WO2023179617A1

WO2023179617A1 - Locating method and apparatus, terminal and network side device

Info

Publication number: WO2023179617A1
Application number: PCT/CN2023/082838
Authority: WO
Inventors: 贾承璐; 杨昂; 王园园
Original assignee: 维沃移动通信有限公司
Priority date: 2022-03-25
Filing date: 2023-03-21
Publication date: 2023-09-28
Also published as: CN116847456A

Abstract

The present application relates to the technical field of communications. Disclosed are a locating method and apparatus, a terminal, and a network side device. The locating method in the embodiments of the present application comprises: a terminal acquiring a model for locating (201); and the terminal using the acquired model to locate (202).

Description

Positioning methods, devices, terminals and network side equipment

Cross-references to related applications

This application claims priority to the Chinese patent application with application number 202210307761.6 and the invention title "Positioning Method, Device, Terminal and Network Side Equipment" submitted on March 25, 2022, which is fully incorporated into this application by reference.

Technical field

This application belongs to the field of communication technology, and specifically relates to a positioning method, device, terminal and network side equipment.

Background technique

With the development of communication technology, location services, as part of communication services, have become more and more important in people's daily lives. Location information in positioning services is an important type of perceptual information, which can not only be used to meet diverse business needs, such as sidelinks or smart factories, but can also be used as prior information for communication links to help Improve the overall performance and service experience of the communication system. Wireless communication network positioning is for a terminal to estimate its current geographical location by measuring reference signals. Specifically, the terminal measures positioning reference signals from multiple positioning base stations, and reports the measurement information of the positioning reference signals to the core network through the serving base station. The positioning management function of the core network performs position estimation; finally, the core network sends the terminal's geographical location information to the terminal through the service base station to complete the positioning of the terminal. However, when the wireless link communication quality is poor or the terminal is in an idle state, the reference signal may not be measured, which will lead to low positioning accuracy.

Contents of the invention

Embodiments of the present application provide a positioning method, device, terminal and network side equipment, which can solve the problem of low positioning accuracy.

The first aspect provides a positioning method, which includes:

The terminal obtains the model used for positioning;

The terminal uses the obtained model to perform positioning.

The second aspect provides a positioning method, which includes:

The network side device obtains the model used for positioning;

The network side device uses the obtained model to perform positioning.

In a third aspect, a positioning device is provided, which device includes:

The first acquisition module is used to acquire the model used for positioning;

The first positioning module is used to use the obtained model for positioning.

In a fourth aspect, a positioning device is provided, which device includes:

The second acquisition module is used to acquire the model used for positioning;

The second positioning module is used to use the obtained model for positioning.

In a fifth aspect, a terminal is provided. The terminal includes a processor and a memory. The memory stores programs or instructions that can be run on the processor. When the program or instructions are executed by the processor, the following implementations are implemented: The steps of the method described in one aspect.

In a sixth aspect, a terminal is provided, including a processor and a communication interface; wherein the processor is configured to: obtain a model for positioning; and use the obtained model to perform positioning.

In a seventh aspect, a network side device is provided. The network side device includes a processor and a memory. The memory stores programs or instructions that can be run on the processor. The program or instructions are executed by the processor. When implementing the steps of the method described in the second aspect.

In an eighth aspect, a network side device is provided, including a processor and a communication interface; wherein the processor is configured to: obtain a model for positioning; and use the obtained model to perform positioning.

A ninth aspect provides a positioning system, including: a terminal and a network side device. The terminal can be used to perform the steps of the method described in the first aspect. The network side device can be used to perform the steps of the method described in the second aspect. steps of the method.

In a tenth aspect, a readable storage medium is provided. Programs or instructions are stored on the readable storage medium. When the programs or instructions are executed by a processor, the steps of the method described in the first aspect are implemented, or the steps of the method are implemented as described in the first aspect. The steps of the method described in the second aspect.

In an eleventh aspect, a chip is provided. The chip includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the method described in the first aspect. method, or implement a method as described in the second aspect.

In a twelfth aspect, a computer program/program product is provided, 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 as described in the first aspect The steps of the method, or the steps of implementing the method as described in the second aspect.

In the embodiment of the present application, the terminal obtains a model for positioning and uses the obtained model for positioning. Through the above method, the terminal uses the obtained model for positioning from the model for positioning, which satisfies the differentiated positioning of the terminal. capabilities, positioning scenarios and positioning accuracy requirements, so that appropriate positioning models can be selected for different needs, thereby effectively improving positioning accuracy.

Description of the drawings

Figure 1 is a schematic diagram of a wireless communication system applicable to the embodiment of the present application;

Figure 2 is one of the flow diagrams of the positioning method provided by the embodiment of the present application;

Figure 3 is the second schematic flowchart of the positioning method provided by the embodiment of the present application;

Figure 4 is one of the structural schematic diagrams of the positioning device provided by the embodiment of the present application;

Figure 5 is the second structural schematic diagram of the positioning device provided by the embodiment of the present application;

Figure 6 is a schematic structural diagram of a communication device provided by an embodiment of the present application;

Figure 7 is a schematic structural diagram of a terminal provided by an embodiment of the present application;

Figure 8 is one of the structural schematic diagrams of the network side device provided by the embodiment of the present application;

Figure 9 is a second structural schematic diagram of a network side device provided by an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be clearly described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art fall within the scope of protection of this application.

The terms "first", "second", etc. in the description and claims of this application are used to distinguish similar objects and are not used to describe a specific order or sequence. It is to be understood that the terms so used are interchangeable under appropriate circumstances so that the embodiments of the present application can be practiced in sequences other than those illustrated or described herein, and that "first" and "second" are distinguished objects It is usually one type, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "and/or" in the description and claims indicates at least one of the connected objects, and the character "/" generally indicates that the related objects are in an "or" relationship.

It is worth pointing out that the technology described in the embodiments of this application is not limited to Long Term Evolution (Long Term Evolution, LTE)/LTE Evolution (LTE-Advanced, LTE-A) systems, and can also be used in other wireless communication systems, such as code 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 (SC-FDMA) and other systems. The terms "system" and "network" in the embodiments of this application are often used interchangeably, and the described technology can be used not only for the above-mentioned systems and radio technologies, but also for other systems and radio technologies. The following description describes a New Radio (NR) system for example purposes, and uses NR terminology in most of the following description, but these techniques can also be applied to communication systems other than NR system applications, such as 6th generation Generation, 6G) communication system.

Figure 1 is a schematic diagram of a wireless communication system applicable to the embodiment of the present application. The wireless communication system shown in Figure 1 includes a terminal 11 and a network side device 12. The terminal 11 may be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer), or a notebook computer, a personal digital assistant (Personal Digital Assistant, PDA), a palmtop computer, a netbook, or a super mobile personal computer. (ultra-mobile personal computer, UMPC), mobile Internet device (Mobile Internet Device, MID), augmented reality (AR)/virtual reality (VR) equipment, robots, wearable devices (Wearable Device) , vehicle-mounted equipment (VUE), pedestrian terminal (PUE), smart home (home equipment with wireless communication functions equipment, such as refrigerators, TVs, washing machines or furniture, etc.), game consoles, personal computers (PCs), teller machines or self-service machines and other terminal-side devices. Wearable devices include: smart watches, smart bracelets, smart headphones, Smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc. It should be noted that the embodiment of the present application does not limit the specific type of the terminal 11.

The network side device 12 may include an access network device or a core network device, where the access network device may also be called a radio access network device, a radio access network (Radio Access Network, RAN), a radio access network function or a wireless device. access network unit. Access network equipment may include base stations, WLAN access points or WiFi nodes, etc. The base stations may be called Node B, Evolved Node B (eNB), Access Point, Base Transceiver Station (BTS), Radio Base Station , radio transceiver, Basic Service Set (BSS), Extended Service Set (ESS), Home B-Node, Home Evolved B-Node, Transmitting Receiving Point (TRP) or the above Some other appropriate terminology in the field, as long as the same technical effect is achieved, the base station is not limited to specific technical terms. It should be noted that in the embodiment of this application, only the base station in the NR system is used as an example for introduction. Define the specific type of base station. Core network equipment may include but is not limited to at least one of the following: core network nodes, core network functions, mobility management entities (Mobility Management Entity, MME), access mobility management functions (Access and Mobility Management Function, AMF), session management functions (Session Management Function, SMF), User Plane Function (UPF), Policy Control Function (PCF), Policy and Charging Rules Function (PCRF), Edge Application Service Discovery function (Edge Application Server Discovery Function, EASDF), Unified Data Management (UDM), Unified Data Repository (UDR), Home Subscriber Server (HSS), centralized network configuration ( Centralized network configuration (CNC), Network Repository Function (NRF), Network Exposure Function (NEF), Local NEF (Local NEF, or L-NEF), Binding Support Function (Binding Support Function, BSF), application function (Application Function, AF), location management function (LMF), Enhanced Serving Mobile Location Center (E-SMLC), network data analytics function (NWDAF), etc. It should be noted that in the embodiment of this application, only the core network equipment in the NR system is used as an example for introduction, and the specific type of the core network equipment is not limited.

The positioning method provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings through some embodiments and application scenarios.

In the positioning method provided by the embodiment of this application, the terminal obtains a model used for positioning and uses the obtained model to perform positioning. Through the above method, the terminal uses the obtained model from the model used for positioning to perform positioning, which meets the terminal's needs for differentiated positioning capabilities, positioning scenarios, and positioning accuracy, so that it can select an appropriate positioning model for different needs, and then Effectively improve positioning accuracy.

Figure 2 is one of the flow diagrams of the positioning method provided by the embodiment of the present application. As shown in Figure 2, the method includes steps 201-202; wherein:

Step 201: The terminal obtains the model used for positioning;

Step 202: The terminal uses the obtained model to perform positioning.

It should be noted that the embodiments of the present application can be applied to the scenario of positioning based on the positioning model in the communication network. Terminals include, but are not limited to, the types of terminals 11 listed above; network side devices include, but are not limited to, types of network side devices 12 listed above. For example, network side devices include at least one of the following: core network nodes; access network nodes (such as base station); neural network processing node. Core network nodes include network data analysis function (NWDAF) network elements and/or location management function (LMF) network elements.

The time units involved in the embodiments of this application include at least one of the following: reference signal period; prediction period; time slot; half time slot; symbol (such as Orthogonal Frequency Division Multiplex, OFDM)); subframe ;Wireless frame; milliseconds; seconds.

The reference signal involved in the embodiment of this application includes at least one of the following: channel state information reference signal (CSI Reference Signal, CSI-RS); sounding reference signal (Sounding Reference Signal, SRS); synchronization signal block (Synchronization Signal Block, SSB) ); positioning reference Signal (Positioning Reference Signal, PRS).

Optionally, the type of any one of the M models used for positioning includes: an AI-based positioning model or a non-AI-based positioning model. The type of any model among the N models used for positioning includes: an AI-based positioning model or a non-AI-based positioning model. Specifically, non-AI-based positioning models can include non-AI-based positioning methods, such as network-assisted Global Navigation Satellite System (GNSS) positioning method; downlink Observed Time Difference of Arrival (OTDOA) ) positioning method; motion sensor positioning method; air pressure sensor positioning method, etc. AI-based positioning models can be, for example, fully-connection network (Full-connection network), convolutional neural network (CNN) models; Vision Transformer (Vision Transformer) models, etc.

It can be understood that the first task performed by the positioning model in the embodiment of the present application may include tasks such as positioning and/or channel state information (Channel State Information, CSI) estimation. In practical applications, the terminal uses N positioning models to perform the first task and obtains prediction results output by the N positioning models. The N positioning models can respectively output N prediction results; or a fusion result of the N prediction results. The terminal determines N positioning models from the M positioning models to perform the first task, thereby obtaining more accurate prediction results.

In the positioning method provided by the embodiment of the present application, the terminal obtains a model used for positioning and uses the obtained model to perform positioning. Through the above method, the terminal uses the obtained model from the model used for positioning to perform positioning, which meets the terminal's needs for differentiated positioning capabilities, positioning scenarios, and positioning accuracy, so that it can select an appropriate positioning model for different needs, and then Effectively improve positioning accuracy.

In a possible implementation manner of the embodiments of this application, the terminal obtains a model used for positioning, including:

The terminal determines N models among the M models used for positioning based on at least one of the first information related to model prediction, protocol predefinition, or preconfiguration;

Alternatively, the terminal receives the second information sent by the network side device; the terminal determines N models among M models used for positioning based on the second information; wherein the second information is used for positioning Indicate or determine the model identification ID information of the N models; M is greater than or equal to N; M and N are positive integers.

The specific method for the terminal to obtain the model used for positioning is explained below:

Method 1: The terminal determines N models among M models used for positioning based on the first information related to model prediction; M is greater than or equal to N; M and N are positive integers.

Method 2: The terminal determines N models among M models used for positioning based on protocol predefinition; M is greater than or equal to N; M and N are positive integers.

Method 3: Based on preconfiguration, the terminal determines N models among M models used for positioning; M is greater than or equal to N; M and N are positive integers.

Method 4: The terminal receives the second information sent by the network side device; the terminal determines N models among the M models used for positioning based on the second information; wherein the second information is used to indicate or determine the model identifiers of the N models ID information; M is greater than or equal to N; M and N are positive integers.

It can be understood that for different tasks and different methods, the number and type of models used by the terminal for positioning may be different; for different tasks and different methods, the number and type of models used by the network side device for positioning may also be different.

Here, methods 1 to 4 are explained respectively:

Optionally, after the terminal determines N models among M models used for positioning, the terminal uses the N models to perform the first task and obtains prediction results output by the N models.

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

a) Service quality QoS requirements;

Specifically, the quality of service QoS requirements may be, for example, positioning QoS requirements, including delay, accuracy, etc.

b)Terminal type;

c) Terminal capabilities;

Specifically, the terminal capability may include at least one of the following: terminal computing power (for example, the terminal's total computing power power or remaining computing power); terminal storage (such as terminal overall storage or remaining storage).

d) Channel status information;

Specifically, the channel state information may include at least one of the following: time domain, frequency domain, spatial domain, and delay Doppler domain channels. The terminal can select a positioning model related to the specified channel state information for positioning based on the channel state information.

e) Sensor information;

Specifically, the sensor information may include at least one of the following: visual sensor information, infrared sensor information, position sensor information, radar sensor information, air pressure sensor information, motion sensor information, etc. The terminal can select a positioning model related to the specified sensor information for positioning based on the sensor information.

f) Multipath information;

Specifically, the multipath information may include: the number of LOS paths, the number of channel paths, multipath delay, multipath launch angle, and multipath arrival angle.

To select a positioning model based on multipath information, the terminal can use at least one of the following methods:

1) According to the LOS/NLOS path, select a positioning model suitable for LOS/NLOS. In practice, the number of LOS paths refers to how many base stations have LOS paths with the user.

2) Select an anti-multipath positioning model based on the number of multipaths.

3) Based on the accuracy of multipath delay and angle acquisition, select a positioning model related to delay and angle.

g) Reference signal quality information;

Specifically, the reference signal quality can include Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), SNR and SINR, etc. Select a positioning model with good anti-interference or robustness. .

h) Cell identification ID information;

Specifically, the cell in which the terminal is located can be determined based on the cell identification ID information. Further, after determining the cell in which the terminal is located, a positioning model can be selected according to the environment of the cell. For example, if the community is located in an urban environment, the probability of LOS paths is relatively low, so the NLOS positioning model is selected; conversely, if the community is located in a suburban environment, the probability of LOS paths is relatively high, so the LOS positioning model is selected. Positioning model; alternatively, the model ID can be associated with the cell ID, for example, a cell corresponds to one or more AI positioning models.

i) Area identification ID information;

Specifically, in practical applications, the areas can be numbered, and the area where the terminal is located can be determined based on the area ID. Further, after determining the area where the terminal is located, the terminal selects a positioning model according to the environment of the area. For example, if the area where the terminal is located is a supermarket environment, the probability of LOS path is relatively low, so the NLOS positioning model is selected; conversely, if the area where the terminal is located is a square environment, the probability of LOS path is relatively high, so the positioning model of NLOS is selected. Select the LOS positioning model; alternatively, the model ID can be associated with the area ID, for example, a certain area corresponds to one or more AI positioning models.

j) Timing advance information;

Specifically, the timing advance information can be used to determine the approximate distance between the terminal and the base station, and then select a positioning model adapted to the specific distance based on the distance between the terminal and the base station.

k) Historical model selection information;

Specifically, in practical applications, for example, if the terminal was located in area 1 10 days ago, the terminal selected positioning model A. At this time, the terminal directly selects model A as the positioning model based on historical model selection information.

l) Model ID information of at least one model among the M models;

Specifically, the terminal can select the positioning model with the largest or smallest model ID based on the model ID information.

m) Positioning reference signal PRS type information, used to indicate model ID information;

For example, the terminal may select the positioning model corresponding to the model ID indicated by the PRS type information according to the PRS type information.

n) PRS resource configuration information or PRS resource set configuration information, used to indicate model ID information;

Specifically, the PRS resource configuration information may include at least one of the following: PRS resource ID; PRS resource set ID; and the number of Transmitting Receiving Points (TRPs).

o) Report configuration information, used to indicate model ID information;

Specifically, the terminal can select the positioning model corresponding to the model ID indicated by the report configuration information according to the report configuration information (or ReportConfigID).

p) Model structure information of at least one model among the M models;

Specifically, the model structure information may include: model computing power; model complexity; model storage size; model parameter amount, etc. For example, the terminal can select a positioning model with strong model computing power and a small number of parameters based on the model structure information.

q) The confidence of at least one model among the M models;

Specifically, the confidence of the model refers to the degree of reliability of the model. The terminal can select a positioning model with high confidence among M models.

r) The weight of at least one model among the M models.

Specifically, a weight value can be assigned to each of the M models, and then the terminal can select a positioning model with a high weight value among the M models.

In the above embodiment, the terminal determines N models among M models used for positioning based on the first information related to model prediction, provides more refined parameter information for the selection of positioning models, and uses the first information to perform The selection of the positioning model meets the needs of the terminal's differentiated positioning capabilities, positioning scenarios and positioning accuracy, so that the appropriate positioning model can be selected for different needs, thereby effectively improving the positioning accuracy.

Specifically, the terminal can determine N models among the M models used for positioning according to the protocol predefinition. Optionally, after determining the N models, the terminal uses the N models to perform the first task and obtains the prediction results output by the N models.

In the above embodiment, the terminal determines N models among M models used for positioning based on the protocol predefinition, providing more refined parameter information for the selection of the positioning model, and uses the protocol predefinition to select the positioning model. It meets the needs of terminals for differentiated positioning capabilities, positioning scenarios and positioning accuracy, so that appropriate positioning models can be selected for different needs, thereby effectively improving positioning accuracy.

Specifically, the terminal can determine N models among M models used for positioning based on preconfiguration. Optionally, after determining the N models, the terminal uses the N models to perform the first task and obtains the prediction results output by the N models.

In the above embodiment, the terminal determines N models among M models used for positioning based on pre-configuration, providing more refined parameter information for the selection of positioning models, and using pre-configuration to select positioning models satisfies the following requirements The terminal has differentiated positioning capabilities, positioning scenarios and positioning accuracy requirements, so that appropriate positioning models can be selected for different needs, thereby effectively improving positioning accuracy.

Optionally, after determining the N models, the terminal uses the N models to perform the first task and obtains the prediction results output by the N models.

Optionally, the second information includes at least one of the following:

a) Model ID information of at least one model among the N models;

Specifically, for example, the terminal may select the positioning model with the largest or smallest model ID based on the model ID information sent by the network side device.

b) PRS type information, used to indicate model ID information;

Specifically, the terminal may select the positioning model corresponding to the model ID indicated by the PRS type information according to the PRS type information sent by the network side device.

c) PRS resource configuration information or PRS resource set configuration information, used to indicate model ID information;

Specifically, the PRS resource configuration information may be, for example, the number of TRPs.

d) report configuration information, used to indicate model ID information;

Specifically, the terminal can select the positioning model corresponding to the model ID indicated by the report configuration information according to the report configuration information sent by the network side device.

e) Model configuration information;

Specifically, the model configuration information includes PRS resource configuration information or PRS resource set configuration information; PRS resource configuration information or PRS resource set configuration information is used to indicate model ID information.

In the above embodiment, the terminal receives the second information sent by the network side device, and then selects N models among the M models based on the second information. Through the above method, more refined parameter information is provided for the selection of the positioning model, and the second information is used to select the positioning model, which meets the needs of the terminal's differentiated positioning capabilities, positioning scenarios and positioning accuracy, so that it can target different situations. It is necessary to select an appropriate positioning model to effectively improve the positioning accuracy.

In a possible implementation manner of the embodiments of this application, before the terminal acquires the model used for positioning, the method further includes: the terminal sending fourth information related to the model prediction to the network side device. Through the above method, the network side device can select a positioning model based on the fourth information related to model prediction sent by the terminal, and then select an appropriate positioning model according to different needs, effectively improving the positioning accuracy.

In a possible implementation manner of the embodiments of this application, the fourth information includes at least one of the following:

a) Service quality QoS requirements;

The quality of service QoS requirements may be, for example, positioning QoS requirements, including delay, accuracy, etc.

b)Terminal type;

c) Terminal capabilities;

Specifically, the terminal capability may include at least one of the following: terminal computing power (such as the terminal's overall computing power or remaining computing power); terminal storage (such as the terminal's overall storage or remaining storage).

d) Channel status information;

Specifically, the channel state information may include at least one of the following: time domain, frequency domain, spatial domain, and delay Doppler domain channels. The terminal can send channel state information to the network side device, and the network side device selects a positioning model related to the specified channel state information based on the channel state information sent by the terminal. For example, when using AI positioning model 1 for positioning, the input of model 1 is the time domain channel and the output is position information.

e) Sensor information;

Specifically, the sensor information may include at least one of the following: visual sensor information, infrared sensor information, position sensor information, radar sensor information, air pressure sensor information, motion sensor information, etc. The terminal can send sensor information to the network side device, and the network side device sends sensor information based on the information sent by the terminal. The sensor information sent selects the positioning model related to the specified sensor information.

f) Multipath information;

The terminal sends multipath information to the network side device, and the network side device selects a positioning model based on the multipath information sent by the terminal. It can use at least one of the following methods:

Select a positioning model adapted to LOS/NLOS based on the LOS/NLOS path. For example, based on the environment between base stations, determine whether there is a LOS path between base stations. If a LOS path exists, select a positioning model adapted to LOS; if not, The LOS path selects a positioning model suitable for NLOS;

Select an anti-multipath positioning model based on the number of multipaths;

Select the positioning model related to delay and angle based on the accuracy of multipath delay and angle acquisition.

g) Reference signal quality information;

Specifically, the reference signal quality may include RSRP, RSRQ, etc.

h) Cell identification ID information;

Specifically, the cell in which the terminal is located can be determined based on the cell identification ID information. Further, after determining the cell where the terminal is located, the terminal sends the cell identification ID corresponding to the cell where the terminal is located to the network side device, and the network side device selects a positioning model according to the environment of the cell. For example, if the community is located in an urban environment, the probability of LOS paths is relatively low, so the NLOS positioning model is selected; conversely, if the community is located in a suburban environment, the probability of LOS paths is relatively high, so the LOS positioning model is selected.

i) Area identification ID information;

Specifically, in practical applications, the areas can be numbered, and the area where the terminal is located can be determined based on the area ID. Further, after determining the area where the terminal is located, the terminal sends the area where the terminal is located to the network side device, and the network side device selects a positioning model according to the environment of the area. For example, if the area where the terminal is located is a supermarket environment, the probability of LOS path is relatively low, so the NLOS positioning model is selected; conversely, if the area where the terminal is located is a square environment, the probability of LOS path is relatively high, so the positioning model of NLOS is selected. Select the positioning model of LOS.

j) Timing advance information;

Specifically, in practical applications, the timing advance information can be used to determine the approximate distance between the terminal and the base station, and then the terminal sends the distance between the terminal and the base station to the network side device, and the network side device selects based on the distance between the terminal and the base station Positioning model adapted to specific distances.

k) Historical model selection information;

Specifically, in practical applications, for example, if the terminal was located in area 1 10 days ago, the terminal selected positioning model A. At this time, the terminal directly sends the historical model selection information to the network side device, and the network side device is based on the historical model. Select information selection model A as the positioning model.

l) PRS resource configuration information or PRS resource set configuration information, used to indicate model ID information;

m) The confidence of at least one model among the M models;

Specifically, the confidence of the model refers to the degree of reliability of the model. The terminal can send the confidence of at least one model among the M models to the network side device, and the network side device selects a positioning model with high confidence among the M models.

n) The weight of at least one model among the M models.

Specifically, a weight value can be assigned to each of the M models, and then the terminal sends the weight of at least one model among the M models to the network side device, and the network side device can select a positioning model with a high weight value among the M models. .

Through the above method, the network side device can select a model based on the fourth information related to model prediction sent by the terminal, and then select an appropriate positioning model according to different needs, effectively improving the positioning accuracy.

In a possible implementation manner of the embodiments of this application, before the terminal obtains the model used for positioning, the method further includes: the terminal receives configuration information of M models from the network side device, wherein the model used for positioning is in M determined in a model.

In this embodiment, the model configuration information includes PRS resource configuration information or PRS resource set configuration information; the PRS resource configuration information or PRS resource set configuration information is used to indicate model ID information.

Specifically, before the terminal obtains the model used for positioning, the terminal needs to receive Configuration information of M models, and then based on the model configuration information, N models are determined among the M models used for positioning.

When the target conditions are met, the terminal obtains the model used for positioning.

In this embodiment, the target condition refers to the triggering condition for model selection. That is, when the target conditions are met, the terminal starts model selection. Through the above method, the efficiency of terminal positioning model selection can be improved and the calculation amount of terminal positioning model selection can be reduced.

Specifically, the target conditions include at least one of the following:

a) The prediction accuracy of the current model is lower than the first threshold;

In this embodiment, the current model refers to the positioning model currently being used. Specifically, if the prediction accuracy of the positioning model currently used by the terminal is lower than the first threshold, positioning model selection is performed.

b) The change amount of the first information related to the model prediction reaches the second threshold.

Specifically, the change amount of the first information related to the model prediction reaches the second threshold, including the change amount of the first information related to the model prediction exceeding the second threshold or the change amount of the first information related to the model prediction being lower than the second threshold. Two thresholds. That is, when the change amount of the first information related to the model prediction exceeds the second threshold or the change amount of the first information related to the model prediction is lower than the second threshold, positioning model selection is performed.

c) The current positioning accuracy is lower than the third threshold.

In the above embodiment, when the target conditions are met, the terminal starts positioning model selection. Through the above method, the efficiency of terminal positioning model selection can be improved and the calculation amount of terminal positioning model selection can be reduced.

In a possible implementation manner of the embodiment of this specification, after the terminal obtains the model used for positioning, the method further includes: the terminal sends third information to the network side device; wherein the third information is used to indicate or determine the N models. Model ID information.

In this embodiment, after the terminal obtains the models used for positioning, it also needs to send third information for indicating or determining the model ID information of N models to the network side device. The network side device can The positioning model is selected based on the N models determined by the terminal.

In this embodiment of the present application, the terminal determines the N model IDs based on at least one of the first information related to model prediction, protocol predefinition, and preconfiguration, or based on the second information used to indicate or determine the N model IDs. N models are determined among the models used for positioning. Through the above method, the terminal uses at least one of the first information, protocol predefined information, and preconfigured information, or uses the second information to select N models out of M models, providing a more refined selection of positioning models. Parameter information, using this parameter information to select a positioning model, meets the needs of the terminal's differentiated positioning capabilities, positioning scenarios and positioning accuracy, so that an appropriate positioning model can be selected for different needs, thereby effectively improving the positioning model positioning accuracy.

Figure 3 is the second schematic flowchart of the positioning method provided by the embodiment of the present application. As shown in Figure 3, the method includes step 301; wherein:

Step 301: The network side device obtains the model used for positioning;

Step 302: The network side device uses the obtained model to perform positioning.

It should be noted that the embodiments of the present application can be applied to the scenario of model-based positioning in a communication network. Terminals include, but are not limited to, the types of terminals 11 listed above; network side devices include, but are not limited to, types of network side devices 12 listed above. For example, network side devices include at least one of the following: core network nodes; access network nodes (such as base station); neural network processing node. Core network nodes include network data analysis function (NWDAF) network elements and/or location management function (LMF) network elements.

The reference signal involved in the embodiment of this application includes at least one of the following: Channel State Information Reference Signal (CSI Reference Signal, CSI-RS); Sounding Reference Signal (Sounding Reference Signal, SRS); Synchronization Signal Block (SSB) ); Positioning Reference Signal (PRS).

Optionally, the type of any one of the M models used for positioning includes: AI-based Positioning model or non-AI based positioning model. The type of any positioning model among the N models used for positioning includes: an AI-based positioning model or a non-AI-based positioning model. Specifically, non-AI based positioning models may include non-AI based positioning methods, such as network-assisted Global Navigation Satellite System (GNSS) positioning method; downlink observed time difference of arrival (OTDOA) ) positioning method; motion sensor positioning method; air pressure sensor positioning method, etc. The AI-based positioning model can be, for example, a fully connected neural network (Full-connection network), a vision transformer (Vision Transformer) model, etc.

It can be understood that the first task performed by the positioning model in the embodiment of the present application may include tasks such as positioning and/or channel state information (Channel State Information, CSI) estimation. In actual applications, the network-side device uses N positioning models to perform the first task and obtain prediction results output by the N positioning models. Among them, the N positioning models can output N prediction results respectively; or, the fusion of N prediction results result. The network side device determines N positioning models from the M positioning models to perform the first task, so as to obtain more accurate prediction results.

In the positioning method provided by the embodiment of this application, the network side device obtains a model used for positioning and uses the obtained model to perform positioning. Through the above method, the network-side device uses the model obtained from the model used for positioning to perform positioning, which meets the needs of the network-side device for differentiated positioning capabilities, positioning scenarios and positioning accuracy, so that it can choose the appropriate one for different needs. Positioning model, thereby effectively improving positioning accuracy.

In a possible implementation manner of the embodiments of this application, the network side device obtains a model used for positioning, including:

The network side device determines N models among the M models used for positioning based on at least one of fourth information related to model prediction, protocol predefinition, or preconfiguration;

Alternatively, the network side device receives the third information sent by the terminal; the network side device determines N models among the M models used for positioning based on the third information; wherein the third information is used to indicate or determine the models of the N models Identifies ID information; M is greater than or equal to N; M and N are positive integers.

The following describes the specific method for the network side device to obtain the model used for positioning:

Method A: The network side device determines N models among M models used for positioning based on the fourth information related to model prediction; M is greater than or equal to N; M and N are positive integers.

Method B: The network side device determines N models among M models used for positioning based on protocol predefinition; M is greater than or equal to N; M and N are positive integers.

Method C: The network side device determines N models among M models used for positioning based on pre-configuration; M is greater than or equal to N; M and N are positive integers.

Method D: The network side device receives the third information sent by the terminal; the network side device determines N models among the M models used for positioning based on the third information; wherein the third information is used to indicate or determine N models Model identification ID information; M is greater than or equal to N; M and N are positive integers.

Here, methods A to D are explained respectively:

Optionally, after the network side device determines N models among the M models used for positioning, the network side device uses the N models to perform the first task and obtain prediction results output by the N models.

Optionally, the fourth information includes at least one of the following:

a) Service quality QoS requirements;

b)Terminal type;

c) Terminal capabilities;

Specifically, terminal capabilities may include at least one of the following: terminal computing power (such as the terminal's overall computing power or remaining computing power); terminal storage (such as the terminal's overall storage or remaining storage);

In addition, in this embodiment, in addition to terminal capabilities, network side capabilities may also be included; network side capabilities include at least one of the following: radio access network (Radio Access Network, RAN); core Network elements on the heart network side (for example, whether it supports inference and training of the AI positioning model).

d) Channel status information;

Specifically, the channel state information may include at least one of the following: time domain, frequency domain, spatial domain, and delay Doppler domain channels. The network side device can select a positioning model related to the specified channel state information for positioning based on the channel state information.

e) Sensor information;

Specifically, the sensor information may include at least one of the following: visual sensor information, infrared sensor information, position sensor information, radar sensor information, air pressure sensor information, motion sensor information, etc. The network-side device can select a positioning model related to the specified sensor information for positioning based on the sensor information.

f) Multipath information;

The network-side device selects a positioning model based on multipath information and can use at least one of the following methods:

1) Select a positioning model suitable for LOS/NLOS based on the LOS/NLOS path.

2) Select an anti-multipath positioning model based on the number of multipaths;

3) Select a delay- and angle-related positioning model based on the accuracy of multipath delay and angle acquisition.

g) Reference signal quality information;

Specifically, the reference signal quality may include RSRP, RSRQ, etc.

h) Cell identification ID information;

Specifically, the cell in which the terminal is located can be determined based on the cell identification ID information. Further, after determining the cell where the terminal is located, the network side device can select a positioning model according to the environment of the cell. For example, if the community is located in an urban environment, the probability of LOS paths is relatively low, so the NLOS positioning model is selected; conversely, if the community is located in a suburban environment, the probability of LOS paths is relatively high, so the LOS positioning model is selected.

i) Area identification ID information;

Specifically, in practical applications, the areas can be numbered, and the terminal location can be determined based on the area ID. in the area. Further, after determining the area where the terminal is located, the network side device selects a positioning model according to the environment of the area. For example, if the area where the terminal is located is a supermarket environment, the probability of LOS path is relatively low, so the NLOS positioning model is selected; conversely, if the area where the terminal is located is a square environment, the probability of LOS path is relatively high, so the positioning model of NLOS is selected. Select the positioning model of LOS.

j) Timing advance information;

Specifically, the timing advance information can be used to determine the approximate distance between the terminal and the base station, and then the network side device selects a positioning model adapted to the specific distance based on the distance between the terminal and the base station.

k) Historical model selection information;

Specifically, in practical applications, for example, if the terminal was located in area 1 10 days ago, the terminal selected positioning model A. At this time, the network side device directly selects model A as the positioning model based on historical model selection information.

l) Positioning reference signal PRS resource configuration information or PRS resource set configuration information, used to indicate model ID information;

m) Model ID information of at least one model among the M models;

n) Model structure information of at least one model among the M models;

Specifically, the model structure information may include: model computing power; model complexity; model storage size; model parameter amount, etc. For example, the network-side device can select a positioning model with strong model computing power and a small number of parameters based on the model structure information.

o) The confidence of at least one model among the M models;

Specifically, the confidence of the model refers to the degree of reliability of the model. The network-side device can select a positioning model with high confidence among M models.

p) The weight of at least one model among the M models.

Specifically, a weight value can be assigned to each of the M models, and then the network side device can select a positioning model with a high weight value among the M models.

In the above embodiment, the network side device determines N models among the M models used for positioning based on the fourth information related to model prediction, providing more refined parameter information for the selection of the positioning model, using the parameters The selection of the positioning model based on the information satisfies the differentiated positioning capabilities, positioning scenarios and positioning accuracy requirements of network-side devices, so that appropriate positioning models can be selected for different needs, thereby effectively improving positioning accuracy.

Specifically, the network side device can determine N models among the M models used for positioning according to the protocol predefinition. Optionally, after determining the N models, the network side device uses the N models to perform the first task and obtains prediction results output by the N models.

In the above embodiment, the network side device determines N models among the M models used for positioning based on the protocol predefinition, providing more refined parameter information for the selection of the positioning model, and uses the protocol predefinition to perform positioning model selection. The selection satisfies the needs of differentiated positioning capabilities, positioning scenarios and positioning accuracy of network-side devices, so that appropriate positioning models can be selected for different needs, thereby effectively improving positioning accuracy.

Specifically, the network side device may determine N models among M models used for positioning based on preconfiguration. Optionally, after determining the N models, the network side device uses the N positioning models to perform the first task and obtains prediction results output by the N models.

In the above embodiment, the network side device determines N models among the M models used for positioning based on pre-configuration, providing more refined parameter information for the selection of positioning models, and uses the pre-configuration to select the positioning model. It meets the needs of differentiated positioning capabilities, positioning scenarios and positioning accuracy of network-side devices, so that appropriate positioning models can be selected for different needs, thereby effectively improving positioning accuracy.

In a possible implementation manner of the embodiments of the present application, before the network side device obtains the model used for positioning, the method further includes: the network side device receives a third parameter related to the model prediction sent by the terminal. Four information; the fourth information is used by the network side device to select N models among the M models.

In this embodiment, the network side device may determine N models among the M models used for positioning by using the fourth information related to model prediction sent by the terminal. Through the above method, the network side device can select an appropriate positioning model according to different needs, effectively improving the positioning accuracy.

Optionally, after determining the N models, the network side device uses the N models to perform the first task and obtains prediction results output by the N models.

Optionally, the third information includes: model ID information of at least one model among the N models.

Specifically, for example, the network side device may receive the model ID information of at least one model sent by the terminal, and then select the positioning model with the largest or smallest model ID.

In a possible implementation manner of the embodiments of this application, the network side device selects N models among the M models based on the third information, including:

Based on the third information, the network side device determines T models among M models used for positioning; M is greater than or equal to T; T is a positive integer;

The network-side device performs at least one of the following operations:

a) When the model used for positioning obtained in advance by the network side device is not exactly the same as the T models, N models are determined based on the model used for positioning obtained in advance;

Specifically, for example, when the network side determines T models among M models for positioning based on the third information, and compares them with pre-acquired models for positioning, when the pre-acquired positioning model is different from the T models. Under exactly the same situation, the network side selects the pre-obtained models for positioning as N models for positioning.

b) The network side device determines N models among T models based on the terminal distribution information;

Specifically, the terminal distribution information may include the geometric location distribution of user equipment (User Equipment, UE), the distribution of measurement quantities (such as the distribution of CSI), etc.

In practical applications, for example, the network side device can be distributed based on the geometric location of the UE and be the same Combined with the location of the UE, the same N models among the T models are selected for positioning.

c) The network side device is based on the statistical information of the terminal recommendation model, N models among the T models.

Specifically, for example, terminals located in the same area may recommend different models, such as model A, model B, and model C. Then the network side device counts the models recommended by different terminals. The result is that the terminals recommending model A are the most, so the network The side directly selects model A for positioning of all terminals in the area, thereby improving the efficiency of positioning model selection.

When the target conditions are met, the network side device obtains the model used for positioning.

In this embodiment, the target condition refers to the triggering condition for model selection. That is, when the target conditions are met, the network side device starts model selection. Through the above method, the efficiency of network-side device model selection can be improved and the calculation amount of network-side device model selection can be reduced.

Specifically, the target conditions include at least one of the following:

In this embodiment, the current model refers to the model currently being used. Specifically, if the prediction accuracy of the model currently used by the network side device is lower than the first threshold, model selection is performed.

Specifically, the change amount of the first information related to the model prediction reaches the second threshold, including the change amount of the first information related to the model prediction exceeding the second threshold or the change amount of the first information related to the model prediction being lower than the second threshold. Two thresholds. That is, when the change amount of the first information related to the model prediction exceeds the second threshold or the change amount of the first information related to the model prediction is lower than the second threshold, model selection is performed.

c) The current positioning accuracy is lower than the third threshold.

In the above embodiment, when the target conditions are met, the network side device starts model selection. Through the above method, while improving the efficiency of network side device model selection, it can Reduce the calculation amount of network-side device model selection.

In a possible implementation manner of the embodiments of this application, after the network side device obtains the model used for positioning, it also includes:

The network side device sends second information to the terminal; wherein the second information is used to indicate or determine model identification ID information of N models; M is greater than or equal to N; M and N are positive integers.

In this embodiment, after the network side device obtains the models used for positioning, it also needs to send second information to the terminal for indicating or determining the model ID information of N models. The terminal can be based on the N models selected by the network side device. Model selection for positioning model.

In a possible implementation manner of the embodiments of this application, the second information includes at least one of the following:

a) Model ID information of at least one model among the N models;

Specifically, for example, the network side device may select the positioning model with the largest or smallest model ID based on the model ID information sent by the terminal.

b) Positioning reference signal PRS type information, used to indicate model ID information;

Specifically, the network side device may select the positioning model corresponding to the model ID indicated by the PRS type information according to the PRS type information sent by the terminal.

d) Report report configuration information, used to indicate model ID information;

e) Model configuration information, including PRS resource configuration information or PRS resource set configuration information; PRS resource configuration information or PRS resource set configuration information is used to indicate model ID information.

In the above embodiment, the network side device sends the second information to the terminal, and then the terminal can determine N models among the M models used for positioning based on the second information. Through the above method, for positioning The selection of the model provides more refined parameter information. The second information is used to select the positioning model, which meets the needs of the terminal's differentiated positioning capabilities, positioning scenarios and positioning accuracy, so that the appropriate positioning model can be selected for different needs. , thereby effectively improving the positioning accuracy.

The network side device sends the fifth information to the terminal; the fifth information includes the model ID information of the N models; the fifth information is used to instruct the terminal to obtain the measurement results corresponding to the measurement quantities associated with each model ID information;

Specifically, after obtaining the model used for positioning, the network side device sends the fifth information including the model ID information of N models to the terminal; after receiving the fifth information, the terminal determines the location according to the N information in the fifth information. The model ID information of the model is indexed to the required measurement quantity information and the measurement is performed based on the measurement quantity information to obtain the measurement results.

Alternatively, the network side device sends sixth information to the terminal; the sixth information includes measurement quantities corresponding to the N models; and the sixth information is used to instruct the terminal to obtain the measurement results corresponding to the measurement quantities.

Specifically, after the network side device selects N models among the M models, it sends the sixth information including the measurement quantities corresponding to the N models to the terminal; after receiving the sixth information, the terminal Measure the measurement quantities corresponding to N models and obtain the measurement results.

In the above embodiment, after acquiring the model used for positioning, the network side device sends the fifth information including the model ID information of the N models or the sixth information including the corresponding measurement quantities of the N models to the terminal; the terminal can According to the fifth information or the sixth information, the measurement quantities corresponding to the N models can be obtained for measurement and the measurement results can be obtained.

For the positioning method provided by the embodiment of the present application, the execution subject may be a positioning device. In the embodiment of the present application, the positioning device performing the positioning method is taken as an example to illustrate the positioning device provided by the embodiment of the present application.

Figure 4 is one of the structural schematic diagrams of a positioning device provided by an embodiment of the present application. As shown in Figure 4, the positioning device 400 is applied to a terminal and includes:

The first acquisition module 401 is used to acquire the model used for positioning;

The first positioning module 402 is used to use the obtained model to perform positioning.

In the positioning device provided by the embodiments of the present application, by obtaining a model for positioning, using the obtained model to perform positioning, and using the obtained model from the model for positioning to perform positioning, the differentiated positioning capabilities of the terminal are satisfied. , positioning scenarios and positioning accuracy requirements, so that appropriate positioning models can be selected for different needs, thereby effectively improving positioning accuracy.

Optionally, the first acquisition module 401 is further used to:

Determine N models among the M models used for positioning based on at least one of the first information related to model prediction, protocol predefinition, or preconfiguration;

Or, receive the second information sent by the network side device; based on the second information, determine N models among the M models used for positioning; wherein the second information is used to indicate or determine the model identification ID information of the N models; M is greater than or equal to N; M and N are positive integers.

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

Quality of service QoS requirements;

terminal type;

terminal capabilities;

Channel status information;

sensor information;

multipath information;

Reference signal quality information;

Community identification ID information;

Area identification ID information;

Timing advance information;

Historical model selection information;

Model ID information of at least one model among the M models;

Positioning reference signal PRS type information, used to indicate model ID information;

PRS resource configuration information or PRS resource set configuration information, used to indicate model ID information;

Report report configuration information, used to indicate model ID information;

Model structure information of at least one model among the M models;

The confidence of at least one model among the M models;

The weight of at least one model among M models.

Optionally, the second information includes at least one of the following:

Model ID information of at least one model among N models;

PRS type information, used to indicate model ID information;

report configuration information, used to indicate model ID information;

Model configuration information includes PRS resource configuration information or PRS resource set configuration information; PRS resource configuration information or PRS resource set configuration information is used to indicate model ID information.

Optionally, the first acquisition module 401 is further used to:

When the target conditions are met, obtain the model used for positioning; where the target conditions include at least one of the following:

The prediction accuracy of the current model is below the first threshold;

The amount of change in the first information related to the model prediction reaches the second threshold;

The current positioning accuracy is lower than the third threshold.

Optionally, the device also includes:

The first sending module is configured to send third information to the network side device; wherein the third information is used to indicate or determine model ID information of N models.

Optionally, the device also includes:

The second sending module is configured to send fourth information related to model prediction to the network side device; wherein the fourth information includes at least one of the following:

Quality of service QoS requirements;

terminal type;

terminal capabilities;

Channel status information;

sensor information;

multipath information;

Reference signal quality information;

Community identification ID information;

Area identification ID information;

Timing advance information;

Historical model selection information;

The confidence of at least one model among the M models;

The weight of at least one model among M models.

Optionally, the device also includes:

The first receiving module is configured to receive configuration information of M models from the network side device, and the model used for positioning is determined among the M models.

Figure 5 is the second structural schematic diagram of the positioning device provided by the embodiment of the present application. As shown in Figure 5, the positioning device 500 is applied to network side equipment and includes:

The second acquisition module 501 is used to acquire the model used for positioning;

The second positioning module 502 is used to use the obtained model to perform positioning.

In the positioning device provided by the embodiments of the present application, by obtaining a model for positioning and using the obtained model to perform positioning, the needs for differentiated positioning capabilities, positioning scenarios and positioning accuracy of network-side devices are met, thereby being able to target different situations. It is necessary to select an appropriate positioning model to effectively improve the positioning accuracy.

Optionally, the second acquisition module 501 is further used to:

Determine N models among the M models used for positioning based on at least one of the fourth information related to model prediction, protocol predefinition, or preconfiguration;

Optionally, the fourth information includes at least one of the following:

Quality of service QoS requirements;

terminal type;

terminal capabilities;

Channel status information;

sensor information;

multipath information;

Reference signal quality information;

Community identification ID information;

Area identification ID information;

Timing advance information;

Historical model selection information;

Positioning reference signal PRS resource configuration information or PRS resource set configuration information, used to indicate model ID information;

Model ID information of at least one model among the M models;

Model structure information of at least one model among the M models;

The confidence of at least one model among the M models;

The weight of at least one model among M models.

Optionally, the second acquisition module 501 is further used to:

The prediction accuracy of the current model is below the first threshold;

The current positioning accuracy is lower than the third threshold.

Optionally, the device also includes:

The third sending module is used to send second information to the terminal; wherein the second information is used to indicate or determine the model identification ID information of N models; M is greater than or equal to N; M and N are positive integers.

Optionally, the second information includes at least one of the following:

Model ID information of at least one model among N models;

Report report configuration information, used to indicate model ID information;

Optionally, the device also includes:

The second receiving module is configured to receive fourth information related to model prediction sent by the terminal; the fourth information is used by the network side device to select N models among the M models.

Optionally, the second acquisition module 501 is further used to:

Based on the third information, T models are determined among M models used for positioning; M is greater than or equal to T; T is a positive integer;

The network-side device performs at least one of the following operations:

In the case where the models used for positioning obtained in advance by the network side device are not exactly the same as the T models, N models are determined based on the models used for positioning obtained in advance;

Based on the terminal distribution information, the network side device determines N models among T models;

The network side device determines N models among the T models based on the statistical information of the terminal recommended models.

Optionally, the device also includes:

The fourth sending module is used to send the fifth information to the terminal; the fifth information includes the model ID information of the N models; the fifth information is used to instruct the terminal to obtain the measurement results corresponding to the measurement quantities associated with each model ID information;

Alternatively, sixth information is sent to the terminal; the sixth information includes measurement quantities corresponding to the N models; and the sixth information is used to instruct the terminal to obtain the measurement results corresponding to the measurement quantities.

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

The positioning device provided by the embodiments of the present application can implement each process implemented by the method embodiments in Figures 1 to 3 and achieve the same technical effect. To avoid duplication, details will not be described here.

Figure 6 is a schematic structural diagram of a communication device provided by an embodiment of the present application. As shown in Figure 6, the communication device 600 includes a processor 601 and a memory 602. The memory 602 stores programs that can run on the processor 601. or instructions. For example, when the communication device 600 is a terminal, when the program or instructions are executed by the processor 601, each step of the above positioning method embodiment is implemented, and the same technical effect can be achieved. When the communication device 600 is a network-side device, when the program or instruction is executed by the processor 601, each step of the above positioning method embodiment is implemented, and the same technical effect can be achieved. To avoid duplication, the details are not repeated here.

An embodiment of the present application also provides a terminal, including a processor and a communication interface. The processor is configured to: obtain a model for positioning; and use the obtained model to perform positioning.

This terminal embodiment corresponds to the above-mentioned terminal-side method embodiment. Each implementation process and implementation manner of the above-mentioned method embodiment can be applied to this terminal embodiment, and can achieve the same technical effect.

Figure 7 is a schematic structural diagram of a terminal provided by an embodiment of the present application. As shown in Figure 7, the terminal 700 includes but is not limited to: a radio frequency unit 701, a network module 702, an audio output unit 703, an input unit 704, a sensor 705, and a display unit. 706. At least some components of the user input unit 707, the interface unit 708, the memory 709, the processor 710, etc.

Those skilled in the art can understand that the terminal 700 may also include a power supply (such as a battery) that supplies power to various components. The power supply may be logically connected to the processor 710 through a power management system, thereby managing charging, discharging, and power consumption through the power management system. Management and other functions. The terminal structure shown in FIG. 7 does not constitute a limitation on the terminal. The terminal may include more or fewer components than shown in the figure, or some components may be combined or arranged differently, which will not be described again here.

It should be understood that in the embodiment of the present application, the input unit 704 may include a graphics processing unit (GPU) 7041 and a microphone 7042. The graphics processor 7041 is responsible for the image capture device (GPU) in the video capture mode or the image capture mode. Process the image data of still pictures or videos obtained by cameras (such as cameras). The display unit 706 may include a display panel 7061, which may The display panel 7061 is configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 707 includes a touch panel 7071 and at least one of other input devices 7072 . Touch panel 7071, also called 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 physical keyboards, function keys (such as volume control keys, switch keys, etc.), trackballs, mice, and joysticks, which will not be described again here.

In this embodiment of the present application, after receiving downlink data from the network side device, the radio frequency unit 701 can transmit it to the processor 710 for processing; in addition, the radio frequency unit 701 can send uplink data to the network side device. Generally, the radio frequency unit 701 includes, but is not limited to, an antenna, amplifier, transceiver, coupler, low noise amplifier, duplexer, etc.

Memory 709 may be used to store software programs or instructions as well as various data. The memory 709 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required for at least one function (such as a sound playback function, Image playback function, etc.) etc. Additionally, memory 709 may include volatile memory or non-volatile memory, or memory 709 may include both volatile and non-volatile memory. Among them, non-volatile memory can be read-only memory (Read-Only Memory, ROM), programmable read-only memory (Programmable ROM, PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electrically removable memory. Erase programmable read-only memory (Electrically EPROM, EEPROM) or flash memory. Volatile memory can be random access memory (Random Access Memory, RAM), static random access memory (Static RAM, SRAM), dynamic random access memory (Dynamic RAM, DRAM), synchronous dynamic random access memory (Synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDRSDRAM), enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), synchronous link dynamic random access memory (Synch link DRAM) , SLDRAM) and direct memory bus random access memory (Direct Rambus RAM, DRRAM). Memory 709 in embodiments of the present application includes, but is not limited to, these and any other suitable types of memory.

The processor 710 may include one or more processing units; optionally, the processor 710 integrates application Processor and modem processor, among which the application processor mainly processes operations involving the operating system, user interface and application programs, etc., and the modem processor mainly processes wireless communication signals, such as a baseband processor. It can be understood that the above-mentioned modem processor may not be integrated into the processor 710.

Among them, the processor 710 is used to obtain a model for positioning; and use the obtained model to perform positioning.

The terminal provided by the embodiment of the present application obtains a model for positioning and uses the obtained model to perform positioning. Through the above method, the terminal uses the obtained model from the model used for positioning to perform positioning, which meets the terminal's needs for differentiated positioning capabilities, positioning scenarios, and positioning accuracy, so that it can select an appropriate positioning model for different needs, and then Effectively improve positioning accuracy.

An embodiment of the present application also provides a network side device, including a processor and a communication interface; wherein:

The processor is used to: obtain the model used for positioning; use the obtained model to perform positioning.

This network-side device embodiment corresponds to the above-mentioned network-side device method embodiment. Each implementation process and implementation manner of the above-mentioned method embodiment can be applied to this network-side device embodiment, and can achieve the same technical effect.

Figure 8 is one of the structural schematic diagrams of network side equipment provided by the embodiment of the present application. As shown in Figure 8, the network side equipment 800 includes: an antenna 801, a radio frequency device 802, a baseband device 803, a processor 804 and a memory 805. Antenna 801 is connected to radio frequency device 802. In the uplink direction, the radio frequency device 802 receives information through the antenna 801 and sends the received information to the baseband device 803 for processing. In the downlink direction, the baseband device 803 processes the information to be sent and sends it to the radio frequency device 802. The radio frequency device 802 processes the received information and then sends it out through the antenna 801.

The method performed by the network side device in the above embodiment can be implemented in the baseband device 803, which includes a baseband processor.

The baseband device 803 may include, for example, at least one baseband board on which multiple chips are disposed, as shown in FIG. Program to perform the network device operations shown in the above method embodiments.

The network side device may also include a network interface 806, which is, for example, a universal public wireless interface. Port (common public radio interface, CPRI).

Specifically, the network side device 800 in this embodiment of the present invention also includes: instructions or programs stored in the memory 805 and executable on the processor 804. The processor 804 calls the instructions or programs in the memory 805 to perform the positioning as described above. method and achieve the same technical effect. To avoid repetition, we will not repeat it here.

Figure 9 is the second structural schematic diagram of a network side device provided by an embodiment of the present application. As shown in Figure 9, the network side device 900 includes: a processor 901, a network interface 902 and a memory 903. The network interface 902 is, for example, a common public radio interface (CPRI).

Specifically, the network side device 900 in the embodiment of the present invention also includes: instructions or programs stored in the memory 903 and executable on the processor 901. The processor 901 calls the instructions or programs in the memory 903 to execute the network side device side. The steps of the positioning method and achieve the same technical effect will not be repeated here to avoid repetition.

Embodiments of the present application also provide a positioning system, including: a terminal and a network side device. The terminal can be used to perform the steps of the positioning method as described above. The network side device can be used to perform the positioning method as described above. step.

Embodiments of the present application also provide a readable storage medium. The readable storage medium may be volatile or non-volatile. The readable storage medium stores a program or instructions. The program Or when the instruction is executed by the processor, each process of the above positioning method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, the details will not be described here.

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

An embodiment of the present application further provides a chip. The chip includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement each of the above positioning method embodiments. The process can achieve the same technical effect. To avoid repetition, it will not be described again here.

It should be understood that the chip mentioned in the embodiment of this application can also be called a system-level chip, system chip, System-on-a-chip or system-on-chip, etc.

Embodiments of the present application further provide a computer program/program product. The computer program/program product is stored in a storage medium. The computer program/program product is executed by at least one processor to implement the above positioning method embodiment. Each process can achieve the same technical effect. To avoid duplication, it will not be described again here.

It should be noted that, in this document, the terms "comprising", "comprising" or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article or device that includes a series of elements not only includes those elements, It also includes other elements not expressly listed or inherent in the process, method, article or apparatus. Without further limitation, an element defined by the statement "comprises a..." does not exclude the presence of additional identical elements in a process, method, article, or device that includes that element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, but may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved. Functions may be performed, for example, the methods described may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is better. implementation. Based on this understanding, the technical solution of the present application can be embodied in the form of a computer software product that is essentially or contributes to the existing technology. The computer software product is stored in a storage medium (such as ROM/RAM, disk , CD), including several instructions to cause a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of this application.

The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above-mentioned specific implementations. The above-mentioned specific implementations are only illustrative and not restrictive. Those of ordinary skill in the art will Inspired by this application, many forms can be made without departing from the purpose of this application and the scope protected by the claims, all of which fall within the protection of this application.

Claims

A positioning method that includes:

The terminal obtains the model used for positioning;

The terminal uses the obtained model to perform positioning.
The positioning method according to claim 1, wherein the terminal obtains a model for positioning, including:

The terminal determines N models among the M models used for positioning based on at least one of first information related to model prediction, protocol predefinition, or preconfiguration;

Alternatively, the terminal receives the second information sent by the network side device; the terminal determines N models among the M models used for positioning based on the second information; wherein the second information is used to indicate or Determine the model identification ID information of the N models; M is greater than or equal to N; M and N are positive integers.
The positioning method according to claim 2, wherein the first information includes at least one of the following:

Quality of service QoS requirements;

terminal type;

terminal capabilities;

Channel status information;

sensor information;

multipath information;

Reference signal quality information;

Community identification ID information;

Area identification ID information;

Timing advance information;

Historical model selection information;

Model ID information of at least one model among the M models;

Positioning reference signal PRS type information, used to indicate model ID information;

PRS resource configuration information or PRS resource set configuration information, used to indicate model ID information;

Report report configuration information, used to indicate model ID information;

Model structure information of at least one model among the M models;

The confidence of at least one model among the M models;

The weight of at least one model among M models.
The positioning method according to claim 2, wherein the second information includes at least one of the following:

Model ID information of at least one model among the N models;

PRS type information, used to indicate model ID information;

PRS resource configuration information or PRS resource set configuration information, used to indicate model ID information;

report configuration information, used to indicate model ID information;

Model configuration information includes PRS resource configuration information or PRS resource set configuration information; the PRS resource configuration information or PRS resource set configuration information is used to indicate model ID information.
The positioning method according to claim 1 or 2, wherein the terminal obtains a model for positioning, including:

The terminal obtains a model for positioning when the target conditions are met; wherein the target conditions include at least one of the following:

The prediction accuracy of the current model is below the first threshold;

The amount of change in the first information related to the model prediction reaches the second threshold;

The current positioning accuracy is lower than the third threshold.
The positioning method according to any one of claims 1 to 5, wherein after the terminal acquires the model used for positioning, the method further includes:

The terminal sends third information to the network side device; wherein the third information is used to indicate or determine model ID information of the N models.
The positioning method according to any one of claims 1 to 5, wherein before the terminal acquires a model for positioning, the method further includes:

The terminal sends fourth information related to model prediction to the network side device; wherein the fourth information includes at least one of the following:

Quality of service QoS requirements;

terminal type;

terminal capabilities;

Channel status information;

sensor information;

multipath information;

Reference signal quality information;

Community identification ID information;

Area identification ID information;

Timing advance information;

Historical model selection information;

PRS resource configuration information or PRS resource set configuration information, used to indicate model ID information;

The confidence of at least one model among the M models;

The weight of at least one model among M models.
The positioning method according to any one of claims 1 to 7, wherein before the terminal acquires a model for positioning, the method further includes:

The terminal receives configuration information of M models from the network side device, and the model used for positioning is determined among the M models.
A positioning method that includes:

The network side device obtains the model used for positioning;

The network side device uses the obtained model to perform positioning.
The positioning method according to claim 9, wherein the network side device obtains a model for positioning, including:

The network side device determines N models among the M models used for positioning based on at least one of fourth information related to model prediction, protocol predefinition, or preconfiguration;

Alternatively, the network side device receives the third information sent by the terminal; the network side device determines N models among M models used for positioning based on the third information; wherein the third information is used for Indicate or determine the model identification ID information of the N models; M is greater than or equal to N; M and N are positive integers.
The positioning method according to claim 10, wherein the fourth information includes at least one of the following:

Quality of service QoS requirements;

terminal type;

terminal capabilities;

Channel status information;

sensor information;

multipath information;

Reference signal quality information;

Community identification ID information;

Area identification ID information;

Timing advance information;

Historical model selection information;

Positioning reference signal PRS resource configuration information or PRS resource set configuration information, used to indicate model ID information;

Model ID information of at least one model among the M models;

Model structure information of at least one model among the M models;

The confidence of at least one model among the M models;

The weight of at least one model among M models.
The positioning method according to claim 10, wherein the third information includes: model ID information of at least one model among the N models.
The positioning method according to claim 9 or 10, wherein the network side device obtains a model for positioning, including:

The network side device obtains a model for positioning when the target condition is met; wherein the target condition includes at least one of the following:

The prediction accuracy of the current model is below the first threshold;

The amount of change in the first information related to the model prediction reaches the second threshold;

The current positioning accuracy is lower than the third threshold.
The positioning method according to any one of claims 9 to 13, wherein after the network side device obtains the model used for positioning, the method further includes:

The network side device sends second information to the terminal; wherein the second information is used to indicate or determine model identification ID information of the N models.
The positioning method according to claim 14, wherein the second information includes at least one of the following:

Model ID information of at least one model among the N models;

Positioning reference signal PRS type information, used to indicate model ID information;

PRS resource configuration information or PRS resource set configuration information, used to indicate model ID information;

Report report configuration information, used to indicate model ID information;

Model configuration information includes PRS resource configuration information or PRS resource set configuration information; the PRS resource configuration information or PRS resource set configuration information is used to indicate model ID information.
The positioning method according to claim 9, wherein before the network side device obtains the model for positioning, the method further includes:

The network side device receives fourth information related to model prediction sent by the terminal; the fourth information is used by the network side device to select N models among M models for positioning.
The positioning method according to any one of claims 9 to 16, wherein the network side device obtains a model for positioning, including:

The network side device determines T models among M models used for positioning based on the third information; M is greater than or equal to T; T is a positive integer;

The network side device performs at least one of the following operations:

The model used for positioning obtained in advance by the network side device is not completely consistent with the T models. Under the same circumstances, N models are determined based on the pre-acquired model used for positioning;

The network side device determines N models among the T models based on terminal distribution information;

The network side device determines N models among the T models based on the statistical information of the terminal recommended models.
The positioning method according to any one of claims 9 to 17, wherein after the network side device obtains the model used for positioning, the method further includes:

The network side device sends fifth information to the terminal; the fifth information includes model ID information of the N models; the fifth information is used to instruct the terminal to obtain the model ID information associated with each of the models. The measurement result corresponding to the measured quantity;

Alternatively, the network side device sends sixth information to the terminal; the sixth information includes measurement quantities corresponding to the N models; the sixth information is used to instruct the terminal to obtain the measurement quantities corresponding to measurement results.
A positioning device including:

The first acquisition module is used to acquire the model used for positioning;

The first positioning module is used to use the obtained model for positioning.
A positioning device including:

The second acquisition module is used to acquire the model used for positioning;

The second positioning module is used to use the obtained model for positioning.
A terminal includes a processor and a memory, the memory stores programs or instructions that can be run on the processor, and when the programs or instructions are executed by the processor, the implementation of any one of claims 1 to 8 is achieved. The steps of the positioning method described above.
A network side device, including a processor and a memory. The memory stores programs or instructions that can be run on the processor. When the program or instructions are executed by the processor, any one of claims 9 to 18 is implemented. The steps of the positioning method described in the item.
A readable storage medium on which programs or instructions are stored. When the programs or instructions are executed by a processor, the positioning method as described in any one of claims 1 to 8 is implemented, or the positioning method as claimed in claims 1-8 is implemented. The steps of the positioning method described in any one of 9 to 18.