Reporting method and apparatus, communication device, communication system, and storage medium

Abstract

The present disclosure provides a reporting method and apparatus, a device, and a storage medium. The method comprises: a first device receives a positioning purpose reference signal sent by at least one second device, and determines first information on the basis of the positioning purpose reference signal, the first information being used for implementing artificial intelligence (AI)-based positioning; the first device performs first processing on the first information to obtain second information, the first processing being used for performing dimension reduction on the first information; and the first device reports to a third device on the basis of the second information. The method of the present disclosure reduces the information transmission overhead, saves transmission resources, the model storage overhead and the calculation overhead, and improves the AI-based positioning efficiency.

Description

Reporting method and device, communication equipment, communication system, and storage medium Technical Field

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

[0002] In the New Radio (NR) system, artificial intelligence (AI)-based positioning is introduced to improve positioning accuracy.

[0003] Summary of the Invention

[0004] The present disclosure provides a reporting method and apparatus, communication equipment, a communication system, and a storage medium.

[0005] According to a first aspect of an embodiment of the present disclosure, a reporting method is proposed, including at least one of the following:

[0006] The first device receives a positioning reference signal sent by at least one second device, and determines first information based on the positioning reference signal, where the first information is used to implement artificial intelligence (AI) positioning;

[0007] The first device performs a first processing on the first information to obtain second information, where the first processing is used to reduce the dimension of the first information;

[0008] The first device reports to the third device based on the second information.

[0009] According to a second aspect of an embodiment of the present disclosure, a reporting method is proposed, including at least one of the following:

[0010] The third device receives a report from the first device based on second information, wherein the second information is obtained by performing a first processing on the first information, the first processing being used to reduce the dimension of the first information; the first information is used to implement AI positioning, the first information is determined based on the positioning reference signal, and the positioning reference signal is sent by at least one second device to the first device;

[0011] The third device performs AI positioning based on the report of the first device.

[0012] According to a third aspect of an embodiment of the present disclosure, a reporting method is provided for use in a communication system, the communication system including a first device and a third device, the method including at least one of the following:

[0013] The first device receives a positioning reference signal sent by at least one second device, and determines first information based on the positioning reference signal, where the first information is used to implement artificial intelligence (AI) positioning;

[0014] The first device performs a first processing on the first information to obtain second information, where the first processing is used to reduce the dimension of the first information;

[0015] The first device reports to the third device based on the second information;

[0016] The third device receives a report from the first device based on the second information;

[0017] The third device performs AI positioning based on the report of the first device.

[0018] According to a fourth aspect of the embodiments of the present disclosure, a first device is provided, comprising at least one of the following:

[0019] a receiving module, configured to receive a positioning reference signal sent by at least one second device, and determine first information based on the positioning reference signal, where the first information is used to implement artificial intelligence (AI) positioning;

[0020] a processing module, configured to perform a first processing on the first information to obtain second information, wherein the first processing is used to reduce the dimension of the first information;

[0021] A sending module is used to report to a third device based on the second information.

[0022] According to a fifth aspect of the embodiments of the present disclosure, a third device is provided, comprising at least one of the following:

[0023] a receiving module, configured to receive a report from a first device based on second information; wherein the second information is obtained by performing a first processing on the first information, the first processing being used to reduce the dimension of the first information; the first information is used to implement AI positioning, the first information is determined based on the positioning reference signal, and the positioning reference signal is sent by at least one second device to the first device;

[0024] A processing module is used to perform AI positioning based on the report of the first device.

[0025] According to a sixth aspect of an embodiment of the present disclosure, a communication device is provided, including:

[0026] one or more processors;

[0027] The processor is used to call instructions to enable the communication device to execute the reporting method described in the first aspect or the second aspect.

[0028] According to the seventh aspect of an embodiment of the present disclosure, a communication system is proposed, characterized in that it includes a first device and a third device, wherein the first device is configured to implement the reporting method described in the first aspect, and the third device is configured to implement the reporting method described in the second aspect.

[0029] According to an eighth aspect of an embodiment of the present disclosure, a storage medium is proposed, which stores instructions, and is characterized in that when the instructions are executed on a communication device, the communication device executes the reporting method described in the first aspect or the second aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The above and / or additional aspects and advantages of the present disclosure will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0031] FIG1 is a schematic diagram of the architecture of some communication systems provided by embodiments of the present disclosure;

[0032] FIG2A is an interactive diagram of a reporting method provided by an embodiment of the present disclosure;

[0033] FIG3A is a flowchart of a reporting method provided in yet another embodiment of the present disclosure;

[0034] FIG4A is a flowchart of a reporting method provided in yet another embodiment of the present disclosure;

[0035] FIG5A is a flowchart of a reporting method provided in yet another embodiment of the present disclosure;

[0036] FIG5B is a flowchart of a reporting method provided in yet another embodiment of the present disclosure;

[0037] FIG6A is a schematic structural diagram of a first device provided by an embodiment of the present disclosure;

[0038] FIG6B is a schematic structural diagram of a third device provided by an embodiment of the present disclosure;

[0039] FIG7A is a schematic structural diagram of a communication device provided by an embodiment of the present disclosure;

[0040] FIG7B is a schematic structural diagram of a chip provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0041] The embodiments of the present disclosure provide a reporting method and apparatus, a communication device, a communication system, and a storage medium.

[0042] In a first aspect, an embodiment of the present disclosure provides a reporting method, which is performed by a first device. The method includes at least one of the following:

[0043] The first device receives a positioning reference signal sent by at least one second device, and determines first information based on the positioning reference signal, where the first information is used to implement artificial intelligence (AI) positioning;

[0044] The first device performs a first processing on the first information to obtain second information, where the first processing is used to reduce the dimension of the first information;

[0045] The first device reports to the third device based on the second information.

[0046] In the above embodiment, after the first device determines the first information for implementing AI positioning based on the positioning reference signal, it does not directly report the original first information to the third device to implement AI positioning. Instead, it first performs a first process on the first information to reduce the dimensionality of the first information to obtain second information. Then, based on the second information, it reports to the third device so that the third device inputs the information reported by the first device into the AI ​​model deployed on the third device to implement AI positioning. Therefore, it can be seen that in the embodiment of the present disclosure, when the first device reports the information for AI positioning to the third device, it reports based on the information after dimensionality reduction. This makes the dimension of the information for AI positioning reported by the first device to the third device smaller, reducing information transmission overhead and saving transmission resources. In addition, because the dimension of the information for AI positioning received by the third device is smaller, the data dimension of the input data of the AI ​​model is smaller. Since the complexity of the AI ​​model is positively correlated with the data dimension of the AI ​​model input data, the complexity of the AI ​​model is also reduced, thereby saving model storage overhead and computing overhead, and improving AI positioning efficiency.

[0047] In conjunction with some embodiments of the first aspect, in some embodiments, performing the first processing on the first information to obtain the second information includes at least one of the following:

[0048] performing singular value decomposition on the first information to obtain the second information;

[0049] performing eigenvalue decomposition on the first information to obtain the second information;

[0050] Performing principal component analysis (PCA) on the first information to obtain the second information;

[0051] The first information is intercepted and processed to obtain the second information.

[0052] In the above embodiment, a method is provided for how the first device specifically reduces the dimension of the first information to ensure that the first device can successfully reduce the dimension of the first information, so as to achieve the purpose of "reducing information transmission overhead, saving transmission resources, saving model storage overhead and computing overhead, and improving AI positioning efficiency".

[0053] In combination with some embodiments of the first aspect, in some embodiments, the first information includes an M×N matrix, where M and N are both positive integers, where M is the number of second devices that send positioning reference signals, and N is the time domain sampling dimension of the positioning reference signal received by the first device. The element in the i-th row and j-th column of the M×N matrix is ​​determined based on the data at the j-th time domain sampling position of the positioning reference signal sent by the i-th second device received by the first device.

[0054] With reference to some embodiments of the first aspect, in some embodiments, performing singular value decomposition on the first information to obtain the second information includes:

[0055] Performing singular value decomposition on the first information to obtain a unitary matrix U, a first matrix Σ, and a unitary matrix V corresponding to the first information;

[0056] The second information is determined based on at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V.

[0057] In conjunction with some embodiments of the first aspect, in some embodiments, determining the second information based on at least one of the unitary matrix, the first matrix, and the unitary matrix includes at least one of the following:

[0058] Determine at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V as the second information;

[0059] intercepting part of the data of at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V, and determining the intercepted data of at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V as the second information;

[0060] Partial data of the unitary matrix U, the first matrix Σ, and the unitary matrix V are respectively intercepted, and the product of the intercepted data of the unitary matrix U, the first matrix Σ, and the unitary matrix V is determined as the second information.

[0061] In conjunction with some embodiments of the first aspect, in some embodiments, performing eigenvalue decomposition on the first information to obtain the second information includes:

[0062] Performing eigenvalue decomposition on the first information to obtain an eigenvector matrix Q and a second matrix Σ corresponding to the first information;

[0063] Determine at least one of the eigenvector matrix Q and the second matrix Σ as the second information; and / or intercept part of the data in the eigenvector matrix Q and / or the second matrix Σ, and determine the intercepted data of at least one of the eigenvector matrix Q and the second matrix Σ as the second information.

[0064] In conjunction with some embodiments of the first aspect, in some embodiments, performing PCA on the first information to obtain the second information includes:

[0065] Performing PCA on the first information to obtain third information, and determining the third information as the second information; and / or intercepting part of the data in the third information, and determining the intercepted data as the second information.

[0066] In the above embodiment, a method is provided for how the first device specifically reduces the dimension of the first information through singular value decomposition, eigenvalue decomposition, and PCA to ensure that the first device can successfully reduce the dimension of the first information, so as to achieve the purpose of "reducing information transmission overhead, saving transmission resources, saving model storage overhead and computing overhead, and improving AI positioning efficiency". In addition, when the present disclosure uses singular value decomposition, eigenvalue decomposition, and PCA to reduce the dimension of the first information, when the first device decomposes the first information based on singular value decomposition or eigenvalue decomposition to obtain a submatrix, or when the first device analyzes the first information based on PCA to obtain third information, the first device will also intercept the submatrix or the third information, and then obtain the second information based on the intercepted data. Since the singular value decomposition, eigenvalue decomposition, and PCA algorithms themselves can already reduce the dimensionality of the first information, the present disclosure further extracts the submatrix or third information obtained by the singular value decomposition, eigenvalue decomposition, or PCA, thereby significantly reducing the data dimension. This greatly achieves the goal of "reducing information transmission overhead, saving transmission resources, saving model storage overhead and computational overhead, and improving AI positioning efficiency." Furthermore, since singular value decomposition, eigenvalue decomposition, and PCA not only reduce the dimensionality of the original data but also retain the key feature information of the original data when acquiring low-latitude data, when the first information is reduced in dimension based on singular value decomposition, eigenvalue decomposition, or PCA to obtain the second information, the reduced second information can be guaranteed to include the key features of the original first information. Subsequently, when the first device reports the reduced second information to the third device, and the third device performs AI positioning based on the report from the first device, not only can the overhead be reduced and the efficiency of AI positioning be improved, but the accuracy of AI positioning can also be ensured.

[0067] In conjunction with some embodiments of the first aspect, in some embodiments, intercepting and processing the first information to obtain the second information includes at least one of the following:

[0068] intercepting data at first K time domain sampling positions from data at N time domain sampling positions corresponding to the reference signal sent by the second device to obtain the second information;

[0069] Uniformly extracting K data from the data at N time domain sampling positions corresponding to the reference signal sent by the second device to obtain the second information;

[0070] intercepting first K data with the largest power values ​​from the data at N time domain sampling positions corresponding to the reference signal sent by the second device to obtain the second information;

[0071] Wherein, K is a positive integer, K≤N.

[0072] In the above embodiment, a method is provided for how the first device specifically reduces the dimension of the first information through interception processing, ensuring that the first device can successfully reduce the dimension of the first information to achieve the purpose of "reducing information transmission overhead, saving transmission resources, saving model storage overhead and computing overhead, and improving AI positioning efficiency". In addition, when the above-mentioned interception processing is used to intercept the data at the first K time domain sampling positions from the data at the N time domain sampling positions corresponding to the reference signal sent from the second device, or when the first K data with the largest power value are intercepted from the data at the N time domain sampling positions corresponding to the reference signal sent from the second device, it can be ensured that the data with larger power value in the first information is intercepted as the second information, thereby ensuring that the key features of the original first information can be retained in the second information, and when the above-mentioned interception processing is used to uniformly intercept the data at the N time domain sampling positions corresponding to the reference signal sent from the second device. When K data are output, the distribution characteristics of the intercepted data can be made close to the distribution characteristics of the data in the first information, so that the second information retains the distribution characteristics of the data in the original first information. That is, when the first information is reduced in dimension by interception processing, the distribution characteristics of the data in the first information will not be destroyed or the key data in the first information will not be omitted. Therefore, when the first device subsequently reports to the third device based on the reduced-dimensional second information and the third device implements AI positioning based on the report of the first device, it can not only save overhead and improve the efficiency of AI positioning, but also ensure the accuracy of AI positioning.

[0073] In conjunction with some embodiments of the first aspect, in some embodiments, the first information includes a channel impulse response CIR, the M×N matrix is ​​a complex matrix, and the data in the M×N matrix is ​​complex data;

[0074] The data in the second information is plural data.

[0075] In conjunction with some embodiments of the first aspect, in some embodiments, reporting to the third device based on the second information includes at least one of the following:

[0076] reporting the second information to the third device;

[0077] Report the power value of the data in the second information to the third device.

[0078] In combination with some embodiments of the first aspect, in some embodiments, the power value is the modulus of the complex data in the second information.

[0079] In the above embodiment, a method is provided for how the first device specifically reports to the third device based on the second information, so as to ensure that the first device can successfully report information used for AI positioning to the third device based on the second information, thereby ensuring that the third device can subsequently implement AI positioning based on the information used for AI positioning reported by the first device, thereby ensuring the successful execution of the AI ​​positioning process.

[0080] In combination with some embodiments of the first aspect, in some embodiments, the first device is a terminal, the second device is a base station, and the positioning reference signal is a positioning reference signal PRS; or,

[0081] The first device is a base station, the second device is a terminal, and the positioning reference signal is a positioning sounding reference signal SRS-Pos.

[0082] In combination with some embodiments of the first aspect, in some embodiments, the third device is a location management function LMF.

[0083] In a second aspect, an embodiment of the present disclosure provides a reporting method, which is executed by a terminal. The method includes at least one of the following:

[0084] The third device receives a report from the first device based on second information, wherein the second information is obtained by performing a first processing on the first information, the first processing being used to reduce the dimension of the first information; the first information is used to implement AI positioning, the first information is determined based on the positioning reference signal, and the positioning reference signal is sent by at least one second device to the first device;

[0085] The third device performs AI positioning based on the report of the first device.

[0086] In the above embodiment, after the first device determines the first information for implementing AI positioning based on the positioning reference signal, it does not directly report the original first information to the third device to implement AI positioning. Instead, it first performs a first process on the first information to reduce the dimensionality of the first information to obtain second information. Then, based on the second information, it reports to the third device so that the third device inputs the information reported by the first device into the AI ​​model deployed on the third device to implement AI positioning. Therefore, it can be seen that in the embodiment of the present disclosure, when the first device reports the information for AI positioning to the third device, it reports based on the information after dimensionality reduction. This makes the dimension of the information for AI positioning reported by the first device to the third device smaller, reducing information transmission overhead and saving transmission resources. In addition, because the dimension of the information for AI positioning received by the third device is smaller, the data dimension of the input data of the AI ​​model is smaller. Since the complexity of the AI ​​model is positively correlated with the data dimension of the AI ​​model input data, the complexity of the AI ​​model is also reduced, thereby saving model storage overhead and computing overhead, and improving AI positioning efficiency.

[0087] In combination with some embodiments of the second aspect, in some embodiments, the first information includes an M×N matrix, where M and N are both positive integers, where M is the number of second devices that send positioning reference signals, and N is the time domain sampling dimension of the positioning reference signal received by the first device. The element in the i-th row and j-th column of the M×N matrix is ​​determined based on the data at the j-th time domain sampling position of the positioning reference signal sent by the i-th second device received by the first device.

[0088] In conjunction with some embodiments of the second aspect, in some embodiments, receiving a report from the first device based on the second information includes at least one of the following:

[0089] receiving the second information reported by the first device;

[0090] Receive a power value of data in the second information reported by the first device.

[0091] In conjunction with some embodiments of the second aspect, in some embodiments, performing AI positioning based on a report from the first device includes:

[0092] The information reported by the first device is input into the AI ​​model to obtain an AI positioning result.

[0093] In a third aspect, an embodiment of the present disclosure provides a reporting method for a communication system, wherein the communication system includes a first device and a third device, and the method includes at least one of the following:

[0094] The first device receives a positioning reference signal sent by at least one second device, and determines first information based on the positioning reference signal, where the first information is used to implement artificial intelligence (AI) positioning;

[0095] The first device performs a first processing on the first information to obtain second information, where the first processing is used to reduce the dimension of the first information;

[0096] The first device reports to the third device based on the second information;

[0097] The third device receives a report from the first device based on the second information;

[0098] The third device performs AI positioning based on the report of the first device.

[0099] In a fourth aspect, an embodiment of the present disclosure provides a first device, comprising at least one of the following:

[0100] a receiving module, configured to receive a positioning reference signal sent by at least one second device, and determine first information based on the positioning reference signal, where the first information is used to implement artificial intelligence (AI) positioning;

[0101] a processing module, configured to perform a first processing on the first information to obtain second information, wherein the first processing is used to reduce the dimension of the first information;

[0102] A sending module is used to report to a third device based on the second information.

[0103] In conjunction with some embodiments of the fourth aspect, in some embodiments, the processing module is used for at least one of the following:

[0104] performing singular value decomposition on the first information to obtain the second information;

[0105] performing eigenvalue decomposition on the first information to obtain the second information;

[0106] Performing principal component analysis (PCA) on the first information to obtain the second information;

[0107] The first information is intercepted and processed to obtain the second information.

[0108] In combination with some embodiments of the fourth aspect, in some embodiments, the first information includes an M×N matrix, where M and N are both positive integers, where M is the number of second devices that send positioning reference signals, and N is the time domain sampling dimension of the positioning reference signal received by the first device. The element in the i-th row and j-th column of the M×N matrix is ​​determined based on the data at the j-th time domain sampling position of the positioning reference signal sent by the i-th second device received by the first device.

[0109] In conjunction with some embodiments of the fourth aspect, in some embodiments, the processing module is further configured to:

[0110] Performing singular value decomposition on the first information to obtain a unitary matrix U, a first matrix Σ, and a unitary matrix V corresponding to the first information;

[0111] The second information is determined based on at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V.

[0112] In conjunction with some embodiments of the fourth aspect, in some embodiments, the processing module is further configured to:

[0113] Determine at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V as the second information;

[0114] intercepting part of data of at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V, and determining the intercepted data of at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V as the second information;

[0115] Partial data of the unitary matrix U, the first matrix Σ, and the unitary matrix V are respectively intercepted, and the product of the intercepted data of the unitary matrix U, the first matrix Σ, and the unitary matrix V is determined as the second information.

[0116] In conjunction with some embodiments of the fourth aspect, in some embodiments, the processing module is further configured to:

[0117] Performing eigenvalue decomposition on the first information to obtain an eigenvector matrix Q and a second matrix Σ corresponding to the first information;

[0118] Determine at least one of the eigenvector matrix Q and the second matrix Σ as the second information; and / or intercept part of the data in the eigenvector matrix Q and / or the second matrix Σ, and determine the intercepted data of at least one of the eigenvector matrix Q and the second matrix Σ as the second information.

[0119] In conjunction with some embodiments of the fourth aspect, in some embodiments, the processing module is further configured to:

[0120] Performing PCA on the first information to obtain third information, and determining the third information as the second information; and / or intercepting part of the data in the third information, and determining the intercepted data as the second information.

[0121] In conjunction with some embodiments of the fourth aspect, in some embodiments, intercepting and processing the first information to obtain the second information includes at least one of the following:

[0122] intercepting data at first K time domain sampling positions from data at N time domain sampling positions corresponding to the reference signal sent by the second device to obtain the second information;

[0123] Uniformly extracting K data from the data at N time domain sampling positions corresponding to the reference signal sent by the second device to obtain the second information;

[0124] intercepting first K data with the largest power values ​​from the data at N time domain sampling positions corresponding to the reference signal sent by the second device to obtain the second information;

[0125] Wherein, K is a positive integer, K≤N.

[0126] In conjunction with some embodiments of the fourth aspect, in some embodiments, the first information includes a channel impulse response CIR, the M×N matrix is ​​a complex matrix, and the data in the M×N matrix is ​​complex data;

[0127] The data in the second information is plural data.

[0128] In conjunction with some embodiments of the fourth aspect, in some embodiments, the sending module is further used for at least one of the following:

[0129] reporting the second information to the third device;

[0130] Report the power value of the data in the second information to the third device.

[0131] In combination with some embodiments of the fourth aspect, in some embodiments, the power value is the modulus of the complex data in the second information.

[0132] In conjunction with some embodiments of the fourth aspect, in some embodiments, the first device is a terminal, the second device is a base station, and the positioning reference signal is a positioning reference signal PRS; or,

[0133] The first device is a base station, the second device is a terminal, and the positioning reference signal is a positioning sounding reference signal SRS-Pos.

[0134] In combination with some embodiments of the fourth aspect, in some embodiments, the third device is a location management function LMF.

[0135] In a fifth aspect, an embodiment of the present disclosure provides a third device, comprising at least one of the following:

[0136] a receiving module, configured to receive a report from a first device based on second information; wherein the second information is obtained by performing a first processing on the first information, the first processing being used to reduce the dimension of the first information; the first information is used to implement AI positioning, the first information is determined based on the positioning reference signal, and the positioning reference signal is sent by at least one second device to the first device;

[0137] A processing module is used to perform AI positioning based on the report of the first device.

[0138] In combination with some embodiments of the fifth aspect, in some embodiments, the first information includes an M×N matrix, where M and N are both positive integers, where M is the number of second devices that send positioning reference signals, and N is the time domain sampling dimension of the positioning reference signal received by the first device. The element in the i-th row and j-th column of the M×N matrix is ​​determined based on the data at the j-th time domain sampling position of the positioning reference signal sent by the i-th second device received by the first device.

[0139] In conjunction with some embodiments of the fifth aspect, in some embodiments, the receiving module is further used for at least one of the following:

[0140] receiving the second information reported by the first device;

[0141] Receive a power value of data in the second information reported by the first device.

[0142] In conjunction with some embodiments of the fifth aspect, in some embodiments, the processing module is further configured to:

[0143] The information reported by the first device is input into the AI ​​model to obtain an AI positioning result.

[0144] In the sixth aspect, an embodiment of the present disclosure proposes a communication device, which includes: one or more processors; one or more memories for storing instructions; wherein the processor is used to call the instructions so that the communication device executes the reporting method described in the first aspect, the optional implementation of the first aspect, the second aspect, and the optional implementation of the second aspect.

[0145] In the seventh aspect, an embodiment of the present disclosure proposes a communication system, which includes: a first device and a third device; wherein the first device is configured to execute the method described in the first aspect and the optional implementation of the first aspect, and the third device is configured to execute the method described in the second aspect and the optional implementation of the second aspect.

[0146] In the eighth aspect, an embodiment of the present disclosure proposes a storage medium, which stores instructions. When the instructions are executed on a communication device, the communication device executes the reporting method described in the first aspect, the optional implementation of the first aspect, the second aspect, and the optional implementation of the second aspect.

[0147] In the ninth aspect, an embodiment of the present disclosure proposes a program product. When the program product is executed by a communication device, the communication device executes the reporting method described in the first aspect, the optional implementation of the first aspect, the second aspect, and the optional implementation of the second aspect.

[0148] In a tenth aspect, an embodiment of the present disclosure proposes a computer program, which, when running on a computer, enables the computer to execute the reporting method described in the first aspect, the optional implementation of the first aspect, the second aspect, and the optional implementation of the second aspect.

[0149] It is understandable that the first device, the third device, the communication device, the communication system, the storage medium, the program product, and the computer program are all used to perform the method proposed in the embodiment of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding method and will not be repeated here.

[0150] The present disclosure provides invention titles. In some embodiments, the terms "reporting method" and "information processing method," "information sending method," and "information receiving method" are interchangeable; the terms "communication device" and "information processing device," "information sending device," and "information receiving device" are interchangeable; and the terms "information processing system," "communication system," "information sending system," and "information receiving system" are interchangeable.

[0151] The embodiments of the present disclosure are not exhaustive and are merely illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined. For example, some or all steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0152] In each embodiment of the present disclosure, unless otherwise specified or provided for by logic, the terms and / or descriptions between the embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their inherent logical relationships.

[0153] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0154] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular, such as "a", "an", "the", "above", "said", "the", "the", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun following the article may be understood as a singular expression or a plural expression.

[0155] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0156] In some embodiments, the terms "at least one of", "at least one of", "at least one of", "one or more", "a plurality of", "multiple", etc. can be used interchangeably.

[0157] In the embodiments of the present disclosure, descriptions such as “at least one of A, B, C…”, “A and / or B and / or C…”, etc. include the situation where any one of A, B, C… exists alone, and also include any combination of any multiple of A, B, C…, and each situation can exist alone; for example, “at least one of A, B, C” includes the situation where A exists alone, B exists alone, C exists alone, the combination of A and B, the combination of A and C, the combination of B and C, and the combination of A, B, and C; for example, A and / or B includes the situation where A exists alone, B exists alone, and the combination of A and B.

[0158] In some embodiments, descriptions such as "in one case A, in another case B," or "in response to one case A, in response to another case B," may include the following technical solutions depending on the situation: executing A independently of B (in some embodiments, A); executing B independently of A (in some embodiments, B); selectively executing A and B (in some embodiments, selecting between A and B); and executing both A and B (in some embodiments, A and B). The same applies when there are more branches, such as A, B, and C.

[0159] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restriction on the position, order, priority, quantity or content of the description objects. For the statement of the description object, please refer to the description in the context of the claims or embodiments, and no unnecessary restriction should be constituted by the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does not limit the position or order between the "fields". "First" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of the "first field" and the "second field". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the number of description objects is not limited by the ordinal number and can be one or more. Taking "first device" as an example, the number of "devices" can be one or more. In addition, the objects modified by different prefixes can be the same or different. For example, if the description object is "device", then the "first device" and the "second device" can be the same device or different devices, and their types can be the same or different; for another example, if the description object is "information", then the "first information" and the "second information" can be the same information or different information, and their contents can be the same or different.

[0160] In some embodiments, “including A,” “comprising A,” “used to indicate A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0161] In some embodiments, terms such as "in response to...", "in response to determining...", "in the case of...", "at the time of...", "when...", "if...", "if...", etc. can be used interchangeably.

[0162] In some embodiments, terms such as "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not less than", and "above" can be replaced with each other, and terms such as "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "not more than", "lower than", "lower than or equal to", "not higher than", and "below" can be replaced with each other.

[0163] In some embodiments, devices, etc. can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. Terms such as "device", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", and "subject" can be used interchangeably.

[0164] In some embodiments, "network" can be interpreted as devices included in the network (eg, access network equipment, core network equipment, etc.).

[0165] In some embodiments, the terms "access network device (AN device)", "radio access network device (RAN device)", "base station (BS)", "radio base station" "fixed station", "node", "access point", "transmission point (TP)", "reception point (RP)", "transmission / reception point (TRP)", "panel", "antenna panel", "antenna array", "cell", "macro cell", "small cell", "femto cell", "pico cell", "sector", "cell group", "carrier", "component carrier", "bandwidth part (BWP)" and the like may be used interchangeably.

[0166] In some embodiments, the terms "terminal", "terminal device", "user equipment (UE)", "user terminal", "mobile station (MS)", "mobile terminal (MT)", subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, etc. can be used interchangeably.

[0167] In some embodiments, the access network device, the core network device, or the network device can be replaced by a terminal. For example, the various embodiments of the present disclosure can also be applied to a structure in which the communication between the access network device, the core network device, or the network device and the terminal is replaced by communication between multiple terminals (for example, it can also be called device-to-device (D2D), vehicle-to-everything (V2X), etc.). In this case, it can also be set as a structure in which the terminal has all or part of the functions of the access network device. In addition, language such as "uplink" and "downlink" can also be replaced by language corresponding to communication between terminals (for example, "side"). For example, uplink channels, downlink channels, etc. can be replaced by side channels, and uplinks, downlinks, etc. can be replaced by side links.

[0168] In some embodiments, the terminal may be replaced by an access network device, a core network device, or a network device. In this case, the access network device, the core network device, or the network device may have a structure that has all or part of the functions of the terminal.

[0169] In some embodiments, obtaining data, information, etc. may comply with the laws and regulations of the country where the data is obtained.

[0170] In some embodiments, data, information, etc. may be obtained with the user's consent.

[0171] In addition, each element, each row, or each column in the table of the embodiment of the present disclosure can be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns can also be implemented as an independent embodiment.

[0172] The correspondences shown in the tables of the present disclosure can be configured or predefined. The values ​​of the information in each table are merely examples and can be configured to other values, which are not limited by the present disclosure. When configuring the correspondences between information and parameters, it is not necessarily required to configure all the correspondences shown in each table. For example, in the tables of the present disclosure, the correspondences shown in certain rows may not be configured. For another example, appropriate deformation adjustments can be made based on the above tables, such as splitting, merging, etc. The names of the parameters shown in the titles of the above tables may also adopt other names that can be understood by the communication device, and the values ​​or representations of the parameters may also adopt other values ​​or representations that can be understood by the communication device. When implementing the above tables, other data structures may also be used, such as arrays, queues, containers, stacks, linear lists, pointers, linked lists, trees, graphs, structures, classes, heaps, hash tables or hash tables, etc.

[0173] The predefined in the present disclosure may be understood as defined, predefined, stored, pre-stored, pre-negotiated, pre-configured, solidified, or pre-burned.

[0174] Figure 1 is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure. As shown in Figure 1, the communication system 100 may include a first device, a second device, and a third device. The first device may be a channel data measurement node, such as a network device or a terminal, and the second device may be a reference signal sending node, such as a network device or a terminal. When the first device is a network device, the second device is a terminal; when the first device is a terminal, the second device is a network device. The third device may be a positioning model inference node, such as a core network device (such as a location management function (LMF)). The above-mentioned network device may include at least one of an access network device and a core network device.

[0175] In some embodiments, the terminal includes, for example, a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and at least one of a wireless terminal device in a smart home, but is not limited thereto.

[0176] In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network. The access network device may include an evolved NodeB (eNB), a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved nodeB (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, and at least one of an access node in a wireless fidelity (WiFi) system, but is not limited thereto.

[0177] In some embodiments, the technical solution of the present disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within the access network devices involved in the embodiments of the present disclosure can be transformed into internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces can be implemented through software or programs.

[0178] In some embodiments, the access network device can be composed of a centralized unit (CU) and a distributed unit (DU), where the CU can also be called a control unit. The CU-DU structure can be used to split the protocol layer of the access network device, with the functions of some protocol layers centrally controlled by the CU, and the functions of the remaining part or all of the protocol layers distributed in the DU, which is centrally controlled by the CU, but is not limited to this.

[0179] In some embodiments, the core network device may be a device including one or more network elements, or may be multiple devices or a group of devices, each including all or part of one or more network elements. The network element may be virtual or physical. The core network, for example, includes at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC). Alternatively, the core network device may also be a location management function network element. Exemplarily, the location management function network element includes a location server (location server), which may be implemented as any one of the following: Location Management Function (LMF), Enhanced Serving Mobile Location Centre (E-SMLC), Secure User Plane Location (SUPL), and Secure User Plane Location Platform (SUPLLP).

[0180] It can be understood that the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution proposed in the embodiment of the present disclosure. Ordinary technicians in this field can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution proposed in the embodiment of the present disclosure is also applicable to similar technical problems.

[0181] The following embodiments of the present disclosure may be applied to the communication system 100 shown in Figure 1, or a portion thereof, but are not limited thereto. The entities shown in Figure 1 are illustrative only. The communication system may include all or part of the entities shown in Figure 1, or may include other entities outside of Figure 1. The number and form of the entities may be arbitrary. The connection relationship between the entities is illustrative only. The entities may be connected or disconnected, and the connection may be in any manner, including direct or indirect, wired or wireless.

[0182] The embodiments of the present disclosure can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), future radio access (FRA), new radio access technology (RAT), new radio (NR), new radio access (NX), future generation radio access (FX), Global System for Mobile Communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X), systems utilizing other reporting methods, and next-generation systems based on and extending these systems. Furthermore, multiple systems may be combined (for example, a combination of LTE or LTE-A with 5G).

[0183] Optionally, the method for positioning based on AI may include: receiving a positioning reference signal by a signal measurement node, and measuring the positioning reference signal to obtain channel measurement data for positioning, such as a channel impulse response (CIR), and then inputting the channel measurement data for positioning into an AI model so that the AI ​​model outputs the required AI positioning result, or inputting the channel measurement data for positioning into the AI ​​model so that the AI ​​model outputs intermediate positioning parameters, such as time of arrival (ToA), angle of arrival (AoA), etc., and then using a positioning method to calculate based on the intermediate positioning parameters to obtain the required AI positioning result. However, in some embodiments, the AI ​​model may not be deployed in the signal measurement node, but may be deployed in a positioning server (such as a location management function (LMF)). In this case, the signal measurement node generally needs to send the measured channel measurement data for positioning to the positioning server so that the positioning server inputs the received channel measurement data for positioning into the AI ​​model to obtain the required AI positioning result.

[0184] However, the data dimension of the channel measurement data used for positioning is relatively large. Therefore, when the first device sends the channel measurement data used for positioning to the third device, it will result in a large transmission overhead, which is not conducive to the application of AI-based positioning technology in actual communication systems. In addition, the complexity of the AI ​​model is positively correlated with the data dimension of the AI ​​model input data. Therefore, when the data dimension of the channel measurement data used for positioning as the input of the AI ​​model is relatively large, the complexity of the AI ​​model is also relatively large, resulting in large model storage overhead and computational overhead.

[0185] FIG2A is an interactive diagram of a reporting method according to an embodiment of the present disclosure. As shown in FIG2A , the present disclosure embodiment relates to a reporting method for a communication system 100, the method comprising:

[0186] Step 2101: At least one second device sends a positioning reference signal to a first device.

[0187] Optionally, the first device may be a channel data measurement node, and the second device may be a reference signal sending node. In some embodiments, the first device may be a network device (such as a base station or TRP) or a terminal, and the second device may also be a network device (such as a base station or TRP) or a terminal. When the first device is a network device and the second device is a terminal, the positioning reference signal may be a positioning sounding reference signal (Sounding Reference Signal-position, SRS-Pos). When the first device is a terminal and the second device is a network device, the positioning reference signal may be a positioning reference signal (Positioning Reference Signal, PRS).

[0188] Step 2102: The first device determines first information based on a positioning reference signal.

[0189] Optionally, in some embodiments, the first information can be used to implement AI positioning. For example, the first information can be used to determine the required AI positioning result (such as the positioning position of the terminal). Optionally, the first information can be, for example, CIR, where CIR refers to the impact on the signal during channel propagation, which can reflect the changes that occur to the signal after it propagates through the channel, including path loss and shadow fading causing the energy of the pulse signal to attenuate, and the superposition of multiple different pulse signals received successively at the receiving end due to the presence of multiple propagation paths in the channel. The CIR is in plural form.

[0190] Optionally, the first device may determine the first information based on the positioning reference signal received from the second device. Optionally, the method for the first device to determine the first information based on the positioning reference signal received from the second device may include: the first device determines the time domain sampling dimension N of the positioning reference signal, where N is a positive integer, wherein the time domain sampling dimension may be agreed upon by the protocol, or may be configured by the network device. Thereafter, the first device may determine the time domain sampling position in the positioning reference signal received by the first device based on the time domain sampling dimension N, and determine the above-mentioned first information based on the data at each time domain sampling position. Optionally, assuming that the first device receives a symbol sent by the second device, the symbol carries the positioning reference signal, and its signal waveform may be the superposition of multiple signal waveforms propagated through multipath. If the duration of the symbol is T, then when the time domain sampling dimension is N, the first device may use "the time interval between two adjacent time domain sampling positions is This criterion determines at least one time domain sampling position in the symbol, wherein the data at each time domain sampling position includes amplitude and delay, and the first device can calculate the above-mentioned first information based on the data at each time domain sampling position.

[0191] Therefore, optionally, the first information may include an M×N matrix, where M and N are both positive integers, where M is the number of second devices that send positioning reference signals, N is the time domain sampling dimension of the positioning reference signal received by the first device, and the element in the i-th row and j-th column of the M×N matrix is ​​determined based on the data at the j-th time domain sampling position of the positioning reference signal sent by the i-th second device received by the first device.

[0192] Optionally, in some embodiments, when the first information is a CIR, the element in the i-th row and j-th column of the aforementioned M×N matrix may be: the CIR corresponding to the data at the j-th time domain sampling position of the positioning reference signal sent by the i-th second device and received by the first device. Since the CIR is in complex form, the amplitude and delay of the data at the j-th time domain sampling position of the positioning reference signal sent by the i-th second device may be converted to complex form to obtain the element in the i-th row and j-th column of the M×N matrix, thereby making the aforementioned M×N matrix a complex matrix and the data in the aforementioned M×N matrix complex data.

[0193] Step 2103: The first device performs a first process on the first information to obtain second information.

[0194] Optionally, the first processing can be used to reduce the dimension of the first information.

[0195] In some embodiments, the first processing may be singular value decomposition, wherein the singular value decomposition mainly multiplies a complex matrix by three sub-matrices with lower dimensions and simpler dimensions, which is specifically defined as: for a matrix A of dimension m×n, there exists a unitary matrix U of dimension m×m, a first matrix Σ of dimension m×n, and a unitary matrix V of dimension n×n, such that A=UΣV H , where H represents the complex conjugate transpose, and the diagonal elements of the first matrix Σ are the singular values ​​of the matrix A, which are AA T The square roots of the eigenvalues ​​of , arranged in descending order, correspond to the important information implicit in the matrix. The larger the data, the more important it is in the matrix data. Except for the diagonal elements, all other elements of the first matrix are 0. Based on this, optionally, the method of performing singular value decomposition on the first information to obtain the second information may include: performing singular value decomposition on the first information to obtain a unitary matrix U, a first matrix Σ, and a unitary matrix V corresponding to the first information, and then determining the second information based on at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V.

[0196] Optionally, in some embodiments, the above-mentioned method of "determining the second information based on at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V" can be: directly determining at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V as the second information. For example, the unitary matrix U can be determined as the second information, or the first matrix Σ can be determined as the second information.

[0197] Alternatively, in other embodiments, the method of "determining the second information based on at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V" may be as follows: extracting partial data of at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V, and determining the extracted data of at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V as the second information. In some embodiments, for example, the method of "extracting partial data of at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V" may be as follows: extracting the first G data of at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V, where G is a positive integer, and determining the first G extracted data of at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V as the second information. For example, the first G data of the first matrix Σ may be extracted, and the first G extracted data of the first matrix Σ may be determined as the second information.

[0198] Alternatively, in some further embodiments, the method of "determining the second information based on at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V" may include: partially extracting data from each of the unitary matrix U, the first matrix Σ, and the unitary matrix V, and determining the second information as the product of the extracted data from the unitary matrix U, the first matrix Σ, and the unitary matrix V. For a description of extracting data from the unitary matrix U, the first matrix Σ, and the unitary matrix V, refer to the above content.

[0199] In some other embodiments, the first processing can also be eigenvalue decomposition, wherein the eigenvalue decomposition is mainly to decompose a matrix A into A = QΣQ -1, where Q is a matrix composed of eigenvectors of matrix A, and Σ obtained by eigenvalue decomposition is a diagonal matrix whose diagonal elements are eigenvalues ​​arranged from largest to smallest. The eigenvectors corresponding to the eigenvalues ​​can describe the main information of matrix A. Based on this, optionally, the method of performing eigenvalue decomposition on the first information to obtain the second information may include: performing eigenvalue decomposition on the first information to obtain an eigenvector matrix Q and a second matrix Σ corresponding to the first information; determining at least one of the eigenvector matrix Q and the second matrix Σ as the second information; and / or truncating a portion of the data in the eigenvector matrix Q and / or the second matrix Σ, and determining the truncated data of at least one of the eigenvector matrix Q and the second matrix Σ as the second information. For example, the eigenvector matrix Q may be determined as the second information, and / or the eigenvector matrix Q may be truncated and the truncated data of the eigenvector matrix Q may be determined as the second information. The matrix truncation method herein may refer to the relevant description above when introducing "truncating the unitary matrix U, the first matrix Σ, and the unitary matrix V."

[0200] In some other embodiments, the first processing mentioned above may be principal component analysis (PCA), wherein PCA is a common data analysis method that transforms the original data into a set of linearly independent representations of each dimension through linear transformation. PCA is often used for dimensionality reduction of high-dimensional data and can be used to extract the main characteristic components of the data. Optionally, the method of "performing PCA on the first information to obtain the second information" may include: performing PCA on the first information to obtain third information, and determining the third information as the second information; and / or intercepting part of the data in the third information, and determining the intercepted data as the second information. The third information is also in matrix form, and the method for intercepting the third information can refer to the relevant description of "intercepting the unitary matrix U, the first matrix Σ, and the unitary matrix V" mentioned above.

[0201] It can be seen from the above content that a method is provided for how the first device specifically reduces the dimension of the first information through singular value decomposition, eigenvalue decomposition, and PCA to ensure that the first device can successfully reduce the dimension of the first information to achieve the purpose of "reducing information transmission overhead, saving transmission resources, saving model storage overhead and computing overhead, and improving AI positioning efficiency". In addition, when the present disclosure adopts singular value decomposition, eigenvalue decomposition, and PCA to reduce the dimension of the first information, when the first device decomposes the first information based on singular value decomposition or eigenvalue decomposition to obtain a submatrix, or when the first device analyzes the first information based on PCA to obtain the third information, the first device will also intercept the submatrix or the third information, and then obtain the second information based on the intercepted data. Among them, since the singular value decomposition, eigenvalue decomposition, and PCA algorithms themselves can already reduce the dimensionality of the first information, on this basis, the present disclosure will further intercept the submatrix or third information obtained by the singular value decomposition, eigenvalue decomposition, or PCA, thereby greatly reducing the data dimension, thereby greatly achieving the purpose of "reducing information transmission overhead, saving transmission resources, saving model storage overhead and computing overhead, and improving AI positioning efficiency." Moreover, since singular value decomposition, eigenvalue decomposition, and PCA can not only reduce the dimensionality of the original data, but also retain the key feature information of the original data when acquiring low-latitude data, therefore, when the first information is reduced in dimension based on singular value decomposition, eigenvalue decomposition, and PCA to obtain the second information, it can be ensured that the reduced second information includes the key features of the original first information. When the first device subsequently reports based on the reduced second information to achieve AI positioning, it can not only save overhead and improve AI positioning efficiency, but also ensure the accuracy of AI positioning.

[0202] In some further embodiments, the first processing may be an interception processing. Optionally, the method of intercepting the first information to obtain the second information may include at least one of the following:

[0203] The data at the first K time domain sampling positions are intercepted from the data at the N time domain sampling positions corresponding to the reference signal sent by the second device to obtain the second information, where K is a positive integer, K≤N; for example, the first 64 data can be intercepted from the data at 256 time domain sampling positions.

[0204] K data are uniformly intercepted from the data at N time domain sampling positions corresponding to the reference signal sent by the second device to obtain the second information; optionally, the uniform interception can be understood as: the data is uniformly divided into F data subsets, the number of data included in different data subsets is the same, and the different data subsets may overlap or not overlap, and then B data are selected from each data subset, B < the total number of data included in the data subset, wherein, when selecting B data from the data subset, any B data can be selected, or the first B data with the largest power value can be selected to achieve uniform interception. Optionally, the above-mentioned power value can be the modulus of the complex data at the time domain sampling position. And, the uniform interception here can mean sampling the original data, using the data at a time domain sampling position to represent the data at its adjacent time domain sampling position. Optionally, for example, 64 data can be uniformly selected from the data at 256 time domain sampling positions.

[0205] The first K data with the largest power values ​​are intercepted from the data at N time domain sampling positions corresponding to the reference signal sent by the second device to obtain the second information; for example, 64 data with the largest power values ​​can be selected from the data at 256 time domain sampling positions.

[0206] For a detailed introduction to the above-mentioned interception process, please refer to the above description.

[0207] Optionally, the principle of "extracting the data at the first K time domain sampling positions from the data at the N time domain sampling positions corresponding to the reference signal sent by the second device to obtain the second information" is described below: In the time domain dimension, the signal at the earlier time domain position in the positioning reference signal received by the first device generally has a shorter propagation path. A shorter propagation path requires a shorter propagation time, which causes the signal to reach the first device earlier, i.e., the time domain position at which the first device receives the signal is earlier. The signal with a shorter propagation path experiences less reflection and / or refraction, resulting in less loss. Therefore, the value of the first information (e.g., CIR) obtained based on the signal with a shorter propagation path (i.e., the real part of the complex data, the imaginary part of the complex data, or the modulus of the complex data) is larger. In other words, the value of the first information corresponding to the reference signal with an earlier time domain position is larger. Based on this, in some embodiments, in the time domain sampling dimension, non-zero data is typically concentrated at the earlier sampling positions of the first information, and near-zero data is typically concentrated at the later sampling positions of the first information. That is, the value of the first information corresponding to the sampling position closer to the front in the first information is larger, and the value of the first information corresponding to the sampling position closer to the back is closer to zero. Since the larger the value of the data in the first information, the more important it will be in the subsequent AI positioning, when intercepting the first information for dimensionality reduction, it is possible to first consider intercepting the non-zero data at the first K time domain sampling positions. This not only achieves the purpose of dimensionality reduction, but also intercepts key data. When AI positioning is subsequently performed based on the intercepted second information, positioning accuracy can be ensured.

[0208] Optionally, the following describes the principle of "intercepting the first K data with the largest power values ​​from the data at the N time domain sampling positions corresponding to the reference signal sent by the second device to obtain the second information": because the data with larger power values ​​in the first information plays a more important role in subsequent AI positioning. Therefore, by intercepting the first K data with the largest power values, not only the purpose of dimensionality reduction is achieved, but also key data is intercepted. When AI positioning is subsequently performed based on the intercepted second information, positioning accuracy can be ensured.

[0209] From the above content, it can be seen that a method is provided for how the first device specifically reduces the dimension of the first information through interception processing, ensuring that the first device can successfully reduce the dimension of the first information, so as to achieve the purpose of "reducing information transmission overhead, saving transmission resources, saving model storage overhead and computing overhead, and improving AI positioning efficiency". Furthermore, when the above-mentioned truncation processing is used to truncate the data at the first K time domain sampling positions from the data at the N time domain sampling positions corresponding to the reference signal sent from the second device, or when the first K data with the largest power values ​​are truncate from the data at the N time domain sampling positions corresponding to the reference signal sent from the second device, it can be ensured that the data with larger power values ​​in the first information is truncate as the second information, thereby ensuring that the key features of the original first information can be retained in the second information. Furthermore, when the above-mentioned truncation processing is used to uniformly truncate K data from the data at the N time domain sampling positions corresponding to the reference signal sent from the second device, the distribution features of the truncation data can be made close to the distribution features of the data in the first information, thereby ensuring that the second information retains the distribution features of the data in the original first information. That is, when the truncation processing is used to reduce the dimensionality of the first information, the distribution features of the data in the first information are not destroyed or the key data in the first information is not omitted. Therefore, when the first device subsequently reports based on the reduced dimensionality second information to achieve AI positioning, it can not only save overhead and improve AI positioning efficiency, but also ensure the accuracy of AI positioning.

[0210] Step 2104: The first device reports to the third device based on the second information.

[0211] Optionally, in some embodiments, the first device may report the second information to the third device; and / or, the first device may report the power value of the data in the second information to the third device.

[0212] Optionally, the third device may be a LMF.

[0213] Step 2105: The third device performs AI positioning based on the report from the first device.

[0214] Optionally, an AI model may be deployed on the third device, which may be used to implement AI positioning. After receiving the report from the first device, the third device may input the information reported by the first device (such as the second information and / or the power value of the data in the second information) into the AI ​​model to obtain the required AI positioning result. Optionally, the third device may input the information reported by the first device into the AI ​​model so that the AI ​​model directly outputs the required AI positioning result, or the third device may input the information reported by the first device into the AI ​​model so that the AI ​​model outputs intermediate positioning parameters, such as ToA, AoA, etc., and then use the positioning method to perform calculations based on the intermediate positioning parameters to obtain the required AI positioning result.

[0215] In the above embodiment, after the first device determines the first information for implementing AI positioning based on the positioning reference signal, it does not directly report the original first information to the third device to implement AI positioning. Instead, it first performs a first process on the first information to reduce the dimensionality of the first information to obtain second information. Then, based on the second information, it reports to the third device so that the third device inputs the information reported by the first device into the AI ​​model deployed on the third device to implement AI positioning. Therefore, it can be seen that in the embodiment of the present disclosure, when the first device reports the information for AI positioning to the third device, it reports based on the information after dimensionality reduction. This makes the dimension of the information for AI positioning reported by the first device to the third device smaller, reducing information transmission overhead and saving transmission resources. In addition, because the dimension of the information for AI positioning received by the third device is smaller, the data dimension of the input data of the AI ​​model is smaller. Since the complexity of the AI ​​model is positively correlated with the data dimension of the AI ​​model input data, the complexity of the AI ​​model is also reduced, thereby saving model storage overhead and computing overhead, and improving AI positioning efficiency.

[0216] The reporting method involved in the embodiments of the present disclosure may include at least one of steps S2101 to S2105. For example, step S2101 may be implemented as an independent embodiment, step S2102 may be implemented as an independent embodiment, step S2103 may be implemented as an independent embodiment, and steps S2101+S2102 may be implemented as independent embodiments, but are not limited thereto.

[0217] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other embodiments or other examples.

[0218] FIG3A is an interactive diagram of a reporting method according to an embodiment of the present disclosure. As shown in FIG3A , the present disclosure embodiment relates to a reporting method for a first device, the method comprising:

[0219] Step 3101: A first device receives a positioning reference signal sent by at least one second device.

[0220] Step 3102: The first device determines first information based on a positioning reference signal.

[0221] Step 3103: The first device performs a first process on the first information to obtain second information.

[0222] Step 3104: The first device reports based on the second information.

[0223] Optionally, the first processing is used to reduce the dimension of the first information.

[0224] Optionally, performing a first processing on the first information to obtain the second information includes at least one of the following:

[0225] performing singular value decomposition on the first information to obtain the second information;

[0226] performing eigenvalue decomposition on the first information to obtain the second information;

[0227] Performing principal component analysis (PCA) on the first information to obtain the second information;

[0228] The first information is intercepted and processed to obtain the second information.

[0229] Optionally, the first information includes an M×N matrix, where M and N are both positive integers, where M is the number of second devices that send positioning reference signals, and N is the time domain sampling dimension of the positioning reference signal received by the first device. The element in the i-th row and j-th column of the M×N matrix is ​​determined based on the data at the j-th time domain sampling position of the positioning reference signal sent by the i-th second device received by the first device.

[0230] Optionally, performing singular value decomposition on the first information to obtain the second information includes:

[0231] Performing singular value decomposition on the first information to obtain a unitary matrix U, a first matrix Σ, and a unitary matrix V corresponding to the first information;

[0232] The second information is determined based on at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V.

[0233] Optionally, the determining the second information based on at least one of the unitary matrix, the first matrix, and the unitary matrix includes at least one of the following:

[0234] Determine at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V as the second information;

[0235] intercepting part of the data of at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V, and determining the intercepted data of at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V as the second information;

[0236] Partial data of the unitary matrix U, the first matrix Σ, and the unitary matrix V are respectively intercepted, and the product of the intercepted data of the unitary matrix U, the first matrix Σ, and the unitary matrix V is determined as the second information.

[0237] Optionally, performing eigenvalue decomposition on the first information to obtain the second information includes:

[0238] Performing eigenvalue decomposition on the first information to obtain an eigenvector matrix Q and a second matrix Σ corresponding to the first information;

[0239] Determine at least one of the eigenvector matrix Q and the second matrix Σ as the second information; and / or intercept part of the data in the eigenvector matrix Q and / or the second matrix Σ, and determine the intercepted data of at least one of the eigenvector matrix Q and the second matrix Σ as the second information.

[0240] Optionally, performing PCA on the first information to obtain the second information includes:

[0241] Performing PCA on the first information to obtain third information, and determining the third information as the second information; and / or intercepting part of the data in the third information, and determining the intercepted data as the second information.

[0242] Optionally, the intercepting and processing the first information to obtain the second information includes at least one of the following:

[0243] intercepting data at first K time domain sampling positions from data at N time domain sampling positions corresponding to the reference signal sent by the second device to obtain the second information;

[0244] Uniformly extracting K data from the data at N time domain sampling positions corresponding to the reference signal sent by the second device to obtain the second information;

[0245] intercepting first K data with the largest power values ​​from the data at N time domain sampling positions corresponding to the reference signal sent by the second device to obtain the second information;

[0246] Wherein, K is a positive integer, K≤N.

[0247] Optionally, the first information includes a channel impulse response CIR, the M×N matrix is ​​a complex matrix, and the data in the M×N matrix is ​​complex data;

[0248] The data in the second information is plural data.

[0249] Optionally, the reporting to the third device based on the second information includes at least one of the following:

[0250] reporting the second information to the third device;

[0251] Report the power value of the data in the second information to the third device.

[0252] Optionally, the power value is the modulus of the complex data in the second information.

[0253] Optionally, the first device is a terminal, the second device is a base station, and the positioning reference signal is a positioning reference signal PRS; or,

[0254] The first device is a base station, the second device is a terminal, and the positioning reference signal is a positioning sounding reference signal SRS-Pos.

[0255] Optionally, the third device is a location management function LMF.

[0256] For a detailed description of steps 3101 - 3104 , please refer to the above embodiment description.

[0257] The reporting method involved in the embodiments of the present disclosure may include at least one of steps S3101 to S3104. For example, step S3101 may be implemented as an independent embodiment, step S3102 may be implemented as an independent embodiment, and steps S3101+S3102 may be implemented as independent embodiments, but are not limited thereto.

[0258] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other embodiments or other examples.

[0259] FIG4A is an interactive diagram of a reporting method according to an embodiment of the present disclosure. As shown in FIG4A , the present disclosure embodiment relates to a reporting method for a third device, the method comprising:

[0260] Step 4101: The third device receives a report from the first device based on the second information.

[0261] Step 4102: The third device performs AI positioning based on the report from the first device.

[0262] Optionally, the second information is obtained by performing a first processing on the first information, and the first processing is used to reduce the dimension of the first information; the first information is used to implement AI positioning, and the first information is determined based on the positioning reference signal, and the positioning reference signal is sent by at least one second device to the first device.

[0263] Optionally, the first information includes an M×N matrix, where M and N are both positive integers, where M is the number of second devices that send positioning reference signals, and N is the time domain sampling dimension of the positioning reference signal received by the first device. The element in the i-th row and j-th column of the M×N matrix is ​​determined based on the data at the j-th time domain sampling position of the positioning reference signal sent by the i-th second device received by the first device.

[0264] Optionally, the receiving a report performed by the first device based on the second information includes at least one of the following:

[0265] receiving the second information reported by the first device;

[0266] Receive a power value of data in the second information reported by the first device.

[0267] Optionally, performing AI positioning based on the report from the first device includes:

[0268] The information reported by the first device is input into the AI ​​model to obtain an AI positioning result.

[0269] For a detailed description of steps 4101-4102, please refer to the above embodiment.

[0270] The reporting method involved in the embodiment of the present disclosure may include at least one of steps S4101 and S4102. For example, step S4101 may be implemented as an independent embodiment, and step S4102 may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0271] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other embodiments or other examples.

[0272] Figure 5A is an interactive diagram of a reporting method according to an embodiment of the present disclosure. As shown in Figure 5A, the embodiment of the present disclosure relates to a reporting method for a communication system including a first device and a third device, wherein the method includes at least one of the following:

[0273] Step 5101: A first device receives a positioning reference signal sent by at least one second device, and determines first information based on the positioning reference signal.

[0274] Step 5102: The first device performs a first process on the first information to obtain second information;

[0275] Step 5103: The first device reports to the third device based on the second information;

[0276] Step 5104: The third device receives the report made by the first device based on the second information;

[0277] Step 5105: The third device performs AI positioning based on the report from the first device.

[0278] Optional implementations of steps 5101 to 5105 can be found in the above embodiments.

[0279] In some embodiments, the above method may include the method described in the above embodiments of the communication system side, the first device side, the third device side, etc., which will not be repeated here.

[0280] The reporting method involved in the embodiment of the present disclosure may include at least one of steps S5101 to S5105. For example, step S5101 may be implemented as an independent embodiment, and step S5102 may be implemented as an independent embodiment, but the present invention is not limited thereto.

[0281] In this embodiment or example, unless there is any contradiction, each step can be independent, arbitrarily combined or exchanged in order, the optional methods or optional examples can be arbitrarily combined, and can be arbitrarily combined with any steps of other embodiments or other examples.

[0282] The following is an exemplary introduction to the above method.

[0283] Application scenarios of the disclosed technical solution

[0284] For application scenarios where terminal positioning is completed based on AI models, the AI ​​model used for positioning is deployed in the LMF. The UE or BS obtains channel measurement data based on reference signal calculations and reports it to the LMF, and then inputs the AI ​​positioning model to obtain the positioning results.

[0285] Technical problems solved by the disclosed technical solution

[0286] Because the UE or BS needs to report channel measurement data used for AI positioning to the LMF, directly reporting raw channel measurement data such as CIR will result in significant data reporting overhead. While ensuring positioning accuracy, processing the channel measurement data to reduce its data dimension can reduce positioning data transmission overhead and the complexity of the AI ​​positioning model, which is conducive to promoting the practical application of AI-based terminal positioning technology in communication systems.

[0287] The complete content of the disclosed technical solution

[0288] The data dimension of the complete CIR is relatively high, but the data itself has certain distribution characteristics. In the dimension of time-domain sampling points, non-zero data usually concentrates on the front-segment sampling points, indicating the propagation path information of the high-power signals received by the receiving end. Near-zero data usually concentrates on the last-segment sampling points, and the CIR value is close to 0. Generally, the data with larger values in the CIR plays a more important role in positioning. Therefore, it can be considered to first process the CIR data in the dimension of time-domain sampling points to reduce the data volume, and then report the processed channel measurement data to the LMF, and input it into the AI model to complete the terminal positioning. The CIR is a complex matrix.

[0289] When processing the CIR data from the dimension of time-domain sampling points, some relatively direct data dimensionality reduction methods can be considered. Suppose the original dimension of the time-domain sampling points of the CIR data is N, and the data processing methods that can be adopted include:

[0290] (1)截取前M个数据作为AI定位模型的输入,M<N,例如选择256个时域采样点数据中的前64个数据,表示主要选取原CIR中的非零数据完成定位; (1) Intercept the first M data as the input of the AI positioning model, where M < N. For example, select the first 64 data from 256 time-domain sampling point data, indicating that mainly the non-zero data in the original CIR is selected to complete the positioning;

[0291] (2)均匀选取K个数据,K<N,例如在256个时域采样点数据中均匀选取64个数据,表示对原数据进行抽样,使用一个采样点处的数据代表其临近采样点的数据; (2) Uniformly select K data, where K < N. For example, uniformly select 64 data from 256 time-domain sampling point data, indicating that the original data is sampled, and the data at one sampling point is used to represent the data of its adjacent sampling points;

[0292] (3)首先计算每个时域采样点处数据的功率值,即每个采样点复数数据的模,然后选取功率值最大的P个数据,P<N,例如在256个时域采样点数据中选取64个功率值最大的数据,表示基于功率最强的接收路径完成定位。 (3) First calculate the power value of the data at each time-domain sampling point, that is, the modulus of the complex data at each sampling point, and then select the P data with the largest power values, where P < N. For example, select 64 data with the largest power values from 256 time-domain sampling point data, indicating that the positioning is completed based on the receiving path with the strongest power.

[0293] 上述处理方法直接对原始CIR进行数据的选取或抽样,不涉及数据本身的变换。在数据选取或抽样的过程中,可能造成对定位起到较大作用的关键数据的丢失,进而影响定位精确度。奇异值分解、特征分解、主成分分析等数据分析和降维方法,能够实现原始数据的降维,并且能够在获取的低纬度数据中保留关键特征信息,是对原始CIR进行数据处理更为合适的方法。 The above processing methods directly select or sample the original CIR data, without involving the transformation of the data itself. During the process of data selection or sampling, it may cause the loss of key data that plays a greater role in positioning, thereby affecting the positioning accuracy. Data analysis and dimensionality reduction methods such as singular value decomposition, eigen decomposition, and principal component analysis can achieve the dimensionality reduction of the original data and can retain key feature information in the obtained low-dimensional data, which are more suitable methods for processing the original CIR data.

[0294] 因此,本方案提出一种低数据传输开销的AI定位方法,在现有基于AI的终端定位方案的基础上引入信道测量数据处理方法,该方法主要实现原始CIR数据中关键信息的提取,获取低维度的关键信道测量数据作为AI定位模型的输入,以降低UE或BS向LMF上报信道测量数据的开销。 Therefore, this solution proposes an AI positioning method with low data transmission overhead, introducing a channel measurement data processing method based on the existing AI-based terminal positioning solution. This method mainly realizes the extraction of key information in the original CIR data, obtains low-dimensional key channel measurement data as the input of the AI positioning model, so as to reduce the overhead of the UE or BS reporting channel measurement data to the LMF.

[0295] FIG5B is an interactive diagram of a reporting method according to an embodiment of the present disclosure. As shown in FIG5B , the steps of this solution are as follows:

[0296] (1) The reference signal sending node sends a positioning reference signal to the channel data measurement node. When the reference signal sending node is the base station, the positioning reference signal is the PRS, and the channel data measurement node is the user equipment terminal (UE). When the reference signal sending node is the user equipment terminal (UE), the positioning reference signal is the SRS-Pos, and the channel data measurement node is the base station.

[0297] (2) The channel data measurement node receives the positioning reference signal and calculates and obtains the complete CIR data;

[0298] (3) The channel data measurement node applies data analysis and dimensionality reduction methods to process the original CIR measurement data in the time domain sampling point dimension to obtain low-dimensional key channel measurement data. Such methods may include singular value decomposition, eigenvalue decomposition, principal component analysis (PCA) dimensionality reduction, etc.

[0299] (4) The channel data measurement node reports the low-dimensional key channel measurement data to the LMF.

[0300] (5)LMF receives low-dimensional key channel measurement data, inputs it into the AI ​​positioning model, and obtains the positioning results, i.e., the terminal location coordinates.

[0301] In the above process, the channel data measurement node needs to report low-dimensional key channel measurement data to the positioning model inference node. New measurement values ​​are defined in the information reported by the measurement node UE or BS to the model inference node LMF: nr-KeyInformation nr-RSRP-KeyInformation

[0302] nr-DL-KeyInformation represents the low-dimensional key channel measurement data obtained by the channel data measurement node after processing the raw CIR data. nr-DL-RSRP-KeyInformation represents the power value of the low-dimensional key channel measurement data, that is, the modulus of the complex number at each position in nr-DL-KeyInformation. The power value has a similar effect to the original complex data. Therefore, in actual application of this solution, you can choose to report either one.

[0303] Optionally, the present disclosure is directed to application scenarios in which terminal positioning is completed based on an AI model, and is directed to an application mode in which the LMF receives channel measurement data reported by the UE or BS and inputs the AI ​​positioning model to obtain the terminal location coordinates. In order to solve the problem of high reporting overhead of channel measurement data used for positioning, the present disclosure proposes an AI positioning method with low data transmission overhead. After the UE or BS obtains the channel measurement data, it completes data analysis and dimensionality reduction processing. The reported processed low-latitude key channel measurement data can effectively reduce the reporting overhead of the channel measurement data used for positioning and the complexity of the AI ​​model while ensuring positioning accuracy, which is conducive to promoting the application of AI-based positioning solutions in actual communication systems.

[0304] The embodiments of the present disclosure further provide an apparatus for implementing any of the above methods. For example, an apparatus is provided, comprising units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another apparatus is provided, comprising units or modules for implementing each step performed by a network device (e.g., an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0305] It should be understood that the division of the various units or modules in the above device is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a physical entity, or they may be physically separated. In addition, the units or modules in the device may be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the various units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory within the device or a memory outside the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits, and the functions of some or all of the units or modules can be realized by designing the hardware circuits. The above-mentioned hardware circuits can be understood as one or more processors; for example, in one implementation, the above-mentioned hardware circuit is an application-specific integrated circuit (ASIC), which realizes the functions of some or all of the above units or modules by designing the logical relationship of the components in the circuit; for example, in another implementation, the above-mentioned hardware circuit can be realized by a programmable logic device (PLD). Taking a field programmable gate array (FPGA) as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by configuring the configuration file, thereby realizing the functions of some or all of the above units or modules. All units or modules of the above devices can be realized in the form of software called by the processor, or in the form of hardware circuits, or in part by the form of software called by the processor, and the rest by hardware circuits.

[0306] In the embodiments of the present disclosure, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationship of the hardware circuit. The logical relationship of the above-mentioned hardware circuit is fixed or reconfigurable. For example, the processor is a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and implementing the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc.

[0307] FIG6A is a schematic diagram of the structure of the first device proposed in an embodiment of the present disclosure. As shown in FIG6A , it includes:

[0308] a receiving module, configured to receive a positioning reference signal sent by at least one second device, and determine first information based on the positioning reference signal, where the first information is used to implement artificial intelligence (AI) positioning;

[0309] a processing module, configured to perform a first processing on the first information to obtain second information, wherein the first processing is used to reduce the dimension of the first information;

[0310] A sending module is used to report to a third device based on the second information.

[0311] Optionally, the processing module is used to execute the steps related to "processing" performed by the first device in any of the above methods, and the sending module is used to execute the steps related to "sending" performed by the first device in any of the above methods. The receiving module is used to execute the steps related to receiving performed by the first device in any of the above methods, and will not be repeated here.

[0312] Optionally, the processing module includes at least one of the following:

[0313] a first decomposition unit, configured to perform singular value decomposition on the first information to obtain the second information;

[0314] a second decomposition unit, configured to perform eigenvalue decomposition on the first information to obtain the second information;

[0315] an analyzing unit, configured to perform principal component analysis (PCA) on the first information to obtain the second information;

[0316] An interception unit is used to intercept and process the first information to obtain the second information.

[0317] Optionally, the first information includes an M×N matrix, where M and N are both positive integers, where M is the number of second devices that send positioning reference signals, and N is the time domain sampling dimension of the positioning reference signal received by the first device. The element in the i-th row and j-th column of the M×N matrix is ​​determined based on the data at the j-th time domain sampling position of the positioning reference signal sent by the i-th second device received by the first device.

[0318] Optionally, the first decomposition unit is further used for:

[0319] Performing singular value decomposition on the first information to obtain a unitary matrix U, a first matrix Σ, and a unitary matrix V corresponding to the first information;

[0320] The second information is determined based on at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V.

[0321] Optionally, the first decomposition unit is further used for at least one of the following:

[0322] Determine at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V as the second information;

[0323] intercepting part of the data of at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V, and determining the intercepted data of at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V as the second information;

[0324] Partial data of the unitary matrix U, the first matrix Σ, and the unitary matrix V are respectively intercepted, and the product of the intercepted data of the unitary matrix U, the first matrix Σ, and the unitary matrix V is determined as the second information.

[0325] Optionally, the second decomposition unit is further used for:

[0326] Performing eigenvalue decomposition on the first information to obtain an eigenvector matrix Q and a second matrix Σ corresponding to the first information;

[0327] Determine at least one of the eigenvector matrix Q and the second matrix Σ as the second information; and / or intercept part of the data in the eigenvector matrix Q and / or the second matrix Σ, and determine the intercepted data of at least one of the eigenvector matrix Q and the second matrix Σ as the second information.

[0328] Optionally, the analysis unit is further configured to:

[0329] Performing PCA on the first information to obtain third information, and determining the third information as the second information; and / or intercepting part of the data in the third information, and determining the intercepted data as the second information.

[0330] Optionally, the interception unit is further configured to perform at least one of the following:

[0331] intercepting data at first K time domain sampling positions from data at N time domain sampling positions corresponding to the reference signal sent by the second device to obtain the second information;

[0332] Uniformly extracting K data from the data at N time domain sampling positions corresponding to the reference signal sent by the second device to obtain the second information;

[0333] intercepting first K data with the largest power values ​​from the data at N time domain sampling positions corresponding to the reference signal sent by the second device to obtain the second information;

[0334] Wherein, K is a positive integer, K≤N.

[0335] Optionally, the first information includes a channel impulse response CIR, the M×N matrix is ​​a complex matrix, and the data in the M×N matrix is ​​complex data;

[0336] The data in the second information is plural data.

[0337] Optionally, the sending module further includes at least one of the following:

[0338] a first sending unit, configured to report the second information to the third device;

[0339] The second sending unit is configured to report the power value of the data in the second information to the third device.

[0340] Optionally, the power value is the modulus of the complex data in the second information.

[0341] Optionally, the first device is a terminal, the second device is a base station, and the positioning reference signal is a positioning reference signal PRS; or,

[0342] The first device is a base station, the second device is a terminal, and the positioning reference signal is a positioning sounding reference signal SRS-Pos.

[0343] Optionally, the third device is a location management function LMF.

[0344] FIG6B is a schematic diagram of the structure of the third device proposed in an embodiment of the present disclosure. As shown in FIG6B , it includes:

[0345] a receiving module, configured to receive a report from a first device based on second information; wherein the second information is obtained by performing a first processing on the first information, the first processing being used to reduce the dimension of the first information; the first information is used to implement AI positioning, the first information is determined based on the positioning reference signal, and the positioning reference signal is sent by at least one second device to the first device;

[0346] A processing module is used to perform AI positioning based on the report of the first device.

[0347] Optionally, the processing module is configured to execute the steps related to "processing" performed by the third device in any of the above methods, and the receiving module is configured to execute the steps related to receiving performed by the third device in any of the above methods. The third device may further include a sending module, wherein the sending module is configured to execute the steps related to "sending" performed by the third device in any of the above methods.

[0348] Optionally, the first information includes an M×N matrix, where M and N are both positive integers, where M is the number of second devices that send positioning reference signals, and N is the time domain sampling dimension of the positioning reference signal received by the first device. The element in the i-th row and j-th column of the M×N matrix is ​​determined based on the data at the j-th time domain sampling position of the positioning reference signal sent by the i-th second device received by the first device.

[0349] Optionally, the receiving module further includes at least one of the following:

[0350] a first receiving unit, configured to receive the second information reported by the first device;

[0351] The second receiving unit is configured to receive a power value of data in the second information reported by the first device.

[0352] Optionally, the processing module includes:

[0353] An input unit is used to input the information reported by the first device into the AI ​​model to obtain an AI positioning result.

[0354] Figure 7A is a schematic diagram of the structure of a communication device 7100 proposed in an embodiment of the present disclosure. Communication device 7100 can be a network device (e.g., an access network device, a core network device, etc.), a terminal (e.g., a user equipment, etc.), a chip, a chip system, or a processor that supports a network device to implement any of the above methods, or a chip, a chip system, or a processor that supports a terminal to implement any of the above methods. Communication device 7100 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.

[0355] As shown in Figure 7A, the communication device 7100 includes one or more processors 7101. The processor 7101 can be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication device (such as a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute programs, and process program data. The processor 7101 is used to call instructions to enable the communication device 7100 to perform any of the above methods.

[0356] In some embodiments, the communication device 7100 further includes one or more memories 7102 for storing instructions. Optionally, all or part of the memories 7102 may be located outside the communication device 7100.

[0357] In some embodiments, the communication device 7100 further includes one or more transceivers 7103. When the communication device 7100 includes one or more transceivers 7103, the communication steps such as sending and receiving in the above method are performed by the transceiver 7103, and the other steps are performed by the processor 7101.

[0358] In some embodiments, a transceiver may include a receiver and a transmitter, which may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, and transceiver circuit may be used interchangeably; the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be used interchangeably; and the terms receiver, receiving unit, receiver, and receiving circuit may be used interchangeably.

[0359] Optionally, the communication device 7100 further includes one or more interface circuits 7104, which are connected to the memory 7102. The interface circuits 7104 may be configured to receive signals from the memory 7102 or other devices, and may be configured to send signals to the memory 7102 or other devices. For example, the interface circuits 7104 may read instructions stored in the memory 7102 and send the instructions to the processor 7101.

[0360] The communication device 7100 described in the above embodiment may be a network device or a terminal, but the scope of the communication device 7100 described in the present disclosure is not limited thereto, and the structure of the communication device 7100 may not be limited by FIG. 7a. The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data or programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0361] 7B is a schematic diagram of the structure of a chip 7200 proposed in an embodiment of the present disclosure. If the communication device 7100 can be a chip or a chip system, please refer to the schematic diagram of the structure of the chip 7200 shown in FIG7B , but the present disclosure is not limited thereto.

[0362] The chip 7200 includes one or more processors 7201 , and the processor 7201 is used to call instructions so that the chip 7200 executes any of the above methods.

[0363] In some embodiments, chip 7200 further includes one or more interface circuits 7202, which are connected to memory 7203. Interface circuit 7202 can be used to receive signals from memory 7203 or other devices, and can be used to send signals to memory 7203 or other devices. For example, interface circuit 7202 can read instructions stored in memory 7203 and send the instructions to processor 7201. Optionally, the terms interface circuit, interface, transceiver pin, and transceiver are interchangeable.

[0364] In some embodiments, the chip 7200 further includes one or more memories 7203 for storing instructions. Alternatively, all or part of the memories 7203 may be located outside the chip 7200.

[0365] The present disclosure also proposes a storage medium having instructions stored thereon. When the instructions are executed on the communication device 7100, the communication device 7100 executes any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited thereto and may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but is not limited thereto and may also be a temporary storage medium.

[0366] The present disclosure also provides a program product, which, when executed by the communication device 7100, enables the communication device 7100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0367] The present disclosure also proposes a computer program, which, when executed on a computer, causes the computer to perform any one of the above methods.

[0368] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer programs. When the computer program is loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present disclosure are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer program can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer program can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0369] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0370] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0371] The above description is merely a specific embodiment of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this disclosure should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A reporting method, characterized in that: The method comprises: The first device receives a positioning reference signal sent by at least one second device, and determines first information based on the positioning reference signal, where the first information is used to implement artificial intelligence AI positioning; The first device performs a first process on the first information to obtain second information, wherein the first process is used to reduce the dimension of the first information; The first device reports to the third device based on the second information.

2. The method according to claim 1, characterized in that The performing a first processing on the first information to obtain the second information includes at least one of the following: Performing singular value decomposition on the first information to obtain the second information; Performing eigenvalue decomposition on the first information to obtain the second information; Performing principal component analysis (PCA) on the first information to obtain the second information; The first information is intercepted and processed to obtain the second information.

3. The method according to claim 2, characterized in that The first information includes an M×N matrix, where M and N are both positive integers, wherein M is the number of second devices that send positioning reference signals, and N is the time domain sampling dimension of the positioning reference signal received by the first device, and the element in the i-th row and j-th column of the M×N matrix is ​​determined based on the data at the j-th time domain sampling position of the positioning reference signal sent by the i-th second device received by the first device.

4. The method according to claim 2 or 3, characterized in that The performing singular value decomposition on the first information to obtain the second information includes: Performing singular value decomposition on the first information to obtain a unitary matrix U, a first matrix Σ, and a unitary matrix V corresponding to the first information; The second information is determined based on at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V.

5. The method according to claim 4, characterized in that The determining the second information based on at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V comprises at least one of the following: Determine at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V as the second information; intercepting part of the data of at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V, and determining the intercepted data of at least one of the unitary matrix U, the first matrix Σ, and the unitary matrix V as the second information; Partial data of the unitary matrix U, the first matrix Σ, and the unitary matrix V are respectively intercepted, and the product of the intercepted data of the unitary matrix U, the first matrix Σ, and the unitary matrix V is determined as the second information.

6. The method according to claim 2 or 3, characterized in that: The performing eigenvalue decomposition on the first information to obtain the second information includes: Performing eigenvalue decomposition on the first information to obtain an eigenvector matrix Q and a second matrix Σ corresponding to the first information; Determine at least one of the eigenvector matrix Q and the second matrix Σ as the second information; and / or intercept part of the data in the eigenvector matrix Q and / or the second matrix Σ, and determine the intercepted data of at least one of the eigenvector matrix Q and the second matrix Σ as the second information.

7. The method according to claim 2 or 3, characterized in that: The performing PCA on the first information to obtain the second information includes: Perform PCA on the first information to obtain third information, and determine the third information as the second information; and / or intercept part of the data in the third information, and determine the intercepted data as the second information.

8. The method according to claim 2 or 3, characterized in that: The intercepting and processing the first information to obtain the second information includes at least one of the following: intercepting data at first K time domain sampling positions from data at N time domain sampling positions corresponding to the reference signal sent by the second device to obtain the second information; Uniformly extracting K data from the data at N time domain sampling positions corresponding to the reference signal sent by the second device to obtain the second information; Extracting first K data with the largest power value from the data at N time domain sampling positions corresponding to the reference signal sent by the second device to obtain the second information; Wherein, K is a positive integer, K≤N.

9. The method according to any one of claims 3 to 8, characterized in that: The first information includes a channel impulse response CIR, the M×N matrix is ​​a complex matrix, and the data in the M×N matrix is ​​complex data; The data in the second information is plural data.

10. The method according to any one of claims 1 to 9, characterized in that: The reporting to the third device based on the second information includes at least one of the following: Reporting the second information to the third device; Report the power value of the data in the second information to the third device.

11. The method according to claim 10, characterized in that The power value is the modulus of the complex data in the second information.

12. The method according to any one of claims 1 to 11, characterized in that: The first device is a terminal, the second device is a base station, and the positioning reference signal is a positioning reference signal PRS; or The first device is a base station, the second device is a terminal, and the positioning reference signal is a positioning detection reference signal SRS-Pos.

13. The method according to any one of claims 1 to 12, characterized in that: The third device is a location management function LMF.

14. A reporting method, characterized in that: The method comprises: The third device receives a report from the first device based on the second information; wherein the second information is obtained by performing a first processing on the first information, and the first processing is used to reduce the dimension of the first information; the first information is used to implement AI positioning, and the first information is determined based on the positioning reference signal, and the positioning reference signal is sent by at least one second device to the first device; The third device performs AI positioning based on the report of the first device.

15. The method according to claim 14, characterized in that The first information includes an M×N matrix, where M and N are both positive integers, wherein M is the number of second devices that send positioning reference signals, and N is the time domain sampling dimension of the positioning reference signal received by the first device, and the element in the i-th row and j-th column of the M×N matrix is ​​determined based on the data at the j-th time domain sampling position of the positioning reference signal sent by the i-th second device received by the first device.

16. The method according to claim 14 or 15, characterized in that The receiving a report performed by the first device based on the second information includes at least one of the following: receiving the second information reported by the first device; Receive a power value of data in the second information reported by the first device.

17. The method according to any one of claims 14 to 16, characterized in that: The performing AI positioning based on the report of the first device includes: The information reported by the first device is input into the AI ​​model to obtain an AI positioning result.

18. A reporting method, characterized in that: Used in a communication system, the communication system comprising a first device and a third device, the method comprising at least one of the following: The first device receives a positioning reference signal sent by at least one second device, and determines first information based on the positioning reference signal, where the first information is used to implement artificial intelligence AI positioning; The first device performs a first process on the first information to obtain second information, wherein the first process is used to reduce the dimension of the first information; The first device reports to a third device based on the second information; The third device receives a report made by the first device based on the second information; The third device performs AI positioning based on the report of the first device.

19. A first device, comprising at least one of the following: A receiving module, configured to receive a positioning reference signal sent by at least one second device, and determine first information based on the positioning reference signal, wherein the first information is used to implement artificial intelligence AI positioning; A processing module is used to perform a first processing on the first information to obtain second information, wherein the first processing is used to perform a first processing on the first information Row dimensionality reduction; A sending module is used to report to a third device based on the second information.

20. A third device, comprising at least one of the following: A receiving module is used to receive a report made by the first device based on the second information; wherein, The second information is obtained by performing a first processing on the first information, the first processing is used to reduce the dimension of the first information; the first information is used to implement AI positioning, the first information is determined based on the positioning reference signal, and the positioning reference signal is sent by at least one second device to the first device; A processing module is used to perform AI positioning based on the report of the first device.

21. A communication device, characterized in that: include: one or more processors; The one or more processors are used to call instructions so that the communication device executes the reporting method described in any one of claims 1-13 and 14-17.

22. A communication system, characterized in that: It comprises a first device and a third device, wherein the first device is configured to implement the reporting method according to any one of claims 1 to 13, and the third device is configured to implement the reporting method according to any one of claims 14 to 17.

23. A storage medium storing instructions, characterized in that: When the instruction is executed on the communication device, the communication device executes the reporting method as described in any one of claims 1-13 and 14-17.

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