CN116170871A

CN116170871A - Positioning method, positioning device, terminal and network side equipment

Info

Publication number: CN116170871A
Application number: CN202111389341.9A
Authority: CN
Inventors: 庄子荀; 王园园; 司晔; 邬华明; 孙鹏
Original assignee: Vivo Mobile Communication Co Ltd
Current assignee: Vivo Mobile Communication Co Ltd
Priority date: 2021-11-22
Filing date: 2021-11-22
Publication date: 2023-05-26
Also published as: WO2023088423A1

Abstract

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

Description

Positioning method, positioning device, terminal and network side equipment

Technical Field

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

Background

At present, the existing positioning mode has lower positioning precision and can not meet the high-precision positioning requirement. In order to improve positioning accuracy, a model may be used for positioning, such as a machine learning model, an error model or a preprocessing model, but there is no clear solution to the problem of how to determine model information for positioning, how to report positioning information, and the like.

Disclosure of Invention

The embodiment of the application provides a positioning method, a positioning device, a terminal and network side equipment, which can solve the problem of lower positioning accuracy of a positioning mode in the related technology.

In a first aspect, a positioning method is provided, including:

the terminal determines configuration information;

and the terminal executes at least one of the following operations according to the configuration information:

determining first model information, and positioning according to the first model information;

and reporting the positioning information.

In a second aspect, there is provided a positioning method comprising:

the network side equipment sends configuration information which is used for positioning the terminal and/or reporting positioning information by the terminal.

In a third aspect, there is provided a positioning device comprising:

the determining module is used for determining configuration information;

the execution module is used for executing at least one of the following operations according to the configuration information:

and reporting the positioning information.

In a fourth aspect, there is provided a positioning device comprising:

and the sending module is used for sending configuration information, wherein the configuration information is used for positioning the terminal and/or reporting positioning information by the terminal.

In a fifth aspect, there is provided a terminal comprising a processor and a memory storing a program or instructions executable on the processor, which when executed by the processor, implement the steps of the method as described in the first aspect.

In a sixth aspect, a terminal is provided, including a processor and a communication interface, where 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 positioning according to the first model information; and reporting the positioning information.

In a seventh aspect, a network side device is provided, comprising a processor and a memory storing a program or instructions executable on the processor, which when executed by the processor, implement the steps of the method as described in the second aspect.

In an eighth aspect, a network side device is provided, where the network side device includes a processor and a communication interface, where the communication interface is configured to send configuration information, where the configuration information is used for positioning a terminal and/or reporting positioning information by the terminal.

In a ninth aspect, there is provided a communication system comprising: a terminal operable to perform the steps of the positioning method as described in the first aspect, and a network side device operable to perform the steps of the positioning method as described in the second aspect.

In a tenth aspect, there is provided a readable storage medium having stored thereon a program or instructions which when executed by a processor, performs the steps of the method according to the first aspect or performs the steps of the method according to the second aspect.

In an eleventh aspect, there is provided a chip comprising a processor and a communication interface, the communication interface and the processor being coupled, the processor being for running a program or instructions to implement the method according to the first aspect or to implement the steps of the method according to the second aspect.

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

In the embodiment of the application, the terminal determines configuration information; and the terminal executes at least one of the following operations according to the configuration information: determining first model information, and positioning according to the first model information; and reporting the positioning information. The terminal can determine the first model information according to the configuration information, so as to select a corresponding model for positioning, and improve positioning accuracy.

Drawings

Fig. 1 is a block diagram of a network system according to an embodiment of the present application;

FIG. 2 is a flow chart of a positioning method provided in an embodiment of the present application;

FIG. 3 is another flow chart of a positioning method provided by an embodiment of the present application;

fig. 4 is a block diagram of a first positioning device according to an embodiment of the present application;

FIG. 5 is a block diagram of a second positioning device according to an embodiment of the present application;

fig. 6 is a block diagram of a communication device provided in an embodiment of the present application;

fig. 7 is a block diagram of a terminal provided in an embodiment of the present application;

fig. 8 is a block diagram of a network side device according to an embodiment of the present application.

Detailed Description

Technical solutions in the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application are within the scope of the protection of the present application.

The terms first, second and the like in the description and in the claims, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application are capable of operation in sequences other than those illustrated or otherwise described herein, and that the terms "first" and "second" are generally intended to be used in a generic sense and not to limit the number of objects, for example, the first object may be one or more. Furthermore, in the description and claims, "and/or" means at least one of the connected objects, and the character "/" generally means a relationship in which the associated object is an "or" before and after.

It is noted that the techniques described in embodiments of the present application are not limited to long term evolution (Long Term Evolution, LTE)/LTE evolution (LTE-Advanced, LTE-a) systems, but may also be used in other wireless communication systems, such as code division multiple access (Code Division Multiple Access, CDMA), time division multiple access (Time Division Multiple Access, TDMA), frequency division multiple access (Frequency Division Multiple Access, FDMA), orthogonal frequency division multiple access (Orthogonal Frequency Division Multiple Access, OFDMA), single carrier frequency division multiple access (Single-carrier Frequency Division Multiple Access, SC-FDMA), and other systems. The terms "system" and "network" in embodiments of the present application are often used interchangeably, and the techniques described may be used for both the above-mentioned systems and radio technologies, as well as other systems and radio technologies. The following description describes a New air interface (NR) system for purposes of example and NR terminology is used in much of the description below, but these techniques may also be applied to applications other than NR system applications, such as the 6th generation (6th Generation,6G) communication system.

Fig. 1 shows a block diagram of a wireless communication system to which embodiments of the present application are applicable. The wireless communication system includes a terminal 11 and a network device 12. The terminal 11 may be a mobile phone, a tablet (Tablet Personal Computer), a Laptop (Laptop Computer) or a terminal-side Device called a notebook, a personal digital assistant (Personal Digital Assistant, PDA), a palm top, a netbook, an ultra-mobile personal Computer (ultra-mobile personal Computer, UMPC), a mobile internet appliance (Mobile Internet Device, MID), an augmented reality (augmented reality, AR)/Virtual Reality (VR) Device, a robot, a Wearable Device (weather Device), a vehicle-mounted Device (VUE), a pedestrian terminal (PUE), a smart home (home Device with a wireless communication function, such as a refrigerator, a television, a washing machine, or a furniture), a game machine, a personal Computer (personal Computer, PC), a teller machine, or a self-service machine, and the Wearable Device includes: intelligent wrist-watch, intelligent bracelet, intelligent earphone, intelligent glasses, intelligent ornament (intelligent bracelet, intelligent ring, intelligent necklace, intelligent anklet, intelligent foot chain etc.), intelligent wrist strap, intelligent clothing etc.. Note that, the specific type of the terminal 11 is not limited in the embodiment of the present application. The network-side device 12 may comprise an access network device or a core network device, wherein the access network device 12 may also be referred to as a radio access network device, a radio access network (Radio Access Network, RAN), a radio access network function or a radio access network element. Access network device 12 may include a base station, a WLAN access point, a WiFi node, or the like, which may be referred to as a node B, an evolved node B (eNB), an access point, a base transceiver station (Base Transceiver Station, BTS), a radio base station, a radio transceiver, a basic service set (Basic Service Set, BSS), an extended service set (Extended Service Set, ESS), a home node B, a home evolved node B, a transmission and reception point (Transmitting Receiving Point, TRP), or some other suitable terminology in the art, and the base station is not limited to a particular technical vocabulary so long as the same technical effect is achieved, and it should be noted that in the embodiments of the present application, only a base station in an NR system is described as an example, and the specific type of the base station is not limited.

The positioning method provided by the embodiment of the application is described in detail below by some embodiments and application scenes thereof with reference to the accompanying drawings.

Referring to fig. 2, fig. 2 is a flowchart of a positioning method according to an embodiment of the present application, where the positioning method includes:

step 201, the terminal determines configuration information.

Step 202, the terminal executes at least one of the following operations according to the configuration information:

and reporting the positioning information.

Specifically, the terminal may receive configuration information sent by the network side device. The configuration information may or may not include the first model information (in this case, the terminal already has the first model information), or the terminal may acquire the first model information through other means (for example, a pre-configuration means). The terminal can determine first model information according to the configuration information and position according to the first model information. The terminal may also report the obtained positioning information (the positioning information may be obtained by positioning according to the first model information or may be obtained by positioning according to another method, which is not limited herein) to the network device according to the configuration information.

In the foregoing, the "positioning according to the first model information" may be understood as a positioning-related process, for example, determining positioning measurement information, processing the positioning measurement information, and reporting the positioning measurement information; determining a positioning error, processing the positioning error and reporting the positioning error; determining location information, processing location information, reporting location information, and the like.

Different first model information can be determined according to the configuration information, and the different first model information can be specifically: 1) The model types are different, if the models are a machine learning model, a preprocessing model and an error model respectively, the terminal can determine that the first model information comprises machine learning model information, preprocessing model information or error model information according to different contents or configuration modes of the configuration information; 2) The input type or the output type of the model is different, for example, the terminal can determine the input and the output of the model according to the different content or the different configuration mode of the configuration information, so as to determine the first model information (the first model information comprises the input and the output of the model); 3) The parameter structures of the models are different, for example, the terminal can determine the parameter structures of the models according to different content or configuration modes of the configuration information, so as to determine first model information (the first model information comprises the parameter structures of the models); 4) The generalization capability of the model is different, for example, the terminal can determine the generalization capability of the model in the first model information according to different content or configuration modes of the configuration information, so as to determine the first model information. Specifically, the content of the configuration information includes positioning reference signal resource configuration information, positioning reference signal resource set configuration information, TRP configuration information, frequency layer configuration information, positioning method configuration information, positioning scene configuration information and the like; the configuration information includes a configuration of each positioning reference signal resource (per PRS resource), a configuration of each set of positioning reference signal resources (per PRS resource set), a configuration of each TRP (per TRP), a configuration of each frequency layer (per Frequency layer), a configuration of each positioning method (per positioning method), and a configuration of each positioning scenario (per positioning scenario).

In this embodiment, the terminal determines configuration information; and the terminal executes at least one of the following operations according to the configuration information: determining first model information, and positioning according to the first model information; and reporting the positioning information. The terminal can determine the first model information according to the configuration information, so as to select a corresponding model to position, improve positioning accuracy, and can report positioning information according to the configuration information, for example, select a corresponding reporting mode according to the configuration information to report the positioning information, so that in positioning based on machine learning, the reporting mode of the positioning information determined under different models (for example, a machine learning model, error model information or a preprocessing model, etc.) or different configurations (different configurations can be understood as different contents included in the configuration information or different configuration modes) can be clarified, and ambiguity is avoided.

In the foregoing, the configuration information includes at least one of:

(1) Positioning reference signal resource configuration information, e.g., positioning reference signal (Positioning Reference Signal, PRS) resources or sounding reference signal (sounding reference signal, SRS) resources;

(2) Positioning reference signal resource set configuration information, e.g., a PRS resource set or an SRS resource set;

(3) Frequency layer configuration information;

(4) TRP configuration information;

(5) Positioning method configuration information, wherein the positioning method includes but is not limited to: downlink time difference of arrival (DownLink Time delay of arrival, DL-TDOA), multiple round trip times (multi round triptime, multi-RTT), downlink departure angle (Downlink Angle Of Departure, DL-AOD), enhanced Cell identity ((Enhanced Cell-ID, E-CID), observed time difference of arrival (Observed Time Difference of Arrival, OTDOA) positioning, etc.;

(6) Positioning scene configuration information, wherein the positioning scene includes, but is not limited to: urban macros (Uma), urban Microscopes (UMi), indoor (industry), intelligent factories (industry), narrowband internet of things (Narrow Band Internet of Things, NB-IoT), lightweight devices (RedCap), augmented reality (XR), and the like.

In the foregoing, the positioning information includes at least one of:

(1) Measurement information, wherein the measurement information comprises at least one of: channel impulse response (Channel Impulse Response, CIR), time-delay power spectrum (Power Delay Profile, PDP), reference signal Time difference (Reference Signal Time Difference, RSTD), round-trip Time (RTT), angle of Arrival (AoA), reference signal received power (Reference Signal Receiving Power, RSRP), time of Arrival (TOA), power of the head path; time delay of the first path; TOA of the first path; reference signal time difference of first path (Reference Signal Time Difference, RSTD); the arrival angle of the head path; antenna sub-carrier phase difference of the first path; power of other paths; time delay of other paths; TOA of other paths; RSTD of other paths; other paths of antenna subcarrier phase differences, line of Sight (LoS) identification information, non-Line of Sight (Not Line of Sight, NLoS) identification information, average excess delay root mean square, delay expansion, coherence bandwidth and the like.

(2) Error information, wherein the error information comprises at least one of: measurement errors of measurement quantity, errors of a model, errors of related parameters of the model and errors of a positioning result.

(3) Positioning results, wherein the positioning results include at least one of: absolute position coordinate information calculated by the terminal, relative position coordinate information calculated by the terminal and coordinate system related information.

(4) Machine learning model update information, which may include updates of machine learning model parameters, updates of machine learning model structure, and the like;

(5) Error model update information, which may include updates of error model parameters, updates of error model structure, etc.;

(6) The pretreatment model update information may include an update of pretreatment model parameters, an update of pretreatment model structures, and the like.

The other path (additional path) is a path other than the first path (e.g., multipath), the other path may include at least one path, and the maximum number of other paths may include one of: 4,8, 16, 32, 64 paths.

In one embodiment of the present application, the method further includes that the terminal receives first information sent by a network side device;

the first information includes at least one of:

(1) The first model information; wherein the first model information includes at least one of: machine learning model information; error model information; preprocessing model information.

(2) The indication information is used for indicating the terminal to report the positioning information.

Specifically, the indication information is used for indicating at least one of the following:

(a) The method is used for indicating the terminal to report measurement information, and the measurement information comprises at least one of the following items: CIR, PDP, RSTD, RTT, aoA, RSRP, TOA, power of the head path; time delay of the first path; TOA of the first path; RSTD of the first path; the arrival angle of the head path; antenna sub-carrier phase difference of the first path; power of other paths; time delay of other paths; TOA of other paths; RSTD of other paths; other paths of antenna subcarrier phase differences, loS identification information, NLoS identification information, average excess delay root mean square, delay expansion, coherence bandwidth and the like;

(b) The method is used for indicating the terminal to report error information, and the first error information comprises, but is not limited to, at least one of the following: measuring errors of the measured quantity, errors of the model, errors of related parameters of the model and errors of a positioning result;

(c) The method is used for indicating the terminal to report the positioning result, and the positioning result comprises, but is 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 and coordinate system related information.

The first information may be sent separately from the configuration information, or may be sent in the configuration information, where the first information may be sent in at least one of:

carrying the information in the resource allocation of each positioning reference signal and sending the information;

carrying the information in the resource set configuration of each positioning reference signal and sending the information;

carrying the configuration information of each frequency layer and sending the configuration information;

carrying and sending in each TRP configuration information;

carrying and sending the configuration information of each positioning method;

and the information is carried and sent in each positioning scene configuration information.

The indication information is used for indicating the terminal to report the positioning information, or the indication information only indicates the terminal to report the positioning information, or the indication information indicates the terminal to report the positioning information by indicating the reporting mode of the positioning information, or the indication information indicates the terminal to report the positioning information and indicates the reporting mode of the positioning information.

The reporting mode of the positioning information is determined by at least one of the following steps:

(1) Determining according to the type of the first model information; the first model information includes at least one of: machine learning model information; error model information; preprocessing model information. The type of the first model information may be determined according to the content included in the first model information being different, for example, a case where the first model information includes machine learning model information and a case where the first model information includes preprocessing model information, both corresponding to different types; the first model information includes machine learning model information and the first model information includes error model information, both corresponding to different types.

The type of the first model information may also be determined based on information including machine learning model information, error model information, or preprocessing model information itself. For example, for a machine learning model, model types with different input and output amounts are different; model types with different generalization capacities are different; the model structure and the parameter information are different and are also different in type;

for the preprocessing model, model types with different input and output quantities are different; the structure and parameter information are different and the type is also different; for the error model information, the error types may be different, such as the error is mean square error or euclidean distance; the model structure and parameter information are different and are also of different types.

The reporting mode of the positioning information is related to the type of the first model information, for example, if the used or configured machine learning model information is of each positioning reference signal resource, the positioning information is also related to each positioning reference signal resource reporting.

(2) And determining according to the transmission mode 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 in association with each positioning reference signal resource.

(3) And determining according to the indication information. For example, the indication information may indicate a reporting manner of the positioning information. 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 transmission;

each frequency layer is transmitted;

each positioning method is sent;

each positioning scene is sent.

(4) And determining according to the sending mode of the indication information. The reporting mode of the positioning information is the same as the sending mode (i.e. the sending mode) of the indication information, for example, if the indication information is related to the sending of the positioning reference signal resource set, the positioning information is also related to the reporting of the positioning reference signal resource set.

(5) Besides the above reporting mode for determining the positioning information, the terminal can also determine the reporting mode by itself.

In the case that the reporting modes of the positioning information are determined in the above (1) - (5), the terminal needs to report the identification information corresponding to the target reporting mode at the same time, where the target mode is the reporting mode determined by the terminal according to one of the methods in the above (1) - (5). Namely, when 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 items:

Positioning reference signal resource identification information, e.g., an ID of a positioning reference signal resource;

positioning reference signal resource set identification information, e.g., IDs of positioning reference signal resource sets;

TRP identification information, e.g., an ID of the TRP;

frequency layer identification information, for example, an ID of the frequency layer;

positioning method identification information, for example, an ID of a positioning method;

locating scene identification information, for example, an ID of a locating scene.

In one embodiment of the present application, the machine learning model information includes at least one of:

(1) At least one machine learning model. The at least one machine learning model may include a common machine learning model, a neural network model, or a deep neural network model, the at least one machine learning model including at least one of:

a convolutional neural network (Convolutional Neural Networks, CNN);

a recurrent neural network (Recurrent Neural Network, RNN);

recurrent neural networks (Long-Short Term Memory, LSTM);

a recursive tensor neural network (Recursive Neural Tensor Network, RNTN);

generating an antagonism network (Generative Adversarial Networks, GAN);

a deep belief network (Deep Belief Network, DBN);

A limited boltzmann machine (Restricted Boltzmann Machine, RBM).

Optionally, the at least one machine learning model includes a multi-step machine learning model (one step may correspond to one machine learning model). The multi-step machine learning model includes at least one of:

a multi-step machine learning model that distinguishes between input information types and output information types;

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

a multi-step machine learning model differentiated according to generalization capability.

The multi-step machine learning model may be transmitted in association with information included in the configuration information, for example, the multi-step machine learning model may be transmitted in association with different configuration information according to model types. The model types are different, including different input and output quantities, different generalization capacities, different model structures and parameter information and the like; depending on the type, each of the multiple step machine learning models may be associated with a different configuration information transmission, e.g., a first step model associated with each positioning method transmission, a second step model associated with each positioning reference signal resource transmission, etc.

(2) Parameters of the at least one machine learning model. The parameters of the at least one machine learning model include at least one of: weights of all layers; step length; the average value; the variance.

(3) Input information of the machine learning model; the input information of the machine learning model includes at least one of:

CIR; a 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; the arrival angle of the head path; antenna sub-carrier phase difference of the first path; power of other paths; time delay of other paths; TOA of other paths; RSTD of other paths; angle of arrival of other paths; other paths of antenna sub-carrier wave phase differences; loS identification information; NLoS identification information; average excess delay; expanding root mean square time delay; a coherence bandwidth.

Further, the input information may be single-station or multi-station, where the single-station or multi-station information is determined by information about the number of base stations issued by the network side device, and the number of base stations includes 1-maxTRPNumber (maximum number of TRPs), where maxTRPNumber is the maximum number of TRPs in a specific scenario.

(4) And outputting information of the machine learning model. The output information of the machine learning model includes 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; the arrival angle of the head path; power of other paths; time delay of other paths; TOA of other paths; RSTD of other paths; angle of arrival of other paths; loS identification information; NLoS identification information.

In another embodiment of the present application, the error model information includes at least one of:

(1) At least one error value estimated by network side equipment; wherein the error value comprises at least one of: position error values, measurement error values, model error values and parameter error values.

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

(3) Parameters of an error model estimated by the at least one network side device;

(4) Input information of the error model;

(5) And outputting information of the error model.

If the error model information is used for calibrating 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.

If the error model information is used to calibrate the measurement information, the input information for the error model includes initial first measurement information including 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, 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, average excessive delay root mean square, delay expansion and coherence bandwidth; the output information of the error model comprises first measurement information after error calibration.

If the error model information is used for calibrating the model information or the parameter information related to the model, the input information of the error model comprises at least one of the following: a machine learning model, a parameter of a machine learning model, a preprocessing model, or a parameter of a preprocessing model; the output information of the error model includes at least one of: a calibrated machine learning model, parameters of a calibrated pre-processing model, or parameters of a calibrated pre-processing model.

In one embodiment of the present application, the preprocessing model information is used to preprocess terminal measurement information, so that the processed measurement information can be better trained or processed by a machine learning model, where the preprocessing model information includes at least one of the following:

filter parameters or structures;

convolutional layer parameters or structures;

pooling layer parameters or structures;

discrete cosine transform (Discrete Cosine Transform, DCT) parameters or structures;

wavelet transform parameters or structures;

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.

The input information of the preprocessing model information comprises second measurement information, and the second 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, reference signal waveform and related sequence of reference signal; the output information of the preprocessing model information includes second measurement information after preprocessing.

In one embodiment of the present application, the method further comprises: the terminal sends request information, wherein the request information is used for requesting a sending mode of the first information. The sending mode of the first 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 transmission; each frequency layer is transmitted; each positioning method is sent; each positioning scene is sent.

In an embodiment of the present application, at least one of the error model information and the preprocessing model information may be further sent in association with machine learning model information, that is, the per machine learning model broadcasts the error model information and/or the preprocessing model information, that is, under each machine learning model, the preprocessing model and the error model corresponding to the machine learning model are issued, so as to perform preprocessing on the input quantity of the machine learning, or perform error processing on errors of the machine learning model, model parameters and output quantity.

In one embodiment of the present application, the method further comprises: the terminal sends terminal positioning capability information, wherein the terminal positioning capability information comprises at least one of the following items:

whether machine learning based positioning is supported;

whether 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 receives machine learning model information;

whether 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, receives error model information;

whether 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, receives the preprocessing model information;

whether receiving a plurality of machine learning models is supported;

a maximum number of receivable machine learning models;

whether parameters of the plurality of machine learning models are supported;

a maximum number of parameters that can receive the machine learning model;

whether receiving a plurality of pre-processing models is supported;

The maximum number of pre-processing models that can be received;

whether parameters of a plurality of preprocessing models are supported to be received;

a maximum number of parameters that can receive the pre-processing model;

whether or not receiving a plurality of error models is supported;

the maximum number of receivable error models;

whether parameters of the plurality of error models are supported;

the maximum number of parameters that can receive the error model;

input information of a supported machine learning model;

output information of the supported machine learning model;

input information of the supported preprocessing model;

output information of the supported preprocessing model;

input information of the supported error model;

output information of the supported error model.

Referring to fig. 3, fig. 3 is a flowchart of a positioning method according to an embodiment of the present application, where the positioning method includes:

In the above description, "positioning" may be understood as a positioning-related process, such as determining positioning measurement information, processing the positioning measurement information, and reporting the positioning measurement information; determining a positioning error, processing the positioning error and reporting the positioning error; determining location information, processing location information, reporting location information, and the like.

In this embodiment, the network side device sends configuration information, where the configuration information is used for positioning a terminal and/or reporting positioning information by the terminal. The terminal can determine the first model information according to the configuration information, so as to select a corresponding model for positioning, thereby improving positioning accuracy, or the terminal can report the positioning information according to the configuration information, for example, select a corresponding reporting mode according to the configuration information.

Optionally, the configuration information includes at least one of:

positioning reference signal resource allocation information;

positioning reference signal resource set configuration information;

frequency layer configuration information;

transmitting receiving point TRP configuration information;

positioning method configuration information;

positioning scene configuration information.

Optionally, the configuration information carries first information:

the first information includes at least one of:

First model information;

the indication information is used for indicating the terminal to report the positioning information.

Optionally, the indication information is used for indicating a reporting mode of the positioning information.

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

determining according to the type of the first model information;

determining according to a transmission mode of the first model information;

determining according to the indication information;

and determining according to the sending mode of the indication information.

Optionally, the first model information includes at least one of:

machine learning model information;

error model information;

preprocessing model information.

Optionally, the machine learning model information includes at least one of:

at least one machine learning model;

parameters of the at least one machine learning model;

input information of the machine learning model;

and outputting information of the machine learning model.

Optionally, the at least one machine learning model includes at least one of:

a convolutional neural network CNN;

a recurrent neural network RNN;

recurrent neural network LSTM;

a recursive tensor neural network RNTN;

generating an antagonizing network GAN;

a deep belief network DBN;

Boltzmann machines RBM are limited.

Optionally, the parameters of the at least one machine learning model include at least one of:

weights of all layers; step length; the average value; the variance.

Optionally, the input information of the machine learning model includes at least one of:

channel impulse response CIR; a delay power spectrum PDP; reference signal time difference RSTD; round trip time RTT; angle of arrival AoA; reference signal received power RSRP; arrival time TOA; power of the first path; time delay of the first path; TOA of the first path; RSTD of the first path; the arrival angle of the head path; antenna sub-carrier phase difference of the first path; power of other paths; time delay of other paths; TOA of other paths; RSTD of other paths; angle of arrival of other paths; other paths of antenna sub-carrier wave phase differences; loS identification information; NLoS identification information; average excess delay; expanding root mean square time delay; a coherence bandwidth.

Optionally, the output information of the machine learning model includes at least one of:

Optionally, the error model information includes at least one of:

at least one error value estimated by network side equipment;

the error model estimated by the at least one network side device;

parameters of an error model estimated by the at least one network side device;

input information of the error model;

and outputting information of the error model.

Optionally, the error value includes at least one of: position error values, measurement error values, model error values and parameter error values.

Optionally, the error model includes at least one of: a position error model, a measurement error model and a parameter error model.

Optionally, the input information of the error model includes an initial position of the terminal or a position calculated by the terminal, and the output information of the error model includes position information after error calibration.

Optionally, the input information of the error model includes initial first measurement information, and the first measurement information includes 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, average excessive delay root mean square, delay expansion and coherence bandwidth;

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

Optionally, the input information of the error model includes at least one of: a machine learning model, a parameter of a machine learning model, a preprocessing model, or a parameter of a preprocessing model;

the output information of the error model includes at least one of: a calibrated machine learning model, parameters of a calibrated pre-processing model, or parameters of a calibrated pre-processing model.

Optionally, the preprocessing model information includes at least one of the following:

filter parameters or structures;

convolutional 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 the measurement information.

Optionally, 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 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, reference signal waveform and related sequence of reference signal;

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

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

Optionally, the multi-step machine learning model includes at least one of:

Optionally, the positioning information includes at least one of:

measuring information;

error information;

positioning a result;

machine learning model update information

Updating information of the error model;

the preprocessing model updates the information.

Optionally, the method further comprises:

the network side equipment receives request information sent by the terminal, wherein the request information is used for requesting a sending mode of the first information.

Optionally, the method further comprises:

the network side equipment receives terminal positioning capability information sent by the terminal, wherein the terminal positioning capability information comprises at least one of the following items:

whether machine learning based positioning is supported;

whether receiving a plurality of machine learning models is supported;

a maximum number of receivable machine learning models;

whether parameters of the plurality of machine learning models are supported;

a maximum number of parameters that can receive the machine learning model;

whether receiving a plurality of pre-processing models is supported;

the maximum number of pre-processing models that can be received;

a maximum number of parameters that can receive the pre-processing model;

whether or not receiving a plurality of error models is supported;

The maximum number of receivable error models;

whether parameters of the plurality of error models are supported;

the maximum number of parameters that can receive the error model;

input information of a supported machine learning model;

output information of the supported machine learning model;

input information of the supported preprocessing model;

output information of the supported preprocessing model;

input information of the supported error model;

output information of the supported error model.

In the positioning method provided in fig. 2 of the present application, the execution body may be a first positioning device. In the embodiment of the present application, the positioning method performed by the first positioning device is taken as an example, and the device of the positioning method provided in the embodiment of fig. 2 of the present application is described.

As shown in fig. 4, an embodiment of the present application provides a first positioning device 400, including:

a configuration module 401, configured to determine configuration information;

an execution module 402, configured to execute at least one of the following operations according to the configuration information:

and reporting the positioning information.

Optionally, the configuration information includes at least one of:

positioning reference signal resource allocation information;

positioning reference signal resource set configuration information;

Frequency layer configuration information;

transmitting receiving point TRP configuration information;

positioning method configuration information;

positioning scene configuration information.

Optionally, the apparatus further includes a receiving module, configured to receive first information sent by the network side device;

the first information includes at least one of:

the first model information;

Optionally, the first information is carried in the configuration information.

determining according to the type of the first model information;

determining according to a transmission mode of the first model information;

determining according to the indication information;

and determining according to the sending mode of the indication information.

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

each positioning reference signal resource is sent;

each positioning reference signal resource set is sent;

each TRP transmission;

each frequency layer is transmitted;

each positioning method is sent;

each positioning scene is sent.

Optionally, when the positioning information is reported in a target reporting manner, the positioning information further includes identification information corresponding to the target reporting manner, where the identification information includes 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 scene identification information.

Optionally, the first model information includes at least one of:

machine learning model information;

error model information;

preprocessing model information.

Optionally, the machine learning model information includes at least one of:

at least one machine learning model;

parameters of the at least one machine learning model;

input information of the machine learning model;

and outputting information of the machine learning model.

Optionally, the at least one machine learning model includes at least one of:

a convolutional neural network CNN;

a recurrent neural network RNN;

recurrent neural network LSTM;

a recursive tensor neural network RNTN;

generating an antagonizing network GAN;

a deep belief network DBN;

boltzmann machines RBM are limited.

weights of all layers; step length; the average value; the variance.

Optionally, the error model information includes at least one of:

at least one error value estimated by network side equipment;

the error model estimated by the at least one network side device;

parameters of an error model estimated by the at least one network side device;

input information of the error model;

and outputting information of the error model.

Filter parameters or structures;

convolutional 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 the measurement information.

Optionally, the multi-step machine learning model includes at least one of:

Optionally, the positioning information includes at least one of:

measuring information;

error information;

positioning a result;

machine learning model update information

Updating information of the error model;

the preprocessing model updates the information.

Optionally, the device further includes a first sending module, configured to send request information, where the request information is used to request a sending manner of the first information.

Optionally, the apparatus further includes a second sending module, configured to send terminal location capability information, where the terminal location capability information includes at least one of the following:

whether machine learning based positioning is supported;

whether receiving a plurality of machine learning models is supported;

a maximum number of receivable machine learning models;

whether parameters of the plurality of machine learning models are supported;

a maximum number of parameters that can receive the machine learning model;

whether receiving a plurality of pre-processing models is supported;

the maximum number of pre-processing models that can be received;

a maximum number of parameters that can receive the pre-processing model;

whether or not receiving a plurality of error models is supported;

the maximum number of receivable error models;

whether parameters of the plurality of error models are supported;

the maximum number of parameters that can receive the error model;

input information of a supported machine learning model;

output information of the supported machine learning model;

input information of the supported preprocessing model;

output information of the supported preprocessing model;

input information of the supported error model;

output information of the supported error model.

The first positioning apparatus 400 in the embodiment of the present application may be an electronic device, for example, an electronic device with an operating system, or may be a component in an electronic device, for example, an integrated circuit or a chip. The electronic device may be a terminal, or may be other devices than a terminal. By way of example, terminals may include, but are not limited to, the types of terminals 11 listed above, other devices may be servers, network attached storage (Network Attached Storage, NAS), etc., and embodiments of the application are not specifically limited.

The first positioning device 400 provided in this embodiment of the present application can implement each process implemented by the method embodiment of fig. 2, and achieve the same technical effects, so that repetition is avoided, and no further description is provided herein.

In the positioning method provided in fig. 3 of the present application, the execution body may be a second positioning device. In the embodiment of the present application, the device for executing the positioning method by using the second positioning device is described as an example.

As shown in fig. 5, an embodiment of the present application provides a second positioning device 500, including:

the sending module 501 is configured to send configuration information, where the configuration information is used for positioning a terminal and/or reporting positioning information by the terminal.

Optionally, the configuration information includes at least one of:

positioning reference signal resource allocation information;

positioning reference signal resource set configuration information;

frequency layer configuration information;

transmitting receiving point TRP configuration information;

positioning method configuration information;

positioning scene configuration information.

Optionally, the configuration information carries first information:

the first information includes at least one of:

first model information;

determining according to the type of the first model information;

determining according to a transmission mode of the first model information;

determining according to the indication information;

and determining according to the sending mode of the indication information.

Optionally, the first model information includes at least one of:

machine learning model information;

error model information;

preprocessing model information.

Optionally, the machine learning model information includes at least one of:

at least one machine learning model;

Parameters of the at least one machine learning model;

input information of the machine learning model;

and outputting information of the machine learning model.

Optionally, the at least one machine learning model includes at least one of:

a convolutional neural network CNN;

a recurrent neural network RNN;

recurrent neural network LSTM;

a recursive tensor neural network RNTN;

generating an antagonizing network GAN;

a deep belief network DBN;

boltzmann machines RBM are limited.

weights of all layers; step length; the average value; the variance.

Optionally, the error model information includes at least one of:

at least one error value estimated by network side equipment;

the error model estimated by the at least one network side device;

parameters of an error model estimated by the at least one network side device;

input information of the error model;

and outputting information of the error model.

Filter parameters or structures;

convolutional 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 the measurement information.

Optionally, the multi-step machine learning model includes at least one of:

Optionally, the positioning information includes at least one of:

measuring information;

error information;

positioning a result;

machine learning model update information

Updating information of the error model;

the preprocessing model updates the information.

Optionally, the second positioning device further includes a first receiving module, configured to receive request information sent by the terminal, where the request information is used to request a sending manner of the first information.

Optionally, the second positioning device further includes a second receiving module, configured to receive terminal positioning capability information sent by the terminal, where the terminal positioning capability information includes at least one of the following:

whether machine learning based positioning is supported;

whether receiving a plurality of machine learning models is supported;

a maximum number of receivable machine learning models;

whether parameters of the plurality of machine learning models are supported;

a maximum number of parameters that can receive the machine learning model;

whether receiving a plurality of pre-processing models is supported;

the maximum number of pre-processing models that can be received;

a maximum number of parameters that can receive the pre-processing model;

whether or not receiving a plurality of error models is supported;

the maximum number of receivable error models;

whether parameters of the plurality of error models are supported;

the maximum number of parameters that can receive the error model;

input information of a supported machine learning model;

output information of the supported machine learning model;

input information of the supported preprocessing model;

output information of the supported preprocessing model;

input information of the supported error model;

output information of the supported error model.

The second positioning device 500 provided in this embodiment of the present application can implement each process implemented by the method embodiment of fig. 3, and achieve the same technical effects, so that repetition is avoided, and no further description is provided herein.

Optionally, as shown in fig. 6, the embodiment of the present application further provides a communication device 600, including a processor 601 and a memory 602, where the memory 602 stores a program or instructions that can be executed on the processor 601, for example, when the communication device 600 is a terminal, the program or instructions implement, when executed by the processor 601, the steps of the positioning method embodiment shown in fig. 2 and achieve the same technical effects. When the communication device 600 is a network side device, the program or the instruction, when executed by the processor 601, implements the steps of the positioning method embodiment shown in fig. 3, and the same technical effects can be achieved, so that repetition is avoided, and no further description is given here.

The embodiment of the application also provides a terminal, which comprises a processor and a communication interface, wherein the processor is used for executing at least one of the following operations according to the configuration information: determining first model information, and positioning according to the first model information; reporting the positioning information, wherein the communication interface is used for acquiring the configuration information. The terminal embodiment corresponds to the terminal-side method embodiment, and each implementation process and implementation manner of the method embodiment can be applied to the terminal embodiment, and the same technical effects can be achieved. Specifically, fig. 7 is a schematic hardware structure of a terminal for implementing an embodiment of the present application.

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

Those skilled in the art will appreciate that the terminal 700 may further include a power source (e.g., a battery) for powering the various components, and the power source may be logically coupled to the processor 7 by a power management system for performing functions such as managing charging, discharging, and power consumption by the power management system. The terminal structure shown in fig. 7 does not constitute a limitation of the terminal, and the terminal may include more or less components than shown, or may combine certain components, or may be arranged in different components, which will not be described in detail herein.

It should be appreciated that in embodiments of the present application, the input unit 704 may include a graphics processing unit (Graphics Processing Unit, GPU) 7041 and a microphone 7042, with the graphics processor 7041 processing image data of still pictures or video obtained by an image capturing device (e.g., a camera) in a video capturing mode or an image capturing mode. The display unit 706 may include a display panel 7061, and the display panel 7061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. 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 referred to as a touch screen. The touch panel 7071 may include two parts, a touch detection device and a touch controller. Other input devices 7072 may include, but are not limited to, a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and so forth, which are not described in detail herein.

In this embodiment, after receiving downlink data from the network side device, the radio frequency unit 701 may transmit the downlink data to the processor 710 for processing; in addition, the radio frequency unit 701 may send uplink data to the network side device. Typically, the radio unit 701 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like.

The memory 709 may be used to store software programs or instructions and various data. The memory 709 may mainly include a first storage area storing programs or instructions and a second storage area storing data, wherein the first storage area may store an operating system, application programs or instructions (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like. Further, the memory 709 may include volatile memory or nonvolatile memory, or the memory 709 may include both volatile and nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable EPROM (EEPROM), or a flash Memory. The volatile memory may be random access memory (Random Access Memory, RAM), static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (ddr SDRAM), enhanced SDRAM (Enhanced SDRAM), synchronous DRAM (SLDRAM), and Direct RAM (DRRAM). Memory 709 in embodiments of the present application includes, but is not limited to, these and any other suitable types of memory.

Processor 710 may include one or more processing units; optionally, processor 710 integrates an application processor that primarily processes operations involving an operating system, user interface, application programs, and the like, and a modem processor that primarily processes wireless communication signals, such as a baseband processor. It will be appreciated that the modem processor described above may not be integrated into the processor 710.

The radio frequency unit 701 is configured to obtain configuration information;

a processor 710, configured to perform at least one of the following operations according to the configuration information:

and reporting the positioning information.

Optionally, the configuration information includes at least one of:

positioning reference signal resource allocation information;

positioning reference signal resource set configuration information;

frequency layer configuration information;

transmitting receiving point TRP configuration information;

positioning method configuration information;

positioning scene configuration information.

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

the first information includes at least one of:

the first model information;

Optionally, the first information is carried in the configuration information.

determining according to the type of the first model information;

determining according to a transmission mode of the first model information;

determining according to the indication information;

and determining according to the sending mode of the indication information.

each positioning reference signal resource is sent;

each positioning reference signal resource set is sent;

each TRP transmission;

each frequency layer is transmitted;

each positioning method is sent;

each positioning scene is sent.

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 scene identification information.

Optionally, the first model information includes at least one of:

machine learning model information;

error model information;

preprocessing model information.

Optionally, the machine learning model information includes at least one of:

at least one machine learning model;

parameters of the at least one machine learning model;

input information of the machine learning model;

and outputting information of the machine learning model.

Optionally, the at least one machine learning model includes at least one of:

a convolutional neural network CNN;

a recurrent neural network RNN;

recurrent neural network LSTM;

a recursive tensor neural network RNTN;

generating an antagonizing network GAN;

a deep belief network DBN;

boltzmann machines RBM are limited.

weights of all layers;

step length;

the average value;

the variance.

Optionally, the error model information includes at least one of:

at least one error value estimated by network side equipment;

the error model estimated by the at least one network side device;

parameters of an error model estimated by the at least one network side device;

input information of the error model;

and outputting information of the error model.

Filter parameters or structures;

convolutional 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 the measurement information.

Optionally, the multi-step machine learning model includes at least one of:

Optionally, the positioning information includes at least one of:

measuring information;

error information;

positioning a result;

machine learning model update information

Updating information of the error model;

the preprocessing model updates the information.

Optionally, the radio frequency unit 701 is further configured to send request information, where the request information is used to request a sending manner of the first information.

Optionally, the radio frequency unit 701 is further configured to send terminal location capability information, where the terminal location capability information includes at least one of the following:

whether machine learning based positioning is supported;

Whether receiving a plurality of machine learning models is supported;

a maximum number of receivable machine learning models;

whether parameters of the plurality of machine learning models are supported;

a maximum number of parameters that can receive the machine learning model;

whether receiving a plurality of pre-processing models is supported;

the maximum number of pre-processing models that can be received;

a maximum number of parameters that can receive the pre-processing model;

whether or not receiving a plurality of error models is supported;

the maximum number of receivable error models;

whether parameters of the plurality of error models are supported;

the maximum number of parameters that can receive the error model;

input information of a supported machine learning model;

output information of the supported machine learning model;

input information of the supported preprocessing model;

output information of the supported preprocessing model;

input information of the supported error model;

output information of the supported error model.

The embodiment of the application also provides network side equipment, which comprises a processor and a communication interface, wherein the communication interface is used for sending configuration information, and the configuration information is used for positioning a terminal and/or reporting positioning information by the terminal. The network side device embodiment corresponds to the network side device method embodiment, and 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.

Specifically, the embodiment of the application also provides network side equipment. As shown in fig. 8, the network side device 800 includes: an antenna 81, a radio frequency device 82, a baseband device 83, a processor 84 and a memory 85. The antenna 81 is connected to a radio frequency device 82. In the uplink direction, the radio frequency device 82 receives information via the antenna 81, and transmits the received information to the baseband device 83 for processing. In the downlink direction, the baseband device 83 processes information to be transmitted, and transmits the processed information to the radio frequency device 82, and the radio frequency device 82 processes the received information and transmits the processed information through the antenna 81.

The method performed by the network side device in the above embodiment may be implemented in the baseband apparatus 83, and the baseband apparatus 83 includes a baseband processor.

The baseband device 83 may, for example, include at least one baseband board, where a plurality of chips are disposed on the baseband board, as shown in fig. 8, where one chip, for example, a baseband processor, is connected to the memory 85 through a bus interface, so as to call a program in the memory 85, and perform the network side device operation shown in the foregoing method embodiment.

The network-side device may also include a network interface 86, such as a common public radio interface (common public radio interface, CPRI).

Specifically, the network side device 800 of the embodiment of the present invention further includes: instructions or programs stored in the memory 85 and executable on the processor 84, the processor 84 invokes the instructions or programs in the memory 85 to perform the method performed by the modules shown in fig. 5, and achieve the same technical effects, and are not repeated here.

The embodiment of the present application further provides a readable storage medium, where a program or an instruction is stored, and when the program or the instruction is executed by a processor, the processes of the embodiment of the positioning method are implemented, and the same technical effects can be achieved, so that repetition is avoided, and no further description is given here.

Wherein the processor is a processor in the terminal described in the above embodiment. The readable storage medium includes computer readable storage medium such as computer readable memory ROM, random access memory RAM, magnetic or optical disk, etc.

The embodiment of the application further provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled with the processor, the processor is used for running a program or an instruction, implementing each process of the above positioning method embodiment, and achieving the same technical effect, so as to avoid repetition, and no redundant description is provided herein.

It should be understood that the chips referred to in the embodiments of the present application may also be referred to as system-on-chip chips, or the like.

The embodiments of the present application further provide a computer program/program product, where the computer program/program product is stored in a storage medium, and the computer program/program product is executed by at least one processor to implement each process of the above positioning method embodiment, and achieve the same technical effects, so that repetition is avoided, and details are not repeated herein.

The embodiment of the application also provides a communication system, which comprises: the terminal may be configured to perform the steps of the method embodiment shown in fig. 2 and the network side device may be configured to perform the steps of the method embodiment shown in fig. 3.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. Furthermore, it should be noted that the scope of the methods and apparatus in the embodiments of the present application is not limited to performing the functions in the order shown or discussed, but may also include performing the functions in a substantially simultaneous manner or in an opposite order depending on the functions involved, e.g., the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a computer software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network side device, etc.) to perform the method described in the embodiments of the present application.

The embodiments of the present application have been described above with reference to the accompanying drawings, but the present application is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those of ordinary skill in the art without departing from the spirit of the present application and the scope of the claims, which are also within the protection of the present application.

Claims

1. A positioning method, comprising:

the terminal determines configuration information;

and reporting the positioning information.

2. The method of claim 1, wherein the configuration information comprises at least one of:

positioning reference signal resource allocation information;

positioning reference signal resource set configuration information;

frequency layer configuration information;

transmitting receiving point TRP configuration information;

positioning method configuration information;

positioning scene configuration information.

3. The method according to claim 1, further comprising the terminal receiving first information sent by a network side device;

the first information includes at least one of:

the first model information;

4. A method according to claim 3, characterized in that the first information is carried in the configuration information.

5. The method of claim 3, wherein the indication information is used to indicate a reporting manner of the positioning information.

6. The method of claim 5, wherein the reporting manner of the positioning information is determined by at least one of:

determining according to the type of the first model information;

determining according to a transmission mode of the first model information;

determining according to the indication information;

and determining according to the sending mode of the indication information.

7. The method of claim 5, wherein the reporting manner of the positioning information includes at least one of:

each positioning reference signal resource is sent;

each positioning reference signal resource set is sent;

each TRP transmission;

each frequency layer is transmitted;

each positioning method is sent;

each positioning scene is sent.

8. The method according to any one of claims 5-7, wherein, in the case where the positioning information is reported in a target reporting manner, the positioning information further includes identification information corresponding to the target reporting manner, where the identification information includes at least one of:

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 scene identification information.

9. The method of claim 1, wherein the first model information comprises at least one of:

machine learning model information;

error model information;

preprocessing model information.

10. The method of claim 9, wherein the machine learning model information comprises at least one of:

at least one machine learning model;

parameters of the at least one machine learning model;

input information of the machine learning model;

and outputting 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:

a convolutional neural network CNN;

a recurrent neural network RNN;

recurrent neural network LSTM;

a recursive tensor neural network RNTN;

generating an antagonizing network GAN;

a deep belief network DBN;

boltzmann machines RBM are limited.

12. The method of claim 10, wherein the parameters of the at least one machine learning model include at least one of:

weights of all layers;

step length;

the average value;

the variance.

13. The method of claim 10, wherein the input information of the machine learning model comprises at least one of:

Channel impulse response CIR;

a delay power spectrum PDP;

reference signal time difference RSTD;

round trip time RTT;

angle of arrival AoA;

reference signal received power RSRP;

arrival time TOA;

power of the first path;

time delay of the first path;

TOA of the first path;

RSTD of the first path;

the arrival angle of the head path;

antenna sub-carrier phase difference of the first path;

power of other paths;

time delay of other paths;

TOA of other paths;

RSTD of other paths;

angle of arrival of other paths;

other paths of antenna sub-carrier wave phase differences;

LoS identification information;

NLoS identification information;

average excess delay;

expanding root mean square time delay;

a coherence 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;

the arrival angle of the head path;

power of other paths;

time delay of other paths;

TOA of other paths;

RSTD of other paths;

angle of arrival of other paths;

LoS identification information;

NLoS identification information.

15. The method of claim 9, wherein the error model information comprises at least one of:

At least one error value estimated by network side equipment;

the error model estimated by the at least one network side device;

parameters of an error model estimated by the at least one network side device;

input information of the error model;

and outputting information of the error model.

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

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

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

19. The method of claim 15, wherein the input information of the error model comprises initial first measurement information, the first measurement information comprising 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, 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, average excessive delay root mean square, delay expansion and coherence bandwidth;

20. The method of claim 15, wherein the input information of the error model comprises at least one of: a machine learning model, a parameter of a machine learning model, a preprocessing model, or a parameter of a preprocessing model;

21. The method of claim 9, wherein the preprocessing model information comprises at least one of:

filter parameters or structures;

convolutional 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 the measurement information.

22. The method of claim 9, wherein the input information of the pre-processing model information includes second measurement information;

23. The method of claim 10, wherein the at least one machine learning model comprises a multi-step machine learning model.

24. The method of claim 23, wherein the multi-step machine learning model comprises at least one of:

25. The method of claim 1, wherein the positioning information comprises at least one of:

measuring information;

error information;

positioning a result;

machine learning model update information

Updating information of the error model;

the preprocessing model updates the information.

26. A method according to claim 3, characterized in that the method further comprises:

the terminal sends request information, wherein the request information is used for requesting a sending mode of the first information.

27. The method according to claim 1, wherein the method further comprises:

The terminal sends terminal positioning capability information, wherein the terminal positioning capability information comprises at least one of the following items:

whether machine learning based positioning is supported;

whether receiving a plurality of machine learning models is supported;

a maximum number of receivable machine learning models;

whether parameters of the plurality of machine learning models are supported;

a maximum number of parameters that can receive the machine learning model;

whether receiving a plurality of pre-processing models is supported;

the maximum number of pre-processing models that can be received;

a maximum number of parameters that can receive the pre-processing model;

whether or not receiving a plurality of error models is supported;

the maximum number of receivable error models;

whether parameters of the plurality of error models are supported;

the maximum number of parameters that can receive the error model;

input information of a supported machine learning model;

output information of the supported machine learning model;

input information of the supported preprocessing model;

output information of the supported preprocessing model;

input information of the supported error model;

output information of the supported error model.

28. A positioning method, comprising:

29. The method of claim 28, wherein the configuration information comprises at least one of:

positioning reference signal resource allocation information;

positioning reference signal resource set configuration information;

frequency layer configuration information;

transmitting receiving point TRP configuration information;

positioning method configuration information;

positioning scene configuration information.

30. The method of claim 28, wherein the configuration information carries first information:

The first information includes at least one of:

first model information;

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

32. The method of claim 31, wherein the reporting manner of the positioning information is determined by at least one of:

determining according to the type of the first model information;

determining according to a transmission mode of the first model information;

determining according to the indication information;

and determining according to the sending mode of the indication information.

33. The method of claim 30, wherein the first model information comprises at least one of:

machine learning model information;

error model information;

preprocessing model information.

34. The method of claim 33, wherein the machine learning model information comprises at least one of:

at least one machine learning model;

parameters of the at least one machine learning model;

input information of the machine learning model;

and outputting information of the machine learning model.

35. The method of claim 34, wherein the at least one machine learning model comprises at least one of:

a convolutional neural network CNN;

a recurrent neural network RNN;

recurrent neural network LSTM;

a recursive tensor neural network RNTN;

generating an antagonizing network GAN;

a deep belief network DBN;

boltzmann machines RBM are limited.

36. The method of claim 34, wherein the parameters of the at least one machine learning model include at least one of:

weights of all layers;

step length;

the average value;

the variance.

37. The method of claim 34, wherein the input information of the machine learning model comprises at least one of:

channel impulse response CIR;

a delay power spectrum PDP;

reference signal time difference RSTD;

round trip time RTT;

angle of arrival AoA;

reference signal received power RSRP;

arrival time TOA;

power of the first path;

time delay of the first path;

TOA of the first path;

RSTD of the first path;

the arrival angle of the head path;

antenna sub-carrier phase difference of the first path;

power of other paths;

time delay of other paths;

TOA of other paths;

RSTD of other paths;

angle of arrival of other paths;

other paths of antenna sub-carrier wave phase differences;

LoS identification information;

NLoS identification information;

average excess delay;

expanding root mean square time delay;

a coherence bandwidth.

38. The method of claim 34, 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;

the arrival angle of the head path;

power of other paths;

time delay of other paths;

TOA of other paths;

RSTD of other paths;

angle of arrival of other paths;

LoS identification information;

NLoS identification information.

39. The method of claim 33, wherein the error model information comprises at least one of:

at least one error value estimated by network side equipment;

the error model estimated by the at least one network side device;

parameters of an error model estimated by the at least one network side device;

input information of the error model;

and outputting information of the error model.

40. The method of claim 39, wherein the error value comprises at least one of: position error values, measurement error values, model error values and parameter error values.

41. The method of claim 39, wherein the error model comprises at least one of: a position error model, a measurement error model and a parameter error model.

42. The method of claim 39, wherein the input information of the error model comprises an initial position of the terminal or a calculated position of the terminal, and the output information of the error model comprises position information after error calibration.

43. The method of claim 39, wherein the input information for the error model comprises initial first measurement information, the first measurement information comprising 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, 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, average excessive delay root mean square, delay expansion and coherence bandwidth;

44. The method of claim 39, wherein the input information of the error model comprises at least one of: a machine learning model, a parameter of a machine learning model, a preprocessing model, or a parameter of a preprocessing model;

45. The method of claim 33, wherein the preprocessing model information comprises at least one of:

filter parameters or structures;

convolutional 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 the measurement information.

46. The method of claim 33, wherein the input information of the pre-processing model information includes second measurement information;

47. The method of claim 34, wherein the at least one machine learning model comprises a multi-step machine learning model.

48. The method of claim 47, wherein the multi-step machine learning model comprises at least one of:

49. The method of claim 28, wherein the positioning information comprises at least one of:

measuring information;

error information;

positioning a result;

machine learning model update information

Updating information of the error model;

the preprocessing model updates the information.

50. The method of claim 30, wherein the method further comprises:

51. The method of claim 28, wherein the method further comprises:

whether machine learning based positioning is supported;

whether receiving a plurality of machine learning models is supported;

a maximum number of receivable machine learning models;

whether parameters of the plurality of machine learning models are supported;

a maximum number of parameters that can receive the machine learning model;

whether receiving a plurality of pre-processing models is supported;

The maximum number of pre-processing models that can be received;

a maximum number of parameters that can receive the pre-processing model;

whether or not receiving a plurality of error models is supported;

the maximum number of receivable error models;

whether parameters of the plurality of error models are supported;

the maximum number of parameters that can receive the error model;

input information of a supported machine learning model;

output information of the supported machine learning model;

input information of the supported preprocessing model;

output information of the supported preprocessing model;

input information of the supported error model;

output information of the supported error model.

52. A positioning device, comprising:

the determining module is used for determining configuration information;

and reporting the positioning information.

53. The apparatus of claim 52, wherein the configuration information comprises at least one of:

positioning reference signal resource allocation information;

positioning reference signal resource set configuration information;

Frequency layer configuration information;

transmitting receiving point TRP configuration information;

positioning device configuration information;

positioning scene configuration information.

54. A positioning device, comprising:

55. The apparatus of claim 54, wherein the configuration information comprises at least one of:

positioning reference signal resource allocation information;

positioning reference signal resource set configuration information;

frequency layer configuration information;

transmitting receiving point TRP configuration information;

positioning device configuration information;

positioning scene configuration information.

56. A terminal comprising a processor and a memory storing a program or instructions executable on the processor, which when executed by the processor, performs the steps of the positioning method according to any one of claims 1 to 27.

57. A network side device comprising a processor and a memory storing a program or instructions executable on the processor, which when executed by the processor, implement the steps of the positioning method of any of claims 28 to 51.

58. A readable storage medium, characterized in that the readable storage medium has stored thereon a program or instructions which, when executed by a processor, implement the positioning method according to any of claims 1-27 or the steps of the positioning method according to any of claims 28-51.