Communication method and communication apparatus

Abstract

The application provides a communication method and a communication device, and applies to the field of communication. In the technical scheme of the application, corresponding label data is obtained based on the collection time of training data, or corresponding training data is obtained based on the collection time of label data, a training sample with higher accuracy can be obtained, so as to help improve the accuracy of training data, and then help improve the accuracy of an AI model obtained by training.

Description

Technical Field

[0001] This application relates to the field of communications, and more particularly to communication methods and communication devices. Background Technology

[0002] 3GPP proposes applying artificial intelligence (AI) to wireless communication systems, for example, by intelligently collecting and analyzing data to improve network performance and user experience. One approach to applying AI in communication systems includes training an AI model using training samples containing training and labeled data, and then using the trained AI model for inference.

[0003] In some scenarios, training data and label data need to be time-aligned to ensure the accuracy of the trained AI model. However, in many cases, training data and label data are not collected simultaneously, which can lead to lower accuracy in the trained AI model. Summary of the Invention

[0004] The communication method and communication device provided in this application help improve the accuracy of the output results of the trained AI model.

[0005] Firstly, this application provides a communication method, which can be executed by a first device. The first device can be a communication device, or a device within the communication device (e.g., a module, communication module, circuit or chip responsible for communication functions (such as a modem chip, also known as a baseband chip, or a system-on-chip (SoC) chip containing a modem core or a system-in-package (SIP) chip), a chip system, or a processor), or a logical node, logical module, or software capable of implementing all or part of the functions of the communication device. As an example, the communication device is an access network device or a terminal.

[0006] This method includes: sending first information, which requests second data corresponding to a first time, where the first time is the collection time of the first data, the first data is one of training data and label data, and the second data is the other of training data and label data; and receiving the second data corresponding to the first time.

[0007] In this method, the second data corresponding to the first time can be understood as the second data collection time satisfying a preset correlation with the first time.

[0008] In this method, when the first device requests the second data, it also indicates the collection time of the second data, and the indicated collection time of the second data corresponds to the collection time of the first data. This ensures that the collection time of the second data received by the first device corresponds to the collection time of the first data, so that the training data and label data in the training dataset generated based on the first data and the second data are corresponding in time, thereby improving the accuracy of the model trained based on the training dataset.

[0009] In some possible implementations, the first information includes the first time. In this implementation, the first information directly carries the first time, which can both flexibly and accurately indicate the first time to the second data collection end.

[0010] In some possible implementations, the second data received by the first device includes: at least one of the second data whose collection time is closest to the first time.

[0011] In this implementation, the second data received by the first device is the one or more second data whose acquisition time is closest to that of the first data. Therefore, the training data generated based on the first data and the received second data has higher accuracy, which helps to improve the performance of the trained model.

[0012] In some possible implementations, the second data received by the first device includes: at least one second data in which the difference between the collection time and the first time is less than or equal to a preset time difference.

[0013] In one possible implementation, the first data is training data, and the second data is labeled data. Alternatively, the first data is labeled data, and the second data is training data.

[0014] In some possible implementations, the first data includes the measurement results of the reference signal; and / or, the second data includes at least one of the following: the location information of the terminal, the direct path information of the reference signal, the indirect path information of the reference signal, the phase information of the reference signal, or the measurement time information of the reference signal.

[0015] Secondly, this application provides a communication method that can be executed by a second device, which may be a communication device, or a device within the communication device (e.g., a module, communication module, circuit or chip responsible for communication functions, chip system or processor), or a logical node, logical module or software capable of implementing all or part of the functions of the communication device. As an example, the communication device is a core network element, such as a location management function entity.

[0016] This method includes: receiving first information, which is used to request second data corresponding to a first time, wherein the first time is the collection time of the first data, the first data is one of training data and label data, and the second data is the other of training data and label data; and sending the second data corresponding to the first time.

[0017] In this method, the second device receives first information indicating a first time, thereby knowing that second data corresponding to the first time should be sent. Since the first time is the collection time of the first data, the collection time of the second data sent by the second device corresponds to the collection time of the first data. This ensures that the second data sent by the second device corresponds to the first data, thereby improving the accuracy of the training dataset generated based on the first and second data, and ultimately improving the accuracy of the trained model.

[0018] In some implementations, the first information includes the first moment.

[0019] In this implementation, the second device directly obtains the first time based on the first information. On the one hand, it can flexibly obtain the accurate first time; on the other hand, it can reduce the complexity and latency of the second device obtaining the first time, thereby enabling it to send the second data corresponding to the first time more efficiently, and thus improve the efficiency of training data collection.

[0020] In some possible implementations, the second data transmitted by the second device includes: at least one of the second data, which is the first data whose collection time is closest to the first time.

[0021] In this implementation, the second data sent by the second device is the one or more second data whose acquisition time is closest to that of the first data. Therefore, the training data generated based on the first data and the received second data has higher accuracy, which helps to improve the performance of the trained model.

[0022] In some possible implementations, the second data sent by the second device includes: at least one of the second data in which the difference between the collection time and the first time is less than or equal to a preset time difference.

[0023] This implementation helps to improve the accuracy of training data generated based on the first data and the received second data, thereby helping to improve the performance of the trained model.

[0024] In one possible implementation, the first data is training data, and the second data is labeled data. Alternatively, the first data is labeled data, and the second data is training data.

[0025] In some possible implementations, the first data includes the measurement results of the reference signal; and / or, the second data includes at least one of the following: the location information of the terminal, the direct path information of the reference signal, the indirect path information of the reference signal, the phase information of the reference signal, or the measurement time information of the reference signal.

[0026] Thirdly, this application provides a communication method, which can be executed by a first device. The first device can be a communication device, or a device within the communication device (e.g., a module, communication module, circuit or chip responsible for communication functions (such as a modem chip, also known as a baseband chip, or a system-on-chip (SoC) chip containing a modem core or a system-in-package (SIP) chip), a chip system, or a processor), or a logical node, logical module, or software capable of implementing all or part of the functions of the communication device. As an example, the communication device is an access network device or a terminal.

[0027] This method includes: receiving second data and a second time, wherein the second time is the collection time of the second data and the second data is one of training data and label data; determining the first data corresponding to the second time, wherein the first data is the other of training data and label data.

[0028] In this method, the first device receives the second data and the second time, thereby determining the first data corresponding to the second time based on the second time, which can improve the accuracy of the training dataset generated based on the first data and the second data, and ultimately improve the accuracy of the trained model.

[0029] In some possible implementations, the second data received by the first device includes: at least one of the second data whose collection time is closest to the first time.

[0030] In this implementation, the second data received by the first device is the one or more second data whose acquisition time is closest to that of the first data. Therefore, the training data generated based on the first data and the received second data has higher accuracy, which helps to improve the performance of the trained model.

[0031] In some possible implementations, the second data received by the first device includes: at least one second data in which the difference between the collection time and the first time is less than or equal to a preset time difference.

[0032] This implementation helps to improve the accuracy of training data generated based on the first data and the received second data, thereby helping to improve the performance of the trained model.

[0033] In one possible implementation, the first data is training data, and the second data is labeled data. Alternatively, the first data is labeled data, and the second data is training data.

[0034] In some possible implementations, the first data includes the measurement results of the reference signal; and / or, the second data includes at least one of the following: the location information of the terminal, the direct path information of the reference signal, the indirect path information of the reference signal, the phase information of the reference signal, or the measurement time information of the reference signal.

[0035] Fourthly, this application provides a communication method, which can be executed by a second device. The second device can be a communication device, or a device within the communication device (e.g., a module, communication unit, circuit or chip responsible for communication functions, chip system, or processor), or a logical node, logical module, or software capable of implementing all or part of the functions of the communication device. As an example, the communication device is a core network element, such as a location management function entity.

[0036] This method includes: acquiring second data and a second time, wherein the second time is the time when the second data is collected, and the second time is used to determine the first data, wherein the second data is one of training data and label data, and the first data is the other of training data and label data; and sending the second data and the second time.

[0037] In this implementation, the second device acquires the second data and the second time, so that the first device can determine the first data corresponding to the second time based on the second time, thereby improving the accuracy of the training dataset generated based on the first data and the second data, and ultimately improving the accuracy of the trained model.

[0038] In some possible implementations, the second data transmitted by the second device includes: at least one of the second data whose collection time is closest to the first time.

[0039] In this implementation, the second data sent by the second device is the second data among one or more second data whose acquisition time is closest to that of the first data. Therefore, the training data generated based on the first data and the received second data has higher accuracy, which helps to improve the performance of the trained model.

[0040] In some possible implementations, the second data sent by the second device includes: at least one of the second data in which the difference between the collection time and the first time is less than or equal to a preset time difference.

[0041] In one possible implementation, the first data is training data, and the second data is labeled data. Alternatively, the first data is labeled data, and the second data is training data.

[0042] In some possible implementations, the first data includes the measurement results of the reference signal; and / or, the second data includes at least one of the following: the location information of the terminal, the direct path information of the reference signal, the indirect path information of the reference signal, the phase information of the reference signal, or the measurement time information of the reference signal.

[0043] Fifthly, this application provides a communication device. This device may be a communication equipment, or a component within a communication equipment (e.g., a module, communication module, circuit or chip responsible for communication functions (such as a modem chip, also known as a baseband chip, or a SoC chip or SIP chip containing a modem core), a chip system, or a processor), or a logical node, logical module, or software capable of implementing all or part of the functions of the communication equipment. As an example, this communication equipment is an access network device.

[0044] This device may include modules that perform the methods / operations / steps / actions described in the first aspect or any of the possible implementations thereof. These modules may be hardware circuits, software, or a combination of hardware circuits and software.

[0045] In one design, the device may include a processing module; further, it may also include a communication module. The communication module is used to perform the sending and receiving actions in the method described in the first aspect or any of its possible implementations above, while the processing module is used to perform the processing actions involved in the method described in the first aspect or any of its possible implementations above.

[0046] Sixthly, this application provides a communication device. This device may be a communication equipment, or a component within a communication equipment (e.g., a module, communication module, circuit or chip responsible for communication functions, chip system, or processor), or a logical node, logical module, or software capable of implementing all or part of the functions of the communication equipment. As an example, the communication equipment is a core network element, such as a location management function entity.

[0047] This device may include modules that perform the methods / operations / steps / actions described in the second aspect or any of the possible implementations thereof. These modules may be hardware circuits, software, or a combination of hardware circuits and software.

[0048] In one design, the device may include a processing module and a communication module. The communication module is used to perform the sending and receiving actions in the method described in the second aspect above or any possible implementation thereof, while the processing module is used to perform the processing actions involved in the method described in the second aspect above or any possible implementation thereof.

[0049] Seventhly, this application provides a communication device. This device may be a communication equipment, or a component within a communication equipment (e.g., a module, communication module, circuit or chip responsible for communication functions (such as a modem chip, also known as a baseband chip, or a SoC chip or SIP chip containing a modem core), a chip system, or a processor), or a logical node, logical module, or software capable of implementing all or part of the functions of the communication equipment. As an example, this communication equipment is an access network device.

[0050] This device may include modules that perform the methods / operations / steps / actions described in the third aspect or any of the possible implementations thereof. These modules may be hardware circuits, software, or a combination of hardware circuits and software.

[0051] In one design, the device may include a processing module; further, it may also include a communication module. The communication module is used to perform the sending and receiving actions in the method described in the third aspect above or any of its possible implementations, while the processing module is used to perform the processing actions involved in the method described in the third aspect above or any of its possible implementations.

[0052] Eighthly, this application provides a communication device. This device may be a communication equipment, or a component within a communication equipment (e.g., a module, communication module, circuit or chip responsible for communication functions, chip system, or processor), or a logical node, logical module, or software capable of implementing all or part of the functions of the communication equipment. As an example, the communication equipment is a core network element, such as a location management function entity.

[0053] This device may include modules that perform the methods / operations / steps / actions described in the fourth aspect or any of the possible implementations thereof. These modules may be hardware circuits, software, or a combination of hardware circuits and software.

[0054] In one design, the device may include a processing module and a communication module. The communication module is used to perform the sending and receiving actions in the method described in the fourth aspect above or any possible implementation thereof, while the processing module is used to perform the processing actions involved in the method described in the fourth aspect above or any possible implementation thereof.

[0055] Ninthly, this application provides a communication device including a processor, wherein instructions are executed by the processor to cause the method as described in the first aspect or any possible implementation thereof to be implemented.

[0056] Optionally, the device may further include a storage medium that stores the instructions executed by the processor.

[0057] In some implementations, the storage medium is integrated with the processor, for example, the storage medium is integrated into the processor.

[0058] In a tenth aspect, this application provides a communication device including a processor, wherein instructions are executed by the processor to cause the method as described in the second aspect or any possible implementation thereof to be implemented.

[0059] Optionally, the device may further include a storage medium that stores the instructions executed by the processor.

[0060] In some implementations, the storage medium is integrated with the processor, for example, the storage medium is integrated into the processor.

[0061] In one aspect, this application provides a communication device including a processor, wherein instructions are executed by the processor to cause the method as described in the third aspect or any possible implementation thereof to be implemented.

[0062] Optionally, the device may further include a storage medium that stores the instructions executed by the processor.

[0063] In some implementations, the storage medium is integrated with the processor, for example, the storage medium is integrated into the processor.

[0064] In a twelfth aspect, this application provides a communication device including a processor, wherein instructions are executed by the processor to cause the method as described in the fourth aspect or any possible implementation thereof to be implemented.

[0065] Optionally, the device may further include a storage medium that stores the instructions executed by the processor.

[0066] In some implementations, the storage medium is integrated with the processor, for example, the storage medium is integrated into the processor.

[0067] In a thirteenth aspect, this application provides a chip including processing circuitry for running programs or instructions to implement methods as described in the first aspect or any possible implementation thereof.

[0068] Optionally, the chip may further include a memory for storing programs or instructions.

[0069] Optionally, the chip may also include the transceiver circuit, or an input / output interface.

[0070] In a fourteenth aspect, this application provides a chip including processing circuitry for running programs or instructions to implement methods as described in the second aspect or any possible implementation thereof.

[0071] Optionally, the chip may further include a memory for storing programs or instructions.

[0072] Optionally, the chip may also include the transceiver circuit, or an input / output interface.

[0073] In a fifteenth aspect, this application provides a chip including processing circuitry for running programs or instructions to implement methods as described in the third aspect or any of its possible implementations.

[0074] Optionally, the chip may further include a memory for storing programs or instructions.

[0075] Optionally, the chip may also include the transceiver circuit, or an input / output interface.

[0076] In a sixteenth aspect, this application provides a chip including processing circuitry for running programs or instructions to implement methods as described in the fourth aspect or any possible implementation thereof.

[0077] Optionally, the chip may further include a memory for storing programs or instructions.

[0078] Optionally, the chip may also include the transceiver circuit, or an input / output interface.

[0079] In a seventeenth aspect, this application provides a computer-readable storage medium including instructions that, when executed by a processor, cause a method as described in the first aspect or any possible implementation thereof to be implemented.

[0080] In an eighteenth aspect, this application provides a computer-readable storage medium including instructions that, when executed by a processor, cause the method as described in the second aspect or any possible implementation thereof to be implemented.

[0081] In a nineteenth aspect, this application provides a computer-readable storage medium including instructions that, when executed by a processor, cause a method as described in the third aspect or any of its possible implementations to be implemented.

[0082] In a twentieth aspect, this application provides a computer-readable storage medium including instructions that, when executed by a processor, cause the method as described in the fourth aspect or any of its possible implementations to be implemented.

[0083] In a twentieth aspect, this application provides a computer program product comprising computer program code or instructions that, when executed, cause the method as described in the first aspect or any of its possible implementations to be implemented.

[0084] In a twentieth aspect, this application provides a computer program product comprising computer program code or instructions that, when executed, cause the method as described in the second aspect or any of its possible implementations to be implemented.

[0085] In a twentieth aspect, this application provides a computer program product comprising computer program code or instructions that, when executed, cause the method described in the third aspect or any of its possible implementations to be implemented.

[0086] In a twentieth aspect, this application provides a computer program product comprising computer program code or instructions that, when executed, cause the method as described in the fourth aspect or any of its possible implementations to be implemented.

[0087] In a twentieth aspect, this application provides a communication system for performing the methods described in the first aspect or any possible implementation thereof, the methods described in the second aspect or corresponding implementation thereof, the methods described in the third aspect or corresponding implementation thereof, and the methods described in the fourth aspect or corresponding implementation thereof.

[0088] It is understood that the technical effects in any of the fifth to twenty-fifth aspects can be referenced from the technical effects in the corresponding method aspects, and will not be elaborated here. Attached Figure Description

[0089] Figure 1 An exemplary flowchart of a training data acquisition method for an AI model used for terminal positioning;

[0090] Figure 2 This is a schematic diagram of a communication system according to an embodiment of this application;

[0091] Figure 3 This is a schematic diagram of a communication system according to yet another embodiment of this application;

[0092] Figures 4 to 9 This is a flowchart illustrating the communication method according to an embodiment of this application;

[0093] Figure 10 This is a schematic diagram of the structure of a communication device according to an embodiment of this application;

[0094] Figure 11 This is a schematic diagram of the structure of another communication device according to an embodiment of this application. Detailed Implementation

[0095] Figure 1 This is an exemplary flowchart of a method for collecting training data for an AI model used for terminal positioning. The method includes steps S101 to S106. Here, gNB is an abbreviation for Next Generation NodeB, LMF is an abbreviation for Location Management Function, and the AI ​​model can be deployed on the gNB.

[0096] S101, LMF indicates to gNB that AI positioning is supported.

[0097] S102, gNB sends a data acquisition request to LMF.

[0098] It is understandable that after receiving the instruction information, the gNB sends a data acquisition request to the LMF.

[0099] S103, LMF executes the traditional positioning process to obtain the UE's location.

[0100] It is understandable that after receiving the data collection request, the LMF executes the traditional positioning process to obtain Part B information, that is, the UE's location information.

[0101] S104, LMF sends a message to gNB, triggering gNB to collect part A information.

[0102] As an example, the LMF sends an NRPPa MEASUREMENT REQUEST message to the gNB, triggering the collection of Part A information. The message sent by the LMF can carry Part B information.

[0103] S105, gNB obtains information from part A.

[0104] For example, gNB collects measurement data for Part A.

[0105] S106, gNB notifies LMF that the collection of part A information by gNB has ended.

[0106] As an example, Part A contains training data, and Part B contains ground truth label data.

[0107] Because the measurement information Part A collected by the gNB and Part B acquired by the LMF do not start collecting simultaneously, and the UE is moving, by the time the gNB finishes collecting Part A, the Part B information provided by the LMF may no longer be accurate. At this point, Part B information is no longer suitable as label data for Part A. If Part B information is used as label data for Part A, there will be a problem of mismatch or non-correspondence between the training data and the label data in time; in other words, the training dataset containing this training data and label data may be inaccurate. This will lead to inaccurate localization results from the localization model trained based on this training data and label data.

[0108] It is understandable that the AI ​​model used for terminal positioning mentioned above is just an example, intended only to better illustrate the problems existing in current technology. Even AI models with this function may have inaccurate output results due to the mismatch between the training data and the label data in time. In other words, any AI model that requires the time correspondence between training data and label data, or that requires the time correspondence between input and output, may have inaccurate output results after being trained using training datasets collected by existing methods.

[0109] To address the aforementioned problems, this application provides a new technical solution. The technical solution provided by this application helps to ensure that the time of association between training data and the time of association between label data satisfy a preset relationship, thereby improving the accuracy of the training dataset and consequently improving the accuracy of the trained AI model.

[0110] In this application, the time of association between training data and the time of association between label data satisfy a preset relationship, which can also be understood as: the time of association between training data and the time of association between label data are aligned or matched.

[0111] In some implementations of this application, the time associated with training data represents the time of collection of training data, and the time associated with label data represents the time of collection of label data.

[0112] In the embodiments of this application, the training data and label data constitute training samples, and multiple training samples constitute a training dataset.

[0113] An example of training a model based on training samples is as follows: input the training data into the model to obtain the model's output data; calculate the loss between the model's output data and the label data; and adjust the model's parameters based on this loss.

[0114] The technical solution provided in this application will be described below from several aspects, including system architecture, method, and apparatus.

[0115] In the description of the embodiments of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can mean A or B. "And / or" in this application is merely a description of the relationship between the related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. A and B can be singular or plural.

[0116] In the description of the embodiments of this application, unless otherwise stated, "a plurality of" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0117] In the description of the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner to facilitate understanding.

[0118] In the description of the embodiments of this application, the terms "information", "signal", "message", "channel", and "signaling" may sometimes be used interchangeably. It should be noted that when their distinctions are not emphasized, their intended meanings are matched.

[0119] In the description of the embodiments of this application, the terms "of", "corresponding (relevant)" and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing their distinction, their intended meanings are matched.

[0120] In the description of the embodiments of this application, the order of the process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0121] In the description of the embodiments of this application, "preset," "predefined," or "preconfigured" can be implemented by pre-storing corresponding codes, tables, or other means that can be used to indicate relevant information in the device (e.g., including terminals and wireless access network devices), or by being pre-defined in a protocol. This application does not limit the specific implementation method. "Stored" can refer to storing in one or more memories. The one or more memories can be separate settings or integrated into an encoder or decoder, processor, or communication device. The one or more memories can also be partially separate settings and partially integrated into a decoder, processor, or communication device. The type of memory can be any form of storage medium, and this application does not limit this.

[0122] In the description of the embodiments of this application, "protocol" may refer to standard protocols in the field of communications, such as 3GPP LTE protocols (such as technical specification (TS) 36, i.e., the TS36 series of technical specifications), NR protocols (such as the TS38 series of technical specifications), and related protocols applied to future communication systems. This application does not limit this.

[0123] It is understood that in this application, "...when" and "if" both refer to the corresponding processing that will be carried out under certain objective circumstances, and are not limited to a specific time, nor do they require a judgment action to be performed during implementation.

[0124] It is understood that some optional features in the embodiments of this application can be implemented independently in certain scenarios without relying on other features, such as the current solution on which they are based, to solve the corresponding technical problems and achieve the corresponding effects. Alternatively, they can be combined with other features as needed in certain scenarios. Correspondingly, the apparatus given in the embodiments of this application can also implement these features or functions, which will not be elaborated here.

[0125] In this application, unless otherwise specified, the same or similar parts between the various embodiments can be referred to each other. In the various embodiments of this application, unless otherwise specified or there is a logical conflict, the terminology and / or descriptions between different embodiments are consistent and can be mutually referenced. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships. The following descriptions of the embodiments of this application do not constitute a limitation on the scope of protection of this application.

[0126] In this application, entity A sends information to entity B, either directly or indirectly through other entities. Similarly, entity B receives information from entity A, either directly or indirectly through other entities. Entities A and B can be RAN nodes or terminals, or modules within RAN nodes or terminals. Information transmission and reception can be between RAN nodes and terminals, such as between a base station and a terminal; between two RAN nodes, such as between a CU and a DU; or between different modules within a single device, such as between a terminal chip and other modules of the terminal, or between a base station chip and other modules of the base station.

[0127] It is understood that the network architecture and business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0128] The method provided in this application can be used in various communication systems, including 3rd Generation Partnership Project (3GPP) communication systems such as Long Term Evolution (LTE) systems, 5th Generation Mobile Communication Technology (5G) systems such as 5G New Radio (NR) systems, and various future communication systems and networks. The method can be applied to terrestrial network communication systems as well as non-terrestrial network (NTN) communication systems. The NTN system can be an NTN system integrated with 4G, 5G, and any future communication system, such as NR NTN, IoT NTN, etc. NTN communication systems can be, for example, satellite communication systems, and can also include unmanned aerial vehicles (UAVs), high altitude platform stations (HAPS), and other aerial access network equipment; this application does not limit this.

[0129] The methods provided in this application can also be applied to Internet of Things (IoT) systems, vehicle-to-everything (V2X) systems, device-to-device (D2D) communication systems, machine-to-machine (M2M) communication systems, or other similar future-oriented systems, such as future communication systems. This application does not specifically limit these applications. Furthermore, the terms "system" and "network" are interchangeable.

[0130] In a communication system, a network element can send signals to or receive signals from another network element. These signals can include information, signaling, or data. The term "network element" can also be replaced by an entity, network entity, device, communication equipment, communication module, node, communication node, etc. This disclosure uses a network element as an example. For instance, a communication system can include at least one terminal device and at least one network device. The network device can send downlink signals to the terminal device, and / or the terminal device can send uplink signals to the network device.

[0131] In the communication system of this application embodiment, the terminal equipment may also be referred to as user equipment (UE), access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication equipment, user agent, or user device.

[0132] Terminal devices can be devices that provide voice / data, such as handheld devices with wireless connectivity, in-vehicle devices, etc. Currently, examples of terminals include: mobile phones, tablets, laptops, PDAs, mobile internet devices (MIDs), wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, cellular phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), handheld devices with wireless communication capabilities, computing devices or other processing devices connected to a wireless modem, wearable devices, terminal devices in 5G networks, or future public land mobile communication networks. Terminal devices in a network (PLMN), etc., are not limited to this in the embodiments of this application.

[0133] By way of example and not limitation, in this embodiment, the terminal device can also be a wearable device. Wearable devices, also known as wearable smart devices, are a general term for devices that utilize wearable technology to intelligently design and develop everyday wearables, such as glasses, gloves, watches, clothing, and shoes. Wearable devices are portable devices that are worn directly on the body or integrated into the user's clothing or accessories. Wearable devices are not merely hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are feature-rich, large in size, and can achieve complete or partial functions without relying on a smartphone, such as smartwatches or smart glasses, as well as those that focus on a specific type of application function and require the use of other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0134] The network device in this application embodiment can be a device for communicating with a terminal device. This network device can also be called an access network device or a wireless access network device, such as a base station. In this application embodiment, the network device can refer to a radio access network (RAN) node (or device) that connects the terminal device to the wireless network. A base station can broadly encompass, or be replaced by, various names including: NodeB, evolved NodeB (eNB), next-generation NodeB (gNB), relay station, access point, transmitting and receiving point (TRP), transmitting point (TP), master station, auxiliary 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. A base station can be a macro base station, micro base station, relay node, donor node, or similar entities, or combinations thereof. A base station can also refer to a communication module, modem, or chip installed within the aforementioned equipment or apparatus. A base station can also be a mobile switching center, equipment performing base station functions in D2D, V2X, and M2M communications, network-side equipment in future communication networks, or equipment performing base station functions in future communication systems. A base station can support networks using the same or different access technologies. Optionally, a RAN node can also be a server, wearable device, vehicle, or in-vehicle equipment. For example, the access network equipment in vehicle-to-everything (V2X) technology can be a roadside unit (RSU). The embodiments of this application do not limit the specific technologies or equipment forms used in the network equipment.

[0135] Base stations can be fixed or mobile. For example, a helicopter or drone can be configured to act as a mobile base station, and one or more cells can move depending on the location of the mobile base station. In other examples, a helicopter or drone can be configured as a device to communicate with another base station.

[0136] In the communication system of this application embodiment, network devices and / or terminal devices can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; and they can also be deployed in the air on airplanes, balloons, and satellites. This application embodiment does not limit the scenario in which the network devices and terminal devices are located. Furthermore, terminal devices and network devices can be hardware devices, or software functions running on dedicated hardware, or software functions running on general-purpose hardware, such as virtualization functions instantiated on a platform (e.g., a cloud platform), or entities that include dedicated or general-purpose hardware devices and software functions. This application does not limit the specific form of the terminal devices and network devices.

[0137] In wireless communication networks, such as mobile communication networks, the services supported by the networks are becoming increasingly diverse, thus requiring increasingly diverse demands. For example, networks need to support ultra-high speeds, ultra-low latency, and / or massive connectivity. This characteristic makes network planning, network configuration, and / or resource scheduling increasingly complex. Furthermore, as network functions become more powerful, such as supporting higher spectrum, supporting higher-order multiple-input multiple-output (MIMO) technologies, supporting beamforming, and / or supporting beam management, network energy efficiency has become a hot research topic. These new demands, new scenarios, and new characteristics bring unprecedented challenges to network planning, operation, and efficient operation. To meet these challenges, artificial intelligence (AI) technology can be introduced into wireless communication networks to achieve network intelligence. To support AI technology in wireless networks, AI nodes may also be introduced.

[0138] Optionally, the AI ​​node can be deployed in one or more of the following locations within the communication system: access network devices, terminal devices, or core network devices, etc. Alternatively, the AI ​​node can be deployed independently, for example, in a location other than any of the aforementioned devices, such as in the host or cloud server of an over-the-top (OTT) system. The AI ​​node can communicate with other devices in the communication system, which can be, for example, one or more of the following: network devices, terminal devices, or core network elements, etc.

[0139] It is understood that this application does not limit the number of AI nodes. For example, when there are multiple AI nodes, they can be divided based on function, such as different AI nodes being responsible for different functions.

[0140] It can also be understood that AI nodes can be independent devices, or they can be integrated into the same device to achieve different functions. Alternatively, they can be network elements in hardware devices, software functions running on dedicated hardware, or virtualization functions instantiated on a platform (e.g., a cloud platform). This application does not limit the specific form of the aforementioned AI nodes.

[0141] AI nodes can be AI network elements or AI modules.

[0142] AI modules are used to implement corresponding AI functions. AI modules deployed in different network elements can be the same or different. Depending on the parameter configuration, the AI ​​module can implement different functions. The AI ​​module model can be configured based on one or more of the following parameters: structural parameters (e.g., at least one of the following: number of neural network layers, neural network width, inter-layer connections, neuron weights, neuron activation function, or biases in the activation function), input parameters (e.g., the type and / or dimension of the input parameters), or output parameters (e.g., the type and / or dimension of the output parameters). The biases in the activation function can also be referred to as the neural network biases.

[0143] An AI module can have one or more models. A model can infer an output, which includes one or more parameters. The learning, training, or inference processes of different models can be deployed on different nodes or devices, or they can be deployed on the same node or device.

[0144] Figure 2 This is a schematic diagram of a communication system according to an embodiment of this application. Figure 2 As shown, the communication system 200 may include at least one network device, such as Figure 2 The network device 210 shown; the communication system 200 may also include at least one terminal device, such as Figure 2 The terminal device 220 is shown. The network device 210 and the terminal device 220 can communicate via a wireless link. Communication devices in this communication system, for example, between the network device 210 and the terminal device 220, can communicate using multi-antenna technology.

[0145] The communication system may also include a core network element 230. Terminals can connect to the core network element. The core network element and network devices can be independent physical devices, or they can be the same physical device integrating the logical functions of the core network element and the network device. As an example, a core network element may contain an LMF entity.

[0146] In practical applications, this communication system may include multiple network devices or multiple terminal devices. This application does not limit the number of network devices and terminal devices included in the communication system.

[0147] In some possible scenarios, multiple radio access network (RAN) devices collaborate to assist a terminal in achieving wireless access, with each RAN device performing some of the functions of a base station. In this scenario, as an example, the RAN devices can be a central unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU), etc.

[0148] CU and DU can be configured separately or included in the same network element, such as in a baseband unit (BBU). RU can be included in radio frequency equipment or radio frequency units, such as in a remote radio unit (RRU), an active antenna unit (AAU), or a remote radio head (RRH).

[0149] It is understood that in the description of the following embodiments, the radio access network device can be a CU node, a DU node, or a device including both CU nodes and DU nodes. Furthermore, a CU can be classified as a network device in the access network (RAN) or as a network device in the core network (CN), and no limitation is imposed here.

[0150] In some implementations, the CU performs some of the functions of layer 2 (L2) and layer 3 (L3), the DU performs some of the functions of layer 1 (L1) and L2, and the RU performs the computation of L1 and the digital part of RF.

[0151] The midhaul interface carries traffic between the CU and DU, the backhaul interface carries traffic between the CU and CN, and the fronthaul interface carries traffic between the RU and DU. The integrated DU includes the functions of both the DU and RU mentioned above.

[0152] The CU and / or DU include processors and hardware accelerators. The processors may include x86 processors or non-x86 processors, and the hardware accelerators may include FPGAs, GPUs, or other accelerators.

[0153] Taking DU as an example, DU can be implemented using a multi-core processor and one or more hardware accelerators. Parts of the DU protocol stack can be implemented in software running on a multi-core processor, while computationally intensive L1 and L2 functions can be offloaded to FPGA- or GPU-based hardware accelerators; or all L1 functions can be offloaded to FPGA- or GPU-based hardware accelerators, while other protocol stack components are implemented in software running on the processor; or the entire protocol stack can be implemented in software running on the processor. The hardware accelerator supports interconnection with x86 or non-x86 processors. Similarly, the accelerator has a multi-channel PCIe interface pointing to the CPU and external connections via GbE.

[0154] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an ORAN system, CU can also be called O-CU (Open CU), DU can also be called Open Distributed Unit (O-DU), CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called Open Radio Unit (O-RU). O-DU has baseband processing capabilities and complete protocol layer functions, primarily responsible for higher-level protocol functions such as data encryption and integrity protection, while also possessing high-level physical layer processing capabilities. O-RU possesses low-level physical layer signal processing capabilities, primarily responsible for the transmission and reception of radio frequency signals.

[0155] In an ORAN system, a RU can include three parts: an O-RAN processing unit (OPU), an O-RU digital processing unit (DPU), and a radio frequency (RF) processing unit.

[0156] The OPU receives eCPRI frames from the O-RAN fronthaul and performs fronthaul interface, lowest-level L1 (encoding, scrambling, modulation, layer mapping, precoding), synchronization, beamforming, and resource unit mapping. The OPU can be a CPU, FPGA, or ASIC.

[0157] The DPU can perform synchronous, DDC (digital downconversion in UL), and DUC (digital upconversion in DL) operations, improving power amplifier efficiency by reducing PAPR / ACLR at the RF front end; the DPU can be an FPGA or an ASIC.

[0158] The RF processing unit may include a transceiver module, up / down converters, power amplifiers (PAs), low-noise amplifiers (LNAs), and Tx / Rx filters. All conversions between the analog and digital domains (DAC and ADC), such as RF sampling, frequency conversion using RF, IF, and LO mixing during up-conversion and down-conversion, are performed within the transceiver module. In some implementations, the physical and logical partitions within the RF processing unit do not require specific boundaries.

[0159] Figure 3 This is a schematic diagram of a communication system according to another embodiment of this application. In some scenarios, this communication system may be referred to as an open radio access network (ORAN). Figure 3 As shown, this communication system includes a RAN intelligent controller (RIC). For example, the RIC can be an AI module used to implement AI-related functions.

[0160] RICs include near-real-time RICs (near-RT RICs) and non-real-time RICs (non-RT RICs). Non-real-time RICs primarily process non-real-time information, such as data that is not sensitive to latency (with latency on the order of seconds). Real-time RICs primarily process near-real-time information, such as data that is relatively sensitive to latency (with latency on the order of tens of milliseconds).

[0161] Near real-time (NRT) RICs are used for model training and inference. For example, they are used to train AI models and then use those models for inference. NRT RICs can obtain network-side and / or terminal-side information from network devices (e.g., CUs, CU-CPs, CU-UPs, DUs, and / or RUs) and / or terminals. This information can be used as training data or inference data. Optionally, the NRT RIC can deliver inference results to network devices and / or terminals. Optionally, inference results can be exchanged between CUs and DUs, and / or between DUs and RUs. For example, the NRT RIC delivers inference results to the DU, and the DU sends them to the RU.

[0162] Non-real-time RICs are also used for model training and inference. For example, they can be used to train AI models and then use those models for inference. Non-real-time RICs can obtain network-side and / or terminal-side information from network devices (e.g., CUs, CU-CPs, CU-UPs, DUs, and / or RUs) and / or terminals. This information can be used as training data or inference data, and the inference results can be delivered to the network devices and / or terminals. Optionally, inference results can be exchanged between CUs and DUs, and / or between DUs and RUs; for example, a non-real-time RIC delivers the inference result to a DU, which then forwards it to an RU.

[0163] For example, near real-time RICs are set up in network devices (e.g., CU, DU), while non-real-time RICs are set up in OAM, cloud servers, core network devices, or other network devices. RICs can be trained by obtaining subsets from multiple end devices from network devices (e.g., CU, CU-CP, CU-UP, DU, and / or RU), recombining them into a training dataset #2, and training on the training dataset #2.

[0164] For example, near real-time RIC and non-real-time RIC can also be set up separately as a network element, and the network device can be a near real-time RIC or a non-real-time RIC.

[0165] Near real-time RICs and non-real-time RICs can also be configured as separate network elements. Optionally, near real-time RICs and non-real-time RICs can also be part of other devices. For example, near real-time RICs can be set in network devices (e.g., CU, DU), while non-real-time RICs can be set in OAM, cloud servers, core network devices, or other network devices.

[0166] Figure 4 This is a flowchart illustrating a communication method according to an embodiment of this application. The method includes steps S410 and S420. Figure 4 The illustrated embodiment uses the first and second devices as examples of execution entities. It can be understood that... Figure 4 The first device in the method shown can be replaced by a processor, processor system, chip, chip system, circuit unit, or circuit system applied to the first device; the second device can be replaced by a processor, processor system, chip, chip system, circuit unit, or circuit system applied to the second device. The first device and the second device can be terminal equipment, network equipment, or core network elements, respectively.

[0167] As an example, the first device can be a network device; the second device is a core network element, such as an LMF entity.

[0168] S410, the first device sends first information, which requests second data corresponding to a first time, where the first time is the collection time of the first data, and the first data is one of training data and label data, while the second data is the other of training data and label data. Correspondingly, the second device receives the first information.

[0169] It is understandable that before sending the first information, the first device collects the first data and records the collection time of the first data, which is recorded as the first time.

[0170] Taking the UE positioning scenario as an example, the first device is a network device. The network device performs measurement operations according to the measurement configuration information configured by the LMF entity, obtains the measurement result, and timestamps the measurement result. The measurement result is the first data, and the timestamp is the first time.

[0171] It is understandable that during the same period that the first device collects the first data, the second device is also collecting the second data.

[0172] The first information is used to request the second data corresponding to the first time, which can be understood as: the first information is used to trigger the second device to send the second data corresponding to the first time.

[0173] In some implementations, the first information includes the first moment.

[0174] In some implementations, timestamps are used to represent the time immediately.

[0175] In some implementations, the first data is the training data, and the second data is the labeled data. In other implementations, the first data is the labeled data, and the second data is the training data.

[0176] In some implementations, the training data includes measurement results of the reference signal. For example, the training data includes measurement results obtained by the network device measuring the reference signal sent by the terminal device. Alternatively, the training data includes measurement results at the network device after the reference signal sent by the network device is transmitted by the terminal device. Yet another example is that the training data includes measurement results obtained by the terminal device measuring the reference signal sent by the network device.

[0177] In some implementations, the tag data includes at least one of the following: the location information of the terminal, the direct path information of the reference signal, the indirect path information of the reference signal, the phase information of the reference signal, the power information of the reference signal, or the measurement time information of the reference signal.

[0178] For example, in a scenario where a model for training terminal localization is acquired, the label data includes at least one of the following: the terminal's location information, the direct path information of the reference signal sent by the terminal to the network device, the indirect path information of the reference signal sent by the terminal to the network device, the direct path information of the reference signal sent by the network device to the terminal, the indirect path information of the reference signal sent by the network device to the terminal, the phase information of the reference channel sent by the terminal reaching the network device, the phase information of the reference signal sent by the network device reaching the terminal, the measurement time information of the network device measuring the reference signal sent by the terminal device, or the measurement time information of the terminal device measuring the reference signal sent by the network device.

[0179] S420, the second device sends the second data corresponding to the first time. Correspondingly, the first device receives the second data corresponding to the first time.

[0180] It is understood that the second device collects the second data and records the collection time of the second data before sending the second data corresponding to the first time. For ease of description, the collection time of the second data is referred to as the second time.

[0181] Taking a UE positioning scenario where the second device is an LMF entity as an example, the LMF entity periodically collects the UE's location information and timestamps each UE location information after collection. The UE location information is the second data, and the timestamp of the UE location information is the second time.

[0182] Before receiving the first information, the second device may have collected one or more pieces of second data. After receiving the first information, the second device, in response to the first information, determines the second data corresponding to the first time from the one or more pieces of second data.

[0183] In some implementations, the second device determines the second data whose collection time is closest to the first time from among the one or more second data as the second data corresponding to the first time.

[0184] In some implementations, the second device determines the second data corresponding to the first time as the second data in the one or more second data whose difference between the collection time and the first time is less than or equal to a difference threshold.

[0185] In some implementations, the second device first determines the second data among the one or more second data whose collection time is less than or equal to the first time difference threshold; then, among the second data whose collection time is less than or equal to the first time difference threshold, the second data whose collection time is closest to the first time is determined as the second data corresponding to the first time.

[0186] In some implementations, the second device stops collecting second data after receiving the first information to avoid wasting resources.

[0187] In some implementations, after the second device sends the second data corresponding to the first time, it deletes all the second data collected before that time to avoid occupying storage resources.

[0188] In this embodiment, after receiving the second data corresponding to the first time, the first device can obtain a training dataset based on the first data and the second data. For example, the first data can be used as training data, and the second data can be used as the label data of the first data, thereby obtaining a training sample in the training dataset. Alternatively, the second data can be used as training data, and the first data can be used as the label data of the second data, thereby obtaining a training sample in the training dataset.

[0189] The communication method in this embodiment helps improve the accuracy of the training samples obtained based on the second data and the first data because the collection time of the second data corresponds to, matches, or aligns with the collection time of the first data, thereby helping to improve the accuracy of the model trained based on the training samples.

[0190] In some implementations, when the second device sends the second data corresponding to the first time, it also sends the collection time of the second data, i.e., it also sends the second time. In this case, the first device can determine whether to associate the first data and the second data to obtain training samples based on the second time, thereby further ensuring the accuracy of the obtained training samples.

[0191] For example, the first device determines whether the difference between the second time and the first time is less than or equal to a time difference threshold. If the difference between the second time and the first time is less than or equal to the time difference threshold, the first data and the second data are associated to obtain a training sample.

[0192] In some implementations, this method further includes: a second device sending second information, the second information being used to request, instruct, trigger, or activate the first device to collect first data. The first device begins collecting the first data and records the collection time of the first data only after receiving the second information.

[0193] Optionally, after sending the second information, the second device begins collecting the second data and records the collection time of the second data. For example, after sending the second information, the second device begins periodically collecting the second data and recording the collection time of the second data.

[0194] In some implementations, this method further includes: the first device sending third information, which is used to request, instruct, trigger, or activate the second device to collect second data. After receiving the third information, the second device begins to collect the second data and records the collection time of the second data.

[0195] In the scenario where the second device sends second information to the first device, and the first device sends third information to the second device, optionally, the first device sends the third information before the second device sends the second information. For example, the second device sends the second information only after receiving the third information. In this case, the order in which the second device starts collecting the second data and sends the second information is not limited.

[0196] In this embodiment, the operations performed by the first device can be performed separately by the O-DU and O-RU under the ORAN architecture. For example, the O-RU is responsible for receiving and transmitting signals and data, while the O-DU is responsible for processing signals and data.

[0197] The following example uses a gNB as the first device, an LMF as the second device, and training data for UE positioning AI model training. Figure 5 The communication method in the embodiments of this application is described exemplarily. It is understood that the gNB in ​​this example can be replaced with any other type of network device, and the LMF in this example can be replaced with any other network element capable of UE location. In this embodiment, the AI ​​model is deployed in the gNB. This method includes steps S501 to S507.

[0198] S501, LMF indicates to gNB that AI positioning is supported.

[0199] This step can be referenced from relevant content in existing technologies, and will not be elaborated here.

[0200] S502, gNB sends a data acquisition request message to LMF.

[0201] The data collection request is a message carrying the first piece of information.

[0202] S503, LMF begins executing the traditional positioning process to obtain the UE's location information.

[0203] The UE's location information can also be referred to as Part B information.

[0204] As an example, after receiving a data collection request, LMF begins to periodically execute the traditional positioning process to obtain the UE's location information and timestamps the obtained UE location information.

[0205] S504, LMF sends a message to gNB, which triggers gNB to collect information from part A.

[0206] As an example, the LMF sends an NRPPa MEASUREMENT REQUEST message to the gNB, triggering the gNB to collect Part A information.

[0207] In this step, the message sent by LMF to gNB to trigger gNB to collect Part A information is the message carrying the second information.

[0208] S505 and gNB perform measurements and obtain information from part A based on the measurement configuration configured in the LMF.

[0209] After receiving the message sent by LMF, gNB performs the measurement according to the measurement configuration configured by LMF, obtains Part A information, and adds a timestamp.

[0210] S506, gNB notifies LMF that the collection of part A information is complete. This notification message carries the timestamp of part A information and is used to request part B information corresponding to the timestamp.

[0211] It is understandable that this notification is the primary information.

[0212] S507, LMF sends the B part of the information corresponding to the timestamp to gNB.

[0213] Optionally, after sending Part B information, the LMF deletes the Part B information collected starting from S503. In this implementation, deleting Part A information reduces the storage space occupancy of the LMF and avoids wasting storage resources.

[0214] Optionally, LMF stops data acquisition after sending Part B information to avoid wasting resources.

[0215] Figure 5 In the example shown, the operations performed by the gNB can be performed separately by the O-DU and O-RU under the ORAN architecture. Figure 6 This is an example process under the ORAN architecture. The example process under the ORAN architecture is... Figure 5 Some differences in the process shown are:

[0216] Figure 5 In the example shown, the signal and data reception and transmission operations handled by the gNB are handled by the O-RU in the ORAN architecture; Figure 5 The signal and data processing operations, which are handled by the gNB in ​​the example shown, are handled by the O-DU in the ORAN architecture.

[0217] Figure 6The illustrated embodiment uses UE, O-RU, O-DU, and LMF as examples for the execution entities. This method includes steps S601 to S607.

[0218] S601, LMF instructs O-RU to support AI positioning.

[0219] S602, O-RU sends a data acquisition request to LMF.

[0220] S603, LMF begins executing the traditional positioning process to obtain the UE's location information.

[0221] S604, LMF sends a message to O-RU, which is used to trigger O-SU to collect information from part A.

[0222] S605, O-DU performs measurements and obtains information from part A based on the measurement configuration configured in the LMF.

[0223] It is understandable that the O-DU here performs the measurement based on the trigger message received by the O-RU from the LMF.

[0224] S606, the O-RU notifies the LMF that the collection of part A information is complete. This notification message carries a request message, which is used to request part B information corresponding to the timestamp.

[0225] It is understandable that the A-part information sent by the O-RU here is collected by the O-DU.

[0226] S607, LMF sends the B part information corresponding to the time to O-RU.

[0227] Figure 7 This is a flowchart illustrating a communication method according to an embodiment of this application. The method includes steps S710 and S720. Figure 7 The illustrated embodiment uses the first and second devices as examples of execution entities. It can be understood that... Figure 7 The first device in the method shown can be replaced by a processor, processor system, chip, chip system, circuit unit, or circuit system applied to the first device; the second device can be replaced by a processor, processor system, chip, chip system, circuit unit, or circuit system applied to the second device. The first device and the second device can be terminal equipment, network equipment, or core network elements, respectively.

[0228] As an example, the first device can be a network device; the second device is a core network element, such as an LMF entity.

[0229] S710, the second device acquires second data and a second time, the second time being the time when the second data is collected, and the second data being either labeled data or training data.

[0230] For example, the second device collects second data and records the collection time of the second data, which is denoted as the second time.

[0231] It is understandable that during the same period that the second device collects the second data, the first device is also collecting the first data. For example, the first device periodically collects the first data and records the collection time of the first data.

[0232] Taking a UE positioning scenario where the second device is an LMF entity as an example, the LMF entity periodically collects the UE's location information and timestamps each UE location information after collection. The UE location information is the second data, and the timestamp of the UE location information is the second time.

[0233] The second data is either the labeled data or the training data. It can be understood as either the second data being used as the labeled data or the second data being used as the training data.

[0234] Examples of training data and label data in this embodiment can be found in the previous embodiment, and will not be repeated here.

[0235] S720, the second device transmits the second data and the second time. Correspondingly, the first device receives the second data and the second time.

[0236] For example, after the second device collects the second data and records the second time, it sends the second data to the first device and then to the second device.

[0237] After receiving the second data and the second time, the first device determines the first data corresponding to the second time, and determines the training sample based on the first data and the second data. The first data is another type of data between the label data and the training data.

[0238] For example, when the first data is the label data, the second data is the training data; or, when the first data is the training data, the second data is the label data.

[0239] In some implementations, the first device determines the first data whose collection time is closest to the second time from the first data collected.

[0240] In some other implementations, the first device determines the first data in the first data collected, where the difference between the collection time and the second time is less than or equal to a time difference threshold, as the first data corresponding to the second time.

[0241] Accordingly, the first device receives the second data and the second time.

[0242] In this method, because the collection time of the first data for generating training samples corresponds to the collection time of the second data, it helps to improve the accuracy of the generated training samples, and thus helps to improve the accuracy of the model trained using the training samples.

[0243] In some implementations, after the first device determines the first data corresponding to the second time, it deletes the other first data collected to avoid wasting storage resources.

[0244] In some implementations, the first device stops collecting the first data after receiving the second data and the second time, in order to avoid wasting resources.

[0245] In some implementations, the method further includes: the first device sending fourth information, which requests, triggers, instructs, or activates the second device to collect second data. Accordingly, after receiving the fourth information, the second device begins collecting the second data and records the collection time of the second data, i.e., this collection time is designated as the second time. After collecting the second data and recording the second time, the second device sends the second data and the second time to the first device.

[0246] In some implementations, this method further includes: the second device sending a fifth message, the fifth message being used to request, trigger, instruct, or activate the first device to collect the first data. Accordingly, after receiving the fifth message, the first device begins collecting the first data and records the collection time of the first data.

[0247] Taking the UE positioning scenario as an example, the first device is a network device. The network device performs measurement operations according to the measurement configuration information configured by the LMF entity, obtains the measurement result, and timestamps the measurement result. The measurement result is the first data, and the timestamp is the collection time of the first data.

[0248] As an example, when the first device sends the fourth message, after receiving the fifth message, the first device starts to periodically collect the first data and record the collection time of the first data, and then sends the fourth message.

[0249] In this embodiment, the operations performed by the first device can be performed separately by the O-DU and O-RU under the ORAN architecture. For example, the O-RU is responsible for receiving and transmitting signals and data, while the O-DU is responsible for processing signals and data.

[0250] The following example uses a gNB as the first device, an LMF as the second device, and training data for UE positioning AI model training. Figure 8The communication method in the embodiments of this application is described exemplarily. It is understood that the gNB in ​​this example can be replaced with any other type of network device, and the LMF in this example can be replaced with any other network element capable of UE location. In this embodiment, the AI ​​model is deployed in the gNB. This method includes steps S801 to S808.

[0251] S801, LMF indicates to gNB that AI positioning is supported.

[0252] S802, gNB sends a data acquisition request to LMF.

[0253] S803, LMF sends a message to gNB, which triggers gNB to collect information from part A.

[0254] In this step, the message sent by LMF to gNB to trigger gNB to collect Part A information is the message carrying the second information.

[0255] As an example, the LMF sends an NRPPa MEASUREMENT REQUEST message to the gNB, triggering the collection of Part A information.

[0256] S804 and gNB perform measurements and obtain information from part A based on the measurement configuration configured in the LMF.

[0257] After receiving the message sent by LMF, gNB performs the measurement according to the measurement configuration configured by LMF, obtains Part A information, and adds a timestamp.

[0258] S805, the gNB notifies the LMF that the collection of part A information is complete. This notification message carries a timestamp of part A information and is used to request part B information corresponding to that timestamp. S806, the LMF begins executing the traditional positioning procedure to obtain the UE's location information.

[0259] The UE's location information can also be referred to as Part B information.

[0260] In some implementations, after receiving a data acquisition request, the LMF begins to periodically execute the traditional positioning process, obtains the UE's reference signal, calculates the UE's position, and timestamps the obtained UE position.

[0261] S807, LMF sends part B information to gNB.

[0262] After receiving the request information sent by the gNB, the LMF sends Part B information to the gNB, sending the UE's location at the time corresponding to the measurement time of the reference signal.

[0263] After receiving part B of the information, S808 and gNB select part A of the information at the corresponding time for training.

[0264] After receiving Part B information, gNB selects Part A information from the corresponding time period for training.

[0265] After the LMF sends Part B information, it deletes the Part B information collected since S806. In this implementation, deleting Part A information reduces the storage space usage of the LMF and avoids wasting storage resources. In some implementations, the gNB stops acquiring Part A data.

[0266] Optionally, after receiving information from part B, the gNB stops collecting information from part A to avoid wasting resources.

[0267] Figure 8 The example shown is the same as Figure 5 Some differences in the examples shown are as follows: Figure 5 In the example shown, the LMF starts first to obtain Part B information, and the gNB starts later to obtain Part A information. The gNB requests the LMF to send Part B information at the time corresponding to the Part A information, so the gNB receives Part B information at the matching time. Figure 8 In the example shown, gNB first starts to obtain Part A information. After receiving Part B information, gNB selects Part B information at the time corresponding to Part A information.

[0268] Figure 8 In the example shown, the operations performed by the gNB can be performed separately by the O-DU and O-RU under the ORAN architecture. Figure 9 This is another exemplary process under the ORAN architecture. The exemplary process under the ORAN architecture is... Figure 8 Some differences in the process shown are: Figure 8 In the example shown, the signal and data reception and transmission operations handled by the gNB are handled by the O-RU in the ORAN architecture; Figure 8 The signal and data processing operations, which are handled by the gNB in ​​the example shown, are handled by the O-DU in the ORAN architecture.

[0269] Figure 9 The illustrated embodiment uses UE, O-RU, O-DU, and LMF as examples of execution entities. This method includes S901 to S908.

[0270] S901, LMF instructs O-RU to support AI positioning.

[0271] S902, O-RU sends a data acquisition request to LMF.

[0272] S903, LMF sends a message to O-RU, which is used to trigger gNB to collect part A information.

[0273] S904, O-DU performs measurements and obtains information from part A based on the measurement configuration configured in the LMF.

[0274] It is understandable that the O-DU here performs the measurement based on the trigger message received by the O-RU from the LMF.

[0275] S905, the O-RU notifies the LMF that the collection of part A information is complete. This notification message carries a request message, which is used to request part B information corresponding to the timestamp.

[0276] S906, LMF begins executing the traditional positioning process to obtain the UE's location information.

[0277] S907, LMF sends part B information to O-RU.

[0278] After receiving information from part B, the S908 O-DU selects part A information from the corresponding time period for training.

[0279] It is understandable that the B part of the information used by the O-DU here is received by the O-RU from the LMF.

[0280] Figure 10 This is a schematic diagram of the structure of a communication device according to an embodiment of this application. Figure 10 As shown, the communication device 1000 may include a processing module 1010 and a communication module 1020.

[0281] As a first example, device 1000 can be used to implement Figure 4 or Figure 7 The communication method implemented by the first device in the embodiment may be used to implement... Figure 5 or Figure 8 The communication method implemented by gNB in ​​the embodiment shown may be used to implement... Figure 6 or Figure 9 The communication method is implemented by O-RU. The processing module 1010 can be used to implement processing-related steps, and the communication module 1020 can be used to implement sending and / or receiving steps.

[0282] As a second example, device 1000 can be used to implement Figure 4 or Figure 7 The communication method implemented by the second device in the embodiment may be used to implement... Figure 5 , Figure 6 , Figure 9 or Figure 8The embodiment shown illustrates a communication method implemented by LMF. The processing module 1010 can be used to implement processing-related steps, and the communication module 1020 can be used to implement steps such as sending and / or receiving.

[0283] Figure 11 This is a schematic diagram of another communication device according to an embodiment of this application, which can be used to implement this application. Figure 5 or Figure 8 The communication method shown is as follows. Figure 11 As shown, the communication device 1100 includes a processor 1110 and a communication circuit 1120. The processor 1110 and the communication circuit 1120 are coupled to each other. It is understood that the communication circuit 1120 can be a transceiver or an input / output interface.

[0284] Optionally, the communication device 1100 may further include a memory 1130 for storing instructions executed by the processor 1110, or storing input data required by the processor 1110 to execute instructions, or storing data generated after the processor 1110 executes instructions. It is understood that the memory 1130 may be located outside the processor 1110, or inside the processor 1110.

[0285] As an example, processor 1110 is used to implement the functions of the processing module 1010 described above, and communication circuit 1020 is used to implement the functions of the communication module 1120 described above.

[0286] As an example, when the above-mentioned device is a chip used in a communication device, the chip receiving information can be understood as the information being received first by other modules (such as an RF module or antenna) in the communication device, and then sent to the chip by these modules. Similarly, the chip sending information can be understood as the information being first sent to other modules (such as an RF module or antenna) in the communication device, and then sent by these modules.

[0287] In some embodiments of this application, a computer program product is also provided. When the computer program product is run on a processor, it can implement the method implemented by the terminal device in any of the above embodiments, or it can implement the method implemented by the service satellite in any of the above method embodiments.

[0288] In some embodiments of this application, a computer-readable storage medium is also provided, which contains computer instructions that, when executed on a processor, can implement the methods implemented by the terminal device in any of the above embodiments, or can implement the methods implemented by the service satellite in any of the above method embodiments.

[0289] In some embodiments of this application, a communication system is also provided, which can implement the methods implemented by terminal devices and service satellites in any of the above embodiments.

[0290] It is understood that the processor in the embodiments of this application may be any of the following devices or all or part of the circuitry used for processing functions: a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor may be a microprocessor or any conventional processor.

[0291] The method steps in the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory, flash memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. Alternatively, the ASIC can reside in a service satellite or terminal device. Of course, the processor and storage medium can also exist as discrete components in the service satellite or terminal device.

[0292] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this application are performed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video optical disc; or it can be a semiconductor medium, such as a solid-state drive.

[0293] In the various embodiments of this application, unless otherwise specified or in case of logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

[0294] It is understood that the various numerical designations used in the embodiments of this application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of this application. The order of the process numbers described above does not imply the order of execution; the execution order of each process should be determined by its function and internal logic.

Claims

1. A communication method, characterized in that, The method includes: Send a first message, the first message being used to request second data corresponding to a first time, the first time being the collection time of the first data, the first data being one of training data and label data, and the second data being the other of the training data and the label data; Receive the second data corresponding to the first time.

2. The method according to claim 1, characterized in that, The first information includes the first time.

3. The method according to claim 1 or 2, characterized in that, The second data corresponding to the first time includes: at least one of the second data whose collection time is closest to the first time.

4. The method according to any one of claims 1 to 3, characterized in that, The first data is the training data, and the second data is the label data.

5. The method according to any one of claims 1 to 4, characterized in that, The first data includes measurement result information of the reference signal; and / or, the second data includes at least one of the following: terminal location information, direct path information of the reference signal, indirect path information of the reference signal, phase information of the reference signal, or measurement time information of the reference signal.

6. A communication method, characterized in that, The method includes: Receive first information, the first information is used to request second data corresponding to a first time, the first time is the collection time of the first data, the first data is one of training data and label data, and the second data is the other of the training data and the label data; Send the second data corresponding to the first time.

7. The method according to claim 6, characterized in that, The first information includes the first time.

8. The method according to claim 6 or 7, characterized in that, The second data corresponding to the first time includes: at least one of the second data whose collection time is closest to the first time.

9. The method according to any one of claims 6 to 8, characterized in that, The first data is the training data, and the second data is the label data.

10. The method according to any one of claims 6 to 9, characterized in that, The first data includes measurement result information of the reference signal; and / or, the second data includes at least one of the following: terminal location information, direct path information of the reference signal, indirect path information of the reference signal, phase information of the reference signal, or measurement time information of the reference signal.

11. A communication method, characterized in that, The method includes: Acquire second data and second time, where the second time is the collection time of the second data, and the second time is used to determine the first data. The second data is one of training data and label data, and the first data is the other of the training data and the label data. Send the second data and the second time.

12. The method according to claim 11, characterized in that, The first data corresponding to the second time includes: at least one first data whose collection time is closest to the second time.

13. The method according to claim 11 or 12, characterized in that, The first data is the training data, and the second data is the label data.

14. The method according to any one of claims 11 to 13, characterized in that, The first data includes measurement result information of the reference signal; and / or, the second data includes at least one of the following: terminal location information, direct path information of the reference signal, indirect path information of the reference signal, phase information of the reference signal, or measurement time information of the reference signal.

15. A communication method, characterized in that, The method includes: Receive second data and a second time, where the second time is the collection time of the second data, and the second data is one of training data and label data; Determine the first data corresponding to the second time, wherein the first data is one of the training data and the label data.

16. The method according to claim 15, characterized in that, The first data corresponding to the second time includes: at least one first data whose collection time is closest to the second time.

17. The method according to claim 15 or 16, characterized in that, The first data is the training data, and the second data is the label data.

18. The method according to any one of claims 15 to 17, characterized in that, The first data includes measurement result information of the reference signal; and / or, the second data includes at least one of the following: terminal location information, direct path information of the reference signal, indirect path information of the reference signal, phase information of the reference signal, or measurement time information of the reference signal.

19. A communication device, characterized in that, include: A module or unit for performing the method as described in any one of claims 1 to 5, 6 to 10, 11 to 14, or 15 to 18.

20. A communication device, characterized in that, include: A processor coupled to a memory for storing a computer program, wherein when the processor invokes the computer program, the communication device performs the method as claimed in any one of claims 1 to 5, or any one of claims 6 to 10, or any one of claims 11 to 14, or any one of claims 15 to 18.

21. A computer-readable storage medium, characterized in that, For storing a computer program, the computer program comprising: instructions for implementing the method as claimed in any one of claims 1 to 5, or any one of claims 6 to 10, or any one of claims 11 to 14, or any one of claims 15 to 18.

22. A computer program product, the computer program product comprising instructions, characterized in that, When the instructions are executed on a computer, the computer performs the method as claimed in any one of claims 1 to 5, 6 to 10, 11 to 14, or any one of claims 15 to 18.

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