network that communicates with wireless terminal devices through one or more sectors over an air interface, or other names. The network device can be used to exchange received air frames and Internet Protocol (IP) packets as a router between the wireless terminal device and the rest of the access network, which can include an Internet Protocol (IP) communication network. The network device can also coordinate the management of the properties of the air interface. For example, the network device related to the embodiments of the present disclosure can be a network device (Base Transceiver Station, BTS) in the Global System for Mobile Communications (GSM) or Code Division Multiple Access (CDMA), and can also be a network device (NodeB) in Wide-band Code Division Multiple Access (WCDMA), and can also be an evolved network device (evolutional Node B, eNB or e-NodeB) in a Long Term Evolution (LTE) system, a 5G base station (gNB) in a next generation system, and can also be a Home evolved Node B (HeNB), a relay node, a femto, a pico, etc., which are not limited in the embodiments of the present disclosure. In some network structures, the network device can include a Centralized Unit (CU) node and a Distributed Unit (DU) node, and the centralized unit and the distributed unit can also be geographically separated.

[0662] The network device and the terminal device can each use one or more antennas for multi-input multi-output (MIMO) transmission, which can be single-user MIMO (SU-MIMO) or multiple-user MIMO (MU-MIMO). According to the form and number of root antenna combinations, the MIMO transmission can be two-dimensional MIMO (2D-MIMO), three-dimensional MIMO (3D-MIMO), full-dimensional MIMO (FD-MIMO), or massive-MIMO, and can also be diversity transmission or precoding transmission or beamforming transmission, etc.

[0663] Those skilled in the art will understand that the embodiments of the present disclosure can be provided as a method, a system, or a computer program product. Therefore, the present disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present disclosure can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage, etc.) containing computer-usable program code.

[0664] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer executable instructions. These computer executable instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a means for implementing the functions specified in one or more flows of the flowcharts and / or one or more blocks of the block diagrams.

[0665] These processor executable instructions can also be stored in a processor readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the processor readable memory produce a product including instruction means, which implements the functions specified in one or more flows of the flowcharts and / or one or more blocks of the block diagrams.

[0666] These processor-executable instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operational steps are performed on the computer or other programmable device to generate a computer-implemented process such that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows in the flowchart and / or one or more blocks in the block diagram.

[0667] In addition, it should be noted that in the apparatus and method of the present disclosure, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions of the present disclosure. Moreover, the steps of performing the above series of processes can naturally be executed in time sequence according to the order of description, but do not necessarily have to be executed in time sequence, and certain steps can be executed in parallel or independently of each other. It can be understood by those skilled in the art that all or any steps or components of the method and apparatus of the present disclosure can be implemented in hardware, firmware, software or a combination thereof in any computing device (including processors, storage media, etc.) or network of computing devices, which can be implemented by those skilled in the art using their basic programming skills after reading the description of the present disclosure.

[0668] It should be noted that it should be understood that the division of each module above is only a logical division of functions, and in actual implementation, it can be integrated into a physical entity in whole or in part, or physically separated. And these modules can all be implemented in the form of software called by a processing element; all can be implemented in the form of hardware; some modules can be implemented in the form of software called by a processing element, and some modules can be implemented in the form of hardware. For example, a certain module can be a separately established processing element, or can be integrated into a certain chip of the above device, and in addition, it can also be stored in the form of program code in the memory of the above device, and called and executed by a certain processing element of the above device to determine the function of the above module. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together or independently implemented. The processing element described herein can be an integrated circuit with signal processing capability. In the implementation process, each step of the above method or each of the above modules can be completed by the integrated logic circuit of hardware or the instruction of software in the processing element.

[0669] For example, each module, unit, sub-unit or sub-module can be one or more integrated circuits configured to implement the above method, such as one or more application-specific integrated circuits (ASICs), one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs). For another example, when a module is implemented by scheduling program code through a processing element, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0670] The terms "first," "second," and the like in the specification and claims of the present disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present disclosure described herein may be implemented in a sequence other than that illustrated or described herein. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device that includes a series of steps or units need not be limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or devices. In addition, the use of "and / or" in the specification and claims to indicate at least one of the connected objects, for example, A and / or B and / or C, means that seven situations are included: A alone, B alone, C alone, both A and B present, both B and C present, both A and C present, and all A, B, and C present. Similarly, the use of "at least one of A and B" in the specification and claims should be understood to mean "A alone, B alone, or both A and B present."

[0671] Obviously, those skilled in the art may make various changes and modifications to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is intended to include these modifications and variations.

Claims

1. An information reporting method applied to a terminal device, comprising: obtaining performance monitoring related information for a first object, the first object being an artificial intelligence model or an artificial intelligence function; reporting the performance monitoring related information to a network side device, wherein the performance monitoring related information comprises one or more of the following information: characteristic values of performance monitoring indicators at N time instants; performance monitoring related information at each of the N time instants; time instant related information corresponding to the characteristic values of the performance monitoring indicators at the N time instants, N being a positive integer greater than 1; characteristic values of performance monitoring indicators corresponding to multiple model inferences; time instant related information corresponding to the characteristic values of the performance monitoring indicators corresponding to the multiple model inferences.

2. The method of claim 1, wherein, The characteristic values comprise one or more of the mean value, the maximum value, and the minimum value.

3. The method of claim 1, wherein, The obtaining of the performance monitoring related information for the first object comprises: receiving first reference signals corresponding to M time instants configured by the network side device, one or more first reference signals corresponding to each time instant; measuring the first reference signals corresponding to the M time instants; wherein M is a positive integer; obtaining prediction results at each of the N time instants according to the measurement results of the first reference signals corresponding to the M time instants; receiving second reference signals corresponding to the N time instants configured by the network side device, one or more second reference signals corresponding to each time instant; measuring the second reference signals corresponding to the N time instants; obtaining the performance monitoring related information for the first object according to the prediction results at each of the N time instants and the measurement results at each of the N time instants.

4. The method according to any one of claims 1 to 3, wherein, The reporting of the performance monitoring related information to the network side device comprises: reporting, to the network side device, a number of time instants correctly predicted or a correct prediction rate among the N time instants, the characteristic values of the performance monitoring indicators at the N time instants comprising the number of time instants correctly predicted or the correct prediction rate among the N time instants; and / or reporting, to the network side device, first indication information, the first indication information being used to indicate whether model inference at each of the N time instants is correct, the performance monitoring related information at each of the N time instants comprising the first indication information.

5. The method of claim 3, wherein, The prediction results at each of the N time instants comprise K indexes of the N time instants predicted, and the measurement results at each of the N time instants comprise indexes of the maximum or strongest K reference signals at each of the N time instants measured, K being a positive integer; The obtaining of the performance monitoring related information for the first object according to the prediction results at each of the N time instants and the measurement results at each of the N time instants comprises: obtaining, according to the K indexes of the N time instants predicted and the indexes of the maximum or strongest K reference signals at each of the N time instants measured, the number of time instants correctly predicted or the correct prediction rate among the N time instants; and / or According to the K indexes of each of the N moments obtained by prediction and the indexes of the maximum or strongest K reference signals of each of the N moments obtained by measurement, first indication information is obtained, the first indication information being used to indicate whether the model inference of each of the N moments is correct.

6. The method of claim 4, wherein, The first indication information is an N-bit bitmap.

7. The method of claim 5, wherein, The measurement of the second reference signals corresponding to the N moments comprises: The measurement of the second reference signals corresponding to the N moments obtains first RSRP measurement values of each of the N moments; The first RSRP measurement values of each of the N moments are sorted to obtain indexes of the maximum or strongest K reference signals of each of the N moments.

8. The method according to any one of claims 1 to 3, wherein, The reporting of the performance monitoring related information to the network side device comprises: The reporting of one or more of the following information to the network side device comprises: a first average value, the first average value being an average value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N moments; a maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N moments; time related information corresponding to the maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N moments; a minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N moments; time related information corresponding to the minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N moments.

9. The method of claim 3, wherein, The prediction result of each of the N moments comprises an RSRP prediction value of the target reference signal corresponding to each of the N moments; and the measurement result of each of the N moments comprises an RSRP measurement value of the target reference signal corresponding to each of the N moments; The obtaining of the performance monitoring related information for the first object according to the prediction result of each of the N moments and the measurement result of each of the N moments comprises: a difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to each of the N moments is calculated; one or more of the following information is obtained according to the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to each of the N moments: a first average value, the first average value being an average value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N moments; a maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N moments; time related information corresponding to the maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N moments; a minimum value of a difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants; time instant information corresponding to the minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants.

10. The method of claim 9, wherein, The obtaining of the prediction result of each of the N time instants from the measurement result of the first reference signal corresponding to the M time instants comprises: inputting the measurement result of the first reference signal corresponding to the M time instants into an artificial intelligence model to obtain the RSRP prediction value of the second reference signal corresponding to each of the N time instants; obtaining the RSRP prediction value of the target reference signal corresponding to each of the N time instants from the RSRP prediction value of the second reference signal corresponding to each of the N time instants.

11. The method of claim 1, wherein, The reporting of the performance monitoring related information to the network side device comprises: reporting one or more of the following information to the network side device: a maximum value of a difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to P measurements or measurements within a first time period, P being a positive integer greater than 1; time instant information corresponding to the maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to P measurements or measurements within a first time period, or an index value of the measurement; a minimum value of a difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to P measurements or measurements within a first time period; time instant information corresponding to the minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to P measurements or measurements within a first time period, or an index value of the measurement; The feature value of the performance monitoring indicator corresponding to the multiple model inferences comprises: the maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to P measurements or measurements within a first time period, and / or the minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to P measurements or measurements within a first time period.

12. The method of claim 11, wherein, The obtaining of the performance monitoring related information for the first object comprises: receiving a first reference signal configured by the network side device for one measurement; performing measurement on the first reference signal; obtaining a RSRP prediction value for a target reference signal from the measurement result of the first reference signal; calculating a difference between the RSRP prediction value and the RSRP measurement value for the target reference signal from the RSRP prediction value for the target reference signal and the RSRP measurement value for the target reference signal; After performing P measurements or measurements within a first time period, obtaining one or more of the following information from the difference between the RSRP prediction value and the RSRP measurement value for the target reference signal corresponding to each measurement: a maximum value of a difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to P measurements or measurements within a first time period; a time point corresponding to a maximum value of a difference between a RSRP prediction value for a target reference signal and a RSRP measurement value corresponding to P measurements or measurements within a first time period, or an index value of the measurement; a minimum value of a difference between a RSRP prediction value for a target reference signal and a RSRP measurement value corresponding to P measurements or measurements within a first time period; a time point corresponding to a minimum value of a difference between a RSRP prediction value for a target reference signal and a RSRP measurement value corresponding to P measurements or measurements within a first time period, or an index value of the measurement.

13. The method according to any one of claims 8 to 12, wherein, The target reference signal is determined by the first reference signal or the prediction result.

14. The method of claim 1, wherein, The method further comprises: receiving configuration information sent by a network side device, the configuration information being used to indicate content of performance monitoring related information reported by the terminal device for a first object.

15. An information transmission method applied to a network side device, comprising: receiving performance monitoring related information reported by a terminal device, wherein the performance monitoring related information comprises one or more of the following information: a characteristic value of a performance monitoring index at N time points; performance monitoring related information at each of N time points; time point related information corresponding to a characteristic value of a performance monitoring index at N time points, N being a positive integer greater than 1; a characteristic value of a performance monitoring index corresponding to multiple model inferences; time point related information corresponding to a characteristic value of a performance monitoring index corresponding to multiple model inferences.

16. The method of claim 15, wherein, The characteristic value comprises one or more of a mean value, a maximum value, and a minimum value.

17. The method of claim 15 or 16, wherein, The method further comprises: configuring, for the terminal device, a first reference signal corresponding to M time points, one or more first reference signals corresponding to each time point, the first reference signal corresponding to the M time points being used to obtain a prediction result at each of N time points, M being a positive integer; configuring, for the terminal device, a second reference signal corresponding to N time points, one or more second reference signals corresponding to each time point, the second reference signal corresponding to the N time points being used to obtain a measurement result at each of N time points.

18. The method of claim 15 or 16, wherein, The method further comprises: configuring, for the terminal device, a first reference signal for each measurement in P measurements or within a first time period, the first reference signal being used to obtain a RSRP prediction value for a target reference signal, the target reference signal being determined by the first reference signal or a prediction result, P being a positive integer greater than 1.

19. The method of claim 15 or 16, wherein, The method further comprises: sending, to the terminal device, configuration information, the configuration information being used to indicate content of performance monitoring related information reported by the terminal device for a first object.

20. A terminal device comprising: a memory, a transceiver, and a processor: the memory is used to store program instructions; the transceiver is used to transceive data under control of the processor; the processor is used to read the program instructions in the memory and perform the following operations: obtaining performance monitoring related information for a first object, the first object being an artificial intelligence model or an artificial intelligence function; reporting, to a network side device, the performance monitoring related information, wherein the performance monitoring related information comprises one or more of the following information: a characteristic value of a performance monitoring index at N time points; performance monitoring related information of each of the N time instants; time instant related information corresponding to the characteristic values of the performance monitoring indicators of the N time instants, N being a positive integer greater than 1; characteristic values of the performance monitoring indicators corresponding to the multiple times of model inference; time instant related information corresponding to the characteristic values of the performance monitoring indicators corresponding to the multiple times of model inference.

21. The terminal device of claim 20, wherein, The characteristic values include one or more of a mean value, a maximum value, and a minimum value.

22. The terminal device of claim 20, wherein, The operations further include: receiving first reference signals corresponding to M time instants configured by the network side device, one or more first reference signals corresponding to each time instant; performing measurement on the first reference signals corresponding to the M time instants; M being a positive integer; obtaining prediction results of each of the N time instants according to the measurement results of the first reference signals corresponding to the M time instants; receiving second reference signals corresponding to the N time instants configured by the network side device, one or more second reference signals corresponding to each time instant; performing measurement on the second reference signals corresponding to the N time instants; obtaining performance monitoring related information of the first object according to the prediction results of each of the N time instants and the measurement results of each of the N time instants.

23. The terminal device of any one of claims 20 to 22, wherein, The operations further include: reporting, to the network side device, a number of correctly predicted time instants or a correct prediction rate in the N time instants, the characteristic values of the performance monitoring indicators of the N time instants including the number of correctly predicted time instants or the correct prediction rate in the N time instants; and / or, reporting, to the network side device, first indication information, the first indication information being used to indicate whether model inference of each of the N time instants is correct, the performance monitoring related information of each of the N time instants including the first indication information.

24. The terminal device of claim 22, wherein, The prediction results of each of the N time instants include K indexes of each of the N time instants predicted, the measurement results of each of the N time instants include indexes of the maximum or strongest K reference signals of each of the N time instants measured, K being a positive integer; the operations further include: obtaining the number of correctly predicted time instants or the correct prediction rate in the N time instants according to the K indexes of each of the N time instants predicted and the indexes of the maximum or strongest K reference signals of each of the N time instants measured; and / or, obtaining the first indication information according to the K indexes of each of the N time instants predicted and the indexes of the maximum or strongest K reference signals of each of the N time instants measured, the first indication information being used to indicate whether model inference of each of the N time instants is correct.

25. The terminal device of claim 23, wherein, The first indication information is an N-bit bitmap.

26. The terminal device of claim 24, wherein, The operations further include: performing measurement on the second reference signals corresponding to the N time instants to obtain first RSRP measurement values of each of the N time instants; performing sorting on the first RSRP measurement values of each of the N time instants to obtain indexes of the maximum or strongest K reference signals of each of the N time instants.

27. The terminal device of any one of claims 20 to 22, wherein, The operations further include: reporting, to the network side device, one or more of the following information: a first average value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants; a maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants; time information corresponding to the maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants; a minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants; time information corresponding to the minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants.

28. The terminal device of claim 22, wherein, The prediction result of each time instant of the N time instants includes the RSRP prediction value of the target reference signal corresponding to each time instant of the N time instants; the measurement result of each time instant of the N time instants includes the RSRP measurement value of the target reference signal corresponding to each time instant of the N time instants; and the operation further includes: calculating the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to each time instant according to the RSRP prediction value of the target reference signal corresponding to each time instant of the N time instants and the RSRP measurement value of the target reference signal corresponding to each time instant of the N time instants; obtaining one or more of the following information according to the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to each time instant: a first average value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants; a maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants; time information corresponding to the maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants; a minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants; time information corresponding to the minimum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to the N time instants.

29. The terminal device of claim 28, wherein, The operation further includes: inputting the measurement result of the first reference signal corresponding to the M time instants into an artificial intelligence model to obtain the RSRP prediction value of the second reference signal corresponding to each time instant of the N time instants; obtaining the RSRP prediction value of the target reference signal corresponding to each time instant of the N time instants according to the RSRP prediction value of the second reference signal corresponding to each time instant of the N time instants.

30. The terminal device of claim 20, wherein, The operation further includes: reporting one or more of the following information to the network side device: a maximum value of the difference between the RSRP prediction value and the RSRP measurement value of the target reference signal corresponding to P times of measurement or measurement within a first time period, P being a positive integer greater than 1; a maximum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to the P measurements or the measurements within the first time period; a minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to the P measurements or the measurements within the first time period; a minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to the P measurements or the measurements within the first time period; The feature values of the performance monitoring indicators corresponding to the multiple model inferences include: a maximum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to the P measurements or the measurements within the first time period, and / or a minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to the P measurements or the measurements within the first time period.

31. The terminal device of claim 21, wherein, The operations further include: receiving a first reference signal configured by the network side device for one measurement; performing measurement on the first reference signal; obtaining an RSRP prediction value for a target reference signal according to the measurement result of the first reference signal; calculating the difference between the RSRP prediction value and the RSRP measurement value for the target reference signal according to the RSRP prediction value for the target reference signal and the RSRP measurement value for the target reference signal; After performing the P measurements or the measurements within the first time period, one or more of the following information is obtained according to the difference between the RSRP prediction value and the RSRP measurement value for the target reference signal corresponding to each measurement: a maximum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to the P measurements or the measurements within the first time period; a maximum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to the P measurements or the measurements within the first time period; a minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to the P measurements or the measurements within the first time period; a minimum value of the difference between the RSRP prediction value and the RSRP measurement value corresponding to the P measurements or the measurements within the first time period.

32. The terminal device of any of claims 27 to 31, wherein, The target reference signal is determined by the first reference signal or the prediction result.

33. The terminal device of claim 20, wherein, The operations further include: receiving configuration information sent by the network side device, the configuration information being used to indicate the content of the performance monitoring related information reported by the terminal device for the first object.

34. An information reporting apparatus, comprising: an obtaining unit configured to obtain performance monitoring related information for a first object, the first object being an artificial intelligence model or an artificial intelligence function; and an obtaining unit configured to obtain performance monitoring related information for a first object, the first object being an artificial intelligence model or an artificial intelligence function; and reporting, to a network-side device, performance monitoring related information, wherein the performance monitoring related information comprises one or more of the following: characteristic values of performance monitoring indexes at N time instants; performance monitoring related information at each of the N time instants; time instant related information corresponding to the characteristic values of the performance monitoring indexes at the N time instants, N being a positive integer greater than 1; characteristic values of performance monitoring indexes corresponding to multiple model inferences; time instant related information corresponding to the characteristic values of the performance monitoring indexes corresponding to the multiple model inferences.

35. A network-side device comprising: a memory, a transceiver, and a processor: the memory is configured to store program instructions; the transceiver is configured to transceive data under control of the processor; the processor is configured to read the program instructions in the memory and perform the following operations: receiving performance monitoring related information reported by a terminal device, wherein the performance monitoring related information comprises one or more of the following: characteristic values of performance monitoring indexes at N time instants; performance monitoring related information at each of the N time instants; time instant related information corresponding to the characteristic values of the performance monitoring indexes at the N time instants, N being a positive integer greater than 1; characteristic values of performance monitoring indexes corresponding to multiple model inferences; time instant related information corresponding to the characteristic values of the performance monitoring indexes corresponding to the multiple model inferences.

36. The network-side device of claim 35, wherein, The characteristic values comprise one or more of a mean value, a maximum value, and a minimum value.

37. The network-side device of claim 35 or 36, wherein, The operations further comprise: configuring, for the terminal device, first reference signals corresponding to M time instants, one or more first reference signals corresponding to each time instant, the first reference signals corresponding to the M time instants being used to obtain predicted results at each of the N time instants, M being a positive integer; configuring, for the terminal device, second reference signals corresponding to the N time instants, one or more second reference signals corresponding to each time instant, the second reference signals corresponding to the N time instants being used to obtain measurement results at each of the N time instants.

38. The network-side device of claim 35 or 36, wherein, The operations further comprise: configuring, for the terminal device, a first reference signal for each measurement in P measurements or in a first time period, the first reference signal being used to obtain an RSRP prediction value for a target reference signal, the target reference signal being determined by the first reference signal or a predicted result, P being a positive integer greater than 1.

39. The network-side device of claim 35 or 36, wherein, The operations further comprise: sending, to the terminal device, configuration information, the configuration information being used to instruct the terminal device to report content of performance monitoring related information for a first object.

40. An information transmission apparatus, comprising: a first receiving unit configured to receive performance monitoring related information reported by a terminal device, wherein the performance monitoring related information comprises one or more of the following: characteristic values of performance monitoring indexes at N time instants; performance monitoring related information at each of the N time instants; time instant related information corresponding to the characteristic values of the performance monitoring indexes at the N time instants, N being a positive integer greater than 1; characteristic values of performance monitoring indexes corresponding to multiple model inferences; time instant related information corresponding to the characteristic values of the performance monitoring indexes corresponding to the multiple model inferences. 41.A processor-readable storage medium, storing a computer program, the computer program being configured to cause the processor to perform the steps of the information reporting method in any one of claims 1 to 14, or the steps of the information transmitting method in any one of claims 15 to 19. 42.A computer program product, comprising computer instructions, which, when executed by a processor, implement the steps of the information reporting method in any one of claims 1 to 14, or the steps of the information transmitting method in any one of claims 15 to 19.

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