Method for determining prediction accuracy, terminal, device, and storage medium

Abstract

The present disclosure provides a method for determining prediction accuracy, a device, and a storage medium. The method for determining prediction accuracy comprises: at a first node, performing radio resource management (RRM) measurement on a reference signal to obtain first information, the first information comprising a measured value of a signal parameter; on the basis of the first information and a prediction model, determining second information at a second node, the second information comprising a value obtained by performing first processing on the measured value, the first processing at least comprising prediction by the prediction model, and the first node and the second node being processing nodes in an RRM measurement processing flow; and sending the second information to a network device or a test device, or sending the second information and third information, the second information being used to determine the prediction accuracy of the signal parameter, and the third information comprising a reference value of the signal parameter. The present method can reduce the processing load of RRM measurements at a terminal, and enables accurate evaluation of the prediction accuracy of a signal parameter.

Description

Method, terminal, device and storage medium for determining prediction accuracy TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of communication, and in particular to a method, a terminal, a device and a storage medium for determining prediction accuracy. BACKGROUND

[0002] Radio resource management (RRM) measurement on a reference signal (RS) plays an important role in the handover process of a wireless network. By performing RRM measurement on the RS, the signal quality of the RS can be obtained, and the connection quality between the terminal and the network device can be evaluated through the signal quality. The higher the signal quality, the more likely the corresponding cell is a better candidate for handover. When the terminal moves between different cells, the network device determines the target cell most suitable for handover for the terminal according to the RRM measurement result. The accuracy and timeliness of the signal quality directly affect the efficiency of the handover process, and the influencing factors include call continuity, data transmission quality and overall user experience.

[0003] The signal quality in the RRM measurement result can be predicted by an artificial intelligence (AI) technology, so that the measurement period can be shortened.

[0004] SUMMARY

[0005] How to ensure the prediction accuracy of the signal quality after the prediction processing is a technical problem to be solved.

[0006] In a first aspect, an embodiment of the present disclosure provides a method for determining prediction accuracy, executed by a terminal, comprising:

[0007] performing radio resource management (RRM) measurement on a reference signal (RS) at a first node to obtain first information, the first information comprising a measured value of a signal parameter;

[0008] determining second information located at a second node according to the first information and a prediction model, the second information comprising a value after first processing of the measured value; the first processing at least comprises prediction of the prediction model, and the first node and the second node are processing nodes in an RRM measurement processing flow;

[0009] sending the second information to a network device or a test device, or sending the second information and third information, the second information being used to determine the prediction accuracy of the signal parameter, and the third information comprising a reference value of the signal parameter.

[0010] In a second aspect, the embodiments of the present disclosure provide a method for determining prediction accuracy, executed by a network device or a test device, comprising:

[0011] receiving second information sent by the terminal, the second information comprising a value of the measurement value after first processing, the first processing at least comprising prediction of the prediction model;

[0012] determining the prediction accuracy of the signal parameter according to the second information and third information;

[0013] or

[0014] receiving second information and third information sent by the terminal, the second information comprising a value of the measurement value after first processing, the first processing at least comprising prediction of the prediction model, and the third information comprising a reference value of the signal parameter;

[0015] determining the prediction accuracy of the signal parameter according to the second information and the third information;

[0016] wherein the second information is determined by the terminal according to the following method:

[0017] performing radio resource management (RRM) measurement on a reference signal at a first node to obtain first information, the first information comprising a measurement value of a signal parameter;

[0018] determining second information at a second node according to the first information and a prediction model, the second information comprising a value of the measurement value after first processing, the first processing at least comprising prediction of the prediction model, the first node and the second node being processing nodes in an RRM measurement processing flow.

[0019] In a third aspect, the embodiments of the present disclosure provide a terminal, comprising:

[0020] a processing module configured to perform radio resource management (RRM) measurement on a reference signal at a first node to obtain first information, the first information comprising a measurement value of a signal parameter; and determine second information at a second node according to the first information and a prediction model, the second information comprising a value of the measurement value after first processing, the first processing at least comprising prediction of the prediction model, the first node and the second node being processing nodes in an RRM measurement processing flow.

[0021] a transceiver module configured to send the second information to a network device or a test device, or send the second information and third information to the network device or the test device, the second information being used to determine the prediction accuracy of the signal parameter, and the third information comprising a reference value of the signal parameter.

[0022] In a fourth aspect, the embodiments of the present disclosure provide a network device, comprising a transceiver module and a processing module:

[0023] comprising a transceiver module and a processing module:

[0024] The transceiver module is configured to receive second information sent by a terminal, the second information comprising a value of the measurement value after first processing; the first processing at least comprises prediction of the prediction model;

[0025] The processing module is configured to determine the prediction accuracy of the signal parameter according to the second information and third information;

[0026] Or,

[0027] The transceiver module is configured to receive second information and third information sent by a terminal, the second information comprising a value of the measurement value after first processing, the first processing at least comprising prediction of the prediction model, and the third information comprising a reference value of the signal parameter;

[0028] The processing module is configured to determine the prediction accuracy of the signal parameter according to the second information and the third information;

[0029] The second information is determined by the terminal according to the following method:

[0030] At a first node, performing radio resource management (RRM) measurement on a reference signal to obtain first information, the first information comprising a measurement value of a signal parameter;

[0031] According to the first information and a prediction model, determining second information located at a second node, the second information comprising a value of the measurement value after first processing; the first processing at least comprises prediction of the prediction model, and the first node and the second node are processing nodes in an RRM measurement processing flow.

[0032] In a fifth aspect, the embodiments of the present disclosure provide a test device, comprising a transceiver module and a processing module:

[0033] The transceiver module is configured to receive second information sent by a terminal, the second information comprising a value of the measurement value after first processing; the first processing at least comprises prediction of the prediction model;

[0034] The processing module is configured to determine the prediction accuracy of the signal parameter according to the second information and third information;

[0035] Or,

[0036] The transceiving module is configured to receive second information and third information sent by the terminal, the second information comprising a value of the measurement value after first processing, the first processing at least comprising prediction of the prediction model, and the third information comprising a reference value of the signal parameter;

[0037] The processing module is configured to determine the prediction accuracy of the signal parameter according to the second information and the third information.

[0038] The second information is determined by the terminal according to the following method:

[0039] At a first node, a radio resource management (RRM) measurement is performed on a reference signal to obtain first information, the first information comprising a measurement value of a signal parameter;

[0040] Second information located at a second node in the RRM measurement processing flow is determined according to the first information and a prediction model, the second information comprising a value of the measurement value after first processing, the first processing at least comprising prediction of the prediction model, and the first node and the second node being processing nodes in the RRM measurement processing flow.

[0041] In a sixth aspect, an embodiment of the present disclosure provides a terminal, comprising:

[0042] One or more processors;

[0043] The terminal is configured to implement the optional implementation manner of the first aspect.

[0044] In a seventh aspect, an embodiment of the present disclosure provides a network device, comprising:

[0045] One or more processors;

[0046] The network device is configured to implement the optional implementation manner of the second aspect.

[0047] In an eighth aspect, an embodiment of the present disclosure provides a test device, comprising:

[0048] One or more processors;

[0049] The test device is configured to implement the optional implementation manner of the second aspect.

[0050] In a ninth aspect, an embodiment of the present disclosure provides a communication system, comprising a terminal, a network device and a test device, wherein:

[0051] The terminal is configured to perform the method described in the optional implementation manner of the first aspect;

[0052] The network device is configured to perform the method described in the optional implementation of the second aspect.

[0053] The test device is configured to perform the method described in the optional implementation of the second aspect.

[0054] In a tenth aspect, the embodiments of the present disclosure provide a storage medium, the storage medium storing instructions, wherein,

[0055] When the instructions run on the communication device, the communication device performs the method described in the optional implementation of the first aspect or the second aspect.

[0056] In an eleventh aspect, the embodiments of the present disclosure provide a program product, wherein,

[0057] When the program product is executed by the communication device, the communication device performs the method described in the optional implementation of the first aspect or the second aspect.

[0058] In the embodiments of the present disclosure, the data processing amount of the RRM measurement of the terminal can be reduced, and more reasonable second information can be provided, so as to more accurately evaluate the prediction accuracy of the signal parameter and the quality of the prediction model. BRIEF DESCRIPTION OF DRAWINGS

[0059] The accompanying drawings, which are included to provide a further understanding of the embodiments of the present disclosure and constitute a part of the application, illustrate embodiments of the present disclosure and are used to explain the principles of the present disclosure, but do not limit the present disclosure. In the drawings:

[0060] The accompanying drawings are incorporated into the specification and form a part of the specification, show embodiments consistent with the embodiments of the present disclosure, and together with the specification, serve to explain the principles of the present disclosure.

[0061] FIG. 1A is a schematic diagram of a wireless communication system architecture provided by the embodiments of the present disclosure;

[0062] FIG. 1B is a schematic diagram of a processing flow of RRM measurement provided by the embodiments of the present disclosure;

[0063] FIG. 2A is an interaction schematic diagram of a method for determining prediction accuracy provided by the embodiments of the present disclosure;

[0064] FIG. 2A1-1 is an interaction schematic diagram of a method for determining second information provided by the embodiments of the present disclosure;

[0065] FIG. 2A1-2 is an interaction schematic diagram of another method for determining second information provided by the embodiments of the present disclosure;

[0066] FIG. 2A1-3 is an interaction schematic diagram of another method for determining the second information according to an embodiment of the present disclosure;

[0067] FIG. 2A2-1 is an interaction schematic diagram of another method for determining the second information according to an embodiment of the present disclosure;

[0068] FIG. 2A2-2 is an interaction schematic diagram of another method for determining the second information according to an embodiment of the present disclosure;

[0069] FIG. 2A2-3 is an interaction schematic diagram of another method for determining the second information according to an embodiment of the present disclosure;

[0070] FIG. 2A3-1 is an interaction schematic diagram of another method for determining the second information according to an embodiment of the present disclosure;

[0071] FIG. 2A3-2 is an interaction schematic diagram of another method for determining the second information according to an embodiment of the present disclosure;

[0072] FIG. 2A3-3 is an interaction schematic diagram of another method for determining the second information according to an embodiment of the present disclosure;

[0073] FIG. 2A4-1 is an interaction schematic diagram of another method for determining the second information according to an embodiment of the present disclosure;

[0074] FIG. 2A4-2 is an interaction schematic diagram of another method for determining the second information according to an embodiment of the present disclosure;

[0075] FIG. 2A4-3 is an interaction schematic diagram of another method for determining the second information according to an embodiment of the present disclosure;

[0076] FIG. 2A4-4 is an interaction schematic diagram of another method for determining the second information according to an embodiment of the present disclosure;

[0077] FIG. 2B is an interaction schematic diagram of another method for determining the prediction accuracy according to an embodiment of the present disclosure;

[0078] FIG. 3A is a flowchart of a method for determining the prediction accuracy by a terminal according to an embodiment of the present disclosure;

[0079] FIG. 3B is a flowchart of another method for determining the prediction accuracy by a terminal according to an embodiment of the present disclosure;

[0080] FIG. 4A is a flowchart of a method for determining the prediction accuracy by a network device according to an embodiment of the present disclosure;

[0081] FIG. 4B is a flowchart of another method for determining the prediction accuracy by a network device according to an embodiment of the present disclosure;

[0082] FIG. 4C is a flowchart of a method for determining signal prediction accuracy by a test device, according to an embodiment of the present disclosure;

[0083] FIG. 4D is a flowchart of another method for determining prediction accuracy by a test device, according to an embodiment of the present disclosure;

[0084] FIG. 5A is a schematic diagram of a terminal, according to an embodiment of the present disclosure;

[0085] FIG. 5B is a schematic diagram of a network device, according to an embodiment of the present disclosure;

[0086] FIG. 5C is a schematic diagram of a test device, according to an embodiment of the present disclosure;

[0087] FIG. 6A is a schematic diagram of a communication device, according to an embodiment of the present disclosure;

[0088] FIG. 6B is a schematic diagram of a communication device, according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0089] The embodiments of the present disclosure provide a method for determining prediction accuracy, a terminal, a device and a storage medium.

[0090] In a first aspect, the embodiments of the present disclosure provide a method for determining prediction accuracy, performed by a terminal, comprising:

[0091] performing a radio resource management (RRM) measurement on a reference signal at a first node to obtain first information, the first information comprising a measured value of a signal parameter;

[0092] determining second information located at a second node according to the first information and a prediction model, the second information comprising a value of the measured value after a first processing, the first processing comprising at least a prediction of the prediction model, the first node and the second node being processing nodes in a RRM measurement processing flow;

[0093] sending the second information to a network device or a test device, or sending the second information and third information, the second information being used to determine prediction accuracy of the signal parameter, the third information comprising a reference value of the signal parameter.

[0094] In the above embodiments, the data processing amount of RRM measurement of the terminal can be reduced, and more reasonable second information can be provided, so that the prediction accuracy of the signal parameter can be more accurately evaluated, and the quality of the prediction model can be more accurately evaluated.

[0095] In some embodiments of the first aspect, in some embodiments, the first node and the second node are the same node in the RRM measurement processing flow, or the first node is before the second node in the RRM measurement processing flow.

[0096] In some embodiments of the first aspect, in some embodiments, the signal parameter comprises at least one of:

[0097] Reference Signal Received Power, RSRP;

[0098] Reference Signal Received Quality, RSRQ.

[0099] In some embodiments of the first aspect, in some embodiments, the first node is at an input end of a beam-level Layer 1 filter, and the second node is at an output end of the beam-level Layer 1 filter.

[0100] In the above embodiments, by setting the second node at the output end of the beam-level Layer 1 filter, the prediction accuracy can be determined according to the beam-level information without considering the cell-level data, thereby improving the accuracy of the evaluation.

[0101] In some embodiments of the first aspect, in some embodiments, the prediction model is a first model;

[0102] The determining, according to the first information and a prediction model, of second information at a second node comprises:

[0103] predicting, by the first model, the first information to obtain fourth information, the fourth information comprising a predicted value of the signal parameter at the first node;

[0104] filtering, by a beam-level Layer 1 filter, the fourth information to obtain the second information at the second node.

[0105] In the above embodiments, prediction is performed first and then filtering is performed, which can reduce the influence of filtering on the prediction accuracy.

[0106] In some embodiments of the first aspect, in some embodiments, the prediction model is a second model;

[0107] The determining, according to the first information and a prediction model, of second information at a second node comprises:

[0108] predicting, by the second model, the first information to obtain the second information at the second node.

[0109] In the above embodiments, only prediction is performed without filtering, which can reduce the influence of filtering on the prediction accuracy.

[0110] In some embodiments of the first aspect, the prediction model is a third model.

[0111] The second information located at the second node is determined according to the first information and a prediction model.

[0112] The first information is filtered by a beam-level layer 1 filter to obtain fifth information located at the second node.

[0113] The second information located at the second node is obtained by predicting the fifth information by using the third model.

[0114] In the above embodiments, the filtering is performed before the prediction, which can reduce the interference between the prediction input data and improve the accuracy of the prediction.

[0115] In some embodiments of the first aspect, the third information is one of the following:

[0116] The first measurement result filtered by a beam-level layer 1 filter, wherein the first measurement result is measured at the first node based on a first signal-to-noise ratio (SNR);

[0117] The second measurement result filtered by a beam-level layer 1 filter, wherein the second measurement result is measured at the first node based on a second signal-to-noise ratio (SNR), and the first SNR is greater than the second SNR;

[0118] The terminal determines the evaluation result based on a simulation assumption.

[0119] In the above embodiments, the use of reasonable data as reference data can improve the accuracy of the evaluation.

[0120] In some embodiments of the first aspect, the first node is located at the input end of the beam-level layer 1 filter, and the second node is located at the input end of the cell-level layer 3 filter.

[0121] In the above embodiments, the second node is arranged at the input end of the cell-level layer 3 filter, and the prediction is performed using cell-level data without considering beam-level data, which can reduce the data processing amount.

[0122] In some embodiments of the first aspect, the determination of the second information located at the second node according to the first information and a prediction model includes:

[0123] The sixth information located at a third node is determined according to the first information and a prediction model, and the third node is located at the output end of the beam-level layer 1 filter.

[0124] The sixth information is subjected to beam combining or selection processing to obtain second information located at a second node.

[0125] In the above embodiment, multiplexing multiple steps in the existing processing flow can save processing workload.

[0126] In combination with some embodiments of the first aspect, in some embodiments, the third information is one of the following:

[0127] The first measurement result is filtered by a beam-level layer 1 filter and then subjected to beam combining or selection processing to obtain the third information, wherein the first measurement result is measured at the first node based on a first signal-to-noise ratio (SNR).

[0128] The second measurement result is filtered by a beam-level layer 1 filter and then subjected to beam combining or selection processing to obtain the third information, wherein the second measurement result is measured at the first node based on a second signal-to-noise ratio (SNR), and the first SNR is greater than the second SNR.

[0129] The terminal determines the third information based on simulation assumptions.

[0130] In the above embodiment, using reasonable data as reference data can improve the accuracy of evaluation.

[0131] In combination with some embodiments of the first aspect, in some embodiments, the first node is located at the input end of the beam-level layer 1 filter, and the second node is located at the output end of the cell-level layer 3 filter.

[0132] In the above embodiment, the second node is arranged after the cell-level layer 3 filter, and the prediction accuracy is determined according to the filtered cell-level information, without considering the beam-level data, thereby improving the evaluation speed.

[0133] In combination with some embodiments of the first aspect, in some embodiments, the determining the second information located at the second node according to the first information and the prediction model comprises:

[0134] The sixth information located at a third node is determined according to the first information and the prediction model, wherein the third node is located at the output end of the beam-level layer 1 filter.

[0135] The seventh information located at a fourth node is obtained by performing beam combining or selection processing according to the sixth information, wherein the fourth node is located at the input end of the cell-level layer 3 filter.

[0136] The second information at the second node is obtained by filtering the seventh information through the cell-level layer 3 filter.

[0137] In the above embodiment, multiplexing multiple steps in the existing processing flow can save processing workload.

[0138] In combination with some embodiments of the first aspect, in some embodiments, the determining, according to the first information and a prediction model, the sixth information located at the third node comprises one of:

[0139] The prediction model is a first model, the first information is predicted by the first model to obtain fourth information, the fourth information comprises a predicted value of the signal parameter at the first node, and the fourth information is filtered by a beam-level layer 1 filter to obtain the sixth information located at the third node; or

[0140] The prediction model is a second model, the first information is predicted by the second model to obtain the sixth information located at the third node; or

[0141] The prediction model is a third model, the first information is filtered by a beam-level layer 1 filter to obtain fifth information located at the third node, and the fifth information is predicted by the third model to obtain the sixth information at the third node.

[0142] In combination with some embodiments of the first aspect, in some embodiments, the prediction model is a fourth model;

[0143] The determining, according to the first information and a prediction model, the second information located at the second node comprises:

[0144] The eighth information located at the second node is determined according to the first information,

[0145] The eighth information is predicted by a fourth model to obtain the second information located at the second node.

[0146] In the above embodiment, performing prediction at the second node can multiplex as many existing steps in the existing flow as possible, thereby improving the evaluation speed.

[0147] In combination with some embodiments of the first aspect, in some embodiments, the determining, according to the first information, the eighth information located at the second node comprises:

[0148] The first information is filtered by a beam-level layer 1 filter to obtain fifth information located at the third node;

[0149] The ninth information located at the fourth node is obtained by performing beam combining or selection processing according to the fifth information;

[0150] Filter the ninth information through a cell-level layer 3 filter to obtain eighth information located at the second node.

[0151] In some embodiments of the first aspect, the prediction model is a fifth model.

[0152] The determining the second information located at the second node according to the first information and the prediction model comprises:

[0153] The first information is predicted through the fifth model to obtain the second information located at the second node.

[0154] In the above embodiments, the second information is directly predicted according to the first information, avoiding the execution of the existing processing procedure between the first node and the second node, and the evaluation speed can be improved.

[0155] In some embodiments of the first aspect, the prediction model is a sixth model.

[0156] The determining the second information located at the second node according to the first information and the prediction model comprises:

[0157] The first information is filtered through a beam-level layer 1 filter to obtain fifth information located at a third node.

[0158] The fifth information is predicted through the sixth model to obtain the second information located at the second node.

[0159] In the above embodiments, the beam-level information is used for prediction, the evaluation accuracy can be improved, and the execution of more existing processing steps between the first node and the second node is avoided, and the evaluation speed can be improved.

[0160] In some embodiments of the first aspect, the third information is one of the following:

[0161] The terminal obtains, through a beam-level layer 1 filter, a first measurement result filtered again through beam combining or selection processing and a cell-level layer 3 filter, wherein the first measurement result is a result measured at the first node based on a first signal-to-noise ratio (SNR).

[0162] The terminal obtains, through a beam-level layer 1 filter, a second measurement result filtered again through beam combining or selection processing and a cell-level layer 3 filter, wherein the second measurement result is a result measured at the first node based on a second signal-to-noise ratio (SNR), and the first SNR is greater than the second SNR.

[0163] The terminal determines based on an analog hypothesis.

[0164] In some embodiments of the first aspect, the prediction accuracy of the signal parameter is determined according to a difference between the second information and the third information.

[0165] In a second aspect, the embodiments of the present disclosure provide a method for determining prediction accuracy, executed by a network device or a test device, comprising:

[0166] receiving second information sent by a terminal, the second information comprising a value of the measurement value after first processing; the first processing at least comprising prediction of the prediction model;

[0167] determining the prediction accuracy of the signal parameter according to the second information and third information;

[0168] or

[0169] receiving second information and third information sent by a terminal, the second information comprising a value of the measurement value after first processing, the first processing at least comprising prediction of the prediction model, and the third information comprising a reference value of the signal parameter;

[0170] determining the prediction accuracy of the signal parameter according to the second information and the third information;

[0171] wherein the second information is determined by the terminal according to the following method:

[0172] performing radio resource management (RRM) measurement on a reference signal at a first node to obtain first information, the first information comprising a measurement value of a signal parameter;

[0173] determining second information at a second node according to the first information and a prediction model, the second information comprising a value of the measurement value after first processing; the first processing at least comprising prediction of the prediction model, and the first node and the second node being processing nodes in an RRM measurement processing flow.

[0174] In some embodiments of the first aspect, the first node and the second node are the same node in the RRM measurement processing flow, or the first node is located before the second node in the RRM measurement processing flow.

[0175] In some embodiments of the second aspect, the signal parameter comprises at least one of:

[0176] reference signal received power (RSRP);

[0177] reference signal received quality (RSRQ).

[0178] In some embodiments of the second aspect, in some embodiments, the first node is located at an input end of a beam-level Layer 1 filter, and the second node is located at an output end of the beam-level Layer 1 filter.

[0179] In some embodiments of the second aspect, in some embodiments, the prediction model is a first model.

[0180] The determining, according to the first information and a prediction model, of second information located at a second node comprises:

[0181] The prediction, by the first model, of the first information obtains fourth information, the fourth information comprising a predicted value of the signal parameter at the first node;

[0182] The filtering, by a beam-level Layer 1 filter, of the fourth information obtains second information located at a second node.

[0183] In some embodiments of the second aspect, in some embodiments, the prediction model is a second model.

[0184] The determining, according to the first information and a prediction model, of second information located at a second node comprises:

[0185] The prediction, by the second model, of the first information obtains second information located at a second node.

[0186] In some embodiments of the second aspect, in some embodiments, the prediction model is a third model.

[0187] The determining, according to the first information and a prediction model, of second information located at a second node comprises:

[0188] The filtering, by a beam-level Layer 1 filter, of the first information obtains fifth information located at a second node;

[0189] The prediction, by the third model, of the fifth information obtains second information located at a second node.

[0190] In some embodiments of the second aspect, in some embodiments, the third information is one of:

[0191] The first measurement result is measured at the first node based on a first signal-to-noise ratio (SNR);

[0192] the terminal obtains after filtering a first measurement result by a beam level layer 1 filter, wherein the first measurement result is measured at the first node based on a first signal noise ratio (SNR);

[0193] the terminal determines based on an analog hypothesis.

[0194] In some embodiments of the second aspect, the first node is located at an input end of the beam level layer 1 filter, and the second node is located at an output end of the cell level layer 3 filter.

[0195] In some embodiments of the second aspect, determining the second information located at the second node according to the first information and the prediction model comprises:

[0196] determining sixth information located at a third node according to the first information and the prediction model, the third node being located at an output end of the beam level layer 1 filter;

[0197] performing beam combining or selection processing on the sixth information to obtain the second information located at the second node.

[0198] In some embodiments of the second aspect, the third information is one of:

[0199] the terminal obtains after filtering a first measurement result by a beam level layer 1 filter, wherein the first measurement result is measured at the first node based on a first signal noise ratio (SNR);

[0200] the terminal obtains after filtering a second measurement result by a beam level layer 1 filter, wherein the second measurement result is measured at the first node based on a second signal noise ratio (SNR), the first SNR being greater than the second SNR;

[0201] the terminal determines based on an analog hypothesis.

[0202] In some embodiments of the second aspect, the first node is located at an input end of the beam level layer 1 filter, and the second node is located at an output end of the cell level layer 3 filter.

[0203] In some embodiments of the second aspect, determining the second information located at the second node according to the first information and the prediction model comprises:

[0204] determining sixth information at a third node according to the first information and a prediction model, the third node being located at an output of a beam-level Layer 1 filter;

[0205] performing beam combining or selection processing according to the sixth information to obtain seventh information at a fourth node, the fourth node being located at an input of a cell-level Layer 3 filter;

[0206] filtering the seventh information by the cell-level Layer 3 filter to obtain the second information at the second node.

[0207] With some embodiments of the second aspect, in some embodiments, the determining the sixth information at the third node according to the first information and the prediction model comprises one of:

[0208] the prediction model is a first model, the first information is predicted by the first model to obtain fourth information, the fourth information comprising predicted values of the signal parameters at the first node, the fourth information is filtered by the beam-level Layer 1 filter to obtain the sixth information at the third node; or

[0209] the prediction model is a second model, the first information is predicted by the second model to obtain the sixth information at the third node; or

[0210] the prediction model is a third model, the first information is filtered by the beam-level Layer 1 filter to obtain fifth information at the third node, the fifth information is predicted by the third model to obtain the sixth information at the third node.

[0211] With some embodiments of the second aspect, in some embodiments, the prediction model is a fourth model;

[0212] the determining the second information at the second node according to the first information and the prediction model comprises:

[0213] determining eighth information at the second node according to the first information,

[0214] predicting the eighth information by a fourth model to obtain the second information at the second node.

[0215] With some embodiments of the second aspect, in some embodiments, the determining the eighth information at the second node according to the first information comprises:

[0216] filtering the first information by the beam-level Layer 1 filter to obtain fifth information at a third node;

[0217] performing beam combining or selection processing on the fifth information according to the fifth information to obtain ninth information located at a fourth node;

[0218] filtering the ninth information by a cell-level layer 3 filter to obtain eighth information located at a second node.

[0219] In some embodiments of the second aspect, the prediction model is a fifth model.

[0220] The determining the second information located at the second node according to the first information and the prediction model comprises:

[0221] performing prediction on the first information by the fifth model to obtain the second information located at the second node.

[0222] In some embodiments of the second aspect, the prediction model is a sixth model.

[0223] The determining the second information located at the second node according to the first information and the prediction model comprises:

[0224] filtering the first information by a beam-level layer 1 filter to obtain fifth information located at a third node;

[0225] performing prediction on the fifth information by the sixth model to obtain the second information located at the second node.

[0226] In some embodiments of the second aspect, the third information is one of:

[0227] obtained by filtering, by a beam-level layer 1 filter, the first measurement result, then performing beam combining or selection processing, and then filtering by a cell-level layer 3 filter, wherein the first measurement result is measured at the first node based on a first signal-to-noise ratio (SNR);

[0228] obtained by filtering, by a beam-level layer 1 filter, the second measurement result, then performing beam combining or selection processing, and then filtering by a cell-level layer 3 filter, wherein the second measurement result is measured at the first node based on a second signal-to-noise ratio (SNR), and the first SNR is greater than the second SNR;

[0229] determined by the terminal based on a simulation assumption.

[0230] In some embodiments of the second aspect, the method comprises determining a prediction accuracy of the signal parameter according to a difference between the second information and the third information.

[0231] In a third aspect, the embodiments of the present disclosure provide a terminal, comprising at least one of a transceiver module and a processing module; wherein the terminal is configured to perform the optional implementation manner of the first aspect.

[0232] In a fourth aspect, the embodiments of the present disclosure provide a network device, comprising at least one of a transceiver module and a processing module; wherein the network device is configured to perform the optional implementation manner of the second aspect.

[0233] In a fifth aspect, the embodiments of the present disclosure provide a test device, comprising at least one of a transceiver module and a processing module; wherein the test device is configured to perform the optional implementation manner of the second aspect.

[0234] In a sixth aspect, the embodiments of the present disclosure provide a terminal, comprising one or more processors; wherein the terminal is configured to perform the optional implementation manner of the first aspect.

[0235] In a seventh aspect, the embodiments of the present disclosure provide a network device, comprising one or more processors; wherein the terminal is configured to perform the optional implementation manner of the second aspect.

[0236] In a ninth aspect, the embodiments of the present disclosure provide a test device, comprising one or more processors; wherein the terminal is configured to perform the optional implementation manner of the second aspect.

[0237] In a tenth aspect, the embodiments of the present disclosure provide a communication system, comprising a terminal, a network device and a test device; wherein the terminal is configured to perform the method described in the optional implementation manner of the first aspect, and the network device or the test device is configured to perform the method described in the optional implementation manner of the second aspect.

[0238] In an eleventh aspect, the embodiments of the present disclosure provide a storage medium, which stores instructions, when the instructions are run on a communication device, causing the communication device to perform the method described in the optional implementation manner of the first aspect and the second aspect.

[0239] In a twelfth aspect, the embodiments of the present disclosure provide a program product, which, when executed by a communication device, causes the communication device to perform the method described in the optional implementation manner of the first aspect and the second aspect.

[0240] In a thirteenth aspect, the embodiments of the present disclosure provide a computer program, which, when run on a computer, causes the computer to perform the method described in the optional implementation manner of the first aspect and the second aspect.

[0241] In a fourteenth aspect, the embodiments of the present disclosure provide a chip or chip system. The chip or chip system includes processing circuitry configured to perform the method described in the first aspect and the optional implementation of the second aspect.

[0242] The embodiments of the present disclosure are not exhaustive, but only illustrate some embodiments, and are not specific limitations on the protection scope of the present disclosure. In the case of no contradiction, each step in an embodiment can be implemented as an independent embodiment, and the steps can be combined arbitrarily, for example, the scheme after removing part of the steps in an embodiment can also be implemented as an independent embodiment, and the order of the steps in an embodiment can be exchanged arbitrarily, in addition, the optional implementation in an embodiment can be combined arbitrarily; in addition, the embodiments can be combined arbitrarily, for example, part or all steps of different embodiments can be combined arbitrarily, an embodiment can be combined with the optional implementation of other embodiments.

[0243] In the embodiments of the present disclosure, the terms and / or descriptions between the embodiments are consistent if there is no special description and logical conflict, and can be referred to each other, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0244] The terms used in the embodiments of the present disclosure are only for the purpose of describing the specific embodiments, and not as a limitation on the present disclosure.

[0245] In the embodiments of the present disclosure, unless otherwise specified, the elements expressed in singular form, such as "one", "a", "the", "above", "said", "preceding", "this" and the like, can represent "one and only one", and can also represent "one or more", "at least one" and the like. For example, in the case of using articles such as "a", "an", "the" and the like in English, the noun after the article can be understood as singular expression, and can also be understood as plural expression.

[0246] In the embodiments of the present disclosure, "a plurality of" means two or more.

[0247] In some embodiments, the terms "at least one of", "at least one of", "at least one of", "one or more", "a plurality of", "multiple" and the like can be replaced with each other.

[0248] The description manner such as "at least one of A, B, C, …", "A and / or B and / or C, …" and the like in the embodiments of the present disclosure includes any one of A, B, C, … existing alone, and also includes any combination of any multiple of A, B, C, …, each of which can exist alone; for example, "at least one of A, B, C" includes a case of A alone, a case of B alone, a case of C alone, a case of combination of A and B, a case of combination of A and C, a case of combination of B and C, and a case of combination of A and B and C; for example, A and / or B includes a case of A alone, a case of B alone, and a case of combination of A and B.

[0249] In some embodiments, the description manner such as "A in a case, B in another case", "in response to a case A, in response to another case B" and the like can include the following technical solutions according to the case: A is executed regardless of B, that is, A in some embodiments; B is executed regardless of A, that is, B in some embodiments; A and B are selectively executed, that is, one of A and B is executed in some embodiments; A and B are both executed, that is, A and B in some embodiments. When there are more branches of A, B, C and the like, it is similar to the above.

[0250] The prefix words "first", "second" and the like in the embodiments of the present disclosure are only used to distinguish different description objects, and do not constitute limitation on the position, order, priority, quantity or content of the description objects. The description of the description objects should be referred to the description in the context of the claims or embodiments, and should not be limited by the prefix words. For example, the description object is "field", and the ordinal words before "field" in "first field" and "second field" do not limit the position or order between "fields", and "first" and "second" do not limit whether the "fields" modified thereby are in the same message or not, nor limit the order of "first field" and "second field". For another example, the description object is "level", and the ordinal words before "level" in "first level" and "second level" do not limit the priority between "levels". For another example, the quantity of the description object is not limited by the ordinal words, and can be one or more. For example, "first device", wherein the quantity of "device" can be one or more. In addition, the objects modified by different prefix words can be the same or different, for example, the description object is "device", and "first device" and "second device" can be the same device or different devices, and the types thereof can be the same or different; for another example, the description object is "information", and "first information" and "second information" can be the same information or different information, and the content thereof can be the same or different.

[0251] In some embodiments, "including A", "containing A", "for indicating A", "carrying A" can be interpreted as directly carrying A, or indirectly indicating A.

[0252] In some embodiments, the terms "in response to", "in response to determining", "in the event that", "when", "if", and the like can be replaced with each other.

[0253] In some embodiments, the terms "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not lower than", "above", and the like can be replaced with each other, and the terms "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "not more than", "lower than", "lower than or equal to", "not higher than", "below", and the like can be replaced with each other.

[0254] In some embodiments, an apparatus and the like can be interpreted as an entity, and can also be interpreted as virtual, and the name thereof is not limited to the name described in the embodiments, and the terms "apparatus", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "subject", and the like can be replaced with each other.

[0255] In some embodiments, the terms “access network device (AN device),” “radio access network device (RAN device),” “base station (BS),” “radio base station,” “fixed station,” “node,” “access point,” “transmission point (TP),” “reception point (RP),” “transmission / reception point (TRP),” “panel,” “antenna panel,” “antenna array,” “cell,” “macro cell,” “small cell,” “femto cell,” “pico cell,” “sector,” “cell group,” “carrier,” “component carrier,” “bandwidth part (BWP),” and the like can be used interchangeably.

[0256] In some embodiments, the terms "terminal," "terminal device," "user equipment (UE)," "user terminal," "mobile station (MS)," "mobile terminal (MT)," "subscriber station," "mobile unit," "subscriber unit," "wireless unit," "remote unit," "mobile device," "wireless device," "wireless communication device," "remote device," "mobile subscriber station," "access terminal," "mobile terminal," "wireless terminal," "remote terminal," "handset," "user agent," "mobile client," "client," and so on can be replaced with each other.

[0257] In some embodiments, a network device can be replaced with a terminal. For example, in a structure in which communication between an access network device, a core network device, or a network device and a terminal is replaced with communication between a plurality of terminals (for example, also referred to as device-to-device (D2D), vehicle-to-everything (V2X), and so on), embodiments of the present disclosure can also be applied. In this case, a structure in which a terminal has all or part of the functions of an access network device can also be provided. Furthermore, the language of "uplink," "downlink," and so on can also be replaced with language corresponding to communication between terminals (for example, "side"). For example, an uplink channel, a downlink channel, and so on can be replaced with a side channel, and an uplink, a downlink, and so on can be replaced with a sidelink.

[0258] In some embodiments, a terminal can be replaced with an access network device, a core network device, or a network device. In this case, a structure in which an access network device, a core network device, or a network device has all or part of the functions of a terminal can also be provided.

[0259] In some embodiments, "acquire", "obtain", "get", "receive", "transmit", "send and / or receive" can be replaced with each other, which can be interpreted as receiving from other subjects, acquiring from protocols, obtaining by oneself, autonomously implementing, and various meanings.

[0260] In some embodiments, the terms "send", "transmit", "report", "issue", "transmit", "send and / or receive" can be replaced with each other.

[0261] In some embodiments, "predetermined" and "preset" can be interpreted as being previously specified in protocols and the like, or as being previously set by devices and the like.

[0262] In some embodiments, "determining" can be interpreted as judging, deciding, judging, calculating, computing, processing, deriving, investigating, searching, looking up, searching, inquiring, ascertaining, receiving, transmitting, inputting, outputting, accessing, resolving, selecting, choosing, establishing, comparing, "assuming", "expecting", "considering", broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, assigning, and the like, but is not limited thereto.

[0263] In some embodiments, determination or judgment can be made by a value represented by 1 bit (0 or 1), or by a true or false value (Boolean value) represented by true or false, or by comparison of numerical values (for example, comparison with a predetermined value), but is not limited thereto.

[0264] In some embodiments, "network" can be interpreted as a device (for example, an access network device, a core network device, and the like) included in the network.

[0265] In some embodiments, "not expecting to receive" can be interpreted as not receiving on the time domain resource and / or the frequency domain resource, and can also be interpreted as, after receiving the data, etc., not performing subsequent processing on the data, etc.; "not expecting to send" can be interpreted as not sending, and can also be interpreted as sending but not expecting the receiving party to respond to the content of the sending.

[0266] In some embodiments, obtaining data, information, etc. can comply with the laws and regulations of the country where the location is located.

[0267] In some embodiments, data, information, etc. can be obtained after obtaining the consent of the user.

[0268] In addition, each element, each row, or each column in the table of the embodiments of the present disclosure can be implemented as an independent embodiment, and any combination of any element, any row, or any column can also be implemented as an independent embodiment.

[0269] FIG. 1A is a schematic diagram of an architecture of a communication system according to an embodiment of the present disclosure.

[0270] As shown in FIG. 1A, the communication system 100 includes a terminal 101 and a network device 102 and a test device 103.

[0271] In some embodiments, the terminal 101 includes at least one of a mobile phone, a wearable device, an Internet of Things device, a communication-capable car, a smart car, a tablet computer (Pad), a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in smart grid, a wireless terminal device in transportation safety, a wireless terminal device in smart city, a wireless terminal device in smart home, and the like, but is not limited thereto.

[0272] In some embodiments, the network device 102 is, for example, a node or device that accesses the terminal 101 or the test device 103 to the wireless network, and the access network device can include at least one of an evolved NodeB (eNB), a next generation eNB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved node B (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, an access node in a Wi-Fi system, but is not limited thereto.

[0273] In some embodiments, the test device 103 is used to test various performances of the terminal 101, and can also simulate the behavior of the network device.

[0274] FIG. 1B is a schematic diagram of an RRM measurement processing flow according to an embodiment of the present disclosure. The RRM measurement processing flow includes multiple points, such as point A, point A1, point B, point C, point D, etc. The terminal 101 can perform measurement on the reference signal at point A to obtain a measurement result, which is beam-level, and if the number of beams is K, K is an integer greater than zero, the measurement result includes K sub-results, each corresponding to a beam. The measurement result is input to a beam-level layer 1 (L1) filter, filtered by the beam-level L1 filter to obtain information at point A1, and the information is processed by beam consolidation or selection to obtain information at point B. The information is filtered by a cell-level L3 (layer 3, L3) filter to obtain information at point C. The processing of other points is shown in FIG. 1B and will not be described in detail here.

[0275] The processing before point A1 (including point A1) belongs to the processing within the physical layer, and the processing after point A1 belongs to the processing within the higher layer.

[0276] Hereinafter, a beam-level layer 1 (L1) filter is referred to as a beam-level L1 filter, and a cell-level L3 (layer 3) filter is referred to as a cell-level L3 filter.

[0277] Hereinafter, the node is a processing node in the RRM measurement processing flow.

[0278] FIG. 2A is an interaction schematic diagram of a method for determining prediction accuracy, according to an embodiment of the present disclosure. As shown in FIG. 2A, the embodiment of the present disclosure provides a method for determining prediction accuracy, which is performed by the terminal 101, and the method comprises:

[0279] In step S2101, the terminal 101 performs a radio resource management (RRM) measurement on a reference signal at a first node to obtain first information.

[0280] In some embodiments, the first node is a processing node in the RRM measurement processing flow.

[0281] In some embodiments, the first node is located at the input end of a beam-level L1 filter in the RRM measurement processing flow.

[0282] In an example, the first node is an A node in the RRM measurement processing flow.

[0283] In some embodiments, the reference signal comprises at least one of the following:

[0284] A synchronization signal block (SSB);

[0285] A channel state information reference signal (CSI-RS).

[0286] The SSB is a reference signal for synchronization and initial access. It includes a primary synchronization signal (PSS) and a secondary synchronization signal (SSS), as well as a physical broadcast channel (PBCH). These signals are used to help the terminal 101 synchronize to the network and perform the initial access procedure.

[0287] The CSI-RS is a reference signal for downlink channel state information measurement. The main purpose of the CSI-RS is to help the terminal 101 estimate the channel state information of the downlink.

[0288] For ease of description, the first information is labeled as Info_1, and the first information in the following text will be described as the first information Info_1.

[0289] In some embodiments, the first information Info_1 includes measured values ​​of signal parameters.

[0290] In some embodiments, the first information Info_1 includes measured values ​​of the signal parameters of the reference signal.

[0291] In some embodiments, the signal parameters are at least one of the following:

[0292] Reference signal receiving power (RSRP);

[0293] Reference signal receiving quality (RSRQ).

[0294] In one example, when the first node is node A in the RRM measurement process, the first information Info_1 includes the measured values ​​of the signal parameters of multiple beams. For example, when the reference signal is CSI-RS and the signal parameter is RSRP, the first information Info_1 includes the measured values ​​of the RSRP of the CSI-RS for multiple beams.

[0295] In step S2102, terminal 101 determines the second information located at the second node based on the first information Info_1 and the prediction model.

[0296] In some embodiments, the prediction model is an artificial intelligence-based prediction model.

[0297] In some embodiments, the second node is a processing node in the RRM measurement processing flow.

[0298] In some embodiments, the second node is a reference point for determining the accuracy of the signal parameters.

[0299] In some embodiments, the second node and the first node are the same node in the RRM measurement processing flow.

[0300] In one example, both the second node and the first node are nodes A in the RRM measurement processing flow.

[0301] In some embodiments, the first node is located before the second node in the RRM measurement processing flow, that is, the second node is located after the first node in the RRM measurement processing flow.

[0302] In some embodiments, the second node is located at the output of a beam level L1 filter.

[0303] In an example, the second node is an A1 node in the RRM measurement processing flow.

[0304] In some embodiments, the second node is located at the input of a cell level L3 filter.

[0305] In an example, the second node is a B node in the RRM measurement processing flow.

[0306] In some embodiments, the second node is located at the output of a cell level L3 filter.

[0307] In an example, the second node is a C node in the RRM measurement processing flow.

[0308] For convenience of description, the second information is denoted as Info_2, and the second information in the following is all described as the second information Info_2.

[0309] In some embodiments, the second information Info_2 includes a value after a first processing of a measurement value in the first information Info_1.

[0310] In some embodiments, the first processing at least includes a prediction of a prediction model.

[0311] In some embodiments, the first processing only includes a prediction of a prediction model.

[0312] In some embodiments, the first processing at least includes a prediction of a prediction model and at least one processing in the RRM measurement processing flow.

[0313] In an example, the first processing at least includes a prediction of a prediction model and a filtering in the RRM measurement processing flow.

[0314] In some embodiments, the second information Info_2 corresponds to third information. The third information includes a reference value of a signal parameter. The specific content of the third information will be introduced in the subsequent step S2103.

[0315] For convenience of description, the third information is denoted as Info_3. The third information in the following is all described as the third information Info_3.

[0316] In some embodiments, the prediction model is different in the position in the RRM measurement processing flow, and the second node is different in the position in the RRM measurement processing flow, corresponding to different second information Info_2.

[0317] In some embodiments, in the case that the first node is located at the input end of the beam-level L1 filter, the following three methods (Method 1, Method 2, and Method 3) are used respectively according to the location of the second node.

[0318] Method 1

[0319] In Method 1, the second node is located at the output end of the beam-level L1 filter.

[0320] Method 1 includes three implementation manners, specifically Method 1-1, Method 1-2, and Method 1-3. Details will be described below.

[0321] Method 1-1

[0322] The prediction model is a first model. The first model can predict a new value according to the measured value of the actually measured signal parameter at the first node, and input the new value into subsequent processing.

[0323] Method 1-1 includes: predicting the first information Info_1 by the first model to obtain fourth information, the fourth information including the predicted value of the signal parameter at the first node; filtering the fourth information by the beam-level L1 filter to obtain the second information Info_2 at the second node.

[0324] For convenience of description, the fourth information is marked as Info_4, and the fourth information in the following description is all described as the fourth information Info_4.

[0325] The following is described by way of example:

[0326] The second node is an A1 node. The first node is an A node, and the first information Info_1 is as described in step S2101.

[0327] Referring to FIG. 2A1-1, the first information Info_1 is predicted by the first model to obtain the fourth information Info_4, the fourth information Info_4 including the predicted value of the signal parameter at the A node; the fourth information Info_4 is filtered by the beam-level L1 filter to obtain the second information Info_2 at the A1 node.

[0328] When the signal parameter is RSRP, the fourth information Info_4 is the new RSRP at the A node, and the second information Info_2 is the new RSRP at the A1 node. Since the first information Info_1 can include the RSRP of multiple beams, the fourth information Info_4 can include the RSRP of multiple beams. For example, if the first information Info_1 includes the RSRP of K beams, the fourth information Info_4 includes the RSRP of K beams.

[0329] Method 1-2

[0330] The prediction model is a second model. The second model can predict a new RSRP at the second node according to the actually measured signal parameter at the first node.

[0331] The method 1-2 comprises: predicting the first information Info_1 by a second model to obtain second information Info_2 at the second node.

[0332] The following is described by examples:

[0333] The second node is the A1 node. The first node is the A node, and the first information Info_1 is as described in step S2101.

[0334] Referring to FIG. 2A1-2, the first information Info_1 is predicted by a second model to obtain second information Info_2 at the A1 node.

[0335] When the signal parameter is RSRP, the second information Info_2 is a new RSRP at the A1 node.

[0336] Method 1-3

[0337] The prediction model is a third model. The third model can predict a new signal parameter at the second node according to an existing signal parameter at the second node.

[0338] The method 1-3 comprises: filtering the first information Info_1 by a beam-level L1 filter to obtain fifth information at the second node; predicting the fifth information by a third model to obtain second information Info_2 at the second node.

[0339] For convenience of description, the fifth information is marked as Info_5, and the fifth information in the following is described as the fifth information Info_5.

[0340] The following is described by examples:

[0341] The second node is the A1 node. The first node is the A node, and the first information Info_1 is as described in step S2101.

[0342] Referring to FIG. 2A1-3, the first information Info_1 is filtered by a beam-level L1 filter to obtain fifth information Info_5 at the A1 node; the fifth information Info_5 is predicted by a third model to obtain second information Info_2 at the A1 node.

[0343] When the signal parameter is RSRP, the fifth information Info_5 is the RSRP obtained after the first information Info_1 is filtered by the beam level L1 filter. The second information Info_2 is the new RSRP at the A1 node.

[0344] Method 2

[0345] In method 2, the second node is located at the input end of the cell level L3 filter.

[0346] Method 2 includes: determining the sixth information located at the third node according to the first information Info_1 and the prediction model, the third node being located at the output end of the beam level L1 filter; and performing beam combining or selection processing on the sixth information to obtain the second information Info_2 located at the second node.

[0347] For convenience of description, the sixth information is marked as Info_6, and the sixth information in the following is described as the sixth information Info_6.

[0348] The method of determining the sixth information Info_6 is the same as the method of determining the second information Info_2 in method 1-1, method 1-2, and method 1-3. That is, the sixth information Info_6 in method 2 is the second information Info_2 in method 1.

[0349] In an example: the second node is the B node. The third node is the A1 node. The first information Info_1 is as described in step S2101.

[0350] Method 2 includes three implementation manners, specifically, manner 2-1, manner 2-2, and manner 2-3. Details will be described below.

[0351] Manner 2-1

[0352] Referring to FIG. 2A2-1, the sixth information Info_6 is determined using method 1-1, and the sixth information Info_6 is subjected to beam combining or selection processing to obtain the second information Info_2 located at the B node.

[0353] Manner 2-2

[0354] Referring to FIG. 2A2-2, the sixth information Info_6 is determined using method 1-2, and the sixth information Info_6 is subjected to beam combining or selection processing to obtain the second information Info_2 located at the B node.

[0355] Manner 2-3

[0356] Referring to FIG. 2A2-3, the sixth information Info_6 is determined using method 1-3, and the sixth information Info_6 is subjected to beam combining or selection processing to obtain the second information Info_2 located at the B node.

[0357] Method 3

[0358] In method 3, the second node is located at the output end of the cell-level L3 filter.

[0359] In an example, the second node is a C node.

[0360] Method 3 includes 4 implementation manners, specifically, method 3-1, method 3-2, method 3-3, and method 3-4. Details will be described below.

[0361] Method 3-1

[0362] Method 3-1 includes:

[0363] According to the first information Info_1 and the prediction model, the sixth information Info_6 located at the third node is determined, the third node is located at the output end of the beam-level L1 filter;

[0364] According to the sixth information Info_6, beam combining or selection processing is performed to obtain the seventh information located at the fourth node, the fourth node is located at the input end of the cell-level L3 filter;

[0365] The seventh information is filtered through the cell-level L3 filter to obtain the second information Info_2 at the second node.

[0366] For the convenience of description, the seventh information is marked as Info_7, and the seventh information in the following description is described as the seventh information Info_7.

[0367] Wherein, the method for determining the sixth information Info_6 is the same as the method for determining the second information Info_2 in method 1-1, method 1-2, and method 1-3. That is, the sixth information Info_6 in method 3 is the second information Info_2 in method 1.

[0368] Method 3-1 includes 3 implementation manners, specifically, method 3-1-1, method 3-1-2, and method 3-1-3. Details will be described below.

[0369] The following is described by examples.

[0370] The second node is a C node, the third node is an A1 node, and the fourth node is a B node.

[0371] In method 3-1-1, referring to FIG. 2A3-1, the sixth information Info_6 at the A1 node is determined using method 1-1, according to the sixth information Info_6, beam combining or selection processing is performed to obtain the seventh information located at the B node, and the seventh information is filtered through the cell-level L3 filter to obtain the second information Info_2 at the C node.

[0372] In the mode 3-1-2, referring to FIG. 2A3-2, the sixth information Info_6 at the A1 node is determined by using the method 1-2, beam combining or selection processing is performed according to the sixth information Info_6, the seventh information at the B node is obtained, the second information Info_2 at the C node is obtained by filtering the seventh information through the cell-level L3 filter.

[0373] In the mode 3-1-3, referring to FIG. 2A3-3, the sixth information Info_6 at the A1 node is determined by using the method 1-3, beam combining or selection processing is performed according to the sixth information Info_6, the seventh information at the B node is obtained, the second information Info_2 at the C node is obtained by filtering the seventh information through the cell-level L3 filter.

[0374] Mode 3-2

[0375] The mode 3-2 includes:

[0376] According to the first information Info_1, the eighth information at the second node is determined,

[0377] The second information Info_2 at the second node is obtained by predicting the eighth information through the fourth model.

[0378] For convenience of description, the eighth information is marked as Info_8, and the eighth information in the following is described as the eighth information Info_8.

[0379] The following is described by examples.

[0380] The second node is the C node, the third node is the A1 node, and the fourth node is the B node.

[0381] Referring to FIG. 2A4-1, the eighth information Info_8 at the C node is determined according to the first information Info_1, and the second information Info_2 at the C node is obtained by predicting the eighth information Info_8 through the fourth model.

[0382] Referring to FIG. 2A4-2, the method for determining the eighth information Info_8 at the C node according to the first information Info_1 includes:

[0383] The first information Info_1 is filtered through the beam-level L1 filter to obtain the fifth information Info_5 at the A1 node;

[0384] Beam combining or selection processing is performed according to the fifth information Info_5 to obtain the ninth information at the B node;

[0385] The ninth information is filtered through the beam-level L3 filter to obtain the eighth information Info_8 at the C node.

[0386] The method in FIG. 2A4-2 is using the existing processing flow corresponding to FIG. 2A.

[0387] Method 3-3

[0388] The prediction model is a fifth model; the fifth model is used to predict second information Info_2 at a second node through first information Info_1 at a first node.

[0389] Method 3-1 includes: predicting the first information Info_1 through the fifth model to obtain the second information Info_2 at the second node.

[0390] The following is illustrated by examples.

[0391] The second node is a C node.

[0392] Referring to FIG. 2A4-3, the first information Info_1 at the A node is predicted through the fifth model to obtain the second information Info_2 at the C node.

[0393] Method 3-4

[0394] The prediction model is a sixth model; the sixth model is used to predict second information Info_2 at a second node through data filtered by a beam-level L1 filter from first information Info_1 at a first node.

[0395] Method 3-1 includes: filtering the first information Info_1 through the beam-level L1 filter to obtain fifth information Info_5 at a third node; predicting the fifth information Info_5 through the sixth model to obtain the second information Info_2 at the second node.

[0396] The following is illustrated by examples.

[0397] The second node is a C node.

[0398] Referring to FIG. 2A4-4, the first information Info_1 at the A node is filtered through the beam-level L1 filter to obtain the fifth information Info_5 at the A1 node; the fifth information Info_5 is predicted through the sixth model to obtain the second information Info_2 at the C node.

[0399] Step S2103, the terminal 101 determines third information Info_3.

[0400] In some embodiments, the third information Info_3 includes a reference value of the signal parameter.

[0401] Optionally, the reference value can also be referred to as a benchmark value, a standard value.

[0402] In the above method 1, the terminal 101 determines the third information Info_3 according to one of the following methods:

[0403] The third information Info_3 is obtained by filtering a first measurement result through a beam-level L1 filter, wherein the first measurement result is measured at the first node based on a first signal-to-noise ratio (SNR).

[0404] The third information Info_3 is obtained by filtering a second measurement result through a beam-level L1 filter, wherein the second measurement result is measured at the first node based on a second signal-to-noise ratio (SNR), and the first SNR is greater than the second SNR.

[0405] The third information Info_3 is determined based on a simulation assumption.

[0406] In the above method 2, the terminal 101 determines the third information Info_3 according to one of the following methods:

[0407] The third information Info_3 is obtained by filtering a first measurement result through a beam-level L1 filter, and then performing beam combining or selection processing, wherein the first measurement result is measured at the first node based on a first signal-to-noise ratio (SNR).

[0408] The third information Info_3 is obtained by filtering a second measurement result through a beam-level L1 filter, and then performing beam combining or selection processing, wherein the second measurement result is measured at the first node based on a second signal-to-noise ratio (SNR), and the first SNR is greater than the second SNR.

[0409] The terminal 101 determines the third information Info_3 based on a simulation assumption.

[0410] In the above method 3, the terminal 101 determines the third information Info_3 according to one of the following methods:

[0411] The third information Info_3 is obtained by filtering a first measurement result through a beam-level L1 filter, and then performing beam combining or selection processing, and further filtering through a cell-level L3 filter, wherein the first measurement result is measured at the first node based on a first signal-to-noise ratio (SNR).

[0412] The third information Info_3 is obtained by filtering the second measurement result through a beam-level L1 filter, then performing beam combining or selection processing, and then filtering through a cell-level L3 filter. The second measurement result is a result measured at the first node based on a second signal-to-noise ratio (SNR), and the first SNR is greater than the second SNR.

[0413] The terminal 101 determines the third information Info_3 based on the simulation assumption.

[0414] In step S2104, the terminal 101 sends the second information Info_2 and the third information Info_3 to at least one of the network device 102 and the test device 103.

[0415] In step S2105, the network device 102 determines the prediction accuracy of the signal parameter according to the second information Info_2 and the third information Info_3.

[0416] In some embodiments, the network device 102 determines the prediction accuracy of the signal parameter according to a difference between the second information Info_2 and the third information Info_3.

[0417] In some embodiments, the network device 102 determines the prediction accuracy of the signal parameter according to whether the difference is within a set range.

[0418] In some embodiments, the network device 101 obtains a plurality of second information Info_2 and a plurality of third information Info_3, calculates a plurality of differences from the plurality of second information Info_2 and the plurality of third information Info_3, determines whether each difference is within a set range, and determines the prediction accuracy of the signal parameter according to the number of differences within the set range.

[0419] In an example, the network device 101 obtains N second information Info_2 and N third information Info_3, N is an integer greater than 1, calculates N differences, and determines whether each difference is within a set range. If M of the differences are within the set range, M is less than N, and the ratio of M to N is determined as the prediction accuracy of the signal parameter.

[0420] In step S2106, the test device 103 determines the prediction accuracy of the signal parameter according to the second information Info_2 and the third information Info_3.

[0421] In some embodiments, the test device 103 determines the prediction accuracy of the signal parameter according to a difference between the second information Info_2 and the third information Info_3.

[0422] In some embodiments, the test device 103 determines the prediction accuracy of the signal parameter according to the second information Info_2 and the third information Info_3 in the same way as in step S2105, which will not be repeated here.

[0423] In some embodiments, the order of step S2105 and step S2106 can be interchanged.

[0424] FIG. 2B is an interaction schematic diagram of a method for determining prediction accuracy according to an embodiment of the present disclosure. As shown in FIG. 2B, the embodiment of the present disclosure provides a method for determining prediction accuracy, which comprises:

[0425] In step S2201, the terminal 101 performs RRM measurement on the reference signal at the first node to obtain first information.

[0426] In some embodiments, the implementation of step S2201 can refer to the implementation of step S2101, which will not be repeated here.

[0427] In step S2202, the terminal 101 determines the second information located at the second node according to the first information Info_1 and the prediction model.

[0428] In some embodiments, the implementation of step S2202 can refer to the implementation of step S2101, which will not be repeated here.

[0429] In step S2203, the terminal 101 sends the second information Info_2 to at least one of the network device 102 and the test device.

[0430] In step S2204, the network device 102 determines the prediction accuracy of the signal parameter according to the second information Info_2 and the third information Info_3.

[0431] In some embodiments, the third information Info_3 is determined by the network device 102.

[0432] In some embodiments, the third information Info_3 is determined by the network device 102 based on simulation assumptions.

[0433] In some embodiments, the network device 102 determines the prediction accuracy of the signal parameter according to the difference between the second information Info_2 and the third information Info_3.

[0434] In step S2205, the test device 103 determines the prediction accuracy of the signal parameter according to the second information Info_2 and the third information Info_3.

[0435] In some embodiments, the third information Info_3 is determined by the test device 103.

[0436] In some embodiments, step S2204 and step S2205 can be interchanged in order.

[0437] FIG. 3A is a flow chart illustrating a method for determining prediction accuracy, according to an embodiment of the present disclosure. As shown in FIG. 3A, an embodiment of the present disclosure relates to a method for determining prediction accuracy, which is performed by a terminal 101, and the above method comprises the following steps:

[0438] In step S3101, the terminal 101 performs a radio resource management (RRM) measurement on a reference signal at a first node to obtain first information Info_1.

[0439] In some embodiments, the implementation of step S3101 can refer to the implementation of step S2101, and details are not described herein.

[0440] In step S3102, the terminal 101 determines second information Info_2 located at a second node according to the first information Info_1 and a prediction model.

[0441] In some embodiments, the implementation of step S3102 can refer to the implementation of step S2102, and details are not described herein.

[0442] In step S3103, the terminal 101 determines third information Info_3.

[0443] In some embodiments, the implementation of step S3302 can refer to the implementation of step S2103, and details are not described herein.

[0444] In step S3104, the terminal 101 sends the second information Info_2 and the third information Info_3 to a network device 102.

[0445] In some embodiments, the implementation of step S3104 can refer to the implementation of step S2104, and details are not described herein.

[0446] FIG. 3B is a flow chart illustrating a method for determining prediction accuracy, according to an embodiment of the present disclosure. As shown in FIG. 3B, an embodiment of the present disclosure relates to a method for determining prediction accuracy, which is performed by a terminal 101, and the above method comprises the following steps:

[0447] In step S3201, the terminal 101 performs a radio resource management (RRM) measurement on a reference signal at a first node to obtain first information Info_1.

[0448] In some embodiments, the implementation of step S3101 can refer to the implementation of step S2101, and details are not described herein.

[0449] At step S3202, the terminal 101 determines, according to the first information Info_1 and the prediction model, second information Info_2 located at the second node.

[0450] In some embodiments, the implementation of step S3102 can refer to the implementation of step S2102, which will not be repeated here.

[0451] At step S3203, the terminal 101 sends the second information Info_2 to the network device 102.

[0452] In some embodiments, the implementation of step S3203 can refer to the implementation of step S2203, which will not be repeated here.

[0453] FIG. 4A is a flow chart illustrating a method for determining prediction accuracy, according to an embodiment of the present disclosure. As shown in FIG. 4A, the embodiment of the present disclosure relates to a method for determining prediction accuracy, which is performed by the network device 102 and includes the following steps:

[0454] At step S4101, the network device 102 receives the second information Info_2 and the third information Info_3 sent by the terminal 101.

[0455] In some embodiments, the implementation of step S4101 can refer to the implementation of step S2104, which will not be repeated here.

[0456] At step S4102, the network device 102 determines the prediction accuracy of the signal parameter according to the second information Info_2 and the third information Info_3.

[0457] In some embodiments, the implementation of step S4102 can refer to the implementation of step S2105, which will not be repeated here.

[0458] FIG. 4B is a flow chart illustrating a method for determining prediction accuracy, according to an embodiment of the present disclosure. As shown in FIG. 4B, the embodiment of the present disclosure relates to a method for determining prediction accuracy, which is performed by the network device 102 and includes the following steps:

[0459] At step S4201, the network device 102 receives the second information Info_2 sent by the terminal 101.

[0460] In some embodiments, the implementation of step S4201 can refer to the implementation of step S2203, which will not be repeated here.

[0461] At step S4202, the network device 102 determines the prediction accuracy of the signal parameter according to the second information Info_2 and the third information Info_3.

[0462] In some embodiments, implementation of step S4202 can refer to implementation of step S2204, which will not be repeated here.

[0463] FIG. 4C is a flow chart illustrating a method for determining prediction accuracy, according to embodiments of the present disclosure. As shown in FIG. 4C, embodiments of the present disclosure relate to a method for determining prediction accuracy, performed by the test device 103, and the above method comprises the following steps:

[0464] In step S4301, the test device 103 receives the second information Info_2 and the third information Info_3 sent by the terminal 101.

[0465] In some embodiments, implementation of step S4301 can refer to implementation of step S2104, which will not be repeated here.

[0466] In step S4302, the test device 103 determines the prediction accuracy of the signal parameter according to the second information Info_2 and the third information Info_3.

[0467] In some embodiments, implementation of step S4302 can refer to implementation of step S2105, which will not be repeated here.

[0468] FIG. 4D is a flow chart illustrating a method for determining prediction accuracy, according to embodiments of the present disclosure. As shown in FIG. 4D, embodiments of the present disclosure relate to a method for determining prediction accuracy, performed by the test device 103, and the above method comprises the following steps:

[0469] In step S4401, the test device 103 receives the second information Info_2 sent by the terminal 101.

[0470] In some embodiments, implementation of step S4401 can refer to implementation of step S2203, which will not be repeated here.

[0471] In step S4402, the test device 103 determines the prediction accuracy of the signal parameter according to the second information Info_2 and the third information Info_3.

[0472] In some embodiments, implementation of step S4402 can refer to implementation of step S2204, which will not be repeated here.

[0473] Embodiments of the present disclosure also propose an apparatus for implementing any of the above methods, for example, an apparatus comprising units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another apparatus is also proposed, comprising units or modules for implementing each step performed by a network device (such as an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0474] It should be understood that the division of each unit or module in the above apparatus is only a logical function division, and all or part of them can be integrated into a physical entity or physically separated in actual implementation. In addition, the units or modules in the apparatus can be implemented in the form of processor calling software: for example, the apparatus includes a processor connected with a memory, the memory stores instructions, and the processor calls the instructions stored in the memory to implement any of the above methods or realize the functions of the units or modules of the above apparatus, wherein the processor is a general processor such as a central processing unit (CPU) or a microprocessor, and the memory is a memory in the apparatus or a memory outside the apparatus. Alternatively, the units or modules in the apparatus can be implemented in the form of hardware circuit, and the functions of part or all of the units or modules can be realized by the design of the hardware circuit. The above hardware circuit can be understood as one or more processors; for example, in one implementation, the above hardware circuit is an application-specific integrated circuit (ASIC), and the functions of part or all of the units or modules are realized by the design of the logical relationship of the elements in the circuit; for another example, in another implementation, the above hardware circuit is a programmable logic device (PLD), and a field programmable gate array (FPGA) is taken as an example, which can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by a configuration file, so as to realize the functions of part or all of the units or modules. All units or modules of the above apparatus can be implemented in the form of processor calling software, or all units or modules can be implemented in the form of hardware circuit, or part of the units or modules are implemented in the form of processor calling software, and the remaining part is implemented in the form of hardware circuit.

[0475] In the embodiments of the present disclosure, the processor is a circuit with signal processing capability. In one implementation, the processor can be a circuit with instruction reading and running capability, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), a digital signal processor (DSP), or the like. In another implementation, the processor can implement certain functions through a logical relationship of a hardware circuit, and the logical relationship of the hardware circuit is fixed or reconfigurable. For example, the processor is a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In the reconfigurable hardware circuit, the processor loads a configuration document to implement the configuration of the hardware circuit. It can be understood that the processor loads instructions to implement the functions of the above part or all units or modules. In addition, the hardware circuit can also be designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), or the like.

[0476] FIG. 5A is a structural schematic diagram of a terminal according to an embodiment of the present disclosure. As shown in FIG. 5A, the terminal 5100 can include at least one of a processing module 5101, a transceiver module 5102, and the like.

[0477] In some embodiments, the processing module 5102 is configured to perform a radio resource management (RRM) measurement on a reference signal at a first node to obtain first information, the first information including a measurement value of a signal parameter; determine second information located at a second node according to the first information and a prediction model, the second information including a value after first processing of the measurement value; the first processing at least includes a prediction of the prediction model, and the first node and the second node are processing nodes in an RRM measurement processing flow.

[0478] The transceiver module 5102 is configured to send the second information to a network device or a test device, or send the second information and third information, the second information being used to determine a prediction accuracy of the signal parameter, and the third information including a reference value of the signal parameter.

[0479] Optionally, the processing module 5101 described above is configured to perform at least one of other steps performed by the terminal 101 in any of the methods described above, which will not be repeated here. Optionally, the transceiver module 5102 described above is configured to perform at least one of the communication steps such as sending and / or receiving performed by the terminal 101 in any of the methods described above, which will not be repeated here.

[0480] Figure 5B is a structural schematic diagram of a network device according to an embodiment of the present disclosure. As shown in Figure 5B, the network device 5200 can include at least one of a processing module 5201 and a transceiver module 5202.

[0481] The transceiver module 5202 described above is configured to receive second information sent by the terminal, the second information including a value of the measurement value after first processing; the first processing at least includes prediction of the prediction model;

[0482] The processing module 5201 described above is configured to determine the prediction accuracy of the signal parameter according to the second information and third information.

[0483] Alternatively,

[0484] The transceiver module 5202 described above is configured to receive second information and third information sent by the terminal, the second information including a value of the measurement value after first processing, the first processing at least including prediction of the prediction model, and the third information including a reference value of the signal parameter.

[0485] The processing module 5201 described above is configured to determine the prediction accuracy of the signal parameter according to the second information and the third information.

[0486] The second information is determined by the terminal according to the following method:

[0487] At the first node, performing a radio resource management (RRM) measurement on a reference signal to obtain first information, the first information including a measurement value of a signal parameter;

[0488] According to the first information and a prediction model, determining second information located at a second node, the second information including a value of the measurement value after first processing; the first processing at least including prediction of the prediction model, and the first node and the second node being processing nodes in an RRM measurement processing flow.

[0489] Figure 5C is a structural schematic diagram of a network device according to an embodiment of the present disclosure. As shown in Figure 5C, the test device 5300 can include at least one of a processing module 5301 and a transceiver module 5302.

[0490] In some embodiments, the transceiver module 5302 described above is configured to

[0491] The transceiver 5302 is configured to receive second information sent by the terminal, the second information comprising a value of the measurement value after first processing, the first processing at least comprising prediction of the prediction model.

[0492] The processing module 5301 is configured to determine the prediction accuracy of the signal parameter according to the second information and third information.

[0493] Alternatively,

[0494] The transceiver 5302 is configured to receive second information and third information sent by the terminal, the second information comprising a value of the measurement value after first processing, the first processing at least comprising prediction of the prediction model, and the third information comprising a reference value of the signal parameter.

[0495] The processing module 5301 is configured to determine the prediction accuracy of the signal parameter according to the second information and the third information.

[0496] The second information is determined by the terminal according to the following method:

[0497] At the first node, performing a radio resource management (RRM) measurement on a reference signal to obtain first information, the first information comprising a measurement value of a signal parameter;

[0498] According to the first information and a prediction model, determining second information located at a second node, the second information comprising a value of the measurement value after first processing, the first processing at least comprising prediction of the prediction model, the first node and the second node being processing nodes in a RRM measurement processing flow.

[0499] Optionally, the transceiver 5302 is configured to perform at least one of the communication steps such as sending and / or receiving performed by the test equipment in any of the above methods, which will not be described herein. In some embodiments, the transceiver can comprise a sending module and / or a receiving module, which can be separate or integrated together. Optionally, the transceiver can be replaced by a transceiver.

[0500] FIG. 6A is a structural schematic diagram of a communication device 6100 according to the embodiments of the present disclosure. The communication device 6100 can be a network device (such as an access network device, a core network device, etc.), a terminal (such as a user equipment, etc.), a chip, a chip system, or a processor supporting the network device to implement any of the above methods, or a chip, a chip system, or a processor supporting the terminal to implement any of the above methods. The communication device 6100 can be used to implement the methods described in the above method embodiments, and details can be referred to the descriptions in the above method embodiments.

[0501] As shown in FIG. 6A, the communication device 6100 includes one or more processors 6101. The processor 6101 can be a general processor or a special purpose processor, etc., such as a baseband processor or a central processing unit. The baseband processor can be configured to process communication protocols and communication data, and the central processing unit can be configured to control a communication apparatus (e.g., a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute programs, and process data of the programs. Optionally, the communication device 6100 is configured to perform any of the above methods. Optionally, the one or more processors 6101 are configured to invoke instructions to cause the communication device 6100 to perform any of the above methods.

[0502] In some embodiments, the communication device 6100 further includes one or more transceivers 6102. When the communication device 6100 includes one or more transceivers 6102, the transceiver 6102 performs at least one of the communication steps such as transmitting and / or receiving in the above methods, and the processor 6101 performs at least one of the other steps. In optional embodiments, the transceiver can include a receiver and / or a transmitter, which can be separate or integrated together. Optionally, the terms transceiver, transceiving unit, transceiver, transceiving circuit, interface circuit, interface, etc. can be replaced with each other, and the terms transmitter, transmitting unit, transmitter, transmitting circuit, etc. can be replaced with each other, and the terms receiver, receiving unit, receiver, receiving circuit, etc. can be replaced with each other.

[0503] In some embodiments, the communication device 6100 further includes one or more memories 6103 for storing data. Optionally, all or part of the memory 6103 can also be outside the communication device 6100. In optional embodiments, the communication device 6100 can include one or more interface circuits 6104. Optionally, the interface circuit 6104 is connected to the memory 6103, and the interface circuit 6104 can be configured to receive data from the memory 6103 or other devices, and can be configured to send data to the memory 6103 or other devices. For example, the interface circuit 6104 can read data stored in the memory 6103 and send the data to the processor 6101.

[0504] The communication device 6100 described in the above embodiments can be a network device or a terminal, but the scope of the communication device 6100 described in the present disclosure is not limited thereto, and the structure of the communication device 6100 can not be limited by FIG. 6A. The communication device can be a standalone device or can be part of a larger device. For example, the communication device can be: 1) a standalone integrated circuit (IC), or a chip, or a chip system or subsystem; (2) a set of one or more ICs, which can optionally also include storage components for storing data, programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, a smart terminal device, a cellular phone, a wireless device, a handset, a mobile unit, a vehicle-mounted device, a network device, a cloud device, an artificial intelligence device, and the like; (6) other devices, and the like.

[0505] FIG. 6B is a structural schematic diagram of a chip 6200 according to an embodiment of the present disclosure. For the case where the communication device 6100 is a chip or a chip system, the structural schematic diagram of the chip 6200 shown in FIG. 6B can be referred to, but is not limited thereto.

[0506] The chip 6200 includes one or more processors 6201. The chip 6200 is configured to perform any of the above methods.

[0507] In some embodiments, the chip 6200 further includes one or more interface circuits 6202. Optionally, the terms interface circuit, interface, transceiver pin, and the like can be replaced with each other. In some embodiments, the chip 6200 further includes one or more memories 6203 for storing data. Optionally, all or part of the memory 6203 can be outside the chip 6200. Optionally, the interface circuit 6202 is connected to the memory 6203, and the interface circuit 6202 can be configured to receive data from the memory 6203 or other devices, and the interface circuit 6202 can be configured to send data to the memory 6203 or other devices. For example, the interface circuit 6202 can read data stored in the memory 6203 and send the data to the processor 6201.

[0508] In some embodiments, the interface circuit 6202 performs at least one of the communication steps such as sending and / or receiving in the above methods. The interface circuit 6202 performing the communication steps such as sending and / or receiving in the above methods means that the interface circuit 6202 performs data interaction between the processor 6201, the chip 6200, the memory 6203, or a transceiver device. In some embodiments, the processor 6201 performs at least one of the other steps.

[0509] The modules and / or devices described in various embodiments of virtual devices, physical devices, chips, etc. can be combined or separated according to circumstances. Optionally, part or all of the steps can also be performed by multiple modules and / or devices in cooperation, which is not limited here.

[0510] The present disclosure further provides a storage medium having stored instructions which, when executed on the communication device 6100, cause the communication device 6100 to perform any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited to this, and can also be a storage medium readable by other devices. Optionally, the storage medium can be a non-transitory storage medium, but is not limited to this, and can also be a transitory storage medium.

[0511] The present disclosure further provides a program product which, when executed by the communication device 6100, causes the communication device 6100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0512] The present disclosure further provides a computer program which, when executed on a computer, causes the computer to perform any of the above methods. Industrial applicability

[0513] The data processing amount of the RRM measurement of the terminal can be reduced, and more reasonable second information can be provided, so as to more accurately evaluate the prediction accuracy of the signal parameter and the quality of the prediction model.

Claims

1. A method for determining prediction accuracy, performed by a terminal, comprising: performing, at a first node, a radio resource management (RRM) measurement on a reference signal to obtain first information, the first information comprising a measured value of a signal parameter; determining, according to the first information and a prediction model, second information at a second node, the second information comprising a value of the signal parameter after a first processing, the first processing comprising at least a prediction of the prediction model; the first node and the second node being processing nodes in a RRM measurement processing flow; and sending, to a network device or a test device, the second information, or the second information and third information, the second information being used to determine a prediction accuracy of the signal parameter, the third information comprising a reference value of the signal parameter. The first node and the second node are the same node in the RRM measurement processing flow, or the first node is located before the second node in the RRM measurement processing flow. The signal parameter comprises at least one of: a reference signal received power (RSRP) ; 2. The method of claim 1, wherein, a reference signal received quality (RSRQ).

3. The method of claim 1 or 2, wherein, The first node is located at an input end of a beam-level layer 1 filter, and the second node is located at an output end of the beam-level layer 1 filter. The prediction model is a first model. The determining, according to the first information and a prediction model, of second information at a second node comprises:

4. The method of any one of claims 1 to 3, wherein, performing, by the first model, a prediction on the first information to obtain fourth information, the fourth information comprising a predicted value of the signal parameter at the first node; and performing, by a beam-level layer 1 filter, a filtering on the fourth information to obtain the second information at the second node.

5. The method of claim 4, wherein, The prediction model is a second model. The determining, according to the first information and a prediction model, of second information at a second node comprises: performing, by the second model, a prediction on the first information to obtain the second information at the second node. The prediction model is a third model.

6. The method of claim 4, wherein, The determining, according to the first information and a prediction model, of second information at a second node comprises: performing, by a beam-level layer 1 filter, a filtering on the first information to obtain fifth information at the second node; and performing, by the third model, a prediction on the fifth information to obtain the second information at the second node. The third information is one of:

7. The method of claim 4, wherein, a first measurement result filtered by a beam-level layer 1 filter, the first measurement result being measured at the first node based on a first signal-to-noise ratio (SNR) ; a second measurement result filtered by a beam-level layer 1 filter, the second measurement result being measured at the first node based on a second signal-to-noise ratio (SNR), the first SNR being greater than the second SNR; and determined by the terminal based on an analog hypothesis. The first node is located at an input end of a beam-level layer 1 filter, and the second node is located at an input end of a cell-level layer 3 filter.

8. The method of any one of claims 4 to 7, wherein, The determining, according to the first information and a prediction model, of second information at a second node comprises: ​ ​ ​ 9. The method of any one of claims 1 to 3, wherein, ​ 10. The method of claim 9, wherein, ​ determining sixth information at a third node according to the first information and a prediction model, the third node being located at an output end of a beam-level layer 1 filter; performing beam combining or selection processing on the sixth information to obtain second information at a second node.

11. The method of claim 9 or 10, wherein, The third information is one of: obtained by performing beam combining or selection processing on a first measurement result filtered by a beam-level layer 1 filter, wherein the first measurement result is measured at the first node based on a first signal-to-noise ratio (SNR); obtained by performing beam combining or selection processing on a second measurement result filtered by a beam-level layer 1 filter, wherein the second measurement result is measured at the first node based on a second signal-to-noise ratio (SNR), the first SNR being greater than the second SNR; determined by the terminal based on a simulation assumption.

12. The method of any one of claims 1 to 3, wherein, The first node is located at an input end of a beam-level layer 1 filter, and the second node is located at an output end of a cell-level layer 3 filter.

13. The method of claim 12, wherein, The determining the second information at the second node according to the first information and the prediction model comprises: determining sixth information at a third node according to the first information and a prediction model, the third node being located at an output end of a beam-level layer 1 filter; performing beam combining or selection processing on the sixth information to obtain seventh information at a fourth node, the fourth node being located at an input end of a cell-level layer 3 filter; filtering the seventh information by a cell-level layer 3 filter to obtain the second information at the second node.

14. The method of claim 10 or 13, wherein, The determining the sixth information at the third node according to the first information and the prediction model comprises one of: the prediction model is a first model, the first information is predicted by the first model to obtain fourth information, the fourth information comprises a predicted value of the signal parameter at the first node, and the fourth information is filtered by a beam-level layer 1 filter to obtain the sixth information at the third node; or the prediction model is a second model, the first information is predicted by the second model to obtain the sixth information at the third node; or the prediction model is a third model, the first information is filtered by a beam-level layer 1 filter to obtain fifth information at the third node, and the fifth information is predicted by the third model to obtain the sixth information at the third node.

15. The method of claim 12, wherein, The prediction model is a fourth model. The determining the second information at the second node according to the first information and the prediction model comprises: determining eighth information at the second node according to the first information, the eighth information is predicted by the fourth model to obtain the second information at the second node.

16. The method of claim 15, wherein, The determining the eighth information at the second node according to the first information comprises: filtering the first information by a beam-level layer 1 filter to obtain fifth information at a third node; performing beam combining or selection processing on the fifth information to obtain ninth information at a fourth node; and filtering the ninth information by a cell-level layer 3 filter to obtain the second information at the second node. Filter the ninth information through a cell-level layer 3 filter to obtain eighth information located at a second node.

17. The method of claim 12, wherein, The prediction model is a fifth model. The determining the second information located at the second node according to the first information and the prediction model comprises: The second information located at the second node is obtained by predicting the first information through a fifth model.

18. The method of claim 12, wherein, The prediction model is a sixth model. The determining the second information located at the second node according to the first information and the prediction model comprises: The fifth information located at a third node is obtained by filtering the first information through a beam-level layer 1 filter. The second information located at the second node is obtained by predicting the fifth information through a sixth model.

19. The method of any one of claims 12 to 18, wherein, The third information is one of the following: The terminal obtains the third information by filtering a first measurement result through a beam-level layer 1 filter, then performing beam combining or selection processing, and then filtering through a cell-level layer 3 filter, wherein the first measurement result is a result measured at the first node based on a first signal-to-noise ratio (SNR); The terminal obtains the third information by filtering a second measurement result through a beam-level layer 1 filter, then performing beam combining or selection processing, and then filtering through a cell-level layer 3 filter, wherein the second measurement result is a result measured at the first node based on a second signal-to-noise ratio (SNR), and the first SNR is greater than the second SNR; The terminal determines the third information based on a simulation assumption.

20. The method of claims 1-19, wherein, The prediction accuracy of the signal parameter is determined according to a difference between the second information and the third information.

21. A method for determining prediction accuracy, performed by a network device or a test device, comprising: receiving second information sent by a terminal, the second information comprising a value of the measurement value after first processing; the first processing at least comprising prediction of the prediction model; determining the prediction accuracy of the signal parameter according to the second information and third information; or receiving second information and third information sent by a terminal, the second information comprising a value of the measurement value after first processing, the first processing at least comprising prediction of the prediction model, and the third information comprising a reference value of the signal parameter; determining the prediction accuracy of the signal parameter according to the second information and the third information; wherein the second information is determined by the terminal according to the following method: performing radio resource management (RRM) measurement on a reference signal at a first node to obtain first information, the first information comprising a measurement value of a signal parameter; determining second information located at a second node according to the first information and a prediction model, the second information comprising a value of the measurement value after first processing; the first processing at least comprising prediction of the prediction model, and the first node and the second node being processing nodes in an RRM measurement processing flow.

22. The method of claim 21, wherein, The first node and the second node are the same node in the RRM measurement processing flow, or the first node is located before the second node in the RRM measurement processing flow.

23. The method of claim 21 or 22, wherein, The signal parameter comprises at least one of the following: reference signal received power (RSRP); Reference signal received quality, RSRQ.

24. The method of any one of claims 21 to 23, wherein, The first node is located at an input end of a beam-level layer 1 filter, and the second node is located at an output end of the beam-level layer 1 filter.

25. The method of claim 24, wherein, The prediction model is a first model. The determining, according to the first information and the prediction model, of second information located at the second node comprises: predicting, by the first model, the first information to obtain fourth information, the fourth information comprising a predicted value of the signal parameter at the first node; filtering, by the beam-level layer 1 filter, the fourth information to obtain the second information located at the second node.

26. The method of claim 24, wherein, The prediction model is a second model. The determining, according to the first information and the prediction model, of second information located at the second node comprises: predicting, by the second model, the first information to obtain the second information located at the second node.

27. The method of claim 24, wherein, The prediction model is a third model. The determining, according to the first information and the prediction model, of second information located at the second node comprises: filtering, by the beam-level layer 1 filter, the first information to obtain fifth information located at the second node; predicting, by the third model, the fifth information to obtain the second information located at the second node.

28. The method of any one of claims 24 to 27, wherein, The third information is one of: a result measured at the first node based on a first signal-to-noise ratio (SNR) and filtered by the beam-level layer 1 filter by the terminal; a result measured at the first node based on a second signal-to-noise ratio (SNR) and filtered by the beam-level layer 1 filter by the terminal, the first SNR being greater than the second SNR; determined by the terminal based on an analog hypothesis.

29. The method of any one of claims 21 to 23, wherein, The first node is located at an input end of a beam-level layer 1 filter, and the second node is located at an input end of a cell-level layer 3 filter.

30. The method of claim 29, wherein, The determining, according to the first information and the prediction model, of second information located at the second node comprises: determining, according to the first information and a prediction model, sixth information located at a third node, the third node being located at an output end of the beam-level layer 1 filter; performing beam combining or selection processing on the sixth information to obtain the second information located at the second node.

31. The method of claim 29 or 30, wherein, The third information is one of: a result measured at the first node based on a first signal-to-noise ratio (SNR) and filtered by the beam-level layer 1 filter by the terminal, and then subjected to beam combining or selection processing; a result measured at the first node based on a second signal-to-noise ratio (SNR) and filtered by the beam-level layer 1 filter by the terminal, the first SNR being greater than the second SNR, and then subjected to beam combining or selection processing; determined by the terminal based on an analog hypothesis.

32. The method of claim 31 or 33, wherein, The first node is located at an input end of a beam-level layer 1 filter, and the second node is located at an output end of a cell-level layer 3 filter.

33. The method of claim 32, wherein, The determining, according to the first information and a prediction model, of the second information located at the second node comprises: The determining, according to the first information and a prediction model, of the sixth information located at the third node comprises one of: The prediction model is a first model, the fourth information is obtained by predicting the first information through the first model, the fourth information comprises a predicted value of the signal parameter at the first node, and the sixth information located at the third node is obtained by filtering the fourth information through a beam-level Layer 1 filter; or The prediction model is a second model, the sixth information located at the third node is obtained by predicting the first information through the second model; or 34. The method of claim 30 or 33, wherein, The prediction model is a third model, the fifth information located at the third node is obtained by filtering the first information through a beam-level Layer 1 filter, the sixth information located at the third node is obtained by predicting the fifth information through the third model. The prediction model is a fourth model; The determining, according to the first information and a prediction model, of the second information located at the second node comprises: The determining, according to the first information, of the eighth information located at the second node comprises:

35. The method of claim 32, wherein, The predicting, through a fourth model, of the eighth information to obtain the second information located at the second node. The determining, according to the first information, of the eighth information located at the second node comprises: The filtering, through a beam-level Layer 1 filter, of the first information to obtain the fifth information located at the third node; The performing, according to the fifth information, of beam combining or selection processing to obtain the ninth information located at the fourth node; 36. The method of claim 35, wherein, The filtering, through a cell-level Layer 3 filter, of the ninth information to obtain the eighth information located at the second node. The prediction model is a fifth model; The determining, according to the first information and a prediction model, of the second information located at the second node comprises: The predicting, through a fifth model, of the first information to obtain the second information located at the second node.

37. The method of claim 32, wherein, The prediction model is a sixth model; The determining, according to the first information and a prediction model, of the second information located at the second node comprises: The filtering, through a beam-level Layer 1 filter, of the first information to obtain the fifth information located at the third node; 38. The method of claim 32, wherein, The predicting, through a sixth model, of the fifth information to obtain the second information located at the second node. The third information is one of: The first measurement result is measured at the first node based on a first signal noise and interference ratio (SNR). ​ 39. The method of any one of claims 32 to 38, wherein, ​ ​ The terminal obtains the second information by filtering a second measurement result through a beam-level layer 1 filter, performing beam combining or selection processing, and filtering again through a cell-level layer 3 filter, wherein the second measurement result is a result measured at the first node based on a second signal noise and interference ratio (SNR), and the first SNR is greater than the second SNR. The terminal determines the second information based on a simulation assumption.

40. The method of claims 21-39, wherein, The method comprises: According to a difference between the second information and the third information, determining a prediction accuracy of the signal parameter.

41. A terminal, comprising: a processing module configured to perform a radio resource management (RRM) measurement on a reference signal at a first node to obtain first information, the first information comprising a measurement value of a signal parameter; and determine second information at a second node according to the first information and a prediction model, the second information comprising a value of the measurement value after first processing, the first processing comprising at least a prediction of the prediction model; the first node and the second node being processing nodes in a RRM measurement processing flow. a transceiver module configured to send the second information to a network device or a test device, or send the second information and third information, the second information being used to determine a prediction accuracy of the signal parameter, and the third information comprising a reference value of the signal parameter.

42. A network device, comprising a transceiver module and a processing module: the transceiver module is configured to receive second information sent by a terminal, the second information comprising a value of a measurement value after first processing, the first processing comprising at least a prediction of a prediction model; the processing module is configured to determine a prediction accuracy of the signal parameter according to the second information and third information; or, the transceiver module is configured to receive second information and third information sent by a terminal, the second information comprising a value of a measurement value after first processing, the first processing comprising at least a prediction of a prediction model, and the third information comprising a reference value of the signal parameter; the processing module is configured to determine a prediction accuracy of the signal parameter according to the second information and the third information; wherein the second information is determined by the terminal according to the following method: performing a radio resource management (RRM) measurement on a reference signal at a first node to obtain first information, the first information comprising a measurement value of a signal parameter; determining second information at a second node according to the first information and a prediction model, the second information comprising a value of the measurement value after first processing, the first processing comprising at least a prediction of the prediction model, the first node and the second node being processing nodes in a RRM measurement processing flow.

43. A test device, comprising a transceiver module and a processing module: the transceiver module is configured to receive second information sent by a terminal, the second information comprising a value of a measurement value after first processing, the first processing comprising at least a prediction of a prediction model; the processing module is configured to determine a prediction accuracy of the signal parameter according to the second information and third information; or, the transceiver module is configured to receive second information and third information sent by a terminal, the second information comprising a value of a measurement value after first processing, the first processing comprising at least a prediction of a prediction model, and the third information comprising a reference value of the signal parameter; the processing module is configured to determine a prediction accuracy of the signal parameter according to the second information and the third information. The transceiving module is configured to receive second information and third information sent by the terminal, the second information comprising a value of the measurement value after first processing, the first processing comprising at least prediction of the prediction model, and the third information comprising a reference value of the signal parameter; The processing module is configured to determine the prediction accuracy of the signal parameter according to the second information and the third information; wherein The second information is determined by the terminal according to the following method: At a first node, performing a radio resource management (RRM) measurement on a reference signal to obtain first information, the first information comprising a measurement value of a signal parameter; According to the first information and a prediction model, determining second information at a second node, the second information comprising a value of the measurement value after first processing, the first processing comprising at least prediction of the prediction model, the first node and the second node being processing nodes in a RRM measurement processing flow.

44. A terminal, comprising: one or more processors; wherein the terminal is configured to implement the method of any one of claims 1 to 20.

45. A network device, comprising: one or more processors; wherein the network device is configured to implement the method of any one of claims 21 to 40.

46. A test device, comprising: one or more processors; wherein the test device is configured to implement the method of any one of claims 21 to 40.

47. A storage medium, the storage medium storing instructions, wherein, when the instructions run on a communication device, the communication device is caused to perform as claimed in any one of claims 1 to 20, or, perform as claimed in any one of claims 21 to 40.

48. A program product, wherein, when the program product is executed by a communication device, the communication device is caused to perform as claimed in any one of claims 1 to 20, or, perform as claimed in any one of claims 21 to 40.

Images