WO2023206028A1

WO2023206028A1 - Assistance information receiving method and apparatus, assistance information sending method and apparatus, communication apparatus, and storage medium

Info

Publication number: WO2023206028A1
Application number: PCT/CN2022/089095
Authority: WO
Inventors: 李小龙
Original assignee: 北京小米移动软件有限公司
Priority date: 2022-04-25
Filing date: 2022-04-25
Publication date: 2023-11-02
Also published as: CN117296402A

Abstract

The present disclosure relates to an assistance information receiving method and apparatus, an assistance information sending method and apparatus, a communication apparatus, and a storage medium. The assistance information receiving method comprises: receiving assistance information sent by a second device, wherein the assistance information is used for assisting a first device in determining an AI model, and the AI model is used for positioning a terminal. According to the present disclosure, a network device can send assistance information to a terminal, so that the terminal can accurately determine, on the basis of the assistance information, an AI model that needs to be used for positioning in the current application scenario, and can then perform accurate positioning according to the determined AI model.

Description

Auxiliary information receiving and sending method and device, communication device and storage medium

Technical field

The present disclosure relates to the field of communication technology, and specifically, to an auxiliary information receiving method, an auxiliary information sending method, an auxiliary information receiving device, an auxiliary information sending device, a communication device and a computer-readable storage medium.

Background technique

5G NR (New Radio, New Radio) introduces a variety of positioning technologies to realize the positioning of terminals. But in order to further improve positioning accuracy, 5G NR began to consider positioning based on artificial intelligence (Artificial Intelligence, AI).

Contents of the invention

In view of this, embodiments of the present disclosure propose an auxiliary information receiving method, an auxiliary information sending method, an auxiliary information receiving device, an auxiliary information sending device, a communication device and a computer-readable storage medium to solve technical problems in related technologies.

According to a first aspect of an embodiment of the present disclosure, a method for receiving auxiliary information is proposed, which is suitable for a first device. The method includes: receiving auxiliary information sent by a second device, wherein the auxiliary information is used to assist the third device. A device determines an AI model, and the AI model is used for terminal positioning.

According to a second aspect of the embodiment of the present disclosure, a method for sending auxiliary information is proposed, which is suitable for a second device. The method includes: sending auxiliary information to a first device, wherein the auxiliary information is used to assist the first device. The device determines an AI model, which is used for terminal positioning.

According to a third aspect of the embodiment of the present disclosure, an auxiliary information receiving device is proposed, which is suitable for a first device. The device includes: a receiving module configured to receive auxiliary information sent by a second device, wherein the auxiliary information Used to assist the first device in determining an AI model, where the AI model is used for terminal positioning.

According to a fourth aspect of the embodiment of the present disclosure, an auxiliary information sending device is proposed, which is suitable for a second device. The device includes: a sending module configured to send auxiliary information to the first device, wherein the auxiliary information is To assist the first device in determining an AI model, the AI model is used for terminal positioning.

According to a fifth aspect of the embodiment of the present disclosure, a communication device is proposed, including: a processor; a memory for storing a computer program; wherein when the computer program is executed by the processor, the above-mentioned auxiliary information receiving method is implemented.

According to a sixth aspect of the embodiment of the present disclosure, a communication device is proposed, including: a processor; and a memory for storing a computer program; wherein when the computer program is executed by the processor, the above-mentioned auxiliary information sending method is implemented.

According to a seventh aspect of the embodiment of the present disclosure, a computer-readable storage medium is proposed for storing a computer program. When the computer program is executed by a processor, the steps in the above auxiliary information receiving method are implemented.

According to an eighth aspect of the embodiment of the present disclosure, a computer-readable storage medium is proposed for storing a computer program. When the computer program is executed by a processor, the steps in the above auxiliary information sending method are implemented.

According to embodiments of the present disclosure, the network device can send auxiliary information to the terminal, so that the terminal can accurately determine the AI model required for positioning in the current application scenario based on the auxiliary information, and then perform accurate positioning based on the determined AI model.

The terminal can send auxiliary information to the network device, so that the network device can accurately determine the AI model required for positioning in the current application scenario based on the auxiliary information, and then perform accurate positioning based on the determined AI model.

Description of the drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without exerting any creative effort.

Figure 1 is a schematic flow chart of a model determination method according to an embodiment of the present disclosure.

FIG. 2 is a schematic flow chart of another model determination method according to an embodiment of the present disclosure.

Figure 3 is a schematic flow chart of yet another model determination method according to an embodiment of the present disclosure.

Figure 4 is a schematic flow chart of a method for sending auxiliary information according to an embodiment of the present disclosure.

Figure 5 is a schematic block diagram of an auxiliary information receiving device according to an embodiment of the present disclosure.

Figure 6 is a schematic block diagram of an auxiliary information sending device according to an embodiment of the present disclosure.

FIG. 7 is a schematic block diagram of a device for assisting information transmission according to an embodiment of the present disclosure.

FIG. 8 is a schematic block diagram of a device for assisting information reception according to an embodiment of the present disclosure.

Detailed ways

The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only some of the embodiments of the present disclosure, rather than all of the embodiments. Based on the embodiments in this disclosure, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of this disclosure.

The terminology used in the embodiments of the present disclosure is for the purpose of describing specific embodiments only and is not intended to limit the embodiments of the present disclosure. As used in the embodiments of the present disclosure and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It will also be understood that the term "and/or" as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

It should be understood that although the terms first, second, third, etc. may be used to describe various information in the embodiments of the present disclosure, the information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of the embodiments of the present disclosure, the first information may also be called second information, and similarly, the second information may also be called first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "when" or "in response to determining."

For the purpose of simplicity and ease of understanding, the terms used in this article are "greater than" or "less than", "higher than" or "lower than" when characterizing size relationships. But for those skilled in the art, it can be understood that: the term "greater than" also covers the meaning of "greater than or equal to", and "less than" also covers the meaning of "less than or equal to"; the term "higher than" covers the meaning of "higher than or equal to". "The meaning of "less than" also covers the meaning of "less than or equal to".

Figure 1 is a schematic flow chart of a method for receiving auxiliary information according to an embodiment of the present disclosure. The auxiliary information receiving method shown in this embodiment may be applicable to the first device.

As shown in Figure 1, the auxiliary information receiving method may include the following steps:

In step S101, auxiliary information sent by the second device is received, where the auxiliary information is used to assist the first device in determining an AI model, and the AI model is used for terminal positioning (determining terminal location).

Wherein, the first device includes a terminal, and the second device includes a network device. Alternatively, the first device includes a network device, and the second device includes a terminal.

The terminals include but are not limited to mobile phones, tablets, wearable devices, sensors, Internet of Things devices and other communication devices. The terminal can communicate with network equipment, which includes but is not limited to network equipment in 4G, 5G, 6G and other communication systems, such as base stations, core networks, etc.

The following embodiments first illustrate the technical solution of the present disclosure in the case where the first device includes a terminal and the second device includes a network device. It should be understood that some embodiments are applicable in both scenarios where the first device includes a terminal and the second device includes a network device, and the first device includes a network device and the second device includes a terminal. For example, the following specific assistance Regarding the limitation of information, in order to avoid redundancy, this disclosure does not repeat the description. Those skilled in the art can select the application scenarios of the embodiments as needed.

In one embodiment, the AI model can be obtained by training a sample set through a machine learning (deep learning) algorithm. For different application scenarios, the sample set is different, and the resulting AI model can also be different.

Therefore, for different application scenarios, positioning needs to be based on the corresponding AI model. Therefore, the positioning device (such as a terminal or network device) needs to accurately determine the AI model to be used.

In the case where the first device includes a terminal and the second device includes a network device, when the terminal performs terminal positioning based on the AI model, in order to determine the AI model to be used, there are still certain deficiencies based solely on the information it possesses. , for example, the terminal mainly grasps user-side information and only selects an AI model among multiple AI models based on user-side information. The selected AI model may be less applicable to network-side information. Therefore, the network device can send auxiliary information to the terminal, so that the terminal can accurately determine the AI model required for positioning based on the auxiliary information. For example, auxiliary information #1 corresponds to AI model #1, and auxiliary information #2 corresponds to AI model #2. When receiving auxiliary information #1, the AI corresponding to auxiliary information #1 can be determined among multiple AI models (for example, pre-stored). Model #1, and then perform accurate positioning based on the determined AI model.

In one embodiment, the method further includes: sending an acquisition request to the network device for requesting the auxiliary information.

When the terminal needs the auxiliary information (for example, when the AI model needs to be determined for terminal positioning), the terminal can send an acquisition request to the network device. After receiving the acquisition request, the network device can send the auxiliary information to the terminal. Of course, when the network device does not receive the acquisition request, it can also send the auxiliary information to the terminal as needed.

In one embodiment, the auxiliary information includes at least one of the following:

Conditions for use of AI models;

The application scenario in which the terminal is currently located;

Recommended AI model, where when the first device includes a terminal and the second device includes a network device, the terminal can use the AI model recommended by the network device for terminal positioning; and when the first device includes a network device and the second device includes When a terminal is used, the network device can choose to use the AI model recommended by the terminal for terminal positioning, or it can select other AI models for terminal positioning as needed.

In one embodiment, the usage conditions of the AI model include at least one of the following:

The first correlation between the AI model and the application scenario;

The second correlation between the AI model and measurement information.

In one embodiment, the terminal can determine the current conditions, determine which AI model's usage conditions the current conditions meet, and then select the AI model that meets the usage conditions for terminal positioning.

For example, the current application scenario can be determined, and then the AI model corresponding to the current application scenario can be determined based on the first association relationship, and then the terminal positioning can be performed using the determined AI model.

For example, specific information (such as positioning reference signals, base station information, etc.) can be measured to obtain measurement information, and then the AI model corresponding to the measured measurement information is determined according to the second association relationship, and terminal positioning is performed through the determined AI model.

In one embodiment, when the network device includes a base station, the auxiliary information further includes:

The type of base station, such as macro base station Macro BS and micro base station Micro BS;

The transmit power of the base station.

By sending some of its own parameters to the terminal as auxiliary information, the base station helps the terminal determine the AI model to be used more accurately.

In one embodiment, the measurement information includes at least one of the following:

The transmit power of the base station;

Number of base stations;

Measurement results of the positioning reference signal.

For example, the transmit power of the base station can be obtained by measurement, and then the AI model corresponding to the measured transmit power of the base station can be determined based on the second association relationship, and the terminal positioning can be performed using the determined AI model.

For example, the number of base stations can be obtained through measurement, and then the AI model corresponding to the measured number of base stations can be determined according to the second association relationship, and the terminal positioning can be performed through the determined AI model.

For example, the positioning reference signal sent by the base station can be measured to obtain the measurement result of the positioning reference signal, and then the AI model corresponding to the measurement result of the positioning reference signal can be obtained according to the second correlation relationship, and the terminal positioning can be performed through the determined AI model.

Among them, the measurement results of the positioning reference signal include but are not limited to Positioning Reference Signal Reference Signal Receiving Power (PRS-RSRP), reference signal time difference (Reference signal time difference, RSTD), receiving time and sending time. The time difference (Rx-Tx timing difference).

In all embodiments of the present disclosure, the application scenario includes at least one of the following:

Urban micro cell UMi;

Urban macro community UMa;

indoor office open office indoor office open office;

indoor office mixed office indoor office mixed office;

Indoor Factory Sparse clutter low-power base station InF-SL (Indoor Factory Sparse clutter, Low BS);

Indoor Factory Dense clutter low-power base station InF-DL (Indoor Factory Dense clutter, Low BS);

Indoor Factory Sparse clutter high-power base station InF-SH (Indoor Factory Sparse clutter, High BS);

Indoor Factory Dense clutter high-power base station InF-DH (Indoor Factory Dense clutter, High BS);

Indoor Factory High Power Transmit High Power Receive InF-HH (Indoor Factory High Tx, High Rx).

FIG. 2 is a schematic flow chart of another model determination method according to an embodiment of the present disclosure. As shown in Figure 2, when the auxiliary information includes the first association relationship, the method further includes:

In step S201, determine the current application scenario;

In step S202, the AI model corresponding to the current application scenario is determined according to the first association relationship.

In one embodiment, the first association relationship may be sent to the terminal by the network device, and the terminal may determine the current application scenario, and then determine the AI model corresponding to the current application scenario based on the first association relationship, Then the terminal is positioned through the determined AI model.

In another embodiment, the first association relationship may be pre-stored by the terminal, and the network device may carry the application scenario in which the terminal is located in auxiliary information and send it to the terminal (of course, it may also be independently determined by the terminal), so that the terminal can The first association determines the AI model corresponding to the current application scenario, and then uses the determined AI model to perform terminal positioning.

Figure 3 is a schematic flow chart of yet another model determination method according to an embodiment of the present disclosure. As shown in Figure 3, in the case where the auxiliary information includes the second association relationship, the method further includes:

In step S301, obtain the measurement information;

In step S302, the AI model corresponding to the obtained measurement information is determined according to the second association relationship.

In one embodiment, the terminal can measure specific information (such as positioning reference signals, base station information, etc.) to obtain measurement information, and then determine the AI model corresponding to the measured measurement information based on the second association relationship, and use the determined AI model performs terminal positioning.

In one embodiment, when the network device includes a base station, the auxiliary information is carried in a Radio Resource Control (RRC) message;

And/or, when the network device includes a core network, the assistance information is carried in a Long Term Evolution Positioning Protocol LPP (LTE Positioning Protocol) message, for example, LPP provide assistance information (LPP provides assistance information).

The following embodiments illustrate the technical solution of the present disclosure in the case where the first device includes a network device and the second device includes a terminal.

In the case where the first device includes a network device and the second device includes a terminal, when the network device performs terminal positioning based on the AI model, in order to determine the AI model to be used, there is still a certain problem based only on the information it possesses. Inadequacy, for example, the core network mainly grasps the information on the network side, and only selects an AI model among multiple AI models based on the information on the network side. The selected AI model may be less applicable to the information on the terminal side. Therefore, the terminal can send auxiliary information to the network device, so that the network device can accurately determine the AI model required for positioning based on the auxiliary information. For example, auxiliary information #1 corresponds to AI model #1, and auxiliary information #2 corresponds to AI model #2. When receiving auxiliary information #1, the AI corresponding to auxiliary information #1 can be determined among multiple AI models (for example, pre-stored). Model #1, and then perform accurate positioning based on the determined AI model.

According to embodiments of the present disclosure, the terminal can send auxiliary information to the network device, so that the network device can accurately determine the AI model required for positioning in the current application scenario based on the auxiliary information, and then perform accurate positioning based on the determined AI model.

It should be noted that when the terminal sends auxiliary information to the network device, the network device may choose to determine the AI model based on the auxiliary information, or may choose not to determine the AI model based on the auxiliary information, but determine the AI model based on the needs of the network device.

In one embodiment, the method further includes: sending an acquisition request to the terminal for requesting the auxiliary information.

When the network device needs the auxiliary information (for example, when the AI model needs to be determined for terminal positioning), the network device can send an acquisition request to the terminal. After receiving the acquisition request, the terminal can send the auxiliary information to the network device. Of course, when the terminal does not receive the acquisition request, the terminal may also send the auxiliary information (for example, periodically, or when the terminal needs to perform terminal positioning) to the network device.

In one embodiment, when the network device includes a base station, the assistance information is carried in a radio resource control RRC message;

And/or, when the network device includes a core network, the auxiliary information is carried in a Long Term Evolution Positioning Protocol (LPP) message, such as LPP provide location information (LPP provides location information).

In one embodiment, when the network device includes a core network, the method further includes: receiving auxiliary information sent by a base station, where the auxiliary information sent by the base station is used to assist in determining the AI model.

In one embodiment, the assistance information sent by the base station includes at least one of the following:

The transmit power of the base station.

By sending some of its own parameters to the core network as auxiliary information, the base station helps the core network more accurately determine the AI model to be used. Among them, the base station can carry the auxiliary information in the NRPPa (NR Positioning Protocol A, NR Positioning Protocol A) message and send it to the core network, for example, in the NRPPa positioning information request.

Figure 4 is a schematic flow chart of a method for sending auxiliary information according to an embodiment of the present disclosure. The auxiliary information sending method shown in this embodiment can be applied to the second device.

As shown in Figure 4, the auxiliary information sending method may include the following steps:

In step S401, auxiliary information is sent to the first device, where the auxiliary information is used to assist the first device in determining an AI model, and the AI model is used for terminal positioning.

In one embodiment, the method further includes: receiving an acquisition request sent by the terminal for requesting the auxiliary information.

Conditions for use of AI models;

The application scenario in which the terminal is currently located;

Recommended AI models to use.

The first correlation between the AI model and the application scenario;

The second correlation between the AI model and measurement information.

The transmit power of the base station.

The transmit power of the base station;

Number of base stations;

Measurement results of the positioning reference signal.

For example, the number of base stations can be obtained by measurement, and then the AI model corresponding to the measured number of base stations can be determined based on the second association relationship, and the terminal positioning can be performed using the determined AI model.

Among them, the measurement results of the positioning reference signal include but are not limited to the positioning reference signal received power PRS-RSRP, the reference signal time difference RSTD, and the time difference Rx-Tx timing difference between the receiving time and the sending time.

Urban micro cell UMi;

Urban macro community UMa;

indoor office open office indoor office open office;

indoor office mixed office indoor office mixed office;

Indoor factory sparse clutter low-power base station InF-SL;

InF-DL, low-power base station with dense clutter in indoor factories;

Indoor factory sparse clutter high-power base station InF-SH;

InF-DH, a high-power base station with dense clutter in indoor factories;

Indoor factory high power transmit high power receive InF-HH.

And/or, when the network device includes a core network, the assistance information is carried in a Long Term Evolution Positioning Protocol (LPP) message, such as LPP provide assistance information (LPP provides assistance information).

In one embodiment, the method further includes: receiving an acquisition request sent by the network device for requesting the auxiliary information.

Corresponding to the foregoing embodiments of the auxiliary information receiving method and the auxiliary information sending method, the present disclosure also provides embodiments of an auxiliary information receiving device and an auxiliary information sending device.

Figure 5 is a schematic block diagram of an auxiliary information receiving device according to an embodiment of the present disclosure. The auxiliary information receiving device shown in this embodiment can be applied to the first device.

As shown in Figure 5, the auxiliary information receiving device may include:

The receiving module is configured to receive auxiliary information sent by the second device, where the auxiliary information is used to assist the first device in determining an AI model, and the AI model is used for terminal positioning.

In one embodiment, the auxiliary information includes at least one of the following: usage conditions of the AI model; the application scenario where the terminal is currently located; and the AI model recommended for use.

In one embodiment, the usage conditions of the AI model include at least one of the following: a first association relationship between the AI model and the application scenario; a second association relationship between the AI model and measurement information.

In one embodiment, the measurement information includes at least one of the following: the transmission power of the base station; the number of base stations; and the measurement result of the positioning reference signal.

In one embodiment, the first device includes a terminal, and the second device includes a network device.

In one embodiment, in the case where the auxiliary information includes the first association relationship between the AI model and the application scenario, the device further includes: a processing module configured to determine the current application scenario; according to the The above association relationship determines the AI model corresponding to the current application scenario.

In one embodiment, in the case where the auxiliary information includes the second association relationship between the AI model and the measurement information, the device further includes: a processing module configured to obtain the measurement information; according to the first The second correlation relationship determines the AI model corresponding to the obtained measurement information.

In one embodiment, when the network device includes a base station, the auxiliary information is carried in a radio resource control RRC message; and/or when the network device includes a core network, the auxiliary information is carried In the Long Term Evolution Positioning Protocol LPP message.

In one embodiment, when the network device includes a base station, the auxiliary information further includes: the type of the base station; and the transmission power of the base station.

In one embodiment, the apparatus further includes: a sending module configured to send an acquisition request to the network device for requesting the auxiliary information.

In one embodiment, the first device includes a network device, and the second device includes a terminal.

In one embodiment, when the network device includes a core network, the receiving module is further configured to receive auxiliary information sent by a base station, wherein the auxiliary information sent by the base station is used to assist in determining the AI model. .

In one embodiment, the auxiliary information sent by the base station includes at least one of the following: the type of the base station; the transmission power of the base station.

In one embodiment, the apparatus further includes: a sending module configured to send an acquisition request to the terminal for requesting the auxiliary information.

Figure 6 is a schematic block diagram of an auxiliary information sending device according to an embodiment of the present disclosure. The auxiliary information sending apparatus shown in this embodiment can be applied to the second device.

As shown in Figure 6, the auxiliary information sending device may include:

The sending module 601 is configured to send auxiliary information to the first device, where the auxiliary information is used to assist the first device in determining an AI model, and the AI model is used for terminal positioning.

In one embodiment, when the network device includes a base station, the auxiliary information further includes at least one of the following: the type of the base station; and the transmit power of the base station.

In one embodiment, the apparatus further includes: a receiving module configured to receive an acquisition request sent by the terminal for requesting the auxiliary information.

In one embodiment, the apparatus further includes: a receiving module configured to receive an acquisition request sent by the network device for requesting the auxiliary information.

Regarding the devices in the above embodiments, the specific manner in which each module performs operations has been described in detail in the embodiments of the relevant methods, and will not be described in detail here.

As for the device embodiment, since it basically corresponds to the method embodiment, please refer to the partial description of the method embodiment for relevant details. The device embodiments described above are only illustrative. The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules, that is, they may be located in One place, or it can be distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Persons of ordinary skill in the art can understand and implement the method without any creative effort.

An embodiment of the present disclosure also provides a communication device, including: a processor; a memory for storing a computer program; wherein, when the computer program is executed by the processor, the auxiliary information reception described in any of the above embodiments is implemented method.

An embodiment of the present disclosure also provides a communication device, including: a processor; a memory for storing a computer program; wherein, when the computer program is executed by the processor, the auxiliary information transmission described in any of the above embodiments is implemented method.

Embodiments of the present disclosure also provide a computer-readable storage medium for storing a computer program. When the computer program is executed by a processor, the steps in the auxiliary information receiving method described in any of the above embodiments are implemented.

Embodiments of the present disclosure also provide a computer-readable storage medium for storing a computer program. When the computer program is executed by a processor, the steps in the auxiliary information sending method described in any of the above embodiments are implemented.

As shown in FIG. 7 , FIG. 7 is a schematic block diagram of a device 700 for assisting information transmission according to an embodiment of the present disclosure. The apparatus 700 may be provided as a base station. Referring to Figure 7, apparatus 700 includes a processing component 722, which may further include one or more processors, a wireless transmit/receive component 724, an antenna component 726, and a signal processing portion specific to the wireless interface. One of the processors in the processing component 722 may be configured to implement the auxiliary information sending method described in any of the above embodiments.

FIG. 8 is a schematic block diagram of a device 800 for assisting information reception according to an embodiment of the present disclosure. For example, the device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, or the like.

Referring to FIG. 8 , the apparatus 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power supply component 806 , a multimedia component 808 , an audio component 810 , an input/output (I/O) interface 812 , a sensor component 814 , and Communication component 816.

Processing component 802 generally controls the overall operations of device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above-mentioned auxiliary information receiving method. Additionally, processing component 802 may include one or more modules that facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

Memory 804 is configured to store various types of data to support operations at device 800 . Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EEPROM), Programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.

Power supply component 806 provides power to the various components of device 800. Power supply components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to device 800 .

Multimedia component 808 includes a screen that provides an output interface between the device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide action. In some embodiments, multimedia component 808 includes a front-facing camera and/or a rear-facing camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and/or the rear camera may receive external multimedia data. Each front-facing camera and rear-facing camera can be a fixed optical lens system or have a focal length and optical zoom capabilities.

Audio component 810 is configured to output and/or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when device 800 is in operating modes, such as call mode, recording mode, and speech recognition mode. The received audio signal may be further stored in memory 804 or sent via communication component 816 . In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module, which may be a keyboard, a click wheel, a button, etc. These buttons may include, but are not limited to: Home button, Volume buttons, Start button, and Lock button.

Sensor component 814 includes one or more sensors that provide various aspects of status assessment for device 800 . For example, the sensor component 814 can detect the open/closed state of the device 800, the relative positioning of components, such as the display and keypad of the device 800, and the sensor component 814 can also detect a change in position of the device 800 or a component of the device 800. , the presence or absence of user contact with the device 800 , device 800 orientation or acceleration/deceleration and temperature changes of the device 800 . Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor component 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

Communication component 816 is configured to facilitate wired or wireless communication between apparatus 800 and other devices. The device 800 can access a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G LTE, 5G NR, or a combination thereof. In one exemplary embodiment, the communication component 816 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communications component 816 also includes a near field communications (NFC) module to facilitate short-range communications. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

In an exemplary embodiment, apparatus 800 may be configured by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable Gate array (FPGA), controller, microcontroller, microprocessor or other electronic components are implemented for executing the above auxiliary information receiving method.

In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions, such as a memory 804 including instructions, which can be executed by the processor 820 of the device 800 to complete the above auxiliary information receiving method is also provided. For example, the non-transitory computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.

Other embodiments of the disclosure will be readily apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. The present disclosure is intended to cover any variations, uses, or adaptations of the disclosure that follow the general principles of the disclosure and include common common sense or customary technical means in the technical field that are not disclosed in the disclosure. . It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It is to be understood that the present disclosure is not limited to the precise structures described above and illustrated in the accompanying drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the disclosure is limited only by the appended claims.

It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that these entities or operations are mutually exclusive. any such actual relationship or sequence exists between them. The terms "comprises," "comprises," or any other variation thereof are intended to cover a non-exclusive inclusion such that a process, method, article or apparatus including a list of elements includes not only those elements but also others not expressly listed elements, or elements inherent to such process, method, article or equipment. 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 apparatus that includes the stated element.

The methods and devices provided by the embodiments of the present disclosure have been introduced in detail above. Specific examples are used in this article to illustrate the principles and implementations of the present disclosure. The description of the above embodiments is only used to help understand the methods and methods of the present disclosure. The core idea; at the same time, for those of ordinary skill in the art, there will be changes in the specific implementation and application scope based on the ideas of this disclosure. In summary, the content of this description should not be understood as a limitation of this disclosure. .

Claims

A method for receiving auxiliary information, characterized in that it is suitable for a first device, and the method includes:

Receive auxiliary information sent by the second device, where the auxiliary information is used to assist the first device in determining an AI model, and the AI model is used for terminal positioning.
The method according to claim 1, characterized in that the auxiliary information includes at least one of the following:

Conditions for use of AI models;

The application scenario in which the terminal is currently located;

Recommended AI models to use.
The method according to claim 2, characterized in that the usage conditions of the AI model include at least one of the following:

The first correlation between the AI model and the application scenario;

The second correlation between the AI model and measurement information.
The method according to claim 3, characterized in that the measurement information includes at least one of the following:

The transmit power of the base station;

Number of base stations;

Measurement results of the positioning reference signal.
The method according to any one of claims 1 to 4, characterized in that the first device includes a terminal, and the second device includes a network device.
The method according to claim 5, characterized in that, in the case where the auxiliary information includes the first association relationship between the AI model and the application scenario, the method further includes:

Determine the current application scenario;

The AI model corresponding to the current application scenario is determined based on the association relationship.
The method according to claim 5, characterized in that, in the case where the auxiliary information includes the second association relationship between the AI model and the measurement information, the method further includes:

Obtain the measurement information;

The AI model corresponding to the obtained measurement information is determined according to the second association relationship.
The method according to claim 5, characterized in that, when the network device includes a base station, the auxiliary information is carried in a radio resource control RRC message; and/or

In the case where the network device includes a core network, the auxiliary information is carried in a Long Term Evolution Positioning Protocol (LPP) message.
The method according to claim 5, characterized in that, when the network device includes a base station, the auxiliary information further includes:

The type of base station;

The transmit power of the base station.
The method of claim 5, further comprising:

Send an acquisition request to the network device for requesting the auxiliary information.
The method according to any one of claims 1 to 4, characterized in that the first device includes a network device, and the second device includes a terminal.
The method according to claim 11, characterized in that, when the network device includes a base station, the auxiliary information is carried in a radio resource control RRC message; and/or

In the case where the network device includes a core network, the auxiliary information is carried in a Long Term Evolution Positioning Protocol (LPP) message.
The method according to claim 11, characterized in that, when the network device includes a core network, the method further includes:

Receive auxiliary information sent by the base station, where the auxiliary information sent by the base station is used to assist in determining the AI model.
The method according to claim 13, characterized in that the auxiliary information sent by the base station includes at least one of the following:

The type of base station;

The transmit power of the base station.
The method according to claim 11, characterized in that, the method further includes:

Send an acquisition request to the terminal for requesting the auxiliary information.
A method for sending auxiliary information, characterized in that it is suitable for a second device, and the method includes:

Send auxiliary information to the first device, where the auxiliary information is used to assist the first device in determining an AI model, and the AI model is used for terminal positioning.
The method according to claim 16, characterized in that the auxiliary information includes at least one of the following:

Conditions for use of AI models;

The application scenario in which the terminal is currently located;

Recommended AI models to use.
The method according to claim 17, characterized in that the usage conditions of the AI model include at least one of the following:

The first correlation between the AI model and the application scenario;

The second correlation between the AI model and measurement information.
The method according to claim 18, characterized in that the measurement information includes at least one of the following:

The transmit power of the base station;

Number of base stations;

Measurement results of the positioning reference signal.
The method according to any one of claims 16 to 19, wherein the first device includes a terminal, and the second device includes a network device.
The method according to claim 20, characterized in that, when the network device includes a base station, the auxiliary information is carried in a radio resource control RRC message; and/or

In the case where the network device includes a core network, the auxiliary information is carried in a Long Term Evolution Positioning Protocol (LPP) message.
The method according to claim 21, characterized in that, when the network device includes a base station, the auxiliary information further includes at least one of the following:

The type of base station;

The transmit power of the base station.
The method of claim 20, further comprising:

Receive an acquisition request sent by the terminal for requesting the auxiliary information.
The method according to any one of claims 16 to 19, wherein the first device includes a network device, and the second device includes a terminal.
The method according to claim 24, characterized in that, when the network device includes a base station, the auxiliary information is carried in a radio resource control RRC message; and/or

In the case where the network device includes a core network, the auxiliary information is carried in a Long Term Evolution Positioning Protocol (LPP) message.
The method of claim 24, further comprising:

Receive an acquisition request sent by the network device for requesting the auxiliary information.
An auxiliary information receiving device, characterized in that it is suitable for first equipment, and the device includes:

The receiving module is configured to receive auxiliary information sent by the second device, where the auxiliary information is used to assist the first device in determining an AI model, and the AI model is used for terminal positioning.
An auxiliary information sending device, characterized in that it is suitable for a second device, and the device includes:

The sending module is configured to send auxiliary information to the first device, where the auxiliary information is used to assist the first device in determining an AI model, and the AI model is used for terminal positioning.
A communication device, characterized by including:

processor;

Memory used to store computer programs;

Wherein, when the computer program is executed by the processor, the auxiliary information receiving method described in any one of claims 1 to 15 is implemented.
A communication device, characterized by including:

processor;

Memory used to store computer programs;

Wherein, when the computer program is executed by the processor, the auxiliary information sending method described in any one of claims 16 to 26 is implemented.
A computer-readable storage medium used to store a computer program, characterized in that when the computer program is executed by a processor, the steps in the auxiliary information receiving method described in any one of claims 1 to 15 are implemented.
A computer-readable storage medium used to store a computer program, characterized in that when the computer program is executed by a processor, the steps in the auxiliary information sending method described in any one of claims 16 to 26 are implemented.