Communication method, communication device and communication system

Abstract

The embodiment of the invention relates to a communication method, communication equipment and a communication system. The communication method comprises the following steps: a first device receives an L3-RSRP predicted value sent by a UE; and, according to the L3-RSRP predicted value and at least one RSRP precision margin, determining the precision of L3-RSRP prediction performed by the UE, wherein the first equipment comprises test equipment TE or network equipment, and the RSRP precision margin comprises at least one of the following items: a first margin based on time offset, a second margin based on L1 filtering sample number difference, a third margin based on L1 filtering mode difference, and a fourth margin; wherein the fourth margin is obtained based on time offset, L1 filtering sample number difference and L1 filtering mode difference; in this way, a mobility management mechanism based on artificial intelligence is further enhanced.

Description

Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a communication method, communication equipment, and communication system. Background Art

[0002] In wireless communication systems, seamless handovers between cells are crucial to maintaining high-quality connections as users move across different coverage areas. However, traditional methods for measuring Reference Signal Received Power (RSRP) for handover decisions can face significant challenges, such as increased measurement overhead and potential delays in detecting the optimal handover opportunity.

[0003] With the development of artificial intelligence, the mobility management mechanism based on artificial intelligence can improve the efficiency of RSRP estimation and switching management to a certain extent, but the mechanism is not mature enough and needs to be further improved. Summary of the Invention

[0004] The embodiments of the present disclosure provide a communication method, a communication device, and a communication system to further enhance the mobility management mechanism based on artificial intelligence.

[0005] In one aspect, an embodiment of the present disclosure provides a communication method, comprising: a first device, wherein the first device includes testing equipment (TE) or a network device, and the method includes:

[0006] Receive a predicted value of layer 3 reference signal received power (layer 3-RSRP, L3-RSRP) sent by the UE;

[0007] Determining an accuracy of L3-RSRP prediction performed by the UE according to the L3-RSRP prediction value and at least one RSRP accuracy margin;

[0008] The RSRP accuracy margin includes at least one of the following: a first margin based on time offset, a second margin based on the difference in the number of layer 1 (L1) filtered samples, a third margin based on the difference in L1 filtering methods, and a fourth margin; wherein the fourth margin is obtained based on the time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods.

[0009] On the other hand, an embodiment of the present disclosure provides a communication method, performed by a user equipment UE, the method including:

[0010] L3-RSRP prediction value sent to TE;

[0011] The TE determines the accuracy of the L3-RSRP prediction performed by the UE according to the L3-RSRP prediction value and at least one RSRP accuracy margin;

[0012] The RSRP accuracy margin includes at least one of the following: a first margin based on time offset, a second margin based on the difference in the number of L1 filtered samples, a third margin based on the difference in L1 filtering methods, and a fourth margin; wherein the fourth margin is obtained based on the time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods.

[0013] On the other hand, an embodiment of the present disclosure further provides a communication device, which is used to execute the above communication method.

[0014] On the other hand, an embodiment of the present disclosure further provides a communication device, including:

[0015] one or more processors;

[0016] Wherein, the communication device is used to execute the above communication method.

[0017] On the other hand, an embodiment of the present disclosure further provides a communication system, including a communication device; wherein the communication device is configured to implement the above-mentioned communication method.

[0018] On the other hand, an embodiment of the present disclosure further provides a storage medium storing instructions, which, when executed on a communication device, enables the communication device to execute the above-mentioned communication method.

[0019] On the other hand, an embodiment of the present disclosure further provides a program product, including at least one of a program and an instruction, wherein the at least one of the program and the instruction implements the above-mentioned communication method when executed by a communication device.

[0020] In the embodiment of the present disclosure, when determining the accuracy of the L3-RSRP prediction performed by the UE, by considering at least one of a first margin based on a time offset, a second margin based on a difference in the number of L1 filtered samples, a third margin based on a difference in the L1 filtering method, and a fourth margin obtained based on the time offset, the difference in the number of L1 filtered samples, and the difference in the L1 filtering method, multiple uncertainties that may lead to an inability to accurately determine whether the UE meets the preset accuracy requirements for the L3-RSRP prediction can be considered, thereby improving the accuracy of the evaluation of the L3-RSRP prediction accuracy of the UE, and thus achieving better verification of the L3-RSRP prediction performance of the UE.

[0021] Additional aspects and advantages of the embodiments of the present disclosure will be given in part in the following description, which will become apparent from the following description or be learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following drawings required for describing the embodiments are introduced. The following drawings are merely some embodiments of the present disclosure and do not impose specific limitations on the protection scope of the present disclosure.

[0023] Figure 1 A schematic diagram of the architecture of a communication system provided according to an embodiment of the present disclosure;

[0024] Figure 2 This is one of the interactive schematic diagrams of the communication method provided according to an embodiment of the present disclosure;

[0025] Figure 3 This is a second interactive schematic diagram of the communication method provided according to an embodiment of the present disclosure;

[0026] Figure 4 This is one of the scenario diagrams of the communication method provided according to an embodiment of the present disclosure;

[0027] Figure 5 This is a second scenario diagram of the communication method provided according to an embodiment of the present disclosure;

[0028] Figure 6 This is a third scenario diagram of the communication method provided according to an embodiment of the present disclosure;

[0029] Figure 7 This is a fourth scenario diagram of the communication method provided according to an embodiment of the present disclosure;

[0030] Figure 8 This is a fifth scenario diagram of the communication method provided according to an embodiment of the present disclosure;

[0031] Figure 9 This is a seventh scenario diagram of the communication method provided according to an embodiment of the present disclosure;

[0032] Figure 10 This is a third interactive diagram of the communication method provided according to an embodiment of the present disclosure;

[0033] Figure 11 This is a fourth interactive diagram of the communication method provided according to an embodiment of the present disclosure;

[0034] Figure 12 This is a fifth interactive diagram of the communication method provided according to an embodiment of the present disclosure;

[0035] Figure 13 A flow chart of a communication method provided in an embodiment of the present disclosure;

[0036] Figure 14 A schematic diagram of the structure of the test equipment proposed in an embodiment of the present disclosure;

[0037] Figure 15 A schematic diagram of the structure of a terminal proposed in an embodiment of the present disclosure;

[0038] Figure 16 This is a schematic diagram of the structure of the chip proposed in the embodiment of the present disclosure. DETAILED DESCRIPTION

[0039] The embodiments of the present disclosure provide a communication method, a communication device, and a communication system.

[0040] In a first aspect, an embodiment of the present disclosure provides a communication method, which is performed by a first device, where the first device includes a test device TE or a network device NW. The method includes:

[0041] Receive the L3-RSRP prediction value sent by the UE;

[0042] Determining an accuracy of L3-RSRP prediction performed by the UE according to the L3-RSRP prediction value and at least one RSRP accuracy margin;

[0043] The RSRP accuracy margin includes at least one of the following: a first margin based on time offset, a second margin based on the difference in the number of L1 filtered samples, a third margin based on the difference in L1 filtering methods, and a fourth margin; wherein the fourth margin is obtained based on the time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods.

[0044] In the above embodiment, when determining the accuracy of the UE's L3-RSRP prediction, by considering at least one of the first margin based on the time offset, the second margin based on the difference in the number of L1 filtered samples, the third margin based on the difference in the L1 filtering method, and the fourth margin obtained based on the time offset, the difference in the number of L1 filtered samples, and the difference in the L1 filtering method, it is possible to consider multiple uncertainties that make it impossible to accurately determine whether the UE meets the preset accuracy requirements for the L3-RSRP prediction, thereby improving the accuracy of the evaluation of the UE's L3-RSRP prediction accuracy, thereby achieving better verification of the UE's L3-RSRP prediction performance.

[0045] In combination with some embodiments of the first aspect, in some embodiments, determining the accuracy of L3-RSRP prediction performed by the UE based on the L3-RSRP prediction value and at least one RSRP accuracy margin includes:

[0046] Determining an absolute error between the predicted L3-RSRP value and an ideal true L3-RSRP value;

[0047] When the absolute error is less than or equal to the first parameter value, determining that the UE meets a preset accuracy requirement for L3-RSRP prediction;

[0048] The first parameter value includes: the sum of a preset RSRP prediction accuracy threshold and the at least one L3-RSRP accuracy margin.

[0049] In the above embodiment, the absolute error between the L3-RSRP prediction value of the UE and the ideal true value of the L3-RSRP can be determined, the sum of the preset RSRP prediction accuracy threshold and at least one L3-RSRP accuracy margin is determined as the first parameter value, and the first parameter value is used as an evaluation parameter of the preset accuracy requirement of the L3-RSRP prediction. According to the relationship between the absolute error and the first parameter value, the accuracy of the L3-RSRP prediction is evaluated. On the basis of the original prediction accuracy, it is possible to consider various uncertain factors that make it impossible to accurately determine whether the UE meets the preset accuracy requirement of the L3-RSRP prediction, thereby improving the accuracy of the evaluation of the L3-RSRP prediction accuracy of the UE, thereby achieving better verification of the L3-RSRP prediction performance of the UE.

[0050] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0051] Sending first indication information to the UE; wherein the first indication information instructs: the UE to report first information to the first device;

[0052] The first information includes at least one of the following: the time of the L3-RSRP prediction value; the number of L1 filtering samples used by the UE for L3-RSRP prediction; and the L1 filtering method used by the UE for L3-RSRP prediction.

[0053] In the above embodiment, the first device can configure the UE to report the first information to determine the corresponding ideal true value of L3-RSRP based on the information indicated by the first information, and try to avoid various uncertain factors that affect the verification process of determining whether the UE meets the preset accuracy requirements of the L3-RSRP prediction.

[0054] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0055] receiving first information sent by the UE;

[0056] Wherein, at a moment when the first information includes the L3-RSRP prediction value, the at least one RSRP accuracy margin does not include the first margin;

[0057] In a case where the first information includes the number of L1 filtered samples used by the UE to perform L3-RSRP prediction, the at least one RSRP accuracy margin does not include the second margin;

[0058] In a case where the first information includes an L1 filtering mode adopted by the UE for L3-RSRP prediction, the at least one RSRP accuracy margin does not include the third margin.

[0059] In the above embodiment, the first device determines the factors that specifically affect the verification process of determining whether the UE meets the preset accuracy requirement of L3-RSRP prediction based on the content carried in the first information reported by the UE, and then ignores the margin corresponding to the factors that will not affect the verification process.

[0060] In conjunction with some embodiments of the first aspect, in some embodiments, the first margin is obtained based on the following method:

[0061] A first RSRP difference fluctuation curve based on a preset timing offset is generated, and an RSRP difference within a first confidence interval is extracted as the first margin.

[0062] In the above embodiment, a first RSRP difference fluctuation curve can be generated according to the L3-RSRP prediction value under different preset timing offsets, and then the RSRP difference within the first confidence interval is extracted from the first RSRP difference fluctuation curve. When evaluating the L3-RSRP prediction performance, the first margin based on the timing offset can be fully considered, resulting in the uncertainty factor that makes it impossible to accurately determine whether the UE meets the preset accuracy requirement of the L3-RSRP prediction, thereby improving the accuracy of the evaluation of the L3-RSRP prediction accuracy of the UE, thereby achieving better verification of the L3-RSRP prediction performance of the UE.

[0063] In conjunction with some embodiments of the first aspect, in some embodiments, generating a first RSRP difference fluctuation curve based on a preset timing offset includes:

[0064] Obtain a first L1-RSRP value; wherein the first L1-RSRP value is obtained by measuring the first channel based on the first channel measurement information;

[0065] Filtering a first preset number of adjacent first L1-RSRP values to obtain a first L3-RSRP value;

[0066] The first RSRP difference fluctuation curve is generated according to a first RSRP difference between L3-RSRP values with a first preset timing offset.

[0067] In conjunction with some embodiments of the first aspect, in some embodiments, the second margin is obtained based on the following method:

[0068] A second RSRP difference fluctuation curve based on different numbers of L1 filtered samples is generated, and an RSRP difference within a second confidence interval is extracted as the second margin.

[0069] In the above embodiment, a second RSRP difference fluctuation curve can be generated according to the L3-RSRP prediction value under different L1 filter sample numbers, and then the RSRP difference within the second confidence interval is extracted from the second RSRP difference fluctuation curve. When evaluating the L3-RSRP prediction performance, the second margin based on different L1 filter sample numbers can be fully considered, resulting in the uncertainty factor that cannot accurately determine whether the UE meets the preset accuracy requirement of the L3-RSRP prediction, thereby improving the accuracy of the evaluation of the L3-RSRP prediction accuracy of the UE, thereby achieving better verification of the L3-RSRP prediction performance of the UE.

[0070] In conjunction with some embodiments of the first aspect, in some embodiments, generating a second RSRP difference fluctuation curve based on different numbers of L1 filtered samples includes:

[0071] Obtain a second L1-RSRP value; wherein the second L1-RSRP value is obtained by measuring the second channel based on the second channel measurement information;

[0072] filtering a second preset number of adjacent second L1-RSRP values to obtain a second L3-RSRP value; and filtering a third preset number of second L1-RSRP values among the second preset number of adjacent second L1-RSRP values to obtain a third L3-RSRP value; wherein the third preset number is less than the second preset number;

[0073] The second RSRP difference fluctuation curve is generated according to a second RSRP difference between the second L3-RSRP value and a corresponding third L3-RSRP value.

[0074] In conjunction with some embodiments of the first aspect, in some embodiments, the third margin is obtained based on the following method:

[0075] generating a third RSRP difference fluctuation curve based on different L1 filtering modes and L3 filtering coefficients, and extracting an RSRP difference within a third confidence interval as the third margin;

[0076] The L1 filtering method includes non-sliding window filtering and sliding window filtering.

[0077] In the above embodiment, a third RSRP difference fluctuation curve can be generated according to the L3-RSRP prediction value under different L1 filtering modes and L3 filtering coefficients, and then the RSRP difference within the third confidence interval can be extracted from the third RSRP difference fluctuation curve. When evaluating the L3-RSRP prediction performance, the third margin based on different L1 filtering modes and L3 filtering coefficients can be fully considered, resulting in uncertainty factors that make it impossible to accurately determine whether the UE meets the preset accuracy requirements of the L3-RSRP prediction, thereby improving the accuracy of the evaluation of the L3-RSRP prediction accuracy of the UE, thereby achieving better verification of the L3-RSRP prediction performance of the UE.

[0078] In conjunction with some embodiments of the first aspect, in some embodiments, generating a third RSRP difference fluctuation curve based on different L1 filtering modes and L3 filtering coefficients includes:

[0079] Obtaining a third L1-RSRP value; wherein the third L1-RSRP value is obtained by measuring the third channel based on the third channel measurement information;

[0080] For a fourth preset number of adjacent third L1-RSRP values, perform non-sliding window filtering on the third L1-RSRP values to obtain a first RSRP value, and filter the first RSRP value according to a preset first L3 filter coefficient to obtain a fourth L3-RSRP value; and perform sliding window filtering on the third L1-RSRP value to obtain a second RSRP value, and filter the second RSRP value according to the first L3 filter coefficient to obtain a fifth L3-RSRP value;

[0081] The third RSRP difference fluctuation curve is generated according to a third RSRP difference between the fourth L3-RSRP value and a corresponding fifth L3-RSRP value.

[0082] In conjunction with some embodiments of the first aspect, in some embodiments, the fourth margin is obtained based on the following method:

[0083] Based on the time offset, the difference in the number of L1 filtering samples, and the difference in the L1 filtering method, a fourth RSRP difference fluctuation curve is generated, and the RSRP difference in a fourth confidence interval is extracted as the fourth margin.

[0084] In the above embodiment, under the combined influence of time offset, difference in the number of L1 filtering samples, and difference in L1 filtering mode, a fourth RSRP difference fluctuation curve can be generated according to the L3-RSRP prediction value, and then the RSRP difference within the fourth confidence interval can be extracted from the fourth RSRP difference fluctuation curve. When evaluating the L3-RSRP prediction performance, the fourth margin based on comprehensive factors such as time offset, difference in the number of L1 filtering samples, and difference in L1 filtering mode can be fully considered, resulting in uncertainty factors that make it impossible to accurately determine whether the UE meets the preset accuracy requirements of the L3-RSRP prediction, thereby improving the accuracy of the evaluation of the L3-RSRP prediction accuracy of the UE, thereby achieving better verification of the L3-RSRP prediction performance of the UE.

[0085] In combination with some embodiments of the first aspect, in some embodiments, generating a fourth RSRP difference fluctuation curve based on the time offset, the difference in the number of L1 filtered samples, and the difference in the L1 filtering method includes:

[0086] Obtaining a fourth L1-RSRP value; wherein the fourth L1-RSRP value is obtained by measuring the fourth channel based on the fourth channel measurement information;

[0087] For a fifth preset number of adjacent fourth L1-RSRP values, perform non-sliding window filtering on the fourth L1-RSRP values to obtain a third RSRP value, and filter the third RSRP value according to a preset second L3 filter coefficient to obtain a sixth L3-RSRP value; and perform sliding window filtering on a sixth preset number of the fourth L1-RSRP values to obtain a fourth RSRP value, and filter the fourth RSRP value according to the second L3 filter coefficient to obtain a fifth RSRP value, and determine an RSRP difference between the fifth RSRP values having a second preset timing offset to obtain a seventh L3-RSRP value.

[0088] The fourth RSRP difference fluctuation curve is generated according to a fourth RSRP difference between the sixth L3-RSRP value and a corresponding seventh RSRP value.

[0089] In a second aspect, an embodiment of the present disclosure further provides a communication method, which is performed by a user equipment UE, and the method includes:

[0090] The predicted L3-RSRP value sent to the first device;

[0091] The first device includes a TE or a network device; the TE determines the accuracy of the L3-RSRP prediction performed by the UE according to the L3-RSRP prediction value and at least one RSRP accuracy margin;

[0092] The RSRP accuracy margin includes at least one of the following: a first margin based on time offset, a second margin based on the difference in the number of L1 filtered samples, a third margin based on the difference in L1 filtering methods, and a fourth margin; wherein the fourth margin is obtained based on the time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods.

[0093] In conjunction with some embodiments of the second aspect, in some embodiments, determining the accuracy of L3-RSRP prediction performed by the UE based on the L3-RSRP prediction value and at least one RSRP accuracy margin includes:

[0094] Determining an absolute error between the predicted L3-RSRP value and an ideal true L3-RSRP value;

[0095] When the absolute error is less than or equal to the first parameter value, determining that the UE meets a preset accuracy requirement for L3-RSRP prediction;

[0096] The first parameter value includes: the sum of a preset RSRP prediction accuracy threshold and the at least one L3-RSRP accuracy margin.

[0097] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes:

[0098] receiving first indication information sent by the first device; wherein the first indication information instructs the UE to report first information to the first device;

[0099] The first information includes at least one of the following: the time of the L3-RSRP prediction value; the number of L1 filtering samples used by the UE for L3-RSRP prediction; and the L1 filtering method used by the UE for L3-RSRP prediction.

[0100] In conjunction with some embodiments of the second aspect, in some embodiments, the method further includes:

[0101] sending the first information to the first device;

[0102] Wherein, at a moment when the first information includes the L3-RSRP prediction value, the at least one RSRP accuracy margin does not include the first margin;

[0103] In a case where the first information includes the number of L1 filtered samples used by the UE to perform L3-RSRP prediction, the at least one RSRP accuracy margin does not include the second margin;

[0104] In a case where the first information includes an L1 filtering mode adopted by the UE for L3-RSRP prediction, the at least one RSRP accuracy margin does not include the third margin.

[0105] In the above embodiment, the first device may directly report the first information, or may send the first information to the first device in response to the first indication information sent by the first device.

[0106] In conjunction with some embodiments of the second aspect, in some embodiments, the first margin is obtained based on the following method:

[0107] A first RSRP difference fluctuation curve based on a preset timing offset is generated, and an RSRP difference within a first confidence interval is extracted as the first margin.

[0108] In conjunction with some embodiments of the second aspect, in some embodiments, generating a first RSRP difference fluctuation curve based on a preset timing offset includes:

[0109] Obtain a first L1-RSRP value; wherein the first L1-RSRP value is obtained by measuring the first channel based on the first channel measurement information;

[0110] Filtering a first preset number of adjacent first L1-RSRP values to obtain a first L3-RSRP value;

[0111] The first RSRP difference fluctuation curve is generated according to a first RSRP difference between L3-RSRP values with a first preset timing offset.

[0112] In conjunction with some embodiments of the second aspect, in some embodiments, the second margin is obtained based on the following method:

[0113] A second RSRP difference fluctuation curve based on different numbers of L1 filtered samples is generated, and an RSRP difference within a second confidence interval is extracted as the second margin.

[0114] In conjunction with some embodiments of the second aspect, in some embodiments, generating a second RSRP difference fluctuation curve based on different numbers of L1 filtered samples includes:

[0115] Obtain a second L1-RSRP value; wherein the second L1-RSRP value is obtained by measuring the second channel based on the second channel measurement information;

[0116] filtering a second preset number of adjacent second L1-RSRP values to obtain a second L3-RSRP value; and filtering a third preset number of second L1-RSRP values among the second preset number of adjacent second L1-RSRP values to obtain a third L3-RSRP value; wherein the third preset number is less than the second preset number;

[0117] The second RSRP difference fluctuation curve is generated according to a second RSRP difference between the second L3-RSRP value and a corresponding third L3-RSRP value.

[0118] In conjunction with some embodiments of the second aspect, in some embodiments, the third margin is obtained based on the following method:

[0119] generating a third RSRP difference fluctuation curve based on different L1 filtering modes and L3 filtering coefficients, and extracting an RSRP difference within a third confidence interval as the third margin;

[0120] The L1 filtering method includes non-sliding window filtering and sliding window filtering.

[0121] In conjunction with some embodiments of the second aspect, in some embodiments, generating a third RSRP difference fluctuation curve based on different L1 filtering modes and L3 filtering coefficients includes:

[0122] Obtaining a third L1-RSRP value; wherein the third L1-RSRP value is obtained by measuring the third channel based on the third channel measurement information;

[0123] For a fourth preset number of adjacent third L1-RSRP values, perform non-sliding window filtering on the third L1-RSRP values to obtain a first RSRP value, and filter the first RSRP value according to a preset first L3 filter coefficient to obtain a fourth L3-RSRP value; and perform sliding window filtering on the third L1-RSRP value to obtain a second RSRP value, and filter the second RSRP value according to the first L3 filter coefficient to obtain a fifth L3-RSRP value;

[0124] The third RSRP difference fluctuation curve is generated according to a third RSRP difference between the fourth L3-RSRP value and a corresponding fifth L3-RSRP value.

[0125] In conjunction with some embodiments of the second aspect, in some embodiments, the fourth margin is obtained based on the following method:

[0126] Based on the time offset, the difference in the number of L1 filtering samples, and the difference in the L1 filtering method, a fourth RSRP difference fluctuation curve is generated, and the RSRP difference in a fourth confidence interval is extracted as the fourth margin.

[0127] In conjunction with some embodiments of the second aspect, in some embodiments, generating a fourth RSRP difference fluctuation curve based on the time offset, the difference in the number of L1 filtered samples, and the difference in the L1 filtering method includes:

[0128] Obtaining a fourth L1-RSRP value; wherein the fourth L1-RSRP value is obtained by measuring the fourth channel based on the fourth channel measurement information;

[0129] For a fifth preset number of adjacent fourth L1-RSRP values, perform non-sliding window filtering on the fourth L1-RSRP values to obtain a third RSRP value, and filter the third RSRP value according to a preset second L3 filter coefficient to obtain a sixth L3-RSRP value; and perform sliding window filtering on a sixth preset number of the fourth L1-RSRP values to obtain a fourth RSRP value, and filter the fourth RSRP value according to the second L3 filter coefficient to obtain a fifth RSRP value, and determine an RSRP difference between the fifth RSRP values having a second preset timing offset to obtain a seventh L3-RSRP value.

[0130] The fourth RSRP difference fluctuation curve is generated according to a fourth RSRP difference between the sixth L3-RSRP value and a corresponding seventh L3-RSRP value.

[0131] In a third aspect, an embodiment of the present disclosure further provides a communication device, which is used to execute an optional implementation of the first aspect or the second aspect.

[0132] In a fourth aspect, an embodiment of the present disclosure further provides a communication device, including:

[0133] one or more processors;

[0134] The communication device is used to execute the optional implementation of the first aspect.

[0135] In a fifth aspect, an embodiment of the present disclosure further provides a communication system, comprising a communication device; wherein the communication device is configured as the optional implementation method described in the first aspect.

[0136] In a sixth aspect, an embodiment of the present disclosure further provides a storage medium storing instructions, which, when executed on a communication device, enables the communication device to execute the optional implementation method described in the first aspect.

[0137] In a seventh aspect, an embodiment of the present disclosure proposes a program product. When the program product is executed by a communication device, the communication device executes the method described in the optional implementation manner of the first aspect.

[0138] In an eighth aspect, an embodiment of the present disclosure proposes a computer program, which, when executed on a computer, enables the computer to execute the method described in the optional implementation manner of the first aspect.

[0139] In a ninth aspect, an embodiment of the present disclosure provides a chip or a chip system, which includes a processing circuit configured to execute the method described in the optional implementation of the first aspect.

[0140] It is understandable that the above-mentioned communication devices, communication systems, storage media, program products, computer programs, chips, or chip systems are all used to perform the methods proposed in the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding methods and will not be repeated here.

[0141] The embodiments of the present disclosure provide a communication method, a communication device, and a communication system. In some embodiments, the terms communication method, signal transmission method, wireless frame transmission method, etc. can be used interchangeably, and the terms information processing system, communication system, etc. can be used interchangeably.

[0142] The embodiments of the present disclosure are not exhaustive and are merely illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined. For example, some or all steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0143] In each embodiment of the present disclosure, unless otherwise specified or provided for by logic, the terms and / or descriptions between the embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their inherent logical relationships.

[0144] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0145] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0146] In some embodiments, the terms "at least one," "one or more," "a plurality of," "multiple," etc. may be used interchangeably.

[0147] In some embodiments, descriptions such as "at least one of A and B," "A and / or B," "A in one case, B in another case," or "in response to one case A, in response to another case B" may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); and in some embodiments, A and B (both A and B are executed). The above is also applicable when there are more branches such as A, B, and C.

[0148] In some embodiments, "A or B" and other descriptions may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The above is also applicable when there are more branches such as A, B, C, etc.

[0149] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restriction on the position, order, priority, quantity or content of the description objects. For the statement of the description object, please refer to the description in the context of the claims or embodiments, and no unnecessary restriction should be constituted due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields". "First" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by the ordinal number and can be one or more. Taking "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes can be the same or different. For example, if the description object is "device", then the "first device" and the "second device" can be the same device or different devices, and their types can be the same or different; for another example, if the description object is "information", then the "first information" and the "second information" can be the same information or different information, and their contents can be the same or different.

[0150] In some embodiments, “including A,” “comprising A,” “used to indicate A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0151] In some embodiments, terms such as "in response to...", "in response to determining...", "in the case of...", "at the time of...", "when...", "if...", "if...", etc. can be used interchangeably.

[0152] In some embodiments, terms such as "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 less than", and "above" can be replaced with each other, and terms such as "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", and "below" can be replaced with each other.

[0153] In some embodiments, devices and equipment can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. In some cases, they can also be understood as "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", "subject", etc.

[0154] In some embodiments, "network" can be interpreted as devices included in the network, such as access network equipment, core network equipment, etc.

[0155] In some embodiments, "access network device (AN device)" may also be referred to as "radio access network device (RAN device)", "base station (BS)", "radio base station", "fixed station", and in some embodiments may also be understood as "node", "access point", "transmission point (TP)", "reception point (RP)", "transmission and / or reception point (TRP)" "panel", "antenna panel", "antenna array", "cell", "macro cell", "small cell", "femto cell", "pico cell", "sector", "cell group", "serving cell", "carrier", "component carrier", "bandwidth part (BWP)", etc.

[0156] In some embodiments, "terminal" or "terminal device" may be referred to as "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, etc.

[0157] In some embodiments, the access network device, the core network device, or the network device can be replaced by a terminal. For example, the various embodiments of the present disclosure can also be applied to a structure in which the communication between the access network device, the core network device, or the network device and the terminal is replaced by communication between multiple terminals (for example, device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, it is also possible to set the structure in which the terminal has all or part of the functions of the access network device. In addition, terms such as "uplink" and "downlink" can also be replaced by terms corresponding to communication between terminals (for example, "side"). For example, uplink channels, downlink channels, etc. can be replaced by side channels, and uplinks, downlinks, etc. can be replaced by side links.

[0158] In some embodiments, the terminal may be replaced by an access network device, a core network device, or a network device. In this case, the access network device, the core network device, or the network device may have a structure that has all or part of the functions of the terminal.

[0159] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0160] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0161] In addition, each element, each row, or each column in the table of the embodiment of the present disclosure can be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns can also be implemented as an independent embodiment.

[0162] Figure 1 It is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure.

[0163] like Figure 1 As shown, the communication system 100 includes a first device 101 and a user equipment (UE) 102 .

[0164] In some embodiments, the first device 101 may include test equipment (TE) or a network device.

[0165] In some embodiments, when the first device includes a network device, it can be used to perform UE predictive performance monitoring and network communication; in some embodiments, when the first device includes a test device (for example, a test instrument), it can be used to send a signal to the UE by simulating a network device to perform UE predictive performance testing.

[0166] In some embodiments, the test device may be a separate device or a functional module configured in a certain device (eg, a network device), which is not limited in the embodiments of the present disclosure.

[0167] In some embodiments, when the first device includes a test device, the communication system 100 may further include a network device, and the network device, the test device, and the user equipment may communicate with each other. Optionally, the network device may send a test signal to the test device, instructing the test device to perform a UE prediction performance test. Optionally, if the test device determines that the UE prediction accuracy meets preset requirements through the UE prediction performance test, it may send a signal to the network device, instructing the network device to perform subsequent operations based on the UE prediction results.

[0168] In some embodiments, the network device may include at least one of an access network device and a core network device. For example, the network device may be a base station.

[0169] In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network. The access network device may include an evolved NodeB (eNB), a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved nodeB (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 (CloudRAN), a base station in other communication systems, and at least one of an access node in a Wi-Fi system, but is not limited thereto.

[0170] In some embodiments, the technical solution of the present disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within the access network devices involved in the embodiments of the present disclosure can be transformed into internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces can be implemented through software or programs.

[0171] In some embodiments, the access network device can be composed of a centralized unit (CU) and a distributed unit (DU), where the CU can also be called a control unit. The CU-DU structure can be used to split the protocol layer of the access network device, with the functions of some protocol layers centrally controlled by the CU, and the functions of the remaining part or all of the protocol layers distributed in the DU, which is centrally controlled by the CU, but is not limited to this.

[0172] In some embodiments, a core network device may be a device including one or more network elements, or may be multiple devices or a group of devices. A network element may be virtual or physical. The core network may include, for example, at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), or a Next Generation Core (NGC).

[0173] In some embodiments, the user device 102 may also be referred to as a terminal, and may include, for example, but not limited to, at least one of a mobile phone, a wearable device, an Internet of Things device, a car with communication capabilities, a smart car, a tablet computer, a computer with wireless transceiver capabilities, 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 a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and a wireless terminal device in a smart home, but is not limited thereto.

[0174] It can be understood that the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution proposed in the embodiment of the present disclosure. Ordinary technicians in this field can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution proposed in the embodiment of the present disclosure is also applicable to similar technical problems.

[0175] The following embodiments of the present disclosure can be applied to Figure 1 The communication system 100, or a portion thereof, is shown but is not limited thereto. Figure 1 The various entities shown are examples, and the communication system may include Figure 1 All or part of the subject, and may also include Figure 1 The number and form of other subjects are arbitrary, each subject can be physical or virtual, the connection relationship between the subjects is illustrative, the subjects can be connected or disconnected, and the connection can be in any way, which can be direct or indirect, wired or wireless.

[0176] The embodiments of the present disclosure can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), future radio access (FRA), new radio access technology (RAT), new radio (NR), new radio access (NX), future generation radio access (FX), Global System for Mobile Communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X) systems, Open Radio Access Network (O-RAN) systems, systems utilizing other resource determination methods, and next-generation systems based on and extended from these systems, such as the sixth-generation mobile communication system (6G). Furthermore, a combination of multiple systems (e.g., a combination of LTE or LTE-A with 5G) may also be employed.

[0177] Figure 2 FIG. 1 is an interactive diagram of a communication method according to an embodiment of the present disclosure. Figure 2 As shown, the above method includes:

[0178] Step 201: The UE sends a predicted L3-RSRP value to a first device. Correspondingly, the first device receives the predicted L3-RSRP value sent by the UE.

[0179] In some embodiments, traditional RSRP measurements require frequent and resource-intensive signal strength checks, which consumes significant network resources and increases latency. In some embodiments, an artificial intelligence (AI)-based mobility management mechanism leverages the power of AI and machine learning to provide a more efficient, accurate, and adaptable method for maintaining seamless handovers between cells. The reduced measurement overhead and improved handover performance not only improve user satisfaction but also contribute to the overall efficiency and scalability of wireless networks. As the demand for high-speed, reliable connectivity continues to grow, AI mobility has become a key solution for modern communication systems.

[0180] In some embodiments, an AI-based mobility management mechanism can minimize the frequency of RSRP measurements by using predictive analysis, for example, by identifying patterns and trends in signal behavior through AI models, enabling the network to make accurate switching decisions with fewer actual measurements, which not only saves measurement overhead associated with RSRP estimation but also improves overall system efficiency.

[0181] In some embodiments, AI-based mobility management mechanisms can improve RSRP estimation accuracy, translating into better handover performance. By more accurately predicting signal strength fluctuations and identifying optimal handover points, the likelihood of connection interruptions and handover failures can be significantly reduced, leading to a more stable and reliable user experience, for example, in high-mobility scenarios such as vehicular communications or densely populated urban environments.

[0182] In some embodiments, AI-based mobility management mechanisms can dynamically adapt to changing network conditions and user needs. For example, in areas with fluctuating traffic loads or changing environmental factors (such as urban canyons or rural terrain), AI models can adjust their predictions in real time to ensure optimal handover performance. This adaptability ensures that network equipment remains responsive and efficient under a variety of operating conditions.

[0183] In some embodiments, in an artificial intelligence-based communication system, a UE can use AI (Artificial Intelligence) technology to predict channel quality at future times and report its predicted channel quality value to a network device. In some embodiments, channel quality includes at least one of the following: Layer 3 Reference Signal Received Power (L3-RSRP), Reference Signal Received Quality (RSRQ), and Signal-to-Interference-plus-Noise Ratio (SINR).

[0184] In some embodiments, the UE can measure the channel in advance, and construct training samples based on the historical channel quality measurement values obtained. Based on the training samples, AI technology is used for training to obtain a channel quality prediction model that can predict channel quality. In this way, the UE can predict channel quality through the channel quality prediction model.

[0185] In some embodiments, in order to adapt to channel quality prediction under different channel characteristics, when the UE trains the channel quality prediction model, the results of channel quality measurement based on channels with different channel characteristics can be used as training samples to predict the neural network model to obtain a trained channel quality prediction model.

[0186] In some embodiments, the channel characteristics include at least one of the following:

[0187] Fading, multipath interference, Doppler, channel initialization random seed, channel time duration, and transient response characteristics of the channel in the time domain.

[0188] Optionally, when the channel characteristics include fading, it can be used to characterize the amplitude and phase fluctuations of the signal caused by environmental factors during the channel propagation process. Optionally, the fading includes at least one of the following types: Rayleigh fading, Rician fading, and Nakagami fading, which are not limited in the embodiments of the present disclosure.

[0189] Optionally, when the channel characteristics include multipath interference, it can be used to characterize the delay spread and superposition effect caused by the signal propagating through different paths in the channel.

[0190] Optionally, when the channel characteristics include Doppler, it can be used to characterize the signal frequency offset caused by the relative motion of the transmitting and receiving ends. Optionally, Doppler can include at least one of the following: Doppler Shift, Doppler Spread, and Doppler Power Spectrum.

[0191] Optionally, when the channel characteristics include a channel initialization random seed, a channel impulse response (CIR) conforming to a specific statistical distribution may be generated based on the channel initialization random seed, that is, a repeatable channel simulation environment may be generated.

[0192] Optionally, when the channel characteristics include the channel time length, it can be used to define the effective time range of each channel characteristic.

[0193] Optionally, when the channel characteristics include instantaneous response characteristics of the channel in the time domain, they may specifically include amplitude and phase values of the channel at each time instance (Amplitude and Phase Values at Each Time Instance).

[0194] Optionally, the channel condition of a channel may include one or more channel characteristics, which is not limited in the embodiments of the present disclosure.

[0195] Step 202: The first device determines the accuracy of L3-RSRP prediction performed by the UE based on the L3-RSRP prediction value and at least one RSRP accuracy margin.

[0196] The RSRP accuracy margin includes at least one of the following: a first margin based on time offset, a second margin based on the difference in the number of L1 filtered samples, a third margin based on the difference in L1 filtering methods, and a fourth margin; wherein the fourth margin is obtained based on the time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods.

[0197] In some embodiments, in order to avoid errors in UE prediction, which may cause the network device to perform subsequent operations based on the channel quality value predicted by the UE, the predicted channel quality value reported by the UE and the corresponding channel measurement true value can be compared in advance by a first device (for example, test equipment, TE), and the performance of the UE's channel quality prediction can be evaluated. When the test equipment determines that the performance of the UE's channel quality prediction passes the evaluation, the network device performs subsequent operations based on the channel quality value predicted by the UE.

[0198] In some embodiments, when performing a traditional RSRP measurement accuracy test, the user equipment (UE) measures the reference signal to obtain the layer 1 reference signal received power (L1 RSRP, L1 is layer 1, layer one; RSRP is Reference Signal Received Power, reference signal received power). By filtering multiple L1 RSRP values (for example, averaging multiple L1RSRP values), the layer 3 filtered reference signal received power (L3 RSRP, L3 is layer 3, layer three) is obtained, and the L3 RSRP value is reported to the first device. During the RSRP accuracy test, the first device can obtain the ideal reference signal power transmitted by the base station. Therefore, the first device can directly determine the terrestrial L3RSRP value based on the ideal reference signal power transmitted by the base station. By comparing the L3-RSRP value reported by the UE with the true terrestrial L3-RSRP value, it is evaluated whether the UE meets the required accuracy and passes the test.

[0199] In some embodiments, when performing a traditional RSRP measurement accuracy test, RSRP measurement is performed by assuming an additive white Gaussian noise (AWGN) channel. Since the AWGN channel does not exhibit time-varying characteristics, when the UE filters the L1 RSRP value to obtain the L3 RSRP value, the number of L1 RSRP values used in the filtering (i.e., the number of L1 RSRP filtering samples) does not affect the final L3 RSRP value. In addition, since the RSRP value of the channel does not change over time, there is no time offset between the measured RSRP value and the actual RSRP value. Therefore, during the test, there is no need to relax the requirements for RSRP measurement accuracy.

[0200] However, in some embodiments, under fading channel conditions, many factors can introduce uncertainty into RSRP measurements. For example, if T) does not know the measurement time point corresponding to the RSRP value reported by the UE, a discrepancy may exist between the RSRP value reported by the UE and its corresponding true RSRP. Furthermore, different L1 filtering methods used by the UE and the first device may also result in different true L3 RSRP values.

[0201] In summary, when formulating RSRP accuracy test requirements, at least one of these factors needs to be considered to better meet the needs of AI-based communication systems.

[0202] In some embodiments, the first device may enter into an agreement with the UE to define a method for determining an ideal true value of L3-RSRP. After receiving the predicted L3-RSRP value sent by the UE, the first device may determine the corresponding ideal true value of L3-RSRP, and evaluate the channel quality prediction performance of the UE based on the difference between the predicted L3-RSRP value and the ideal true value of L3-RSRP. For example, if the difference is less than or equal to the sum of at least one RSRP precision margin, it is determined that the accuracy of the L3-RSRP prediction performed by the UE meets the preset accuracy requirement of the L3-RSRP prediction; if the difference is greater than the sum of at least one RSRP precision margin, it is determined that the accuracy of the L3-RSRP prediction performed by the UE does not meet the preset accuracy requirement of the L3-RSRP prediction.

[0203] In some embodiments, margin refers to the error compensation parameter value brought about by considering different uncertain factors that may lead to the inability to accurately evaluate the channel quality prediction performance of the UE when evaluating the channel quality prediction performance of the UE. It can also be called "error compensation value", "margin", etc. The embodiments of the present disclosure do not limit this name.

[0204] In some embodiments, a first margin (Margin 1) based on time offset is generated considering the situation where the time on both sides of the UE and the first device are not synchronized, resulting in a time offset between the L3-RSRP prediction value determined by the UE and the L3-RSRP true value determined by the first device, thereby making it impossible to accurately evaluate the channel quality prediction performance of the UE.

[0205] It should be understood that in actual application, the time between the UE and the first device may be synchronized or asynchronous. The embodiment of the present disclosure considers the first margin in order to avoid the situation where the channel quality prediction performance of the UE cannot be accurately evaluated due to the asynchronous time between the UE and the first device.

[0206] In some embodiments, a second margin (Margin 2) based on the difference in the number of L1 filtered samples is generated to account for the difference between the first number and the second number, resulting in an error in alignment between the L3-RSRP predicted value determined by the UE and the true L3-RSRP value determined by the first device, thereby making it impossible to accurately assess the channel quality prediction performance of the UE. The first number is the number of L1-RSRP values (i.e., the number of L1 filtered samples) used when the UE determines the L3-RSRP predicted value. The second number is the number of L1-RSRP values used when the first device determines the true L3-RSRP value.

[0207] It should be understood that in actual application, the first number and the second number may be the same or different. The embodiment of the present disclosure considers the second margin in order to avoid the situation where the channel quality prediction performance of the UE cannot be accurately evaluated due to the difference between the first number and the second number.

[0208] In some embodiments, a third margin (Margin 3) based on the difference in L1 filtering methods is generated, i.e., considering the difference between the first L1 filtering method and the second L1 filtering method, resulting in an error between the L3-RSRP predicted value determined by the UE and the L3-RSRP true value determined by the first device, which makes it impossible to accurately evaluate the channel quality prediction performance of the UE. The first L1 filtering method is the method used to filter the L1-RSRP value when the UE determines the L3-RSRP predicted value. The second L1 filtering method is the method used to filter the L1-RSRP value when the first device determines the L3-RSRP true value.

[0209] It should be understood that in actual application, the first L1 filtering method and the second L1 filtering method may be the same or different. The embodiment of the present disclosure considers the third margin in order to avoid the situation where the channel quality prediction performance of the UE cannot be accurately evaluated due to the difference between the first L1 filtering method and the second L1 filtering method.

[0210] In some embodiments, a fourth margin (Margin 4) is obtained based on the time offset, the difference in the number of L1 filtered samples, and the difference in the L1 filtering method, that is, the margin is generated by comprehensively considering the time offset, the difference in the number of L1 filtered samples, and the difference in the L1 filtering method, resulting in an error that cannot be aligned between the L3-RSRP prediction value determined by the UE and the L3-RSRP true value determined by the first device, thereby making it impossible to accurately evaluate the channel quality prediction performance of the UE.

[0211] Optionally, in some embodiments, by simulating specific scenarios under the above factors respectively, the RSRP difference in each scenario can be quantified to generate a corresponding RSRP difference curve. According to actual needs, the confidence interval corresponding to each factor is selected, and the RSRP difference within the corresponding confidence interval is extracted from the corresponding RSRP difference curve as the corresponding margin.

[0212] In the above embodiment, when determining the accuracy of the UE's L3-RSRP prediction, by considering at least one of the first margin based on the time offset, the second margin based on the difference in the number of L1 filtered samples, the third margin based on the difference in the L1 filtering method, and the fourth margin obtained based on the time offset, the difference in the number of L1 filtered samples, and the difference in the L1 filtering method, it is possible to consider multiple uncertainties that make it impossible to accurately determine whether the UE meets the preset accuracy requirements for the L3-RSRP prediction, thereby improving the accuracy of the evaluation of the UE's L3-RSRP prediction accuracy, thereby achieving better verification of the UE's L3-RSRP prediction performance.

[0213] In some embodiments, see Figure 3 , the above communication method may include:

[0214] Step 301: The UE sends a predicted L3-RSRP value to a first device. Correspondingly, the first device receives the predicted L3-RSRP value sent by the UE.

[0215] In some embodiments, the UE can measure the channel in advance, and construct training samples based on the historical channel quality measurement values obtained. Based on the training samples, AI technology is used for training to obtain a channel quality prediction model that can predict channel quality. In this way, the UE can predict channel quality through the channel quality prediction model.

[0216] In some embodiments, in order to adapt to channel quality prediction under different channel characteristics, when the UE trains the channel quality prediction model, the results of channel quality measurement based on channels with different channel characteristics can be used as training samples to predict the neural network model to obtain a trained channel quality prediction model.

[0217] In some embodiments, before the UE sends the L3-RSRP prediction value to the first device, the first device may send measurement indication information to the UE, instructing the UE to perform L3-RSRP measurement; after receiving the measurement indication information, the UE may perform L3-RSRP measurement according to the measurement indication information, and input the measured L3-RSRP value into the channel quality prediction model, obtain the L3-RSRP prediction value through the channel quality prediction model, and send the obtained L3-RSRP prediction value to the first device.

[0218] Optionally, the embodiment of the present disclosure does not limit the number of L3-RSRP prediction values sent by the UE, and can be set according to actual conditions, such as accuracy requirements, which will not be elaborated here.

[0219] Step 302: The first device determines an absolute error between the predicted L3-RSRP value and an ideal true L3-RSRP value.

[0220] Optionally, the ideal true value of L3-RSRP may also be referred to as “L3-RSRP ground truth”, which is not limited in the embodiments of the present disclosure.

[0221] In some embodiments, the first device may enter into an agreement with the UE to define a method for determining an ideal true L3-RSRP value. After receiving the predicted L3-RSRP value sent by the UE, the first device may determine the ideal true L3-RSRP value corresponding to the predicted L3-RSRP value.

[0222] Optionally, the absolute error between the predicted L3-RSRP value and the ideal true L3-RSRP value may include an absolute value of a difference between the predicted L3-RSRP value and the ideal true L3-RSRP value.

[0223] Step 303: When the absolute error is less than or equal to a first parameter value, the first device determines that the UE meets a preset accuracy requirement for L3-RSRP prediction;

[0224] The RSRP accuracy margin includes at least one of the following: a first margin based on a time offset, a second margin based on a difference in the number of L1 filtered samples, a third margin based on a difference in an L1 filtering method, and a fourth margin; wherein the fourth margin is obtained based on the time offset, the difference in the number of L1 filtered samples, and the difference in the L1 filtering method;

[0225] The first parameter value includes: the sum of a preset RSRP prediction accuracy threshold and the at least one L3-RSRP accuracy margin.

[0226] As mentioned above, in different situations, the corresponding L3-RSRP accuracy margin is different. After determining the L3-RSRP accuracy margin based on actual conditions, the preset RSRP prediction accuracy threshold can be added with at least one determined L3-RSRP accuracy margin to obtain a first parameter value, and further based on the size relationship between the above absolute error and the first parameter value, it is determined whether the UE meets the preset accuracy requirements of the L3-RSRP prediction.

[0227] Optionally, the preset RSRP prediction accuracy threshold can be determined based on actual conditions, and the embodiments of the present disclosure do not limit this. For example, the RSRP prediction accuracy threshold can be set to a value range of 5-6dBm (decibel-milliwatts) or 2-3dBm.

[0228] In some embodiments, when the time of the UE and the first device is completely synchronized, Margin 1 is 0, that is, there is no need to consider the impact of the time offset on the L3-RSRP prediction performance verification of the UE.

[0229] In some embodiments, there is signaling interaction between the UE and the first device, that is, the two determine through signaling interaction that the number of L1 filtering samples used by both are the same, and Margin 2 is 0, that is, there is no need to consider the impact of the difference in the number of L1 filtering samples on the L3-RSRP prediction performance verification of the UE.

[0230] In some embodiments, the third margin based on the L1 filtering method difference may include a third margin determined based on the L1 filtering method difference and an L3 filtering coefficient.

[0231] Optionally, the L1 filtering method may include non-sliding window filtering and sliding window filtering. The L3 filter coefficient identifies the impact of a previous L3-RSRP value on a current L3-RSRP value.

[0232] In some embodiments, the L1 filtering method adopted by the UE may include non-sliding window filtering and sliding window filtering. The key differences between non-sliding window filtering and sliding window filtering are as follows:

[0233] (1) In the L1 / L3 non-sliding window filtering method, an L3 RSRP value is generated based on every 5 L1 RSRP samples.

[0234] (2) In the L1 / L3 sliding window filtering method, an L3 RSRP value is generated based on each L1 RSRP sample.

[0235] In some embodiments, the L3-RSRP value obtained based on different filtering methods is determined as shown in the following formula (1):

[0236] F n =(1-a)·F n-1 +α·M n Formula (1)

[0237] Among them, F n F is the updated filtered measurement result (ie, the latest L3-RSRP value) used for evaluating reporting conditions or for measurement reporting. n-1The initial value F0 is set to the first measurement result M1 received from the physical layer. n is the latest measurement result received from the physical layer. α is the filter coefficient corresponding to the L1 filter mode, α=1 / 2 (k / 4) , where k is the L3 filter coefficient, i.e., the impact of the previous L3-RSRP value on the current L3-RSRP value.

[0238] Optionally, when the filter coefficient α corresponding to the L1 filtering mode is 1, or the L3 filtering coefficient k is 0, there is no need to set Margin 3, that is, there is no need to consider the impact of the L1 filtering mode difference on the L3-RSRP prediction performance verification of the UE.

[0239] In some embodiments, it is assumed that α = 0.5. This means that the previous L3-RSRP value will affect the current L3-RSRP value. The weights of the influence of each previous L3-RSRP value on the current L3-RSRP value are 1 / 2, 1 / 4, 1 / 8, 1 / 16, and 1 / 32 respectively. Since the periodicity of the sliding window is less than that of the non-sliding window when determining the L3-RSRP value, see Figure 7 , T1 is less than T2. In the sliding window filtering method, the influence of the previous L3-RSRP value on the current L3-RSRP value will decrease more quickly. Figure 7 As shown, when considering that the influence of the previous L3-RSRP value on the current L3-RSRP value is 1 / 16, the sliding window filtering method corresponds to 8 L1 filtering samples, and the non-sliding window filtering method corresponds to 32 L1 filtering samples.

[0240] Based on the above, at time T, the ground truth values of L3-RSRP obtained by the sliding filtering method and the non-sliding filtering method are different. Therefore, it is necessary to consider the impact of the difference in L1 filtering methods on the evaluation of UE channel quality prediction performance.

[0241] Optionally, for each L3-RSRP prediction value, it may be determined that the UE meets a preset accuracy requirement for L3-RSRP prediction when an absolute error corresponding to the L3-RSRP prediction value is less than or equal to a first parameter value.

[0242] Optionally, in order to improve the accuracy of L3-RSRP prediction of the UE and perform more accurate verification, the UE can be instructed to report M L3-RSRP prediction values. When the number of L3-RSRP prediction values whose corresponding absolute errors are less than or equal to the first parameter value reaches 90%×M, it is determined that the UE has passed the L3-RSRP prediction performance test.

[0243] In some embodiments, when at least one L3-RSRP accuracy margin includes Margin 1, Margin 2, Margin 3, the preset RSRP prediction accuracy threshold includes Z, the L3-RSRP prediction value reported by the UE is X, and the ideal true value of L3-RSRP determined by the first device is Y, it can be determined that the current L3-RSRP prediction value meets the preset accuracy requirements when the following formula (2) or formula (3) is satisfied:

[0244] Y - Z - (Margin1 + Margin2 + Margin3) < X < Y + Z + (Margin1 + Margin2 + Margin3) Formula (2)

[0245] |X - Y| < |Z + margin 1 + margin2 + margin3| Formula (3)

[0246] It can be understood that the above formula (2) and formula (3) are equivalent, and either one of them can be used to determine whether the current L3-RSRP prediction value meets the preset accuracy requirements.

[0247] Optionally, in some embodiments, the first margin is obtained based on the following method:

[0248] Generate a first RSRP difference fluctuation curve based on a preset timing offset, and extract the RSRP difference within the first confidence interval as the first margin.

[0249] Optionally, the preset timing offset can be set according to actual needs, and the embodiments of the present disclosure do not limit this. For example, the preset timing offset t = 40ms / 80ms / 160ms / 200ms.

[0250] In some embodiments, when determining the L3-RSRP value, a preset number of L1-RSRP values can be filtered to obtain the L3-RSRP difference. Correspondingly, the timing offset between two L3-RSRP values can include: the time interval between the time intervals where the preset number of L1-RSRP values corresponding to these two L3-RSRP values are located.

[0251] Optionally, the embodiments of the present disclosure do not limit the specific number of the preset number, which can be determined according to the device performance. For example, it can be 3 or 5. Based on the different device performances of the UE, some UEs with higher device performances can use fewer L1 RSRP samples (i.e., L1-RSRP values) to determine the L3 RSRP value. For example, 3 L1 RSRP samples can be used for filtering to obtain the L3-RSRP value, so that the filtered L1 RSRP can be reported to the upper layer (such as the network device) faster.

[0252] Optionally, when determining the L3-RSRP value, the method of filtering a preset number of L1-RSRP values may include: calculating an average value of the preset number of L1-RSRP values, and further obtaining a corresponding L3-RSRP value (for example, the above formula (1) may be used to further obtain it).

[0253] As an example, the time interval of the preset number of L1-RSRP values corresponding to the first L3-RSRP value is (t1-t2), and the time interval of the preset number of L1-RSRP values corresponding to the second L3-RSRP value is (t3-t4), and (t1-t2) is before (t3-t4), then the timing offset between the two L3-RSRP values can be (t3-t1) or (t4-t2).

[0254] In some embodiments, the moment corresponding to the L3-RSRP value can be the median of the time interval in which the preset number of L1-RSRP values corresponding to it are located. Taking the time interval in which the preset number of L1-RSRP values corresponding to the first L3-RSRP value mentioned above are located as (t1-t2) as an example, the moment corresponding to the L3-RSRP value can be: (t2-t1) / 2.

[0255] Optionally, a first confidence interval may be set according to actual accuracy test requirements. For example, the first confidence interval may include values between 5% and 95%.

[0256] Optionally, in the embodiment of the present disclosure, the first RSRP difference fluctuation curve may include but is not limited to a CDF (Cumulative Distribution Function) curve.

[0257] Optionally, a first RSRP difference fluctuation curve based on a preset timing offset may be generated based on differences between multiple groups of L3-RSRP values having the same preset time offset, and RSRP differences within a first confidence interval may be extracted as the first margin.

[0258] Optionally, in some embodiments, generating a first RSRP difference fluctuation curve based on a preset timing offset includes:

[0259] Obtain a first L1-RSRP value; wherein the first L1-RSRP value is obtained by measuring the first channel based on the first channel measurement information;

[0260] Filtering a first preset number of adjacent first L1-RSRP values to obtain a first L3-RSRP value;

[0261] The first RSRP difference fluctuation curve is generated according to a first RSRP difference between L3-RSRP values with a first preset timing offset.

[0262] In some embodiments, the first channel measurement information may include but is not limited to at least one of a channel model, a Doppler, a measurement interval, a moving speed of the UE, and the like.

[0263] Optionally, the first channel may be a simulated channel (also referred to as an "instant channel") generated based on the first channel measurement information, or may be a real channel based on the first channel measurement information, and the embodiments of the present disclosure do not impose any restrictions on this.

[0264] Optionally, the first preset number may be determined according to actual conditions, for example, may include but is not limited to 3 or 5.

[0265] For example, taking the example that the number of first L1-RSRP values includes 10 and the first preset number includes 5, the number of obtained L3-RSRP values may include 6, specifically: one L3-RSRP is obtained based on the first to fifth of the 10 first L1-RSRPs, one L3-RSRP is obtained based on the second to sixth of the 10 first L1-RSRPs, one L3-RSRP is obtained based on the third to seventh of the 10 first L1-RSRPs, one L3-RSRP is obtained based on the fourth to eighth of the 10 first L1-RSRPs, one L3-RSRP is obtained based on the fifth to ninth of the 10 first L1-RSRPs, and one L3-RSRP is obtained based on the sixth to tenth of the 10 first L1-RSRPs.

[0266] Optionally, the first preset timing offset may be set according to actual needs, which is not limited in the embodiment of the present disclosure. For example, the first preset timing offset t=40ms / 80ms / 160ms / 200ms.

[0267] Optionally, after determining multiple L3-RSRP values, a first RSRP difference fluctuation curve corresponding to the first preset timing offset can be constructed based on the first RSRP difference between two L3-RSRP values with a first preset timing offset among the multiple L3-RSRP values, and the RSRP difference within the first confidence interval in the first RSRP difference fluctuation curve can be extracted as the first margin.

[0268] That is, in some embodiments, the method of determining the first margin may include:

[0269] 1. Set first channel measurement information including at least one of a channel model, a Doppler, and a measurement interval.

[0270] 2. Generate an instant channel (i.e., simulate the first channel) based on the first channel measurement information, measure the first channel, and obtain an L1 RSRP value. Average every five L1 RSRP samples to obtain an L3-RSRP value.

[0271] 3. Based on the time interval of the five L1 RSRP samples corresponding to each L3-RSRP value, calculate the RSRP difference (i.e., the first RSRP difference) between two L3-RSRP values with a timing offset T, and plot a CDF (Cumulative Distribution Function) curve (i.e., the first RSRP difference curve) of the RSRP difference.

[0272] 4. Select the difference between the RSRP difference of 5% and the RSRP difference of 95% (ie, the first confidence interval) in the CDF curve as Margin 1.

[0273] Optionally, in actual application, a corresponding first margin may be selected based on the first preset timing offset.

[0274] See also Figure 5 , taking the first preset number as 5, the first RSRP difference fluctuation curve as the CDF curve, and the first confidence interval including values between 5% and 95% as an example, in the case of the first preset timing offset t=40ms, the first L3-RSRP difference corresponds to the CDF curve corresponding to the "double dashed line", in the case of the first preset timing offset t=80ms, the first L3-RSRP difference corresponds to the CDF curve corresponding to the "short line-dot", in the case of the first preset timing offset t=160ms, the first L3-RSRP difference corresponds to the CDF curve corresponding to the "solid line", in the case of the first preset timing offset t=200ms, the first L3-RSRP difference corresponds to the CDF curve corresponding to the "double dotted line". Based on Figure 5 As can be seen, the RSRP difference increases with increasing timing offset. In 90% of cases (i.e., between 5% and 95%), when the timing offset is 40ms, the first RSRP difference is within 3dB (±3dB). When the timing offset increases to 200ms, the first RSRP difference increases to 5.5dB (±5.5dB).

[0275] Optionally, in some embodiments, the second margin is obtained based on the following method:

[0276] A second RSRP difference fluctuation curve based on different numbers of L1 filtered samples is generated, and an RSRP difference within a second confidence interval is extracted as the second margin.

[0277] Optionally, the number of L1 filtering samples is the number of L1-RSRP values required to determine one L3-RSRP value.

[0278] Optionally, the number of L1 filter samples can be set according to actual needs, and the embodiment of the present disclosure does not limit this. For example, the number of L1 filter samples can be 3 or 5. The specific determination method can be referred to above and will not be repeated here.

[0279] Optionally, when determining the second margin, it may be assumed that the RSRP difference is not affected by other factors, for example, the timing offsets corresponding to the L3-RSRP values are the same.

[0280] Optionally, a second confidence interval may be set according to actual accuracy test requirements. For example, the second confidence interval may include values between 5% and 95%.

[0281] Optionally, in the embodiment of the present disclosure, the second RSRP difference fluctuation curve may include but is not limited to a CDF curve.

[0282] Optionally, a second RSRP difference fluctuation curve may be generated based on the difference between two L3-RSRP values in multiple groups of L3-RSRP value combinations based on different numbers of L1 filtered samples, and the RSRP difference within the second confidence interval may be extracted as the second margin.

[0283] Optionally, in some embodiments, generating a second RSRP difference fluctuation curve based on different numbers of L1 filtered samples includes:

[0284] Obtain a second L1-RSRP value; wherein the second L1-RSRP value is obtained by measuring the second channel based on the second channel measurement information;

[0285] filtering a second preset number of adjacent second L1-RSRP values to obtain a second L3-RSRP value; and filtering a third preset number of second L1-RSRP values among the second preset number of adjacent second L1-RSRP values to obtain a third L3-RSRP value; wherein the third preset number is less than the second preset number;

[0286] The second RSRP difference fluctuation curve is generated according to a second RSRP difference between the second L3-RSRP value and a corresponding third L3-RSRP value.

[0287] In some embodiments, the second channel measurement information may include but is not limited to at least one of a channel model, a Doppler, a measurement interval, a moving speed of the UE, and the like.

[0288] Optionally, the second channel may be a simulated channel (also referred to as an "instant channel") generated based on the second channel measurement information, or a real channel based on the second channel measurement information, which is not limited in the embodiments of the present disclosure.

[0289] Optionally, the second preset number and the third preset number may be determined according to actual conditions. For example, the second preset number may be 5, and the third preset number may be 3.

[0290] Optionally, when determining the third L3-RSRP value, filtering may be performed based on any three of a third preset number of second L1-RSRP values corresponding to the corresponding second L3-RSRP value. For example, filtering may be performed based on the last third preset number of second L1-RSRP values among the third preset number of second L1-RSRP values.

[0291] Optionally, the second L3-RSRP value and the corresponding third L3-RSRP value, that is, the second preset number of adjacent second L1-RSRP values used when determining the second L3-RSRP value, are the same as the second preset number of adjacent second L1-RSRP values used when determining the third L3-RSRP value.

[0292] Optionally, the second L3-RSRP value and the third L3-RSRP value corresponding to each other may be referred to as a group of L3-RSRP values.

[0293] Optionally, after determining multiple groups of L3-RSRP values, a second RSRP difference fluctuation curve can be constructed based on the second RSRP differences between the multiple groups of L3-RSRP values, and the RSRP differences within the second confidence interval in the second RSRP difference fluctuation curve can be extracted as the second margin.

[0294] That is, in some embodiments, the second margin may be determined in the following manner:

[0295] 1. Set second channel measurement information including at least one of a channel model, a Doppler, and a measurement interval.

[0296] 2. Generate an instant channel (ie, simulate the second channel) based on the second channel measurement information, measure the second channel, and obtain an L1 RSRP value (ie, a second L1-RSRP value).

[0297] 3. Averaging every five L1 RSRP samples (i.e., L1-RSRP values) yields an L3 RSRP value (i.e., a second L3-RSRP value). Averaging three L1 RSRP samples (i.e., L1-RSRP values) out of every five L1 RSRP samples yields an L3 RSRP value (i.e., a third L3-RSRP value).

[0298] 4. Calculate the RSRP difference between the two L3-RSRP values corresponding to the mismatched L1 filter sample numbers in step 3, and plot the CDF curve of the RSRP difference (see Figure 6 , i.e. the second RSRP difference curve).

[0299] 5. Select the difference between the RSRP difference of 5% and the RSRP difference of 95% (ie, the second confidence interval) in the CDF curve as Margin 2.

[0300] See also Figure 5 For example, when the second preset number is 5, the third preset number is 3, the second RSRP difference fluctuation curve is a CDF curve, and the second confidence interval includes values between 5% and 95%, using 3 L1 RSRP samples to derive the L1 filtered RSRP value (i.e., the L3-RSRP value) and using 5 L1 RSRP samples to derive the L1 filtered RSRP value, the corresponding RSRP difference is within 3dB in 90% of cases. Therefore, when it is necessary to set a second margin based on the difference in the number of L1 filtered samples, the second margin can be set to 3dB.

[0301] Optionally, in some embodiments, the third margin is obtained based on the following method:

[0302] generating a third RSRP difference fluctuation curve based on different L1 filtering modes and L3 filtering coefficients, and extracting an RSRP difference within a third confidence interval as the third margin;

[0303] The L1 filtering method includes non-sliding window filtering and sliding window filtering.

[0304] Optionally, as mentioned above, the L1 filtering method includes non-sliding window filtering and sliding window filtering. For details, see Figure 7 , I will not go into details here.

[0305] Optionally, the L3 filter coefficient, i.e., the effect of the previous L3-RSRP value on the current L3-RSRP value, can be set to a parameter value of the first L3 filter coefficient according to actual needs, which is not limited in the present embodiment. For example, it can be 1 / 2, 1 / 4, 1 / 8, 1 / 16, 1 / 32, etc.

[0306] Optionally, when determining the third margin, it may be assumed that the RSRP difference is not affected by other factors, for example, the timing offsets corresponding to the L3-RSRP values and the number of L1 filtered samples used are the same.

[0307] Optionally, a third confidence interval may be set according to actual accuracy test requirements. For example, the third confidence interval may include values between 5% and 95%.

[0308] Optionally, in the embodiment of the present disclosure, the third RSRP difference fluctuation curve may include but is not limited to a CDF curve.

[0309] Optionally, a third RSRP difference fluctuation curve can be generated based on a third RSRP difference between two L3-RSRP values in multiple groups of L3-RSRP value combinations generated based on different L1 filtering methods and L3 filtering coefficients, and the RSRP difference within the third confidence interval can be extracted as the third margin.

[0310] Optionally, in some embodiments, generating a third RSRP difference fluctuation curve based on different L1 filtering modes and L3 filtering coefficients includes:

[0311] Obtaining a third L1-RSRP value; wherein the third L1-RSRP value is obtained by measuring the third channel based on the third channel measurement information;

[0312] For a fourth preset number of adjacent third L1-RSRP values, perform non-sliding window filtering on the third L1-RSRP values to obtain a first RSRP value, and filter the first RSRP value according to a preset first L3 filter coefficient to obtain a fourth L3-RSRP value; and perform sliding window filtering on the third L1-RSRP value to obtain a second RSRP value, and filter the second RSRP value according to the first L3 filter coefficient to obtain a fifth L3-RSRP value;

[0313] The third RSRP difference fluctuation curve is generated according to a third RSRP difference between the fourth L3-RSRP value and a corresponding fifth L3-RSRP value.

[0314] In some embodiments, the third channel measurement information may include but is not limited to at least one of a channel model, a Doppler, a measurement interval, a moving speed of the UE, and the like.

[0315] Optionally, the third channel may be a simulated channel (also referred to as an "instant channel") generated based on the third channel measurement information, or a real channel based on the third channel measurement information, which is not limited in the embodiments of the present disclosure.

[0316] Optionally, the fourth preset number may be determined according to actual conditions. For example, the fourth preset number may be 5.

[0317] Optionally, for a fourth preset number of adjacent third L1-RSRP values, non-sliding window filtering can be directly performed on these third L1-RSRP values to obtain a first RSRP value, and then the first L3 filter coefficient is used to filter the first RSRP value to obtain a fourth L3-RSRP value; sliding window filtering can be directly performed on these third L1-RSRP values to obtain a second RSRP value, and then the first L3 filter coefficient is used to filter the second RSRP value to obtain a fifth L3-RSRP value.

[0318] Optionally, the fourth L3-RSRP value and the corresponding fifth L3-RSRP value, that is, the fourth preset number of adjacent second L1-RSRP values used when determining the fourth L3-RSRP value, are the same as the fourth preset number of adjacent second L1-RSRP values used when determining the fifth L3-RSRP value.

[0319] Optionally, the fourth L3-RSRP value and the fifth L3-RSRP value corresponding to each other may be referred to as a set of L3-RSRP values.

[0320] Optionally, after determining multiple groups of L3-RSRP values, a third RSRP difference fluctuation curve can be constructed based on the third RSRP difference between the multiple groups of L3-RSRP values, and the RSRP difference within the third confidence interval in the third RSRP difference fluctuation curve can be extracted as the third margin.

[0321] That is, in some embodiments, the third margin may be determined in the following manner:

[0322] 1. Set third channel measurement information including at least one of a channel model, a Doppler, and a measurement interval.

[0323] 2. Generate an instant channel (ie, simulate the third channel) based on the third channel measurement information, measure the third channel, and obtain an L1 RSRP value (ie, a third L1-RSRP value).

[0324] 3. Calculate the RSRP difference between the two L3 RSRP values (i.e., the third RSRP difference) by using the sliding and non-sliding methods, and draw a CDF curve of the RSRP difference (i.e., the third RSRP difference curve).

[0325] 4. Select the difference between the RSRP difference of 5% and the RSRP difference of 95% (ie, the third confidence interval) in the CDF curve as Margin 2.

[0326] In some embodiments, different Margin 3 values may be set based on different L3 filter coefficients. Optionally, when α = 1 or k = 0, Margin 3 does not need to be set, i.e., the impact of differences in L1 filtering methods on the L3-RSRP prediction performance verification of the UE does not need to be considered.

[0327] See also Figure 8 Taking the fourth preset number being 5, the third RSRP difference fluctuation curve being a CDF curve, and the third confidence interval including values between 5% and 95% as an example, the RSRP difference between the L3-RSRP values obtained based on sliding window filtering and non-sliding window filtering under different L3 filter coefficients is shown, wherein, when the L3 filter coefficient k=0, the third L3-RSRP difference corresponds to the CDF curve corresponding to the "double dashed line", when the L3 filter coefficient k=1, the third L3-RSRP difference corresponds to the CDF curve corresponding to the "short line-dot", when the L3 filter coefficient k=2, the third L3-RSRP difference corresponds to the CDF curve corresponding to the "solid line", and when the L3 filter coefficient k=4, the third L3-RSRP difference corresponds to the CDF curve corresponding to the "double-dotted line". Assuming k = 0, which means no L3 filtering coefficient is used, the previous L3-RSRP value has no effect on the current L3-RSRP value. This means there is no difference in the L3-RSRP values obtained by using a non-sliding window L1 filtering scheme or a sliding window L1 filtering scheme. Assuming k = 4 and α = 0.5, the RSRP difference between the L3-RSRP values obtained by using a non-sliding window L1 filtering scheme and a sliding window L1 filtering scheme is within 1.8 dB in 90% of cases. Assuming the influence of α is ignored, the L3-RSRP value is equivalent to the measured value when α = 1, equivalent to the value obtained by directly taking the average of the L1-RSRP values. In this case, the ideal true L3 RSRP value is independent of the L1 / L3 filtering scheme. Regardless of the L1 / L3 filtering scheme used, the L3-RSRP value is equivalent to the average of M instantaneous L1-RSRP values.

[0328] Optionally, in some embodiments, the fourth margin is obtained based on the following method:

[0329] Based on the time offset, the difference in the number of L1 filtering samples, and the difference in the L1 filtering method, a fourth RSRP difference fluctuation curve is generated, and the RSRP difference in a fourth confidence interval is extracted as the fourth margin.

[0330] In some embodiments, the fourth margin is generated based on the time offset, the difference in the number of L1 filtered samples, and the difference in the L1 filtering method. That is, the fourth margin is determined by comprehensively considering the impact of the time offset, the difference in the number of L1 filtered samples, and the difference in the L1 filtering method on the evaluation of the L3-RSRP prediction accuracy of the UE.

[0331] Optionally, a fourth confidence interval may be set according to actual accuracy test requirements. For example, the fourth confidence interval may include values between 5% and 95%.

[0332] Optionally, in the embodiment of the present disclosure, the fourth RSRP difference fluctuation curve may include but is not limited to a CDF curve.

[0333] Optionally, a fourth RSRP difference fluctuation curve can be generated based on a fourth RSRP difference between two L3-RSRP values in multiple groups of L3-RSRP value combinations generated based on time offset, difference in the number of L1 filtering samples, and difference in L1 filtering methods, and the RSRP difference within the fourth confidence interval can be extracted as the fourth margin.

[0334] Optionally, in some embodiments, generating a fourth RSRP difference fluctuation curve based on the time offset, the difference in the number of L1 filtering samples, and the difference in the L1 filtering mode includes:

[0335] Obtaining a fourth L1-RSRP value; wherein the fourth L1-RSRP value is obtained by measuring the fourth channel based on the fourth channel measurement information;

[0336] For a fifth preset number of adjacent fourth L1-RSRP values, perform non-sliding window filtering on the fourth L1-RSRP values to obtain a third RSRP value, and filter the third RSRP value according to a preset second L3 filter coefficient to obtain a sixth L3-RSRP value; and perform sliding window filtering on a sixth preset number of the fourth L1-RSRP values to obtain a fourth RSRP value, and filter the fourth RSRP value according to the second L3 filter coefficient to obtain a fifth RSRP value, and determine an RSRP difference between the fifth RSRP values having a second preset timing offset to obtain a seventh L3-RSRP value.

[0337] The fourth RSRP difference fluctuation curve is generated according to a fourth RSRP difference between the sixth L3-RSRP value and a corresponding seventh RSRP value.

[0338] In some embodiments, the fourth channel measurement information may include but is not limited to at least one of a channel model, a Doppler, a measurement interval, a moving speed of the UE, and the like.

[0339] Optionally, the fourth channel may be a simulated channel (also referred to as an "instant channel") generated based on the fourth channel measurement information, or a real channel based on the fourth channel measurement information, which is not limited in the embodiments of the present disclosure.

[0340] Optionally, the fifth preset number and the sixth preset number may be determined according to actual conditions. For example, the fifth preset number may be 5, and the sixth preset number may be 3.

[0341] Optionally, for a fifth preset number of adjacent fourth L1-RSRP values, non-sliding window filtering can be directly performed on these fourth L1-RSRP values to obtain a third RSRP value, and then the second L3 filter coefficient is used to filter the third RSRP value to obtain a sixth L3-RSRP value; sliding window filtering can be directly performed on these fourth L1-RSRP values to obtain a fourth RSRP value, and then the second L3 filter coefficient is used to filter the fourth RSRP value to obtain a fifth RSRP value, and the RSRP difference between the fifth RSRP values with the second preset timing offset is further determined to obtain a seventh L3-RSRP value.

[0342] Optionally, the second preset timing offset may be set according to actual needs, which is not limited in the embodiment of the present disclosure. For example, the second preset timing offset t=40ms / 80ms / 160ms / 200ms.

[0343] Optionally, the L3 filter coefficient, i.e., the effect of the previous L3-RSRP value on the current L3-RSRP value, can be set to a parameter value of the second L3 filter coefficient according to actual needs, which is not limited in the embodiments of the present disclosure. For example, it can be 1 / 2, 1 / 4, 1 / 8, 1 / 16, 1 / 32, etc.

[0344] Optionally, the sixth L3-RSRP value and the corresponding seventh L3-RSRP value, that is, the fourth preset number of adjacent fourth L1-RSRP values used when determining the sixth L3-RSRP value, are the same as the fourth preset number of adjacent fourth L1-RSRP values used when determining the seventh L3-RSRP value.

[0345] Optionally, the sixth L3-RSRP value and the seventh L3-RSRP value corresponding to each other may be referred to as a set of L3-RSRP values.

[0346] Optionally, after determining multiple groups of L3-RSRP values, a fourth RSRP difference fluctuation curve can be constructed based on the fourth RSRP difference between the multiple groups of L3-RSRP values, and the RSRP difference within the third confidence interval in the fourth RSRP difference fluctuation curve can be extracted as the fourth margin.

[0347] It can be understood that in the embodiment of the present disclosure, the first channel measurement information, the second channel measurement information, the third channel measurement information and the fourth channel measurement information may be entirely or partially the same, or may all be different, and the embodiment of the present disclosure does not limit this.

[0348] It can be understood that in the embodiment of the present disclosure, the first channel, the second channel, the third channel and the fourth channel may be entirely or partially the same, or may all be different, and the embodiment of the present disclosure does not limit this.

[0349] It can be understood that in the embodiment of the present disclosure, the first confidence interval, the second confidence interval, the third confidence interval and the fourth confidence interval may be entirely or partially the same, or may all be different, and the embodiment of the present disclosure does not limit this.

[0350] In some embodiments, see Figure 9 , the above communication method may include:

[0351] Step 901: A first device sends first indication information to the UE; wherein the first indication information instructs the UE to report first information to the first device;

[0352] The first information includes at least one of the following: the time of the L3-RSRP prediction value; the number of L1 filtering samples used by the UE for L3-RSRP prediction; and the L1 filtering method used by the UE for L3-RSRP prediction.

[0353] Step 902: The UE sends first information to the first device.

[0354] Step 903: The UE sends a predicted L3-RSRP value to the first device. Correspondingly, the first device receives the predicted L3-RSRP value sent by the UE.

[0355] In some embodiments, the UE can measure the channel in advance, and construct training samples based on the historical channel quality measurement values obtained. Based on the training samples, AI technology is used for training to obtain a channel quality prediction model that can predict channel quality. In this way, the UE can predict channel quality through the channel quality prediction model.

[0356] In some embodiments, in order to adapt to channel quality prediction under different channel characteristics, when the UE trains the channel quality prediction model, the results of channel quality measurement based on channels with different channel characteristics can be used as training samples to predict the neural network model to obtain a trained channel quality prediction model.

[0357] In some embodiments, before the UE sends the L3-RSRP prediction value to the first device, the first device may send measurement indication information to the UE, instructing the UE to perform L3-RSRP measurement; after receiving the measurement indication information, the UE may perform L3-RSRP measurement according to the measurement indication information, and input the measured L3-RSRP value into the channel quality prediction model, obtain the L3-RSRP prediction value through the channel quality prediction model, and send the obtained L3-RSRP prediction value to the first device.

[0358] Optionally, the embodiment of the present disclosure does not limit the number of L3-RSRP prediction values sent by the UE, and can be set according to actual conditions, such as accuracy requirements, which will not be elaborated here.

[0359] Step 904: The first device determines an absolute error between the predicted L3-RSRP value and an ideal true L3-RSRP value.

[0360] Optionally, the ideal true value of L3-RSRP may also be referred to as a “ground truth value of L3-RSRP”, which is not limited in the embodiments of the present disclosure.

[0361] In some embodiments, the first device may enter into an agreement with the UE to define a method for determining an ideal true L3-RSRP value. After receiving the predicted L3-RSRP value sent by the UE, the first device may determine the ideal true L3-RSRP value corresponding to the predicted L3-RSRP value.

[0362] Optionally, the absolute error between the predicted L3-RSRP value and the ideal true L3-RSRP value may include an absolute value of a difference between the predicted L3-RSRP value and the ideal true L3-RSRP value.

[0363] Step 905: When the absolute error is less than or equal to a first parameter value, the first device determines that the UE meets a preset accuracy requirement for L3-RSRP prediction;

[0364] The RSRP accuracy margin includes at least one of the following: a first margin based on a time offset, a second margin based on a difference in the number of L1 filtered samples, a third margin based on a difference in an L1 filtering method, and a fourth margin; wherein the fourth margin is obtained based on the time offset, the difference in the number of L1 filtered samples, and the difference in the L1 filtering method;

[0365] The first parameter value includes: the sum of a preset RSRP prediction accuracy threshold and the at least one L3-RSRP accuracy margin.

[0366] As mentioned above, in different situations, the corresponding L3-RSRP accuracy margin is different. After determining the L3-RSRP accuracy margin based on actual conditions, the preset RSRP prediction accuracy threshold can be added with at least one determined L3-RSRP accuracy margin to obtain a first parameter value, and further based on the size relationship between the above absolute error and the first parameter value, it is determined whether the UE meets the preset accuracy requirements of the L3-RSRP prediction.

[0367] Optionally, the preset RSRP prediction accuracy threshold can be determined according to actual conditions, and the embodiments of the present disclosure do not limit this. For example, the RSRP prediction accuracy threshold can be set to a value range of 5-6dBm, or 2-3dBm.

[0368] In some embodiments, when the time of the UE and the first device is completely synchronized, Margin 1 is 0, that is, there is no need to consider the impact of the time offset on the L3-RSRP prediction performance verification of the UE.

[0369] In some embodiments, there is signaling interaction between the UE and the first device, that is, the two determine through signaling interaction that the number of L1 filtering samples used by both are the same, and Margin 2 is 0, that is, there is no need to consider the impact of the difference in the number of L1 filtering samples on the L3-RSRP prediction performance verification of the UE.

[0370] In some embodiments, the third margin based on the L1 filtering method difference may include a third margin determined based on the L1 filtering method difference and an L3 filtering coefficient.

[0371] In some embodiments, when the UE and the first device respectively determine the L3-RSRP prediction value and the L3-RSRP ideal true value based on the same L1 filtering method difference and L3 filtering coefficient, Margin 3 is 0, that is, there is no need to consider the impact of the L1 filtering method difference on the L3-RSRP prediction performance verification of the UE.

[0372] Optionally, for each L3-RSRP prediction value, it may be determined that the UE meets a preset accuracy requirement for L3-RSRP prediction when an absolute error corresponding to the L3-RSRP prediction value is less than or equal to a first parameter value.

[0373] Optionally, when the TE determines the RSRP precision margin, it may be determined in combination with the information carried in the first information, specifically:

[0374] At a moment when the first information includes the L3-RSRP prediction value, the at least one RSRP accuracy margin does not include the first margin;

[0375] In a case where the first information includes the number of L1 filtered samples used by the UE to perform L3-RSRP prediction, the at least one RSRP accuracy margin does not include the second margin;

[0376] In a case where the first information includes an L1 filtering mode adopted by the UE for L3-RSRP prediction, the at least one RSRP accuracy margin does not include the third margin.

[0377] Optionally, when the first information includes the moment of the L3-RSRP prediction value, the TE can determine the ideal true value of the L3-RSRP corresponding to the L3-RSRP prediction value and its corresponding moment, that is, time synchronization of each L3-RSRP prediction value and the corresponding L3-RSRP ideal true value can be achieved. At this time, Margin 1 can be set to 0, that is, at least one RSRP accuracy margin does not include the first margin, and there is no need to consider the time offset, and the impact of L3-RSRP prediction performance verification on the UE.

[0378] Optionally, when the first information includes the number of L1 filtering samples used by the UE for L3-RSRP prediction, the TE can determine to use the same number of L1 filtering samples to determine the corresponding ideal true value of L3-RSRP, that is, the number of L1 filtering samples used for each L3-RSRP prediction value and the corresponding ideal true value of L3-RSRP can be achieved. At this time, Margin2 can be set to 0, that is, at least one RSRP accuracy margin does not include the second margin, and there is no need to consider the difference in the number of L1 filtering samples to verify the L3-RSRP prediction performance of the UE.

[0379] Optionally, when the first information includes the L1 filtering method adopted by the UE for L3-RSRP prediction, the TE can determine to use the same L1 filtering method to determine the corresponding L3-RSRP ideal true value, that is, each L3-RSRP prediction value can be achieved to be the same as the L1 filtering method adopted by the corresponding L3-RSRP ideal true value. At this time, Margin 3 can be set to 0, that is, at least one RSRP accuracy margin does not include the third margin, and there is no need to consider the difference in L1 filtering methods and the impact of L3-RSRP prediction performance verification on the UE.

[0380] Optionally, in order to improve the accuracy of L3-RSRP prediction of the UE and perform more accurate verification, the UE can be instructed to report M L3-RSRP prediction values. When the number of L3-RSRP prediction values whose corresponding absolute errors are less than or equal to the first parameter value reaches 90%×M, it is determined that the UE has passed the L3-RSRP prediction performance test.

[0381] In some embodiments, when at least one L3-RSRP accuracy margin includes Margin 1, Margin 2, and Margin 3, the preset RSRP prediction accuracy threshold includes Z, the L3-RSRP prediction value reported by the UE is X, and the L3-RSRP ideal true value determined by the first device is Y, it can be determined that the current L3-RSRP prediction value meets the preset accuracy requirement when the above formula (2) or formula (3) is satisfied.

[0382] In some embodiments, the method of determining the first margin, the second margin, the third margin, and the fourth margin can refer to the above description and will not be repeated here.

[0383] In some embodiments, see Figure 10 , the above communication method may include:

[0384] Step 1001: The UE sends first information to the first device; wherein the first information includes at least one of the following: the time of the L3-RSRP prediction value; the number of L1 filtering samples used by the UE for L3-RSRP prediction; and the L1 filtering method used by the UE for L3-RSRP prediction.

[0385] Step 1002: The UE sends a predicted L3-RSRP value to the first device. Correspondingly, the first device receives the predicted L3-RSRP value sent by the UE.

[0386] In some embodiments, the UE can measure the channel in advance, and construct training samples based on the historical channel quality measurement values obtained. Based on the training samples, AI technology is used for training to obtain a channel quality prediction model that can predict channel quality. In this way, the UE can predict channel quality through the channel quality prediction model.

[0387] In some embodiments, in order to adapt to channel quality prediction under different channel characteristics, when the UE trains the channel quality prediction model, the results of channel quality measurement based on channels with different channel characteristics can be used as training samples to predict the neural network model to obtain a trained channel quality prediction model.

[0388] In some embodiments, before the UE sends the L3-RSRP prediction value to the first device, the first device may send measurement indication information to the UE, instructing the UE to perform L3-RSRP measurement; after receiving the measurement indication information, the UE may perform L3-RSRP measurement according to the measurement indication information, and input the measured L3-RSRP value into the channel quality prediction model, obtain the L3-RSRP prediction value through the channel quality prediction model, and send the obtained L3-RSRP prediction value to the first device.

[0389] Optionally, the embodiment of the present disclosure does not limit the number of L3-RSRP prediction values sent by the UE, and can be set according to actual conditions, such as accuracy requirements, which will not be elaborated here.

[0390] Step 1003: The first device determines an absolute error between the predicted L3-RSRP value and an ideal true L3-RSRP value.

[0391] Optionally, the ideal true value of L3-RSRP may also be referred to as a “ground truth value of L3-RSRP”, which is not limited in the embodiments of the present disclosure.

[0392] In some embodiments, the first device may enter into an agreement with the UE to define a method for determining an ideal true L3-RSRP value. After receiving the predicted L3-RSRP value sent by the UE, the first device may determine the ideal true L3-RSRP value corresponding to the predicted L3-RSRP value.

[0393] Optionally, the absolute error between the predicted L3-RSRP value and the ideal true L3-RSRP value may include an absolute value of a difference between the predicted L3-RSRP value and the ideal true L3-RSRP value.

[0394] Step 1004: When the absolute error is less than or equal to a first parameter value, the first device determines that the UE meets a preset accuracy requirement for L3-RSRP prediction;

[0395] The RSRP accuracy margin includes at least one of the following: a first margin based on a time offset, a second margin based on a difference in the number of L1 filtered samples, a third margin based on a difference in an L1 filtering method, and a fourth margin; wherein the fourth margin is obtained based on the time offset, the difference in the number of L1 filtered samples, and the difference in the L1 filtering method;

[0396] The first parameter value includes: the sum of a preset RSRP prediction accuracy threshold and the at least one L3-RSRP accuracy margin.

[0397] As mentioned above, in different situations, the corresponding L3-RSRP accuracy margin is different. After determining the L3-RSRP accuracy margin based on actual conditions, the preset RSRP prediction accuracy threshold can be added with at least one determined L3-RSRP accuracy margin to obtain a first parameter value, and further based on the size relationship between the above absolute error and the first parameter value, it is determined whether the UE meets the preset accuracy requirements of the L3-RSRP prediction.

[0398] Optionally, the preset RSRP prediction accuracy threshold can be determined according to actual conditions, and the embodiments of the present disclosure do not limit this. For example, the RSRP prediction accuracy threshold can be set to a value range of 5-6dBm, or 2-3dBm.

[0399] In some embodiments, when the time of the UE and the first device is completely synchronized, Margin 1 is 0, that is, there is no need to consider the impact of the time offset on the L3-RSRP prediction performance verification of the UE.

[0400] In some embodiments, there is signaling interaction between the UE and the first device, that is, the two determine through signaling interaction that the number of L1 filtering samples used by both are the same, and Margin 2 is 0, that is, there is no need to consider the impact of the difference in the number of L1 filtering samples on the L3-RSRP prediction performance verification of the UE.

[0401] In some embodiments, the third margin based on the L1 filtering method difference may include a third margin determined based on the L1 filtering method difference and an L3 filtering coefficient.

[0402] In some embodiments, when the UE and the first device respectively determine the L3-RSRP prediction value and the L3-RSRP ideal true value based on the same L1 filtering method difference and L3 filtering coefficient, Margin 3 is 0, that is, there is no need to consider the impact of the L1 filtering method difference on the L3-RSRP prediction performance verification of the UE.

[0403] Optionally, for each L3-RSRP prediction value, it may be determined that the UE meets a preset accuracy requirement for L3-RSRP prediction when an absolute error corresponding to the L3-RSRP prediction value is less than or equal to a first parameter value.

[0404] Optionally, when the TE determines the RSRP precision margin, it may be determined in combination with the information carried in the first information, specifically:

[0405] At a moment when the first information includes the L3-RSRP prediction value, the at least one RSRP accuracy margin does not include the first margin;

[0406] In a case where the first information includes the number of L1 filtered samples used by the UE to perform L3-RSRP prediction, the at least one RSRP accuracy margin does not include the second margin;

[0407] In a case where the first information includes an L1 filtering mode adopted by the UE for L3-RSRP prediction, the at least one RSRP accuracy margin does not include the third margin.

[0408] Optionally, when the first information includes the moment of the L3-RSRP prediction value, the TE can determine the ideal true value of the L3-RSRP corresponding to the L3-RSRP prediction value and its corresponding moment, that is, time synchronization of each L3-RSRP prediction value and the corresponding L3-RSRP ideal true value can be achieved. At this time, Margin 1 can be set to 0, that is, at least one RSRP accuracy margin does not include the first margin, and there is no need to consider the time offset, and the impact of L3-RSRP prediction performance verification on the UE.

[0409] Optionally, when the first information includes the number of L1 filtering samples used by the UE for L3-RSRP prediction, the TE can determine to use the same number of L1 filtering samples to determine the corresponding ideal true value of L3-RSRP, that is, the number of L1 filtering samples used for each L3-RSRP prediction value and the corresponding ideal true value of L3-RSRP can be achieved. At this time, Margin2 can be set to 0, that is, at least one RSRP accuracy margin does not include the second margin, and there is no need to consider the difference in the number of L1 filtering samples to verify the L3-RSRP prediction performance of the UE.

[0410] Optionally, when the first information includes the L1 filtering method adopted by the UE for L3-RSRP prediction, the TE can determine to use the same L1 filtering method to determine the corresponding L3-RSRP ideal true value, that is, each L3-RSRP prediction value can be achieved to be the same as the L1 filtering method adopted by the corresponding L3-RSRP ideal true value. At this time, Margin 3 can be set to 0, that is, at least one RSRP accuracy margin does not include the third margin, and there is no need to consider the difference in L1 filtering methods and the impact of L3-RSRP prediction performance verification on the UE.

[0411] Optionally, in order to improve the accuracy of L3-RSRP prediction of the UE and perform more accurate verification, the UE can be instructed to report M L3-RSRP prediction values. When the number of L3-RSRP prediction values whose corresponding absolute errors are less than or equal to the first parameter value reaches 90%×M, it is determined that the UE has passed the L3-RSRP prediction performance test.

[0412] In some embodiments, when at least one L3-RSRP accuracy margin includes Margin 1, Margin 2, and Margin 3, the preset RSRP prediction accuracy threshold includes Z, the L3-RSRP prediction value reported by the UE is X, and the L3-RSRP ideal true value determined by the first device is Y, it can be determined that the current L3-RSRP prediction value meets the preset accuracy requirement when the above formula (2) or formula (3) is satisfied.

[0413] In some embodiments, the method of determining the first margin, the second margin, the third margin, and the fourth margin can refer to the above description and will not be repeated here.

[0414] In some embodiments, the disclosed embodiments also define a method for RSRP accuracy margin based on AI mobility, which ensures more accurate RSRP prediction by considering various uncertainty factors (for example, timing mismatch, number of L1 filters, and L1 filtering method, etc.), thereby achieving better performance verification.

[0415] Specifically, the above method may include the following stages:

[0416] A. Calculate the absolute error between the predicted L3-RSRP value based on the AI prediction model and the ideal true L3-RSRP value:

[0417] Absolute L3 Predicted RSRP Accuracy = XY, that is, L3-RSRP prediction absolute error = XY;

[0418] Where X is the L3-RSRP predicted value reported by the UE based on the AI prediction model, and Y is the ideal true value of L3-RSRP determined by the TE.

[0419] B.TE determines three margins:

[0420] Margin 1: Error compensation value based on time offset. Specifically, by simulating the RSRP difference fluctuation curve (i.e., RSRP difference distribution) under different UE speeds or time offsets, the maximum RSRP difference between the corresponding 5% and 95% quantiles (i.e., the first confidence interval is between 5% and 95%) is extracted as the error compensation value.

[0421] Margin2: Error compensation value based on the number of L1 filter samples. Specifically, by comparing the RSRP difference fluctuation curves under different L1 filter sample numbers (for example, 3 or 5 L1 filter samples), the maximum RSRP difference between the corresponding 5% and 95% quantiles (i.e., the second confidence interval is between 5% and 95%) is extracted as the error compensation value.

[0422] Margin3: Error compensation value based on the difference in L3 filtering methods. Specifically, by comparing the RSRP difference fluctuation curves under sliding window filtering and non-sliding window filtering, the maximum RSRP difference between the corresponding 5% and 95% quantiles (i.e., the third confidence interval is between 5% and 95%) is extracted as the error compensation value.

[0423] C. Combine at least one of the above margins to form the final RSRP accuracy requirement:

[0424] YZ-(Margin1+Margin2+Margin3) <X<Y+Z+(Margin1+Margin2+Margin3)

[0425] Where X is the L3-RSRP predicted value reported by the UE, Y is the ideal true L3-RSRP value determined by the TE, and Z is the preset RSRP prediction accuracy threshold (for example, the value range of Z can be 5-6dBm, or the value range of Z can be 2-3dBm). Part or all of Margin 1, Margin 2, and Margin 3 can be selected based on actual needs.

[0426] Optionally, in some embodiments, when considering the error compensation value based on time offset, the error compensation value based on the difference in the number of L1 filtering samples, and the error compensation value based on the difference in L3 filtering methods at the same time, the integrated error compensation value may not be equal to the simple sum of the corresponding margins, and the integrated error compensation value needs to be determined based on the RSRP difference fluctuation curve obtained by re-simulation.

[0427] Optionally, a method for evaluating whether the performance of RSRP prediction by the UE meets the final RSRP accuracy requirement may be:

[0428] Absolute L3 Predicted RSRP Accuracy = reported predicted L3-RSRP – ground truth of L3-RSRP + Margin 1 + Margin 2 + Margin 3, |xy| < |z+margin 1+margin2+margin3| [RSRP accuracy requirements listed in step C above]

[0429] Where margin 1 is possible timing mismatch between predicted L3-RSRPand ground truth L3-RSRP.Margin 2 is possible mismatch due to L1 filteringnumber.Margin 3is possible mismatch due to L3 filtering. That is, the timing mismatch (no timing mismatch) between the L3-RSRP predicted by margin 1 (i.e., the predicted L3-RSRP value reported by the UE) and the ground truth L3-RSRP (i.e., the ideal true value of L3-RSRP determined by the TE) is caused. Margin 2 may be caused by the mismatch in the number of L1 filtering samples. Margin 3 may be caused by the mismatch in the L3 filtering method. Specifically, the specific meanings of margin 1, margin 2, and margin 3 can be found above and will not be elaborated here.

[0430] Optionally, the determination methods of margin 1, margin 2, and margin 3 can be as follows (Next, we will prove analysis and the method how to decide the margin.):

[0431] Margin1:

[0432] In some embodiments, in order to evaluate the accuracy of RSRP prediction, it is necessary to align the time instance of the predicted L3 RSRP and the ground truth L3 RSRP. In the traditional case, it is assumed that the measured RSRP value is not affected by the AWGN (Additive White Gaussian Noise) channel. However, for fading channels, there may be a timing offset, see Figure 4Therefore, the timing needs to be adjusted. To evaluate RSRP prediction accuracy, the time instance for predicted L3 RSRP and ground truth L3 RSRP needs to be aligned. In legacy, AWGN channel is assumed and there is no impact. However, for fading channel, the timing needs to be aligned. We try to analyze the RSRP difference due to timing mismatch.

[0433] In some embodiments, when determining Margin 1, it is possible to assume that the UE speed is 30 kilometers per hour (km / h) and the RS (Reference Signal) periodicity is 40 milliseconds (ms) based on RAN2 simulation. For simplicity, a TDL-C channel (Tapped Delay Line Channel) is used.

[0434] In some embodiments, when the timing offset t (also called "timing mismatch value") = 40ms / 80ms / 160ms / 200ms, a difference curve between two sets of ideal L3 RSRP values can be plotted. Each L3 RSRP value is obtained by taking the average of five L1 RSRP value samples. Figure 5The figure shows the L3-RSRP values obtained based on five L1 samples and the L3-RSRP differences at different timing offsets. The CDF curve for t = 40ms corresponds to the double dashed line, the CDF curve for t = 80ms corresponds to the dashed dotted line, the CDF curve for t = 160ms corresponds to the solid line, and the CDF curve for t = 200ms corresponds to the double-dotted line. It can be seen that the RSRP difference increases with increasing timing offset. When the timing offset is 40ms, the RSRP difference is within 3dB in 90% of cases. When the timing offset increases to 200ms, the RSRP difference increases to 5.5dB. (Here,we plot the two ideal L3 RSRP difference with timemismatch=40ms / 80ms / 160ms / 200ms.Each L3 RSRP is averaged by 5L1RSRPsamples.As shown below,when time gap increases,RSRP difference increases.Whentiming mismatch is 40ms,RSRP difference is within 3dB for 90% cases.Whentiming mismatch increases to 200ms,RSRP difference increases to 5.5dB.)

[0435] In combination with the above, in some embodiments, in combination with the changing relationship between the timing offset and the RSRP difference (i.e., the larger the timing offset, the larger the timing offset margin that needs to be set), different timing offset margins need to be considered for different timing offsets. mismatch =40ms (i.e., the timing offset is 40ms), the corresponding timing offset margin is set to 3dB. mismatch=200ms (that is, the timing mismatch is 200ms), and the corresponding timing mismatch margin is set to 5.5dB. (Therefore, for different timing mismatch, different margin needs be considered. For Tmismatch=40ms, the margin is 3dB. While for Tmismatch=200ms, the margin is 5.5dB. The larger the timing mismatch, the larger margin is needed.)

[0436] In some embodiments, in combination with the above, the method to derive margin for timing mismatch is as follows:

[0437] 1. Setting channel model, Doppler, and measurement interval. That is, setting first channel measurement information including at least one of the channel model, Doppler, and measurement interval.

[0438] 2. Generate an instant channel (i.e., simulate the first channel) based on the first channel measurement information, measure the first channel, and obtain an L1 RSRP value. Average every five L1 RSRP samples (i.e., L1-RSRP values) to obtain an L3-RSRP value (i.e., the first L3-RSRP value). (Generate instant channel RSRP and average every five samples to obtain one L3-RSRP.)

[0439] 3. Calculate RSRP difference from two L3-RSRP values with timing offset T (i.e., first RSRP difference) based on the time interval of the five L1 RSRP samples corresponding to each L3-RSRP value, and plot the CDF (Cumulative Distribution Function) curve of RSRP difference (i.e., first RSRP difference curve).

[0440] 4. Choose the maximum (RSRP difference of 5% and RSRP difference of 95%) as the RSRP margin.

[0441] In some embodiments, when the UE and TE are completely time synchronized, Margin 1 is 0, that is, there is no need to consider the impact of time offset on the L3-RSRP prediction performance verification of the UE.

[0442] Margin2:

[0443] In some embodiments, within a certain measurement period, L1 filtering may include averaging a number of L1 RSRP samples to obtain an L3 RSRP value; wherein the number of L1 RSRP samples used may be 5. Based on different UE device performance, some UEs with higher device performance may use fewer L1 RSRP samples to determine the L3 RSRP value; for example, 3 L1 RSRP samples may be used for filtering to obtain the L3-RSRP value. In this way, the filtered L1 RSRP value can be reported to higher layers (e.g., network devices) more quickly. In other words, the predicted L3-RSRP value may be obtained by further calculation based on the average of 3 L1 RSRP samples, rather than based on the average of 5 L1 RSRP samples. (In current measurement period requirement, 5 L1 RSRP are averaged to derive one L3 RSRP, which his called L1 filtering. In fact, some advanced UEcan use fewer samples to achieve L3 RSRP accuracy,ie3 L1 RSRP samples, and can send filtered L1 RSRP to high layer more quickly. If that is the case, thepredicted L3-RSRP will also be calculated based on average of 3 L1 RSRP samples but not 5samples.)

[0444] In some embodiments, in order to ensure that the TE can generate an ideal true L3-RSRP value that matches the L3-RSRP value predicted by the UE, the TE needs to know how many L1-RSRP samples the UE uses when calculating the ideal true L3-RSRP value. Of course, this also places high restrictions on the UE implementation. At the same time, only when both the UE and TE use the same number of L1-RSRP samples (for example, 5) for averaging can the TE be guaranteed to generate an ideal true L3-RSRP value that matches the L3-RSRP value predicted by the UE; otherwise, a mismatch will occur. Therefore, it is necessary to consider the margin (i.e., Margin 2) generated by the mismatch in the number of L1 filters used by the UE and TE (i.e., the difference in the number of L1 filter samples). (If L3-RSRP will be calculated at TE side, it means that TE needs to know how many L1 samples will be used to generateideal L3-RSRP. It will have highly limitation on UE implementation. It means that UE must average based on 5 samples. Otherwise, there will be mismatch. Therefore, some margin due to L1 filtering number mismatch needs to be considered.)

[0445] In some embodiments, see Figure 6If 3 samples are used to derive L1 filtered RSRP (i.e., L3-RSRP), the RSRP delta will be within 3dB for 90% cases when compared with that case that 5 samples are used. Therefore, 3dB needs to be added for L1 filtering number mismatch.

[0446] In some embodiments, in combination with the above, the method to derive margin for L1 filtering number mismatch is as follows:

[0447] 1. Setting channel model, Doppler, and measurement interval. That is, setting second channel measurement information including at least one of the channel model, Doppler, and measurement interval.

[0448] 2. Generate an instant channel (ie, simulate a second channel) based on the second channel measurement information, measure the second channel, and obtain an L1 RSRP value (ie, a second L1-RSRP value) (Generate instant channel RSRP).

[0449] 3. Average every five L1 RSRP samples (i.e., L1-RSRP values) to obtain an L3 RSRP value (i.e., a second L3-RSRP value). Average three of every five L1 RSRP samples (i.e., L1-RSRP values) to obtain an L3 RSRP value (i.e., a third L3-RSRP value). (Average every five samples to obtain one L3-RSRP. Average the last three samples every five samples to obtain one L3-RSRP.)

[0450] 4. Calculate the RSRP difference between the two L3-RSRP values corresponding to the mismatched L1 filter sample numbers in step 3, and plot the CDF curve of the RSRP difference (see Figure 6 Calculate RSRPdifference from two L3-RSRP with sample mismatch and plot CDF curve of RSRPdifference.

[0451] 5. Choose the maximum RSRP difference of 5% and RSRP difference of 95% (the second confidence interval) in the CDF curve as Margin 2.

[0452] In some embodiments, when there is signaling interaction between the UE and the TE, that is, the two determine through signaling interaction that the number of L1 filtering samples used by both are the same, Margin 2 is 0, that is, there is no need to consider the impact of the difference in the number of L1 filtering samples on the L3-RSRP prediction performance verification of the UE.

[0453] Margin3: Margin based on L1 filtering method and L3 filtering coefficient

[0454] In some embodiments, if the TE does not know the L1 filtering method used by the UE, the TE may not know how to calculate the ideal L3-RSRP value.

[0455] In some embodiments, the L1 filtering method adopted by the UE may include non-sliding window filtering and sliding window filtering. The key difference between non-sliding filtering and sliding filtering is that:

[0456] (1) In the L1 / L3 non-sliding window filtering method, an L3 RSRP value is generated based on every 5 L1 RSRP samples (For L1 / L3 filtering non-sliding window, every 5 new L1 samples will generate a new L3 RSRP.).

[0457] (2) In the L1 / L3 sliding window filtering method, an L3 RSRP value is generated based on each L1 RSRP sample (For L1 / L3 filtering sliding window, every 1 new L1 sample will generate a new L3 RSRP.).

[0458] In some embodiments, the L3-RSRP values obtained based on different filtering methods are determined as follows:

[0459] F n =(1-α)·F n-1 +α·M n

[0460] Among them, F n Fn is the updated filtered measurement result used to evaluate reporting conditions or for measurement reporting.

[0461] F n-1 Fn-1 is the previous filtered measurement result (i.e., the previous L3-RSRP value), with the initial value F0 set to the first measurement result M1 received from the physical layer.

[0462] M n Mn is the latest measurement result received from the physical layer.

[0463] α is the filter coefficient corresponding to the L1 filter mode, α = 1 / 2 (k / 4) , where k is the L3 filtering coefficient, i.e., the effect of the previous L3-RSRP value on the current L3-RSRP value. (αis the filtering coefficient, α=1 / 2 (k / 4) ,where k is the filterCoefficient.)

[0464] In some embodiments, it is assumed that α = 0.5. This means that the previous L3-RSRP value will affect the current L3-RSRP value. The weights of the influence of each previous L3-RSRP value on the current L3-RSRP value are 1 / 2, 1 / 4, 1 / 8, 1 / 16, and 1 / 32 respectively. Since the periodicity of the sliding window is less than that of the non-sliding window when determining the L3-RSRP value, see Figure 7 , T1 is less than T2. In the sliding window filtering method, the influence of the previous L3-RSRP value on the current L3-RSRP value will decrease more quickly. Figure 7 As shown in the figure, when considering impact of previous L3-RSRP to be 1 / 16, there are total 8 samples for sliding window and 32 samples for non-sliding window. (Suppose α=0.5.Many previous L3 result will have impact on current L3 results.the coefficient of each L3 result is 1 / 2, 1 / 4, 1 / 8, 1 / 16, 1 / 32.As the L3 result periodicity of sliding window is smaller than that of non-sliding mode.For sliding window mode,the impact of previous L3 RSRP will reduce more quickly,i.e.,T1 is smaller than T2.As shown in the figure,when considering impact of previous RSRP to be 1 / 16,there are total 8 samples for sliding window and 32 samples for non-sliding window.)

[0465] Consequently, the ground truth of L3-RSRP at time T differs between sliding and non-sliding filtering methods.

[0466] RSRP difference = ideal filtered L3-RSRP of non-sliding method at time T - ideal filtered L3-RSRP of sliding method at time T.

[0467] In some embodiments, see Figure 8 , shows the RSRP difference between the L3-RSRP values obtained based on sliding window filtering and non-sliding window filtering under different L3 filtering coefficients, where k = 0 corresponds to the "double dashed line", k = 1 corresponds to the "short dashed line-dot", k = 2 corresponds to the "solid line", and k = 4 corresponds to the "double dotted line". Assuming k = 0, it means: no L3 filtering coefficient is used, that is, the previous L3-RSRP value will not affect the current L3-RSRP value, that is, the L3-RSRP values obtained by the L1 filtering method based on non-sliding window filtering and the L1 filtering method based on sliding window filtering are the same. (If k = 0, no L3 filtering is applied, as analyzed, there is no RSRP difference between sliding and non-sliding mode.)

[0468] In some embodiments, based on RAN2 simulations, k=4 is assumed, then α=0.5. Then, in 90% of cases, the RSRP difference between the L3-RSRP values obtained by the L1 filtering mode based on non-sliding window filtering and the L1 filtering mode based on sliding window filtering can be controlled within 1.8 dB. (In current RAN2 simulations, k=4 is assumed, then α=0.5. Then the L3 RSRP difference between the two modes can be within 1.8 dB in 90% cases.)

[0469] In some embodiments, the effect of α is disregarded, meaning that the L3-RSRP value is equivalent to the measurement value when α = 1, which is equivalent to the value obtained by directly taking the average of the L1-RSRP values. In this case, the ideal L3-RSRP value is independent of the L1 / L3 filtering method. That is, regardless of the L1 / L3 filtering method used, the L3-RSRP value is equivalent to the average of the M instantaneous L1-RSRP values. (An alternative approach is to disregard the L3 filtering factor α, meaning L3-RSRP is equivalent to the measurement at point C with α = 0, similar to L1-RSRP at point A1. In this scenario, the ideal L3-RSRP is independent of the L1 / L3 filtering method. Regardless of the method applied, L3-RSRP equals the average of the minimum L1-RSRP samples.)

[0470] In some embodiments, in combination with the above, the derivation method of the margin based on the difference in L1 filtering methods is as follows:

[0471] 1. Setting channel model, Doppler, and measurement interval. That is, setting third channel measurement information including at least one of the channel model, Doppler, and measurement interval.

[0472] 2. Generate an instant channel (ie, simulate a third channel) based on the third channel measurement information, measure the third channel, and obtain an L1 RSRP value (ie, a third L1-RSRP value) (Generate instant channel RSRP).

[0473] 3. Calculate RSRP difference from two L3-RSRPs from sliding and non-sliding method and plot CDF curve of RSRP difference.

[0474] 4. Choose the maximum RSRP difference of 5% and RSRP difference of 95% (the third confidence interval) in the CDF curve as Margin 2.

[0475] In some embodiments, different Margin 3 values can be set based on different L3 filtering factors. Optionally, when α = 1 or k = 0, no Margin 3 value is required, meaning that the impact of differences in L1 filtering methods on the UE's L3-RSRP prediction performance verification does not need to be considered. (Different margin needs to be added according to different L3 filtering factors. If α = 1 or k = 0, no margin is needed.)

[0476] In some embodiments, when performing L3-RSRP prediction performance verification on a UE, the following methods may be included:

[0477] 1. The User Equipment (UE) measures L3 RSRP based on the observation window (OW), then predicts the L3 RSRP value for the future duration based on the measured L3 RSRP value. The predicted L3-RSRP is reported, and the reported value is denoted as X.

[0478] 2. Assume that the true value of L3-RSRP is assumed to be Y, and the RSRP prediction accuracy requirement is Z dB. The error compensation value based on time offset is Margin1, the error compensation value based on the difference in the number of L1 filter samples is Margin2, and the error compensation value based on the difference in L3 filtering methods is Margin3.

[0479] 3. The UE passes the test if the following condition is satisfied:

[0480] YZ-margin 1-margin 2-margin 3 <X<Y+Z+margin 1+margin 2+margin 3

[0481] 4. When testing the M predicted L3-RSRP values predicted by the UE, if the number of predicted L3-RSRP values corresponding to step 3 above reaches 90% × M, the UE is determined to have passed the L3-RSRP prediction performance test. (If there are a total of M results, the UE passes the test if it meets the condition for 90% of the results.)

[0482] In some embodiments, the names of information, etc. are not limited to the names described in the embodiments, and terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0483] In some embodiments, terms such as "moment", "time point", "time", and "time position" can be replaced with each other, and terms such as "duration", "period", "time window", "window", and "time" can be replaced with each other.

[0484] In some embodiments, terms such as wireless access scheme and waveform may be used interchangeably.

[0485] In some embodiments, terms such as "certain", "preset", "preset", "setting", "indicated", "a certain", "any", and "first" can be interchangeable. "Specific A", "preset A", "preset A", "setting A", "indicated A", "a certain A", "any A", and "first A" can be interpreted as A pre-specified in a protocol, etc., or as A obtained through setting, configuration, or indication, etc., or as specific A, a certain A, any A, or first A, etc., but not limited to this.

[0486] In some embodiments, the determination or judgment can be performed 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.

[0487] In some embodiments, "not expecting to receive" can be interpreted as not receiving on time domain resources and / or frequency domain resources, or as not performing subsequent processing on the data after receiving it; "not expecting to send" can be interpreted as not sending, or as sending but not expecting the recipient to respond to the content sent.

[0488] The communication method involved in the embodiments of the present disclosure may include at least one of the aforementioned steps and embodiments. For example, step 201 can be implemented as an independent embodiment, step 202 can be implemented as an independent embodiment, step 301 can be implemented as an independent embodiment, step 302 can be implemented as an independent embodiment, step 303 can be implemented as an independent embodiment, step 901 can be implemented as an independent embodiment, step 902 can be implemented as an independent embodiment, step 903 can be implemented as an independent embodiment, step 904 can be implemented as an independent embodiment, step 905 can be implemented as an independent embodiment, step 1001 can be implemented as an independent embodiment, step 1002 can be implemented as an independent embodiment, step 1003 can be implemented as an independent embodiment, and step 1004 can be implemented as an independent embodiment; the combination of step 201 and step 202 can be implemented as an independent embodiment, the combination of step 301 and step 302 can be implemented as an independent embodiment, and step 301, step 302 and step 303 can be implemented as an independent embodiment. The combination of step 901 and step 902 can be implemented as an independent embodiment, the combination of step 903 and step 904 can be implemented as an independent embodiment, the combination of step 903, step 904 and step 905 can be implemented as an independent embodiment, the combination of step 901, step 902, step 903, step 904 and step 905 can be implemented as an independent embodiment, the combination of step 1001 and step 1002 can be implemented as an independent embodiment, the combination of step 1001, step 1002 and step 1003 can be implemented as an independent embodiment, the combination of step 1002, step 1003 and step 1004 can be implemented as an independent embodiment, and the combination of step 1001, step 1002, step 1003 and step 1004 can be implemented as an independent embodiment, but is not limited thereto.

[0489] In some embodiments, see Figures 2 to 10 Other optional implementations recorded before or after the corresponding description.

[0490] Figure 11 This is one of the flow charts of the communication method according to the embodiment of the present disclosure.

[0491] like Figure 11 As shown, the above method can be performed by a first device, the first device includes a test device TE or a network device, and the above method includes:

[0492] Step 1101: receiving a predicted L3-RSRP value sent by a UE;

[0493] Step 1102: Determine the accuracy of L3-RSRP prediction performed by the UE based on the L3-RSRP prediction value and at least one RSRP accuracy margin;

[0494] The RSRP accuracy margin includes at least one of the following: a first margin based on time offset, a second margin based on the difference in the number of L1 filtered samples, a third margin based on the difference in L1 filtering methods, and a fourth margin; wherein the fourth margin is obtained based on the time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods.

[0495] In the above embodiment, when determining the accuracy of the UE's L3-RSRP prediction, by considering at least one of the first margin based on the time offset, the second margin based on the difference in the number of L1 filtered samples, the third margin based on the difference in the L1 filtering method, and the fourth margin obtained based on the time offset, the difference in the number of L1 filtered samples, and the difference in the L1 filtering method, it is possible to consider multiple uncertainties that make it impossible to accurately determine whether the UE meets the preset accuracy requirements for the L3-RSRP prediction, thereby improving the accuracy of the evaluation of the UE's L3-RSRP prediction accuracy, thereby achieving better verification of the UE's L3-RSRP prediction performance.

[0496] Optionally, in the embodiment of the present disclosure, determining the accuracy of L3-RSRP prediction performed by the UE according to the L3-RSRP prediction value and at least one RSRP accuracy margin includes:

[0497] Determining an absolute error between the predicted L3-RSRP value and an ideal true L3-RSRP value;

[0498] When the absolute error is less than or equal to the first parameter value, determining that the UE meets a preset accuracy requirement for L3-RSRP prediction;

[0499] The first parameter value includes: the sum of a preset RSRP prediction accuracy threshold and the at least one L3-RSRP accuracy margin.

[0500] Optionally, in the embodiment of the present disclosure, the method further includes:

[0501] Sending first indication information to the UE; wherein the first indication information instructs: the UE to report first information to the first device;

[0502] The first information includes at least one of the following: the time of the L3-RSRP prediction value; the number of L1 filtering samples used by the UE for L3-RSRP prediction; and the L1 filtering method used by the UE for L3-RSRP prediction.

[0503] Optionally, in the embodiment of the present disclosure, the method further includes:

[0504] receiving first information sent by the UE;

[0505] Wherein, at a moment when the first information includes the L3-RSRP prediction value, the at least one RSRP accuracy margin does not include the first margin;

[0506] In a case where the first information includes the number of L1 filtered samples used by the UE to perform L3-RSRP prediction, the at least one RSRP accuracy margin does not include the second margin;

[0507] In a case where the first information includes an L1 filtering mode adopted by the UE for L3-RSRP prediction, the at least one RSRP accuracy margin does not include the third margin.

[0508] Optionally, in the embodiment of the present disclosure, the first margin is obtained based on the following method:

[0509] A first RSRP difference fluctuation curve based on a preset timing offset is generated, and an RSRP difference within a first confidence interval is extracted as the first margin.

[0510] Optionally, in the embodiment of the present disclosure, generating a first RSRP difference fluctuation curve based on a preset timing offset includes:

[0511] Obtain a first L1-RSRP value; wherein the first L1-RSRP value is obtained by measuring the first channel based on the first channel measurement information;

[0512] Filtering a first preset number of adjacent first L1-RSRP values to obtain a first L3-RSRP value;

[0513] The first RSRP difference fluctuation curve is generated according to a first RSRP difference between L3-RSRP values with a first preset timing offset.

[0514] Optionally, in the embodiment of the present disclosure, the second margin is obtained based on the following method:

[0515] A second RSRP difference fluctuation curve based on different numbers of L1 filtered samples is generated, and an RSRP difference within a second confidence interval is extracted as the second margin.

[0516] Optionally, in the embodiment of the present disclosure, generating a second RSRP difference fluctuation curve based on different numbers of L1 filtered samples includes:

[0517] Obtain a second L1-RSRP value; wherein the second L1-RSRP value is obtained by measuring the second channel based on the second channel measurement information;

[0518] filtering a second preset number of adjacent second L1-RSRP values to obtain a second L3-RSRP value; and filtering a third preset number of second L1-RSRP values among the second preset number of adjacent second L1-RSRP values to obtain a third L3-RSRP value; wherein the third preset number is less than the second preset number;

[0519] The second RSRP difference fluctuation curve is generated according to a second RSRP difference between the second L3-RSRP value and a corresponding third L3-RSRP value.

[0520] Optionally, in the embodiment of the present disclosure, the third margin is obtained based on the following method:

[0521] generating a third RSRP difference fluctuation curve based on different L1 filtering modes and L3 filtering coefficients, and extracting an RSRP difference within a third confidence interval as the third margin;

[0522] The L1 filtering method includes non-sliding window filtering and sliding window filtering.

[0523] Optionally, in the embodiment of the present disclosure, generating a third RSRP difference fluctuation curve based on different L1 filtering modes and L3 filtering coefficients includes:

[0524] Obtaining a third L1-RSRP value; wherein the third L1-RSRP value is obtained by measuring the third channel based on the third channel measurement information;

[0525] For a fourth preset number of adjacent third L1-RSRP values, perform non-sliding window filtering on the third L1-RSRP values to obtain a first RSRP value, and filter the first RSRP value according to a preset first L3 filter coefficient to obtain a fourth L3-RSRP value; and perform sliding window filtering on the third L1-RSRP value to obtain a second RSRP value, and filter the second RSRP value according to the first L3 filter coefficient to obtain a fifth L3-RSRP value;

[0526] The third RSRP difference fluctuation curve is generated according to a third RSRP difference between the fourth L3-RSRP value and a corresponding fifth L3-RSRP value.

[0527] Optionally, in the embodiment of the present disclosure, the fourth margin is obtained based on the following method:

[0528] Based on the time offset, the difference in the number of L1 filtering samples, and the difference in the L1 filtering method, a fourth RSRP difference fluctuation curve is generated, and the RSRP difference in a fourth confidence interval is extracted as the fourth margin.

[0529] Optionally, in the embodiment of the present disclosure, generating a fourth RSRP difference fluctuation curve based on the time offset, the difference in the number of L1 filtering samples, and the difference in the L1 filtering mode includes:

[0530] Obtaining a fourth L1-RSRP value; wherein the fourth L1-RSRP value is obtained by measuring the fourth channel based on the fourth channel measurement information;

[0531] For a fifth preset number of adjacent fourth L1-RSRP values, perform non-sliding window filtering on the fourth L1-RSRP values to obtain a third RSRP value, and filter the third RSRP value according to a preset second L3 filter coefficient to obtain a sixth L3-RSRP value; and perform sliding window filtering on a sixth preset number of the fourth L1-RSRP values to obtain a fourth RSRP value, and filter the fourth RSRP value according to the second L3 filter coefficient to obtain a fifth RSRP value, and determine an RSRP difference between the fifth RSRP values having a second preset timing offset to obtain a seventh L3-RSRP value.

[0532] The fourth RSRP difference fluctuation curve is generated according to a fourth RSRP difference between the sixth L3-RSRP value and a corresponding seventh RSRP value.

[0533] The communication method involved in the embodiments of the present disclosure may include at least one of the aforementioned steps and embodiments. For example, step 1101 may be implemented as an independent embodiment, and step 1102 may be implemented as an independent embodiment; the combination of step 1101 and step 1102 may be implemented as an independent embodiment, but is not limited thereto.

[0534] In some embodiments, see Figure 11 Other optional implementations recorded before or after the corresponding description.

[0535] Figure 12 This is a second flow chart of a communication method according to an embodiment of the present disclosure.

[0536] like Figure 12 As shown, the communication method is performed by a user equipment UE, and the method includes:

[0537] Step 1201: Send a predicted L3-RSRP value to a first device.

[0538] The first device includes a TE or a network device; the TE determines the accuracy of the L3-RSRP prediction performed by the UE according to the L3-RSRP prediction value and at least one RSRP accuracy margin;

[0539] The RSRP accuracy margin includes at least one of the following: a first margin based on time offset, a second margin based on the difference in the number of L1 filtered samples, a third margin based on the difference in L1 filtering methods, and a fourth margin; wherein the fourth margin is obtained based on the time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods.

[0540] Optionally, in the embodiment of the present disclosure, determining the accuracy of L3-RSRP prediction performed by the UE according to the L3-RSRP prediction value and at least one RSRP accuracy margin includes:

[0541] Determining an absolute error between the predicted L3-RSRP value and an ideal true L3-RSRP value;

[0542] When the absolute error is less than or equal to the first parameter value, determining that the UE meets a preset accuracy requirement for L3-RSRP prediction;

[0543] The first parameter value includes: the sum of a preset RSRP prediction accuracy threshold and the at least one L3-RSRP accuracy margin.

[0544] Optionally, in the embodiment of the present disclosure, the method further includes:

[0545] receiving first indication information sent by the first device; wherein the first indication information instructs the UE to report first information to the first device;

[0546] The first information includes at least one of the following: the time of the L3-RSRP prediction value; the number of L1 filtering samples used by the UE for L3-RSRP prediction; and the L1 filtering method used by the UE for L3-RSRP prediction.

[0547] Optionally, in the embodiment of the present disclosure, the method further includes:

[0548] sending the first information to the first device;

[0549] Wherein, at a moment when the first information includes the L3-RSRP prediction value, the at least one RSRP accuracy margin does not include the first margin;

[0550] In a case where the first information includes the number of L1 filtered samples used by the UE to perform L3-RSRP prediction, the at least one RSRP accuracy margin does not include the second margin;

[0551] In a case where the first information includes an L1 filtering mode adopted by the UE for L3-RSRP prediction, the at least one RSRP accuracy margin does not include the third margin.

[0552] In the above embodiment, the first device may directly report the first information, or may send the first information to the first device in response to the first indication information sent by the first device.

[0553] Optionally, in the embodiment of the present disclosure, the first margin is obtained based on the following method:

[0554] A first RSRP difference fluctuation curve based on a preset timing offset is generated, and an RSRP difference within a first confidence interval is extracted as the first margin.

[0555] Optionally, in the embodiment of the present disclosure, generating a first RSRP difference fluctuation curve based on a preset timing offset includes:

[0556] Obtain a first L1-RSRP value; wherein the first L1-RSRP value is obtained by measuring the first channel based on the first channel measurement information;

[0557] Filtering a first preset number of adjacent first L1-RSRP values to obtain a first L3-RSRP value;

[0558] The first RSRP difference fluctuation curve is generated according to a first RSRP difference between L3-RSRP values with a first preset timing offset.

[0559] Optionally, in the embodiment of the present disclosure, the second margin is obtained based on the following method:

[0560] A second RSRP difference fluctuation curve based on different numbers of L1 filtered samples is generated, and an RSRP difference within a second confidence interval is extracted as the second margin.

[0561] In conjunction with some embodiments of the second aspect, in some embodiments, generating a second RSRP difference fluctuation curve based on different numbers of L1 filtered samples includes:

[0562] Obtain a second L1-RSRP value; wherein the second L1-RSRP value is obtained by measuring the second channel based on the second channel measurement information;

[0563] filtering a second preset number of adjacent second L1-RSRP values to obtain a second L3-RSRP value; and filtering a third preset number of second L1-RSRP values among the second preset number of adjacent second L1-RSRP values to obtain a third L3-RSRP value; wherein the third preset number is less than the second preset number;

[0564] The second RSRP difference fluctuation curve is generated according to a second RSRP difference between the second L3-RSRP value and a corresponding third L3-RSRP value.

[0565] Optionally, in the embodiment of the present disclosure, the third margin is obtained based on the following method:

[0566] generating a third RSRP difference fluctuation curve based on different L1 filtering modes and L3 filtering coefficients, and extracting an RSRP difference within a third confidence interval as the third margin;

[0567] The L1 filtering method includes non-sliding window filtering and sliding window filtering.

[0568] Optionally, in the embodiment of the present disclosure, generating a third RSRP difference fluctuation curve based on different L1 filtering modes and L3 filtering coefficients includes:

[0569] Obtaining a third L1-RSRP value; wherein the third L1-RSRP value is obtained by measuring the third channel based on the third channel measurement information;

[0570] For a fourth preset number of adjacent third L1-RSRP values, perform non-sliding window filtering on the third L1-RSRP values to obtain a first RSRP value, and filter the first RSRP value according to a preset first L3 filter coefficient to obtain a fourth L3-RSRP value; and perform sliding window filtering on the third L1-RSRP value to obtain a second RSRP value, and filter the second RSRP value according to the first L3 filter coefficient to obtain a fifth L3-RSRP value;

[0571] The third RSRP difference fluctuation curve is generated according to a third RSRP difference between the fourth L3-RSRP value and a corresponding fifth L3-RSRP value.

[0572] Optionally, in the embodiment of the present disclosure, the fourth margin is obtained based on the following method:

[0573] Based on the time offset, the difference in the number of L1 filtering samples, and the difference in the L1 filtering method, a fourth RSRP difference fluctuation curve is generated, and the RSRP difference in a fourth confidence interval is extracted as the fourth margin.

[0574] Optionally, in the embodiment of the present disclosure, generating a fourth RSRP difference fluctuation curve based on the time offset, the difference in the number of L1 filtering samples, and the difference in the L1 filtering mode includes:

[0575] Obtaining a fourth L1-RSRP value; wherein the fourth L1-RSRP value is obtained by measuring the fourth channel based on the fourth channel measurement information;

[0576] For a fifth preset number of adjacent fourth L1-RSRP values, perform non-sliding window filtering on the fourth L1-RSRP values to obtain a third RSRP value, and filter the third RSRP value according to a preset second L3 filter coefficient to obtain a sixth L3-RSRP value; and perform sliding window filtering on a sixth preset number of the fourth L1-RSRP values to obtain a fourth RSRP value, and filter the fourth RSRP value according to the second L3 filter coefficient to obtain a fifth RSRP value, and determine an RSRP difference between the fifth RSRP values having a second preset timing offset to obtain a seventh L3-RSRP value.

[0577] The fourth RSRP difference fluctuation curve is generated according to a fourth RSRP difference between the sixth L3-RSRP value and a corresponding seventh L3-RSRP value.

[0578] The embodiments of the present disclosure further provide an apparatus for implementing any of the above methods. For example, an apparatus is provided, comprising units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another apparatus is provided, comprising units or modules for implementing each step performed by a network device (e.g., an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0579] It should be understood that the division of the various units or modules in the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into a physical entity, or they can be physically separated. In addition, the units or modules in the device can be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the various units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory within the device or a memory outside the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits, and the functions of some or all of the units or modules can be realized by designing the hardware circuits. The above-mentioned hardware circuits can be understood as one or more processors; for example, in one implementation, the above-mentioned hardware circuit is an application-specific integrated circuit (ASIC), which realizes the functions of some or all of the above units or modules by designing the logical relationship of the components in the circuit; for example, in another implementation, the above-mentioned hardware circuit can be realized by a programmable logic device (PLD). Taking a field programmable gate array (FPGA) as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by configuring the configuration file, thereby realizing the functions of some or all of the above units or modules. All units or modules of the above devices can be realized in the form of software called by the processor, or in the form of hardware circuits, or in part by the form of software called by the processor, and the rest by hardware circuits.

[0580] In the embodiments of the present disclosure, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP); in another implementation, the processor can implement certain functions through the logical relationship of a hardware circuit, and the logical relationship of the above-mentioned hardware circuit is fixed or reconfigurable, such as a hardware circuit implemented by a processor as an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document to implement the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc.

[0581] Figure 13 Schematic diagram of the structure of the first device proposed in the embodiment of the present disclosure. Figure 13 As shown, the first device 1300 may include at least one of a transceiver module 1301 , a processing module 1302 , and the like.

[0582] In some embodiments, the above-mentioned transceiver module 1301 is used to receive the L3-RSRP prediction value sent by the UE; the processing module 1302 is used to determine the accuracy of the L3-RSRP prediction performed by the UE based on the L3-RSRP prediction value and at least one RSRP accuracy margin; wherein the RSRP accuracy margin includes at least one of the following: a first margin based on time offset, a second margin based on the difference in the number of L1 filtering samples, a third margin based on the difference in L1 filtering method, and a fourth margin; wherein the fourth margin is obtained based on the time offset, the difference in the number of L1 filtering samples, and the difference in L1 filtering method.

[0583] Optionally, the transceiver module 1301 is configured to execute at least one of the transceiver steps (e.g., step 201, step 301, step 901, step 902, step 903, step 1001, step 1002, and step 1101, but not limited thereto) performed by the first device 101 in any of the above methods, which are not described in detail here. The processing module 1302 is configured to execute at least one of the communication steps (e.g., step 201, step 202, step 302, step 303, step 904, step 905, step 1003, step 1004, and step 1102, but not limited thereto) performed by the first device 101 in any of the above methods, which are not described in detail here.

[0584] Figure 14 Schematic diagram of the structure of the user equipment proposed in the embodiment of the present disclosure. Figure 14 As shown, the user equipment 1400 may include: a transceiver module 1401.

[0585] In some embodiments, the above-mentioned transceiver module 1401 is used to send an L3-RSRP prediction value to a first device; wherein the first device includes a TE or a network device; the TE determines the accuracy of the L3-RSRP prediction performed by the UE based on the L3-RSRP prediction value and at least one RSRP accuracy margin; wherein the RSRP accuracy margin includes at least one of the following: a first margin based on time offset, a second margin based on the difference in the number of L1 filtering samples, a third margin based on the difference in the L1 filtering method, and a fourth margin; wherein the fourth margin is obtained based on the time offset, the difference in the number of L1 filtering samples, and the difference in the L1 filtering method.

[0586] Optionally, the above-mentioned transceiver module 1401 is used to execute at least one of the transceiver steps (for example, step 201, step 301, step 901, step 902, step 903, step 1001, step 1002, step 1201, but not limited to these) performed by the user equipment 102 in any of the above methods, which will not be repeated here.

[0587] Figure 15 This is a schematic diagram of the structure of a terminal 1500 (e.g., user equipment) proposed in an embodiment of the present disclosure. Terminal 1500 can be a chip, chip system, or processor that supports a network device implementing any of the above methods, or a chip, chip system, or processor that supports a terminal implementing any of the above methods. Terminal 1500 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.

[0588] like Figure 15As shown, terminal 1500 includes one or more processors 1501. Processor 1501 can be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control communication devices (such as base stations, baseband chips, terminal devices, terminal device chips, DUs or CUs, etc.), execute programs, and process program data. Terminal 1500 is used to perform any of the above methods.

[0589] In some embodiments, the terminal 1500 further includes one or more memories 1502 for storing instructions. Optionally, all or part of the memories 1502 may be located outside the terminal 1500.

[0590] In some embodiments, the terminal 1500 further includes one or more transceivers 1504. When the terminal 1500 includes one or more transceivers 1504, the transceiver 1504 performs at least one of the communication steps such as sending and / or receiving in the above method (for example, step 201, step 301, step 901, step 902, step 903, step 1001, step 1002, step 1101, step 1201, but not limited thereto), and the processor 1501 performs at least one of the other steps (for example, step 201, step 202, step 302, step 303, step 904, step 905, step 1003, step 1004, step 1102, but not limited thereto).

[0591] In some embodiments, a transceiver may include a receiver and / or a transmitter. The receiver and transmitter may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, and transceiver circuit may be used interchangeably; the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be used interchangeably; and the terms receiver, receiving unit, receiver, and receiving circuit may be used interchangeably.

[0592] In some embodiments, terminal 1500 may include one or more interface circuits 1503. Optionally, interface circuit 1503 is connected to memory 1502. Interface circuit 1503 may be configured to receive signals from memory 1502 or other devices, and may be configured to send signals to memory 1502 or other devices. For example, interface circuit 1503 may read instructions stored in memory 1502 and send the instructions to processor 1501.

[0593] The terminal 1500 described in the above embodiments may be a communication device such as a user equipment, but the scope of the terminal 1500 described in the present disclosure is not limited thereto, and the structure of the terminal 1500 may not be limited thereto. Figure 15The communication device may be an independent device or a part of a larger device. For example, the communication device may be: (1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data and programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0594] Figure 16 1600 is a schematic diagram of the structure of the chip 1600 proposed in the embodiment of the present disclosure. For the case where the terminal 1500 can be a chip or a chip system, please refer to Figure 16 The structure of the chip 1600 is shown, but is not limited thereto.

[0595] The chip 1600 includes one or more processors 1601 , and the chip 1600 is configured to execute any of the above methods.

[0596] In some embodiments, chip 1600 further includes one or more 1603. Optionally, interface circuit 1603 is connected to memory 1602. Interface circuit 1603 can be used to receive signals from memory 1602 or other devices, and interface circuit 1603 can be used to send signals to memory 1602 or other devices. For example, interface circuit 1603 can read instructions stored in memory 1602 and send the instructions to processor 1601.

[0597] In some embodiments, the interface circuit 1603 performs at least one of the communication steps such as sending and / or receiving in the above method (for example, step 201, step 301, step 901, step 902, step 903, step 1001, step 1002, step 1101, step 1201, but not limited to these), and the processor 1601 performs at least one of the other steps (for example, step 201, step 202, step 302, step 303, step 904, step 905, step 1003, step 1004, step 1102, but not limited to these).

[0598] In some embodiments, terms such as interface circuit, interface, transceiver pin, and transceiver may be used interchangeably.

[0599] In some embodiments, chip 1600 also includes one or more memories 1602 for storing instructions. Alternatively, all or part of memory 1602 may be external to chip 1600.

[0600] The present disclosure further provides a storage medium having instructions stored thereon. When the instructions are executed on the terminal 1500, the terminal 1500 executes 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 thereto and may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitor 16 storage medium, but is not limited thereto and may also be a transient storage medium.

[0601] The present disclosure also provides a program product, which, when executed by the terminal 1500, enables the terminal 1500 to perform any of the above methods. Optionally, the program product is a computer program product.

[0602] The present disclosure also proposes a computer program, which, when executed on a computer, causes the computer to perform any one of the above methods.

Claims

1. A communication method, characterized in that: The method is performed by a first device, where the first device includes a test device TE or a network device, and includes: Receiving a layer 3 reference signal received power L3-RSRP prediction value sent by a user equipment UE; Determining an accuracy of L3-RSRP prediction performed by the UE according to the L3-RSRP prediction value and at least one reference signal received power (RSRP) accuracy margin; The RSRP accuracy margin includes at least one of the following: a first margin based on time offset, a second margin based on the difference in the number of layer 1 L1 filtering samples, a third margin based on the difference in L1 filtering methods, and a fourth margin; wherein the fourth margin is obtained based on the time offset, the difference in the number of L1 filtering samples, and the difference in L1 filtering methods.

2. The communication method according to claim 1, wherein: The determining, according to the L3-RSRP prediction value and at least one RSRP accuracy margin, an accuracy of L3-RSRP prediction performed by the UE includes: Determining an absolute error between the predicted L3-RSRP value and an ideal true L3-RSRP value; When the absolute error is less than or equal to the first parameter value, determining that the UE meets a preset accuracy requirement for L3-RSRP prediction; The first parameter value includes: the sum of a preset RSRP prediction accuracy threshold and the at least one L3-RSRP accuracy margin.

3. The communication method according to claim 1 or 2, characterized in that: The method further comprises: Sending first indication information to the UE; wherein the first indication information instructs: the UE to report first information to the first device; The first information includes at least one of the following: the time of the L3-RSRP prediction value; the number of L1 filtering samples used by the UE for L3-RSRP prediction; and the L1 filtering method used by the UE for L3-RSRP prediction.

4. The communication method according to any one of claims 1 to 3, characterized in that: The method further comprises: receiving first information sent by the UE; Wherein, at a moment when the first information includes the L3-RSRP prediction value, the at least one RSRP accuracy margin does not include the first margin; In a case where the first information includes the number of L1 filtered samples used by the UE to perform L3-RSRP prediction, the at least one RSRP accuracy margin does not include the second margin; In a case where the first information includes an L1 filtering mode adopted by the UE for L3-RSRP prediction, the at least one RSRP accuracy margin does not include the third margin.

5. The communication method according to any one of claims 1 to 4, characterized in that: The first margin is obtained based on the following method: A first RSRP difference fluctuation curve based on a preset timing offset is generated, and an RSRP difference within a first confidence interval is extracted as the first margin. The communication method according to claim 5 , wherein: Generating a first RSRP difference fluctuation curve based on a preset timing offset includes: Obtain a first L1-RSRP value; wherein the first L1-RSRP value is obtained by measuring the first channel based on the first channel measurement information; Filtering a first preset number of adjacent first L1-RSRP values to obtain a first L3-RSRP value; The first RSRP difference fluctuation curve is generated according to a first RSRP difference between L3-RSRP values with a first preset timing offset.

7. The communication method according to any one of claims 1 to 6, characterized in that: The second margin is obtained based on the following method: A second RSRP difference fluctuation curve based on different numbers of L1 filtered samples is generated, and an RSRP difference within a second confidence interval is extracted as the second margin.

8. The communication method according to claim 7, wherein: Generating a second RSRP difference fluctuation curve based on different numbers of L1 filtered samples includes: Obtain a second L1-RSRP value; wherein the second L1-RSRP value is obtained by measuring the second channel based on the second channel measurement information; filtering a second preset number of adjacent second L1-RSRP values to obtain a second L3-RSRP value; and filtering a third preset number of second L1-RSRP values among the second preset number of adjacent second L1-RSRP values to obtain a third L3-RSRP value; wherein the third preset number is less than the second preset number; The second RSRP difference fluctuation curve is generated according to a second RSRP difference between the second L3-RSRP value and a corresponding third L3-RSRP value.

9. The communication method according to any one of claims 1 to 8, characterized in that: The third margin is obtained based on the following method: generating a third RSRP difference fluctuation curve based on different L1 filtering modes and L3 filtering coefficients, and extracting an RSRP difference within a third confidence interval as the third margin; The L1 filtering method includes non-sliding window filtering and sliding window filtering.

10. The communication method according to claim 9, wherein: Generating a third RSRP difference fluctuation curve based on different L1 filtering modes and L3 filtering coefficients includes: Obtaining a third L1-RSRP value; wherein the third L1-RSRP value is obtained by measuring the third channel based on the third channel measurement information; For a fourth preset number of adjacent third L1-RSRP values, perform non-sliding window filtering on the third L1-RSRP values to obtain a first RSRP value, and filter the first RSRP value according to a preset first L3 filter coefficient to obtain a fourth L3-RSRP value; and perform sliding window filtering on the third L1-RSRP value to obtain a second RSRP value, and filter the second RSRP value according to the first L3 filter coefficient to obtain a fifth L3-RSRP value; The third RSRP difference fluctuation curve is generated according to a third RSRP difference between the fourth L3-RSRP value and a corresponding fifth L3-RSRP value.

11. The communication method according to any one of claims 1 to 10, characterized in that: The fourth remainder is obtained based on the following method: Based on the time offset, the difference in the number of L1 filtering samples, and the difference in the L1 filtering method, a fourth RSRP difference fluctuation curve is generated, and the RSRP difference in a fourth confidence interval is extracted as the fourth margin.

12. The communication method according to claim 11, wherein: Generating a fourth RSRP difference fluctuation curve based on the time offset, the difference in the number of L1 filtering samples, and the difference in the L1 filtering mode includes: Obtaining a fourth L1-RSRP value; wherein the fourth L1-RSRP value is obtained by measuring the fourth channel based on the fourth channel measurement information; For a fifth preset number of adjacent fourth L1-RSRP values, perform non-sliding window filtering on the fourth L1-RSRP values to obtain a third RSRP value, and filter the third RSRP value according to a preset second L3 filter coefficient to obtain a sixth L3-RSRP value; and perform sliding window filtering on a sixth preset number of the fourth L1-RSRP values to obtain a fourth RSRP value, and filter the fourth RSRP value according to the second L3 filter coefficient to obtain a fifth RSRP value, and determine an RSRP difference between the fifth RSRP values having a second preset timing offset to obtain a seventh L3-RSRP value. The fourth RSRP difference fluctuation curve is generated according to a fourth RSRP difference between the sixth L3-RSRP value and a corresponding seventh L3-RSRP value.

13. A communication method, characterized in that: The method is performed by a user equipment UE, and includes: The predicted L3-RSRP value sent to the first device; The first device includes a TE or a network device; the TE determines the accuracy of the L3-RSRP prediction performed by the UE according to the L3-RSRP prediction value and at least one RSRP accuracy margin; The RSRP accuracy margin includes at least one of the following: a first margin based on time offset, a second margin based on the difference in the number of L1 filtered samples, a third margin based on the difference in L1 filtering methods, and a fourth margin; wherein the fourth margin is obtained based on the time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods.

14. The communication method according to claim 13, wherein: The determining, according to the L3-RSRP prediction value and at least one RSRP accuracy margin, an accuracy of L3-RSRP prediction performed by the UE includes: Determining an absolute error between the predicted L3-RSRP value and an ideal true L3-RSRP value; When the absolute error is less than or equal to the first parameter value, determining that the UE meets a preset accuracy requirement for L3-RSRP prediction; The first parameter value includes: the sum of a preset RSRP prediction accuracy threshold and the at least one L3-RSRP accuracy margin.

15. The communication method according to claim 13 or 14, characterized in that: The method further comprises: receiving first indication information sent by the first device; wherein the first indication information instructs the UE to report first information to the first device; The first information includes at least one of the following: the time of the L3-RSRP prediction value; the number of L1 filtering samples used by the UE for L3-RSRP prediction; and the L1 filtering method used by the UE for L3-RSRP prediction.

16. The communication method according to any one of claims 13 to 15, characterized in that: The method further comprises: sending first information to the first device; Wherein, at a moment when the first information includes the L3-RSRP prediction value, the at least one RSRP accuracy margin does not include the first margin; In a case where the first information includes the number of L1 filtered samples used by the UE to perform L3-RSRP prediction, the at least one RSRP accuracy margin does not include the second margin; In a case where the first information includes an L1 filtering mode adopted by the UE for L3-RSRP prediction, the at least one RSRP accuracy margin does not include the third margin.

17. The communication method according to any one of claims 13 to 16, characterized in that: The first margin is obtained based on the following method: A first RSRP difference fluctuation curve based on a preset timing offset is generated, and an RSRP difference within a first confidence interval is extracted as the first margin.

18. The communication method according to claim 17, wherein: Generating a first RSRP difference fluctuation curve based on a preset timing offset includes: Obtain a first L1-RSRP value; wherein the first L1-RSRP value is obtained by measuring the first channel based on the first channel measurement information; Filtering a first preset number of adjacent first L1-RSRP values to obtain a first L3-RSRP value; The first RSRP difference fluctuation curve is generated according to a first RSRP difference between L3-RSRP values with a first preset timing offset.

19. The communication method according to any one of claims 13 to 18, characterized in that: The second margin is obtained based on the following method: A second RSRP difference fluctuation curve based on different numbers of L1 filtered samples is generated, and an RSRP difference within a second confidence interval is extracted as the second margin.

20. The communication method according to claim 19, wherein: Generating a second RSRP difference fluctuation curve based on different numbers of L1 filtered samples includes: Obtain a second L1-RSRP value; wherein the second L1-RSRP value is obtained by measuring the second channel based on the second channel measurement information; filtering a second preset number of adjacent second L1-RSRP values to obtain a second L3-RSRP value; and filtering a third preset number of second L1-RSRP values among the second preset number of adjacent second L1-RSRP values to obtain a third L3-RSRP value; wherein the third preset number is less than the second preset number; The second RSRP difference fluctuation curve is generated according to a second RSRP difference between the second L3-RSRP value and a corresponding third L3-RSRP value.

21. The communication method according to any one of claims 3 to 20, characterized in that: The third margin is obtained based on the following method: generating a third RSRP difference fluctuation curve based on different L1 filtering modes and L3 filtering coefficients, and extracting an RSRP difference within a third confidence interval as the third margin; The L1 filtering method includes non-sliding window filtering and sliding window filtering.

22. The communication method according to claim 21, wherein: Generating a third RSRP difference fluctuation curve based on different L1 filtering modes and L3 filtering coefficients includes: Obtaining a third L1-RSRP value; wherein the third L1-RSRP value is obtained by measuring the third channel based on the third channel measurement information; For a fourth preset number of adjacent third L1-RSRP values, perform non-sliding window filtering on the third L1-RSRP values to obtain a first RSRP value, and filter the first RSRP value according to a preset first L3 filter coefficient to obtain a fourth L3-RSRP value; and perform sliding window filtering on the third L1-RSRP value to obtain a second RSRP value, and filter the second RSRP value according to the first L3 filter coefficient to obtain a fifth L3-RSRP value; The third RSRP difference fluctuation curve is generated according to a third RSRP difference between the fourth L3-RSRP value and a corresponding fifth L3-RSRP value.

23. The communication method according to any one of claims 13 to 22, characterized in that: The fourth remainder is obtained based on the following method: Based on the time offset, the difference in the number of L1 filtering samples, and the difference in the L1 filtering method, a fourth RSRP difference fluctuation curve is generated, and the RSRP difference in a fourth confidence interval is extracted as the fourth margin.

24. The communication method according to claim 23, wherein: Generating a fourth RSRP difference fluctuation curve based on the time offset, the difference in the number of L1 filtering samples, and the difference in the L1 filtering mode includes: Obtaining a fourth L1-RSRP value; wherein the fourth L1-RSRP value is obtained by measuring the fourth channel based on the fourth channel measurement information; For a fifth preset number of adjacent fourth L1-RSRP values, perform non-sliding window filtering on the fourth L1-RSRP values to obtain a third RSRP value, and filter the third RSRP value according to a preset second L3 filter coefficient to obtain a sixth L3-RSRP value; and perform sliding window filtering on a sixth preset number of the fourth L1-RSRP values to obtain a fourth RSRP value, and filter the fourth RSRP value according to the second L3 filter coefficient to obtain a fifth RSRP value, and determine an RSRP difference between the fifth RSRP values having a second preset timing offset to obtain a seventh L3-RSRP value. The fourth RSRP difference fluctuation curve is generated according to a fourth RSRP difference between the sixth L3-RSRP value and a corresponding seventh L3-RSRP value.

25. A communication device, characterized in that: The communication device is configured to execute the communication method according to any one of claims 1 to 12 or the communication method according to any one of claims 13 to 24.

26. A communication system, characterized in that: Includes a first device and a user equipment UE; wherein the first device includes a TE or a network device, the first device is configured to implement the communication method according to any one of claims 1 to 12, and the UE is configured to implement the communication method according to any one of claims 13 to 24.

27. A storage medium storing instructions, characterized in that: When the instruction is executed on a communication device, the communication device is caused to execute the communication method according to any one of claims 1 to 12 or the communication method according to any one of claims 13 to 24.

28. A program product, comprising at least one of a program and instructions, characterized in that: When at least one of the program and the instruction is executed by a communication device, the communication method according to any one of claims 1 to 12 or the communication method according to any one of claims 13 to 24 is implemented.

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