CN118303006A

CN118303006A - Information processing method and device, communication equipment and storage medium

Info

Publication number: CN118303006A
Application number: CN202280004870.9A
Authority: CN
Inventors: 李小龙; 牟勤
Original assignee: Beijing Xiaomi Mobile Software Co Ltd
Current assignee: Beijing Xiaomi Mobile Software Co Ltd
Priority date: 2022-11-04
Filing date: 2022-11-04
Publication date: 2024-07-05
Also published as: WO2024092831A1

Abstract

The embodiment of the disclosure provides an information processing method and device, a communication device and a storage medium. Performed by a first communication device, the method comprising: receiving a data set from a second communication device; an artificial intelligence AI/machine learning ML model is trained using the dataset, wherein the AI/ML model is used at least for localization.

Description

Information processing method and device, communication equipment and storage medium

Technical Field

The present disclosure relates to the field of wireless communication technology, and in particular, to an information processing method and apparatus, a communication device, and a storage medium.

Background

With the development of artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) and machine learning (MACHINE LEARNING, ML), AI/ML was applied in the fifth generation mobile communications new wireless (5 ^th Generation New Radio,5G NR), e.g., AI/ML based positioning of devices was available.

For example, the AI/ML model can be used to locate devices such as User Equipment (UE).

However, with respect to training of the location-based AI/ML model, there is no related technical support in the related art.

Disclosure of Invention

The embodiment of the disclosure provides an information processing method and device, a communication device and a storage medium.

A first aspect of an embodiment of the present disclosure provides an information processing method, wherein the method is performed by a first communication device, and the method includes:

Receiving a data set from a second communication device;

An AL/ML model is trained using the data set, wherein the AI/ML model is used at least for localization.

A second aspect of the embodiments of the present disclosure provides an information processing method, wherein the method is performed by a second communication device, the method including:

transmitting the data set to the first communication device; wherein,

The dataset is used for a trained artificial intelligence AL/machine learning ML model, wherein the AI/ML model is used at least for localization.

A third aspect of an embodiment of the present disclosure provides an information processing apparatus, wherein the apparatus includes:

a receiving module configured to receive a data set from a second communication device;

A training module configured to train an artificial intelligence AI/machine learning ML model using the data set, wherein the AI/ML model is used at least for localization.

A fourth aspect of the disclosed embodiments provides an information processing apparatus, wherein the apparatus includes:

a transmission module configured to transmit a data set to a first communication device; wherein the data set is for a trained artificial intelligence AL/machine learning ML model, wherein the AI/ML model is for at least localization.

A fifth aspect of an embodiment of the present disclosure provides an information processing method, including: a first communication device and a second communication device;

The first communication device is configured to receive a data set from a second communication device and to train an artificial intelligence AI/machine learning ML model using the data set, wherein the AI/ML model is used at least for positioning.

The second communication device is configured to send a data set to the first communication device, wherein the data set is used for a trained artificial intelligence, AL, machine learning, ML, model, wherein the AI/ML model is used at least for localization.

A sixth aspect of the disclosed embodiments provides a communication device, including a processor, a transceiver, a memory, and an executable program stored on the memory and capable of being executed by the processor, wherein the processor executes the information processing method provided in the foregoing first or second aspect when the executable program is executed by the processor.

A seventh aspect of the presently disclosed embodiments provides a computer storage medium storing an executable program; the executable program, when executed by a processor, can implement the information processing method provided in the foregoing first aspect or second aspect.

An eighth invention of the presently disclosed embodiments provides a communication system, wherein the communication system includes:

a first communication device configured to perform the information processing method provided by any of the foregoing first aspects.

A second communication device configured to perform the information processing method provided in any of the foregoing first aspects.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of embodiments of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the embodiments of the disclosure.

Fig. 1 is a schematic diagram of a wireless communication system according to an exemplary embodiment;

FIG. 2A is a flow chart of a method of information processing according to an exemplary embodiment;

FIG. 2B is a flow chart of a method of information processing according to an exemplary embodiment;

FIG. 2C is a flow chart of a method of information processing according to an exemplary embodiment;

FIG. 3A is a flow chart illustrating a method of information processing according to an exemplary embodiment;

FIG. 3B is a flow chart illustrating a method of information processing according to an exemplary embodiment;

FIG. 4A is a flow chart illustrating a method of information processing according to an exemplary embodiment;

FIG. 4B is a flow chart of a method of information processing according to an exemplary embodiment;

FIG. 4C is a flow chart illustrating a method of information processing according to an exemplary embodiment;

FIG. 4D is a flow chart illustrating a method of information processing according to an exemplary embodiment;

FIG. 4E is a flow chart illustrating a method of information processing according to an exemplary embodiment;

FIG. 4F is a flow chart illustrating a method of information processing according to an exemplary embodiment;

Fig. 5 is a schematic diagram showing a structure of an information processing apparatus according to an exemplary embodiment;

fig. 6 is a schematic structural view of an information processing apparatus according to an exemplary embodiment;

Fig. 7 is a schematic diagram illustrating a structure of a UE according to an exemplary embodiment;

fig. 8 is a schematic diagram of a communication device according to an exemplary embodiment.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the embodiments of the present disclosure. Rather, they are merely examples of apparatus and methods consistent with aspects of embodiments of the present disclosure.

The terminology used in the embodiments of the disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the embodiments of the disclosure. As used in this disclosure, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the terms and/or terms used herein refer to and encompass 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 in embodiments of the present disclosure to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, the first information may also be referred to as second information, and similarly, the second information may also be referred to as first information, without departing from the scope of embodiments of the present disclosure. Depending on the context, words as used herein may be interpreted if they are interpreted as at … … or when … … or in response to a determination.

Referring to fig. 2, a schematic structural diagram of a wireless communication system according to an embodiment of the disclosure is shown. As shown, the wireless communication system is a communication system based on a cellular mobile communication technology, and may include: a number of UEs 11 and a number of access devices 12.

Wherein UE 11 may be a device that provides voice and/or data connectivity to a user. The UE 11 may communicate with one or more core networks via a radio access network (Radio Access Network, RAN), and the UE 11 may be an internet of things UE, such as a sensor device, a mobile phone (or called a cellular phone), and a computer with an internet of things UE, for example, a fixed, portable, pocket, hand-held, computer-built-in, or vehicle-mounted device. Such as a Station (STA), subscriber unit (subscriber unit), subscriber Station (subscriber Station), mobile Station (mobile Station), mobile Station (mobile), remote Station (remote Station), access point, remote UE (remote terminal), access UE (access terminal), user terminal, user agent (user agent), user device (user equipment), or user UE (UE). Or the UE 11 may be a device of an unmanned aerial vehicle. Alternatively, the UE 11 may be a vehicle-mounted device, for example, a laptop with a wireless communication function, or a wireless communication device externally connected to the laptop. Or the UE 11 may be a roadside device, for example, a street lamp, a signal lamp, or other roadside devices having a wireless communication function, or the like.

Access device 12 may be a network-side device in a wireless communication system. Wherein the wireless communication system may be a fourth generation mobile communication technology (the 4th generation mobile communication,4G) system, also known as a long term evolution (Long Term Evolution, LTE) system; alternatively, the wireless communication system may be a 5G system, also known as a New Radio (NR) system or a 5G NR system. Or the wireless communication system may be a next generation system of the 5G system. Among them, the access network in the 5G system may be called NG-RAN (New Generation-Radio Access Network, new Generation radio access network). Or an MTC system.

Wherein the access device 12 may be an evolved access device (eNB) employed in a 4G system. Or access device 12 may be an access device (gNB) in a 5G system employing a centralized and distributed architecture. When the access device 12 adopts a centralized and distributed architecture, it generally includes a Centralized Unit (CU) and at least two Distributed Units (DUs). A protocol stack of a packet data convergence protocol (PACKET DATA Convergence Protocol, PDCP) layer, a radio link layer Control protocol (Radio Link Control, RLC) layer, and a medium access Control (MEDIA ACCESS Control, MAC) layer is arranged in the centralized unit; a Physical (PHY) layer protocol stack is provided in the distribution unit, and the specific implementation of the access device 12 is not limited by the embodiments of the present disclosure.

A wireless connection may be established between access device 12 and UE 11 over a wireless air interface. In various embodiments, the wireless air interface is a fourth generation mobile communication network technology (4G) standard-based wireless air interface; or the wireless air interface is a wireless air interface based on a fifth generation mobile communication network technology (5G) standard, for example, the wireless air interface is a new air interface; or the wireless air interface can also be a wireless air interface based on the technical standard of the next generation mobile communication network of 5G.

For AI/ML based positioning, there may be a variety of AI models for positioning, different AI models being applied to different positioning application scenarios. That is, for different positioning application scenarios, different data sets are used to train the AI model, thereby obtaining different AI/ML models for different positioning application scenarios.

AI/ML models for positioning can be deployed at the UE, next generation base stations (gNB), and location management functions (Location Management Function, LMF). The entities that train the AI/ML model may include UEs, gnbs, LMFs, operation and maintenance management (Operation Administration AND MAINTENANCE, OAM) and Network data analysis functions (Network DATA ANALYSIS Function, NWDAF), so datasets are needed to train the AI/ML model when UE, gNB, LMF, OAM and NWDAF. In view of this, the following provides a related technique for data set acquisition during AI/ML model training.

As shown in fig. 2A, an embodiment of the present disclosure provides an information processing method, wherein the method is performed by a first communication device, the method including:

s1110: receiving a data set from a second communication device;

S1120: an artificial intelligence AI/machine learning ML model is trained using the dataset, wherein the AI/ML model is used at least for localization.

In some embodiments of the present disclosure, the first communication device may be any device that performs AI/ML model training. The first communication device may be a network device or a UE.

In some embodiments of the present disclosure, the network devices include, but are not limited to, access network devices or core network devices and/or network management devices, and the like.

In some embodiments of the present disclosure, the UE may be a UE with a relatively strong processing capability, e.g., a UE with an image processor (GPU).

Of course the above is merely an example of a first communication device and the specific implementation is not limited to this example.

The second communication device may be any device different from the first communication device.

The second communication device may have the capability to collect data to form the data set or to forward the data set to the first communication device.

The data set may include any data used to train the AI/ML model. Illustratively, the dataset may comprise: sample data of the AI/ML model is trained.

The AI model may include, but is not limited to: various deep learning models. The deep learning model may include: neural network models, such as convolutional neural networks, recurrent neural networks, and/or long and short memory networks, among others.

The ML model may include, but is not limited to: models generated by machine learning or device self learning, e.g., reinforcement learning models.

After the AI/ML model is trained, the AI/ML model can be used for positioning the tested object. As such, if the training set is sample data that trains the AI/ML model, the data set may include data generated during wireless positioning. For example, after the AI/ML model is input into the measurement results, the AI/ML model may output position information that meets the accuracy requirements and/or within a maximum time delay allowed.

In some embodiments of the present disclosure, the AI/ML model may be used to locate based on the wireless capabilities of access network devices such as base stations. Illustratively, the base stations may include terrestrial network (TERRESTRIAL NETWORK, TN) base stations and non-terrestrial network (Non Terrestrial Network, TN) base stations.

In some embodiments of the present disclosure, the S1110 may include:

Receiving the data set actively reported by the second communication equipment;

Or alternatively

And sending an indication or request message to the second communication device, and returning the corresponding data set by the second communication device according to the indication or request message.

The S1110 may include: the data set is received directly or indirectly from the second communication device.

Illustratively, the dataset comprises at least one of the following data:

Measurement results and position information;

Measurement results and indication information;

Measurement results, position information, and indication information;

Measuring results;

Wherein the measurement results include measurement results of positioning reference signals; the position information comprises position information of the measuring equipment and/or position information of the measured equipment; the indication information is used for indicating that the measurement path for obtaining the measurement result is a line-of-sight path LoS or a non-line-of-sight path NLoS.

In some embodiments of the present disclosure, the positioning reference signals include, but are not limited to, uplink positioning reference signals and/or downlink positioning reference signals. The positioning reference signal may include: an uplink Positioning reference signal (reporting REFERENCE SIGNAL, SRS) and/or a downlink Positioning reference signal (Positioning REFERENCE SIGNAL, PRS).

In summary, the positioning reference signal may be a reference signal dedicated to positioning, which may be transmitted and/or received by the base station, by which at least the relative distance between the object to be measured and the access network device transmitting the positioning reference signal may be obtained.

The measurement may include: and measuring the measured value obtained by measuring the positioning reference signal.

The location information includes: the position information of the measuring device participating in the positioning measurement and/or the position information of the measured device calculated by the measuring device preliminarily. The location information includes, but is not limited to, absolute location information such as latitude and longitude and/or relative location information between the device under test and the measurement device.

The measurement device and the device under test may both be a UE or a relay node or a mobile point of reception (RECEIVING AND TRANSMITTING point, TRS).

The LOS is a non-shielding measurement path; NLos is a blocked measurement path. Illustratively, the measurement may include at least one of:

A reference signal time difference (REFERENCE SIGNAL TIME DIFFERENCE, RSTD);

Relative Arrival time (RELATIVE TIME of Arrival, RTOA);

angle of Arrival (AOA);

angle-of-device (AoD);

time of Arrival (TOA);

Reference signal received Power (REFERENCE SIGNAL RECEIVED Power, RSRP);

Reference signal received Power (REFERENCE SIGNAL RECEIVED PATH Power, RSRPP);

Channel impulse response (Channel Impulse Response, CIR);

The time difference of arrival (Observation ARRIVAL TIME DIFFERENCE, OATD) was observed.

The above is merely an example of the measurement result, and the specific implementation is not limited to the above example.

I.e. the data set may comprise solely: the measurement result may also include information related to the measurement condition in addition to the measurement result. The related information includes: location information and/or indication information.

For example, the location information may be location information of the measuring UE and/or the measured UE.

Indication information of whether the measurement path is a line-of-sight path or a non-line-of-sight path, the indication information may include: loS information or NoLS information.

In some embodiments of the present disclosure, the measurement results include: a first measurement and/or a second measurement; wherein the second measurement is different from the first measurement.

Illustratively, the first measurement result may be directly used to calculate the position information of the measured object; and the second measurement result is indirectly used for calculating the position information of the measured object.

Also, for example, the first measurement may be used to calculate the object under test alone, while the second measurement requires additional information to assist in calculating the location information of the object under test.

Of course, the above is merely an example of the first measurement result and the second measurement result, and the specific implementation is not limited to the above example.

The object to be measured includes but is not limited to: UE and/or relay device, and the like.

In some embodiments of the present disclosure, the second measurement includes at least one of:

RSRP obtained by measuring the positioning reference signals;

PRSPP, measuring the positioning reference signal;

And measuring the CIR of the positioning reference signal.

The first measurement may be other measurements than the RSPR, PRSPP, and CIR. The other measurements may include: RSTD, RTOA, AOA, TOA and/or OATD, etc.

Notably, are: the measurement results of the foregoing embodiments may include: the first type of measurement and/or the second type of measurement.

In some embodiments of the present disclosure, the dataset has descriptive information indicating at least one of: the method comprises the steps of data set type, data format, data amount, collection scene, collection position and reference signal configuration corresponding to the data set.

The descriptive information may indicate one or more attributes (characteristics) of the dataset.

By means of the descriptive information, the first communication device can indicate to the second communication device the properties or characteristics of the data set it wants, and the first communication device can be aware of the properties or characteristics of the data set provided by the second communication device.

For example, the type of data set may be used to indicate a particular type of one or more positioning-related data contained within the data set, and different types of data sets may be of different positioning accuracy or of different accuracy.

There are types of data sets that require specific pre-processing prior to AI/ML model training, while there are types of data sets that may not be used, etc.

The different data may be in different data formats, for example, a numerical storage format, a vector storage format, and/or a matrix storage format. The matrix storage format may include: a two-dimensional matrix storage format, a three-dimensional matrix storage format, and/or a higher-dimensional color data storage format. Of course, this is merely an example of a data format.

The data amount may limit a maximum data amount for one data set or a total data amount for one or more data sets received.

In some embodiments of the present disclosure, the data amount may also be an average data amount of the plurality of data sets, if there are a plurality of data sets.

The collection location may be location information of the measuring device and/or area information of an area where the measuring device is located. The area information may include, but is not limited to, a cell, tracking area, notification area, or the like.

The reference signal configuration may include at least one of:

Time domain resource allocation of reference signals;

Frequency domain resource allocation of reference signals;

The type configuration of reference signals, for example, reference signals related to positioning may include: uplink positioning reference signals and/or downlink reference positioning reference signals.

The collection scenarios may be differentiated according to the use of the data set collection site, the type of building, and/or the height, among others.

For example, the collection scenario may be divided into: subsurface and/or above ground, open air and/or occlusion scenes.

For another example, the collection scenario may be divided into: high altitude scenes, ground scenes, submarine scenes or ground scenes, etc.

For another example, the collection scenario may be divided into: people stream hot spot scenes and non-people stream hot spot scenes. The people stream hot spot scenario may include: malls, stadiums, schools, and/or high volume rate residential communities, etc.

Of course, the above is merely an example of the above information, and the specific implementation is not limited to the above example.

As shown in fig. 2B, an embodiment of the present disclosure provides an information processing method, wherein the method is performed by a first communication device, the method including: s1210: and sending a request message to the second communication device, wherein the request message is used for requesting the data set from the second communication device.

S1220: and receiving a data set returned by the second communication device based on the request message. Wherein the data set is used for the AI/ML model at least for positioning.

The first communication device may send a request message to the corresponding second communication device if the data set is to be acquired. The request message includes, but is not limited to, a long term evolution positioning protocol (Long term evolution Positioning Protocol, LPP) message or a new wireless positioning protocol (New Radio Positioning Protocol a, NRPPa) message to send the request message.

In some embodiments of the present disclosure, when the first communication device is an LMF and the second communication device is a UE, the request message may be an LPP message;

In some embodiments of the present disclosure, when the first communication device is an LMF and the second communication device is a gNB, the request message is a NRPPa message;

if the request message is sent using an LPP message, the data set may also be returned to the first communication device based on the LPP message.

If the request message is sent using a NRPPa message, the dataset may also be returned to the first communication device based on the NRPPa message. In one embodiment, the request message includes the description information. If the request message includes the description information, the second communication device may return a data set to the first communication device according to the description information.

In some embodiments of the present disclosure, the first communication device may request a data set with different description information from a different second communication device, where the second communication device sends the data set to the first communication device and simultaneously returns corresponding description information to the first communication device, so that the first communication device is convenient to check.

In some embodiments of the present disclosure, the receiving the data set from the second communication device includes: and receiving the data set provided by the second communication device according to the description information.

Also for example, if the second communication device actively reports the data set, the second communication device may send the data set and the description information to the first communication device together, and similarly, the first communication device may receive the data set and the description information of the data set from the second communication device at the same time.

In some embodiments of the present disclosure, the first communication device is a location management function LMF, and the second communication device is: user equipment UE and/or access network equipment; or alternatively

The first communication equipment is UE, and the second communication equipment is LMF and/or access network equipment; or alternatively

The first communication device is access network equipment, and the second communication device is: UE and/or LMF; or alternatively

The first communication device is used for operation, maintenance and management (OAM), and the second communication device is used for: access network equipment and/or LMF; or alternatively

The first communication device is network data analysis NWDNF, and the second communication device is any one of the following: access network device OAM, LMF, UE.

As shown in fig. 2C, an embodiment of the present disclosure provides an information processing method, where the method is performed by a first communication device, and the second communication device is a UE, and the method includes:

s1310: receiving data capacity information sent by UE; wherein the data capability information indicates whether the UE is capable of providing the data set;

S1320: transmitting a request message to a UE having data set providing capability, wherein the request message is used for requesting the data set from the second communication device;

s1330: the data set is received from a UE having data set provisioning capability.

Some UEs have the capability to provide a data set and some UEs do not have the capability to provide a data set. For example, a UE with a small storage capacity, a UE with poor processing power such as data collection, or a UE with a long standby period may not have data set providing capability. In view of this, if the second communication device includes a UE, it is necessary to know the data set capability information of the corresponding UE in advance, and only the UE having the data set providing capability can receive and/or provide the data set.

In the embodiment of the present disclosure, if the first communication device sends the data capability request information to the UE to which the data set is to be provided, the UE that receives the data capability request information is indicated to report the data capability information.

If there is a UE with data provision capability, the UE may be instructed to provide the data set to the first communication device.

In some embodiments of the present disclosure, the method further comprises:

And transmitting data capability request information to the UE, wherein the data capability request information can instruct the UE to transmit the data capability information to the first communication device.

In some embodiments of the present disclosure, the UE data capability information is further used to indicate at least one of:

The method comprises the steps of determining the type of a data set supported by the UE, the data format supported by the UE, the data amount supported by the UE, the collection scene supported by the UE, the collection position supported by the UE and the reference signal configuration corresponding to the data set supported by the UE.

Different UE capabilities differ, such that the provisioning capability related attributes for the data set may include any one or more of the above.

For example, some UEs do not support transmitting uplink positioning reference signals. Some UEs do have enough power consumption and transmit power to transmit the uplink positioning reference signal.

In some embodiments of the present disclosure, the type of data set is one of:

a first type, the data elements being measurement results and a location information dataset; wherein the measurement result is a measurement result of a positioning reference signal; wherein the location information includes: measuring the position information of the equipment and/or the position information of the equipment to be measured;

A second type, the data elements being data sets of measurement results and indication information; wherein the measurement result is a measurement result of a positioning reference signal; the indication information indicates that the measurement path for obtaining the measurement result is a line-of-sight path LoS or a non-line-of-sight path NLoS;

The third type, the data elements are data sets of measurement results.

Different types of data sets may contain different types of data elements.

In some embodiments of the present disclosure, the collection scenario is one of:

An outdoor scene;

An indoor scene;

Reserving a building scene;

A predetermined usage scenario.

For example, the indoor scene is a scene inside a building, and the outdoor scene may be a scene outside the building.

Illustratively, the predetermined building scene may include, but is not limited to: ground and/or submarine tunnel scenes, subway scenes, highway scenes, railway scenes, etc.

Still further exemplary, the predetermined usage scenario may include, but is not limited to: factory scenes, square scenes, greenfield scenes, park scenes, store scenes, street scenes, school scenes, library scenes, and/or the like.

Of course, the above is merely examples of various scenarios, and the specific implementation is not limited to the above examples.

In some embodiments of the present disclosure, the collection location comprises at least one of:

A cell identifier indicating a cell to which the data set is generated;

a base station identifier for indicating a base station corresponding to the generation place of the data set;

A transmitting point identifier indicating a transmitting point corresponding to the place where the data set is generated;

UE location information indicating the UE location at which the UE acquired data.

The cell identity may include, but is not limited to, a physical cell identity.

The base station identity may include an identity of the eNB and/or an identity of the gNB.

A base station may have multiple transceivers, with different transceivers being distributed at different locations, so that different transceivers identifiers may represent different locations.

The UE location indicated by the UE location information may be location information obtained by satellite positioning or location information obtained by positioning based on a positioning management function (Location Management Function, LMF). Adding a collection location to the descriptive information may instruct the second communication device to collect a data set for a particular location.

As shown in fig. 3A, an embodiment of the present disclosure provides an information processing method, wherein the method is performed by a second communication device, the method including:

S3110: transmitting the data set to the first communication device; wherein the data set is for a trained artificial intelligence AL/machine learning ML model, wherein the AI/ML model is for at least localization.

The second communication device may be a network device and/or a UE.

The second communication device may actively send the data set to the first communication device or send the data set to the first communication device based on a request message of the first communication device.

The data set may be used to train an AI/ML model for localization. The trained AI/ML model is trained to be used at least for calculation of position information, e.g., absolute position information and/or relative position information may be output. After the AI/ML model is trained, it can be used at least to process the input data set to obtain the position information meeting the accuracy and/or delay requirements.

Likewise, if the first communication device is UE, the second communication device is LMF and/or access network device; or the first communication device is access network device, the second communication device is: UE and/or LMF; or the first communication device manages OAM for operation and maintenance, the second communication device is: access network equipment and/or LMF; or the first communication device is network data analysis NWDNF, the second communication device is: access network equipment, OAM, LMF, and/or UE.

In some embodiments of the present disclosure, the data set includes at least one of the following data:

Measurement results and position information;

Measurement results and indication information;

Measurement results, position information, and indication information;

Measuring results;

In some embodiments of the present disclosure, the positioning reference signals include, but are not limited to, uplink positioning reference signals and/or downlink positioning reference signals. The positioning reference signal may include: an uplink Positioning reference signal (reporting REFERENCE SIGNAL, SRS) and/or a downlink Positioning reference signal (Positioning REFERENCE SIGNAL, PRS). In any case, the positioning reference signal may be a reference signal dedicated to positioning, which may be transmitted and/or received by the base station, by which at least the relative distance between the object to be measured and the access network device transmitting the positioning reference signal may be obtained.

The measurement result is a measured value obtained by measuring the positioning reference signal.

A reference signal time difference (REFERENCE SIGNAL TIME DIFFERENCE, RSTD);

Relative Arrival time (RELATIVE TIME of Arrival, RTOA);

angle of Arrival (AOA);

angle-of-device (AoD);

time of Arrival (TOA);

Reference signal received Power (REFERENCE SIGNAL RECEIVED Power, RSRP);

Reference signal received Power (REFERENCE SIGNAL RECEIVED PATH Power, RSRPP);

Channel impulse response (Channel Impulse Response, CIR);

I.e. the data set may comprise solely: the measurement result may also include information related to the measurement condition in addition to the measurement result.

For example, measuring location information of the UE. Indication information of whether the measurement path is a line-of-sight path or a non-line-of-sight path, the indication information may include: loS information or NoLS information.

RSRP obtained by measuring the positioning reference signals; PRSPP, measuring the positioning reference signal; and measuring the CIR of the positioning reference signal. The first measurement may be other measurements than the RSPR, PRSPP, and CIR. The other measurements may include: RSTD, RTOA, AOA, TOA and/or OATD, etc. Notably, are: the measurement results of the foregoing embodiments may include: the first type of measurement and/or the second type of measurement.

For example, the type of data set may be used to indicate: the particular type of one or more positioning-related data contained within the data set, different types of data sets may differ in positioning accuracy or accuracy.

Some types of data sets require specific pre-processing prior to AI/ML model training, while some types of data sets may not be used, etc.

The reference signal configuration may include at least one of:

Time domain resource allocation of reference signals;

Frequency domain resource allocation of reference signals;

Of course, the above is merely an example of the above information, and the specific implementation is not limited to the above example. As shown in fig. 3B, an embodiment of the present disclosure provides an information processing method, where the method is performed by a second communication device, and the second communication device is a UE, the method includes:

s3210: and receiving a data capability request message, wherein the data capability request message is used for requesting to instruct the UE to provide the data capability information.

If the first communication device sends the data capability request information to the UE to be provided with the data set, the UE receiving the data capability request information is indicated to report the data capability information. If there is a UE with data provision capability, the UE may be instructed to provide the data set to the first communication device. Illustratively, the UE data capability information is further for indicating at least one of:

Notably, are: the step S3210 may be an optional step, for example, the first communication device is a network device, and the network device may determine whether the corresponding UE has a capability of providing the data set according to the subscription data or the model data of the UE. Or in some scenarios the first communication device may directly default to the capability provided by the data set for the particular UE.

Illustratively, the type of data set is one of:

The third type, the data elements are data sets of measurement results.

Illustratively, the collection scenario is one of:

An outdoor scene;

An indoor scene;

Reserving a building scene;

A predetermined usage scenario.

A cell identifier indicating a cell to which the data set is generated;

In some embodiments of the present disclosure, the reference signal configuration includes:

configuring a downlink positioning reference signal PRS;

and configuring an uplink positioning reference signal SRS.

The cell identity may include, but is not limited to, a physical cell identity.

The UE location indicated by the UE location information may be location information obtained by satellite positioning or location information obtained by positioning based on a positioning management function (Location Management Function, LMF).

Adding a collection location to the descriptive information may instruct the second communication device to collect a data set for a particular location.

Embodiments of the present disclosure provide a method for data set acquisition, the training set for training an AI/ML model for localization, comprising the following scenarios:

scene 1: the LMF uses the dataset to train the AI/ML model;

as shown in fig. 4A, the LMF acquires a data set from the UE;

The ue capability (LPP provide capability) to provide a data set may include at least one of:

Types of supported datasets, e.g., datasets of measurements (RSRP/RSRPP/TOA/RSTD/CIR) and location, results of measurements (RSRP/RSRPP/TOA/RSTD/CIR) and LoS information or NLoS information; the LoS information indicates that the measurement path is a line-of-sight path. The NLoS information indicates that the measurement path is a non-line-of-sight path.

The format, size (i.e., amount of data) of the supported data set;

The supported scenes corresponding to the data sets, for example, the scenes corresponding to the data sets are indoor, outdoor, factory and other scenes.

The UE may provide the above capability based on the LMF request, i.e., the LMF requests the UE to provide the data set capability, and the information indicating whether the UE has the capability of providing the data set may be carried by the LPP message.

Lmf requests UE to provide a data set (LPP request location information), which may include at least one of:

Types of datasets, such as datasets of measurement results (RSRP/RSRPP/TOA/RSTD/CIR) and location, results of measurement results (RSRP/RSRPP/TOA/RSTD/CIR) and LoS information/NLoS information;

format, size of the dataset;

A scene corresponding to the dataset;

PRS configuration corresponding to the data set;

Cell identity (cell ID) or transmitting point identity (TRP ID) corresponding to the data set.

The UE provides the data set based on the LMF request; or the LMF obtains the data set from the gNB.

Scene 2:

the LMF uses the dataset to train the AI/ML model;

as shown in fig. 4A, the LMF obtains the data set from the gNB.

Lmf requests the gNB to provide a data set, wherein the description information of the data set may include at least one of:

The type of dataset, e.g., measurement (dataset with location information; the measurement may be RSRP, RSRPP, TOA, RSTD and/or CIR and/or measured on the uplink positioning reference signal,

And measuring results and results of LoS information or NLoS information. The location information may include information of a positioning reference unit (positioning reference unit, PRU); the measurement may be RSRP, RSRPP, TOA, aoA and/or the CIR obtained for the positioning reference signal;

format, size of the dataset;

a scene corresponding to the dataset; for example, the scenes corresponding to the data sets are indoor, outdoor, factory, etc.

Configuring uplink positioning reference signals corresponding to the data set;

UE identity corresponding to the data set.

GNB provides the data set based on LMF requests.

Scene 3:

the UE trains an AI/ML model using the data set;

As shown in fig. 4B, the UE acquires a data set from the LMF.

Illustratively, the ue requests the LMF to provide a data set, the description information of which may indicate at least one of:

The type of data set;

data set format, size;

A scene corresponding to the dataset;

PRS configuration corresponding to the data set;

Cell ID or TRP ID corresponding to the data set.

The description information may be carried in the LPP request assistance information.

The LMF provides the data set to the UE. As such, the data set may be carried in LPP provisioning assistance information. Scene 4:

the UE trains an AI/ML model using the data set;

as shown in fig. 4B, the UE acquires a data set from the gNB.

The ue requests that the gNB provide a data set, the description information of which may indicate at least one of:

The type of data set;

data set format, size;

A scene corresponding to the dataset;

PRS configuration corresponding to the data set;

Cell ID or TRP ID corresponding to the data set.

The description information of the data set may be carried in the LPP request assistance information.

The gNB provides the UE with a data set, which may be carried in the LPP request assistance information (LPP provide assistance information).

Of course, the UE may also actively report the data set.

Scene 5:

the gNB uses the dataset to train the AI/ML model:

as shown in fig. 4C, the gNB obtains a data set from the LMF.

Illustratively, the gNB requests the LMF to provide a data set, the description information of which may indicate at least one of:

The type of data set;

data set format, size;

A scene corresponding to the dataset;

SRS configuration corresponding to the data set;

Cell ID or TRP ID corresponding to the data set.

The lmf provides the dataset to the gNB, e.g., based on NRPPa messages.

The description information of the data set provided by the LMF to the gNB may indicate at least one of:

types of datasets, such as results of measurements (RSRP/RSRPP/TOA/AoA/CIR) with LoS information or NLoS information;

A scene corresponding to the dataset; for example, the scenes corresponding to the data sets are indoor, outdoor, factory and other scenes;

Configuring uplink positioning reference signals corresponding to the data set;

A base station identifier corresponding to the data set, for example, TRP ID, cell ID;

scene 6:

the gNB uses the dataset to train the AI/ML model:

As shown in fig. 4D, the gNB obtains the data set from the PRU. The PRU may be a special UE whose current location is known and which has location related functionality. The gNB requests the PRU to provide the data set.

The PRU provides a data set. Illustratively, the data set provided by the PRU may include location information of the PRU, as well as corresponding positioning measurements.

Scene 7:

OAM uses the data set to train the AI/ML model;

Referring to fig. 4E, the OAM may obtain the data set from the gNB or LMF.

An oam request gNB or LMF provides a data set, the description information of which may indicate at least one of:

The type of data set;

data set format, size;

A scene corresponding to the dataset;

SRS configuration or PRS configuration corresponding to the data set;

Cell ID or TRP ID corresponding to the data set.

The data set is provided by the gNB or LMF based on an OAM request.

Scene 8:

NWDAF training an AI/ML model using the dataset;

referring to fig. 4F, NWDAF obtains the data set directly or indirectly from the gNB or OAM or LMF or UE.

Nwdaf directly or indirectly requests the gNB or OAM or LMF or UE to provide a data set, the description information of which may indicate at least one of the following:

The type of data set;

data set format, size;

A scene corresponding to the dataset;

SRS configuration or PRS configuration corresponding to the data set;

A cell identity (cell ID) or a transmission point identity (transmission reception point Identifier, TRP ID) corresponding to the data set.

GNB or OAM or LMF or UE provides the data set based on NWDAF's request.

As shown in fig. 5, an embodiment of the present disclosure provides an information processing apparatus, wherein the apparatus includes:

A receiving module 110 configured to receive a data set from a second communication device;

A training module 120 configured to train an artificial intelligence AI/machine learning ML model using the data set, wherein the AI/ML model is used at least for localization.

The information processing apparatus may be the aforementioned first communication device.

The first communication device may be a network device or a UE.

The second communication device may be any network device or UE other than the first communication device described above.

It will be appreciated that the data set includes at least one of the following data: a measurement result and a location information dataset; wherein the measurement result is a measurement result of a positioning reference signal; wherein the location information includes: measuring the position information of the equipment and/or the position information of the equipment to be measured; a data set of measurement results and indication information; wherein the measurement result is a measurement result of a positioning reference signal; the indication information indicates that the measurement path for obtaining the measurement result is a line-of-sight path LoS or a non-line-of-sight path NLoS; a dataset of measurements.

As can be appreciated, the measurement results include:

The measurement results include: a first measurement and/or a second measurement; wherein the second measurement is different from the first measurement;

wherein the second measurement comprises at least one of:

Reference signal received power RSRP obtained by measuring the positioning reference signal;

A reference signal receiving path power PRSPP obtained by measuring the positioning reference signal;

And measuring the channel impulse response CIR of the positioning reference signal.

It will be appreciated that the dataset has descriptive information indicating at least one of: the method comprises the steps of data set type, data format, data amount, collection scene, collection position and reference signal configuration corresponding to the data set.

It will be appreciated that the method further comprises: and sending a request message to the second communication device, wherein the request message is used for requesting the data set from the second communication device.

It will be appreciated that the request message includes descriptive information for the data set.

It will be appreciated that the receiving module 110 is configured to receive the data set provided by the second communication device according to the description information.

As will be appreciated, the first communication device is a location management function LMF, and the second communication device is: user equipment UE and/or access network equipment; or alternatively

It will be appreciated that the UE data capability information is also used to indicate at least one of:

The method comprises the steps of determining the type of a data set supported by the UE, the data format supported by the UE, the data amount supported by the UE, the collection scene supported by the UE, the collection position supported by the UE and the reference signal configuration corresponding to the data set supported by the UE. It will be appreciated that the type of data set is one of the following:

The third type, the data elements are data sets of measurement results.

It will be appreciated that the collection scenario is one of the following: an outdoor scene; an indoor scene; reserving a building scene; a predetermined usage scenario.

It will be appreciated that the collection location includes at least one of: a cell identifier indicating a cell to which the data set is generated; a base station identifier for indicating a base station corresponding to the generation place of the data set; a transmitting point identifier indicating a transmitting point corresponding to the place where the data set is generated; UE location information indicating the UE location at which the UE acquired data.

As can be appreciated, the reference signal configuration includes: configuring a downlink positioning reference signal PRS; and configuring an uplink positioning reference signal SRS.

As shown in fig. 6, an embodiment of the present disclosure provides an information processing apparatus, wherein the apparatus includes:

A transmitting module 210 configured to transmit the data set to the first communication device; wherein the data set is for a trained artificial intelligence AL/machine learning ML model, wherein the AI/ML model is for at least localization.

The information processing apparatus may include a second communication device.

The information processing apparatus further includes: a storage module; the storage module is connected to the sending module 210 and the sending module 210210, and is configured to store the data set.

As can be appreciated, the measurement results include:

wherein the second measurement comprises at least one of:

As shown in the drawings, an embodiment of the present disclosure provides an information processing method, including: a first communication device and a second communication device;

The first communication device is configured to receive a data set from a second communication device and to train an artificial intelligence AI/machine learning ML model using the data set, wherein the AI/ML model is used at least for positioning

It will be appreciated that the data set includes at least one of the following data:

Measurement results and position information;

Measurement results and indication information;

Measurement results, position information, and indication information;

Measuring results;

As can be appreciated, the measurement results include:

the measurement results include: a first measurement and/or a second measurement; wherein the second measurement is different from the first measurement.

It will be appreciated that the request message includes descriptive information.

It is understood that said receiving a data set from a second communication device comprises: and receiving the data set provided by the second communication device according to the description information.

It will be appreciated that the first communication device is not a UE and the second communication device is a UE; the method further comprises the steps of: receiving data capacity information sent by UE; wherein the data capability information indicates whether the UE is capable of providing the data set.

It will be appreciated that the method further comprises: the method comprises the steps that first communication equipment sends a data capacity request message to the UE, wherein the data capacity request message is used for requesting to instruct the UE to provide the data capacity information; the second communication device receives the data capability request message. The UE data capability information is further configured to indicate at least one of:

It will be appreciated that the type of data set is one of the following:

The third type, the data elements are data sets of measurement results.

The embodiment of the disclosure provides a communication device, comprising:

a memory for storing processor-executable instructions;

The processor is connected with the memories respectively;

Wherein the processor is configured to execute the information processing method provided in any of the foregoing technical solutions.

The processor may include various types of storage medium, which are non-transitory computer storage media, capable of continuing to memorize information stored thereon after a power down of the communication device.

Here, the communication apparatus includes: the first communication device and/or the second communication device. The first communication device and the second communication device may each include: network devices and/or UEs.

The processor may be connected to the memory via a bus or the like for reading an executable program stored on the memory, for example, at least one of the methods shown in fig. 2A to 2C, fig. 3A to 3B, fig. 4A to 4C.

The disclosed embodiments provide a communication system, wherein the communication system includes:

A first communication device configured to execute an information processing method executed by the first communication device.

And a second communication device configured to execute the information processing method executed by the second communication device.

Fig. 8 is a block diagram of a UE800, according to an example embodiment. For example, the UE800 may be a mobile phone, a computer, a digital broadcast user equipment, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.

Referring to fig. 8, a ue800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, and a communication component 816.

The processing component 802 generally controls overall operation of the UE800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to generate all or part of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interactions between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

The memory 804 is configured to store various types of data to support operations at the UE 800. Examples of such data include instructions for any application or method operating on the UE800, contact data, phonebook data, messages, pictures, videos, and the like. The memory 804 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.

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

The multimedia component 808 includes a screen between the UE 800 and the user that provides an output interface. In some embodiments of the present disclosure, 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 a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments of the present disclosure, the multimedia component 808 includes a front-facing camera and/or a rear-facing camera. The front camera and/or the rear camera may receive external multimedia data when the UE 800 is in an operation mode, such as a photographing mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the UE 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments of the present disclosure, the audio component 810 further includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be a keyboard, click wheel, buttons, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.

The sensor component 814 includes one or more sensors that provide status assessment of various aspects for the UE 800. For example, the sensor component 814 may detect an on/off state of the device 800, a relative positioning of components, such as a display and keypad of the UE 800, the sensor component 814 may also detect a change in position of the UE 800 or a component of the UE 800, the presence or absence of user contact with the UE 800, an orientation or acceleration/deceleration of the UE 800, and a change in temperature of the UE 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The 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 of the present disclosure, the sensor assembly 814 may further include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 816 is configured to facilitate communication between the UE 800 and other devices, either wired or wireless. The UE 800 may access a wireless network based on a communication standard, such as WiFi,2G, or 3G, 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 communication component 816 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may 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, the UE800 may be implemented 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 Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for executing the methods described above.

In an exemplary embodiment, a non-transitory computer-readable storage medium is also provided, such as memory 804 including instructions executable by processor 820 of UE800 to generate the above-described method. 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.

As shown in fig. 9, an embodiment of the present disclosure shows a structure of a communication device. For example, the communication device 900 may be provided as a network device. The network device may be gNB, LMF, OAM and/or NWDAF as previously described.

Referring to fig. 9, communication device 900 includes a processing component 922 that further includes one or more processors and memory resources represented by memory 932 for storing instructions, such as applications, executable by processing component 922. The application programs stored in memory 932 may include one or more modules that each correspond to a set of instructions. Further, processing component 922 is configured to execute instructions to perform any of the methods described above as applied to the access device, e.g., at least one of the methods shown in fig. 2A-2C, 3A-3B, and 4A-4C.

The communication device 900 may also include a power supply component 926 configured to perform power management of the communication device 900, a wired or wireless network interface 950 configured to connect the communication device 900 to a network, and an input output (I/O) interface 958. The communication device 900 may operate based on an operating system stored in the memory 932, such as Windows Server TM, mac OS XTM, unixTM, linuxTM, freeBSDTM, or the like.

Other implementations of the disclosed embodiments will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosed embodiments following, in general, the principles of the disclosed embodiments and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosed embodiments being indicated by the following claims.

It is to be understood that the disclosed embodiments are not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the embodiments of the present disclosure is limited only by the appended claims.

Claims

An information processing method, wherein the method is performed by a first communication device, the method comprising:

Receiving a data set from a second communication device;

An artificial intelligence AI/machine learning ML model is trained using the dataset, wherein the AI/ML model is used at least for localization.
The method of claim 1, the dataset comprising at least one of the following:

Measurement results and position information;

Measurement results and indication information;

Measurement results, position information, and indication information;

Measuring results;

Wherein the measurement results include measurement results of positioning reference signals; the position information comprises position information of the measuring equipment and/or position information of the measured equipment; the indication information is used for indicating that the measurement path for obtaining the measurement result is a line-of-sight path LoS or a non-line-of-sight path NLoS.
The method of claim 2, wherein the measurement comprises: a first measurement and a second measurement, the first measurement being different from the second measurement.
A method according to claim 3, wherein the second measurement comprises at least one of:

Reference signal received power RSRP obtained by measuring the positioning reference signal;

A reference signal receiving path power PRSPP obtained by measuring the positioning reference signal;

And measuring the channel impulse response CIR of the positioning reference signal.
The method of any one of claims 1 to 4, wherein the method further comprises:

and sending a request message to the second communication device, wherein the request message is used for requesting the data set from the second communication device.
The method of claim 5, wherein the request message includes descriptive information for the dataset indicating at least one of: the method comprises the steps of data set type, data format, data amount, collection scene, collection position and reference signal configuration corresponding to the data set.
The method of claim 6, wherein the receiving the data set from the second communication device comprises: and receiving the data set provided by the second communication device according to the description information.
The method according to any one of claims 1 to 7, wherein,

The first communication device is a Location Management Function (LMF), and the second communication device is: user equipment UE and/or access network equipment; or alternatively

The first communication equipment is UE, and the second communication equipment is LMF and/or access network equipment; or alternatively

The first communication device is access network equipment, and the second communication device is: UE and/or LMF; or alternatively

The first communication device is used for operation, maintenance and management (OAM), and the second communication device is used for: access network equipment and/or LMF; or alternatively

The first communication device is network data analysis NWDNF, and the second communication device is any one of the following: access network device OAM, LMF, UE.
The method of any of claims 1-8, wherein the second communication device is a UE, the method further comprising: receiving data capacity information sent by UE; wherein the data capability information indicates whether the UE has a capability to provide the data set.
The method of claim 9, wherein the method further comprises: and sending a data capability request message to the UE, wherein the data capability request message is used for requesting to instruct the UE to provide the data capability information.
The method of claim 9 or 10, wherein the UE data capability information is used to indicate at least one of: the method comprises the steps of determining the type of a data set supported by the UE, the data format supported by the UE, the data amount supported by the UE, the collection scene supported by the UE, the collection position supported by the UE and the reference signal configuration corresponding to the data set supported by the UE.
The method of any of claims 1 to 11, wherein the type of dataset is one of:

a first type, the data elements being measurement results and a location information dataset; wherein the measurement result is a measurement result of a positioning reference signal; wherein the location information includes: measuring the position information of the equipment and/or the position information of the equipment to be measured;

A second type, the data elements being data sets of measurement results and indication information; wherein the measurement result is a measurement result of a positioning reference signal; the indication information indicates that the measurement path for obtaining the measurement result is a line-of-sight path LoS or a non-line-of-sight path NLoS;

The third type, the data elements are data sets of measurement results.
The method of claim 3 or 11, wherein the collection scenario is one of: an outdoor scene; an indoor scene; reserving a building scene; a predetermined usage scenario.
The method of claim 3 or 11, wherein the collection location comprises at least one of:

A cell identifier indicating a cell to which the data set is generated;

a base station identifier for indicating a base station corresponding to the generation place of the data set;

A transmitting point identifier indicating a transmitting point corresponding to the place where the data set is generated;

UE location information indicating the UE location at which the UE acquired data.
The method of claim 3 or 11, wherein the reference signal configuration comprises: configuring a downlink positioning reference signal PRS; and configuring an uplink positioning reference signal SRS.
An information processing method, wherein the method is performed by a second communication device, the method comprising:

transmitting the data set to the first communication device; wherein,

The dataset is used for a trained artificial intelligence AL/machine learning ML model, wherein the AI/ML model is used at least for localization.
The method of claim 16, the dataset comprising at least one of:

Measurement results and position information;

Measurement results and indication information;

Measurement results, position information, and indication information;

Measuring results;

Wherein the measurement results include measurement results of positioning reference signals; the position information comprises position information of the measuring equipment and/or position information of the measured equipment; the indication information is used for indicating that the measurement path for obtaining the measurement result is a line-of-sight path LoS or a non-line-of-sight path NLoS.
The method of claim 17, wherein the measurement comprises:

the measurement results include: a first measurement and/or a second measurement; wherein the second measurement is different from the first measurement.
The method of claim 18, wherein the second measurement comprises at least one of:

Reference signal received power RSRP obtained by measuring the positioning reference signal;

A reference signal receiving path power PRSPP obtained by measuring the positioning reference signal;

And measuring the channel impulse response CIR of the positioning reference signal.
The method of any one of claims 16 to 19, wherein the method further comprises: and receiving a request message sent by the first communication device, wherein the request message is used for requesting the data set from the second communication device.
The method of claim 20, wherein the request message includes description information of the dataset indicating at least one of: the method comprises the steps of data set type, data format, data amount, collection scene, collection position and reference signal configuration corresponding to the data set.
The method of claim 21, wherein the transmitting the data set to the first communication device comprises: and transmitting the data set to the first communication device according to the description information.
The method according to any one of claims 17 to 22, wherein,

The first communication device is a Location Management Function (LMF), and the second communication device is: user equipment UE and/or access network equipment; or alternatively

The first communication equipment is UE, and the second communication equipment is LMF and/or access network equipment; or alternatively

The first communication device is access network equipment, and the second communication device is: UE and/or LMF; or alternatively

The first communication device is used for operation, maintenance and management (OAM), and the second communication device is used for: access network equipment and/or LMF; or alternatively

The first communication device is network data analysis NWDNF, and the second communication device is any one of the following: access network device OAM, LMF, UE.
The method of any of claims 17 to 23, wherein the second communication device comprises a UE; the method further comprises the steps of: data capacity information transmitted; wherein the data capability information indicates whether the UE is capable of providing the data set.
The method of claim 24, wherein the method further comprises: and receiving a data capability request message, wherein the data capability request message is used for requesting to instruct the UE to provide the data capability information.
The method of claim 24 or 25, wherein the UE data capability information is further used to indicate at least one of:

The method comprises the steps of determining the type of a data set supported by the UE, the data format supported by the UE, the data amount supported by the UE, the collection scene supported by the UE, the collection position supported by the UE and the reference signal configuration corresponding to the data set supported by the UE.
The method of any of claims 17 to 26, wherein the type of dataset is one of:

A first type, the data elements being data sets of measurement results and location information; wherein the measurement result is a measurement result of a positioning reference signal; wherein the location information includes: measuring the position information of the equipment and/or the position information of the equipment to be measured;

A second type, the data elements being data sets of measurement results and indication information; wherein the measurement result is a measurement result of a positioning reference signal; the indication information indicates that the measurement path for obtaining the measurement result is a line-of-sight path LoS or a non-line-of-sight path NLoS;

The third type, the data elements are data sets of measurement results.
The method of claim 19 or 26, wherein the collection scenario is one of:

An outdoor scene;

An indoor scene;

Reserving a building scene;

A predetermined usage scenario.
The method of claim 19 or 26, wherein the collection location comprises at least one of:

A cell identifier indicating a cell to which the data set is generated;

a base station identifier for indicating a base station corresponding to the generation place of the data set;

A transmitting point identifier indicating a transmitting point corresponding to the place where the data set is generated;

UE location information indicating the UE location at which the UE acquired data.
The method of claim 19 or 26, wherein the reference signal configuration comprises: configuring a downlink positioning reference signal PRS; and configuring an uplink positioning reference signal SRS.
An information processing apparatus, wherein the apparatus comprises:

a receiving module configured to receive a data set from a second communication device;

A training module configured to train an artificial intelligence AI/machine learning ML model using the data set, wherein the AI/ML model is used at least for localization.
An information processing apparatus, wherein the apparatus comprises:

A transmitting module 210 configured to transmit the data set to the first communication device; wherein the data set is for a trained artificial intelligence AL/machine learning ML model, wherein the AI/ML model is for at least localization.
An information processing method, comprising: a first communication device and a second communication device;

The first communication device is configured to receive a data set from a second communication device and to train an artificial intelligence AI/machine learning ML model using the data set, wherein the AI/ML model is used at least for positioning

The second communication device is configured to send a data set to the first communication device, wherein the data set is used for a trained artificial intelligence, AL, machine learning, ML, model, wherein the AI/ML model is used at least for localization.
A communication device comprising a processor, a transceiver, a memory and an executable program stored on the memory and capable of being run by the processor, wherein the processor performs the method as provided in any one of claims 1 to 15, or 16 to 30 when the executable program is run by the processor.
A computer storage medium storing an executable program; the executable program, when executed by a processor, is capable of implementing the method as provided in any one of claims 1 to 15, or 16 to 30.
A communication system, wherein the communication system comprises:

a first communication device configured to perform the method of any of claims 1 to 15;

a second communication device configured to perform the method of any of claims 16 to 30.