Communication method, communication device, and communication system

Abstract

Embodiments of the present disclosure relate to a communication method, a communication device, and a communication system. The communication method comprises: a first device receiving an L3-RSRP predicted value sent by a UE; and on the basis of the L3-RSRP predicted value and at least one RSRP accuracy margin, determining the accuracy of L3‑RSRP prediction performed by the UE, wherein the first device includes a testing equipment (TE) or a network device, and the RSRP accuracy margin includes at least one of the following: a first margin based on a time offset, a second margin based on an L1 filtered sample count difference, a third margin based on an L1 filtering mode difference, and a fourth margin, wherein the fourth margin is obtained on the basis of the time offset, the L1 filtered sample count difference, and the L1 filtering mode difference. In this way, an artificial intelligence-based mobility management mechanism is further enhanced.

Description

Communication methods, communication equipment and communication systems Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a communication method, communication device and communication system. Background Technology

[0002] In wireless communication systems, seamless handover between cells is crucial for maintaining high-quality connectivity when users move between 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 timing.

[0003] With the development of artificial intelligence, AI-based mobility management mechanisms can improve the estimation and switching management efficiency of RSRP to some extent. However, the mechanism is not yet mature and needs further improvement. Summary of the Invention

[0004] This disclosure provides a communication method, communication device, and communication system to further enhance the mobility management mechanism based on artificial intelligence.

[0005] On one hand, embodiments of this disclosure provide a communication method, comprising a first device, the first device including testing equipment (TE) or a network device, the method including:

[0006] The predicted value of the layer 3 reference signal received power (L3-RSRP) transmitted by the UE;

[0007] The accuracy of L3-RSRP prediction by the UE is determined based on 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 time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods.

[0009] On the other hand, embodiments of this disclosure provide a communication method executed by a user equipment (UE), the method comprising:

[0010] L3-RSRP prediction values ​​sent to TE;

[0011] 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.

[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 time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods.

[0013] On the other hand, this disclosure also provides a communication device for performing the above-described communication method.

[0014] On the other hand, embodiments of this disclosure also provide a communication device, including:

[0015] One or more processors;

[0016] The communication device is used to execute the above-described communication method.

[0017] On the other hand, embodiments of this disclosure also provide a communication system, including a communication device; wherein the communication device is configured to implement the above-described communication method.

[0018] On the other hand, this disclosure also provides a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform the above-described communication method.

[0019] On the other hand, embodiments of this disclosure also provide a program product, including at least one of a program and instructions, wherein the program and instructions, when executed by a communication device, implement the above-described communication method.

[0020] In this embodiment of the disclosure, when determining the accuracy of L3-RSRP prediction for the UE, by considering 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 based on time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods, multiple uncertainties that may prevent accurate determination of whether the UE meets the preset accuracy requirements for L3-RSRP prediction can be taken into account. This improves 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.

[0021] Additional aspects and advantages of embodiments of this disclosure will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of this disclosure. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings required for the description of the embodiments are introduced below. The following drawings are only some embodiments of this disclosure and do not impose specific limitations on the protection scope of this disclosure.

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

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

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

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

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

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

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

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

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

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

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

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

[0035] Figure 13 is a flowchart illustrating the communication method provided in an embodiment of this disclosure;

[0036] Figure 14 is a schematic diagram of the structure of the test equipment proposed in the embodiment of this disclosure;

[0037] Figure 15 is a schematic diagram of the terminal structure proposed in the embodiment of this disclosure;

[0038] Figure 16 is a schematic diagram of the chip structure proposed in the embodiments of this disclosure. Detailed Implementation

[0039] This disclosure presents a communication method, communication device, and communication system.

[0040] In a first aspect, embodiments of this disclosure provide a communication method executed by a first device, the first device including a test device TE or a network device NW, the method comprising:

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

[0042] The accuracy of L3-RSRP prediction for the UE is determined based on 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 time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods.

[0044] In the above embodiments, when determining the accuracy of L3-RSRP prediction for the UE, by considering 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 based on time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods, multiple uncertainties that may prevent accurate determination of whether the UE meets the preset accuracy requirements for L3-RSRP prediction can be taken into account. This improves 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.

[0045] In conjunction with some embodiments of the first aspect, in some embodiments, determining 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 includes:

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

[0047] If the absolute error is less than or equal to the value of the first parameter, the UE is determined to meet the preset accuracy requirement of L3-RSRP prediction.

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

[0049] In the above embodiments, the absolute error between the L3-RSRP predicted value of the UE and the ideal true value of 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. The first parameter value is used as the evaluation parameter of the preset accuracy requirement of L3-RSRP prediction. Based on the relationship between the absolute error and the first parameter value, the accuracy of L3-RSRP prediction is evaluated. On the basis of the original prediction accuracy, various uncertainties that may prevent accurate determination of whether the UE meets the preset accuracy requirement of L3-RSRP prediction can be considered, thereby improving the evaluation accuracy of L3-RSRP prediction accuracy of the UE and thus 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] Send a first indication message to the UE; wherein the first indication message indicates that the UE reports 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 embodiments, the first device can configure the UE to report first information in order to determine the corresponding L3-RSRP ideal true value based on the information indicated by the first information, so as to avoid multiple uncertain factors from affecting the verification process of determining whether the UE meets the preset accuracy requirements of L3-RSRP prediction.

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

[0055] Receive the first information sent by the UE;

[0056] Wherein, when the first information includes the L3-RSRP prediction value, the first margin is not included in the at least one RSRP accuracy margin;

[0057] When the first information includes the number of L1 filter samples used by the UE for L3-RSRP prediction, the second margin is not included in the at least one RSRP accuracy margin.

[0058] If the first information includes the L1 filtering method used by the UE for L3-RSRP prediction, the third margin is not included in the at least one RSRP accuracy margin.

[0059] In the above embodiments, the first device determines the factors that specifically affect the verification process of determining whether the UE meets the preset accuracy requirements 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:

[0061] Generate a first RSRP difference fluctuation curve based on a preset time offset, and extract the RSRP difference within the first confidence interval as the first margin.

[0062] In the above embodiments, a first RSRP difference fluctuation curve can be generated based on the L3-RSRP prediction value under different preset time offsets. Then, the RSRP difference within the first confidence interval can be extracted from the first RSRP difference fluctuation curve. When evaluating the L3-RSRP prediction performance, the uncertainty factors that may prevent accurate determination of whether the UE meets the preset accuracy requirements of L3-RSRP prediction due to the first margin based on the time offset can be fully considered. This improves the accuracy of the L3-RSRP prediction accuracy evaluation of the UE and achieves 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 time offset includes:

[0064] Obtain the 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] Filter the first preset number of adjacent first L1-RSRP values ​​to obtain the first L3-RSRP value;

[0066] Based on the first RSRP difference between L3-RSRP values ​​with a first preset timing offset, a first RSRP difference fluctuation curve is generated.

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

[0068] A second RSRP difference fluctuation curve is generated based on different L1 filter sample numbers, and the RSRP difference within the second confidence interval is extracted as the second margin.

[0069] In the above embodiments, a second RSRP difference fluctuation curve can be generated based on the L3-RSRP prediction value under different L1 filter sample numbers. Then, the RSRP difference within the second confidence interval can be 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. This can prevent the uncertainty factors that make it impossible to accurately determine whether the UE meets the preset accuracy requirements of L3-RSRP prediction, thereby improving the accuracy of the L3-RSRP prediction accuracy evaluation of the UE and 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 L1 filter sample numbers includes:

[0071] Obtain the 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 ​​yields a second L3-RSRP value; and filtering a third preset number of second L1-RSRP values ​​from the second preset number of adjacent second L1-RSRP values ​​yields 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 based on the second RSRP difference between the second L3-RSRP value and the 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:

[0075] A third RSRP difference fluctuation curve is generated based on different L1 filtering methods and L3 filtering coefficients, and the RSRP difference within the third confidence interval is extracted as the third margin.

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

[0077] In the above embodiments, a third RSRP difference fluctuation curve can be generated based on the L3-RSRP prediction value under different L1 filtering methods and L3 filtering coefficients. 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 methods and L3 filtering coefficients can be fully considered. This can prevent uncertainties that may prevent accurate determination of whether the UE meets the preset accuracy requirements of L3-RSRP prediction, thereby improving the accuracy of the L3-RSRP prediction accuracy evaluation of the UE and 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 methods and L3 filtering coefficients includes:

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

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

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

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

[0083] Based on time offset, L1 filter sample number difference, and L1 filter method difference, a fourth RSRP difference fluctuation curve is generated, and the RSRP difference within the fourth confidence interval is extracted as the fourth margin.

[0084] In the above embodiments, a fourth RSRP difference fluctuation curve can be generated based on the L3-RSRP prediction value under the combined influence of time offset, L1 filter sample number difference, and L1 filter method difference. 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 the combined factors such as time offset, L1 filter sample number difference, and L1 filter method difference can be fully considered. This addresses the uncertainty factors that prevent accurate determination of whether the UE meets the preset accuracy requirements of L3-RSRP prediction, thereby improving the accuracy of the L3-RSRP prediction accuracy evaluation of the UE and achieving better verification of the L3-RSRP prediction performance of the UE.

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

[0086] Obtain the 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, a non-sliding window filter is applied to the fourth L1-RSRP values ​​to obtain a third RSRP value. The third RSRP value is then filtered according to a preset second L3 filter coefficient to obtain a sixth L3-RSRP value. Additionally, a sliding window filter is applied to the sixth preset number of fourth L1-RSRP values ​​to obtain a fourth RSRP value. The fourth RSRP value is then filtered according to the second L3 filter coefficient to obtain a fifth RSRP value. The RSRP difference between the fifth RSRP values ​​with a second preset timing offset is determined to obtain a seventh L3-RSRP value.

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

[0089] Secondly, embodiments of this disclosure also provide a communication method executed by a user equipment (UE), the method comprising:

[0090] The L3-RSRP prediction 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 based on 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 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 the L3-RSRP prediction performed by the UE based on the L3-RSRP prediction value and at least one RSRP accuracy margin includes:

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

[0095] If the absolute error is less than or equal to the value of the first parameter, the UE is determined to meet the preset accuracy requirement of L3-RSRP prediction.

[0096] The first parameter value includes: the sum of the 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] The UE receives a first indication message sent by the first device; wherein the first indication message indicates that the UE reports 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] Send the first information to the first device;

[0102] Wherein, when the first information includes the L3-RSRP prediction value, the first margin is not included in the at least one RSRP accuracy margin;

[0103] When the first information includes the number of L1 filter samples used by the UE for L3-RSRP prediction, the second margin is not included in the at least one RSRP accuracy margin.

[0104] If the first information includes the L1 filtering method used by the UE for L3-RSRP prediction, the third margin is not included in the at least one RSRP accuracy margin.

[0105] In the above embodiments, the first device can directly report the first information, or it can respond to the first instruction information sent by the first device and send the first information to 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:

[0107] Generate a first RSRP difference fluctuation curve based on a preset time offset, and extract the RSRP difference within the first confidence interval as the first margin.

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

[0109] Obtain the 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] Filter the first preset number of adjacent first L1-RSRP values ​​to obtain the first L3-RSRP value;

[0111] Based on the first RSRP difference between L3-RSRP values ​​with a first preset timing offset, a first RSRP difference fluctuation curve is generated.

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

[0113] A second RSRP difference fluctuation curve is generated based on different L1 filter sample numbers, and the RSRP difference within the 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 L1 filter sample numbers includes:

[0115] Obtain the 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] A second preset number of adjacent second L1-RSRP values ​​are filtered to obtain a second L3-RSRP value; and a third preset number of second L1-RSRP values ​​are filtered 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 based on the second RSRP difference between the second L3-RSRP value and the 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:

[0119] A third RSRP difference fluctuation curve is generated based on different L1 filtering methods and L3 filtering coefficients, and the RSRP difference within the third confidence interval is extracted 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 methods and L3 filtering coefficients includes:

[0122] Obtain the 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, a non-sliding window filter is applied to the third L1-RSRP values ​​to obtain a first RSRP value, and the first RSRP value is filtered according to a preset first L3 filter coefficient to obtain a fourth L3-RSRP value; and a sliding window filter is applied to the third L1-RSRP values ​​to obtain a second RSRP value, and the second RSRP value is filtered according to the first L3 filter coefficient to obtain a fifth L3-RSRP value;

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

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

[0126] Based on time offset, L1 filter sample number difference, and L1 filter method difference, a fourth RSRP difference fluctuation curve is generated, and the RSRP difference within the 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 time offset, L1 filter sample number difference, and L1 filter method difference includes:

[0128] Obtain the 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, a non-sliding window filter is applied to the fourth L1-RSRP values ​​to obtain a third RSRP value. The third RSRP value is then filtered according to a preset second L3 filter coefficient to obtain a sixth L3-RSRP value. Additionally, a sliding window filter is applied to the sixth preset number of fourth L1-RSRP values ​​to obtain a fourth RSRP value. The fourth RSRP value is then filtered according to the second L3 filter coefficient to obtain a fifth RSRP value. The RSRP difference between the fifth RSRP values ​​with a second preset timing offset is determined to obtain a seventh L3-RSRP value.

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

[0131] Thirdly, embodiments of this disclosure also provide a communication device, which is used to perform optional implementations of the first aspect or the second aspect.

[0132] Fourthly, embodiments of this disclosure also provide a communication device, including:

[0133] One or more processors;

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

[0135] Fifthly, embodiments of this disclosure also provide a communication system, including a communication device; wherein the communication device is configured as an optional implementation as described in the first aspect.

[0136] In a sixth aspect, embodiments of this disclosure also provide a storage medium storing instructions that, when executed on a communication device, cause the communication device to perform the optional implementation described in the first aspect.

[0137] In a seventh aspect, embodiments of this disclosure provide a program product that, when executed by a communication device, causes the communication device to perform the method described in the optional implementation of the first aspect.

[0138] In an eighth aspect, embodiments of this disclosure provide a computer program that, when run on a computer, causes the computer to perform the method as described in an alternative implementation of the first aspect.

[0139] In a ninth aspect, embodiments of this disclosure provide a chip or chip system. The chip or chip system includes processing circuitry configured to perform the method described according to an optional implementation of the first aspect above.

[0140] It is understood that the aforementioned communication devices, communication systems, storage media, program products, computer programs, chips, or chip systems are all used to execute the methods proposed in the embodiments of this disclosure. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0141] This disclosure provides communication methods, communication devices, and communication systems. In some embodiments, the terms "communication method" and "signal transmission method," "wireless frame transmission method," etc., can be used interchangeably, as can the terms "information processing system" and "communication system."

[0142] This disclosure is not exhaustive, but merely illustrative of some embodiments, and is not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular 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 particular embodiment can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment can be arbitrarily interchanged. Furthermore, the optional implementation methods in a particular embodiment can be arbitrarily combined; moreover, the embodiments can be arbitrarily combined, for example, some or all steps of different embodiments can be arbitrarily combined, and a particular embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0143] In each of the disclosed embodiments, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of the embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0144] The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure.

[0145] In the embodiments disclosed herein, "multiple" refers to two or more.

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

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

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

[0149] The prefixes "first," "second," etc., used in the embodiments of this disclosure are merely for distinguishing different descriptive objects and do not impose restrictions on the position, order, priority, quantity, or content of the descriptive objects. The description of the descriptive objects is found in the claims or the context of the embodiments, and the use of prefixes should not constitute unnecessary restrictions. For example, if the descriptive object is a "field," the ordinal numbers preceding "field" in "first field" and "second field" do not restrict the position or order of the "fields." "First" and "second" do not restrict whether the "fields" they modify are in the same message, nor do they restrict the order of "first field" and "second field." Similarly, if the descriptive object is a "level," the ordinal numbers preceding "level" in "first level" and "second level" do not restrict the priority between "levels." Furthermore, the number of descriptive objects is not limited by ordinal numbers and can be one or more. For example, in "first device," the number of "devices" can be one or more. Furthermore, the objects modified by different prefixes can be the same or different. For example, if the object being described is "device", then "first device" and "second device" can be the same device or different devices, and their types can be the same or different. Similarly, if the object being described is "information", then "first information" and "second information" can be the same information or different information, and their content can be the same or different.

[0150] In some embodiments, “including A,” “containing A,” “for indicating A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0151] In some embodiments, the terms “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “if…”, “if…”, etc., can be used interchangeably.

[0152] In some embodiments, the terms “greater than,” “greater than or equal to,” “not less than,” “more than,” “more than or equal to,” “not less than,” “higher than,” “higher than or equal to,” “not lower than,” and “above” can be used interchangeably, as can the terms “less than,” “less than or equal to,” “not greater than,” “less than,” “less than or equal to,” “not more than,” “lower than,” “lower than or equal to,” “not higher than,” and “below”.

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

[0154] In some embodiments, "network" can be interpreted as devices included in the network, such as access network devices, core network devices, 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," or "fixed station." In some embodiments, it may also be understood as "node," "access point," "transmission point (TP)," "reception point (RP)," "transmission / reception point (TRP)," "panel," "antenna panel," "antenna array," "cell," "macro cell," "small cell," "femto cell," "pico cell," "sector," "cell group," "serving cell," "carrier," "component carrier," or "bandwidth part (BWP)."

[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, access network devices, core network devices, or network devices can be replaced by terminals. For example, embodiments of this disclosure can also be applied to structures where communication between access network devices, core network devices, or network devices and terminals is replaced by communication between multiple terminals (e.g., device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, the structure can also be configured such that the terminal has all or part of the functions of the access network device. Furthermore, terms such as "uplink" and "downlink" can be replaced with terms corresponding to communication between terminals (e.g., "sidelink"). For example, uplink channel, downlink channel, etc., can be replaced with sidelink channel, and uplink link, downlink, etc., can be replaced with sidelink link.

[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, core network device, or network device may also be configured to have all or some of the functions of the terminal.

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

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

[0161] Furthermore, each element, each row, or each column in the table of this disclosure can be implemented as an independent embodiment, and any combination of any element, any row, or any column can also be implemented as an independent embodiment.

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

[0163] As shown in Figure 1, 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 network device.

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

[0166] In some embodiments, the test device may be a standalone device or a functional module configured in a device (e.g., a network device), and this disclosure does not limit this.

[0167] In some embodiments, where 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 can 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 prediction accuracy of the UE meets preset requirements by performing 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 connects a terminal to a wireless network. The access network device may include at least one of the following in a 5G communication system: evolved Node B (eNB), next-generation eNB (ng-eNB), next-generation Node B (gNB), node B (NB), home node B (HNB), home evolved node B (HeNB), radio backhaul device, radio network controller (RNC), base station controller (BSC), base transceiver station (BTS), base band unit (BBU), mobile switching center, base station in a 6G communication system, open RAN, cloud RAN, base station in other communication systems, and access node in a Wi-Fi system, but is not limited thereto.

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

[0171] In some embodiments, the access network device may be composed of a central unit (CU) and a distributed unit (DU). The CU may also be called a control unit. The CU-DU structure can separate the protocol layer of the access network device. Some of the protocol layer functions are centrally controlled by the CU, while the remaining part or all of the protocol layer functions are distributed in the DU and centrally controlled by the CU. However, this is not the only possibility.

[0172] In some embodiments, a core network device may be a single device comprising one or more network elements, or it may be multiple devices or a group of devices. Network elements 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, user equipment 102 may also be referred to as a terminal, such as including but not limited to mobile phones, wearable devices, Internet of Things devices, automobiles with communication capabilities, smart cars, tablets, computers with wireless transceiver capabilities, virtual reality (VR) terminal devices, augmented reality (AR) terminal devices, wireless terminal devices in industrial control, wireless terminal devices in self-driving, wireless terminal devices in remote medical surgery, wireless terminal devices in smart grids, wireless terminal devices in transportation safety, wireless terminal devices in smart cities, and wireless terminal devices in smart homes, but not limited to these.

[0174] It is understood that the communication system described in this disclosure is for the purpose of more clearly illustrating the technical solutions of this disclosure, and does not constitute a limitation on the technical solutions proposed in this disclosure. As those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions proposed in this disclosure are also applicable to similar technical problems.

[0175] The following embodiments of this disclosure can be applied to the communication system 100 shown in FIG1, or to some of the main bodies, but are not limited thereto. The main bodies shown in FIG1 are illustrative. The communication system may include all or some of the main bodies in FIG1, or may include other main bodies outside of FIG1. ​​The number and form of each main body are arbitrary. Each main body may be physical or virtual. The connection relationship between the main bodies is illustrative. The main bodies may not be connected or may be connected. The connection can be in any way, it can be a direct connection or an indirect connection, it can be a wired connection or a wireless connection.

[0176] The embodiments disclosed herein 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), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), and IEEE 802.20, Ultra-Wideband (UWB), Bluetooth (a 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 extended from them, such as the 6th generation mobile communication system (6G). Furthermore, multiple systems can be combined (e.g., a combination of LTE or LTE-A with 5G).

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

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

[0179] In some embodiments, traditional RSRP measurements require frequent and resource-intensive signal strength checks, consuming significant network resources and increasing latency. In other embodiments, AI-based mobility management mechanisms leverage the power of artificial intelligence and machine learning to provide a more efficient, accurate, and adaptive method for maintaining seamless handover between cells. Simultaneously, reduced measurement overhead and improved handover performance not only enhance user satisfaction but also contribute to improving the overall efficiency and scalability of wireless networks. With the continued growth in demand for high-speed, reliable connectivity, AI-driven mobility has become a key solution for modern communication systems.

[0180] In some embodiments, the AI-based mobility management mechanism can minimize the measurement frequency of RSRP by using predictive analytics, such as identifying patterns and trends in signal behavior through AI models. This enables the network to make accurate switching decisions with fewer actual measurements, saving not only the measurement overhead associated with RSRP estimation but also improving overall system efficiency.

[0181] In some embodiments, AI-based mobility management mechanisms can improve the accuracy of RSRP estimation, thereby 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, resulting in 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 devices remain responsive and efficient under various operating conditions.

[0183] In some embodiments, in an AI-based communication system, the UE can predict the channel quality at future moments using AI (Artificial Intelligence) technology and report the predicted channel quality values ​​to the network equipment. In some embodiments, channel quality includes at least one of the following: Layer 3 Reference Signal Received Power (L3-RSRP, where L3 stands for layer 3 and RSRP stands for Reference Signal Received Power), Reference Signal Received Quality (RSRQ), and Signal-to-Interference-plus-Noise Ratio (SINR).

[0184] In some embodiments, the UE can build training samples in advance based on historical channel quality measurements obtained by measuring the channel, and use AI technology to train a channel quality prediction model that can predict channel quality. In this way, the UE can perform channel quality prediction 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 its channel quality measurement based on different channel characteristics can be used as training samples to predict the neural network model, thereby obtaining 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 the instantaneous response characteristics of the channel in the time domain.

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

[0189] Optionally, when the channel characteristics include multipath interference, it can be used to characterize the delay spread and superposition effects of signals after propagating through different paths in the channel.

[0190] Optionally, when the channel characteristics include Doppler, it can be used to characterize the signal frequency shift caused by the relative motion between 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, if the channel characteristics include a channel initialization random seed, a channel impulse response (CIR) conforming to a specific statistical distribution can be generated based on the channel initialization random seed, that is, a repeatable channel simulation environment can be generated.

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

[0193] Optionally, if the channel characteristics include the instantaneous response characteristics of the channel in the time domain, they may specifically include the amplitude and phase values ​​at each time instance.

[0194] Optionally, the channel conditions of a channel may include one or more channel features, and this disclosure does not limit this.

[0195] Step 202: The first device 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.

[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 time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods.

[0197] In some embodiments, to avoid operational errors that might occur when the network device performs subsequent operations based on the channel quality value predicted by the UE due to errors in UE prediction, the predicted channel quality value reported by the UE and the corresponding true channel measurement value can be compared in advance by a first device (e.g., test equipment, TE) to evaluate the performance of the UE in channel quality prediction. If the test equipment determines that the performance of the UE in channel quality prediction passes the evaluation, the network device then performs subsequent operations based on the predicted channel quality value of the UE.

[0198] In some embodiments, during conventional RSRP measurement accuracy testing, the User Equipment (UE) measures a reference signal to obtain the Layer 1 Reference Signal Received Power (L1 RSRP, where L1 stands for layer 1; RSRP stands for Reference Signal Received Power). By filtering multiple L1 RSRP values ​​(e.g., averaging multiple L1 RSRP values), a Layer 3 filtered Reference Signal Received Power (L3 RSRP, where L3 stands for layer 3) is obtained, and this L3 RSRP value is reported to the first device. During RSRP accuracy testing, the first device can obtain the ideal reference signal power transmitted by the base station. Therefore, the first device can directly determine the ground L3 RSRP 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 ground L3-RSRP value, the UE is evaluated to determine whether it meets the required accuracy and passes the test.

[0199] In some embodiments, conventional RSRP measurement accuracy tests are performed by assuming an Additive White Gaussian Noise (AWGN) channel. Since the AWGN channel does not exhibit time-varying characteristics, the number of L1 RSRP values ​​used for filtering (i.e., the number of L1 RSRP filtered samples) when the UE filters the L1 RSRP value to obtain the L3 RSRP value does not affect the final L3 RSRP value. Furthermore, because the channel's RSRP value does not change over time, there is no time offset between the measured RSRP value and the actual RSRP value. Therefore, it is unnecessary to relax the requirements for RSRP measurement accuracy during testing.

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

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

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

[0203] In some embodiments, the margin is the error compensation parameter value that is considered when evaluating the channel quality prediction performance of a UE, taking into account different uncertainties that may lead to the inaccurate evaluation of the channel quality prediction performance of the UE. It can also be referred to as "error compensation value", "margin", etc., and this disclosure does not limit the name.

[0204] In some embodiments, the margin is generated based on a first margin 1 of time offset, which takes into account the time asynchrony between the UE and the first device, resulting in a time offset between the L3-RSRP prediction value determined by the UE and the true L3-RSRP value determined by the first device, thus making it impossible to accurately evaluate the channel quality prediction performance of the UE.

[0205] It should be understood that in practical applications, the time of the UE and the first device may be synchronized or not. The embodiments disclosed herein are intended to avoid situations where the channel quality prediction performance of the UE cannot be accurately evaluated due to the time asynchrony between the UE and the first device, and therefore a first margin is considered.

[0206] In some embodiments, a second margin (Margin 2) is generated based on the difference in the number of L1 filtered samples. This margin addresses situations where the difference between the first and second margins leads to misalignment between the L3-RSRP predicted value determined by the UE and the true L3-RSRP value determined by the first device, thus making it impossible to accurately assess the UE's channel quality prediction performance. The first margin is the number of L1-RSRP values ​​(i.e., the number of L1 filtered samples) used when determining the L3-RSRP predicted value by the UE. The second margin is the number of L1-RSRP values ​​used when determining the true L3-RSRP value by the first device.

[0207] It should be understood that in practical applications, the first number and the second number may be the same or different. The embodiments disclosed herein are intended to avoid situations 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, and therefore a second margin is considered.

[0208] In some embodiments, a third margin (Margin 3) is generated based on the difference in L1 filtering methods. This margin is generated when the difference between the first and second L1 filtering methods leads to misalignment between the L3-RSRP predicted value determined by the UE and the true L3-RSRP value determined by the first device, thus making it impossible to accurately evaluate the UE's channel quality prediction performance. Specifically, the first L1 filtering method refers to the filtering method used when the L3-RSRP predicted value is determined by the UE. The second L1 filtering method refers to the filtering method used when the L3-RSRP true value is determined by the first device.

[0209] It should be understood that in practical applications, the first L1 filtering method and the second L1 filtering method may be the same or different. The embodiments disclosed herein are intended to avoid situations 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, and therefore a third margin is considered.

[0210] In some embodiments, a fourth margin (Margin 4) is generated based on time offset, L1 filter sample number difference, and L1 filter method difference. This margin is generated when time offset, L1 filter sample number difference, and L1 filter method difference lead to misalignment error between the L3-RSRP prediction value determined by the UE and the L3-RSRP true value determined by the first device, thus making it impossible to accurately evaluate the UE's channel quality prediction performance.

[0211] Optionally, in some embodiments, the RSRP difference under each of the above factors can be quantified by simulating specific scenarios, generating corresponding RSRP difference curves. Based on actual needs, a confidence interval corresponding to each factor can be selected, and the RSRP difference within the corresponding confidence interval can be extracted from the corresponding RSRP difference curve as the corresponding margin.

[0212] In the above embodiments, when determining the accuracy of L3-RSRP prediction for the UE, by considering 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 based on time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods, multiple uncertainties that may prevent accurate determination of whether the UE meets the preset accuracy requirements for L3-RSRP prediction can be taken into account. This improves 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.

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

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

[0215] In some embodiments, the UE can build training samples in advance based on historical channel quality measurements obtained by measuring the channel, and use AI technology to train a channel quality prediction model that can predict channel quality. In this way, the UE can perform channel quality prediction 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 its channel quality measurement based on different channel characteristics can be used as training samples to predict the neural network model, thereby obtaining 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, 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, this 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 the absolute error between the L3-RSRP predicted value and the L3-RSRP ideal true value.

[0220] Alternatively, the L3-RSRP ideal truth value can also be called the "L3-RSRP ground truth value", and this disclosure does not limit this.

[0221] In some embodiments, the first device may agree with the UE to define a method for determining the ideal truth value of L3-RSRP. After receiving the L3-RSRP prediction value sent by the UE, the first device can determine the ideal truth value of L3-RSRP corresponding to the L3-RSRP prediction value.

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

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

[0224] 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 time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods.

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

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

[0227] Optionally, the preset RSRP prediction accuracy threshold can be determined according to the actual situation, and this embodiment does not limit it. For example, the RSRP prediction accuracy threshold can be set to a range of 5-6 dBm (decibel-milliwatts), or 2-3 dBm.

[0228] In some embodiments, when the UE and the first device are fully synchronized in time, Margin 1 is 0, meaning that the impact of time offset on the L3-RSRP prediction performance verification of the UE does not need to be considered.

[0229] In some embodiments, when there is signaling interaction between the UE and the first device, i.e., when the two determine through signaling interaction that the number of L1 filtering samples used by the two is 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.

[0230] In some embodiments, the third margin based on the difference in L1 filtering methods may include a third margin determined based on the difference in L1 filtering methods and the L3 filtering coefficients.

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

[0232] In some embodiments, the L1 filtering method employed 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 by the following formula (1): F n = (1-a)·F n-1 +α·M n Formula (1)

[0236] Among them, F n This refers to the updated filtered measurement results (i.e., the latest L3-RSRP value) used for evaluating reporting conditions or for measurement reporting. n-1For the prior filtered measurement result (i.e., the previous L3-RSRP value), the initial value F0 is set to the first measurement result M1 received from the physical layer. n This represents the latest measurement result received from the physical layer. α is the filter coefficient corresponding to the L1 filtering method, α = 1 / 2 (k / 4) , where k is the L3 filter coefficient, that is, the influence of the previous L3-RSRP value on the current L3-RSRP value.

[0237] Optionally, when the filter coefficient α = 1 for the L1 filtering method, or the filter coefficient k = 0 for the L3 filtering method, it is not necessary to set Margin 3. That is, it is not necessary to consider the impact of the difference in L1 filtering method on the L3-RSRP prediction performance verification of the UE.

[0238] In some embodiments, it is assumed that α = 0.5. This means that the prior L3-RSRP value will affect the current L3-RSRP value. The influence weights of each prior 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 prior L3-RSRP value on the current L3-RSRP value decreases more quickly. As shown in Figure 7, when considering that the influence of the prior L3-RSRP value on the current L3-RSRP value is 1 / 16, the sliding window filtering method corresponds to 8 L1 filtered samples, while the non-sliding window filtering method corresponds to 32 L1 filtered samples.

[0239] In summary, at time T, the ground truth values ​​of L3-RSRP obtained by the sliding filter method and the non-sliding filter method are different. Therefore, it is necessary to consider the impact of the difference in L1 filtering method on the evaluation of the UE's channel quality prediction performance.

[0240] Optionally, for each L3-RSRP prediction value, if the absolute error corresponding to the L3-RSRP prediction value is less than or equal to the first parameter value, it can be determined that the UE meets the preset accuracy requirement of L3-RSRP prediction.

[0241] Optionally, to more accurately verify the accuracy of L3-RSRP prediction for the UE, the UE can be instructed to report M L3-RSRP prediction values. If the number of L3-RSRP prediction values ​​with an absolute error less than or equal to the first parameter value reaches 90% × M, then the UE is determined to have passed the L3-RSRP prediction performance test.

[0242] In some embodiments, when the at least one L3-RSRP accuracy margin includes Margin 1, Margin 2, and Margin 3, the preset RSRP prediction accuracy threshold is Z, the L3-RSRP predicted value reported by the UE is X, and the ideal true value of L3-RSRP determined by the first device is Y, it may be determined that the current L3-RSRP predicted value meets the preset accuracy requirement if the following formula (2) or formula (3) is satisfied: Y-Z-(Margin1+Margin2+Margin3)<X<Y+Z+(Margin1+Margin2+Margin3) Formula (2) |X-Y|<|Z+margin 1+margin2+margin3| Formula (3)

[0243] It can be understood that the above formula (2) and formula (3) are equivalent, and whether the current L3-RSRP predicted value meets the preset accuracy requirement can be determined through one of them.

[0244] Optionally, in some embodiments, the first margin is obtained based on the following method:

[0245] Generating a first RSRP difference fluctuation curve based on a preset timing offset, and extracting an RSRP difference within a first confidence interval as the first margin.

[0246] Optionally, the preset timing offset may be set according to actual requirements, which is not limited in the embodiments of the present disclosure. For example, the preset timing offset t=40ms / 80ms / 160ms / 200ms.

[0247] In some embodiments, when determining an L3-RSRP value, filtering may be performed on a preset number of L1-RSRP values to obtain an L3-RSRP difference. Correspondingly, the timing offset between two L3-RSRP values may include the time interval between the time intervals where the preset number of L1-RSRP values corresponding to the two L3-RSRP values are located.

[0248] Optionally, the embodiments of the present disclosure do not limit the specific number of the preset number, which may be specifically determined according to device performance, for example, it may be 3 or 5. Based on different device performances of UEs, some UEs with higher device performance may use fewer L1 RSRP samples (i.e., L1-RSRP values) to determine the L3 RSRP value; for example, 3 L1 RSRP samples may be used for filtering to obtain the L3-RSRP value, which allows the filtered L1 RSRP to be reported to a higher layer (e.g., a network device) more quickly.

[0249] Optionally, when determining the L3-RSRP value, the filtering method used for a preset number of L1-RSRP values ​​may include: taking the average value of the preset number of L1-RSRP values, and further obtaining the corresponding L3-RSRP value (for example, it may be obtained by using the above formula (1)).

[0250] As an example, if 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 time offset between these two L3-RSRP values ​​can be (t3-t1) or (t4-t2).

[0251] In some embodiments, the time corresponding to the L3-RSRP value can be the median of the time interval of the preset number of L1-RSRP values. Taking the time interval of the preset number of L1-RSRP values ​​corresponding to the first L3-RSRP value as (t1-t2) as an example, the time corresponding to the L3-RSRP value can be (t2-t1) / 2.

[0252] Optionally, a first confidence interval can be set according to the actual accuracy test requirements. For example, the first confidence interval may include values ​​between 5% and 95%.

[0253] Optionally, in this embodiment of the disclosure, the first RSRP difference fluctuation curve may include, but is not limited to, a CDF (Cumulative Distribution Function) curve.

[0254] Optionally, a first RSRP difference fluctuation curve based on a preset time offset can be generated based on the difference between multiple sets of L3-RSRP values ​​with the same preset time offset, and the RSRP difference within the first confidence interval can be extracted as the first margin.

[0255] Optionally, in some embodiments, generating a first RSRP difference fluctuation curve based on a preset time offset includes:

[0256] Obtain the first L1-RSRP value; wherein the first L1-RSRP value is obtained by measuring the first channel based on the first channel measurement information;

[0257] Filter the first preset number of adjacent first L1-RSRP values ​​to obtain the first L3-RSRP value;

[0258] Based on the first RSRP difference between L3-RSRP values ​​with a first preset timing offset, a first RSRP difference fluctuation curve is generated.

[0259] In some embodiments, the first channel measurement information may include, but is not limited to, at least one of the following: channel model, Doppler, measurement interval, UE moving speed, etc.

[0260] Optionally, the first channel can be an analog channel (also known as an "instantaneous channel") generated based on the first channel measurement information, or it can be a real channel based on the first channel measurement information. This disclosure does not limit the specific channel.

[0261] Optionally, the first preset number can be determined according to the actual situation, for example, it can include, but is not limited to, 3 or 5.

[0262] For example, if the number of first L1-RSRP values ​​includes 10 and the first preset number includes 5, the number of L3-RSRP values ​​obtained can include 6. Specifically: an L3-RSRP is obtained based on the first to fifth of the 10 first L1-RSRP values, an L3-RSRP is obtained based on the second to sixth of the 10 first L1-RSRP values, an L3-RSRP is obtained based on the third to seventh of the 10 first L1-RSRP values, an L3-RSRP is obtained based on the fourth to eighth of the 10 first L1-RSRP values, an L3-RSRP is obtained based on the fifth to ninth of the 10 first L1-RSRP values, and an L3-RSRP is obtained based on the sixth to tenth of the 10 first L1-RSRP values.

[0263] Optionally, a first preset timing offset can be set according to actual needs, and this embodiment of the present disclosure does not impose any limitations on this. For example, the first preset timing offset t = 40ms / 80ms / 160ms / 200ms.

[0264] Optionally, after determining multiple L3-RSRP values, a first RSRP difference fluctuation curve corresponding to the first preset time offset can be constructed based on the first RSRP difference between two L3-RSRP values ​​with a first preset time 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.

[0265] In other words, in some embodiments, the method for determining the first margin may include:

[0266] 1. Set first channel measurement information including at least one of the following: channel model, Doppler, and measurement interval.

[0267] 2. Generate an instantaneous channel (i.e., simulate the first channel) based on the first channel measurement information described above, measure the first channel, and obtain the L1 RSRP value. Average every 5 L1 RSRP samples to obtain an L3-RSRP value.

[0268] 3. Based on the time interval of the five L1 RSRP samples corresponding to each L3-RSRP value, calculate the RSRP difference between two L3-RSRP values ​​with a time offset T (i.e., the first RSRP difference), and plot the CDF (Cumulative Distribution Function) curve of the RSRP difference (i.e., the first RSRP difference curve).

[0269] 4. Select the difference between the 5% and 95% RSRP differences (i.e., the first confidence interval) in the CDF curve as Margin 1.

[0270] Optionally, in practical applications, a corresponding first margin can be selected based on a first preset timing offset.

[0271] Referring to Figure 5, taking a first preset number of 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, when the first preset time offset t = 40ms, the first L3-RSRP difference corresponds to the CDF curve corresponding to the "double dashed line"; when the first preset time offset t = 80ms, the first L3-RSRP difference corresponds to the CDF curve corresponding to the "short line-dot"; when the first preset time offset t = 160ms, the first L3-RSRP difference corresponds to the CDF curve corresponding to the "solid line"; and when the first preset time offset t = 200ms, the first L3-RSRP difference corresponds to the CDF curve corresponding to the "double dotted line". Based on Figure 5, it can be seen that as the time offset increases, the RSRP difference also increases. In 90% of cases (i.e., between 5% and 95%), when the timing offset is 40 ms, the first RSRP difference can be within 3 dB (±3 dB); when the timing offset increases to 200 ms, the first RSRP difference increases to 5.5 dB (±5.5 dB).

[0272] Optionally, in some embodiments, the second margin is obtained based on the following:

[0273] A second RSRP difference fluctuation curve is generated based on different L1 filter sample numbers, and the RSRP difference within the second confidence interval is extracted as the second margin.

[0274] Optionally, the number of L1-filtered samples is the number of L1-RSRP values ​​required to determine an L3-RSRP value.

[0275] Optionally, the number of L1 filter samples can be set according to actual needs, and this embodiment does not limit this. For example, the number of L1 filter samples can be 3 or 5, and the specific determination method can be found above, and will not be repeated here.

[0276] Alternatively, when determining the second margin, it can be assumed that the RSRP difference is not affected by other factors, such as that the timing offsets corresponding to each L3-RSRP value are the same.

[0277] Optionally, a second confidence interval can be set according to actual accuracy testing requirements. For example, the second confidence interval may include values ​​between 5% and 95%.

[0278] Optionally, in this embodiment of the disclosure, the second RSRP difference fluctuation curve may include, but is not limited to, the CDF curve.

[0279] Optionally, a second RSRP difference fluctuation curve can be generated based on the difference between two L3-RSRP values ​​within multiple combinations of L3-RSRP values ​​with different L1 filter sample numbers, and the RSRP difference within the second confidence interval can be extracted as the second margin.

[0280] Optionally, in some embodiments, generating a second RSRP difference fluctuation curve based on different L1 filter sample numbers includes:

[0281] Obtain the second L1-RSRP value; wherein the second L1-RSRP value is obtained by measuring the second channel based on the second channel measurement information;

[0282] Filtering a second preset number of adjacent second L1-RSRP values ​​yields a second L3-RSRP value; and filtering a third preset number of second L1-RSRP values ​​from the second preset number of adjacent second L1-RSRP values ​​yields a third L3-RSRP value; wherein the third preset number is less than the second preset number.

[0283] The second RSRP difference fluctuation curve is generated based on the second RSRP difference between the second L3-RSRP value and the corresponding third L3-RSRP value.

[0284] In some embodiments, the second channel measurement information may include, but is not limited to, at least one of the following: channel model, Doppler, measurement interval, UE moving speed, etc.

[0285] Optionally, the second channel can be an analog channel (also known as an "instantaneous channel") generated based on the second channel measurement information, or it can be a real channel based on the second channel measurement information. This disclosure does not limit this.

[0286] Optionally, the second and third preset numbers can be determined according to the actual situation. For example, the second preset number can be 5 and the third preset number can be 3.

[0287] Optionally, when determining the third L3-RSRP value, filtering can be performed based on any three of the third preset number of second L1-RSRP values ​​corresponding to the corresponding second L3-RSRP value. For example, filtering can include the last third preset number of second L1-RSRP values ​​among the third preset number of second L1-RSRP values.

[0288] 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.

[0289] Optionally, the corresponding second L3-RSRP value and the third L3-RSRP value can be referred to as a set of L3-RSRP values.

[0290] Optionally, after determining multiple sets of L3-RSRP values, a second RSRP difference fluctuation curve can be constructed based on the second RSRP difference between the multiple sets of L3-RSRP values, and the RSRP difference within the second confidence interval in the second RSRP difference fluctuation curve can be extracted as the second margin.

[0291] In other words, in some embodiments, the method for determining the second margin may include:

[0292] 1. Set up second channel measurement information including at least one of the following: channel model, Doppler, and measurement interval.

[0293] 2. Generate an instantaneous channel (i.e., simulated second channel) based on the second channel measurement information above, measure the second channel, and obtain the L1 RSRP value (i.e., the second L1-RSRP value).

[0294] 3. An L3 RSRP value (i.e., the second L3-RSRP value) is obtained by averaging the five L1 RSRP samples (i.e., L1-RSRP values). An L3 RSRP value (i.e., the third L3-RSRP value) is obtained by averaging the three L1 RSRP samples (i.e., L1-RSRP values) out of the five L1 RSRP samples.

[0295] 4. Calculate the RSRP difference between the two L3-RSRP values ​​that are mismatched in the number of L1 filtered samples in step 3, and plot the CDF curve of the RSRP difference (see Figure 6, i.e., the second RSRP difference curve).

[0296] 5. Select the difference between the 5% and 95% RSRP differences (i.e., the second confidence interval) in the CDF curve as Margin 2.

[0297] Referring to Figure 5, taking a second preset number of 5, a third preset number of 3, a second RSRP difference fluctuation curve as a CDF curve, and a second confidence interval including values ​​between 5% and 95%, using 3 L1 RSRP samples to derive the RSRP value of the L1 filter (i.e., the L3-RSRP value), compared to using 5 L1 RSRP samples to derive the RSRP value of the L1 filter, in 90% of cases, the corresponding RSRP difference is within 3dB. Therefore, when it is necessary to set a second margin based on the difference in the number of L1 filter samples, the second margin can be set to 3dB.

[0298] Optionally, in some embodiments, the third margin is obtained based on the following:

[0299] A third RSRP difference fluctuation curve is generated based on different L1 filtering methods and L3 filtering coefficients, and the RSRP difference within the third confidence interval is extracted as the third margin.

[0300] The L1 filtering method includes non-sliding window filtering and sliding window filtering.

[0301] Optionally, as mentioned above, L1 filtering methods include non-sliding window filtering and sliding window filtering, as shown in Figure 7, which will not be elaborated here.

[0302] Optionally, the L3 filter coefficient, i.e., the influence of the prior L3-RSRP value on the current L3-RSRP value, can be set according to actual needs. The embodiments of this disclosure do not limit this. For example, it can be 1 / 2, 1 / 4, 1 / 8, 1 / 16, 1 / 32, etc.

[0303] Alternatively, when determining the third margin, it can be assumed that the RSRP difference is not affected by other factors, such as the timing offset corresponding to each L3-RSRP value and the number of L1 filter samples used are the same.

[0304] Optionally, a third confidence interval can be set according to actual accuracy testing requirements. For example, the third confidence interval may include values ​​between 5% and 95%.

[0305] Optionally, in this embodiment of the disclosure, the third RSRP difference fluctuation curve may include, but is not limited to, the CDF curve.

[0306] Optionally, a third RSRP difference fluctuation curve can be generated based on the third RSRP difference between two L3-RSRP values ​​within multiple combinations of L3-RSRP values ​​generated by different L1 filtering methods and L3 filtering coefficients, and the RSRP difference within the third confidence interval can be extracted as the third margin.

[0307] Optionally, in some embodiments, generating a third RSRP difference fluctuation curve based on different L1 filtering methods and L3 filtering coefficients includes:

[0308] Obtain the third L1-RSRP value; wherein, the third L1-RSRP value is obtained by measuring the third new channel based on the third channel measurement information;

[0309] For a fourth preset number of adjacent third L1-RSRP values, a non-sliding window filter is applied to the third L1-RSRP values ​​to obtain a first RSRP value, and the first RSRP value is filtered according to a preset first L3 filter coefficient to obtain a fourth L3-RSRP value; and a sliding window filter is applied to the third L1-RSRP values ​​to obtain a second RSRP value, and the second RSRP value is filtered according to the first L3 filter coefficient to obtain a fifth L3-RSRP value;

[0310] The third RSRP difference fluctuation curve is generated based on the third RSRP difference between the fourth L3-RSRP value and the corresponding fifth L3-RSRP value.

[0311] In some embodiments, the third channel measurement information may include, but is not limited to, at least one of the following: channel model, Doppler, measurement interval, UE moving speed, etc.

[0312] Optionally, the third channel can be a simulated channel (also known as an "instantaneous channel") generated based on the third channel measurement information, or it can be a real channel based on the third channel measurement information. This disclosure does not limit the specific channel.

[0313] Optionally, the fourth preset number can be determined according to the actual situation; for example, the fourth preset number can be 5.

[0314] Optionally, for a fourth preset number of adjacent third L1-RSRP values, these third L1-RSRP values ​​can be directly filtered using a non-sliding window to obtain a first RSRP value, and then filtered using the first L3 filtering coefficient to obtain a fourth L3-RSRP value; similarly, these third L1-RSRP values ​​can be directly filtered using a sliding window to obtain a second RSRP value, and then filtered using the first L3 filtering coefficient to obtain a fifth L3-RSRP value.

[0315] 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.

[0316] Optionally, the corresponding fourth L3-RSRP value and the fifth L3-RSRP value can be referred to as a set of L3-RSRP values.

[0317] Optionally, after determining multiple sets of L3-RSRP values, a third RSRP difference fluctuation curve can be constructed based on the third RSRP difference between the multiple sets 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.

[0318] In other words, in some embodiments, the method for determining the third margin may include:

[0319] 1. Set up third channel measurement information including at least one of the following: channel model, Doppler, and measurement interval.

[0320] 2. Generate an instantaneous channel (i.e., simulated third channel) based on the above third channel measurement information, measure the third channel, and obtain the L1 RSRP value (i.e., the third L1-RSRP value).

[0321] 3. Calculate the RSRP difference (i.e., the third RSRP difference) between two L3 RSRP values ​​using sliding and non-sliding methods, and plot the CDF curve of the RSRP difference (i.e., the third RSRP difference curve).

[0322] 4. Select the difference between the 5% and 95% RSRP differences (i.e., the third confidence interval) in the CDF curve as Margin 2.

[0323] In some embodiments, different Margin 3 values ​​can be set based on different L3 filter coefficients. Optionally, when α = 1 or k = 0, Margin 3 does not need to be set, that is, the impact of differences in L1 filtering methods on the L3-RSRP prediction performance verification of the UE does not need to be considered.

[0324] Referring to Figure 8, taking a fourth preset number of 5, the third RSRP difference fluctuation curve as the CDF curve, and the third confidence interval including values ​​between 5% and 95% as an example, the RSRP difference between L3-RSRP values ​​obtained based on sliding window filtering and non-sliding window filtering are shown under different L3 filter coefficients. Among them, 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". Here, we assume k = 0, meaning that no L3 filter coefficients are used, and the previous L3-RSRP value will not affect the current L3-RSRP value. In other words, there is no difference in 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. Assuming k = 4 and α = 0.5, then in 90% of cases, the RSRP difference between 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 can be controlled within 1.8 dB. Assuming the effect of α is ignored, the L3-RSRP value is equivalent to the measurement value when α = 1, which is equivalent to the value obtained by directly using the average of the L1-RSRP values. In this case, the ideal true value of L3 RSRP 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 M instantaneous L1-RSRP values.

[0325] Optionally, in some embodiments, the fourth margin is obtained based on the following:

[0326] Based on time offset, L1 filter sample number difference, and L1 filter method difference, a fourth RSRP difference fluctuation curve is generated, and the RSRP difference within the fourth confidence interval is extracted as the fourth margin.

[0327] In some embodiments, a fourth margin is generated based on time offset, L1 filter sample number difference, and L1 filter method difference. That is, the fourth margin is determined by comprehensively considering the impact of time offset, L1 filter sample number difference, and L1 filter method difference on the evaluation of UE L3-RSRP prediction accuracy.

[0328] Optionally, a fourth confidence interval can be set according to actual accuracy testing requirements. For example, the fourth confidence interval may include values ​​between 5% and 95%.

[0329] Optionally, in this embodiment of the disclosure, the fourth RSRP difference fluctuation curve may include, but is not limited to, the CDF curve.

[0330] Optionally, a fourth RSRP difference fluctuation curve can be generated based on the fourth RSRP difference between two L3-RSRP values ​​within multiple combinations of L3-RSRP values ​​generated based on time offset, L1 filter sample number difference, and L1 filter method difference, and the RSRP difference within the fourth confidence interval can be extracted as the fourth margin.

[0331] Optionally, in some embodiments, generating the fourth RSRP difference fluctuation curve based on time offset, L1 filter sample number difference, and L1 filtering method difference includes:

[0332] Obtain the fourth L1-RSRP value; wherein, the fourth L1-RSRP value is obtained by measuring the fourth channel based on the fourth channel measurement information;

[0333] For a fifth preset number of adjacent fourth L1-RSRP values, a non-sliding window filter is applied to the fourth L1-RSRP values ​​to obtain a third RSRP value. The third RSRP value is then filtered according to a preset second L3 filter coefficient to obtain a sixth L3-RSRP value. Additionally, a sliding window filter is applied to the sixth preset number of fourth L1-RSRP values ​​to obtain a fourth RSRP value. The fourth RSRP value is then filtered according to the second L3 filter coefficient to obtain a fifth RSRP value. The RSRP difference between the fifth RSRP values ​​with a second preset timing offset is determined to obtain a seventh L3-RSRP value.

[0334] The fourth RSRP difference fluctuation curve is generated based on the fourth RSRP difference between the sixth L3-RSRP value and the corresponding seventh RSRP value.

[0335] In some embodiments, the fourth channel measurement information may include, but is not limited to, at least one of the following: channel model, Doppler, measurement interval, UE moving speed, etc.

[0336] Optionally, the fourth channel can be an analog channel (also known as an "instantaneous channel") generated based on the fourth channel measurement information, or it can be a real channel based on the fourth channel measurement information. This disclosure does not limit the specific channel.

[0337] Optionally, the fifth preset number and the sixth preset number can be determined according to the actual situation. For example, the fifth preset number can be 5 and the sixth preset number can be 3.

[0338] Optionally, for a fifth preset number of adjacent fourth L1-RSRP values, these fourth L1-RSRP values ​​can be directly filtered using a non-sliding window method to obtain a third RSRP value. Then, the third RSRP value is filtered using the second L3 filtering coefficient to obtain a sixth L3-RSRP value. Alternatively, these fourth L1-RSRP values ​​can be directly filtered using a sliding window method to obtain a fourth RSRP value. Then, the fourth RSRP value is filtered using the second L3 filtering coefficient to obtain a fifth RSRP value. Furthermore, the RSRP difference between the fifth RSRP values ​​with a second preset timing offset is determined to obtain a seventh L3-RSRP value.

[0339] Optionally, a second preset timing offset can be set according to actual needs, and this embodiment of the present disclosure does not limit this. For example, the second preset timing offset t = 40ms / 80ms / 160ms / 200ms.

[0340] Optionally, the L3 filter coefficient, i.e., the influence of the prior L3-RSRP value on the current L3-RSRP value, can be set with parameter values ​​of the second L3 filter coefficient according to actual needs. This embodiment of the present disclosure does not limit this. For example, it can be 1 / 2, 1 / 4, 1 / 8, 1 / 16, 1 / 32, etc.

[0341] 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.

[0342] Optionally, the corresponding sixth L3-RSRP value and the seventh L3-RSRP value can be referred to as a set of L3-RSRP values.

[0343] Optionally, after determining multiple sets of L3-RSRP values, a fourth RSRP difference fluctuation curve can be constructed based on the fourth RSRP difference between the multiple sets 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.

[0344] It is understood that in the embodiments of this disclosure, the first channel measurement information, the second channel measurement information, the third channel measurement information and the fourth channel measurement information may be all or partly the same, or all of them may be different, and the embodiments of this disclosure do not limit this.

[0345] It is understood that in the embodiments of this disclosure, the first channel, the second channel, the third channel and the fourth channel may be all or part of the same, or all of them may be different, and the embodiments of this disclosure do not limit this.

[0346] It is understood that in the embodiments of this disclosure, the first confidence interval, the second confidence interval, the third confidence interval and the fourth confidence interval may be all or partly the same, or all of them may be different, and the embodiments of this disclosure do not limit this.

[0347] In some embodiments, referring to FIG9, the above communication method may include:

[0348] Step 901, the first device sends first indication information to the UE; wherein, the first indication information indicates that the UE reports first information to the first device;

[0349] 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.

[0350] Step 902: The UE sends the first information to the first device.

[0351] Step 903: The UE sends the L3-RSRP prediction value to the first device. Correspondingly, the first device receives the L3-RSRP prediction value sent by the UE.

[0352] In some embodiments, the UE can build training samples in advance based on historical channel quality measurements obtained from its channel measurements, and use AI technology to train a channel quality prediction model that can perform channel quality prediction. In this way, the UE can perform channel quality prediction through the channel quality prediction model.

[0353] 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 its channel quality measurement based on different channel characteristics can be used as training samples to predict the neural network model, thereby obtaining a trained channel quality prediction model.

[0354] 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, 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.

[0355] Optionally, this 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.

[0356] Step 904: The first device determines the absolute error between the L3-RSRP predicted value and the L3-RSRP ideal true value.

[0357] Optionally, the L3-RSRP ideal truth value can also be called the "L3-RSRP ground truth value", and this disclosure does not limit this.

[0358] In some embodiments, the first device may agree with the UE to define a method for determining the ideal truth value of L3-RSRP. After receiving the L3-RSRP prediction value sent by the UE, the first device can determine the ideal truth value of L3-RSRP corresponding to the L3-RSRP prediction value.

[0359] Optionally, the absolute error between the L3-RSRP predicted value and the L3-RSRP ideal true value may include the absolute value of the difference between the L3-RSRP predicted value and the L3-RSRP ideal true value.

[0360] Step 905: If the absolute error is less than or equal to the first parameter value, the first device determines that the UE meets the preset accuracy requirement of L3-RSRP prediction.

[0361] 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 time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods.

[0362] The first parameter value includes: the sum of the preset RSRP prediction accuracy threshold and the at least one L3-RSRP accuracy margin.

[0363] As mentioned above, the corresponding L3-RSRP accuracy margin varies under different conditions. After determining the L3-RSRP accuracy margin based on actual conditions, at least one determined L3-RSRP accuracy margin can be added to the preset RSRP prediction accuracy threshold to obtain the first parameter value. Furthermore, based on the relationship between the absolute error and the first parameter value, it can be determined whether the UE meets the preset accuracy requirements of L3-RSRP prediction.

[0364] Optionally, the preset RSRP prediction accuracy threshold can be determined according to actual conditions, and this embodiment does not impose any restrictions on it. For example, the RSRP prediction accuracy threshold can be set to a range of 5-6 dBm, or 2-3 dBm.

[0365] In some embodiments, when the UE and the first device are fully synchronized in time, Margin 1 is 0, meaning that the impact of time offset on the L3-RSRP prediction performance verification of the UE does not need to be considered.

[0366] In some embodiments, when there is signaling interaction between the UE and the first device, i.e., when the two determine through signaling interaction that the number of L1 filtering samples used by the two is 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.

[0367] In some embodiments, the third margin based on the difference in L1 filtering methods may include a third margin determined based on the difference in L1 filtering methods and the L3 filtering coefficients.

[0368] In some embodiments, when the UE and the first device determine the L3-RSRP predicted value and the L3-RSRP ideal true value based on the same L1 filtering method difference and L3 filtering coefficient, respectively, Margin 3 is 0, that is, the impact of the L1 filtering method difference on the L3-RSRP prediction performance verification of the UE does not need to be considered.

[0369] Optionally, for each L3-RSRP prediction value, if the absolute error corresponding to the L3-RSRP prediction value is less than or equal to the first parameter value, it can be determined that the UE meets the preset accuracy requirement of L3-RSRP prediction.

[0370] Optionally, when TE determines the RSRP accuracy margin, it can be determined in conjunction with the information carried in the first information, specifically:

[0371] When the first information includes the L3-RSRP prediction value, the first margin is not included in the at least one RSRP accuracy margin;

[0372] When the first information includes the number of L1 filter samples used by the UE for L3-RSRP prediction, the second margin is not included in the at least one RSRP accuracy margin.

[0373] If the first information includes the L1 filtering method used by the UE for L3-RSRP prediction, the third margin is not included in the at least one RSRP accuracy margin.

[0374] Optionally, when the first information includes the time of the L3-RSRP prediction value, the TE can determine the L3-RSRP ideal true value corresponding to the L3-RSRP prediction value and its corresponding time. That is, the time synchronization between 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 impact of time offset on the L3-RSRP prediction performance verification of the UE.

[0375] Optionally, if the first information includes the number of L1 filtered samples used by the UE for L3-RSRP prediction, the TE can determine that the same number of L1 filtered samples is used to determine the corresponding L3-RSRP ideal true value. That is, each L3-RSRP prediction value can be made to have the same number of L1 filtered samples as the corresponding L3-RSRP ideal true value. In this case, Margin 2 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 impact of the difference in the number of L1 filtered samples on the performance verification of L3-RSRP prediction by the UE.

[0376] Optionally, if the first information includes the L1 filtering method used by the UE for L3-RSRP prediction, the TE can determine that the same L1 filtering method is used and determine the corresponding L3-RSRP ideal true value. That is, each L3-RSRP prediction value can be made to use the same L1 filtering method as the corresponding L3-RSRP ideal true value. In this case, 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 impact of the difference in L1 filtering method on the performance verification of L3-RSRP prediction of the UE.

[0377] Optionally, to more accurately verify the accuracy of L3-RSRP prediction for the UE, the UE can be instructed to report M L3-RSRP prediction values. If the number of L3-RSRP prediction values ​​with an absolute error less than or equal to the first parameter value reaches 90% × M, then the UE is determined to have passed the L3-RSRP prediction performance test.

[0378] In some embodiments, with at least one L3-RSRP accuracy margin including Margin 1, Margin 2, and Margin 3, a preset RSRP prediction accuracy threshold including Z, an L3-RSRP prediction value reported by the UE being X, and an ideal true value of L3-RSRP determined by the first device being Y, the current L3-RSRP prediction value can be determined to meet the preset accuracy requirements if the above formula (2) or formula (3) is satisfied.

[0379] In some embodiments, the methods for determining the first, second, third, and fourth margins can be found in the above description and will not be repeated here.

[0380] In some embodiments, referring to FIG10, the above communication method may include:

[0381] 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 to perform L3-RSRP prediction; and the L1 filtering method used by the UE to perform L3-RSRP prediction.

[0382] Step 1002: The UE sends the L3-RSRP prediction value to the first device. Correspondingly, the first device receives the L3-RSRP prediction value sent by the UE.

[0383] In some embodiments, the UE can build training samples in advance based on historical channel quality measurements obtained from its channel measurements, and use AI technology to train a channel quality prediction model that can perform channel quality prediction. In this way, the UE can perform channel quality prediction through the channel quality prediction model.

[0384] 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 its channel quality measurement based on different channel characteristics can be used as training samples to predict the neural network model, thereby obtaining a trained channel quality prediction model.

[0385] 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, 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.

[0386] Optionally, this 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.

[0387] Step 1003: The first device determines the absolute error between the L3-RSRP predicted value and the L3-RSRP ideal true value.

[0388] Optionally, the L3-RSRP ideal truth value can also be called the "L3-RSRP ground truth value", and this disclosure does not limit this.

[0389] In some embodiments, the first device may agree with the UE to define a method for determining the ideal truth value of L3-RSRP. After receiving the L3-RSRP prediction value sent by the UE, the first device can determine the ideal truth value of L3-RSRP corresponding to the L3-RSRP prediction value.

[0390] Optionally, the absolute error between the L3-RSRP predicted value and the L3-RSRP ideal true value may include the absolute value of the difference between the L3-RSRP predicted value and the L3-RSRP ideal true value.

[0391] Step 1004: If the absolute error is less than or equal to the first parameter value, the first device determines that the UE meets the preset accuracy requirement of L3-RSRP prediction.

[0392] 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 time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods.

[0393] The first parameter value includes: the sum of the preset RSRP prediction accuracy threshold and the at least one L3-RSRP accuracy margin.

[0394] As mentioned above, the corresponding L3-RSRP accuracy margin varies under different conditions. After determining the L3-RSRP accuracy margin based on actual conditions, at least one determined L3-RSRP accuracy margin can be added to the preset RSRP prediction accuracy threshold to obtain the first parameter value. Furthermore, based on the relationship between the absolute error and the first parameter value, it can be determined whether the UE meets the preset accuracy requirements of L3-RSRP prediction.

[0395] Optionally, the preset RSRP prediction accuracy threshold can be determined according to actual conditions, and this embodiment does not impose any restrictions on it. For example, the RSRP prediction accuracy threshold can be set to a range of 5-6 dBm, or 2-3 dBm.

[0396] In some embodiments, when the UE and the first device are fully synchronized in time, Margin 1 is 0, meaning that the impact of time offset on the L3-RSRP prediction performance verification of the UE does not need to be considered.

[0397] In some embodiments, when there is signaling interaction between the UE and the first device, i.e., when the two determine through signaling interaction that the number of L1 filtering samples used by the two is 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.

[0398] In some embodiments, the third margin based on the difference in L1 filtering methods may include a third margin determined based on the difference in L1 filtering methods and the L3 filtering coefficients.

[0399] In some embodiments, when the UE and the first device determine the L3-RSRP predicted value and the L3-RSRP ideal true value based on the same L1 filtering method difference and L3 filtering coefficient, respectively, Margin 3 is 0, that is, the impact of the L1 filtering method difference on the L3-RSRP prediction performance verification of the UE does not need to be considered.

[0400] Optionally, for each L3-RSRP prediction value, if the absolute error corresponding to the L3-RSRP prediction value is less than or equal to the first parameter value, it can be determined that the UE meets the preset accuracy requirement of L3-RSRP prediction.

[0401] Optionally, when TE determines the RSRP accuracy margin, it can be determined in conjunction with the information carried in the first information, specifically:

[0402] When the first information includes the L3-RSRP prediction value, the first margin is not included in the at least one RSRP accuracy margin;

[0403] When the first information includes the number of L1 filter samples used by the UE for L3-RSRP prediction, the second margin is not included in the at least one RSRP accuracy margin.

[0404] If the first information includes the L1 filtering method used by the UE for L3-RSRP prediction, the third margin is not included in the at least one RSRP accuracy margin.

[0405] Optionally, when the first information includes the time of the L3-RSRP prediction value, the TE can determine the L3-RSRP ideal true value corresponding to the L3-RSRP prediction value and its corresponding time. That is, the time synchronization between 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 impact of time offset on the L3-RSRP prediction performance verification of the UE.

[0406] Optionally, if the first information includes the number of L1 filtered samples used by the UE for L3-RSRP prediction, the TE can determine that the same number of L1 filtered samples is used to determine the corresponding L3-RSRP ideal true value. That is, each L3-RSRP prediction value can be made to have the same number of L1 filtered samples as the corresponding L3-RSRP ideal true value. In this case, Margin 2 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 impact of the difference in the number of L1 filtered samples on the performance verification of L3-RSRP prediction by the UE.

[0407] Optionally, if the first information includes the L1 filtering method used by the UE for L3-RSRP prediction, the TE can determine that the same L1 filtering method is used and determine the corresponding L3-RSRP ideal true value. That is, each L3-RSRP prediction value can be made to use the same L1 filtering method as the corresponding L3-RSRP ideal true value. In this case, 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 impact of the difference in L1 filtering method on the performance verification of L3-RSRP prediction of the UE.

[0408] Optionally, to more accurately verify the accuracy of L3-RSRP prediction for the UE, the UE can be instructed to report M L3-RSRP prediction values. If the number of L3-RSRP prediction values ​​with an absolute error less than or equal to the first parameter value reaches 90% × M, then the UE is determined to have passed the L3-RSRP prediction performance test.

[0409] In some embodiments, with at least one L3-RSRP accuracy margin including Margin 1, Margin 2, and Margin 3, a preset RSRP prediction accuracy threshold including Z, an L3-RSRP prediction value reported by the UE being X, and an ideal true value of L3-RSRP determined by the first device being Y, the current L3-RSRP prediction value can be determined to meet the preset accuracy requirements if the above formula (2) or formula (3) is satisfied.

[0410] In some embodiments, the methods for determining the first, second, third, and fourth margins can be found in the above description and will not be repeated here.

[0411] In some embodiments, this disclosure also defines a method for RSRP accuracy margin based on AI mobility, which ensures more accurate RSRP prediction by taking into account various uncertainties (e.g., timing mismatch, number of L1 filters and L1 filtering method), thereby achieving better performance verification.

[0412] Specifically, the above method may include the following stages:

[0413] A. Calculate the absolute error between the L3-RSRP predicted value based on the AI ​​prediction model and the ideal true value of L3-RSRP:

[0414] Absolute L3 Predicted RSRP Accuracy = XY, that is, L3-RSRP prediction absolute error = XY;

[0415] 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.

[0416] B.TE defines three margins:

[0417] Margin1: Error compensation value based on time offset. Specifically, the largest RSRP difference can be extracted as the error compensation value by simulating the RSRP difference fluctuation curves (i.e., RSRP difference distribution) under different UE speeds or time offsets, and the corresponding 5% and 95% quantiles (i.e., the first confidence interval is 5% to 95%).

[0418] Margin2: Error compensation value based on the difference in the number of L1 filtered samples. Specifically, by comparing the RSRP difference fluctuation curves under different numbers of L1 filtered samples (e.g., 3 or 5 L1 filtered samples), the largest RSRP difference between the corresponding 5% and 95th quantiles (i.e., the second confidence interval is from 5% to 95%) is extracted as the error compensation value.

[0419] 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 largest RSRP difference between the corresponding 5% and 95th quantiles (i.e., the third confidence interval is from 5% to 95%) is extracted as the error compensation value.

[0420] C. Combining at least one of the above margins, the final RSRP accuracy requirement is formed:

[0421] YZ-(Margin1+Margin2+Margin3) <X<Y+Z+(Margin1+Margin2+Margin3)

[0422] Where X is the L3-RSRP predicted value reported by the UE, Y is the ideal true value of L3-RSRP determined by the TE, and Z is the preset RSRP prediction accuracy threshold (for example, the value of Z can be 5-6 dBm, or the value of Z can be 2-3 dBm). Some or all of Margin 1, Margin 2, and Margin 3 can be selected according to actual needs.

[0423] Optionally, in some embodiments, when considering error compensation values ​​based on time offset, error compensation values ​​based on L1 filter sample number differences, and error compensation values ​​based on L3 filter method differences, the synthesized error compensation value may not be equal to the simple sum of the corresponding margins. It is necessary to determine the synthesized error compensation value based on the RSRP difference fluctuation curve obtained from the re-simulation.

[0424] Alternatively, the method for evaluating whether the UE's RSRP prediction performance meets the final RSRP accuracy requirements can be:

[0425] Absolute L3 Predicted RSRP Accuracy = Reported predicted L3-RSRP – Ground truth of L3-RSRP + Margin 1 + Margin 2 + Margin 3, |xy| < |z + margin 1 + margin 2 + margin 3| [RSRP accuracy requirements listed in step C above]

[0426] Margin 1 represents a possible timing mismatch between the predicted L3-RSRP and the ground truth L3-RSRP. Margin 2 represents a possible mismatch due to the number of L1 filtering samples. Margin 3 represents a possible mismatch due to L3 filtering. In other words, margin 1 results in a timing mismatch (i.e., no timing mismatch) between the predicted L3-RSRP (the L3-RSRP reported by the UE) and the ground truth L3-RSRP (the ideal L3-RSRP determined by the TE). Margin 2 may be due to a mismatch in the number of L1 filtering samples. Margin 3 may be due to a mismatch in the L3 filtering method. The specific meanings of margins 1, 2, and 3 are explained above and will not be repeated here.

[0427] Alternatively, the margin 1, margin 2, and margin 3 can be determined as follows (Next, we will demonstrate the analysis and the method to determine the margin):

[0428] Margin 1:

[0429] In some embodiments, to evaluate the accuracy of RSRP prediction, the time instance between the predicted L3 RSRP and the ground truth L3 RSRP needs to be aligned. Traditionally, it is assumed that the measured RSRP value is unaffected under AWGN (Additive White Gaussian Noise) channels. However, for fading channels, timing offsets may exist (see Figure 4), thus requiring timing adjustment. This can be addressed by analyzing the RSRP difference caused by timing mismatch.

[0430] In some embodiments, when determining Margin 1, RAN2 simulation can be used, assuming a UE speed of 30 km / h and an RS (Reference Signal) period of 40 ms. For simplicity, a TDL-C channel (Tapped Delay Line Channel) is used.

[0431] In some embodiments, with timing offsets t (also referred to as "timing mismatch values") = 40ms / 80ms / 160ms / 200ms, the difference curves between two sets of ideal L3 RSRP values ​​can be plotted. Each L3 RSRP value is obtained by averaging five L1 RSRP value samples. Figure 5 shows the L3-RSRP difference at different timing offsets based on the L3-RSRP values ​​obtained from five L1 samples. Here, t = 40ms corresponds to the CDF curve corresponding to the "double dashed line," t = 80ms corresponds to the CDF curve corresponding to the "short line-dot," t = 160ms corresponds to the CDF curve corresponding to the "solid line," and t = 200ms corresponds to the CDF curve corresponding to the "double dotted line." It can be seen that the RSRP difference increases with the increase of the 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 time mismatch=40ms / 80ms / 160ms / 200ms. Each L3 RSRP is averaged by 5 L1RSRP samples. As shown below, when time gap increases, RSRP difference increases. When timing mismatch is 40ms, RSRP difference is within 3dB for 90% cases. When timing mismatch increases to 200ms, RSRP difference increases to 5.5dB.)

[0432] In conjunction with the above, in some embodiments, considering the relationship between timing offset and RSRP difference (i.e., the larger the timing offset, the larger the required timing offset margin), different timing offset margins need to be considered for different timing offsets. For example, in T... mismatch With a timing offset of 40ms (i.e., a timing offset of 40ms), the corresponding timing offset margin is set to 3dB. And for T... mismatch=200ms (i.e., timing offset is 200ms), set the corresponding timing offset margin to 5.5dB. (Therefore, for different timing mismatches, different margins need to 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 the margin is needed.)

[0433] In some embodiments, in conjunction with the above, the method to derive margin for timing mismatch is as follows:

[0434] 1. Setting channel model, Doppler, and measurement interval. That is, setting the first channel measurement information, including at least one of the following: channel model, Doppler, and measurement interval.

[0435] 2. Based on the first channel measurement information described above, generate an instant channel (i.e., simulate the first channel), measure the first channel, and obtain the L1 RSRP value. Averaging every 5 L1 RSRP samples (i.e., L1-RSRP values) yields an L3-RSRP value (i.e., the first L3-RSRP value).

[0436] 3. Based on the time intervals 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 time offset T, and plot the CDF (Cumulative Distribution Function) curve of the RSRP difference (i.e., the first RSRP difference curve).

[0437] 4. Select the difference between 5% and 95% of the RSRP difference (i.e., the first confidence interval) in the CDF curve as the margin 1.

[0438] In some embodiments, when the UE and TE are fully synchronized, Margin 1 is 0, meaning that the impact of time offset on the L3-RSRP prediction performance verification of the UE does not need to be considered.

[0439] Margin 2:

[0440] In some embodiments, L1 filtering may include averaging a number of L1 RSRP samples to obtain an L3 RSRP value within a certain measurement period; wherein the number of L1 RSRP samples used may be five. Depending on the device performance of the UE, some UEs with higher device performance may use fewer L1 RSRP samples to determine the L3 RSRP value; for example, three L1 RSRP samples may be used for filtering to obtain the L3-RSRP value, thus allowing for faster reporting of the filtered L1 RSRP to higher layers (e.g., network devices). That is, the predicted L3-RSRP value may be obtained by further calculation based on the average of three L1 RSRP samples, rather than by further calculation based on the average of five 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 UE can 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, the predicted L3-RSRP will also be calculated based on average of 3L1 RSRP samples but not 5 samples.)

[0441] In some embodiments, 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. This, of course, imposes significant limitations on the UE's implementation. Furthermore, only when both the UE and TE use the same number of L1-RSRP samples (e.g., 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) resulting from the mismatch in the number of L1 filters used by the UE and TE (i.e., the difference in the number of L1 filtered 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 generate ideal 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.)

[0442] In some embodiments, referring to Figure 6, using 3 L1 RSRP samples to derive the L1 filtered RSRP value (i.e., the L3-RSRP value), compared to using 5 L1 RSRP samples, the corresponding RSRP difference is within 3dB in 90% of cases. Therefore, when a second margin based on the difference in the number of L1 filtered samples needs to be set, the second margin can be set to 3dB. (If 3 samples are used to derive L1 filtered RSRP, the RSRP delta will be within 3dB for 90% of cases when compared with that case where 5 samples are used. Therefore, 3dB needs to be added for L1 filtering number mismatch.)

[0443] In some embodiments, in conjunction with the above, the method to derive the margin for L1 filtering number mismatch is as follows:

[0444] 1. Setting channel model, Doppler, and measurement interval. That is, setting second channel measurement information that includes at least one of the following: channel model, Doppler, and measurement interval.

[0445] 2. Generate an instant channel (i.e., simulated second channel) based on the second channel measurement information above, measure the second channel, and obtain the L1 RSRP value (i.e., the second L1-RSRP value).

[0446] 3. An L3 RSRP value (the second L3-RSRP value) is obtained by averaging every 5 L1 RSRP samples (i.e., L1-RSRP values). An L3 RSRP value (the third L3-RSRP value) is obtained by averaging the last 3 L1 RSRP samples out of every 5 L1 RSRP samples.

[0447] 4. Calculate the RSRP difference between the two L3-RSRP values ​​corresponding to the sample mismatch in step 3, and plot the CDF curve of the RSRP difference (see Figure 6, i.e., the second RSRP difference curve).

[0448] 5. Select the difference between 5% and 95% of the RSRP difference (i.e., the second confidence interval) on the CDF curve as the margin 2.

[0449] In some embodiments, when there is signaling interaction between the UE and TE, i.e., when the two determine through signaling interaction that the number of L1 filtering samples used by the two is 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.

[0450] Margin 3: Margin based on L1 filtering method and L3 filtering coefficients

[0451] In some embodiments, if the TE is unaware of the L1 filtering method used by the UE, the TE may not know how to calculate the ideal L3-RSRP value.

[0452] In some embodiments, the L1 filtering method employed by the UE may include non-sliding window filtering and sliding window filtering. The key difference between non-sliding filtering and sliding window filtering is as follows:

[0453] (1) In the L1 / L3 non-sliding window filtering method, an L3 RSRP value is generated based on every 5 L1 RSRP samples.

[0454] (2) In the L1 / L3 sliding window filtering method, an L3 RSRP value is generated based on each L1 RSRP sample.

[0455] In some embodiments, the L3-RSRP value obtained based on different filtering methods is determined as follows: F n = (1-α)·F n-1 +α·M n

[0456] Among them, F n Fn is the updated filtered measurement result used to evaluate reporting conditions or for measurement reporting.

[0457] F n-1For the previous filtered measurement result (i.e., the previous L3-RSRP value), the initial value F0 is set to the first measurement result M1 received from the physical layer.

[0458] M n This represents the latest measurement result received from the physical layer.

[0459] α is the filter coefficient corresponding to the L1 filtering method, α = 1 / 2 (k / 4) Where k is the L3 filter coefficient, i.e., the influence of the previous L3-RSRP value on the current L3-RSRP value. (α is the filtering coefficient, α=1 / 2) (k / 4) ,where k is the filterCoefficient.)

[0460] In some embodiments, it is assumed that α = 0.5. This means that the prior L3-RSRP value will affect the current L3-RSRP value. The influence weights of each prior 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 prior L3-RSRP value on the current L3-RSRP value decreases more quickly. As shown in Figure 7, when considering that the influence of the prior L3-RSRP value on the current L3-RSRP value is 1 / 16, the sliding window filtering method corresponds to 8 L1 filtered samples, while the non-sliding window filtering method corresponds to 32 L1 filtered samples. (Supposeα=0.5. Many previous L3 results 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,ieT1 is smaller than T2.As shown in the figure, when considering the impact of previous RSRP to be 1 / 16, there are total 8 samples for sliding window and 32 samples for non-sliding window.)

[0461] In summary, at time T, the ground truth of L3-RSRP obtained by sliding filtering and non-sliding filtering methods differs.

[0462] RSRP difference = Ideal true value of L3-RSRP obtained at time T using the non-sliding window filtering method - Ideal true value of L3-RSRP obtained at time T using the sliding window filtering method. (RSRP difference = ideal filtered L3-RSRP of non-sliding method at time T - ideal filtered L3-RSRP of sliding method at time T.)

[0463] In some embodiments, referring to Figure 8, the RSRP difference between L3-RSRP values ​​obtained based on sliding window filtering and non-sliding window filtering is shown under different L3 filtering coefficients, where k = 0 corresponds to a "double dashed line", k = 1 corresponds to a "short line-dot", k = 2 corresponds to a "solid line", and k = 4 corresponds to a "double dotted line". It is assumed that k = 0, meaning that no L3 filtering coefficients are used, i.e., the previous L3-RSRP value will not affect the current L3-RSRP value; that is, there is no difference in L3-RSRP values ​​obtained by L1 filtering based on non-sliding window filtering and L1 filtering based on sliding window filtering.

[0464] In some embodiments, based on RAN2 simulations, assuming k = 4 and α = 0.5, the RSRP difference between 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 can be controlled within 1.8 dB in 90% of cases.

[0465] In some embodiments, it is assumed that the effect of α is ignored, meaning the L3-RSRP value is equivalent to the measurement at point C with α = 1, which is equivalent to the value obtained by directly using the average of the L1-RSRP values. In this case, the ideal true value of L3 RSRP 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 M instantaneous L1-RSRP values.

[0466] In some embodiments, in conjunction with the above, the derivation method of the margin based on the difference in L1 filtering methods is as follows:

[0467] 1. Setting channel model, Doppler, and measurement interval. That is, setting third-channel measurement information that includes at least one of the following: channel model, Doppler, and measurement interval.

[0468] 2. Generate an instant channel (i.e., simulated third channel) based on the third channel measurement information mentioned above, measure the third channel, and obtain the L1 RSRP value (i.e., the third L1-RSRP value).

[0469] 3. Calculate the RSRP difference between two L3 RSRP values ​​(i.e., the third RSRP difference) using both sliding and non-sliding methods, and plot the CDF curve of the RSRP difference (i.e., the third RSRP difference curve).

[0470] 4. Select the difference between 5% and 95% of the RSRP difference (i.e., the third confidence interval) in the CDF curve as the margin 2.

[0471] In some embodiments, different margins (3) can be set based on different L3 filtering coefficients. Optionally, when α = 1 or k = 0, no margin needs to be set, meaning the impact of differences in L1 filtering methods on the L3-RSRP prediction performance verification of the UE does not need to be considered.

[0472] In some embodiments, the L3-RSRP prediction performance verification of the UE may include the following methods:

[0473] 1. The User Equipment (UE) measures the L3 RSRP value based on the observation window (OW) and predicts the L3 RSRP value over a future period based on the measured L3 RSRP value. It then reports the predicted L3 RSRP value to the Equipment Provider (TE) and denotes it as X.

[0474] 2. Assume that the ideal true value of L3-RSRP is Y, and the preset RSRP prediction accuracy 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 filtered samples is Margin2, and the error compensation value based on the difference in L3 filtering method is Margin3.

[0475] 3. The UE passes the test if the following condition is met:

[0476] YZ - margin 1 - margin 2 - margin 3 <X<Y+Z+margin 1+margin 2+margin 3

[0477] 4. When testing the M L3-RSRP prediction values ​​for the UE, if the number of L3-RSRP prediction values ​​corresponding to step 3 above reaches 90% × M, then the UE passes the L3-RSRP prediction performance test.

[0478] In some embodiments, the names of information, etc., are not limited to the names described in the embodiments. 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.

[0479] In some embodiments, terms such as “moment,” “point in time,” “time,” and “time location” can be used interchangeably, as can terms such as “duration,” “segment,” “time window,” “window,” and “time.”

[0480] In some embodiments, terms such as wireless access scheme and waveform can be used interchangeably.

[0481] In some embodiments, terms such as "certain," "preset," "default," "set," "indicated," "a certain," "any," and "first" can be used interchangeably. "Certain A," "preset A," "default A," "set A," "indicated A," "a certain A," "any A," and "first A" can be interpreted as A pre-defined in a protocol or the like, or as A obtained through setting, configuration, or instruction, or as specific A, a certain A, any A, or first A, but are not limited thereto.

[0482] In some embodiments, the determination or judgment can be made by a value represented by 1 bit (0 or 1), or by a true or false value (boolean), or by a comparison of numerical values ​​(e.g., a comparison with a predetermined value), but is not limited thereto.

[0483] 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 receiver to respond to the sent content.

[0484] The communication method involved in the embodiments of this disclosure may include the foregoing steps and at least one of the 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 steps 301, 302, and step 304 can be implemented as independent embodiments. The combination of steps 901 and 902 can be implemented as an independent embodiment, the combination of steps 903 and 904 can be implemented as an independent embodiment, the combination of steps 903, 904 and 905 can be implemented as an independent embodiment, the combination of steps 901, 902, 903, 904 and 905 can be implemented as an independent embodiment, the combination of steps 1001 and 1002 can be implemented as an independent embodiment, the combination of steps 1001, 1002 and 1003 can be implemented as an independent embodiment, the combination of steps 1002 and 1003 can be implemented as an independent embodiment, the combination of steps 1002, 1003 and 1004 can be implemented as an independent embodiment, and the combination of steps 1001, 1002, 1003 and 1004 can be implemented as an independent embodiment, but is not limited thereto.

[0485] In some embodiments, other alternative implementations described before or after the specification corresponding to Figures 2 to 10 may be referred to.

[0486] Figure 11 is a schematic flowchart of a communication method according to an embodiment of the present disclosure.

[0487] As shown in Figure 11, the above method can be executed by a first device, which includes a test device (TE) or a network device. The above method includes:

[0488] Step 1101: Receive the L3-RSRP prediction value sent by the UE;

[0489] Step 1102: Determine the accuracy of L3-RSRP prediction for the UE based on the L3-RSRP prediction value and at least one RSRP accuracy margin.

[0490] 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 time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods.

[0491] In the above embodiments, when determining the accuracy of L3-RSRP prediction for the UE, by considering 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 based on time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods, multiple uncertainties that may prevent accurate determination of whether the UE meets the preset accuracy requirements for L3-RSRP prediction can be taken into account. This improves 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.

[0492] Optionally, in this embodiment of the disclosure, determining 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 includes:

[0493] Determine the absolute error between the L3-RSRP predicted value and the L3-RSRP ideal true value;

[0494] If the absolute error is less than or equal to the value of the first parameter, the UE is determined to meet the preset accuracy requirement of L3-RSRP prediction.

[0495] The first parameter value includes: the sum of the preset RSRP prediction accuracy threshold and the at least one L3-RSRP accuracy margin.

[0496] Optionally, in this embodiment of the disclosure, the method further includes:

[0497] Send a first indication message to the UE; wherein the first indication message indicates that the UE reports first information to the first device;

[0498] 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.

[0499] Optionally, in this embodiment of the disclosure, the method further includes:

[0500] Receive the first information sent by the UE;

[0501] Wherein, when the first information includes the L3-RSRP prediction value, the first margin is not included in the at least one RSRP accuracy margin;

[0502] When the first information includes the number of L1 filter samples used by the UE for L3-RSRP prediction, the second margin is not included in the at least one RSRP accuracy margin.

[0503] If the first information includes the L1 filtering method used by the UE for L3-RSRP prediction, the third margin is not included in the at least one RSRP accuracy margin.

[0504] Optionally, in this embodiment of the disclosure, the first surplus is obtained based on the following method:

[0505] Generate a first RSRP difference fluctuation curve based on a preset time offset, and extract the RSRP difference within the first confidence interval as the first margin.

[0506] Optionally, in this embodiment of the disclosure, generating the first RSRP difference fluctuation curve based on a preset time offset includes:

[0507] Obtain the first L1-RSRP value; wherein the first L1-RSRP value is obtained by measuring the first channel based on the first channel measurement information;

[0508] Filter the first preset number of adjacent first L1-RSRP values ​​to obtain the first L3-RSRP value;

[0509] Based on the first RSRP difference between L3-RSRP values ​​with a first preset timing offset, a first RSRP difference fluctuation curve is generated.

[0510] Optionally, in this embodiment of the disclosure, the second margin is obtained based on the following method:

[0511] A second RSRP difference fluctuation curve is generated based on different L1 filter sample numbers, and the RSRP difference within the second confidence interval is extracted as the second margin.

[0512] Optionally, in this embodiment of the disclosure, generating the second RSRP difference fluctuation curve based on different L1 filter sample numbers includes:

[0513] Obtain the second L1-RSRP value; wherein the second L1-RSRP value is obtained by measuring the second channel based on the second channel measurement information;

[0514] Filtering a second preset number of adjacent second L1-RSRP values ​​yields a second L3-RSRP value; and filtering a third preset number of second L1-RSRP values ​​from the second preset number of adjacent second L1-RSRP values ​​yields a third L3-RSRP value; wherein the third preset number is less than the second preset number.

[0515] The second RSRP difference fluctuation curve is generated based on the second RSRP difference between the second L3-RSRP value and the corresponding third L3-RSRP value.

[0516] Optionally, in this embodiment of the disclosure, the third margin is obtained based on the following method:

[0517] A third RSRP difference fluctuation curve is generated based on different L1 filtering methods and L3 filtering coefficients, and the RSRP difference within the third confidence interval is extracted as the third margin.

[0518] The L1 filtering method includes non-sliding window filtering and sliding window filtering.

[0519] Optionally, in this embodiment of the disclosure, generating the third RSRP difference fluctuation curve based on different L1 filtering methods and L3 filtering coefficients includes:

[0520] Obtain the third L1-RSRP value; wherein, the third L1-RSRP value is obtained by measuring the third new channel based on the third channel measurement information;

[0521] For a fourth preset number of adjacent third L1-RSRP values, a non-sliding window filter is applied to the third L1-RSRP values ​​to obtain a first RSRP value, and the first RSRP value is filtered according to a preset first L3 filter coefficient to obtain a fourth L3-RSRP value; and a sliding window filter is applied to the third L1-RSRP values ​​to obtain a second RSRP value, and the second RSRP value is filtered according to the first L3 filter coefficient to obtain a fifth L3-RSRP value;

[0522] The third RSRP difference fluctuation curve is generated based on the third RSRP difference between the fourth L3-RSRP value and the corresponding fifth L3-RSRP value.

[0523] Optionally, in this embodiment of the disclosure, the fourth surplus is obtained based on the following method:

[0524] Based on time offset, L1 filter sample number difference, and L1 filter method difference, a fourth RSRP difference fluctuation curve is generated, and the RSRP difference within the fourth confidence interval is extracted as the fourth margin.

[0525] Optionally, in this embodiment of the disclosure, generating a fourth RSRP difference fluctuation curve based on time offset, L1 filter sample number difference, and L1 filter method difference includes:

[0526] Obtain the fourth L1-RSRP value; wherein, the fourth L1-RSRP value is obtained by measuring the fourth channel based on the fourth channel measurement information;

[0527] For a fifth preset number of adjacent fourth L1-RSRP values, a non-sliding window filter is applied to the fourth L1-RSRP values ​​to obtain a third RSRP value. The third RSRP value is then filtered according to a preset second L3 filter coefficient to obtain a sixth L3-RSRP value. Additionally, a sliding window filter is applied to the sixth preset number of fourth L1-RSRP values ​​to obtain a fourth RSRP value. The fourth RSRP value is then filtered according to the second L3 filter coefficient to obtain a fifth RSRP value. The RSRP difference between the fifth RSRP values ​​with a second preset timing offset is determined to obtain a seventh L3-RSRP value.

[0528] The fourth RSRP difference fluctuation curve is generated based on the fourth RSRP difference between the sixth L3-RSRP value and the corresponding seventh RSRP value.

[0529] The communication method involved in the embodiments of this disclosure may include the foregoing steps and at least one of the embodiments. For example, step 1101 may be implemented as a separate embodiment, and step 1102 may be implemented as a separate embodiment; the combination of step 1101 and step 1102 may be implemented as a separate embodiment, but is not limited thereto.

[0530] In some embodiments, other alternative implementations described before or after the specification corresponding to FIG11 may be referred to.

[0531] Figure 12 is a second schematic flowchart illustrating a communication method according to an embodiment of the present disclosure.

[0532] As shown in Figure 12, the communication method, executed by the user equipment (UE), includes:

[0533] Step 1201: Send the L3-RSRP prediction value to the first device;

[0534] 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.

[0535] 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 time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods.

[0536] Optionally, in this embodiment of the disclosure, determining 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 includes:

[0537] Determine the absolute error between the L3-RSRP predicted value and the L3-RSRP ideal true value;

[0538] If the absolute error is less than or equal to the value of the first parameter, the UE is determined to meet the preset accuracy requirement of L3-RSRP prediction.

[0539] The first parameter value includes: the sum of the preset RSRP prediction accuracy threshold and the at least one L3-RSRP accuracy margin.

[0540] Optionally, in this embodiment of the disclosure, the method further includes:

[0541] The UE receives a first indication message sent by the first device; wherein the first indication message indicates that the UE reports first information to the first device.

[0542] 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.

[0543] Optionally, in this embodiment of the disclosure, the method further includes:

[0544] Send the first information to the first device;

[0545] Wherein, when the first information includes the L3-RSRP prediction value, the first margin is not included in the at least one RSRP accuracy margin;

[0546] When the first information includes the number of L1 filter samples used by the UE for L3-RSRP prediction, the second margin is not included in the at least one RSRP accuracy margin.

[0547] If the first information includes the L1 filtering method used by the UE for L3-RSRP prediction, the third margin is not included in the at least one RSRP accuracy margin.

[0548] In the above embodiments, the first device can directly report the first information, or it can respond to the first instruction information sent by the first device and send the first information to the first device.

[0549] Optionally, in this embodiment of the disclosure, the first surplus is obtained based on the following method:

[0550] Generate a first RSRP difference fluctuation curve based on a preset time offset, and extract the RSRP difference within the first confidence interval as the first margin.

[0551] Optionally, in this embodiment of the disclosure, generating the first RSRP difference fluctuation curve based on a preset time offset includes:

[0552] Obtain the first L1-RSRP value; wherein the first L1-RSRP value is obtained by measuring the first channel based on the first channel measurement information;

[0553] Filter the first preset number of adjacent first L1-RSRP values ​​to obtain the first L3-RSRP value;

[0554] Based on the first RSRP difference between L3-RSRP values ​​with a first preset timing offset, a first RSRP difference fluctuation curve is generated.

[0555] Optionally, in this embodiment of the disclosure, the second margin is obtained based on the following method:

[0556] A second RSRP difference fluctuation curve is generated based on different L1 filter sample numbers, and the RSRP difference within the second confidence interval is extracted as the second margin.

[0557] In conjunction with some embodiments of the second aspect, in some embodiments, generating a second RSRP difference fluctuation curve based on different L1 filter sample numbers includes:

[0558] Obtain the second L1-RSRP value; wherein the second L1-RSRP value is obtained by measuring the second channel based on the second channel measurement information;

[0559] A second preset number of adjacent second L1-RSRP values ​​are filtered to obtain a second L3-RSRP value; and a third preset number of second L1-RSRP values ​​are filtered 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.

[0560] The second RSRP difference fluctuation curve is generated based on the second RSRP difference between the second L3-RSRP value and the corresponding third L3-RSRP value.

[0561] Optionally, in this embodiment of the disclosure, the third margin is obtained based on the following method:

[0562] A third RSRP difference fluctuation curve is generated based on different L1 filtering methods and L3 filtering coefficients, and the RSRP difference within the third confidence interval is extracted as the third margin.

[0563] The L1 filtering method includes non-sliding window filtering and sliding window filtering.

[0564] Optionally, in this embodiment of the disclosure, generating the third RSRP difference fluctuation curve based on different L1 filtering methods and L3 filtering coefficients includes:

[0565] Obtain the third L1-RSRP value; wherein, the third L1-RSRP value is obtained by measuring the third channel based on the third channel measurement information;

[0566] For a fourth preset number of adjacent third L1-RSRP values, a non-sliding window filter is applied to the third L1-RSRP values ​​to obtain a first RSRP value, and the first RSRP value is filtered according to a preset first L3 filter coefficient to obtain a fourth L3-RSRP value; and a sliding window filter is applied to the third L1-RSRP values ​​to obtain a second RSRP value, and the second RSRP value is filtered according to the first L3 filter coefficient to obtain a fifth L3-RSRP value;

[0567] The third RSRP difference fluctuation curve is generated based on the third RSRP difference between the fourth L3-RSRP value and the corresponding fifth L3-RSRP value.

[0568] Optionally, in this embodiment of the disclosure, the fourth surplus is obtained based on the following method:

[0569] Based on time offset, L1 filter sample number difference, and L1 filter method difference, a fourth RSRP difference fluctuation curve is generated, and the RSRP difference within the fourth confidence interval is extracted as the fourth margin.

[0570] Optionally, in this embodiment of the disclosure, generating a fourth RSRP difference fluctuation curve based on time offset, L1 filter sample number difference, and L1 filter method difference includes:

[0571] Obtain the fourth L1-RSRP value; wherein, the fourth L1-RSRP value is obtained by measuring the fourth channel based on the fourth channel measurement information;

[0572] For a fifth preset number of adjacent fourth L1-RSRP values, a non-sliding window filter is applied to the fourth L1-RSRP values ​​to obtain a third RSRP value. The third RSRP value is then filtered according to a preset second L3 filter coefficient to obtain a sixth L3-RSRP value. Additionally, a sliding window filter is applied to the sixth preset number of fourth L1-RSRP values ​​to obtain a fourth RSRP value. The fourth RSRP value is then filtered according to the second L3 filter coefficient to obtain a fifth RSRP value. The RSRP difference between the fifth RSRP values ​​with a second preset timing offset is determined to obtain a seventh L3-RSRP value.

[0573] The fourth RSRP difference fluctuation curve is generated based on the fourth RSRP difference between the sixth L3-RSRP value and the corresponding seventh L3-RSRP value.

[0574] This disclosure also provides an apparatus for implementing any of the above methods. For example, an apparatus is provided that includes units or modules for implementing the steps performed by the terminal in any of the above methods. Alternatively, another apparatus is provided that includes units or modules for implementing the steps performed by a network device (e.g., an access network device, a core network functional node, a core network device, etc.) in any of the above methods.

[0575] It should be understood that the division of units or modules in the above device is only a logical functional division. In actual implementation, they can be fully or partially integrated into a single physical entity, or they can be physically separated. Furthermore, the units or modules in the device can be implemented by a processor calling software: for example, the device includes a processor connected to a memory containing instructions. The processor calls the instructions stored in the memory to implement any of the above methods or to implement the functions of the units or modules in the above device. The processor can be, for example, a general-purpose processor, such as a Central Processing Unit (CPU) or a microprocessor, and the memory can be internal or external to the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits. The functionality of some or all of the units or modules can be achieved through the design of these hardware circuits, which can be understood as one or more processors. For example, in one implementation, the hardware circuit is an application-specific integrated circuit (ASIC). The functionality of some or all of the units or modules is achieved through the design of the logical relationships between the components within the circuit. In another implementation, the hardware circuit can be implemented using a programmable logic device (PLD). Taking a field-programmable gate array (FPGA) as an example, it can include a large number of logic gates. The connection relationships between the logic gates are configured through configuration files, thereby achieving the functionality of some or all of the units or modules. All units or modules of the above device can be implemented entirely through processor-called software, entirely through hardware circuits, or partially through processor-called software with the remaining parts implemented through hardware circuits.

[0576] In this embodiment, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction read and execute 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 relationships of hardware circuits. The logical relationships of the aforementioned hardware circuits are fixed or reconfigurable. For example, the processor is a hardware circuit implemented using 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 and configuring the hardware circuit 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. Furthermore, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a Neural Network Processing Unit (NPU), a Tensor Processing Unit (TPU), or a Deep Learning Processing Unit (DPU).

[0577] Figure 13 is a schematic diagram of the structure of the first device proposed in an embodiment of this disclosure. As shown in Figure 13, the first device 1300 may include at least one of a transceiver module 1301, a processing module 1302, etc.

[0578] In some embodiments, the transceiver module 1301 is configured to receive the L3-RSRP prediction value sent by the UE; the processing module 1302 is configured 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 filtered 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 filtered samples, and the difference in L1 filtering method.

[0579] Optionally, the transceiver module 1301 is used to execute at least one of the transceiver steps (e.g., steps 201, 301, 901, 902, 903, 1001, 1002, 1101, but not limited thereto) executed by the first device 101 in any of the above methods, which will not be described in detail here. The processing module 1302 is used to execute at least one of the communication steps (e.g., steps 201, 202, 302, 303, 904, 905, 1003, 1004, 1102, but not limited thereto) executed by the first device 101 in any of the above methods, which will not be described in detail here.

[0580] Figure 14 is a schematic diagram of the structure of a user equipment according to an embodiment of this disclosure. As shown in Figure 14, the user equipment 1400 may include a transceiver module 1401.

[0581] In some embodiments, the transceiver module 1401 is configured 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 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.

[0582] Optionally, the transceiver module 1401 is used to execute at least one of the transceiver steps (e.g., steps 201, 301, 901, 902, 903, 1001, 1002, 1201, but not limited thereto) executed by the user equipment 102 in any of the above methods, which will not be elaborated here.

[0583] Figure 15 is a schematic diagram of the structure of a terminal 1500 (e.g., a user equipment) proposed in an embodiment of this disclosure. The terminal 1500 may be a chip, chip system, or processor that supports network devices in implementing any of the above methods, or it may be a chip, chip system, or processor that supports a terminal in implementing any of the above methods. The terminal 1500 can be used to implement the methods described in the above method embodiments; for details, please refer to the descriptions in the above method embodiments.

[0584] As shown in Figure 15, terminal 1500 includes one or more processors 1501. Processor 1501 can be a general-purpose processor or a dedicated processor, such as a baseband processor or a central processing unit (CPU). The baseband processor can be used to process communication protocols and communication data, while the CPU can be used to control communication devices (e.g., base stations, baseband chips, terminal devices, terminal device chips, DUs or CUs, etc.), execute programs, and process program data. Terminal 1500 is used to execute any of the above methods.

[0585] In some embodiments, terminal 1500 further includes one or more memories 1502 for storing instructions. Optionally, all or part of the memories 1502 may also be located outside of terminal 1500.

[0586] In some embodiments, terminal 1500 further includes one or more transceivers 1504. When terminal 1500 includes one or more transceivers 1504, transceiver 1504 performs at least one of the communication steps such as sending and / or receiving in the above method (e.g., steps 201, 301, 901, 902, 903, 1001, 1002, 1101, 1201, but not limited thereto), and processor 1501 performs at least one of other steps (e.g., steps 201, 202, 302, 303, 904, 905, 1003, 1004, 1102, but not limited thereto).

[0587] In some embodiments, a transceiver may include a receiver and / or a transmitter, which may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, etc., may be used interchangeably; the terms transmitter, transmitting unit, transmitter, transmitting circuit, etc., may be used interchangeably; and the terms receiver, receiving unit, receiver, receiving circuit, etc., may be used interchangeably.

[0588] In some embodiments, terminal 1500 may include one or more interface circuits 1503. Optionally, interface circuit 1503 is connected to memory 1502, and interface circuit 1503 can be used to receive signals from memory 1502 or other devices, and can be used to send signals to memory 1502 or other devices. For example, interface circuit 1503 can read instructions stored in memory 1502 and send the instructions to processor 1501.

[0589] The terminal 1500 described in the above embodiments may be a user equipment or other communication device, but the scope of the terminal 1500 described in this disclosure is not limited thereto, and the structure of the terminal 1500 may not be limited by FIG15. The 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 chip, or chip system or subsystem; (2) a set of one or more ICs, optionally, the IC set may also include storage components 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, terminal device, smart terminal device, cellular phone, wireless device, handheld device, mobile unit, vehicle device, network device, cloud device, artificial intelligence device, etc.; (6) others, etc.

[0590] Figure 16 is a schematic diagram of the structure of the chip 1600 proposed in an embodiment of this disclosure. For cases where the terminal 1500 can be a chip or a chip system, please refer to the schematic diagram of the chip 1600 shown in Figure 16, but it is not limited thereto.

[0591] Chip 1600 includes one or more processors 1601, which are used to perform any of the above methods.

[0592] In some embodiments, chip 1600 further includes one or more 1603s. Optionally, interface circuitry 1603 is connected to memory 1602, and interface circuitry 1603 can be used to receive signals from memory 1602 or other devices, and interface circuitry 1603 can be used to send signals to memory 1602 or other devices. For example, interface circuitry 1603 can read instructions stored in memory 1602 and send the instructions to processor 1601.

[0593] 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 (e.g., steps 201, 301, 901, 902, 903, 1001, 1002, 1101, 1201, but not limited thereto), and the processor 1601 performs at least one of other steps (e.g., steps 201, 202, 302, 303, 904, 905, 1003, 1004, 1102, but not limited thereto).

[0594] In some embodiments, the terms interface circuit, interface, transceiver pin, transceiver, etc., can be used interchangeably.

[0595] In some embodiments, chip 1600 further includes one or more memories 1602 for storing instructions. Optionally, all or part of the memories 1602 may be located outside of chip 1600.

[0596] This disclosure also proposes a storage medium storing instructions that, when executed on terminal 1500, cause terminal 1500 to perform any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but not limited thereto; it may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but not limited thereto; it may also be a temporary storage medium.

[0597] This disclosure also proposes a program product that, when executed by terminal 1500, causes terminal 1500 to perform any of the above methods. Optionally, the program product is a computer program product.

[0598] This disclosure also proposes a computer program that, when run on a computer, causes the computer to perform any of the above methods.

Claims

1. A communication method, characterized in that, Performed by a first device, which includes a test device (TE) or a network device, the method includes: Receive the L3-RSRP predicted value of the Layer 3 reference signal transmitted by the user equipment (UE); The accuracy of the L3-RSRP prediction performed by the UE is determined based on 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 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 time offset, the difference in the number of L1 filtered samples, and the difference in L1 filtering methods.

2. The communication method according to claim 1, characterized in that, Determining the accuracy of L3-RSRP prediction for the UE based on the L3-RSRP prediction value and at least one RSRP accuracy margin includes: Determine the absolute error between the L3-RSRP predicted value and the L3-RSRP ideal true value; If the absolute error is less than or equal to the value of the first parameter, the UE is determined to meet the preset accuracy requirement of L3-RSRP prediction. The first parameter value includes: the sum of the 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 includes: Send a first indication message to the UE; wherein the first indication message indicates that the UE reports 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 includes: Receive the first information sent by the UE; Wherein, when the first information includes the L3-RSRP prediction value, the first margin is not included in the at least one RSRP accuracy margin; When the first information includes the number of L1 filter samples used by the UE for L3-RSRP prediction, the second margin is not included in the at least one RSRP accuracy margin. If the first information includes the L1 filtering method used by the UE for L3-RSRP prediction, the third margin is not included in the at least one RSRP accuracy margin.

5. The communication method according to any one of claims 1 to 4, characterized in that, The first margin is obtained in the following way: Generate a first RSRP difference fluctuation curve based on a preset time offset, and extract the RSRP difference within the first confidence interval as the first margin.

6. The communication method according to claim 5, characterized in that, The generation of the first RSRP difference fluctuation curve based on a preset time offset includes: Obtain the first L1-RSRP value; wherein the first L1-RSRP value is obtained by measuring the first channel based on the first channel measurement information; Filter the first preset number of adjacent first L1-RSRP values ​​to obtain the first L3-RSRP value; Based on the first RSRP difference between L3-RSRP values ​​with a first preset timing offset, a first RSRP difference fluctuation curve is generated.

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

8. The communication method according to claim 7, characterized in that, The generation of the second RSRP difference fluctuation curve based on different L1 filter sample numbers includes: Obtain the second L1-RSRP value; wherein the second L1-RSRP value is obtained by measuring the second channel based on the second channel measurement information; A second preset number of adjacent second L1-RSRP values ​​are filtered to obtain a second L3-RSRP value; and a third preset number of second L1-RSRP values ​​are filtered 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 based on the second RSRP difference between the second L3-RSRP value and the 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: A third RSRP difference fluctuation curve is generated based on different L1 filtering methods and L3 filtering coefficients, and the RSRP difference within the third confidence interval is extracted 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, characterized in that, The generation of the third RSRP difference fluctuation curve based on different L1 filtering methods and L3 filtering coefficients includes: Obtain the 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, a non-sliding window filter is applied to the third L1-RSRP values ​​to obtain a first RSRP value, and the first RSRP value is filtered according to a preset first L3 filter coefficient to obtain a fourth L3-RSRP value; and a sliding window filter is applied to the third L1-RSRP values ​​to obtain a second RSRP value, and the second RSRP value is filtered according to the first L3 filter coefficient to obtain a fifth L3-RSRP value; The third RSRP difference fluctuation curve is generated based on the third RSRP difference between the fourth L3-RSRP value and the corresponding fifth L3-RSRP value.

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

12. The communication method according to claim 11, characterized in that, The generation of the fourth RSRP difference fluctuation curve based on time offset, L1 filter sample number difference, and L1 filter method difference includes: Obtain the 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, a non-sliding window filter is applied to the fourth L1-RSRP values ​​to obtain a third RSRP value. The third RSRP value is then filtered according to a preset second L3 filter coefficient to obtain a sixth L3-RSRP value. Additionally, a sliding window filter is applied to the sixth preset number of fourth L1-RSRP values ​​to obtain a fourth RSRP value. The fourth RSRP value is then filtered according to the second L3 filter coefficient to obtain a fifth RSRP value. The RSRP difference between the fifth RSRP values ​​with a second preset timing offset is determined to obtain a seventh L3-RSRP value. The fourth RSRP difference fluctuation curve is generated based on the fourth RSRP difference between the sixth L3-RSRP value and the corresponding seventh L3-RSRP value.

13. A communication method, characterized in that, Performed by a user equipment (UE), the method includes: The L3-RSRP prediction 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 based on 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 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, characterized in that, Determining the accuracy of L3-RSRP prediction for the UE based on the L3-RSRP prediction value and at least one RSRP accuracy margin includes: Determine the absolute error between the L3-RSRP predicted value and the L3-RSRP ideal true value; If the absolute error is less than or equal to the value of the first parameter, the UE is determined to meet the preset accuracy requirement of L3-RSRP prediction. The first parameter value includes: the sum of the 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 includes: The UE receives a first indication message sent by the first device; wherein the first indication message indicates that the UE reports 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 includes: Send the first information to the first device; Wherein, when the first information includes the L3-RSRP prediction value, the first margin is not included in the at least one RSRP accuracy margin; When the first information includes the number of L1 filter samples used by the UE for L3-RSRP prediction, the second margin is not included in the at least one RSRP accuracy margin. If the first information includes the L1 filtering method used by the UE for L3-RSRP prediction, the third margin is not included in the at least one RSRP accuracy margin.

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

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

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

20. The communication method according to claim 19, characterized in that, The generation of the second RSRP difference fluctuation curve based on different L1 filter sample numbers includes: Obtain the second L1-RSRP value; wherein the second L1-RSRP value is obtained by measuring the second channel based on the second channel measurement information; A second preset number of adjacent second L1-RSRP values ​​are filtered to obtain a second L3-RSRP value; and a third preset number of second L1-RSRP values ​​are filtered 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 based on the second RSRP difference between the second L3-RSRP value and the 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: A third RSRP difference fluctuation curve is generated based on different L1 filtering methods and L3 filtering coefficients, and the RSRP difference within the third confidence interval is extracted 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, characterized in that, The generation of the third RSRP difference fluctuation curve based on different L1 filtering methods and L3 filtering coefficients includes: Obtain the 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, a non-sliding window filter is applied to the third L1-RSRP values ​​to obtain a first RSRP value, and the first RSRP value is filtered according to a preset first L3 filter coefficient to obtain a fourth L3-RSRP value; and a sliding window filter is applied to the third L1-RSRP values ​​to obtain a second RSRP value, and the second RSRP value is filtered according to the first L3 filter coefficient to obtain a fifth L3-RSRP value; The third RSRP difference fluctuation curve is generated based on the third RSRP difference between the fourth L3-RSRP value and the corresponding fifth L3-RSRP value.

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

24. The communication method according to claim 23, characterized in that, The generation of the fourth RSRP difference fluctuation curve based on time offset, L1 filter sample number difference, and L1 filter method difference includes: Obtain the 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, a non-sliding window filter is applied to the fourth L1-RSRP values ​​to obtain a third RSRP value. The third RSRP value is then filtered according to a preset second L3 filter coefficient to obtain a sixth L3-RSRP value. Additionally, a sliding window filter is applied to the sixth preset number of fourth L1-RSRP values ​​to obtain a fourth RSRP value. The fourth RSRP value is then filtered according to the second L3 filter coefficient to obtain a fifth RSRP value. The RSRP difference between the fifth RSRP values ​​with a second preset timing offset is determined to obtain a seventh L3-RSRP value. The fourth RSRP difference fluctuation curve is generated based on the fourth RSRP difference between the sixth L3-RSRP value and the corresponding seventh L3-RSRP value.

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

26. A communication system, characterized in that, The device 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 of any one of claims 1 to 12, and the UE is configured to implement the communication method of any one of claims 13 to 24.

27. A storage medium storing instructions, characterized in that, When the instruction is executed on the communication device, the communication device performs the communication method as described in any one of claims 1 to 12 or 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 programs or instructions is executed by the communication device, it implements the communication method of any one of claims 1 to 12 or any one of claims 13 to 24.

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