Positioning method, apparatus, terminal and network-side device

Abstract

The application discloses a positioning method, a positioning device, a terminal and a network side device, and belongs to the technical field of communication. The positioning method of the application comprises the following steps: a terminal determines configuration information; the terminal performs at least one of the following operations according to the configuration information: determining first model information, and performing positioning according to the first model information; and reporting positioning information.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of communication, and particularly relates to a positioning method, a device, a terminal and a network side equipment. BACKGROUND

[0002] At present, the positioning accuracy of the existing positioning mode is low, and cannot meet the demand of high-precision positioning. In order to improve the positioning accuracy, a model can be used for positioning, such as a machine learning model, an error model or a preprocessing model, etc., but there is no clear solution to the problems of how to determine the model information used for positioning and how to report the positioning information. SUMMARY

[0003] The embodiments of the present application provide a positioning method, a device, a terminal and a network side equipment, which can solve the problem of low positioning accuracy of the positioning mode in the related art.

[0004] In a first aspect, a positioning method is provided, comprising:

[0005] The terminal determines configuration information;

[0006] The terminal performs at least one of the following operations according to the configuration information:

[0007] determines first model information and performs positioning according to the first model information;

[0008] reports positioning information.

[0009] In a second aspect, a positioning method is provided, comprising:

[0010] The network side equipment sends configuration information, which is used for the terminal to perform positioning and / or for the terminal to report positioning information.

[0011] In a third aspect, a positioning device is provided, comprising:

[0012] A determination module is configured to determine configuration information;

[0013] An execution module is configured to perform at least one of the following operations according to the configuration information:

[0014] determines first model information and performs positioning according to the first model information;

[0015] reports positioning information.

[0016] In a fourth aspect, a positioning device is provided, comprising:

[0017] A sending module is configured to send configuration information, which is used for the terminal to perform positioning and / or for the terminal to report positioning information.

[0018] In a fifth aspect, a terminal is provided, which comprises a processor and a memory, the memory storing programs or instructions executable on the processor, and the programs or instructions, when executed by the processor, implement steps of the method according to the first aspect.

[0019] In a sixth aspect, a terminal is provided, which comprises a processor and a communication interface, wherein the processor is configured to determine configuration information, and perform at least one of the following operations according to the configuration information: determining first model information and performing positioning according to the first model information; and reporting positioning information.

[0020] In a seventh aspect, a network-side device is provided, which comprises a processor and a memory, the memory storing programs or instructions executable on the processor, and the programs or instructions, when executed by the processor, implement steps of the method according to the second aspect.

[0021] In an eighth aspect, a network-side device is provided, which comprises a processor and a communication interface, wherein the communication interface is configured to send configuration information, which is used by a terminal to perform positioning and / or by the terminal to report positioning information.

[0022] In a ninth aspect, a communication system is provided, which comprises a terminal and a network-side device, the terminal being configured to perform steps of the positioning method according to the first aspect, and the network-side device being configured to perform steps of the positioning method according to the second aspect.

[0023] In a tenth aspect, a readable storage medium is provided, which stores programs or instructions, and the programs or instructions, when executed by a processor, implement steps of the method according to the first aspect, or implement steps of the method according to the second aspect.

[0024] In an eleventh aspect, a chip is provided, which comprises a processor and a communication interface, the communication interface being coupled to the processor, and the processor being configured to execute programs or instructions to implement the method according to the first aspect, or implement steps of the method according to the second aspect.

[0025] In a twelfth aspect, a computer program / program product is provided, which is stored in a storage medium, and the computer program / program product is executed by at least one processor to implement steps of the positioning method according to the first aspect or the second aspect.

[0026] In the embodiments of the present application, the terminal determines configuration information; the terminal performs at least one of the following operations according to the configuration information: determining first model information and performing positioning according to the first model information; and reporting positioning information. The terminal can determine the first model information according to the configuration information, thereby selecting a corresponding model to perform positioning, improving positioning accuracy. In addition, the terminal can also report positioning information according to the configuration information, for example, selecting a corresponding reporting mode to report the positioning information according to the configuration information, so as to solve the problem of how to report the positioning information. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 is a structural diagram of a network system provided by the embodiments of the present application;

[0028] Figure 2 is a flowchart of a positioning method provided by the embodiments of the present application;

[0029] Figure 3 is another flowchart of a positioning method provided by the embodiments of the present application;

[0030] Figure 4 is a structural diagram of a first positioning device provided by the embodiments of the present application;

[0031] Figure 5 is a structural diagram of a second positioning device provided by the embodiments of the present application;

[0032] Figure 6 is a structural diagram of a communication device provided by the embodiments of the present application;

[0033] Figure 7 is a structural diagram of a terminal provided by the embodiments of the present application;

[0034] Figure 8 is a structural diagram of a network side device provided by the embodiments of the present application. DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present application will be described clearly below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present application.

[0036] The terms "first", "second", and the like in the description and in the claims of the present application are used for distinguishing between similar objects discussed in the specification and claims and do not necessarily describe a particular chronological or sequential order. It is to be understood that the terms so used are interchangeable under appropriate circumstances and that the embodiments of the present application are capable of functioning in other sequences than the one described herein. The term "first", "second", and the like, where used in the specification and in the claims, is used to distinguish between similar objects, not to describe a particular chronological or sequential order. It is to be understood that such terms are not always used consistently in the specification and claims, and that the embodiments of the present application are capable of functioning in other sequences than the one described herein. Furthermore, the terms "comprise", "comprising", "include", "including", and the like used in the specification and in the claims are used in the sense of "including but not limited to". The character " / " is generally used to represent "or", unless otherwise noted.

[0037] It is worth noting that the techniques described in the embodiments of the present application are not limited to Long Term Evolution (LTE) / LTE-Advanced (LTE-A) systems, but can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency Division Multiple Access (SC-FDMA), and other systems. The terms "system" and "network" are often used interchangeably in the embodiments of the present application, and the described techniques can be used in the above-mentioned systems and radio technologies, as well as in other systems and radio technologies. The following description describes a New Radio (NR) system for example purposes, and NR terminology is used in most of the following description, but these techniques can also be applied outside the NR system application, such as in a 6th Generation (6G) communication system.

[0038] Figure 1A block diagram of a wireless communication system to which embodiments of the present application can be applied is shown. The wireless communication system includes a terminal 11 and a network-side device 12. The terminal 11 can be a terminal-side device such as a mobile phone, a Tablet Personal Computer, a Laptop Computer, a Personal Digital Assistant (PDA), a palmtop computer, a netbook, an ultra-mobile personal computer (UMPC), a Mobile Internet Device (MID), an augmented reality (AR) / virtual reality (VR) device, a robot, a wearable device, a vehicle-mounted device (VUE), a pedestrian terminal (PUE), a smart home (a home device with a wireless communication function such as a refrigerator, a television, a washing machine, or furniture), a game console, a personal computer (PC), a kiosk, or a self-service machine, and the wearable device includes a smart watch, a smart bracelet, a smart earphone, smart glasses, smart jewelry (a smart bracelet, a smart necklace, a smart ring, a smart necklace, a smart anklet, a smart necklace, and the like), a smart wristband, smart clothing, and the like. It should be noted that the specific type of the terminal 11 is not limited in the embodiments of the present application. The network-side device 12 can include an access network device or a core network device, and the access network device 12 can also be referred to as a radio access network device, a radio access network (RAN), a radio access network function, or a radio access network unit. The access network device 12 can include a base station, a WLAN access point, or a WiFi node, and the base station can be referred to as a node B, an evolved node B (eNB), an access point, a base transceiver station (BTS), a radio base station, a radio transceiver, a basic service set (BSS), an extended service set (ESS), a home node B, a home evolved node B, a transmitting receiving point (TRP), or some other appropriate terminology in the art, as long as the same technical effects are achieved. The base station is not limited to a specific technical term, and it should be noted that only a base station in an NR system is taken as an example for description in the embodiments of the present application, and the specific type of the base station is not limited.

[0039] The positioning method provided by the embodiments of the present application will be described in detail below in combination with the accompanying drawings and some embodiments and application scenarios.

[0040] Please refer to Figure 2 , Figure 2 is a flowchart of a positioning method provided by the embodiments of the present application. The positioning method comprises the following steps.

[0041] Step 201: The terminal determines configuration information.

[0042] Step 202: The terminal performs at least one of the following operations according to the configuration information:

[0043] determines first model information and performs positioning according to the first model information;

[0044] reports positioning information.

[0045] Specifically, the terminal can receive configuration information sent by a network side device. The configuration information can include first model information or can not include first model information (in this case, the terminal already has the first model information, or the terminal can obtain the first model information through other ways, for example, a pre-configuration way). The terminal can determine the first model information according to the configuration information and perform positioning according to the first model information. The terminal can also report obtained positioning information (the way of obtaining the positioning information can be to obtain the positioning information according to the first model information or to obtain the positioning information according to other ways, which is not limited here) to the network side device according to the configuration information.

[0046] In the above, "performing positioning according to the first model information" can be understood as a process related to positioning, such as determining positioning measurement information, processing positioning measurement information, reporting positioning measurement information; determining positioning error, processing positioning error, reporting positioning error; determining position information, processing position information, reporting position information, etc.

[0047] According to the configuration information, different first model information can be determined, and the different first model information can be: 1) different model types, such as the models being machine learning models, preprocessing models, and error models, respectively; the terminal can determine, according to different contents or configuration manners of the configuration information, that the first model information includes machine learning model information, preprocessing model information, or error model information; 2) different input types or output types of the models, such as the terminal determining, according to different contents or configuration manners of the configuration information, the input and output of the model, and thus determining the first model information (the first model information includes the input and output of the model); 3) different parameter structures of the models, such as the terminal determining, according to different contents or configuration manners of the configuration information, the parameter structure of the model, and thus determining the first model information (the first model information includes the parameter structure of the model); 4) different generalization capabilities of the models, such as the terminal determining, according to different contents or configuration manners of the configuration information, the generalization capability of the model in the first model information, and thus determining the first model information. Specifically, the contents of the configuration information include positioning reference signal resource configuration information, positioning reference signal resource set configuration information, TRP configuration information, frequency layer configuration information, positioning method configuration information, and positioning scenario configuration information; and the configuration manners of the configuration information include per positioning reference signal resource (per PRS resource), per positioning reference signal resource set (per PRS resource set), per TRP (per TRP), per frequency layer (per Frequency layer), per positioning method (per positioning method), and per positioning scenario (per positioning scenario).

[0048] In this embodiment, the terminal determines configuration information; and the terminal performs at least one of the following operations according to the configuration information: determining first model information and performing positioning according to the first model information; and reporting positioning information. The terminal can determine the first model information according to the configuration information, so as to select a corresponding model to perform positioning, thereby improving positioning accuracy; in addition, the terminal can also report positioning information according to the configuration information, for example, selecting a corresponding reporting manner to report the positioning information according to the configuration information, so that in machine learning-based positioning, the reporting manner of the positioning information determined under different models (for example, machine learning models, error model information, or preprocessing models) or different configurations (different configurations can be understood as different contents included in the configuration information or different configuration manners) can be determined, and ambiguity can be avoided.

[0049] In the above, the configuration information includes at least one of the following:

[0050] (1) Positioning Reference Signal resource configuration information, for example, Positioning Reference Signal (PRS) resource or sounding reference signal (SRS) resource;

[0051] (2) Positioning Reference Signal resource set configuration information, for example, PRS resource set or SRS resource set;

[0052] (3) Frequency layer configuration information;

[0053] (4) TRP configuration information;

[0054] (5) Positioning method configuration information, wherein the positioning method includes but is not limited to: DownLink Time delay of arrival (DL-TDOA), multi round trip time (multi-RTT), Downlink Angle Of Departure (DL-AOD), Enhanced Cell-ID (E-CID), Observed Time Difference of Arrival (OTDOA) positioning, etc.

[0055] (6) Positioning scenario configuration information, wherein the positioning scenario includes but is not limited to: Urban Macro (Uma), Urban Micro (UMi), Indoor, Indoor Factory, Narrow Band Internet of Things (NB-IoT), RedCap, Extended Reality (XR), etc.

[0056] Among the above, the positioning information includes at least one of the following:

[0057] (1) measurement information, wherein the measurement information comprises at least one of the following: a channel impulse response (CIR), a power delay profile (PDP), a reference signal time difference (RSTD), a round-trip time (RTT), an angle of arrival (AoA), a reference signal receiving power (RSRP), a time of arrival (TOA), a power of a first path, a delay of a first path, a TOA of a first path, a reference signal time difference (RSTD) of a first path, an angle of arrival of a first path, an antenna subcarrier phase difference of a first path, a power of an additional path, a delay of an additional path, a TOA of an additional path, an RSTD of an additional path, an antenna subcarrier phase difference of an additional path, line of sight (LoS) identification information, not line of sight (NLoS) identification information, a mean excess delay root mean square, a delay spread, a coherence bandwidth, and the like.

[0058] (2) error information, wherein the error information comprises at least one of the following: a measurement error of a measurement quantity, an error of a model, an error of a model-related parameter, an error of a positioning result.

[0059] (3) a positioning result, wherein the positioning result comprises at least one of the following: an absolute position coordinate information calculated by a terminal, a relative position coordinate information calculated by a terminal, coordinate system-related information.

[0060] (4) machine learning model update information, which can comprise an update of a machine learning model parameter, an update of a machine learning model structure, and the like;

[0061] (5) error model update information, which can comprise an update of an error model parameter, an update of an error model structure, and the like;

[0062] (6) pre-processing model update information, which can comprise an update of a pre-processing model parameter, an update of a pre-processing model structure, and the like.

[0063] It should be noted that the additional path is a path (for example, a multipath) other than the first path, and the additional path can comprise at least one path, and the maximum number of additional paths can comprise one of the following: 4, 8, 16, 32, 64 paths.

[0064] In an embodiment of the present application, the method further comprises: receiving, by the terminal, first information sent by the network side device;

[0065] The first information comprises at least one of the following:

[0066] (1) the first model information; wherein the first model information comprises at least one of the following: machine learning model information; error model information; pre-processing model information.

[0067] (2) indication information, the indication information being used to instruct the terminal to report the positioning information.

[0068] Specifically, the indication information is used to instruct at least one of the following:

[0069] (a) used to instruct the terminal to report measurement information, the measurement information comprising but not limited to at least one of the following: CIR, PDP, RSTD, RTT, AoA, RSRP, TOA, power of the first path; time delay of the first path; TOA of the first path; RSTD of the first path; angle of arrival of the first path; antenna subcarrier phase difference of the first path; power of other paths; time delay of other paths; TOA of other paths; RSTD of other paths; antenna subcarrier phase difference of other paths, LoS identification information, NLoS identification information, mean excess delay root mean square, time delay spread, coherence bandwidth, etc.

[0070] (b) used to instruct the terminal to report error information, the first error information comprising but not limited to at least one of the following: measurement error of the measurement quantity, error of the model, error of the model related parameter, error of the positioning result;

[0071] (c) used to instruct the terminal to report the positioning result, the positioning result comprising but not limited to at least one of the following: absolute position coordinate information calculated by the terminal, relative position coordinate information calculated by the terminal, coordinate system related information.

[0072] The first information can be sent separately from the configuration information or can be carried in the configuration information, and when carried in the configuration information, can be carried in at least one of the following:

[0073] carried in each positioning reference signal resource configuration information;

[0074] carried in each positioning reference signal resource set configuration information;

[0075] carried in each frequency layer configuration information;

[0076] carried in each TRP configuration information;

[0077] carried in each positioning method configuration information;

[0078] is carried in each positioning scenario configuration information.

[0079] The indication information is used to instruct the terminal to report the positioning information. The indication information can only instruct the terminal to report the positioning information, or the indication information instructs the terminal to report the positioning information and indicates the reporting manner of the positioning information.

[0080] The reporting manner of the positioning information is determined by at least one of the following:

[0081] (1) According to the type of the first model information. The first model information includes at least one of the following: machine learning model information; error model information; preprocessing model information. The type of the first model information can be determined according to the content included in the first model information. For example, the case where the first model information includes machine learning model information and the case where the first model information includes preprocessing model information correspond to different types; the case where the first model information includes machine learning model information and the case where the first model information includes error model information correspond to different types.

[0082] The type of the first model information can also be determined according to the information of the machine learning model information, error model information, or preprocessing model information itself. For example, for a machine learning model, different model types correspond to different input and output quantities; different model types correspond to different generalization capabilities; different model structures and parameter information also correspond to different types.

[0083] For a preprocessing model, different model types correspond to different input and output quantities; different model structures and parameter information also correspond to different types. For error model information, different error types can be different, such as mean square error or Euclidean distance; different model structures and parameter information also correspond to different types.

[0084] The reporting manner of the positioning information is related to the type of the first model information. For example, if the machine learning model information used or configured is associated with each positioning reference signal resource, the positioning information is also reported on each positioning reference signal resource.

[0085] (2) According to the sending manner of the first model information. For example, if the machine learning model information used or configured is sent in association with each positioning reference signal resource, the positioning information is also reported on each positioning reference signal resource.

[0086] (3) According to the indication information. For example, the indication information can indicate the reporting manner of the positioning information. The reporting manner of the positioning information includes at least one of the following:

[0087] each positioning reference signal resource is transmitted;

[0088] each positioning reference signal resource set is transmitted;

[0089] each TRP is transmitted;

[0090] each frequency layer is transmitted;

[0091] each positioning method is transmitted;

[0092] each positioning scenario is transmitted.

[0093] (4) The reporting manner of the positioning information is determined according to the transmission manner of the indication information. The reporting manner of the positioning information is the same as the transmission manner of the indication information, for example, if the indication information is associated with the transmission of the positioning reference signal resource set, the positioning information is also associated with the reporting of the positioning reference signal resource set.

[0094] (5) In addition to the above-mentioned determination of the reporting manner of the positioning information, the terminal can also determine the reporting manner by itself.

[0095] In the case of determining the reporting manner of the positioning information according to the above (1)-(5), the terminal needs to report the identification information corresponding to the target reporting manner at the same time, wherein the target manner is the reporting manner determined by the terminal according to one of the above (1)-(5). That is, in the case of reporting the positioning information by using the target reporting manner, the positioning information also includes the identification information corresponding to the target reporting manner, and the identification information includes at least one of the following:

[0096] positioning reference signal resource identification information, for example, the ID of the positioning reference signal resource;

[0097] positioning reference signal resource set identification information, for example, the ID of the positioning reference signal resource set;

[0098] TRP identification information, for example, the ID of the TRP;

[0099] frequency layer identification information, for example, the ID of the frequency layer;

[0100] positioning method identification information, for example, the ID of the positioning method;

[0101] positioning scenario identification information, for example, the ID of the positioning scenario.

[0102] In an embodiment of the present application, the machine learning model information includes at least one of the following:

[0103] (1) At least one machine learning model. The at least one machine learning model can include a commonly used machine learning model, a neural network model or a deep neural network model, and the at least one machine learning model includes at least one of the following:

[0104] Convolutional Neural Networks (CNN);

[0105] Recurrent Neural Network (RNN);

[0106] Long-Short Term Memory (LSTM);

[0107] Recursive Neural Tensor Network (RNTN);

[0108] Generative Adversarial Networks (GAN);

[0109] Deep Belief Network (DBN);

[0110] Restricted Boltzmann Machine (RBM).

[0111] Optionally, the at least one machine learning model comprises a multi-step machine learning model (one step can correspond to one machine learning model). The multi-step machine learning model comprises at least one of the following:

[0112] a multi-step machine learning model distinguished according to input information type and output information type;

[0113] a multi-step machine learning model distinguished according to different model parameters;

[0114] a multi-step machine learning model distinguished according to different generalization capabilities.

[0115] The multi-step machine learning model can be sent in association with information included in configuration information, for example, the multi-step machine learning model can be sent in association with different configuration information according to different model types. The different model types include different input and output quantities, different generalization capabilities, different model structures and parameter information, etc. According to the different types, each model in the multi-step machine learning model can be sent in association with different configuration information, for example, the first model is sent in association with each positioning method, the second model is sent in association with each positioning reference signal resource, etc.

[0116] (2) parameters of the at least one machine learning model. The parameters of the at least one machine learning model comprise at least one of the following: weights of layers; step length; mean; variance.

[0117] (3) input information of the machine learning model; the input information of the machine learning model comprises at least one of the following:

[0118] CIR; PDP; RSTD; RTT; AoA; RSRP; TOA; power of a first path; time delay of a first path; TOA of a first path; RSTD of a first path; angle of arrival of a first path; antenna subcarrier phase difference of a first path; power of another path; time delay of another path; TOA of another path; RSTD of another path; angle of arrival of another path; antenna subcarrier phase difference of another path; LoS identification information; NLoS identification information; average excess time delay; root mean square time delay spread; coherent bandwidth.

[0119] Further, the above input information can be single-station or multi-station, and the single-station or multi-station information is determined by base station quantity information issued by a network side device, and the base station quantity comprises 1-maxTRPNumber (maximum TRP quantity), and maxTRPNumber is the maximum quantity of TRPs in a specific scenario.

[0120] (4) output information of the machine learning model. The output information of the machine learning model comprises at least one of the following:

[0121] position coordinate information; RSTD; RTT; AoA; RSRP; TOA; power of a first path; time delay of a first path; TOA of a first path; RSTD of a first path; angle of arrival of a first path; power of another path; time delay of another path; TOA of another path; RSTD of another path; angle of arrival of another path; LoS identification information; NLoS identification information.

[0122] In another embodiment of the present application, the error model information comprises at least one of the following:

[0123] (1) at least one error value estimated by a network side device; wherein the error value comprises at least one of the following: a position error value, a measurement error value, a model error value and a parameter error value.

[0124] (2) an error model estimated by the at least one network side device; wherein the error model comprises at least one of the following: a position error model, a measurement error model and a parameter error model.

[0125] (3) a parameter of the error model estimated by the at least one network side device;

[0126] (4) input information of the error model;

[0127] (5) output information of the error model.

[0128] If the error model information is used to calibrate the position information, the input information of the error model comprises the initial position of the terminal or the position calculated by the terminal, and the output information of the error model comprises the position information after error calibration.

[0129] If the error model information is used to calibrate the measurement information, the input information of the error model comprises the initial first measurement information, and the first measurement information comprises at least one of the following: CIR, PDP, RSTD, RTT, AoA, RSRP, TOA, power of the first path, time delay of the first path, TOA of the first path, RSTD of the first path, arrival angle of the first path, antenna subcarrier phase difference of the first path, power of other paths, time delay of other paths, TOA of other paths, RSTD of other paths, arrival angle of other paths, antenna subcarrier phase difference of other paths, LoS identification information, NLoS identification information, mean excess delay root mean square, time delay spread and coherence bandwidth; and the output information of the error model comprises the first measurement information after error calibration.

[0130] If the error model information is used to calibrate the model information or the model-related parameter information, the input information of the error model comprises at least one of the following: a machine learning model, parameters of the machine learning model, a preprocessing model or parameters of the preprocessing model; and the output information of the error model comprises at least one of the following: a calibrated machine learning model, parameters of the calibrated machine learning model, a calibrated preprocessing model or parameters of the calibrated preprocessing model.

[0131] In an embodiment of the present application, the preprocessing model information is used to preprocess the terminal measurement information, so that the processed measurement information can be better trained or processed by the machine learning model, and the preprocessing model information comprises at least one of the following:

[0132] Filter parameters or structures;

[0133] Convolution layer parameters or structures;

[0134] Pooling layer parameters or structures;

[0135] Discrete cosine transform (DCT) parameters or structures;

[0136] Wavelet transform parameters or structures;

[0137] Parameters or structures for preprocessing the measurement information. That is, parameters or structures of the processing method of the measurement information, for example, sampling, truncation, normalization, simultaneous combination and the like.

[0138] The input information of the preprocessing model information includes second measurement information, and the second measurement information includes at least one of the following: CIR, PDP, RSTD, RTT, AoA, RSRP, TOA, power of a first path, time delay of the first path, TOA of the first path, RSTD of the first path, arrival angle of the first path, antenna subcarrier phase difference of the first path, power of another path, time delay of the another path, TOA of the another path, RSTD of the another path, arrival angle of the another path, antenna subcarrier phase difference of the another path, reference signal waveform, and correlation sequence of the reference signal; and the output information of the preprocessing model information includes the second measurement information after preprocessing.

[0139] In an embodiment of the present application, the method further includes: the terminal sending request information, the request information being used for requesting a sending mode of the first information. The sending mode of the first information includes at least one of the following:

[0140] Each positioning reference signal resource sending; each positioning reference signal resource set sending; each TRP sending; each frequency layer sending; each positioning method sending; and each positioning scenario sending.

[0141] In an embodiment of the present application, at least one of the error model information and the preprocessing model information can also be associated with machine learning model information, that is, the error model information and / or the preprocessing model information are broadcast per machine learning model, that is, under each machine learning model, the preprocessing model and the error model corresponding to the machine learning model are broadcast, used for preprocessing the input quantity of the machine learning, or error processing the error of the machine learning model, the model parameter, and the output quantity.

[0142] In an embodiment of the present application, the method further includes: the terminal sending terminal positioning capability information, the terminal positioning capability information including at least one of the following:

[0143] Whether to support machine learning-based positioning;

[0144] Whether to support receiving machine learning model information of at least one of the following: each positioning reference signal resource, each positioning reference signal resource set, each TRP, each frequency layer, each positioning method, and each positioning scenario;

[0145] Whether to support receiving error model information of at least one of the following: each positioning reference signal resource, each positioning reference signal resource set, each TRP, each frequency layer, each positioning method, and each positioning scenario;

[0146] Whether to support receiving preprocessing model information of at least one of the following: each positioning reference signal resource, each positioning reference signal resource set, each TRP, each frequency layer, each positioning method, and each positioning scenario;

[0147] whether receiving multiple machine learning models is supported;

[0148] a maximum number of machine learning models that can be received;

[0149] whether receiving parameters of multiple machine learning models is supported;

[0150] a maximum number of parameters of machine learning models that can be received;

[0151] whether receiving multiple preprocessing models is supported;

[0152] a maximum number of preprocessing models that can be received;

[0153] whether receiving parameters of multiple preprocessing models is supported;

[0154] a maximum number of parameters of preprocessing models that can be received;

[0155] whether receiving multiple error models is supported;

[0156] a maximum number of error models that can be received;

[0157] whether receiving parameters of multiple error models is supported;

[0158] a maximum number of parameters of error models that can be received;

[0159] input information of a supported machine learning model;

[0160] output information of a supported machine learning model;

[0161] input information of a supported preprocessing model;

[0162] output information of a supported preprocessing model;

[0163] input information of a supported error model;

[0164] output information of a supported error model.

[0165] See Figure 3 , Figure 3 is a flowchart of a positioning method provided by an embodiment of the present application. The positioning method comprises:

[0166] a network-side device sends configuration information, which is used for terminal positioning and / or for the terminal to report positioning information.

[0167] Specifically, the terminal can receive configuration information sent by the network side device. The configuration information can include first model information, or can not include the first model information (in this case, the terminal already has the first model information, or the terminal can obtain the first model information through other manners (for example, pre-configuration manner)). The terminal can determine the first model information according to the configuration information, and perform positioning according to the first model information. The terminal can also report the obtained positioning information (the obtaining manner of the positioning information can be positioning according to the first model information, or positioning according to other manners, which is not limited here) to the network side device according to the configuration information.

[0168] In the above, the "positioning" can be understood as a process related to positioning, such as determining positioning measurement information, processing positioning measurement information, reporting positioning measurement information; determining positioning error, processing positioning error, reporting positioning error; determining position information, processing position information, reporting position information, and the like.

[0169] In this embodiment, the network side device sends configuration information, and the configuration information is used for the terminal to perform positioning and / or the terminal to report positioning information. The terminal can determine the first model information according to the configuration information, so as to select a corresponding model to perform positioning, improve positioning accuracy, or the terminal can also report positioning information according to the configuration information, for example, select a corresponding reporting manner to report the positioning information according to the configuration information.

[0170] Optionally, the configuration information includes at least one of the following:

[0171] Positioning reference signal resource configuration information;

[0172] Positioning reference signal resource set configuration information;

[0173] Frequency layer configuration information;

[0174] Transmission and reception point (TRP) configuration information;

[0175] Positioning method configuration information;

[0176] Positioning scene configuration information.

[0177] Optionally, the configuration information carries first information:

[0178] The first information includes at least one of the following:

[0179] First model information;

[0180] Indication information, the indication information is used to indicate that the terminal reports the positioning information.

[0181] Optionally, the indication information is used to indicate the reporting manner of the positioning information.

[0182] Optionally, the reporting manner of the positioning information is determined by at least one of the following:

[0183] According to the type of the first model information;

[0184] According to the sending manner of the first model information;

[0185] According to the indication information;

[0186] According to the sending manner of the indication information.

[0187] Optionally, the first model information includes at least one of the following:

[0188] Machine learning model information;

[0189] Error model information;

[0190] Preprocessing model information.

[0191] Optionally, the machine learning model information includes at least one of the following:

[0192] At least one machine learning model;

[0193] Parameters of the at least one machine learning model;

[0194] Input information of the machine learning model;

[0195] Output information of the machine learning model.

[0196] Optionally, the at least one machine learning model includes at least one of the following:

[0197] Convolutional neural network CNN;

[0198] Recurrent neural network RNN;

[0199] Long short-term memory LSTM;

[0200] Recursive neural tensor network RNTN;

[0201] Generative adversarial network GAN;

[0202] Deep belief network DBN;

[0203] Restricted Boltzmann machine RBM.

[0204] Optionally, the parameters of the at least one machine learning model include at least one of the following:

[0205] Weights of each layer; step length; mean; variance.

[0206] Optionally, the input information of the machine learning model comprises at least one of the following:

[0207] Channel impulse response (CIR); power delay profile (PDP); reference signal time difference (RSTD); round trip time (RTT); angle of arrival (AoA); reference signal received power (RSRP); time of arrival (TOA); power of the first path; delay of the first path; TOA of the first path; RSTD of the first path; angle of arrival of the first path; antenna subcarrier phase difference of the first path; power of other paths; delay of other paths; TOA of other paths; RSTD of other paths; angle of arrival of other paths; antenna subcarrier phase difference of other paths; LoS identification information; NLoS identification information; mean excess delay; root mean square delay spread; coherence bandwidth.

[0208] Optionally, the output information of the machine learning model comprises at least one of the following:

[0209] Position coordinate information; RSTD; RTT; AoA; RSRP; TOA; power of the first path; delay of the first path; TOA of the first path; RSTD of the first path; angle of arrival of the first path; power of other paths; delay of other paths; TOA of other paths; RSTD of other paths; angle of arrival of other paths; LoS identification information; NLoS identification information.

[0210] Optionally, the error model information comprises at least one of the following:

[0211] An error value estimated by the at least one network-side device;

[0212] An error model estimated by the at least one network-side device;

[0213] A parameter of the error model estimated by the at least one network-side device;

[0214] Input information of the error model;

[0215] Output information of the error model.

[0216] Optionally, the error value comprises at least one of the following: a position error value, a measurement error value, a model error value, and a parameter error value.

[0217] Optionally, the error model comprises at least one of the following: a position error model, a measurement error model, and a parameter error model.

[0218] Optionally, the input information of the error model comprises a terminal initial position or a terminal calculated position, and the output information of the error model comprises position information after error calibration.

[0219] Optionally, the input information of the error model comprises initial first measurement information, and the first measurement information comprises at least one of the following: CIR, PDP, RSTD, RTT, AoA, RSRP, TOA, power of a first path, time delay of a first path, TOA of a first path, RSTD of a first path, angle of arrival of a first path, antenna subcarrier phase difference of a first path, power of another path, time delay of another path, TOA of another path, RSTD of another path, angle of arrival of another path, antenna subcarrier phase difference of another path, LoS identification information, NLoS identification information, mean excess delay root mean square, delay spread, and coherence bandwidth.

[0220] The output information of the error model comprises the first measurement information after error calibration.

[0221] Optionally, the input information of the error model comprises at least one of the following: a machine learning model, parameters of a machine learning model, a preprocessing model, or parameters of a preprocessing model.

[0222] The output information of the error model comprises at least one of the following: a calibrated machine learning model, parameters of a calibrated machine learning model, a calibrated preprocessing model, or parameters of a calibrated preprocessing model.

[0223] Optionally, the preprocessing model information comprises at least one of the following:

[0224] Filter parameters or structures.

[0225] Convolution layer parameters or structures.

[0226] Pooling layer parameters or structures.

[0227] Discrete cosine transform parameters or structures.

[0228] Wavelet transform parameters or structures.

[0229] Parameters or structures for preprocessing measurement information.

[0230] Optionally, the input information of the preprocessing model information comprises second measurement information.

[0231] The second measurement information comprises at least one of the following: CIR, PDP, RSTD, RTT, AoA, RSRP, TOA, power of a first path, time delay of a first path, TOA of a first path, RSTD of a first path, angle of arrival of a first path, antenna subcarrier phase difference of a first path, power of another path, time delay of another path, TOA of another path, RSTD of another path, angle of arrival of another path, antenna subcarrier phase difference of another path, reference signal waveform, and correlation sequence of a reference signal.

[0232] The output information of the pre-processing model information includes second measurement information after pre-processing.

[0233] Optionally, the at least one machine learning model includes a multi-step machine learning model.

[0234] Optionally, the multi-step machine learning model includes at least one of the following:

[0235] A multi-step machine learning model distinguished according to input information types and output information types;

[0236] A multi-step machine learning model distinguished according to different model parameters;

[0237] A multi-step machine learning model distinguished according to different generalization capabilities.

[0238] Optionally, the positioning information includes at least one of the following:

[0239] Measurement information;

[0240] Error information;

[0241] Positioning result;

[0242] Machine learning model update information

[0243] Error model update information;

[0244] Pre-processing model update information.

[0245] Optionally, the method further includes:

[0246] The network-side device receives request information sent by the terminal, and the request information is used to request a sending mode of the first information.

[0247] Optionally, the method further includes:

[0248] The network-side device receives terminal positioning capability information sent by the terminal, and the terminal positioning capability information includes at least one of the following:

[0249] Whether to support machine learning-based positioning;

[0250] Whether to support receiving machine learning model information for at least one of each positioning reference signal resource, each positioning reference signal resource set, each TRP, each frequency layer, each positioning method, and each positioning scenario;

[0251] Whether to support receiving error model information for at least one of each positioning reference signal resource, each positioning reference signal resource set, each TRP, each frequency layer, each positioning method, and each positioning scenario;

[0252] whether support receiving pre-processing model information in at least one of each positioning reference signal resource, each positioning reference signal resource set, each TRP, each frequency layer, each positioning method, and each positioning scenario;

[0253] whether support receiving multiple machine learning models;

[0254] maximum number of machine learning models that can be received;

[0255] whether support receiving parameters of multiple machine learning models;

[0256] maximum number of parameters of machine learning models that can be received;

[0257] whether support receiving multiple pre-processing models;

[0258] maximum number of pre-processing models that can be received;

[0259] whether support receiving parameters of multiple pre-processing models;

[0260] maximum number of parameters of pre-processing models that can be received;

[0261] whether support receiving multiple error models;

[0262] maximum number of error models that can be received;

[0263] whether support receiving parameters of multiple error models;

[0264] maximum number of parameters of error models that can be received;

[0265] input information of supported machine learning models;

[0266] output information of supported machine learning models;

[0267] input information of supported pre-processing models;

[0268] output information of supported pre-processing models;

[0269] input information of supported error models;

[0270] output information of supported error models.

[0271] The present application Figure 2 The provided positioning method can be executed by a first positioning device. In the embodiments of the present application, the first positioning device is taken as an example to illustrate the present application Figure 2 The device of the positioning method provided in the embodiments.

[0272] As Figure 4 shown, the first positioning device 400 provided in the embodiments of the present application comprises:

[0273] The configuration module 401 is configured to determine configuration information.

[0274] The execution module 402 is configured to perform at least one of the following operations according to the configuration information:

[0275] determine first model information and perform positioning according to the first model information;

[0276] report the positioning information.

[0277] Optionally, the configuration information includes at least one of the following:

[0278] positioning reference signal resource configuration information;

[0279] positioning reference signal resource set configuration information;

[0280] frequency layer configuration information;

[0281] transmission and reception point (TRP) configuration information;

[0282] positioning method configuration information;

[0283] positioning scenario configuration information.

[0284] Optionally, the apparatus further includes a receiving module configured to receive first information sent by a network side device.

[0285] The first information includes at least one of the following:

[0286] the first model information;

[0287] indication information, the indication information being used to instruct the terminal to report the positioning information.

[0288] Optionally, the first information is carried in the configuration information.

[0289] Optionally, the indication information is used to instruct a reporting manner of the positioning information.

[0290] Optionally, the reporting manner of the positioning information is determined by at least one of the following:

[0291] according to a type of the first model information;

[0292] according to a sending manner of the first model information;

[0293] according to the indication information;

[0294] according to a sending manner of the indication information.

[0295] Optionally, the reporting manner of the positioning information includes at least one of the following:

[0296] each positioning reference signal resource;

[0297] each set of positioning reference signal resources;

[0298] each TRP;

[0299] each frequency layer;

[0300] each positioning method;

[0301] each positioning scenario.

[0302] Optionally, in the case of reporting the positioning information in a target reporting manner, the positioning information further comprises identification information corresponding to the target reporting manner, and the identification information comprises at least one of the following:

[0303] positioning reference signal resource identification information;

[0304] positioning reference signal resource set identification information;

[0305] TRP identification information;

[0306] frequency layer identification information;

[0307] positioning method identification information;

[0308] positioning scenario identification information.

[0309] Optionally, the first model information comprises at least one of the following:

[0310] machine learning model information;

[0311] error model information;

[0312] preprocessing model information.

[0313] Optionally, the machine learning model information comprises at least one of the following:

[0314] at least one machine learning model;

[0315] parameters of the at least one machine learning model;

[0316] input information of the machine learning model;

[0317] output information of the machine learning model.

[0318] Optionally, the at least one machine learning model comprises at least one of the following:

[0319] convolutional neural network (CNN);

[0320] recurrent neural network (RNN);

[0321] recurrent neural network LSTM;

[0322] recurrent tensor neural network RNTN;

[0323] generative adversarial network GAN;

[0324] deep belief network DBN;

[0325] restricted Boltzmann machine RBM.

[0326] Optionally, the parameters of the at least one machine learning model comprise at least one of:

[0327] weights of layers; steps; mean values; variances.

[0328] Optionally, the input information of the machine learning model comprises at least one of:

[0329] channel impulse response CIR; power delay profile PDP; reference signal time difference RSTD; round trip time RTT; angle of arrival AoA; reference signal received power RSRP; time of arrival TOA; power of a first path; delay of a first path; TOA of a first path; RSTD of a first path; angle of arrival of a first path; antenna subcarrier phase difference of a first path; power of other paths; delay of other paths; TOA of other paths; RSTD of other paths; angle of arrival of other paths; antenna subcarrier phase difference of other paths; LoS identification information; NLoS identification information; mean excess delay; root mean square delay spread; coherence bandwidth.

[0330] Optionally, the output information of the machine learning model comprises at least one of:

[0331] position coordinate information; RSTD; RTT; AoA; RSRP; TOA; power of a first path; delay of a first path; TOA of a first path; RSTD of a first path; angle of arrival of a first path; power of other paths; delay of other paths; TOA of other paths; RSTD of other paths; angle of arrival of other paths; LoS identification information; NLoS identification information.

[0332] Optionally, the error model information comprises at least one of:

[0333] an error value estimated by the at least one network-side device;

[0334] an error model estimated by the at least one network-side device;

[0335] parameters of the error model estimated by the at least one network-side device;

[0336] input information of the error model;

[0337] The output information of the error model.

[0338] Optionally, the error value comprises at least one of a position error value, a measurement error value, a model error value and a parameter error value.

[0339] Optionally, the error model comprises at least one of a position error model, a measurement error model and a parameter error model.

[0340] Optionally, the input information of the error model comprises a terminal initial position or a terminal calculated position, and the output information of the error model comprises position information after error calibration.

[0341] Optionally, the input information of the error model comprises initial first measurement information, and the first measurement information comprises at least one of CIR, PDP, RSTD, RTT, AoA, RSRP, TOA, power of a first path, time delay of a first path, TOA of a first path, RSTD of a first path, arrival angle of a first path, antenna subcarrier phase difference of a first path, power of another path, time delay of another path, TOA of another path, RSTD of another path, arrival angle of another path, antenna subcarrier phase difference of another path, LoS identification information, NLoS identification information, mean excess delay root mean square, delay spread and coherence bandwidth.

[0342] The output information of the error model comprises first measurement information after error calibration.

[0343] Optionally, the input information of the error model comprises at least one of a machine learning model, parameters of a machine learning model, a preprocessing model or parameters of a preprocessing model.

[0344] The output information of the error model comprises at least one of a calibrated machine learning model, parameters of a calibrated machine learning model, a calibrated preprocessing model or parameters of a calibrated preprocessing model.

[0345] Optionally, the preprocessing model information comprises at least one of:

[0346] Filter parameters or structures;

[0347] Convolution layer parameters or structures;

[0348] Pooling layer parameters or structures;

[0349] Discrete cosine transform parameters or structures;

[0350] Wavelet transform parameters or structures;

[0351] Parameters or structures for preprocessing measurement information.

[0352] Optionally, the input information of the preprocessing model information comprises second measurement information.

[0353] The second measurement information comprises at least one of the following: CIR, PDP, RSTD, RTT, AoA, RSRP, TOA, power of a first path, time delay of a first path, TOA of a first path, RSTD of a first path, angle of arrival of a first path, antenna subcarrier phase difference of a first path, power of an other path, time delay of an other path, TOA of an other path, RSTD of an other path, angle of arrival of an other path, antenna subcarrier phase difference of an other path, reference signal waveform, and correlation sequence of a reference signal.

[0354] The output information of the preprocessing model information comprises the second measurement information after preprocessing.

[0355] Optionally, the at least one machine learning model comprises a multi-step machine learning model.

[0356] Optionally, the multi-step machine learning model comprises at least one of the following:

[0357] The multi-step machine learning model is distinguished according to input information types and output information types;

[0358] The multi-step machine learning model is distinguished according to different model parameters;

[0359] The multi-step machine learning model is distinguished according to different generalization capabilities.

[0360] Optionally, the positioning information comprises at least one of the following:

[0361] Measurement information;

[0362] Error information;

[0363] Positioning result;

[0364] Machine learning model update information

[0365] Error model update information;

[0366] Preprocessing model update information.

[0367] Optionally, the apparatus further comprises a first sending module configured to send request information, wherein the request information is used to request a sending mode of the first information.

[0368] Optionally, the apparatus further comprises a second sending module configured to send terminal positioning capability information, wherein the terminal positioning capability information comprises at least one of the following:

[0369] Whether to support machine learning based positioning;

[0370] whether support receiving at least one of machine learning model information in each positioning reference signal resource, each positioning reference signal resource set, each TRP, each frequency layer, each positioning method, and each positioning scenario;

[0371] whether support receiving at least one of error model information in each positioning reference signal resource, each positioning reference signal resource set, each TRP, each frequency layer, each positioning method, and each positioning scenario;

[0372] whether support receiving at least one of pre-processing model information in each positioning reference signal resource, each positioning reference signal resource set, each TRP, each frequency layer, each positioning method, and each positioning scenario;

[0373] whether support receiving multiple machine learning models;

[0374] maximum number of machine learning models that can be received;

[0375] whether support receiving parameters of multiple machine learning models;

[0376] maximum number of parameters of machine learning models that can be received;

[0377] whether support receiving multiple pre-processing models;

[0378] maximum number of pre-processing models that can be received;

[0379] whether support receiving parameters of multiple pre-processing models;

[0380] maximum number of parameters of pre-processing models that can be received;

[0381] whether support receiving multiple error models;

[0382] maximum number of error models that can be received;

[0383] whether support receiving parameters of multiple error models;

[0384] maximum number of parameters of error models that can be received;

[0385] input information of supported machine learning models;

[0386] output information of supported machine learning models;

[0387] input information of supported pre-processing models;

[0388] output information of supported pre-processing models;

[0389] input information of supported error models;

[0390] output information of supported error models.

[0391] The first positioning device 400 in this embodiment can be an electronic device, such as an electronic device with an operating system, or a component in an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal, or other devices besides a terminal. For example, the terminal can include, but is not limited to, the type of terminal 11 listed above; other devices can be servers, network attached storage (NAS), etc., and this embodiment does not impose specific limitations.

[0392] The first positioning device 400 provided in this application embodiment can achieve... Figure 2 The various processes implemented in the method embodiments achieve the same technical effect, and will not be described again here to avoid repetition.

[0393] This application Figure 3 The provided positioning method can be executed by a second positioning device. This application embodiment uses the execution of the positioning method by a second positioning device as an example to illustrate the apparatus of the positioning method provided in this application embodiment.

[0394] like Figure 5 As shown, this application embodiment provides a second positioning device 500, including:

[0395] The sending module 501 is used to send configuration information, which is used by the terminal for positioning and / or by the terminal for reporting positioning information.

[0396] Optionally, the configuration information includes at least one of the following:

[0397] Positioning reference signal resource configuration information;

[0398] Positioning reference signal resource set configuration information;

[0399] Frequency layer configuration information;

[0400] Send the TRP configuration information of the receiving point;

[0401] Location method configuration information;

[0402] Location scene configuration information.

[0403] Optionally, the configuration information carries first information:

[0404] The first information includes at least one of the following:

[0405] First model information;

[0406] Instruction information, which is used to instruct the terminal to report the location information.

[0407] Optionally, the indication information is used for indicating a reporting manner of the positioning information.

[0408] Optionally, the reporting manner of the positioning information is determined by at least one of the following:

[0409] According to a type of the first model information;

[0410] According to a sending manner of the first model information;

[0411] According to the indication information;

[0412] According to a sending manner of the indication information.

[0413] Optionally, the first model information comprises at least one of the following:

[0414] Machine learning model information;

[0415] Error model information;

[0416] Preprocessing model information.

[0417] Optionally, the machine learning model information comprises at least one of the following:

[0418] At least one machine learning model;

[0419] Parameters of the at least one machine learning model;

[0420] Input information of the machine learning model;

[0421] Output information of the machine learning model.

[0422] Optionally, the at least one machine learning model comprises at least one of the following:

[0423] Convolutional neural network CNN;

[0424] Recurrent neural network RNN;

[0425] Long short-term memory LSTM;

[0426] Recursive neural tensor network RNTN;

[0427] Generative adversarial network GAN;

[0428] Deep belief network DBN;

[0429] Restricted Boltzmann machine RBM.

[0430] Optionally, the parameters of the at least one machine learning model comprise at least one of the following:

[0431] Weights of layers, step size, mean, variance.

[0432] Optionally, the input information of the machine learning model comprises at least one of:

[0433] Channel impulse response CIR, power delay profile PDP, reference signal time difference RSTD, round trip time RTT, angle of arrival AoA, reference signal received power RSRP, time of arrival TOA, power of first path, delay of first path, TOA of first path, RSTD of first path, angle of arrival of first path, antenna subcarrier phase difference of first path, power of other paths, delay of other paths, TOA of other paths, RSTD of other paths, angle of arrival of other paths, antenna subcarrier phase difference of other paths, LoS identification information, NLoS identification information, mean excess delay, root mean square delay spread, coherence bandwidth.

[0434] Optionally, the output information of the machine learning model comprises at least one of:

[0435] Position coordinate information, RSTD, RTT, AoA, RSRP, TOA, power of first path, delay of first path, TOA of first path, RSTD of first path, angle of arrival of first path, power of other paths, delay of other paths, TOA of other paths, RSTD of other paths, angle of arrival of other paths, LoS identification information, NLoS identification information.

[0436] Optionally, the error model information comprises at least one of:

[0437] An error value estimated by at least one network side device;

[0438] An error model estimated by the at least one network side device;

[0439] A parameter of the error model estimated by the at least one network side device;

[0440] Input information of the error model;

[0441] Output information of the error model.

[0442] Optionally, the error value comprises at least one of: a position error value, a measurement error value, a model error value and a parameter error value.

[0443] Optionally, the error model comprises at least one of: a position error model, a measurement error model, a parameter error model.

[0444] Optionally, the input information of the error model comprises a terminal initial position or a terminal calculated position, and the output information of the error model comprises position information after error calibration.

[0445] Optionally, the input information of the error model comprises initial first measurement information, and the first measurement information comprises at least one of the following: CIR, PDP, RSTD, RTT, AoA, RSRP, TOA, power of a first path, time delay of a first path, TOA of a first path, RSTD of a first path, angle of arrival of a first path, antenna subcarrier phase difference of a first path, power of another path, time delay of another path, TOA of another path, RSTD of another path, angle of arrival of another path, antenna subcarrier phase difference of another path, LoS identification information, NLoS identification information, mean excess delay root mean square, delay spread, and coherence bandwidth.

[0446] The output information of the error model comprises the first measurement information after error calibration.

[0447] Optionally, the input information of the error model comprises at least one of the following: a machine learning model, parameters of a machine learning model, a preprocessing model, or parameters of a preprocessing model.

[0448] The output information of the error model comprises at least one of the following: a calibrated machine learning model, parameters of a calibrated machine learning model, a calibrated preprocessing model, or parameters of a calibrated preprocessing model.

[0449] Optionally, the preprocessing model information comprises at least one of the following:

[0450] Filter parameters or structures.

[0451] Convolution layer parameters or structures.

[0452] Pooling layer parameters or structures.

[0453] Discrete cosine transform parameters or structures.

[0454] Wavelet transform parameters or structures.

[0455] Parameters or structures for preprocessing measurement information.

[0456] Optionally, the input information of the preprocessing model information comprises second measurement information.

[0457] The second measurement information comprises at least one of the following: CIR, PDP, RSTD, RTT, AoA, RSRP, TOA, power of a first path, time delay of a first path, TOA of a first path, RSTD of a first path, angle of arrival of a first path, antenna subcarrier phase difference of a first path, power of another path, time delay of another path, TOA of another path, RSTD of another path, angle of arrival of another path, antenna subcarrier phase difference of another path, reference signal waveform, and correlation sequence of a reference signal.

[0458] The output information of the pre-processing model information includes second measurement information after pre-processing.

[0459] Optionally, the at least one machine learning model includes a multi-step machine learning model.

[0460] Optionally, the multi-step machine learning model includes at least one of the following:

[0461] A multi-step machine learning model distinguished according to input information types and output information types;

[0462] A multi-step machine learning model distinguished according to model parameters;

[0463] A multi-step machine learning model distinguished according to generalization capabilities.

[0464] Optionally, the positioning information includes at least one of the following:

[0465] Measurement information;

[0466] Error information;

[0467] Positioning results;

[0468] Machine learning model update information

[0469] Error model update information;

[0470] Pre-processing model update information.

[0471] Optionally, the second positioning apparatus further includes a first receiving module configured to receive request information sent by the terminal, the request information being used to request a sending mode of the first information.

[0472] Optionally, the second positioning apparatus further includes a second receiving module configured to receive terminal positioning capability information sent by the terminal, the terminal positioning capability information including at least one of the following:

[0473] Whether to support machine learning-based positioning;

[0474] Whether to support receiving machine learning model information in at least one of each positioning reference signal resource, each set of positioning reference signal resources, each TRP, each frequency layer, each positioning method, and each positioning scenario;

[0475] Whether to support receiving error model information in at least one of each positioning reference signal resource, each set of positioning reference signal resources, each TRP, each frequency layer, each positioning method, and each positioning scenario;

[0476] whether support receiving pre-processing model information in at least one of each positioning reference signal resource, each positioning reference signal resource set, each TRP, each frequency layer, each positioning method, and each positioning scenario;

[0477] whether support receiving multiple machine learning models;

[0478] maximum number of machine learning models that can be received;

[0479] whether support receiving parameters of multiple machine learning models;

[0480] maximum number of parameters of machine learning models that can be received;

[0481] whether support receiving multiple pre-processing models;

[0482] maximum number of pre-processing models that can be received;

[0483] whether support receiving parameters of multiple pre-processing models;

[0484] maximum number of parameters of pre-processing models that can be received;

[0485] whether support receiving multiple error models;

[0486] maximum number of error models that can be received;

[0487] whether support receiving parameters of multiple error models;

[0488] maximum number of parameters of error models that can be received;

[0489] input information of supported machine learning models;

[0490] output information of supported machine learning models;

[0491] input information of supported pre-processing models;

[0492] output information of supported pre-processing models;

[0493] input information of supported error models;

[0494] output information of supported error models.

[0495] The second positioning apparatus 500 provided by the embodiments of the present application can implement each process of the method embodiments and achieve the same technical effects, and thus repeated description is omitted here. Figure 3 The second positioning apparatus 500 provided by the embodiments of the present application can implement each process of the method embodiments and achieve the same technical effects, and thus repeated description is omitted here.

[0496] Optionally, as shown in Figure 6As shown, the embodiments of the present application also provide a communication device 600, comprising a processor 601 and a memory 602, wherein the memory 602 stores programs or instructions executable on the processor 601. For example, when the communication device 600 is a terminal, the programs or instructions are executed by the processor 601 to implement the above-mentioned Figure 2 As shown, the embodiments of the positioning method implement each step of the positioning method and achieve the same technical effects. When the communication device 600 is a network side device, the programs or instructions are executed by the processor 601 to implement the above-mentioned Figure 3 As shown, the embodiments of the positioning method implement each step of the positioning method and achieve the same technical effects. For the sake of brevity, the details are not repeated here.

[0497] The embodiments of the present application also provide a terminal, comprising a processor and a communication interface, wherein the processor is configured to perform at least one of the following operations according to the configuration information: determining first model information and performing positioning according to the first model information; and reporting positioning information, and the communication interface is configured to obtain configuration information. The terminal embodiment corresponds to the above-mentioned terminal side method embodiment, and each implementation process and implementation manner of the above-mentioned method embodiment can be applied to the terminal embodiment and achieve the same technical effects. Specifically, Figure 7 The hardware structure of a terminal for implementing the embodiments of the present application is shown in the figure.

[0498] The terminal 700 includes, but is not limited to, at least part of the components such as a radio frequency unit 701, a network module 702, an audio output unit 703, an input unit 704, a sensor 705, a display unit 706, a user input unit 707, an interface unit 708, a memory 709, and a processor 710.

[0499] Those skilled in the art can understand that the terminal 700 can also include a power supply (such as a battery) for supplying power to each component, and the power supply can be logically connected to the processor 710 through a power management system, so as to realize the functions of power management, discharge management, and power consumption management through the power management system. Figure 7 The terminal structure shown in the figure does not constitute a limitation on the terminal, and the terminal can include more or fewer components than the figure, or combine certain components, or different component arrangements, which are not repeated here.

[0500] It should be understood that in the embodiments of the present application, the input unit 704 can include a graphics processing unit (GPU) 7041 and a microphone 7042. The graphics processor 7041 processes image data of a still picture or a video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The display unit 706 can include a display panel 7061, which can be configured in the form of a liquid crystal display, an organic light-emitting diode, etc. The user input unit 707 includes at least one of a touch panel 7071 and other input devices 7072. The touch panel 7071 is also called a touch screen. The touch panel 7071 can include two parts of a touch detection device and a touch controller. The other input devices 7072 can include, but are not limited to, a physical keyboard, function keys (such as volume control keys, on-off keys, etc.), a trackball, a mouse, a joystick, and the like, which will not be described here.

[0501] In the embodiments of the present application, after the radio frequency unit 701 receives the downlink data from the network side device, it can be transmitted to the processor 710 for processing. In addition, the radio frequency unit 701 can send uplink data to the network side device. Generally, the radio frequency unit 701 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, etc.

[0502] The memory 709 can be used to store software programs or instructions and various data. The memory 709 can mainly include a first storage area storing programs or instructions and a second storage area storing data, wherein the first storage area can store an operating system, application programs or instructions required by at least one function (such as a sound playing function, an image playing function, etc.), and the like. In addition, the memory 709 can include a volatile memory or a non-volatile memory, or the memory 709 can include both volatile and non-volatile memories. The non-volatile memory can be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a Random Access Memory (RAM), a Static RAM (SRAM), a Dynamic RAM (DRAM), a Synchronous DRAM (SDRAM), a Double Data Rate SDRAM (DDR SDRAM), an Enhanced SDRAM (ESDRAM), a Synch link DRAM (SLDRAM), and a Direct Rambus RAM (DRRAM). The memory 709 in the embodiments of the present application includes but is not limited to these and any other suitable types of memories.

[0503] The processor 710 can include one or more processing units; optionally, the processor 710 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and an application program, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It can be understood that the above-mentioned modem processor can also not be integrated into the processor 710.

[0504] The radio frequency unit 701 is configured to obtain configuration information.

[0505] The processor 710 is configured to perform at least one of the following operations according to the configuration information:

[0506] determining first model information and performing positioning according to the first model information;

[0507] reporting positioning information.

[0508] Optionally, the configuration information comprises at least one of the following:

[0509] positioning reference signal resource configuration information;

[0510] positioning reference signal resource set configuration information;

[0511] frequency layer configuration information;

[0512] transmission and reception point (TRP) configuration information;

[0513] positioning method configuration information;

[0514] positioning scenario configuration information.

[0515] Optionally, the radio frequency unit 701 is further configured to receive first information sent by the network side device;

[0516] The first information comprises at least one of the following:

[0517] the first model information;

[0518] indication information, wherein the indication information is used to instruct the terminal to report the positioning information.

[0519] Optionally, the first information is carried in the configuration information.

[0520] Optionally, the indication information is used to indicate the reporting mode of the positioning information.

[0521] Optionally, the reporting mode of the positioning information is determined by at least one of the following:

[0522] determined according to the type of the first model information;

[0523] determined according to the sending mode of the first model information;

[0524] determined according to the indication information;

[0525] determined according to the sending mode of the indication information.

[0526] Optionally, the reporting mode of the positioning information comprises at least one of the following:

[0527] each positioning reference signal resource transmission;

[0528] each positioning reference signal resource set transmission;

[0529] each TRP transmission;

[0530] each frequency layer transmission;

[0531] each positioning method transmission;

[0532] each positioning scenario.

[0533] Optionally, in the case that the positioning information is reported in a target reporting manner, the positioning information further comprises identification information corresponding to the target reporting manner, and the identification information comprises at least one of the following:

[0534] positioning reference signal resource identification information;

[0535] positioning reference signal resource set identification information;

[0536] TRP identification information;

[0537] frequency layer identification information;

[0538] positioning method identification information;

[0539] positioning scenario identification information.

[0540] Optionally, the first model information comprises at least one of the following:

[0541] machine learning model information;

[0542] error model information;

[0543] preprocessing model information.

[0544] Optionally, the machine learning model information comprises at least one of the following:

[0545] at least one machine learning model;

[0546] parameters of the at least one machine learning model;

[0547] input information of the machine learning model;

[0548] output information of the machine learning model.

[0549] Optionally, the at least one machine learning model comprises at least one of the following:

[0550] convolutional neural network (CNN);

[0551] recurrent neural network (RNN);

[0552] long short-term memory (LSTM);

[0553] recurrent tensor neural network (RNTN);

[0554] generative adversarial network (GAN);

[0555] deep belief network (DBN);

[0556] Restricted Boltzmann Machine, RBM.

[0557] Optionally, the parameter of the at least one machine learning model comprises at least one of:

[0558] a weight of each layer;

[0559] a step size;

[0560] a mean value;

[0561] a variance.

[0562] Optionally, the input information of the machine learning model comprises at least one of:

[0563] a channel impulse response (CIR), a power delay profile (PDP), a reference signal time difference (RSTD), a round trip time (RTT), an angle of arrival (AoA), a reference signal received power (RSRP), a time of arrival (TOA), a power of a first path, a delay of the first path, a TOA of the first path, an RSTD of the first path, an angle of arrival of the first path, an antenna subcarrier phase difference of the first path, a power of another path, a delay of the another path, a TOA of the another path, an RSTD of the another path, an angle of arrival of the another path, an antenna subcarrier phase difference of the another path, LoS identification information, NLoS identification information, a mean excess delay, a root mean square delay spread, a coherence bandwidth.

[0564] Optionally, the output information of the machine learning model comprises at least one of:

[0565] position coordinate information, an RSTD, an RTT, an AoA, an RSRP, a TOA, a power of a first path, a delay of the first path, a TOA of the first path, an RSTD of the first path, an angle of arrival of the first path, a power of another path, a delay of the another path, a TOA of the another path, an RSTD of the another path, an angle of arrival of the another path, LoS identification information, NLoS identification information.

[0566] Optionally, the error model information comprises at least one of:

[0567] an error value estimated by the at least one network-side device;

[0568] an error model estimated by the at least one network-side device;

[0569] a parameter of the error model estimated by the at least one network-side device;

[0570] input information of the error model;

[0571] output information of the error model.

[0572] Optionally, the error value comprises at least one of: a position error value, a measurement error value, a model error value, and a parameter error value.

[0573] Optionally, the error model comprises at least one of the following: a position error model, a measurement error model, a parameter error model.

[0574] Optionally, the input information of the error model comprises a terminal initial position or a terminal calculated position, and the output information of the error model comprises position information after error calibration.

[0575] Optionally, the input information of the error model comprises initial first measurement information, and the first measurement information comprises at least one of the following: CIR, PDP, RSTD, RTT, AoA, RSRP, TOA, power of a first path, time delay of a first path, TOA of a first path, RSTD of a first path, angle of arrival of a first path, antenna subcarrier phase difference of a first path, power of another path, time delay of another path, TOA of another path, RSTD of another path, angle of arrival of another path, antenna subcarrier phase difference of another path, LoS identification information, NLoS identification information, mean excess delay root mean square, delay spread and coherence bandwidth.

[0576] The output information of the error model comprises first measurement information after error calibration.

[0577] Optionally, the input information of the error model comprises at least one of the following: a machine learning model, parameters of a machine learning model, a preprocessing model or parameters of a preprocessing model.

[0578] The output information of the error model comprises at least one of the following: a calibrated machine learning model, parameters of a calibrated machine learning model, a calibrated preprocessing model or parameters of a calibrated preprocessing model.

[0579] Optionally, the preprocessing model information comprises at least one of the following:

[0580] Filter parameters or structures;

[0581] Convolution layer parameters or structures;

[0582] Pooling layer parameters or structures;

[0583] Discrete cosine transform parameters or structures;

[0584] Wavelet transform parameters or structures;

[0585] Parameters or structures for preprocessing measurement information.

[0586] Optionally, the input information of the preprocessing model information comprises second measurement information.

[0587] The second measurement information includes at least one of the following: CIR, PDP, RSTD, RTT, AoA, RSRP, TOA, power of a first path, time delay of a first path, TOA of a first path, RSTD of a first path, angle of arrival of a first path, antenna subcarrier phase difference of a first path, power of an other path, time delay of an other path, TOA of an other path, RSTD of an other path, angle of arrival of an other path, antenna subcarrier phase difference of an other path, reference signal waveform, and correlation sequence of a reference signal.

[0588] The output information of the preprocessing model information includes the second measurement information after preprocessing.

[0589] Optionally, the at least one machine learning model includes a multi-step machine learning model.

[0590] Optionally, the multi-step machine learning model includes at least one of the following:

[0591] A multi-step machine learning model distinguished according to input information types and output information types;

[0592] A multi-step machine learning model distinguished according to model parameters;

[0593] A multi-step machine learning model distinguished according to generalization capabilities.

[0594] Optionally, the positioning information includes at least one of the following:

[0595] Measurement information;

[0596] Error information;

[0597] Positioning result;

[0598] Machine learning model update information

[0599] Error model update information;

[0600] Preprocessing model update information.

[0601] Optionally, the radio frequency unit 701 is further configured to send request information, the request information being used to request a sending mode of the first information.

[0602] Optionally, the radio frequency unit 701 is further configured to send terminal positioning capability information, the terminal positioning capability information including at least one of the following:

[0603] Whether to support machine learning-based positioning;

[0604] Whether to support receiving machine learning model information in at least one of the following: each positioning reference signal resource, each set of positioning reference signal resources, each TRP, each frequency layer, each positioning method, and each positioning scenario;

[0605] whether receiving error model information for at least one of each positioning reference signal resource, each set of positioning reference signal resources, each TRP, each frequency layer, each positioning method, and each positioning scenario is supported;

[0606] whether receiving pre-processing model information for at least one of each positioning reference signal resource, each set of positioning reference signal resources, each TRP, each frequency layer, each positioning method, and each positioning scenario is supported;

[0607] whether receiving multiple machine learning models is supported;

[0608] a maximum number of machine learning models that can be received;

[0609] whether receiving parameters of multiple machine learning models is supported;

[0610] a maximum number of parameters of machine learning models that can be received;

[0611] whether receiving multiple pre-processing models is supported;

[0612] a maximum number of pre-processing models that can be received;

[0613] whether receiving parameters of multiple pre-processing models is supported;

[0614] a maximum number of parameters of pre-processing models that can be received;

[0615] whether receiving multiple error models is supported;

[0616] a maximum number of error models that can be received;

[0617] whether receiving parameters of multiple error models is supported;

[0618] a maximum number of parameters of error models that can be received;

[0619] input information of a supported machine learning model;

[0620] output information of a supported machine learning model;

[0621] input information of a supported pre-processing model;

[0622] output information of a supported pre-processing model;

[0623] input information of a supported error model;

[0624] output information of a supported error model.

[0625] The embodiment of the present application further provides a network side device, comprising a processor and a communication interface, the communication interface is used for sending configuration information, the configuration information is used for positioning of a terminal and / or reporting of positioning information by the terminal. The network side device embodiment corresponds to the network side device method embodiment, each implementation process and implementation manner of the method embodiment can be applied to the network side device embodiment, and the same technical effects can be achieved.

[0626] Specifically, the embodiment of the present application further provides a network side device. As shown in the Figure 8 The network side device 800 comprises an antenna 81, a radio frequency device 82, a baseband device 83, a processor 84 and a memory 85. The antenna 81 is connected with the radio frequency device 82. In the uplink direction, the radio frequency device 82 receives information through the antenna 81, and sends the received information to the baseband device 83 for processing. In the downlink direction, the baseband device 83 processes the information to be sent, and sends the information to the radio frequency device 82. The radio frequency device 82 processes the received information, and sends the information out through the antenna 81.

[0627] The method performed by the network side device in the above embodiment can be implemented in the baseband device 83, and the baseband device 83 comprises a baseband processor.

[0628] The baseband device 83 can comprise at least one baseband board, and a plurality of chips are arranged on the baseband board, as shown in the Figure 8 One of the chips is a baseband processor, for example, and is connected with the memory 85 through a bus interface, so as to call a program in the memory 85, and perform the operation of the network side device shown in the above method embodiment.

[0629] The network side device can further comprise a network interface 86, which is a common public radio interface (CPRI), for example.

[0630] Specifically, the network side device 800 of the embodiment of the present application further comprises instructions or programs stored in the memory 85 and executable on the processor 84, and the processor 84 calls the instructions or programs in the memory 85 to perform the method executed by each module shown in the Figure 5 and achieve the same technical effects. To avoid repetition, the details are not described here.

[0631] The embodiment of the present application further provides a readable storage medium, and the readable storage medium stores programs or instructions, which are executed by a processor to implement each process of the above positioning method embodiment, and achieve the same technical effects. To avoid repetition, the details are not described here.

[0632] The processor is the processor in the terminal in the above-described embodiments. The readable storage medium includes a computer readable storage medium, such as a computer readable only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0633] The embodiment of the present application further provides a chip, which comprises a processor and a communication interface, the communication interface is coupled with the processor, the processor is used for running programs or instructions to realize the processes of the above positioning method embodiments and achieve the same technical effects. To avoid repetition, details are not described herein.

[0634] It should be understood that the chip mentioned in the embodiment of the present application can also be referred to as a system chip, a system chip, a chip system or a system on chip, etc.

[0635] The embodiment of the present application further provides a computer program / program product stored in a storage medium, which is executed by at least one processor to realize the processes of the above positioning method embodiments and achieve the same technical effects. To avoid repetition, details are not described herein.

[0636] The embodiment of the present application further provides a communication system, which comprises a terminal and a network side device. The terminal can be used to execute the steps of the above method embodiments. The network side device can be used to execute the steps of the above method embodiments. Figure 2 Figure 3

[0637] It should be noted that in this document, the term "comprising" or "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that processes, methods, articles or devices including a series of elements not only include those elements, but also include other elements not explicitly listed or inherent to such processes, methods, articles or devices. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the method and device in the embodiment of the present application is not limited to the order of functions shown or discussed, but can also include functions performed in a substantially simultaneous manner or in the opposite order, for example, the described method can be performed in an order different from that described, and various steps can also be added, omitted or combined. In addition, the features described with reference to certain examples can be combined in other examples.

[0638] ​​Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned example methods can be realized by means of software and a necessary general hardware platform, and of course, can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a computer software product in essence or in the form of a part that contributes to the prior art, which is stored in a storage medium (such as a ROM / RAM, a magnetic disc, an optical disc), and includes a plurality of instructions for causing a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network side device, etc.) to execute the methods described in various embodiments of the present application.

[0639] The embodiments of the present application are described above in combination with the drawings, but the present application is not limited to the above-mentioned specific embodiments, and the above-mentioned specific embodiments are only illustrative and not restrictive. Those skilled in the art can make many forms under the inspiration of the present application without departing from the scope of the present application and the scope protected by the claims.

Claims

1. A positioning method, characterized by, The method comprises the following steps: A terminal determines configuration information; The terminal performs the following operations according to the configuration information: Determining first model information and positioning according to the first model information; Reporting positioning information; The first model information comprises machine learning model information; The machine learning model information comprises at least one machine learning model and input information of the machine learning model; The input information of the machine learning model comprises a power delay profile (PDP); The at least one machine learning model comprises a multi-step machine learning model; The multi-step machine learning model comprises a multi-step machine learning model distinguished according to input information types and output information types.

2. The method of claim 1, wherein, The configuration information comprises at least one of the following: Positioning reference signal resource configuration information; Positioning reference signal resource set configuration information; Frequency layer configuration information; Transmission and reception point (TRP) configuration information; Positioning method configuration information; Positioning scenario configuration information.

3. The method of claim 1, wherein, The method further comprises that the terminal receives first information sent by a network side device; The first information comprises at least one of the following: The first model information; Indication information used for instructing the terminal to report the positioning information.

4. The method of claim 3, wherein, The first information is carried in the configuration information.

5. The method of claim 3, wherein, The indication information is used for instructing a reporting mode of the positioning information.

6. The method of claim 5, wherein, The reporting mode of the positioning information is determined according to at least one of the following: According to the type of the first model information; According to the sending mode of the first model information; According to the indication information; According to the sending mode of the indication information.

7. The method of claim 5, wherein, The reporting mode of the positioning information comprises at least one of the following: Each positioning reference signal resource is sent; Each positioning reference signal resource set is sent; Each TRP is sent; Each frequency layer is sent; Each positioning method is sent; Each positioning scenario is sent.

8. The method according to any one of claims 5-7, characterized in that, In the case that the positioning information is reported in a target reporting mode, the positioning information further comprises identification information corresponding to the target reporting mode, and the identification information comprises at least one of the following: Positioning reference signal resource identification information; Positioning reference signal resource set identification information; TRP identification information; Frequency layer identification information; Positioning method identification information; Positioning scenario identification information.

9. The method of claim 1, wherein, The first model information further comprises at least one of the following: Error model information; Preprocessing model information.

10. The method of claim 9, wherein, The machine learning model information further comprises at least one of the following: Parameters of the at least one machine learning model; Output information of the machine learning model.

11. The method of claim 10, wherein, The at least one machine learning model comprises at least one of the following: Convolutional neural network (CNN); Recurrent neural network (RNN); Long short-term memory (LSTM); Recursive tensor neural network (RNTN); Generative adversarial network (GAN); Deep belief network (DBN); Restricted Boltzmann machine (RBM).

12. The method of claim 10, wherein, The parameters of the at least one machine learning model comprise at least one of the following: Weights of each layer; Step length; Mean value; Variance.

13. The method of claim 10, wherein, The input information of the machine learning model further comprises at least one of the following: Channel impulse response (CIR); Reference signal time difference (RSTD); Round trip time (RTT); Angle of arrival (AoA); Reference signal received power (RSRP); Time of arrival (TOA); Power of a first path; Delay of a first path; TOA of the first path; RSTD of the first path; Angle of arrival of the first path; Antenna subcarrier phase difference of the first path; Power of the other path; Time delay of the other path; TOA of the other path; RSTD of the other path; Angle of arrival of the other path; Antenna subcarrier phase difference of the other path; LoS identification information; NLoS identification information; Mean excess delay; Root mean square delay spread; Coherent bandwidth.

14. The method of claim 10, wherein, The output information of the machine learning model comprises at least one of: position coordinate information; RSTD; RTT; AoA; RSRP; TOA; Power of the first path; Time delay of the first path; TOA of the first path; RSTD of the first path; Angle of arrival of the first path; Power of the other path; Time delay of the other path; TOA of the other path; RSTD of the other path; Angle of arrival of the other path; LoS identification information; NLoS identification information.

15. The method of claim 9, wherein, The error model information comprises at least one of: error value estimated by at least one network side device; error model estimated by the at least one network side device; parameter of the error model estimated by the at least one network side device; input information of the error model; output information of the error model.

16. The method of claim 15, wherein, The error value comprises at least one of: position error value, measurement error value, model error value and parameter error value.

17. The method of claim 15, wherein, The error model comprises at least one of: position error model, measurement error model, parameter error model.

18. The method of claim 15, wherein, The input information of the error model comprises terminal initial position or terminal calculated position, and the output information of the error model comprises position information after error calibration.

19. The method of claim 15, wherein, The input information of the error model comprises initial first measurement information, and the first measurement information comprises at least one of: CIR, PDP, RSTD, RTT, AoA, RSRP, TOA, power of the first path, time delay of the first path, TOA of the first path, RSTD of the first path, angle of arrival of the first path, antenna subcarrier phase difference of the first path, power of the other path, time delay of the other path, TOA of the other path, RSTD of the other path, angle of arrival of the other path, antenna subcarrier phase difference of the other path, LoS identification information, NLoS identification information, mean excess delay root mean square, time delay spread and coherent bandwidth; The output information of the error model comprises first measurement information after error calibration.

20. The method of claim 15, wherein, The input information of the error model comprises at least one of: machine learning model, parameter of machine learning model, pre-processing model or parameter of pre-processing model; The output information of the error model comprises at least one of: calibrated machine learning model, parameter of calibrated machine learning model, calibrated pre-processing model or parameter of calibrated pre-processing model.

21. The method of claim 9, wherein, The pre-processing model information comprises at least one of: filter parameter or structure; convolution layer parameter or structure; pooling layer parameter or structure; discrete cosine transform parameter or structure; wavelet transform parameter or structure; parameter or structure for pre-processing measurement information.

22. The method of claim 9, wherein, The input information of the pre-processing model information comprises second measurement information; The second measurement information includes at least one of the following: CIR, PDP, RSTD, RTT, AoA, RSRP, TOA, power of a first path, time delay of a first path, TOA of a first path, RSTD of a first path, arrival angle of a first path, antenna subcarrier phase difference of a first path, power of an other path, time delay of an other path, TOA of an other path, RSTD of an other path, arrival angle of an other path, antenna subcarrier phase difference of an other path, reference signal waveform, and correlation sequence of a reference signal; The output information of the pre-processing model information includes the second measurement information after pre-processing.

23. The method of claim 1, wherein, The multi-step machine learning model further includes at least one of the following: The multi-step machine learning model is distinguished according to different model parameters; The multi-step machine learning model is distinguished according to different generalization capabilities.

24. The method of claim 1, wherein, The positioning information includes at least one of the following: Measurement information; Error information; Positioning result; Machine learning model update information Error model update information; Pre-processing model update information.

25. The method of claim 3, wherein, The method further includes: The terminal sends request information, and the request information is used to request a sending mode of the first information.

26. The method of claim 1, wherein, The method further includes: The terminal sends terminal positioning capability information, and the terminal positioning capability information includes at least one of the following: Whether to support machine learning-based positioning; Whether to support receiving machine learning model information in at least one of each positioning reference signal resource, each positioning reference signal resource set, each TRP, each frequency layer, each positioning method, and each positioning scenario; Whether to support receiving error model information in at least one of each positioning reference signal resource, each positioning reference signal resource set, each TRP, each frequency layer, each positioning method, and each positioning scenario; Whether to support receiving pre-processing model information in at least one of each positioning reference signal resource, each positioning reference signal resource set, each TRP, each frequency layer, each positioning method, and each positioning scenario; Whether to support receiving multiple machine learning models; Maximum number of receivable machine learning models; Whether to support receiving parameters of multiple machine learning models; Maximum number of receivable parameters of machine learning models; Whether to support receiving multiple pre-processing models; Maximum number of receivable pre-processing models; Whether to support receiving parameters of multiple pre-processing models; Maximum number of receivable parameters of pre-processing models; Whether to support receiving multiple error models; Maximum number of receivable error models; Whether to support receiving parameters of multiple error models; Maximum number of receivable parameters of error models; Input information of a supported machine learning model; Output information of a supported machine learning model; Input information of a supported pre-processing model; Output information of a supported pre-processing model; Input information of a supported error model; Output information of a supported error model.

27. A positioning method, characterized by, The method further includes: The network-side device sends configuration information, and the configuration information is used for terminal positioning and / or terminal reporting of positioning information; The configuration information carries first information; The first information includes first model information; The first model information includes machine learning model information; The machine learning model information includes at least one of the following: The machine learning model information comprises at least one machine learning model, and input information of the machine learning model. The input information of the machine learning model comprises a delay power spectrum PDP. The at least one machine learning model comprises a multi-step machine learning model. The multi-step machine learning model comprises a multi-step machine learning model distinguished according to types of input information and output information.

28. The method of claim 27, wherein, The configuration information comprises at least one of the following: Positioning reference signal resource configuration information; Positioning reference signal resource set configuration information; Frequency layer configuration information; Transmission and reception point TRP configuration information; Positioning method configuration information; Positioning scenario configuration information.

29. The method of claim 27, wherein The first information further comprises at least one of the following: Indication information, wherein the indication information is used to indicate that the terminal reports the positioning information.

30. The method of claim 29, wherein, The indication information is used to indicate a reporting manner of the positioning information.

31. The method of claim 30, wherein, The reporting manner of the positioning information is determined according to at least one of the following: According to a type of first model information; According to a transmission manner of first model information; According to indication information; According to a transmission manner of indication information.

32. The method of claim 29, wherein, The first model information further comprises at least one of the following: Error model information; Preprocessing model information.

33. The method of claim 32, wherein, The machine learning model information further comprises at least one of the following: Parameters of the at least one machine learning model; Output information of the machine learning model.

34. The method of claim 33, wherein, The at least one machine learning model comprises at least one of the following: Convolutional neural network CNN; Recurrent neural network RNN; Long short-term memory LSTM; Recursive tensor neural network RNTN; Generative adversarial network GAN; Deep belief network DBN; Restricted Boltzmann machine RBM.

35. The method of claim 33, wherein, The parameters of the at least one machine learning model comprise at least one of the following: Weights of each layer; Step length; Mean value; Variance.

36. The method of claim 33, wherein, The input information of the machine learning model further comprises at least one of the following: Channel impulse response CIR; Reference signal time difference RSTD; Round trip time RTT; Angle of arrival AoA; Reference signal received power RSRP; Time of arrival TOA; Power of a first path; Delay of the first path; TOA of the first path; RSTD of the first path; Angle of arrival of the first path; Antenna subcarrier phase difference of the first path; Power of another path; Delay of the another path; TOA of the another path; RSTD of the another path; Angle of arrival of the another path; Antenna subcarrier phase difference of the another path; LoS identification information; NLoS identification information; Average excess delay; Root mean square delay spread; Coherent bandwidth.

37. The method of claim 33, wherein, The output information of the machine learning model comprises at least one of the following: Position coordinate information; RSTD; RTT; AoA; RSRP; TOA; Power of a first path; Delay of the first path; TOA of the first path; RSTD of the first path; Angle of arrival of the first path; Power of another path; Delay of the another path; TOA of the another path; RSTD of the another path; Angle of arrival of the another path; LoS identification information; NLoS identification information.

38. The method of claim 32, wherein, The error model information comprises at least one of the following: Error values estimated by at least one network side device; Error model estimated by the at least one network side device; a parameter of an error model estimated by the at least one network-side device; input information of the error model; output information of the error model.

39. The method of claim 38, wherein, The error value includes at least one of a position error value, a measurement error value, a model error value, and a parameter error value.

40. The method of claim 38, wherein, The error model includes at least one of a position error model, a measurement error model, and a parameter error model.

41. The method of claim 38, wherein, The input information of the error model includes a terminal initial position or a terminal calculated position, and the output information of the error model includes position information after error calibration.

42. The method of claim 38, wherein, The input information of the error model includes initial first measurement information, and the first measurement information includes at least one of CIR, PDP, RSTD, RTT, AoA, RSRP, TOA, power of a first path, time delay of the first path, TOA of the first path, RSTD of the first path, arrival angle of the first path, antenna subcarrier phase difference of the first path, power of another path, time delay of the another path, TOA of the another path, RSTD of the another path, arrival angle of the another path, antenna subcarrier phase difference of the another path, LoS identification information, NLoS identification information, mean excess delay root mean square, time delay spread, and coherence bandwidth. The output information of the error model includes first measurement information after error calibration.

43. The method of claim 38, wherein, The input information of the error model includes at least one of a machine learning model, a parameter of the machine learning model, a preprocessing model, or a parameter of the preprocessing model. The output information of the error model includes at least one of a calibrated machine learning model, a parameter of the calibrated machine learning model, a calibrated preprocessing model, or a parameter of the calibrated preprocessing model.

44. The method of claim 32, wherein, The preprocessing model information includes at least one of: filter parameters or structures; convolution layer parameters or structures; pooling layer parameters or structures; discrete cosine transform parameters or structures; wavelet transform parameters or structures; parameters or structures for preprocessing measurement information.

45. The method of claim 32, wherein, The input information of the preprocessing model information includes second measurement information. The second measurement information includes at least one of CIR, PDP, RSTD, RTT, AoA, RSRP, TOA, power of a first path, time delay of the first path, TOA of the first path, RSTD of the first path, arrival angle of the first path, antenna subcarrier phase difference of the first path, power of another path, time delay of the another path, TOA of the another path, RSTD of the another path, arrival angle of the another path, antenna subcarrier phase difference of the another path, a reference signal waveform, and a correlation sequence of the reference signal. The output information of the preprocessing model information includes second measurement information after preprocessing.

46. The method of claim 27, wherein, The multi-step machine learning model further includes at least one of: a multi-step machine learning model distinguished according to different model parameters; a multi-step machine learning model distinguished according to different generalization capabilities.

47. The method of claim 27, wherein, The positioning information includes at least one of: measurement information; error information; a positioning result; machine learning model update information error model update information; preprocessing model update information.

48. The method of claim 29, wherein, The method further includes: The network-side device receives request information sent by the terminal, and the request information is used to request a sending mode of the first information.

49. The method of claim 27, wherein, The method further comprises: The network side device receives terminal positioning capability information sent by the terminal, and the terminal positioning capability information comprises at least one of the following: Whether to support machine learning based positioning; Whether to support receiving machine learning model information in at least one of each positioning reference signal resource, each positioning reference signal resource set, each TRP, each frequency layer, each positioning method and each positioning scenario; Whether to support receiving error model information in at least one of each positioning reference signal resource, each positioning reference signal resource set, each TRP, each frequency layer, each positioning method and each positioning scenario; Whether to support receiving pre-processing model information in at least one of each positioning reference signal resource, each positioning reference signal resource set, each TRP, each frequency layer, each positioning method and each positioning scenario; Whether to support receiving multiple machine learning models; The maximum number of receivable machine learning models; Whether to support receiving parameters of multiple machine learning models; The maximum number of parameters of receivable machine learning models; Whether to support receiving multiple pre-processing models; The maximum number of receivable pre-processing models; Whether to support receiving parameters of multiple pre-processing models; The maximum number of parameters of receivable pre-processing models; Whether to support receiving multiple error models; The maximum number of receivable error models; Whether to support receiving parameters of multiple error models; The maximum number of parameters of receivable error models; Input information of a supported machine learning model; Output information of a supported machine learning model; Input information of a supported pre-processing model; Output information of a supported pre-processing model; Input information of a supported error model; Output information of a supported error model.

50. A positioning device, characterized by Comprise: A determination module for determining configuration information; An execution module for performing the following operations according to the configuration information: Determining first model information and performing positioning according to the first model information; Reporting positioning information; The first model information comprises machine learning model information; The machine learning model information comprises at least one machine learning model; input information of a machine learning model; The input information of the machine learning model comprises a delay power spectrum PDP; The at least one machine learning model comprises a multi-step machine learning model; The multi-step machine learning model comprises a multi-step machine learning model distinguished according to input information types and output information types.

51. The device of claim 50, wherein, The configuration information comprises at least one of the following: Positioning reference signal resource configuration information; Positioning reference signal resource set configuration information; Frequency layer configuration information; Transmitting and receiving point TRP configuration information; Positioning device configuration information; Positioning scenario configuration information.

52. A positioning device, characterized by Comprise: A sending module for sending configuration information, wherein the configuration information is used for terminal positioning and / or the terminal reporting positioning information; The configuration information carries first information; The first information comprises first model information; The first model information comprises machine learning model information; The machine learning model information comprises at least one machine learning model; input information of a machine learning model; The input information of the machine learning model comprises a delay power spectrum PDP; The at least one machine learning model comprises a multi-step machine learning model. The multi-step machine learning model comprises a multi-step machine learning model distinguished according to input information types and output information types.

53. The device of claim 52, wherein, The configuration information comprises at least one of: Positioning reference signal resource configuration information; Positioning reference signal resource set configuration information; Frequency layer configuration information; Transmission reception point (TRP) configuration information; Positioning device configuration information; Positioning scenario configuration information.

54. A terminal, characterized by A processor and a memory, the memory storing programs or instructions executable on the processor, the programs or instructions being executed by the processor to implement the steps of the positioning method according to any one of claims 1-26.

55. A network-side device, comprising: A processor and a memory, the memory storing programs or instructions executable on the processor, the programs or instructions being executed by the processor to implement the steps of the positioning method according to any one of claims 27-49.

56. A readable storage medium, characterized in that, The readable storage medium stores programs or instructions, the programs or instructions being executed by the processor to implement the positioning method according to any one of claims 1-26, or to implement the steps of the positioning method according to any one of claims 27-49.

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