Communication method and apparatus

Abstract

The present application provides a communication method, which can be applied to an AI-based positioning scenario. In the method, an AI-based positioning model deployed on a location management function network element uses a channel measurement result reported by a channel measurement network element (or a channel feature extracted from a channel measurement result) as an input. The location management function network element determines the reporting granularity of one or more features of at least one path in a channel measurement report according to different positioning precision requirements, and indicates the information of the reporting granularity of the feature to the channel measurement network element, or the channel measurement network element obtains the information of a positioning precision requirement from the location management function network element, and determines the reporting granularity of one or more features of at least one path in the channel measurement report on the basis of the positioning precision requirement and the channel measurement result. Thus, the channel measurement network element can be supported to report, under different channel conditions and / or according to different positioning precision requirements, the channel measurement report carrying the reporting granularities of different features, so as to support the requirement of the channel measurement report conforming to a channel condition.

Description

Method and apparatus of communication TECHNICAL FIELD

[0001] Embodiments of the present application relate to the field of artificial intelligence (AI), and more specifically, to a method and apparatus of communication. BACKGROUND

[0002] In AI-based positioning technology, an AI positioning model is usually deployed in a location management function (LMF), and the AI positioning model takes channel measurement results reported by a channel measurement network element as input and takes a location of a terminal device as output. Therefore, the channel measurement network element usually needs to report a channel measurement report to the location management function network element. However, the current channel measurement report cannot meet different reporting requirements, resulting in reduced positioning accuracy. SUMMARY

[0003] The present application provides a method and apparatus of communication, which can be applied to an AI positioning scenario to support a channel measurement report to meet reporting requirements and thus improve positioning accuracy.

[0004] In a first aspect, a method of communication is provided, which can be executed by a network element, a chip, a chip system, a hardware circuit (such as a logic circuit or an integrated circuit, etc.), a software module, or a combination of a hardware circuit and a software module. Hereinafter, the first network element is taken as an example to illustrate the method. The method comprises:

[0005] sending first indication information to a location management function network element, the first indication information indicating a reporting granularity of one or more features of at least one path in a channel measurement report;

[0006] Optionally, the first indication information is carried in the channel measurement report.

[0007] sending the channel measurement report to the location management function network element, the channel measurement report comprising one or more of the following: a number of the at least one path; and one or more features of the at least one path, the one or more features having the reporting granularity indicated by the first indication information.

[0008] During the research of the present application, it is found that in the existing positioning scenario, the interaction of multipath information between the location management function network element where the AI positioning model is deployed and the channel measurement network element that reports the channel measurement report does not consider that different channel measurement results have different degrees of influence on the positioning performance (e.g., positioning inference accuracy) of the AI positioning model under different channel conditions, resulting in inconsistencies in the understanding of the measurement report between the first network element and the location management function network element. The research results of the present application show that under different channel conditions, the reporting granularity related information of one or more features of at least one path in the channel measurement report reported by the channel measurement network element is different, and the influence on the positioning inference accuracy of the AI positioning model is different. For example, for the same AI positioning model, if the reporting granularity of the time of arrival feature of at least one path in the channel measurement report reported by the channel measurement network element is 5ns under one channel condition, the positioning accuracy can reach sub-meter level; and if the reporting granularity of the time of arrival feature of at least one path in the channel measurement report reported by the channel measurement network element is 10ns under another channel condition, the positioning accuracy can also reach sub-meter level. Therefore, the present application proposes that under the premise of ensuring the positioning accuracy requirement, for different channel conditions, the channel measurement report adopts a flexible reporting granularity of one or more features of at least one path, so as to support the requirement of consistent understanding of the measurement report between the first network element and the location management function network element under different channel conditions.

[0009] In the present application, the reporting granularity of one or more features of at least one path can also be referred to as reporting unit, reporting unit, reporting step, etc.

[0010] In the present application, for the convenience of description, the channel measurement result of the channel information of at least one path represented by the reporting granularity of one or more features is referred to as the reporting granularity related channel measurement result.

[0011] In the present application, the one or more features of at least one path include one or more of the following: time of arrival feature, power feature, amplitude feature, phase feature, angle feature, delay spread feature, or angle spread feature.

[0012] In the present application, the reporting granularity of the time of arrival feature can be 1Ts (one OFDM (Orthogonal Frequency Division Multiplexing) symbol length), 1 minute, 1 second, 1 millisecond, 1 microsecond, 1 nanosecond, 1 picosecond; further, it can also be refined to a fraction (e.g., n / m, n is a positive integer less than m, m is a positive integer greater than 1) times, an integer (e.g., n, n is a positive integer greater than or equal to 1) times of each of the above reporting granularities.

[0013] In the present application, the reporting granularity of the power feature can be 1 picowatt (pW), 1 nanowatt (nW), 1 microwatt (μW), 1 milliwatt (mW), 1 watt (W), 1000 watts (kW), 1 megawatt (mW), 1 dBm (the dB value of the strength of a signal relative to 1 mW), 1 decibel (dB; 1 bel when the power ratio of two sound signals is 10 to 1, and 1 bel is equal to 10 decibels), 1 dBc (the ratio of the power of a signal to the carrier power); further, each of the above reporting granularities can be refined: fractional (e.g., n / m, n is a positive integer less than m, and m is a positive integer greater than 1) times, integer (e.g., n, n is a positive integer greater than or equal to 1) times.

[0014] In the present application, the reporting granularity of the amplitude feature can be 1, and further, the reporting granularity can be refined: fractional (e.g., n / m, n is a positive integer less than m, and m is a positive integer greater than 1) times, integer (e.g., n, n is a positive integer greater than or equal to 1) times; or any of the above values can be represented by an absolute value or a modulus function of a complex number.

[0015] In the present application, the reporting granularity of the phase feature can be 1 degree, or any value between [0, 2π], and further, the reporting granularity can be refined: fractional (e.g., n / m, n is a positive integer less than m, and m is a positive integer greater than 1) times, integer (e.g., n, n is a positive integer greater than or equal to 1) times; or any of the above values can be represented by a trigonometric function of an angle or a combination of angle trigonometric functions.

[0016] In the present application, the reporting granularity of the angle feature can be 1 degree (1°, degree, degree of arc), 1 radian (radian), 1 minute of arc (arc minute or minute arc, abbreviated as arcmin), 1 minute of angle (MOA), 1 arc second / angle second, or any value between [0, 2π]; and further, each of the above reporting granularities can be refined: fractional (e.g., n / m, n is a positive integer less than m, and m is a positive integer greater than 1) times, integer (e.g., n, n is a positive integer greater than or equal to 1) times.

[0017] In the present application, the reporting granularity of the delay spread feature can be 1 Ts (one OFDM (Orthogonal Frequency Division Multiplexing) symbol length), 1 minute, 1 second, 1 millisecond, 1 microsecond, 1 nanosecond, 1 picosecond, etc. Further, it can also be a refinement of each of the above reporting granularities: fractional (for example, n / m, n is a positive integer less than m, and m is a positive integer greater than 1) times, integer (for example, n, n is a positive integer greater than or equal to 1) times.

[0018] In the present application, the reporting granularity of the angle spread feature can be 1 degree (1°, degree, degree of arc), 1 radian (radian), 1 arc minute (minute of arc, arc minute or minute arc, abbreviated as arcmin), 1 angle minute (minute of angle, abbreviated as MOA), 1 arc second / angle second, or any value between [0, 2π]; further, it can also be a refinement of each of the above reporting granularities: fractional (for example, n / m, n is a positive integer less than m, and m is a positive integer greater than 1) times, integer (for example, n, n is a positive integer greater than or equal to 1) times.

[0019] In the technical solution, the first network element (i.e., the channel measurement network element) sends first indication information to the location management function network element, and the first indication information indicates the reporting granularity of one or more features of at least one path in the channel measurement report, for example, the reporting granularity of one or more features of one and / or multiple paths carried in the channel measurement report. Since in the present application, the first network element can send the reporting granularity of one or more features of at least one path to the location management function network element under different channel conditions, the first network element indicates the reporting granularity (which can also be understood as the reporting unit, the reporting unit, the reporting step) of one or more features of at least one path used by the channel measurement report for reporting by the first indication information, so that the location management function decodes the channel measurement report according to the first indication information to obtain multipath information. It can be seen that the technical solution of the present application can indicate a flexible reporting granularity, that is, to adapt to the adjustment of the reporting granularity under different channel conditions, thereby supporting the positioning accuracy requirement as a whole.

[0020] In combination with the first aspect, in some implementations of the first aspect, the method further includes:

[0021] reporting granularity of one or more characteristics of the at least one path is determined according to the positioning accuracy requirement and / or the channel measurement result. In this implementation, the first network element determines the reporting granularity of one or more characteristics of the at least one path in the channel measurement report according to the positioning accuracy requirement and / or the channel condition obtained by analyzing the channel measurement result, so as to support the requirement of the positioning accuracy under the premise of meeting the positioning accuracy requirement.

[0022] In combination with the first aspect, in some implementations of the first aspect, the determining the reporting granularity of one or more characteristics of the at least one path according to the positioning accuracy requirement and the channel measurement result comprises:

[0023] The reporting granularity of one or more characteristics of the at least one path is determined according to a mapping relationship of any one or more of the following:

[0024] The mapping relationship between the reporting granularity of one or more characteristics of the at least one path and the corresponding identification index;

[0025] The mapping relationship between the reporting granularity of one or more characteristics of the at least one path and the positioning accuracy level;

[0026] The mapping relationship between the reporting granularity of one or more characteristics of the at least one path and the channel condition;

[0027] The positioning accuracy level is related to the positioning accuracy requirement, and the channel condition is related to the channel measurement result.

[0028] In this implementation, when determining the reporting granularity of one or more characteristics of the at least one path in the channel measurement report, the first network element can specifically determine the reporting granularity of one or more characteristics of the at least one path in the channel measurement report corresponding to the current positioning accuracy level and the current channel condition according to the mapping relationship between the positioning accuracy level and / or the channel condition and the reporting granularity of one or more characteristics of the at least one path.

[0029] In combination with the first aspect, in some implementations of the first aspect, the first network element is a channel measurement network element, and the method further comprises:

[0030] Receiving information of the positioning accuracy requirement from the location management function network element.

[0031] For example, the information of the positioning accuracy requirement can be carried in a measurement request or in a different message from the measurement request.

[0032] In the implementation, the location management function network element sends corresponding positioning accuracy requirements to the first network element under different positioning accuracy requirements. The first network element correspondingly reports a channel measurement report carrying one or more characteristics of at least one path under different positioning accuracy requirements, which helps to improve positioning accuracy.

[0033] In a second aspect, a method for receiving information is provided, which can be executed by a network element, a chip, a chip system, a hardware circuit (such as a logic circuit or an integrated circuit, etc.), a software module, or a combination of a hardware circuit and a software module. The following takes a location management function network element as an example to illustrate the method. The method comprises:

[0034] receiving first indication information from a first network element, the first indication information indicating a reporting granularity of one or more characteristics of at least one path in a channel measurement report;

[0035] Optionally, the first indication information is carried in the channel measurement report.

[0036] receiving the channel measurement report from the first network element, the channel measurement report comprising one or more of the following: a number of the at least one path; and one or more characteristics of the at least one path, the one or more characteristics having the reporting granularity indicated by the first indication information.

[0037] In combination with the second aspect, in some implementations of the second aspect, the reporting granularity of the one or more characteristics of the at least one path is based on positioning accuracy requirements and / or channel measurement results.

[0038] In combination with the second aspect, in some implementations of the second aspect,

[0039] The determining of the reporting granularity of the one or more characteristics of the at least one path according to the positioning accuracy requirements and / or the channel measurement results comprises:

[0040] determining the reporting granularity of the one or more characteristics of the at least one path according to a mapping relationship of any one or more of the following:

[0041] a mapping relationship between the reporting granularity of the one or more characteristics of the at least one path and a corresponding identification index;

[0042] a mapping relationship between the reporting granularity of the one or more characteristics of the at least one path and a positioning accuracy level;

[0043] a mapping relationship between the reporting granularity of the one or more characteristics of the at least one path and a channel condition;

[0044] wherein the positioning accuracy level is related to the positioning accuracy requirements, and the channel condition is related to the channel measurement results.

[0045] With reference to the second aspect, in some implementations of the second aspect, the method further includes:

[0046] sending, to the first network element, information of the positioning accuracy requirement.

[0047] A third aspect provides a method of communication, which can be performed by a network element, a chip, a chip system, a hardware circuit (e.g., a logic circuit or an integrated circuit, etc.), a software module, or a combination of a hardware circuit and a software module. The following takes a location management function network element as an example for illustration. The method includes:

[0048] determining, according to a positioning-related requirement, a reporting granularity of one or more features of at least one path in a channel measurement report, the positioning-related requirement including at least a positioning accuracy requirement;

[0049] sending third indication information, the third indication information indicating the reporting granularity of the one or more features of the at least one path;

[0050] receiving the channel measurement report.

[0051] Based on the same reasons as described in the first aspect, in the method of the second aspect, the location management function network element can determine, according to a positioning-related requirement, a reporting granularity of one or more features of at least one path in a channel measurement report, and indicate the reporting granularity to the first network element. Under different positioning-related requirements, the location management function network element determines different reporting granularities of the one or more features of the at least one path. Based on the indication of the location management function network element, the first network element reports the channel measurement report carrying the features of each path reported at the reporting granularity of the one or more features of the at least one path, which can support flexible reporting granularity, thereby supporting the requirement of positioning accuracy.

[0052] With reference to the third aspect, in some implementations of the third aspect, the determining, according to a positioning-related requirement, a reporting granularity of one or more features of at least one path in a channel measurement report includes:

[0053] determining, according to a mapping relationship between the positioning-related requirement and the reporting granularity of the one or more features of the at least one path, the reporting granularity of the one or more features of the at least one path.

[0054] With reference to the third aspect, in some implementations of the third aspect, the reporting granularity of the one or more features of the at least one path in the channel measurement report can also be determined according to an identification index.

[0055] In a fourth aspect, a method of communication is provided that can be performed by a network element, a chip, a chip system, a hardware circuit (e.g., a logic circuit or an integrated circuit, etc.), a software module, or a combination of hardware circuit and software module. The method is described below with reference to a first network element. The method includes:

[0056] receiving third indication information from a location management function network element, the third indication information indicating a reporting granularity of one or more characteristics of at least one path in a channel measurement report,

[0057] the channel measurement report including one or more of:

[0058] a number of the at least one path;

[0059] the one or more characteristics of the at least one path having the reporting granularity indicated by the first indication information.

[0060] based on the third indication information, sending the channel measurement report to the location management function network element.

[0061] In some implementations of the fourth aspect, the reporting granularity of the one or more characteristics of the at least one path is determined according to a positioning-related requirement, the positioning-related requirement including at least a positioning accuracy requirement.

[0062] In some implementations of the fourth aspect, the reporting granularity of the one or more characteristics of the at least one path in the channel measurement report can also be determined according to an identification index.

[0063] In a fifth aspect, a method of communication is provided that includes:

[0064] a first network element sending first indication information to a location management function network element, the first indication information indicating a reporting granularity of one or more characteristics of at least one path in a channel measurement report,

[0065] the channel measurement report including one or more of:

[0066] a number of the at least one path;

[0067] the one or more characteristics of the at least one path having the reporting granularity indicated by the first indication information.

[0068] the location management function network element receiving the first indication information from the first network element.

[0069] In some implementations of the fifth aspect, the method further includes:

[0070] The location management function network element decodes the channel measurement report according to the first indication information to obtain the channel measurement result related to the reporting granularity.

[0071] In a sixth aspect, the present application provides a communication method, comprising:

[0072] The location management function network element determines the reporting granularity of one or more features of at least one path in the channel measurement report according to the positioning-related requirement, wherein the positioning-related requirement at least comprises a positioning accuracy requirement,

[0073] The channel measurement report comprises one or more of the following:

[0074] The number of the at least one path;

[0075] The one or more features of the at least one path have the reporting granularity indicated by the first indication information.

[0076] The first network element receives the third indication information;

[0077] The first network element sends the channel measurement report to the location management function network element based on the third indication information;

[0078] The location management function network element receives the channel measurement report from the first network element.

[0079] In the method of any one of the first aspect to the sixth aspect, or some implementations of any aspect, the one or more features of the at least one path comprise one or more of the following: a time of arrival feature, a power feature, an amplitude feature, a phase feature, an angle feature, a delay spread feature, or an angle spread feature.

[0080] In this implementation, by obtaining the channel measurement result, i.e., one or more of the above features of the one or more paths, the current channel condition is analyzed to determine the reporting granularity of the one or more features of the at least one path under the current channel condition on the premise of meeting the positioning accuracy requirement.

[0081] In the method of any one of the first aspect to the sixth aspect, or some implementations of any aspect, the channel measurement result comprises one or more of the following:

[0082] a delay spread characteristic, an angle spread characteristic, an angle-delay-power characteristic, a number of paths whose energy is greater than a preset energy proportion of the energy of a first path, a Rake factor, a Doppler frequency measurement result, a LOS probability, an interference level of a full frequency band or a sub-band, or a RSRP of a full frequency band or a sub-band.

[0083] The implementation provides multiple channel measurement results that can reflect channel conditions. By analyzing the channel measurement results, current channel conditions can be known. In different channel conditions, a channel measurement report carrying reporting granularity of one or more characteristics of at least one path is selected for reporting, so that the positioning accuracy requirement can be guaranteed.

[0084] In a seventh aspect, a communication apparatus is provided. The communication apparatus can include a module corresponding to each of the methods / operations / steps / actions of the first aspect to the fourth aspect, or any implementation of any of the aspects. The module can be a hardware circuit, a software, or a combination of the hardware circuit and the software. The communication apparatus can be a first network element or a location function network element, or a chip or circuit for the first network element or the location function network element.

[0085] In an implementation, the communication apparatus is a communication device. The communication device can include a communication unit and / or a processing unit. The communication unit can be a transceiver or an input / output interface. The processing unit can be at least one processor. Optionally, the transceiver can be a transceiver circuit. Optionally, the input / output interface can be an input / output circuit. In an example, the communication apparatus is a communication device, such as a terminal device, an access network device, or a location management function network element.

[0086] In another implementation, the communication apparatus is a chip, a chip system, or a circuit for a communication device. The communication unit can be an input / output interface, an interface circuit, an input / output circuit, a pin, or a related circuit on the chip, the chip system, or the circuit. The processing unit can be at least one processor, a processing circuit, or a logic circuit.

[0087] In an eighth aspect, a communication apparatus is provided. The communication apparatus includes a processor configured to execute a computer program or instructions stored in a memory to perform the method provided in any of the first aspect to the fourth aspect, or any implementation of any of the aspects. Optionally, the communication apparatus further includes the memory. The communication apparatus can be a first network element or a location management function network element, or a chip or circuit for the first network element or the location management function network element.

[0088] In a ninth aspect, the present application provides a communication apparatus, comprising a processor and a communication interface, configured to execute the method in any of the first aspect to the fourth aspect, or any implementation manner of any of the first aspect to the fourth aspect.

[0089] In a tenth aspect, the present application further provides a computer program, which, when executed on a computer, causes the computer to perform the method in any of the first aspect, the second aspect, the third aspect or the fourth aspect, or any implementation manner of any of the first aspect to the fourth aspect.

[0090] In an eleventh aspect, the present application further provides a computer program product, comprising instructions, which, when executed on a computer, causes the computer to perform the method in any of the first aspect to the fourth aspect, or any implementation manner of any of the first aspect to the fourth aspect.

[0091] In a twelfth aspect, the present application further provides a computer readable storage medium, having a computer program or instructions stored therein, which, when executed on a computer, causes the computer to perform the method in any of the first aspect to the fourth aspect, or any implementation manner of any of the first aspect to the fourth aspect.

[0092] In a thirteenth aspect, the present application further provides a chip, configured to read a computer program stored in a memory, execute the method in any of the first aspect to the fourth aspect, or any implementation manner of any of the first aspect to the fourth aspect, or comprising a circuit configured to execute the method in any of the first aspect to the fourth aspect, or any implementation manner of any of the first aspect to the fourth aspect.

[0093] In a fourteenth aspect, the present application further provides a chip system, comprising a processor configured to support a device to implement the method in any of the first aspect to the fourth aspect, or any implementation manner of any of the first aspect to the fourth aspect. In a possible design, the chip system further comprises a memory configured to store program and data necessary for the device. The chip system can be composed of a chip only, or a chip and other discrete components.

[0094] In a fifteenth aspect, the present application provides a communication system, comprising the first network element and the location management function network element.

[0095] Exemplarily, the first network element can comprise an access network device and / or a terminal device.

[0096] The technical effects of the solutions provided by the fifth aspect to the fifteenth aspect can refer to the descriptions of the technical effects of the corresponding solutions in the first aspect to the fourth aspect, and will not be described again. BRIEF DESCRIPTION OF DRAWINGS

[0097] FIG. 1a is a schematic diagram of a communication system suitable for use with embodiments of the application.

[0098] FIG. 1b is a schematic diagram of another communication system suitable for use with embodiments of the application.

[0099] FIG. 2 is a schematic diagram of a possible application framework in a communication system.

[0100] FIG. 3 is a schematic diagram of another possible application framework in a communication system.

[0101] FIG. 4 is an example of a wireless positioning system suitable for use with embodiments of the application.

[0102] FIG. 5 is a schematic diagram of a network element involved in embodiments of the application.

[0103] FIG. 6 is a schematic diagram of an AI / ML network element or module.

[0104] FIG. 7 is a schematic flowchart of a method 700 of transmitting information provided by embodiments of the application.

[0105] FIG. 8 is a schematic flowchart of a method 800 of transmitting information provided by embodiments of the application.

[0106] FIG. 9 is an example 900 of transmitting information provided by embodiments of the application applied to uplink positioning.

[0107] FIG. 10 is an example 1000 of transmitting information provided by embodiments of the application applied to downlink positioning.

[0108] FIG. 11 is an example 1100 of transmitting information provided by embodiments of the application applied to uplink positioning.

[0109] FIG. 12 is an example 1200 of transmitting information provided by embodiments of the application applied to downlink positioning.

[0110] FIG. 13 is a schematic block diagram of a communication apparatus 1300 provided by embodiments of the application.

[0111] FIG. 14 is a schematic block diagram of another communication apparatus 1400 provided by embodiments of the application. DETAILED DESCRIPTION

[0112] The following describes some terms involved in the application to facilitate understanding by those skilled in the art.

[0113] (1) AI: Let the machine have human intelligence, apply computer hardware and software to simulate some intelligent behavior of human beings, including machine learning and many other methods.

[0114] (2) Machine learning (ML): Learn models or rules from raw data, there are many different machine learning methods, such as neural networks, decision trees, support vector machines, etc.

[0115] (3) AI model: Here refers to a function model that maps a certain dimension of input to a certain dimension of output, and the model parameters are obtained by machine learning training. The type of AI model can be a neural network, a linear regression model, a decision tree model, a support vector machine (SVM), a Bayesian network, a Q learning model, or other machine learning models.

[0116] (4) Neural network (NN): Here refers to an artificial neural network, which is a mathematical model that simulates the behavior characteristics of animal neural networks and performs distributed parallel information processing, and is a special form of AI model.

[0117] (5) Deep neural network (DNN): A neural network with multiple hidden layers.

[0118] (6) Deep learning (DL): Machine learning using deep neural networks.

[0119] (7) Auto-encoders (AE) model: Also known as a bilateral model, a collaborative model, a dual model or a two-side model, etc. The two-side model refers to a model composed of multiple sub-models, and the multiple sub-models constituting the model need to be matched with each other, and the multiple sub-models can be deployed in different nodes.

[0120] (8) CSI: Also known as channel information or channel environment information, it is an information that can reflect the characteristics and quality of the channel.

[0121] (9) Model training: By selecting a suitable loss function, the model parameters are trained using an optimization algorithm to minimize the loss function value.

[0122] (10) Loss function: Used to measure the difference between the predicted value of the model and the true value.

[0123] The embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.

[0124] It should be understood that in the description of the present application, “at least one” means one or more, and “multiple” means two or more. In addition, the words “first”, “second”, and the like, unless otherwise specified, are only used to distinguish descriptions, and cannot be understood as indicating or implying relative importance, nor indicating or implying order.

[0125] The technical solutions provided by the present application can be applied to various communication systems, for example: a fifth generation (5th generation, 5G) or new radio (new radio, NR) system, a long term evolution (long term evolution, LTE) system, an LTE FDD system, an LTE time division duplex (time division duplex, TDD) system, a wireless local area network (wireless local area network, WLAN) system, a satellite communication system, a future communication system such as a sixth generation (6th generation, 6G) mobile communication system, or a fusion system of multiple systems, etc. The technical solutions provided by the present application can also be applied to device to device (device to device, D2D) communication, vehicle to everything (vehicle to everything, V2X) communication, machine to machine (machine to machine, M2M) communication, machine type communication (machine type communication, MTC), and internet of things (internet of things, IoT) communication system or other communication systems.

[0126] A network element in a communication system can send a signal to another network element or receive a signal from another network element. Wherein the signal can include information, signaling or data, etc. Wherein, the network element can also be replaced by entity, network entity, device, communication device, communication module, node, communication node, etc. In the present disclosure, the network element is taken as an example for description. For example, the communication system can include at least one terminal device and at least one network device. The network device can send a downlink signal to the terminal device, and / or the terminal device can send an uplink signal to the network device. It can be understood that the terminal device in the present disclosure can be replaced by a first network element, and the network device can be replaced by a second network element, both of which perform the corresponding communication method in the present disclosure.

[0127] As shown in FIG. 1a, FIG. 1a is a schematic diagram of a communication system applicable to the communication method of embodiments of the present application. The communication system can include at least one network device, for example, network device 1 shown in FIG. 1a; the communication system can also include at least one terminal device, for example, terminal device 120 and terminal device 130 shown in FIG. 1a. The network device 1 and the terminal devices (for example, terminal device 120 and terminal device 130) can communicate through a wireless link. The communication devices in the communication system, for example, the network device 1 and the terminal device 120, can communicate through multi-antenna technology.

[0128] In embodiments of the present application, the terminal device can also be referred to as UE, access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent or user equipment. Among them, UE is the abbreviation of user equipment, which is well known to those skilled in the art.

[0129] The terminal device can be a device that provides voice / data, for example, a handheld device with wireless connection function, a vehicle-mounted device, etc. At present, some examples of terminals are: mobile phone, tablet computer, notebook computer, palm computer, mobile internet device (MID), wearable device, virtual reality (VR) device, augmented reality (AR) device, wireless terminal in industrial control, wireless terminal in self driving, wireless terminal in remote medical surgery, wireless terminal in smart grid, wireless terminal in transportation safety, wireless terminal in smart city, wireless terminal in smart home, cellular phone, cordless phone, session initiation protocol (SIP) phone, wireless local loop (WLL) station, personal digital assistant (PDA), handheld device with wireless communication function, computing device or other processing device connected to wireless modem, wearable device, terminal device in 5G network or terminal device in future evolved public land mobile network (PLMN), etc. Embodiments of the present application are not limited thereto.

[0130] By way of example and not limitation, in embodiments of the present application, the terminal device can also be a wearable device. The wearable device can also be referred to as a wearable smart device, which is a general term for devices that are designed and developed by applying wearable technology to daily wear, such as glasses, gloves, watches, clothes, and shoes. The wearable device is a portable device that is directly worn on the body or integrated into the clothes or accessories of the user. The wearable device is not only a hardware device, but also has powerful functions through software support and data interaction and cloud interaction. The general wearable smart device includes a full function, a large size, and can realize complete or partial functions without relying on a smart phone, such as a smart watch or smart glasses, and focuses on a certain application function and needs to cooperate with other devices such as a smart phone, such as various smart wristbands and smart jewelry for monitoring vital signs.

[0131] In embodiments of the present application, the device for implementing the function of the terminal device can be a terminal device, can be a device capable of supporting the terminal device to implement the function, such as a chip, a chip system, or a processor, can also be a logic node, a logic module, or software capable of implementing all or part of the function of the terminal device, and the device can be installed in the terminal device or used with the terminal device. In embodiments of the present application, the chip system can be composed of a chip, or can include a chip and other discrete devices. In embodiments of the present application, only the device for implementing the function of the terminal device is taken as an example for description, and the scheme of embodiments of the present application is not limited.

[0132] The network side in the embodiments of the present application can include a network device, wherein the network device includes a device for communicating with a terminal device, and the network device includes an access network device or a radio access network device, such as a base station; or an operation, management and maintenance (OAM) device or a CN device. The access network device in the embodiments of the present application can refer to a radio access network (RAN) node (or device) that accesses a terminal device to a wireless network. The base station can broadly cover various names in the following or replace the following names, such as: node B (NodeB), evolved NodeB (eNB), next generation NodeB (gNB), relay station, access point, transmitting and receiving point (TRP), transmitting point (TP), primary station, secondary station, motor slide retainer (MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), radio unit (RU), positioning node, etc. The base station can be a macro base station, a micro base station, a relay node, a donor node or the like, or a combination thereof. The base station can also refer to a communication module, modem or chip used in the foregoing devices or apparatuses. The base station can also be a mobile switching center and a device assuming a base station function in D2D, V2X, M2M communication, a network side device in a 6G network, a device assuming a base station function in a future communication system, etc. The base station can support networks of the same or different access technologies. Optionally, the RAN node can also be a server, a wearable device, a vehicle or a vehicle-mounted device, etc. For example, the access network device in the V2X technology can be a road side unit (RSU). The embodiments of the present application do not limit the specific technology and specific device form of the network device.

[0133] The base station can be fixed or mobile. For example, a helicopter or a drone can be configured to act as a mobile base station, and one or more cells can move according to the location of the mobile base station. In other examples, the helicopter or the drone can be configured to serve as a device communicating with another base station.

[0134] In a possible scenario, the wireless access network device can also be a module or unit that completes the function of the base station part, for example, can be a CU, and can also be a DU. In another possible scenario, a plurality of wireless access network devices cooperate to assist the terminal to implement wireless access, and different wireless access network devices respectively implement part of the function of the base station. For example, the wireless access network device can be a CU, a DU, a CU-control plane (CP), a CU-user plane (UP), or an RU, etc. The CU and the DU can be separately arranged, or can be included in the same network element, for example, in a BBU. The RU can be included in a radio frequency device or a radio frequency unit, for example, included in an RRU, an AAU, or an RRH.

[0135] In different systems, the CU (or CU-CP and CU-UP), DU, or RU can also have different names, but those skilled in the art can understand their meanings. For example, in an ORAN system, the CU can also be referred to as an open central unit (O-CU), the DU can also be referred to as an open distributed unit (O-DU), the CU-CP can also be referred to as an O-CU-CP, the CU-UP can also be referred to as an O-CU-UP, and the RU can also be referred to as an O-RU. For the convenience of description, the DU and the RU are taken as examples for description in this application. Any one of the DU and the RU in this application can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module. The embodiments of this application do not limit the specific technology and specific device form adopted by the wireless access network device. For the convenience of description, the base station is taken as an example of the wireless access network device for description hereinafter. Wherein, ORAN is the abbreviation of open radio access network, which is well known to those skilled in the art.

[0136] The RAN node can support one or more types of fronthaul interfaces, different fronthaul interfaces respectively corresponding to DUs and RUs having different functions. If the fronthaul interface between the DU and the RU is a common public radio interface (CPRI), the DU is configured to implement one or more of the baseband functions, and the RU is configured to implement one or more of the radio frequency functions. If the fronthaul interface between the DU and the RU is another interface, which, relative to the CPRI, moves one or more of the partial baseband functions of the downlink and / or uplink, such as, for the downlink, precoding, digital beamforming (BF), or one or more of inverse fast Fourier transform (IFFT) / adding a cyclic prefix (CP), from the DU to the RU for implementation, and for the uplink, digital BF, or one or more of fast Fourier transform (FFT) / removing the CP, from the DU to the RU for implementation. In a possible implementation, the interface can be an enhanced common public radio interface (eCPRI). Under the eCPRI architecture, the splitting manner between the DU and the RU is different, corresponding to different categories (Cat) of eCPRI, such as eCPRI Cat A, B, C, D, E, and F.

[0137] Taking the eCPRI Cat A as an example, for downlink transmission, the splitting is layer mapping, the DU is configured to implement one or more of the functions before the layer mapping (i.e., one or more of encoding, rate matching, scrambling, modulation, and layer mapping), and one or more of the other functions after the layer mapping (e.g., resource element (RE) mapping, digital BF, or IFFT / adding a CP) are moved to the RU for implementation. For uplink transmission, the splitting is RE demapping, the DU is configured to implement one or more of the functions before the demapping (i.e., one or more of decoding, de-rate matching, de-scrambling, de-modulation, inverse discrete Fourier transform (IDFT), channel equalization, and RE demapping), and one or more of the other functions after the demapping (e.g., digital BF or FFT / removing the CP) are moved to the RU for implementation. It can be understood that the function description of the DU and the RU corresponding to various types of eCPRI can refer to the eCPRI protocol, and will not be described here.

[0138] In the embodiments of the present application, the apparatus for implementing the function of the network device can be a network device, or an apparatus capable of supporting the network device to implement the function, such as a chip system, a hardware circuit, a software module, or a hardware circuit plus a software module, or a logic node, a logic module, or software capable of implementing all or part of the function of the network device. The apparatus can be installed in the network device or used in combination with the network device. In the embodiments of the present application, only the apparatus for implementing the function of the network device is taken as an example for description, and the scheme of the embodiments of the present application is not limited in this way.

[0139] The network device and / or the terminal device can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; can also be deployed on water; and can also be deployed on airplanes, balloons and satellites in the air. The scenarios in which the network device and the terminal device are located are not limited in the embodiments of the present application. In addition, the terminal device and the network device can be hardware devices, or software functions running on special hardware, or software functions running on general hardware, such as virtualized functions instantiated on a platform (for example, a cloud platform), or entities including special or general hardware devices and software functions. The specific forms of the terminal device and the network device are not limited in the present application.

[0140] In a wireless communication network, for example, in a mobile communication network, the services supported by the network are increasingly diverse, and therefore the needs to be met are increasingly diverse. For example, the network needs to be able to support ultra-high rates, ultra-low latencies, and / or ultra-large connections. This feature makes network planning, network configuration, and / or resource scheduling increasingly complex. In addition, as the functions of the network become increasingly powerful, for example, supporting increasingly high frequency spectrums, supporting high-order multiple input multiple output (MIMO) technology, supporting beamforming, and / or supporting new technologies such as beam management, network energy saving has become a hot research topic. These new needs, new scenarios and new features bring unprecedented challenges to network planning, operation and efficient operation. In order to meet this challenge, artificial intelligence technology can be introduced into the wireless communication network, thereby realizing network intelligentization.

[0141] In order to support AI technology in the wireless network, an AI node can also be introduced into the network.

[0142] Optionally, the AI node can be deployed in one or more of the following positions in the communication system: an access network device, a terminal device, or a core network device, etc., or the AI node can also be deployed separately, for example, in a position other than any of the above devices, such as a host or a cloud server of an over the top (OTT) system. The AI node can communicate with other devices in the communication system, which can be one or more of the following: a network device, a terminal device, or a network element of a core network, etc.

[0143] It can be understood that the number of AI nodes is not limited in the present application. For example, when there are multiple AI nodes, the multiple AI nodes can be divided based on functions, such as different AI nodes being responsible for different functions.

[0144] It can also be understood that the AI node can be a separate device, can be integrated into the same device to implement different functions, or can be a network element in a hardware device, or can be a software function running on a dedicated hardware, or a virtualized function instantiated on a platform (e.g., a cloud platform), and the specific form of the AI node is not limited in the present application.

[0145] The AI node can be an AI network element or an AI module.

[0146] As shown in FIG. 1b, FIG. 1b is a schematic diagram of another communication system suitable for the communication method of the embodiments of the present application. Compared with the communication system shown in FIG. 1a, the communication system shown in FIG. 1b further includes an AI network element 140. The AI network element 140 is configured to perform AI-related operations, such as constructing a training data set or training an AI model, etc.

[0147] In a possible implementation, the network device 110 can send data related to the training of the AI model to the AI network element 140, and the AI network element 140 can construct a training data set and train an AI model. For example, the data related to the training of the AI model can include data reported by the terminal device. The AI network element 140 can send the result of the AI model-related operation to the network device 110 and forward it to the terminal device through the network device 110. For example, the result of the AI model-related operation can include at least one of the following: a trained AI model, an evaluation result or a test result of the model, etc. Exemplarily, part of the trained AI model can be deployed on the network device 110, and the other part can be deployed on the terminal device. Alternatively, the trained AI model can be deployed on the network device 110. Or, the trained AI model can be deployed on the terminal device.

[0148] It should be understood that FIG. 1b only illustrates an example in which the AI network element 140 is directly connected with the network device 110, and in other scenarios, the AI network element 140 can also be connected with a terminal device. Alternatively, the AI network element 140 can be connected with both the network device 110 and the terminal device. Alternatively, the AI network element 140 can also be connected with the network device 110 through a third-party network element. The embodiments of the present application do not limit the connection relationship between the AI network element and other network elements.

[0149] The AI network element 140 can also be arranged as a module in the network device and / or the terminal device, for example, in the network device 1 or the terminal device shown in FIG. 1a.

[0150] It should be noted that FIGS. 1a and 1b are only simplified schematic diagrams for ease of understanding, for example, the communication system can also include other devices, such as wireless relay devices and / or wireless backhaul devices, which are not shown in FIGS. 1a and 1b. In actual applications, the communication system can include multiple network devices and can also include multiple terminal devices. The embodiments of the present application do not limit the number of network devices and terminal devices included in the communication system.

[0151] As shown in FIG. 2, FIG. 2 is a possible application framework schematic diagram of a communication system. The network elements in the communication system are connected through interfaces (for example, NG, Xn) or air interfaces. One or more AI modules (only one is shown in FIG. 2 for clarity) are arranged in one or more devices in the network element nodes, such as the core network device, the access network node (RAN node), the terminal, or the OAM. The access network node can be a separate RAN node or can include multiple RAN nodes, for example, including a CU and a DU. The CU and / or the DU can also be arranged with one or more AI modules. Optionally, the CU can also be split into a CU-CP and a CU-UP. One or more AI models are arranged in the CU-CP and / or the CU-UP.

[0152] The AI module is used to implement corresponding AI functions. The AI modules deployed in different network elements can be the same or different. The AI module can implement different functions according to different parameter configurations of the model of the AI module. The model of the AI module can be configured based on one or more of the following parameters: a structural parameter (for example, at least one of the number of neural network layers, the width of the neural network, the connection relationship between layers, the weight of the neuron, the activation function of the neuron, or the bias in the activation function), an input parameter (for example, the type of the input parameter and / or the dimension of the input parameter), or an output parameter (for example, the type of the output parameter and / or the dimension of the output parameter). The bias in the activation function can also be referred to as the bias of the neural network.

[0153] An AI module can have one or more models. A model can infer an output including one or more parameters. The learning process, training process, or inference process of different models can be deployed in different nodes or devices, or can be deployed in the same node or device.

[0154] As shown in FIG. 3, FIG. 3 is another possible application framework in a communication system. The communication system includes a RAN intelligent controller (RIC). The RIC can be an AI module as shown in FIG. 3, for example, to implement AI related functions. The RIC includes a near-real time RIC (near-RT RIC) and a non-real time RIC (Non-RT RIC). The Non-RT RIC mainly processes non-real time information, such as data that is not sensitive to latency, which can be in the order of seconds. The near-RT RIC mainly processes near-real time information, such as data that is relatively sensitive to latency, which can be in the order of tens of milliseconds.

[0155] The near-RT RIC is used for model training and inference. For example, to train an AI model, and to use the AI model for inference. The near-RT RIC can obtain network side and / or terminal side information from RAN nodes (such as CU, CU-CP, CU-UP, DU, and / or RU) and / or terminals. The information can be used as training data or inference data. Optionally, the near-RT RIC can deliver inference results to RAN nodes and / or terminals. Optionally, the inference results can be exchanged between CU and DU, and / or between DU and RU. For example, the near-RT RIC delivers inference results to DU, which sends them to RU.

[0156] The Non-RT RIC is also used for model training and inference. For example, to train an AI model, and to use the AI model for inference. The Non-RT RIC can obtain network side and / or terminal side information from RAN nodes (such as CU, CU-CP, CU-UP, DU, and / or RU) and / or terminals. The information can be used as training data or inference data. The inference results can be delivered to RAN nodes and / or terminals. Optionally, the inference results can be exchanged between CU and DU, and / or between DU and RU. For example, the Non-RT RIC delivers inference results to DU, which sends them to RU.

[0157] The near-real-time RIC and the non-real-time RIC can also be separately set as a network element. Alternatively, the near-real-time RIC and the non-real-time RIC can also be part of other devices, for example, the near-real-time RIC is set in a RAN node (for example, in a CU or a DU), and the non-real-time RIC is set in an OAM, a cloud server, a core network device, or other network devices.

[0158] The machine learning in the embodiments of the present application is further exemplarily described below.

[0159] Machine learning is an important technical approach to realize AI. Machine learning can be divided into supervised learning, unsupervised learning, and reinforcement learning.

[0160] Supervised learning learns the mapping relationship from sample values to sample labels according to the collected sample values and sample labels, and uses a machine learning model to express the learned mapping relationship. The process of training the machine learning model is the process of learning the mapping relationship. For example, in signal detection, the received signal containing noise is the sample, and the real constellation point corresponding to the signal is the label. Machine learning is expected to learn the mapping relationship between the sample and the label through training, that is, to learn a signal detector. During training, the model parameters are optimized by calculating the error between the predicted value of the model and the real label. Once the mapping relationship is learned, the learned mapping can be used to predict the sample label of each new sample. The learned mapping relationship of supervised learning can include linear mapping and nonlinear mapping. According to the type of label, the learned task can be divided into classification task and regression task.

[0161] Unsupervised learning only uses the collected sample values to discover the internal pattern of the sample by using an algorithm. In unsupervised learning, a class of algorithms uses the sample itself as a supervision signal, that is, the model learns the mapping relationship from the sample to the sample, which is called self-supervised learning. During training, the model parameters are optimized by calculating the error between the predicted value of the model and the sample itself. Self-supervised learning can be used in signal compression and decompression recovery applications. Common algorithms include autoencoders and generative adversarial networks.

[0162] Reinforcement learning is different from supervised learning, which is a kind of algorithm that learns the strategy to solve the problem by interacting with the environment. Unlike supervised and unsupervised learning, the reinforcement learning problem does not have clear "correct" action label data. The algorithm needs to interact with the environment, obtain the reward signal of the environment feedback, and then adjust the decision action to obtain a larger reward signal value. For example, in the following power control, the reinforcement learning model adjusts the downlink transmission power of each user according to the system total throughput rate feedback by the wireless network, and then expects to obtain a higher system throughput rate. The goal of reinforcement learning is also to learn the mapping relationship between the environment state and the optimal decision action. However, because the "correct action" label cannot be obtained in advance, the network cannot be optimized by calculating the error between the action and the "correct action". Reinforcement learning is achieved through iterative interaction with the environment.

[0163] DNN is a specific implementation form of machine learning. According to the universal approximation theorem, a neural network can theoretically approximate any continuous function, so that the neural network has the ability to learn any mapping. The traditional communication system needs to rely on rich expert knowledge to design the communication module, while the deep learning communication system based on DNN can automatically discover the implicit pattern structure from a large amount of data set, establish the mapping relationship between the data, and obtain better performance than the traditional modeling method.

[0164] The idea of DNN comes from the neuron structure of the brain tissue. Each neuron performs a weighted sum operation on its input value, and the weighted sum result generates an output through a nonlinear function.

[0165] DNN generally has more than one hidden layer, and the hidden layer often directly affects the ability to extract information and fit functions. Increasing the number of hidden layers of DNN or expanding the width of each layer can improve the function fitting ability of DNN. The weighted value in each neuron is the parameter of the DNN network model. The model parameters are optimized through the training process, so that the DNN network has the ability to extract data features and express mapping relationships. DNN generally uses supervised learning or unsupervised learning strategy to optimize model parameters. According to the construction method of the network, DNN can be divided into feedforward neural network (FNN), convolutional neural network (CNN) and recurrent neural network (RNN).

[0166] CNN is a kind of neural network specially designed to process data with grid-like structure. For example, time series data (time axis discrete sampling) and image data (two-dimensional discrete sampling) can be considered as grid-like structure data. CNN does not use all input information for operation at one time, but uses a fixed size window to extract part of the information for convolution operation, which greatly reduces the calculation of model parameters. In addition, according to the different types of window extraction information (such as people and objects in the same picture are different types of information), each window can use different convolution kernel operation, which makes CNN better extract the features of input data.

[0167] RNN is a kind of DNN network using feedback time series information. Its input includes the new input value at the current time and the output value of itself at the previous time. RNN is suitable for obtaining sequence features with time correlation, and is particularly suitable for speech recognition, channel coding and decoding and other applications.

[0168] In a communication system (for example, an LTE communication system or an NR communication system), a network device needs to determine the configuration of a downlink data channel of a terminal device based on channel state information (CSI), such as scheduling resources, modulation and coding scheme (MCS), and precoding. It can be understood that the CSI is a kind of channel information, which is an information that can reflect the characteristics and quality of the channel.

[0169] The channel information can be determined based on the channel measurement result of the reference signal. Alternatively, the channel information can be the channel measurement result of the reference signal. In the embodiments of the present application, the channel measurement result of the reference signal can also be replaced by the channel information.

[0170] CSI measurement refers to solving the channel information by the receiving end according to the reference signal sent by the sending end, that is, estimating the channel information by using channel estimation method. Exemplarily, the reference signal can include one or more of channel state information reference signal (CSI-RS), synchronizing signal / physical broadcast channel block (SSB), sounding reference signal (SRS), or demodulation reference signal (DMRS). The CSI-RS, SSB, and DMRS can be used to measure the downlink CSI. The SRS and DMRS can be used to measure the uplink CSI.

[0171] In this application, the meaning of CSI is broader than that in the traditional scheme, and is not limited to channel quality indication (CQI), precoding matrix indicator (PMI), rank indicator (RI), or CSI-RS resource indicator (CRI). It can also be one or more of channel response information (such as channel response matrix, frequency domain channel response information, time domain channel response information), weight information corresponding to the channel response, reference signal receiving power (RSRP) or signal to interference plus noise ratio (SINR) and the like.

[0172] It should be understood that in this application, indication includes direct indication (also known as explicit indication) and implicit indication. Among them, direct indication of information A means including information A; implicit indication of information A means indicating information A through the correspondence between information A and information B and directly indicating information B. The correspondence between information A and information B can be pre-defined, pre-stored, pre-burned, or pre-configured.

[0173] It should be understood that in this application, information C is used for the determination of information D, which includes that information D is determined only based on information C, and also includes that information D is determined based on information C and other information. In addition, information C for determining information D can also be indirectly determined, such as the case where information D is determined based on information E, and information E is determined based on information C.

[0174] In addition, in the embodiments of the present application, "network element A sends information A to network element B" can be understood as the destination of the information A or the intermediate network element in the transmission path between the destination is network element B, which can include direct or indirect sending of information to network element B. "Network element B receives information A from network element A" can be understood as the source of the information A or the intermediate network element in the transmission path between the source is network element A, which can include direct or indirect receiving of information from network element A. The information may be processed as necessary between the source and the destination of the information transmission, such as format change, etc., but the destination can understand the valid information from the source. Similar expressions in this application can be similarly understood, and will not be described here.

[0175] The technical solutions of the present application can also be used in various wireless positioning scenarios. Exemplarily, FIG. 4 is an example of a wireless positioning system applicable to the embodiments of the present application. As shown in FIG. 4, the wireless positioning system mainly includes an access network device, a terminal device, and a positioning server. Among them, the positioning server is mainly responsible for receiving a positioning service request, collecting positioning-related measurement results, calculating a positioning result, and providing corresponding wireless positioning services, etc. Optionally, the positioning server can receive a positioning service request from the access network device or an upper-layer application. As an example, the positioning server can be a location management function network element, for example, LMF. For the access network device and the terminal device, refer to the description above.

[0176] FIG. 5 is a schematic diagram of network elements involved in the embodiments of the present application. As shown in FIG. 5, the network elements involved in the present application include a UE, a location management function network element, and an access network device. Optionally, an access mobile management function (AMF) network element is also included. As an example, the access network device can be an ng-eNB or a gNB, where the ng-eNB represents a 4G base station that can access a 5G core network, and the gNB represents a 5G base station, both of which are network elements of NR-RAN. The wireless access network device or base station mentioned in the embodiments of the present application can be an ng-eNB or a gNB, without limitation. The UE and the wireless access network device communicate through corresponding interfaces, for example, the UE and the gNB communicate through an NR-Uu interface, and the UE and the ng-eNB communicate through an LTE-Uu interface. In the embodiments of the present application, the NR-Uu interface and the LTE-Uu interface are used to transmit positioning-related signaling and / or data. In addition, the gNB and the AMF communicate through an NG-C interface, such as transmitting positioning-related signaling. The AMF and the LMF communicate through an NL1 interface, such as transmitting positioning-related signaling. Optionally, the interaction between the UE and the LMF is based on the LTE positioning protocol (LTE positioning protocol, LPP) protocol, the interaction between the NG-RAN and the LMF is based on the NRPPa protocol, and the NRPPa protocol is transparently transmitted across the AMF. It should be understood that the NG-RAN is only an example, and when the technical solutions of the present application are applied to future wireless communication systems, such as a 6G system, the NG-RAN is correspondingly an access network device in the 6G system. Similarly, the names of the network elements, the names of the interfaces between the network elements, and the names of the messages are only examples, and in future wireless communication systems, network elements, interfaces, and interface messages with the same or similar functions can be used to implement the technical solutions of the present application.

[0177] In addition, an AI network element can also be introduced in the present application, as shown in FIG. 6, which is a schematic diagram of an AI / ML network element or module. If an AI network element is introduced, it means that the AI network element corresponds to a separate network element; if an AI module is introduced, the AI module can be located inside a certain network element. As described above, the network elements involved in the embodiments of the present application include a UE, a wireless access network device, and an LMF, and optionally, an AMF can also be included. Inside one or more of these UEs, wireless access network devices, AMFs (if the network element is involved), and LMFs, an AI module can be set up, or one or more of the UEs, wireless access network devices, AMFs, and LMFs introduce a corresponding AI network element, or a combination of the two, without limitation. It should be understood that if one or more of the UEs, wireless access network devices, AMFs, and LMFs introduce a corresponding AI network element, and the AI operation is performed by the corresponding AI network element, the UE, wireless access network device, AMF, or LMF needs to send information related to the AI operation to the corresponding AI network element. For example, the LMF introduces a corresponding AI network element, which performs the inference operation of the AI positioning model, and after the LMF receives the channel measurement report from the first network element (such as an access network device or a UE), the channel measurement result carried in the channel measurement report is sent to the corresponding AI network element. For another example, in uplink positioning, if the access network device introduces a corresponding AI network element, assuming that the input of the AI positioning model is the channel feature extracted from the channel measurement result, and the channel measurement result is obtained by measuring the SRS of the UE by the access network device. Therefore, after the access network device obtains the channel measurement result, the channel measurement result is sent to the corresponding AI network element, and the AI network element extracts the channel feature from the channel measurement result through the AI model, and then returns the extracted channel feature to the access network device, and then the access network device sends the channel feature to the LMF.

[0178] In the research process of the present application, it is found that under different channel conditions, adjusting the reporting granularity of one or more features of at least one path has different effects on the accuracy of the inference of the AI positioning model. The following examples are illustrated in Table 1. Table 1 is an illustrative example of the present application using the arrival time feature in one or more features of at least one path.

[0179] Table 1

[0180] In the above table, n1…n256 represents the multiple of the reporting granularity of the arrival time feature on 1-256 paths under channel condition 1.

[0181] For example, the AI positioning model is deployed on the location management function network element, and the AI positioning model takes the multi-path information of the same number of multi-paths and different reporting granularity represented time of arrival features as input, and takes the position of the UE as output. The multi-path information can be sent by the channel network element to the location management function network element through the channel measurement report. The multi-path information is the time of arrival feature represented by the reporting granularity of the channel measurement report. Therefore, the reporting granularity size of one or more features of at least one path of the channel measurement report actually determines the positioning accuracy. In Table 1, the number of paths and the time of arrival feature on each path are taken as the features of the multi-path information, that is, an example of information related to the reporting granularity of one or more features of at least one path of the channel measurement report. In the case of the same number of paths, if the reporting granularity of one or more features of at least one path is different, the influence on the positioning accuracy of the AI positioning model is also different.

[0182] As can be seen from Table 1:

[0183] (1) In the case of serious non-line of sight (NLOS), for example, the NLOS ratio of channel condition 1 is about 99%, in the case of the same number of multi-paths, the influence on the positioning accuracy is small when the reporting granularity of the time of arrival feature of each path of the AI positioning model input is reduced from 10ns to 5ns, as shown in Table 1, in the case of channel condition 1, the positioning accuracy is 0.95 when the reporting granularity of the time of arrival feature of each path is taken as the input of the AI positioning model, and the positioning accuracy is 0.84 when the reporting granularity of the time of arrival feature of each path is taken as the input of the AI positioning model. The positioning accuracy is still maintained at the level of less than 1 meter (i.e. sub-meter level);

[0184] (2) In the case of light NLOS, for example, the NLOS ratio of channel condition 2 is about 40%, in the case of the same number of multi-paths, the influence on the positioning accuracy is greater when the reporting granularity of the time of arrival feature of each path of the AI positioning model input is reduced from 10ns to 5ns, as shown in Table 1, in the case of channel condition 2, the positioning accuracy is 1.66 when the reporting granularity of the time of arrival feature of each path is taken as the input of the AI positioning model, and the positioning accuracy is 0.88 when the reporting granularity of the time of arrival feature of each path is taken as the input of the AI positioning model. The positioning accuracy is reduced from sub-meter level to 1.66 meters.

[0185] The above research results of the present application show that the reporting granularity of one or more features of at least one path of the input of the AI positioning model required to guarantee positioning accuracy can be different under different channel conditions. For example, under channel condition 1 shown in Table 1, the input of the AI positioning model required to guarantee sub-meter level positioning accuracy can be the arrival time feature of each path with a reporting granularity of 5 ns, while under channel condition 2, the input of the AI positioning model required to guarantee sub-meter level positioning accuracy needs the arrival time feature of each path with a reporting granularity of 10 ns. It should be understood that the input of the AI positioning model is the reporting granularity of one or more features of at least one path of the channel measurement report reported by the channel measurement network element. Based on the above research results, the present application proposes that under different channel conditions, the channel measurement report reporting single-path information with different reporting granularity of one or more features of at least one path is reported to support the positioning accuracy requirement on the premise that the positioning accuracy requirement can be guaranteed. For example, in the example of Table 1, for the same channel condition 1, the arrival time feature of each path with a reporting granularity of 10 ns and the arrival time feature of each path with a reporting granularity of 10 ns as the input of the AI positioning model can both guarantee the sub-meter level positioning accuracy requirement, and in this case, the channel measurement network element can report the arrival time feature with a reporting granularity of 5 ns of at least one path of one or more features.

[0186] Optionally, the arrival time feature information of a single path reported with different reporting granularity of the arrival time feature in the above Table 1 is input to the AI positioning model, which can be the same AI positioning model, or includes multiple AI positioning models for reporting multi-path channel information with different reporting granularity of one or more features, that is, one AI positioning model can indicate the input of multi-path information with reporting granularity of one or more features. For example, under channel condition 1, the AI positioning model with the arrival time feature of each path with a reporting granularity of 10 ns as input can be AI positioning model 1, while the AI positioning model with the arrival time feature of each path with a reporting granularity of 5 ns as input can be AI positioning model 1 or AI positioning model 2. AI positioning model 1 and AI positioning model 2 can be AI positioning models in the model library of the location management function network element.

[0187] In the present application, the channel measurement report is used to report the channel measurement result (or the channel feature extracted from the channel measurement result, which is taken as an example in various embodiments), and the extracted channel feature is used for the AI-related operation performed by the location management function network element, such as the model inference based on the AI positioning model. Therefore, the AI positioning model takes the channel measurement result (or the channel feature extracted from the channel measurement result) as the input and takes the position of the UE as the output, so the purpose of the channel measurement report is to report the input of the AI positioning model. Based on the premise, the reporting granularity of the one or more features of the at least one path in the channel measurement report in the present application actually carries the input of the AI positioning model. Under the premise of ensuring the positioning accuracy, the reporting granularity of the one or more features of the at least one path in the channel measurement report fed back by the channel measurement network element to the location management function network element can support the channel measurement network element to report the channel measurement report of different channel features with the reporting granularity of the one or more features of the at least one path, to support flexible reporting granularity of the one or more features of the at least one path, and thus ensure the positioning accuracy.

[0188] The technical solutions of the present application are described in detail below.

[0189] Referring to FIG. 7, FIG. 7 is a schematic flowchart of a method 700 of sending information provided by the present application. The method 700 can be executed by a corresponding network element, or by a chip or circuit, without limitation. Hereinafter, the execution by the network element is taken as an example for description.

[0190] In the method introduced in FIG. 7, the first network element sends first indication information to the location management function network element, and the first indication information indicates the reporting granularity of the one or more features of the at least one path in the channel measurement report.

[0191] 710. The first network element sends first indication information to the location management function network element, and the first indication information indicates the reporting granularity of the one or more features of the at least one path in the channel measurement report.

[0192] Optionally, the first indication information is carried in the channel measurement report.

[0193] The channel measurement report is sent to the location management function network element, and the channel measurement report includes one or more of the following: the number of the at least one path; the one or more features of the at least one path, which have the reporting granularity indicated by the first indication information.

[0194] The one or more characteristics of the at least one path include one or more of: a time of arrival characteristic, a power characteristic, an amplitude characteristic, a phase characteristic, an angle characteristic, a time delay spread characteristic, or an angle spread characteristic. In the present application, different information on multiple paths is reported using different characteristic reporting granularity, but the same information on different paths needs to be reported using the same characteristic reporting granularity. Taking the time of arrival information as an example, the reporting granularity can be characterized as (path number N, (x1…xN) times the time of arrival characteristic reporting granularity), where x1 represents a multiple of the time of arrival characteristic reporting granularity on the first path, and xN represents a multiple of the time of arrival characteristic reporting granularity on the Nth path. As can be seen, in the embodiments of the present application, the reporting granularity is easier to characterize single-path channel information.

[0195] Further, the first network element sends a channel measurement report to the location management function network element.

[0196] The channel measurement report and the aforementioned first indication information can be carried in the same message, or in different messages.

[0197] In the present application, the first network element sends an indication of the reporting granularity of one or more characteristics of at least one path in the channel measurement report to the location management function network element, so that the first network element can report multipath information with different reporting granularity under different channel conditions, thereby ensuring positioning accuracy.

[0198] In addition, in the embodiments of the present application, "network element A sends information A to network element B", such as "the first network element sends the first indication information to the location management function network element", can be understood as the destination of the information A or the intermediate network element in the transmission path between the destination is network element B, which can include direct or indirect sending information to network element B. "Network element B receives information A from network element A", such as "the location management function network element receives the first indication information from the first network element", can be understood as the source of the information A or the intermediate network element in the transmission path between the source is network element A, which can include direct or indirect receiving information from network element A. The information between the source and the destination of the information transmission can be processed as necessary, for example, format change, etc., but the destination can understand the valid information from the source. Similar expressions in the present application can be understood similarly, and will not be described here.

[0199] Optionally, the method 700 can further include step 720, which can be located before step 710.

[0200] 720、The first network element determines the characteristic reporting granularity of each path according to the positioning accuracy requirement and / or the channel measurement result, or the identification index;

[0201] Optionally, the channel measurement result can include one or more of a channel impulse response (CIR), a channel frequency response (CFR), a power delay profile (PDP), without limitation.

[0202] In addition, the number of sampling points of the CIR and the PDP is not limited, and the CIR can include one or more of a plurality of time-aligned CIR sequences, a cross-correlation sequence of a plurality of CIR sequences, a normalized CIR, etc.

[0203] The number of bandwidths, subbands, or ports corresponding to the CIR, CFR, and PDP, etc. is not limited. The CFR can include a normalized CFR.

[0204] In this application, the channel measurement result can reflect the NLOS degree of the channel environment. According to the above, the influence of the reporting granularity of each path feature of the channel measurement report on the inference accuracy of the AI positioning model is different under different NLOS degrees (i.e., different channel conditions). Therefore, the first network element can obtain the NLOS degree of the channel based on the channel measurement result, thereby obtaining the current channel condition. Under the premise of ensuring the positioning accuracy requirement, the first network element can determine different reporting granularities of each path feature under different channel conditions.

[0205] The above channel measurement result and its corresponding features can satisfy at least one of the following Table 2, i.e., at least one row:

[0206] Table 2

[0207] In Table 2, the K-factor (K-factor, KF) is defined as the ratio of the power sum of the LOS path to the power sum of the NLOS path in the plurality of paths. Like the shadow fading factor (shadow fading, SF), the KF is also related to the geometric position on the map. When the UE moves to different positions, its KF will also change; at the same time, the power of the multipath will also change with the position.

[0208] It should be understood that the channel propagation conditions are usually divided into two categories: LOS (line of sight) path and NLOS path:

[0209] LOS path channel propagation condition: there is a direct path, but it can also contain a NLOS path scenario. Or, at least there is 1 LOS path (i.e. pure LOS), but it is not limited to how many NLOS paths. The overall characteristic is that the energy of the LOS path is higher than the sum of the energy of the NLOS path, and the ratio of the power sum of the LOS path and the NLOS path is usually defined by KF.

[0210] NLOS path channel propagation condition: there is no direct path, that is, a scenario with only NLOS paths.

[0211] Therefore, it can be said that the higher the Rake factor, the higher the energy of the LOS path relative to the energy of the NLOS, and the worse the multipath condition.

[0212] Based on the above description of the reporting granularity of one or more characteristics of at least one path, the first network element determines the reporting granularity of one or more characteristics of at least one path in the channel measurement report, which can be to determine the channel characteristics of one or more paths carried by the channel measurement report characterized by the reporting granularity of the characteristics, for example, one or more of the arrival time characteristics, power characteristics, amplitude information, phase characteristics, angle characteristics, delay spread characteristics, and angle spread characteristics of one or more paths. For example, assuming that the channel measurement report carries information of 32 paths, the channel characteristics of each of the 32 paths are represented by the reporting granularity of the characteristics, for example, the reporting granularity used to represent the arrival time characteristics can be 10ns or 5ns. When in the LOS scenario, using 10ns to represent the arrival time characteristics is more cost-saving than 5ns, but in the channel condition 2, the positioning accuracy decreases when using 10ns than 5ns, therefore, in different channel conditions, different reporting granularity of characteristics can be flexibly selected, which is more conducive to ensuring the positioning accuracy requirement.

[0213] As an example, the first network element can determine the reporting granularity of one or more characteristics of at least one path based on the mapping relationship. Alternatively, the mapping relationship can be a mapping relationship of any one or more of the following:

[0214] The mapping relationship between the reporting granularity of one or more characteristics of the at least one path and the corresponding identification index;

[0215] The mapping relationship between the reporting granularity of one or more characteristics of the at least one path and the positioning accuracy level;

[0216] The mapping relationship between the reporting granularity of one or more characteristics of the at least one path and the channel condition.

[0217] The first network element determines the reporting granularity of one or more characteristics of at least one path based on the above mapping relationship.

[0218] When the reporting granularity of one or more characteristics of at least one path is determined using the identification index, the identification index can represent one or more of the following: a time of arrival characteristic, a power characteristic, a phase characteristic, an angle characteristic, a delay spread characteristic, an angle spread characteristic. As shown in Table 3.1, an example of a mapping relationship between the identification index and the time of arrival characteristic is provided.

[0219] Table 3.1

[0220] As shown in Table 3.2, an example of a mapping relationship between the identification index and the time of arrival characteristic, the power characteristic is provided.

[0221] Table 3.2

[0222] The examples of Tables 3.1 and 3.2 only describe the mapping relationship between the identification index and the time of arrival characteristic, the power characteristic, and the values of the identification index can be any of the values described above for the reporting granularity of the time of arrival characteristic, the power characteristic. In addition, the identification index can also represent a mapping between any one or more of the time of arrival characteristic, the power characteristic, the phase characteristic, the angle characteristic, the delay spread characteristic, the angle spread characteristic. Here, the mapping relationships are not listed one by one. The reporting granularity of one or more characteristics of at least one path can be selected from the reporting granularity values of the various characteristics described above.

[0223] When the reporting granularity of one or more characteristics of at least one path is determined according to the positioning accuracy level and / or the current channel condition, the channel condition can be determined according to the channel measurement result, or in other words, the channel measurement result can reflect the channel condition, and the positioning accuracy requirement can correspond to a certain positioning accuracy level in the mapping relationship. Therefore, after the first network element obtains the channel measurement result through channel measurement, it can determine the channel measurement characteristic, and then determine the reporting granularity of one or more characteristics of at least one path of the channel measurement report according to the mapping relationship and the current positioning accuracy requirement and / or the current channel condition.

[0224] It can be understood that the channel measurement result can include one or more of the items in Table 4, and the mapping relationship between the channel condition and / or the positioning accuracy level corresponding to each channel measurement result, and the mapping relationship between the reporting granularity of one or more characteristics of at least one path, can satisfy at least one of the items in Table 4, and the one or more characteristics of at least one path can be any one or more of the time of arrival characteristic, the power characteristic, the phase characteristic, the angle characteristic, the delay spread characteristic, the angle spread characteristic, and the values of the characteristics can be selected from the reporting granularity values of the various characteristics described above. Here, only the reporting granularity of the time of arrival characteristic is used as an example.

[0225] Table 4

[0226] It should be understood that a plurality of channel measurement results are listed in Table 4, and the first network element can determine the channel condition according to one or more channel measurement results, and determine the reporting granularity of one or more features of at least one path in the channel measurement report in combination with the positioning accuracy level, for example, taking the time delay spread feature as an example, the first network element can determine the NLOS degree of the current channel environment based on the time delay spread feature of the channel measurement result, as an example, assuming that the NLOS degree of the current channel environment corresponds to channel condition 1, and the positioning accuracy requirement corresponds to positioning accuracy level 1, the first network element can determine that the reporting granularity of the time of arrival feature of at least one path of the channel measurement report is 10 ns. For another example, assuming that the NLOS degree of the current channel environment corresponds to channel condition 1, but the positioning accuracy requirement corresponds to positioning accuracy level 3, the first network element can determine that the reporting granularity of the time of arrival feature of at least one path of the channel measurement report is 1 ns.

[0227] Optionally, the channel measurement report can correspond to the number of paths of at least one path, and one or more features, the one or more features of the at least one path are reported with the reporting granularity of the feature (here, the feature reporting granularity is still taken as the time of arrival feature). As an example of Table 5. It can be understood that the channel measurement result can include one or more of Table 5, the mapping relationship between the mapping relationship corresponding to each channel measurement result, i.e. the channel condition and / or the positioning accuracy level, and the feature reporting granularity of each path, can meet at least one of Table 5.

[0228] Table 5

[0229] Only the time delay spread feature, the angle spread feature or the angle-time delay power feature is taken as an example in Table 5, and other channel measurement results are also applicable. As in Table 5, assuming that the first network element determines that the current channel condition corresponds to channel condition 1 according to the channel measurement result, and the positioning accuracy requirement corresponds to positioning accuracy level 3, the first network element can determine that the feature reporting granularity of each path is applicable to 32 paths, and the time of arrival feature of each path in the 32 paths is reported with the reporting granularity of the time of arrival feature. Optionally, assuming that the current channel condition is channel condition 1 according to the channel measurement result, and the positioning accuracy requirement corresponds to positioning accuracy 2, the first network element can determine that the reporting granularity of each time of arrival feature of the channel measurement report can have two options, i.e. (128, 5ns) / (128, 1ns), both of which can meet the positioning accuracy requirement.

[0230] Optionally, when determining the reporting granularity of one or more features of at least one path of the channel measurement report, the first network element can consider other requirements related to positioning in addition to satisfying the positioning accuracy requirement. As some examples, taking the channel condition 1 in Table 5 and the positioning accuracy level 2 as examples, if the positioning accuracy guarantee reliability requirement is high, the first network element selects the reporting granularity of the arrival time of the at least one path to be (128, 1 ns), which corresponds to a more accurate arrival time than (128, 5 ns); or if the transmission delay requirement is high (i.e., low delay requirement), the first network element selects the reporting granularity of the arrival time of the at least one path to be (128, 5 ns), which corresponds to a smaller transmission delay than (128, 1 ns), and can satisfy the timing accuracy requirement while satisfying the low delay requirement. Here, only as examples, the first network element can use other factors as the basis for determining the reporting granularity of the arrival time of the at least one path in the channel measurement report, which is not limited.

[0231] The above Tables 4-5 take the mapping relationship between the channel condition, the positioning accuracy level, and the reporting granularity of the arrival time of the at least one path as examples to describe the technical solutions, and as described above, the mapping relationship can also be a mapping relationship between the channel condition or the positioning accuracy level and the reporting granularity of the arrival time of the at least one path, which is not limited. Optionally, when the mapping relationship includes a mapping relationship between the channel condition and the reporting granularity of the arrival time of the at least one path in the positioning accuracy level, the other of the channel condition and the positioning accuracy level can be known by the first network element, for example, the mapping relationship is a mapping relationship between the positioning accuracy level and the reporting granularity of the arrival time of the at least one path, and the channel condition is known by the first network element through channel measurement estimation. For another example, the mapping relationship is a mapping relationship between the channel condition and the reporting granularity of the arrival time of the at least one path, and the mapping relationship is for a certain positioning accuracy level.

[0232] For example, the first network element has the mapping relationships in Tables 6 and 7, Table 6 is for the positioning accuracy level 1, and Table 7 is for the positioning accuracy level 2. After the first network element obtains the positioning accuracy requirement from the location management function network element, if the positioning accuracy requirement corresponds to the positioning accuracy level 1, the first network element determines the feature reporting granularity of each path of the channel measurement report according to the mapping relationship in Table 6 and the current channel condition. If the positioning accuracy requirement corresponds to the positioning accuracy level 2, the first network element determines the feature reporting granularity of each path of the channel measurement report based on the mapping relationship in Table 7. Tables 6, 7, 8, and 9 all describe the arrival time feature reporting granularity of the arrival time of the at least one path, and the values can be any of the values described above for the arrival time feature reporting granularity, and the values here are only used as examples.

[0233] Table 6

[0234] Table 7

[0235] For another example, the first network element stores the mapping relationship in Table 8 for channel condition 1 and Table 9 for channel condition 2. The first network element determines the current channel condition through the channel measurement result obtained by the channel measurement, and if the corresponding channel condition is channel condition 1, the first network element determines the reporting granularity of the time of arrival of at least one path of the channel measurement report based on the mapping relationship of Table 8 in combination with the positioning accuracy requirement obtained from the location management function network element.

[0236] Table 8

[0237] Table 9

[0238] Based on the description in step 710, the first network element sends the channel measurement report to the location management function network element and indicates the reporting granularity of one or more features of at least one path in the channel measurement report through the first indication information, so as to support flexible reporting granularity related channel measurement result and further meet the positioning accuracy requirement under different channel conditions.

[0239] Optionally, before step 720, step 730 can also be included.

[0240] 730. The first network element receives the information of the positioning accuracy requirement from the location management function network element.

[0241] As an example, the positioning accuracy requirement on which the first network element determines the feature reporting granularity of each path of the channel measurement report can be sent by the location management function network element to the first network element. For example, the location management function network element carries the information of the positioning accuracy requirement in the measurement request sent to the first network element, or the information of the positioning accuracy requirement is sent to the first network element through another information different from the measurement request, without limitation.

[0242] Optionally, the method 700 can also include that the location management function network element sends a measurement request to the first network element. The measurement request is used to instruct the first network element to perform channel measurement. Or in other words, the measurement request is used to trigger the first network element to perform channel measurement.

[0243] Optionally, in the uplink positioning scenario, the first network element is an access network device, and the channel measurement result is based on a first channel measurement, which can be a measurement of a sounding reference signal from the terminal device by the access network device. In the downlink positioning scenario, the first network element is a terminal device, and the channel measurement is based on a second channel measurement, which can be a measurement of a positioning reference signal or a channel state information reference signal (CSI-RS) from the access network device by the terminal device.

[0244] In the method 700, the first network element sends first indication information to the location management function network element, the first indication information indicating a reporting granularity of one or more features of at least one path in the channel measurement report. In this way, the first network element can support reporting granularity-related channel measurement results to support positioning accuracy requirements under different channel conditions. Further, the reporting granularity of the one or more features of the at least one path can be determined by the first network element based on the positioning accuracy requirement and the channel measurement result, so the reporting granularity of the one or more features of the at least one path determined by the first network element is different based on different channel conditions, different positioning accuracy requirements. In this way, based on the channel conditions, different reporting granularities can be used to report related channel measurement results to meet the positioning accuracy requirement, thereby meeting the low-latency requirement.

[0245] Optionally, the first network element can also send a channel measurement report to the location management function network element, the reporting granularity of the one or more features of the at least one path in the channel measurement report being indicated by the first indication information. Or, the first network element sends the channel measurement report and the first indication information to the location management function network element, the first indication information indicating the reporting granularity of the one or more features of the at least one path in the channel measurement report.

[0246] Optionally, as another possible implementation, the first network element sends first indication information to the location management function network element, the first indication information indicating a reporting granularity of one or more features of at least one path in the channel measurement report. After receiving the first indication information, the location management function network element can feed back an acceptance message or a rejection message to the first network element according to the AI positioning model saved in the model library. Illustratively, the first indication information indicates a reporting granularity of one or more features of at least one path in the channel measurement report. The location management function network element feeds back an acceptance message or a response message according to whether there is a corresponding AI positioning model in the model library. For example, if the location management function network element saves an AI positioning model corresponding to a reporting granularity of 10 ns of the arrival time feature of at least one path (or the AI positioning model takes the arrival time feature characterized by 10 ns as input), the location management function network element replies an acceptance message to the first network element; if the location management function network element does not have an AI positioning model corresponding to the arrival time feature of at least one path characterized by 10 ns as granularity, the location management function network element replies a rejection message to the first network element. If the first network element receives the acceptance message, the first network element sends a channel measurement report to the location management function network element. The channel measurement report has a reporting granularity of one or more features of at least one path in the channel measurement report indicated by the first indication information. Optionally, if the location management function network element replies a rejection message to the first network element, the location management function network element can also send second indication information to the first network element, the second indication information indicating a reporting granularity of one or more features of at least one path in a new channel measurement report. In order to distinguish, the reporting granularity of one or more features of at least one path in the channel measurement report indicated by the first indication information sent by the first network element is recorded as reporting granularity 1 of one or more features of at least one path, and the reporting granularity of one or more features of at least one path in the channel measurement report indicated by the second indication information sent by the location management function network element is recorded as reporting granularity 2 of one or more features of at least one path. The reporting granularity 2 of one or more features of at least one path can be indicated by the location management function network element to the first network element based on the AI positioning model in the model library of the location management function network element. For example, taking the arrival time feature as an example, the reporting granularity 1 of the arrival time feature of at least one path includes the arrival time feature characterized by 2 ns, but the location management function network element does not have an AI positioning model corresponding to the arrival time feature characterized by 2 ns, but has an AI positioning model corresponding to the arrival time feature characterized by 5 ns and the arrival time feature characterized by 10 ns, and the AI positioning model corresponding to the arrival time feature characterized by 5 ns and the arrival time feature characterized by 10 ns can also guarantee the positioning accuracy requirement, then the location management function network element indicates the reporting granularity 5 ns of the arrival time feature of at least one path in the channel measurement report through the second indication information, which includes the arrival time feature characterized by 5 ns.Thus, the first network element sends a channel measurement report to the location management function network element based on the indication of the second indication information, the channel measurement report including a time of arrival feature characterized by 5 ns. Optionally, the location management function network element determines a channel condition based on the received first indication information, and in the case where there are multiple AI models corresponding to at least one path with a location accuracy requirement, the time of arrival feature is reported in a reporting granularity (e.g., 2 ns, 5 ns, 10 ns) characterized by the time of arrival feature, the location management function network element selects a time of arrival feature characterized by a reporting granularity that is closer to the reporting granularity of the time of arrival feature indicated by the first indication information, or selects a reporting granularity that is closer to the reporting granularity indicated by the first indication information, and sends the selection result to the first network element through the second indication information. In this way, the case where the channel measurement report sent by the first network element does not match an AI model can be avoided.

[0247] As can be seen, in the implementation manner described later, the first network element first indicates a reporting granularity of one or more features of at least one path in a channel measurement report to the location management function network element, and in the case where the location management function network element accepts the reporting granularity of the one or more features of the at least one path, the corresponding channel measurement report is sent, otherwise, the channel measurement report can be reported based on the reporting granularity of the one or more features of the at least one path indicated by the location management function network element.

[0248] Optionally, to ensure that the first network element and the location management function network element support a same reporting granularity of one or more features of at least one path, the first network element and the location management function network element can perform capability interaction before the first network element sends the channel measurement report. The capability here refers to a reporting granularity of one or more features of at least one path supported by each of the first network element and the location management function network element. For example, the capability of the first network element refers to a reporting granularity of one or more features of at least one path supported by the first network element, and the capability of the location management function network element refers to a reporting granularity of one or more features of at least one path that can be used for AI positioning inference under the premise of meeting the current positioning accuracy requirement. Of course, the capability interaction is based on the premise of meeting the current positioning accuracy requirement. For example, the location management function network element indicates the reporting granularity of one or more features of at least one path when sending the positioning accuracy requirement information to the first network element, to indicate the reporting granularity of one or more features of at least one path that can be used for AI positioning inference under the premise of meeting the current positioning accuracy requirement. The reporting granularity of one or more features of at least one path can correspond to one or more AI positioning models in the model library of the location management function network element. The first network element selects the reporting granularity of one or more features of at least one path that is also supported by the first network element from the reporting granularity of one or more features of at least one path indicated by the location management function network element, and can indicate the reporting granularity of one or more features of at least one path of the channel measurement report, which is specifically the reporting granularity of one or more features of at least one path indicated by the location management function network element.

[0249] Optionally, another implementation is introduced below in combination with FIG. 8, that is, the location management function network element indicates the reporting granularity of one or more features of at least one path in the channel measurement report. Thus, the first network element reports the channel measurement report carrying the reporting granularity of one or more features of corresponding at least one path based on the indication of the location management function network element.

[0250] Referring to FIG. 8, FIG. 8 is a schematic flowchart of a method 800 of sending information provided in the present application. The method 800 can be performed by a corresponding network element, or by a chip or circuit, without limitation. The following is described by taking the network element as an example.

[0251] 810, the location management function network element determines the reporting granularity of one or more features of at least one path in the channel measurement report according to the positioning-related requirement, the positioning-related requirement at least including the positioning accuracy requirement, and the channel measurement report including one or more of the following: the number of at least one path; and one or more features of at least one path, the one or more features having the reporting granularity indicated by the first indication information.

[0252] When the location management function network element determines the reporting granularity of one or more features of at least one path of the channel measurement report according to the positioning-related requirements, the location management function network element can specifically determine in combination with AI model library information of the location management function network element. It should be noted that the AI model library of the location management function network element is not limited to being stored on the location management function network element, but can also be stored on another network element, for example, a second network element (to distinguish from the first network element in the text), and the following embodiments are described by taking the AI model library being stored on the location management function network element as an example. In this application, the AI model in the AI model library is used for positioning, and therefore can also be referred to as an AI positioning model.

[0253] For example, the AI model library stores a plurality of AI positioning models, and each of the plurality of AI positioning models takes a measurement result related to reporting granularity as input and the position of the UE as output. Alternatively, the positioning-related requirements at least include a positioning accuracy requirement, that is, a guaranteed positioning accuracy level, and in addition, can include other requirements, for example, a transmission delay requirement of the channel measurement report, a reliability requirement of the positioning accuracy guarantee, and the like, as shown in Table 10. Tables 10 and 11 both describe the reporting of the arrival time feature of each path with reporting granularity, and the values can be any of the values described above for the reporting granularity of the arrival time feature. The numerical values here are only used for description.

[0254] Table 10

[0255] As shown in Table 10, if the current positioning accuracy requirement of the location management function network element corresponds to a positioning accuracy level 1, and the AI models that can guarantee the positioning accuracy level 1 are AI model 1, AI model 2, and AI model 3, the inputs of the three AI models are channel measurement reports with reporting granularity of 10 ns, 5 ns, and 2 ns of the arrival time feature of at least one path, respectively. At this time, other positioning-related requirements can also be combined, for example, if the transmission delay requirement of the channel measurement report corresponds to a delay requirement level 2, and the reliability requirement of the positioning accuracy guarantee corresponds to a level 1 (for example, low requirement for delay and high requirement for reliability), the location management function network element determines the channel measurement report with reporting granularity of 10 ns of the arrival time feature of at least one path. For another example, if the current positioning accuracy requirement corresponds to a positioning accuracy level 1, the transmission delay requirement of the channel measurement report corresponds to a delay requirement level 1, and the reliability requirement of the positioning accuracy guarantee corresponds to a level 2 (for example, high requirement for delay and low requirement for reliability), the location management function network element determines the channel measurement report with reporting granularity of 5 ns of the arrival time feature of at least one path.

[0256] The reporting granularity of the time of arrival feature of at least one path in Table 10 above is 2ns, 5ns, 10ns, which is only an example. As described above in the method 700, the reporting granularity of one or more features of at least one path can also represent one of the features, or multiple features, without limitation. In these different implementations, the process of the location management function network element determining the reporting granularity of one or more features of at least one path according to the positioning related requirements is similar, that is, based on different positioning related requirements (such as different positioning accuracy requirements), the reporting granularity of one or more features of at least one path is determined to support the reporting of the channel measurement report related to the reporting granularity by the first network element, thereby supporting the requirements of positioning.

[0257] After determining the reporting granularity of one or more features of at least one path in the channel measurement report, the location management function network element indicates the reporting granularity of one or more features of at least one path to the first network element, as in step 820.

[0258] 820. The location management function network element sends third indication information, which indicates the reporting granularity of one or more features of at least one path in the channel measurement report.

[0259] 830. The location management function network element receives the channel measurement report.

[0260] The channel measurement report is reported according to the reporting granularity of one or more features of at least one path in the channel measurement report indicated by the third indication information. Alternatively, the reporting granularity of one or more features of at least one path in the channel measurement report is exactly the same as the reporting granularity of one or more features of at least one path indicated by the third indication information.

[0261] In the method 800, the location management function network element determines the reporting granularity of one or more features of at least one path in the channel measurement report based on the positioning related requirements (such as the positioning accuracy requirement), and indicates the reporting granularity of one or more features of at least one path in the channel measurement report to the channel measurement network element. Under different positioning related requirements, the reporting granularity of one or more features of at least one path indicated by the location management function network element to the channel measurement network element is different. Thus, the channel measurement network element reports the reporting granularity of one or more features of at least one path based on the indication of the location management function network element, and the reporting granularity of one or more features of at least one path reported under different positioning related requirements is different, thereby supporting the channel measurement report related to different reporting granularity to support the positioning accuracy requirement.

[0262] Optionally, in the method 800, the location management function network element can adjust the indicated reporting granularity of one or more features of at least one path based on the identification index.

[0263] Optionally, in the method 800, the location management function network element can adjust the reporting granularity of one or more features of the at least one path based on the performance monitoring of the AI positioning model. For example, based on the current positioning accuracy requirement, the model library of the location management function network element stores multiple AI positioning models that can guarantee the current positioning accuracy requirement, and the reporting granularity of one or more features of the at least one path corresponding to the multiple AI positioning models is different, the location management function network element can first indicate a lower reporting granularity of one or more features of the at least one path to the channel measurement network element. Subsequently, based on the performance monitoring of the corresponding AI positioning model, if the performance of the AI positioning model cannot meet the performance requirement (such as the positioning accuracy requirement), the location management function network element can adjust the reporting granularity of one or more features of the at least one path indicated, for example, to indicate a reporting granularity of one or more features of the at least one path slightly higher than before. Through one or more adjustments, the performance requirement is met. The following is illustrated by taking Table 11 as an example.

[0264] Table 11

[0265] For example, the AI positioning model 1 to the AI positioning model 3 can all meet the positioning accuracy requirement corresponding to the positioning accuracy level 1, but the positioning accuracy levels corresponding to the AI positioning model 1 to the AI positioning model 3 can be for different channel conditions. In this case, the location management function network element can indicate a smaller reporting granularity of the arrival time feature of at least one path to the channel measurement network element, for example, the location management function network element indicates the reporting granularity of the arrival time feature of at least one path corresponding to the AI positioning model 3, and specifically taking the arrival time feature as an example, the reporting granularity of the arrival time of at least one path is 1 ns. The location management function network element performs performance monitoring on the AI positioning model 3, and if it is found that the AI positioning model 3 cannot meet the positioning accuracy requirement, according to the research results of the present application, under the current channel condition, the arrival time feature with the reporting granularity of the arrival time of at least one path being 1 ns as the input of the AI positioning model 3 cannot guarantee the current positioning accuracy requirement. At this time, the location management function network element adjusts the indicated reporting granularity of the arrival time of at least one path, for example, the location management function network element indicates the reporting granularity of the arrival time of at least one path corresponding to the AI positioning model 2 to the channel measurement network element, and specifically the reporting granularity of the arrival time of at least one path is 10 ns. Compared with the arrival time feature with the reporting granularity of the arrival time of at least one path being 10 ns, more channel measurement information is provided, which helps to improve the positioning accuracy. The location management function network element performs inference operation based on the AI positioning model 2, and performs performance monitoring on the AI positioning model 2. If the performance monitoring result of the AI positioning model 2 shows that the AI positioning model 2 can meet the current positioning requirement, it shows that under the current channel condition, the channel measurement network element reports the arrival time feature with the reporting granularity of 10 ns of each path feature as the input of the AI positioning model, which can meet the current positioning accuracy requirement. If the AI positioning model 2 still cannot meet the current positioning accuracy requirement, the location management function network element can continue to modify the indicated reporting granularity of each path feature, for example, the location management function network element indicates the reporting granularity of the arrival time of at least one path corresponding to the AI positioning model 1, and so on. From the above adjustment process, it can be seen that the above adjustment process is actually a process of determining the reporting granularity of one or more features of at least one path of the channel measurement report based on the current channel condition and the positioning accuracy requirement, and finally determining the reporting granularity of one or more features of at least one path that can meet the positioning accuracy requirement under the current channel condition, which can support different reporting granularity related channel measurement reports to support the positioning accuracy requirement.

[0266] As can be seen from the methods introduced in FIG. 7 and FIG. 8, in the embodiments of the present application, the first network element (i.e., the network element performing channel measurement, or simply referred to as the channel measurement network element) or the location management function network element can determine the reporting granularity of one or more features of at least one path in the channel measurement report. As some examples, the method of FIG. 7 can be applied to the case where there are only a small number of models in the model library set on the location management function network element, in which case the location management function network element can indicate the information of the positioning accuracy requirement to the first network element, and then the first network element determines the reporting granularity of one or more features of at least one path in the channel measurement report according to the positioning accuracy requirement and the channel measurement result. While the method of FIG. 8 can be applied to the case where there are more models in the model library set on the location management function network element, in which case the location management function network element determines the reporting granularity of one or more features of at least one path in the channel measurement report according to the positioning related requirement, such as the positioning accuracy requirement, and indicates the reporting granularity of one or more features of at least one path to the first network element, so that the first network element can support reporting the channel measurement report with different reporting granularity of one or more features of at least one path to support the positioning accuracy requirement.

[0267] The following describes the application of the embodiments of the present application in the uplink positioning scenario and the downlink positioning scenario, respectively.

[0268] In the following embodiments, the LMF is taken as an example of the location management function network element, and the base station is taken as an example of the access network device, without limiting the location management function network element to other network elements, modules, etc.

[0269] The reporting granularity of one or more features of at least one path can be any of the above descriptions, and the numbers in the following embodiments are only illustrative. In the following embodiments, the time of arrival feature is taken as an example of the one or more features.

[0270] 1. The first network element determines the reporting granularity of one or more features of at least one path in the channel measurement report.

[0271] Optionally, the method of the first network element sending the channel measurement report to the location management function network element and indicating the reporting granularity of one or more features of at least one path in the channel measurement report, such as the method 700, can be applied to the uplink positioning scenario, and can also be applied to the downlink positioning scenario.

[0272] (1) Uplink positioning

[0273] In uplink positioning, the selection and / or inference of the model occurs at the LMF. In one implementation, the model takes as input channel measurements obtained by the base station measuring SRS transmitted by the UE, and takes as output the position of the UE. Alternatively, in another possible implementation, the model takes as input channel features extracted from the channel measurements, without limitation. In the latter implementation, the base station side can be deployed with an AI model for extracting channel features from channel measurements.

[0274] Exemplarily, the AI module for executing the AI model can be the RIC shown in FIG. 3, such as a near-real-time RIC or a non-real-time RIC, etc. For example, the near-real-time RIC is disposed in the RAN node (e.g., in the CU, the DU), and the non-real-time RIC is disposed in the OAM, the cloud server, the core network device, or other network devices.

[0275] Exemplarily, the near-real-time RIC and the non-real-time RIC can also be respectively disposed as a network element alone, and the network device can be the near-real-time RIC or the non-real-time RIC.

[0276] It should be noted that in the following embodiments, the transmission of information / data between two network elements is not limited to direct transmission or indirect transmission (including transparent transmission), etc. Therefore, the sending of information from the network element A to the network element B includes that the network element A sends the information directly to the network element B through the interface between the network element A and the network element B, and also includes that the network element A sends the information to the network element C, and the network element C sends the information to the network element B. Moreover, the number of relays between the network element A and the network element B is not limited. For example, the sending of the channel measurement report from the UE to the LMF can include that the UE sends the channel measurement report directly to the LMF through the LPP message, or that the UE sends the channel measurement report to the LMF through the base station, or that the UE sends the channel measurement report to the LMF through the base station and the AMF, or that the UE sends the channel measurement report to the LMF through the AMF, etc. The interaction between other network elements is similar, and those skilled in the art can understand without further description. The interface message between the network elements can be referred to the description in FIG. 8.

[0277] Referring to FIG. 9, FIG. 9 is one example 900 of sending information applied to uplink positioning provided by the present application.

[0278] 910, the LMF sends a measurement request to the base station, and the measurement request carries information of positioning accuracy requirement.

[0279] Optionally, the information of positioning accuracy requirement can be carried in the measurement request, or in a different message from the measurement request.

[0280] 920, the base station configures the UE to send SRS based on the measurement request.

[0281] 930、UE sends SRS.

[0282] 940、The base station measures the SRS from the UE to obtain channel measurement results.

[0283] 950、The base station determines the reporting granularity of one or more features of at least one path in the channel measurement report according to the positioning accuracy requirement and / or the channel measurement results, or the identified index. The step 950 can refer to the description in the step 720, and will not be described in detail.

[0284] 960、The base station sends first indication information to the LMF. The first indication information indicates the reporting granularity of one or more features of at least one path in the channel measurement report.

[0285] 970、The base station sends the channel measurement report related to the first indication information to the LMF.

[0286] Optionally, the steps 960 and 970 can be combined and sent through one signaling.

[0287] Optionally, the step 980 can also be included.

[0288] 980、The LMF selects an AI positioning model according to the channel measurement report.

[0289] It can be understood that in the embodiments of the present application, selecting an AI positioning model that is adapted according to the channel measurement report is actually selecting a corresponding AI positioning model according to the number of paths in the channel measurement report and the channel features of one or more features of at least one path reported in the reporting granularity of one or more features of at least one path. Or, if an AI positioning model is based on the features of one or more features of at least one path as input, then the AI positioning model can be selected for positioning inference. For example, if the reporting granularity of the time of arrival feature of at least one path in the channel measurement report is 10ns, and AI positioning model A in the model library of the LMF is input with the reporting granularity of the time of arrival feature of at least one path as 5ns, and AI positioning model B is input with the reporting granularity of the time of arrival feature of at least one path as 10ns, then the LMF selects AI positioning model B for positioning inference.

[0290] Optionally, in a possible case, assuming that only AI positioning model C in the model library of the LMF can meet the positioning accuracy requirement, that is, there is only one AI positioning model that can meet the positioning accuracy requirement, and the AI positioning model C takes the reporting granularity of the time of arrival feature of at least one path as 10ns as input. The LMF indicates the information of the positioning accuracy requirement to the base station. According to the channel measurement result and the positioning accuracy requirement indicated by the LMF, the base station determines that the reporting granularity of the time of arrival feature of at least one path in the channel measurement report is 10ns, and the base station carries the channel feature with the reporting granularity of the time of arrival feature of at least one path as 10ns in the channel measurement report. For the LMF, because only the AI positioning model C meets the positioning accuracy requirement, in this case, the channel feature with the reporting granularity of the time of arrival feature of at least one path as 10ns in the channel measurement report reported by the base station can be processed by zero padding and then used as the input of the AI positioning model C for positioning inference.

[0291] (2) Downlink positioning

[0292] In downlink positioning, the selection / inference of the model occurs in the LMF. In one implementation, the model takes the channel measurement result obtained by the UE measuring the PRS sent by the base station as input, and the position of the UE as output. Optionally, in another possible implementation, the model takes the channel feature extracted from the channel measurement result as input, which is not limited. In the latter implementation, the AI model can be deployed on the UE side, which is used to extract the channel feature from the channel measurement result. Wherein, PRS is the abbreviation of Positioning Reference Signal, which is well known to those skilled in the art.

[0293] Exemplarily, the AI module for executing the AI model can be the RIC shown in FIG. 3, such as near real-time RIC or non-real-time RIC. For example, the near real-time RIC is arranged in the RAN node (for example, in the CU, DU), and the non-real-time RIC is arranged in the OAM, the cloud server, the core network device, or other network devices.

[0294] Exemplarily, the near real-time RIC and the non-real-time RIC can be respectively arranged as a network element alone, and the network device can be the near real-time RIC or the non-real-time RIC.

[0295] Referring to FIG. 10, FIG. 10 is one example 1000 of sending information provided by the present application applied to downlink positioning.

[0296] 1010, the LMF sends the information of the positioning accuracy requirement to the UE.

[0297] Optionally, the positioning accuracy requirement sent by the LMF can be carried in the measurement request sent by the LMF, or can be carried in a different message from the measurement request.

[0298] Optionally, the OTT sends information of the positioning accuracy requirement to the UE.

[0299] 1020、The LMF configures the base station to send PRS.

[0300] 1030、The base station sends PRS to the UE based on the configuration of the LMF.

[0301] 1040、The UE measures PRS from the base station to obtain channel measurement results.

[0302] 1050、The UE determines the reporting granularity of one or more features of at least one path in the channel measurement report according to the positioning accuracy requirement and the channel measurement results.

[0303] Step 1050 can refer to the description in step 720, and will not be repeated here.

[0304] 1060、The UE sends first indication information to the LMF, the first indication information being used to indicate the reporting granularity of one or more features of at least one path in the channel measurement.

[0305] 1070、The UE sends the channel measurement report related to the first indication information to the LMF.

[0306] Optionally, step 1060 and step 1070 can be combined and sent using one signaling.

[0307] Optionally, step 1080 can also be included.

[0308] 1080、The LMF selects an AI positioning model according to the channel measurement report.

[0309] In addition, the LMF can use the selected AI positioning model for inference.

[0310] 2、The location management function network element determines the feature reporting granularity of each path in the channel measurement report.

[0311] Optionally, the location management function network element determines the reporting granularity of one or more features of at least one path in the channel measurement report. And indicates the reporting granularity of one or more features of the at least one path to the first network element. The method, for example, method 800 can be applied to an uplink positioning scenario, and can also be applied to a downlink positioning scenario.

[0312] (1) Uplink positioning

[0313] In uplink positioning, the selection and / or inference of the model occurs in the LMF. The model takes the channel measurement results (or channel features extracted from the channel measurement results) obtained by the base station measuring the SRS sent by the UE as input, and the position of the UE as output.

[0314] Referring to FIG. 11, FIG. 11 is an example 1100 of sending information provided by the present application applied to uplink positioning.

[0315] 1110. The LMF determines the reporting granularity of one or more characteristics of at least one path in the channel measurement report according to the positioning-related requirements or the identified index.

[0316] 1120. The LMF sends third indication information to the base station, the third indication information indicating the reporting granularity of one or more characteristics of the at least one path.

[0317] Optionally, the third indication information can be carried in the measurement request sent by the LMF or in a different message from the measurement request.

[0318] Steps 1110-1120 are described in detail in steps 810-820, and will not be repeated here.

[0319] 1130. The base station configures the UE to send SRS based on the measurement request.

[0320] 1140. The UE sends SRS based on the configuration of the base station.

[0321] 1150. The base station measures the SRS from the UE to obtain channel measurement results.

[0322] 1160. The base station sends a channel measurement report to the LMF according to the third indication information.

[0323] Specifically, the reporting granularity of one or more characteristics of at least one path in the channel measurement report is determined according to the third indication information.

[0324] Optionally, 1170. The LMF selects an AI positioning model according to the channel measurement report.

[0325] (2) Downlink positioning

[0326] In downlink positioning, the selection and / or inference of the model occurs in the LMF. The model takes as input the channel measurement results (or channel characteristics extracted from the channel measurement results) obtained by the UE measuring the PRS sent by the base station, and takes as output the position of the UE.

[0327] Referring to FIG. 12, FIG. 12 is an example 1200 of sending information provided by the present application applied to downlink positioning.

[0328] 1210. The LMF determines the reporting granularity of one or more characteristics of at least one path in the channel measurement report according to the positioning-related requirements. 1220. The LMF sends third indication information to the UE, the third indication information indicating the reporting granularity of one or more characteristics of the at least one path.

[0329] Optionally, the third indication information can be carried in a measurement request sent by the LMF, or in a different message from the measurement request.

[0330] Steps 1210-1220 are described in detail in steps 810-820, and will not be described again.

[0331] 1230, the LMF configures the base station to send PRS.

[0332] 1240, the base station sends PRS to the UE based on the configuration of the LMF.

[0333] 1250, the UE measures the PRS from the base station to obtain channel measurement results.

[0334] 1260, the UE sends a channel measurement report to the LMF based on the third indication information. The reporting granularity of one or more features of at least one path in the channel measurement report is determined according to the third indication information.

[0335] Optionally, 1270, the LMF reports the channel measurement results according to the reporting granularity of one or more features of at least one path in the channel measurement report, and selects an AI positioning model.

[0336] The above describes in detail the method for sending information provided by the embodiments of the application. The communication device provided by the application is introduced below.

[0337] Referring to FIG. 13, the application provides a communication device 1300. As shown in FIG. 13, the communication device 1300 includes a processing module 1310 and a communication module 1320. The communication device 1300 can be a first network element (such as an access network device or a terminal device), or a communication device applied to the first network element or matched with the first network element, capable of realizing the method executed by the first network element, such as a chip, a chip system or a circuit. Alternatively, the communication device 1300 can be a location management function network element, or a communication device applied to the location management function network element or matched with the location management function network element, capable of realizing the method executed by the location management function network element, such as a chip, a chip system or a circuit. Illustratively, the location management function network element can be the LMF in the method embodiments of the application.

[0338] The communication module can also be referred to as a transceiver module, a transceiver, a transceiver, or a transceiver device, etc. The processing module can also be referred to as a processor, a processing board, a processing unit, or a processing device, etc. Optionally, the communication module is used to execute the sending operation and the receiving operation of the first network element (such as an access network device or a terminal device) or the location management function network element in the above method, and the device in the communication module for realizing the receiving function can be regarded as a receiving unit, and the device in the communication module for realizing the sending function can be regarded as a sending unit, that is, the communication module includes a receiving unit and a sending unit.

[0339] When the communication apparatus 1300 is applied to the first network element, the processing module 1310 can be configured to implement the processing function of the first network element (e.g. an access network device or a terminal device) in the embodiments of FIG. 1 to FIG. 12, and the communication module 1320 can be configured to implement the transceiving function of the first network element in the embodiments of FIG. 1 to FIG. 12.

[0340] When the communication apparatus 1300 is applied to the location management function network element, the processing module 1310 can be configured to implement the processing function of the location management function network element in the embodiments of FIG. 1 to FIG. 12, and the communication module 1320 can be configured to implement the transceiving function of the first network element in the embodiments of FIG. 1 to FIG. 12.

[0341] In addition, it should be noted that the aforementioned communication module and / or processing module can be implemented by a virtual module, for example, the processing module can be implemented by a software function unit or a virtual device, and the communication module can be implemented by a software function or a virtual device. Alternatively, the processing module or the communication module can also be implemented by an entity device, for example, if the device is implemented by a chip / circuit (e.g. an integrated circuit or a logic circuit, etc.). The communication module can be an input / output circuit and / or a communication interface, which performs an input operation (corresponding to the aforementioned receiving operation) and an output operation (corresponding to the aforementioned sending operation); and the processing module is an integrated processor or a microprocessor or a circuit (e.g. an integrated circuit or a logic circuit, etc.).

[0342] The division of the modules in the present application is illustrative, and is only a logical function division. In actual implementation, another division manner can be used. In addition, each functional module in each example in the present application can be integrated in one processor, or can be a separate physical existence, or two or more modules can be integrated in one module. The integrated module can be implemented in the form of hardware or in the form of a software function module.

[0343] In addition, referring to FIG. 14, the present application further provides a communication apparatus 1400. Optionally, the communication apparatus 1400 can be a chip or a chip system. Optionally, in the present application, the chip system can be composed of a chip, or can include a chip and other discrete devices.

[0344] The communication device 1400 can be used to implement the function of any network element (e.g., a location management function network element or a first network element, optionally, the first network element is an access network device or a terminal device) in the communication system described in the foregoing example description. The communication device 1400 can include at least one processor 1410. Optionally, the processor 1410 is coupled with a memory, which can be located within the device, or the memory can be integrated with the processor, or the memory can be located outside the device. For example, the communication device 1400 can further include at least one memory 1420. The memory 1420 stores computer programs, computer programs or instructions and / or data necessary for implementing any of the foregoing examples; the processor 1410 can execute the computer programs stored in the memory 1420 to complete the method in any of the foregoing examples.

[0345] The communication device 1400 can further include a communication interface 1430, and the communication device 1400 can exchange information with other devices through the communication interface 1430. For example, the communication interface 1430 can be a transceiver, a circuit, a bus, a module, a pin or other types of communication interfaces. When the communication device 1400 is a chip-type device or a circuit, the communication interface 1430 in the device 1400 can also be an input / output circuit, which can input information (or receive information) and output information (or send information). The processor 1410 is an integrated processor, a microprocessor, an integrated circuit or a logic circuit, etc., and the processor can determine the output information according to the input information.

[0346] The coupling in the present application is an indirect coupling or communication connection between devices, units or modules, which can be electrical, mechanical or other forms, for information exchange between devices, units or modules. The processor 1410 can operate in cooperation with the memory 1420 and the communication interface 1430. The specific connection medium between the processor 1410, the memory 1420 and the communication interface 1430 is not limited in the present application.

[0347] Optionally, as shown in FIG. 14, the processor 1410, the memory 1420 and the communication interface 1430 are connected with each other through a bus 1440. Optionally, the bus can include address bus, data bus, control bus and other types of buses. In addition, for ease of representation, one bus 1440 is shown in FIG. 14, but it does not mean that there is only one bus or only one type of bus.

[0348] In this application, the processor can be a general purpose processor, a digital signal processor, an application specific integrated circuit, a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, can implement or execute the disclosed methods, steps and logic block diagrams in this application. The general purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this application can be directly embodied as hardware processor execution, or executed by a combination of hardware and software modules in the processor.

[0349] The memory can be a non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), and can also be a volatile memory, such as a random-access memory (RAM). The memory can be any other medium capable of carrying or storing desired program code in the form of instructions or data structures and capable of being accessed by a computer, but is not limited to this. The memory in this application can also be a circuit or any other device capable of realizing the storage function, used for storing program instructions and / or data.

[0350] Since the communication device 1400 provided by the present example can be applied to a location management function network element (such as LMF), complete the method executed by the location management function network element, or applied to a first network element, complete the method executed by the first network element. Therefore, the technical effects that can be obtained are described in the above method embodiments, which will not be repeated here.

[0351] Based on the above examples, the present application provides a communication system, which in one example includes a first network element and a location management function network element. Optionally, the communication system can also include at least one AI node. The communication system can implement the method of sending information provided in the embodiments shown in FIGS. 1-12.

[0352] The technical solutions provided in the present application can be realized by software, hardware, firmware or any combination thereof, in whole or in part. When realized by software, the technical solutions can be realized in the form of a computer program product in whole or in part. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal device, an access network device or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that includes one or more available media sets. The available media can be magnetic media (such as floppy disk, hard disk, magnetic tape), optical media (such as digital video disc (DVD)), or semiconductor media, etc.

[0353] In the present application, the examples can be referred to each other without logical contradiction, for example, the methods and / or terms between the method embodiments can be referred to each other, for example, the functions and / or terms between the device embodiments can be referred to each other, for example, the functions and / or terms between the device examples and the method examples can be referred to each other.

[0354] The units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the present embodiment.

[0355] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0356] At least one of the embodiments of the present application refers to one or more. More refers to two or more. "And / or" describes the association between the associated objects, which means that there can be three relationships, for example, A and / or B, which means that there are three cases of A alone, A and B together, and B alone. The character " / " generally represents the relationship between the front and rear associated objects as "or". In addition, it should be understood that although the terms first, second, etc. may be used to describe various objects in the present application, these objects should not be limited by these terms. These terms are only used to distinguish each object from each other.

[0357] The term "comprising" mentioned in the embodiments of the present application and any variation thereof is intended to cover the non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include other steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device. In addition, the words "exemplary" or "for example" are used to mean serving as an example, instance, or illustration. Any method or design scheme described as "exemplary" or "for example" in the present application should not be interpreted as more preferred or more advantageous than other methods or design schemes. Rather, the use of "exemplary" or "for example" and the like is intended to illustrate the relevant technical solutions or concepts in a specific manner.

[0358] If the functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the part of the prior art essentially or the part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and various program code storage media.

[0359] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A communication method characterized by comprising: Applied to a first network element, comprising: sending first indication information to a location management function network element, the first indication information indicating a reporting granularity of one or more characteristics of at least one path in a channel measurement report.

2. The method of claim 1, wherein, The first indication information is carried in the channel measurement report.

3. The method according to claim 1 or 2, characterized in that, Further comprising: sending the channel measurement report to the location management function network element, the channel measurement report comprising one or more of: The number of the at least one path; The one or more characteristics of the at least one path have the reporting granularity indicated by the first indication information.

4. The method of claim 3, wherein, The one or more characteristics of the at least one path comprise one or more of: a time of arrival characteristic, a power characteristic, an amplitude characteristic, a phase characteristic, an angle characteristic, a delay spread characteristic, or an angle spread characteristic.

5. The method according to any one of claims 1 to 4, characterized in that, The method further comprises: determining the reporting granularity of the one or more characteristics of the at least one path according to a positioning accuracy requirement and / or a channel measurement result.

6. The method of claim 5, wherein, The determination of the reporting granularity of the one or more characteristics of the at least one path according to the positioning accuracy requirement and / or the channel measurement result comprises: determining the reporting granularity of the one or more characteristics of the at least one path according to a mapping relationship of any one or more of: A mapping relationship between the reporting granularity of the one or more characteristics of the at least one path and a corresponding identification index; A mapping relationship between the reporting granularity of the one or more characteristics of the at least one path and a positioning accuracy level; A mapping relationship between the reporting granularity of the one or more characteristics of the at least one path and a channel condition; Wherein the positioning accuracy level is related to the positioning accuracy requirement, and the channel condition is related to the channel measurement result.

7. The method according to claim 5 or 6, characterized in that, The channel measurement result comprises one or more of: A delay spread characteristic, an angle spread characteristic, an angle-delay-power characteristic, a number of paths whose energy is greater than a preset energy proportion of the energy of the first path in the sampled paths, a Rake factor, a Doppler frequency measurement result, a LOS(Line of Sight) probability, an interference level of a full frequency band or a sub-band sampled, or a RSRP(Reference Signal Received Power) of a full frequency band or a sub-band.

8. A communication method characterized by comprising: Applied to a location management function network element, comprising: receiving first indication information from a first network element, the first indication information indicating a reporting granularity of one or more characteristics of at least one path in a channel measurement report.

9. The method of claim 8, wherein, The first indication information is carried in the channel measurement report.

10. The method according to claim 8 or 9, characterized in that, Further comprising: receiving the channel measurement report from the first network element, the channel measurement report comprising one or more of: The number of the at least one path; The one or more characteristics of the at least one path have the reporting granularity indicated by the first indication information.

11. The method of claim 10, wherein, The one or more characteristics of the at least one path comprise one or more of: a time of arrival characteristic, a power characteristic, an amplitude characteristic, a phase characteristic, an angle characteristic, a delay spread characteristic, or an angle spread characteristic.

12. The method according to any one of claims 8-11, characterized in that, The method further comprises: reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result.

13. The method of claim 12, wherein, The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result. The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result. The mapping relationship between the reporting granularity of one or more features of the at least one path and the corresponding identification index; The mapping relationship between the reporting granularity of one or more features of the at least one path and the positioning accuracy level; The mapping relationship between the reporting granularity of one or more features of the at least one path and the channel condition; The positioning accuracy level is related to the positioning accuracy requirement, and the channel condition is related to the channel measurement result.

14. The method according to claim 12 or 13, characterized in that, The channel measurement result includes one or more of the following: The channel measurement result includes one or more of the following:

15. A method of communication, comprising: The channel measurement result includes one or more of the following: The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result. The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result. The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result.

16. The method of claim 15, wherein, The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result. The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result.

17. The method according to claim 15 or 16, characterized in that, The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result. The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result.

18. A method of communication, comprising: The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result. The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result. The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result. The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result. The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result.

19. The method of claim 18, wherein, The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result. The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result.

20. The method of claim 18 or 19, wherein, The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result. The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result.

21. A communications device, characterized by The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result. The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result. The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result. The reporting granularity of one or more features of the at least one path is determined according to a mapping relationship between the positioning accuracy requirement and / or the channel measurement result. 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23. A readable storage medium characterized by, a computer program product for storing a computer program or instructions which, when executed, implement the method of any of claims 1-20.

24. A computer program, characterized in that, a computer program product for storing a computer program or instructions which, when executed, implement the method of any of claims 1-20. a computer program product for storing a computer program or instructions which, when executed, implement the method of any of claims 1-20.

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