Communication methods and related apparatuses

Abstract

This invention relates to a communication method and related apparatus, and pertains to the field of communication technology. In one method, a terminal device can report the amount of data M1 that can be collected and fed back within a time period T. This allows the network side to schedule the amount of data collected by the terminal device based on this data amount M1. This avoids situations where the amount of data collected by the terminal device is unknown, leading to scheduling more data than the terminal device can feed back, resulting in data loss. This necessitates rescheduling other terminals for data collection, incurring signaling and latency overhead, and preventing the amount of uncollected or unfeasible data from meeting the required metrics for model training, thus affecting model performance or increasing model training latency.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a communication method and related apparatus. Background Technology

[0002] Machine learning is an important technological approach to achieving artificial intelligence (AI). Before an AI model runs, it is trained on a static dataset (i.e., training data) and then fed into dynamic / constantly changing real-world scenarios / data to perform inference tasks. Therefore, data collection for model training is an issue that needs further research. Summary of the Invention

[0003] This application provides a communication method and related apparatus that can determine a data collection strategy based on the amount of data in the dataset required for model training and the transmission latency.

[0004] In a first aspect, this application provides a communication method that can be applied to a terminal device, a chip or chip module in the terminal device, or a module or unit capable of realizing all or part of the functions of the terminal device. Taking a terminal device as an example, the method includes: the terminal device determining first information, the first information being used to instruct the terminal device to collect data for training a model within a time period T, and the amount of data M1 that can be fed back; and the terminal device sending the first information.

[0005] Based on this method, the terminal device can report the amount of data M1 that can be used for data collection within a time period T. This allows the network side to schedule the amount of data collected by the terminal device according to the amount of data M1. This avoids the situation where the amount of data M1 that the terminal device can collect is unknown, and the scheduling exceeds the amount of data M1 that the terminal device can report, resulting in the inability to obtain data. This would require scheduling other terminals to collect data again, which would incur signaling overhead and latency overhead.

[0006] In addition, based on this method, the network side can schedule the amount of data collected by the terminal device according to the amount of data M1, avoiding the situation where scheduling exceeds the amount of data that the terminal can feed back, resulting in the inability to obtain data or the amount of data fed back not meeting the indicators required for model training, thus affecting the performance of the model or increasing the model training latency.

[0007] In one optional implementation, the terminal device receives a first message, which is used to query the amount of data M1 that the terminal device can return; based on the first message, the terminal device determines and sends the aforementioned first information.

[0008] Based on this implementation method, the network side can actively query the amount of data that the terminal device can report, so as to schedule the terminal to report the corresponding amount of data.

[0009] In one approach, the first message is a capability signaling message used to query the terminal's data collection capabilities, such as the amount of data that can be fed back and / or the transmission latency of the fed-back data. This approach is suitable for situations where the amount of data fed back by the terminal device is static. In another approach, the first message is other information, rather than capability information, which is suitable for situations where the amount of data fed back by the terminal device is dynamically changing.

[0010] Optionally, the method further includes: the terminal device receiving second information, the second information indicating the amount of data M2 that the terminal needs to collect; the terminal device collecting and transmitting data of amount M2 based on the second information, where M2 is less than or equal to M1.

[0011] Based on this method, the terminal device can receive the second information sent by the network side based on the first information, and report the corresponding amount of data.

[0012] In one alternative implementation, the data volume M1 is the total amount of data collected by the terminal device for training the model within time T, or the remaining amount of data that can be fed back; and / or, the value of the data volume M1 is related to the model or scenario used for training.

[0013] Optionally, if time T is periodic and the data volume M1 is the total amount of data that the terminal device can report, then the maximum amount of data that the terminal device can collect every T time interval is M1. If time T is non-periodic, the network side can query the total amount of data that the terminal device can report again after time T, or the terminal device can report the total amount of data that it can report after time T.

[0014] Secondly, this application also provides a communication method applicable to a network-side device, which may be an access network device, a core network device, an operation, administration and maintenance (OAM) network element, a chip or chip module within the network-side device, or a module or unit capable of realizing all or part of the functions of the network-side device. Taking a network device as an example, in this method: the network device receives first information, which instructs the terminal device to collect data for training the model within time T, and the amount of data M1 that can be fed back; based on the first information, the network device sends second information, which instructs the terminal device to feed back the amount of data M2 that needs to be fed back.

[0015] Based on this method, the network device schedules the amount of data M2 that the terminal device needs to return according to the amount of data M1. This avoids the situation where the amount of data M1 that the terminal device can use for data collection is unknown, and the scheduling exceeds the amount of data M1 that the terminal can return, resulting in the inability to obtain data. This would require scheduling other terminals again for data collection, thus avoiding the signaling overhead and latency overhead.

[0016] In one optional implementation, the network device sends a first message, which is used to query the amount of data M1 that the terminal device can return.

[0017] Based on this method, the network device can proactively query the amount of data that the terminal device can return, so as to schedule the terminal device to return the corresponding amount of data.

[0018] In one approach, the first message is a capability signaling message used to query the terminal's data collection capabilities, such as the amount of data that can be fed back and / or the transmission delay of the fed-back data. Optionally, this approach can be applied to cases where the amount of data fed back by the terminal device is static.

[0019] Another approach is to present other information, rather than information about query capabilities, as the first message is suitable for situations where the amount of data returned by the terminal device is dynamically changing.

[0020] In one alternative implementation, the data volume M1 is the total amount of data collected by the terminal device for training the model within time T, or the remaining amount of data that can be fed back; and / or, the value of the data volume M1 is related to the model or scenario used for training.

[0021] Optionally, if time T is periodic and the data volume M1 is the total amount of data that the terminal device can report, then the maximum amount of data that the terminal device can collect every T time interval is M1. If time T is non-periodic, the network side can query the total amount of data that the terminal device can report again after time T, or the terminal device can report the total amount of data that it can report after time T.

[0022] Thirdly, this application provides a communication method that can be applied to a terminal device, a chip or chip module in the terminal device, or a module or unit capable of realizing all or part of the functions of the terminal device. Taking a terminal device as an example, the method includes: the terminal device receiving first information, the first information indicating that the amount of data M2 to be fed back by the terminal for data collection for training the model; and the terminal device sending second information based on the first information, the second information indicating that the amount of data M2 is greater than the amount of remaining data that the terminal can feed back for data collection.

[0023] Based on this method, the terminal device enables the network side to avoid scheduling more data than the terminal can feed back M1, which would result in the inability to obtain data or the amount of data fed back not meeting the indicators required for model training, thus affecting the model's performance or increasing the model training latency.

[0024] Optionally, the second information sent by the terminal may include at least one of the following: a negative acknowledgment (NACK); information indicating the amount of remaining data that the terminal has for data collection; or, information indicating the validity period T of the remaining data.

[0025] In one embodiment, the terminal device receives first information, which indicates the amount of data M2 that the terminal needs to collect for training the model; the terminal device sends a negative response NACK.

[0026] Based on this embodiment, when the data volume M2 is greater than the amount of data that the terminal can collect and feedback for training the model, the terminal device sends a NACK, so that the network side can know in time that the scheduled data volume exceeds the amount of data that the terminal can feedback, thereby adjusting the scheduled data volume in a timely manner, and avoiding the impact on model performance or the increase in model training latency due to the inability to obtain data or the amount of feedback data not meeting the indicators required for model training.

[0027] In another embodiment, the terminal device receives first information, which indicates the amount of data M2 that the terminal needs to collect for training the model; the terminal device then sends information indicating the remaining amount of data for data collection.

[0028] Based on this embodiment, when the amount of data M2 is greater than the amount of data that the terminal can collect and feedback for training the model, the terminal device sends information indicating the remaining amount of data that the terminal can collect. This allows the network side to adjust the amount of data scheduled in a timely manner based on the remaining amount of data, thus avoiding the impact on model performance or the increase in model training latency due to the inability to obtain data or the amount of data fed back not meeting the indicators required for model training.

[0029] In another embodiment, the terminal device receives first information, which indicates the amount of data M2 that the terminal needs to collect for training the model; the terminal device sends an effective time T for the remaining amount of data that the terminal can collect and feed back.

[0030] Based on this embodiment, when the amount of data M2 is greater than the amount of data collected by the terminal for training the model and can be fed back, the terminal device sends an indication of the effective time T of the remaining data amount, so that the network side can adjust the scheduled data amount in a timely manner based on the remaining data amount, or re-determine the remaining data amount after time T, so as to avoid affecting the performance of the model or increasing the model training latency due to the inability to obtain data or the amount of data fed back does not meet the indicators required for model training.

[0031] Optionally, the method in this aspect further includes: the terminal device receiving third information, the third information indicating the amount of data M3 that the terminal needs to collect; and the terminal device collecting and transmitting the data amount M3 based on the third information.

[0032] Optionally, M3 is less than or equal to the amount of data M1 that the terminal can feed back during data collection, or M3 is less than or equal to the amount of data M1 that the terminal can feed back during time T during data collection, or M3 is less than M2.

[0033] Fourthly, this application also provides a communication method applicable to a network-side device, which may be an access network device, a core network device, an operation, administration and maintenance (OAM) network element, a chip or chip module within the network-side device, or a module or unit capable of realizing all or part of the functions of the network-side device. Taking a network device as an example, in this method: the network device sends first information, which indicates that the terminal device needs to return M2 of the data collected for training the model; the network device receives second information, which indicates that the data amount M2 is greater than the remaining data that the terminal device can return for data collection.

[0034] Based on this method, when the network side schedules more data than the terminal can feed back M2, it can receive the second information in a timely manner, adjust the scheduled data volume, and avoid affecting the model's performance or increasing the model training latency due to the inability to obtain data or the insufficient amount of data fed back to meet the indicators required for model training.

[0035] Optionally, the second information may include one or more of the following: a negative response (NACK); information indicating the amount of data remaining for data collection by the terminal device; or, information indicating the effective duration (T) of the remaining data.

[0036] In one embodiment, the network device sends a first message, which instructs the terminal to collect data for training the model and to return a data volume M2; the network device receives a negative response NACK.

[0037] Based on this embodiment, when the data volume M2 is greater than the amount of data that the terminal can collect and feedback for training the model, the network device receives a NACK. In this way, the network side can promptly know that the scheduled data volume exceeds the amount of data M2 that the terminal can feedback, and thus adjust the scheduled data volume in a timely manner. This avoids affecting the model's performance or increasing the model training latency due to the inability to obtain data or the amount of feedback data not meeting the indicators required for model training.

[0038] In another embodiment, the network device sends a first message indicating the amount of data M2 that the terminal needs to collect for training the model; the network device receives information indicating the amount of remaining data that the terminal device needs to collect.

[0039] Based on this embodiment, when the data volume M2 is greater than the amount of data that the terminal can collect and feedback for training the model, the network device receives information indicating the remaining amount of data that the terminal can collect. The network side can adjust the scheduled data volume in a timely manner based on the remaining data volume to avoid affecting the model's performance or increasing the model training latency due to the inability to obtain data or the amount of feedback data not meeting the indicators required for model training.

[0040] In another embodiment, the network device sends a first message indicating to the terminal the amount of data M2 that needs to be fed back for data collection used to train the model; the network device receives a valid time T indicating to the terminal the remaining amount of data that can be fed back for data collection.

[0041] Based on this embodiment, when the data volume M2 is greater than the amount of data collected by the terminal for training the model and can be fed back, the network device receives an indication of the remaining data volume for an effective time T. The network side adjusts the scheduled data volume based on the remaining data volume in a timely manner, or re-determines the remaining data volume after time T, so as to avoid affecting the performance of the model or increasing the model training latency due to the inability to obtain data or the amount of data fed back does not meet the indicators required for model training.

[0042] In one optional implementation, the communication method further includes: the network device sending third information based on the second information, the third information being used to indicate the amount of data M3 that the terminal device needs to return.

[0043] Optionally, M3 is less than or equal to the amount of data M1 that the terminal can feed back during data collection, or M3 is less than or equal to the amount of data M1 that the terminal can feed back during time T during data collection, or M3 is less than M2.

[0044] Based on this method, the network device adjusts the amount of data scheduled based on the second information, thereby avoiding the impact on model performance or the increase in model training latency due to the inability to obtain data or the insufficient amount of feedback data to meet the indicators required for model training.

[0045] Fifthly, this application provides a communication method that can be applied to a terminal device, a chip or chip module in the terminal device, or a module or unit capable of realizing all or part of the functions of the terminal device. Taking a terminal device as an example, the method includes: the terminal device determining first information, the first information indicating a transmission method and / or transmission delay for data transmission; the data being used to train a neural network AI model; and the terminal device sending the first information.

[0046] Based on this method, the terminal device reports the transmission method and / or transmission delay used for data transmission, which helps the network side to schedule the amount of data collected by the terminal device according to the transmission method and / or transmission delay of the terminal device, thus helping to meet the latency requirements of data collection for AI model training.

[0047] Optionally, the first information specifically indicates the transmission delay T used by the terminal device for data transmission.

[0048] Based on this method, it is beneficial for the network side to schedule terminal feedback data based on the transmission delay T.

[0049] In one optional implementation, the method further includes: the terminal device receiving second information, the second information indicating the amount of data M2 that the terminal device needs to return for data collection; and the terminal device collecting and transmitting the data amount M2 based on the second information.

[0050] Based on this method, the amount of data M2 scheduled by the terminal device is determined by the network side in conjunction with the first information, thereby avoiding the situation where the terminal device can only feed back data in specific scenarios, which would prevent the network side from meeting the latency requirements of data collection for training entities.

[0051] Optionally, the data volume M2 is less than or equal to the total amount of data collected by the terminal.

[0052] Based on this method, it is beneficial for the network side to adjust the amount of data that the terminal device needs to feed back, taking into account the transmission method and / or transmission delay of the terminal device.

[0053] In an optional implementation, the method further includes: the terminal device receiving a first message, the first message being used to query the transmission method and / or transmission delay used by the terminal for data transmission.

[0054] Based on this method, the terminal can report the transmission method and / or transmission delay used for data transmission when it receives the first message.

[0055] Optionally, the first message can be capability signaling, used to query the terminal's capability information for data transmission, such as the transmission method and / or transmission latency. This approach is suitable for situations where the amount of data returned by the terminal device is static. Alternatively, the first message can be other information, rather than capability information, suitable for situations where the amount of data returned by the terminal device is dynamically changing.

[0056] In one possible implementation, the transmission method is associated with at least one of the following: the amount of data transmitted, the AI ​​model used for training, or the scenario; and / or, the transmission latency is associated with at least one of the following: the amount of data transmitted, the AI ​​model used for training, or the scenario.

[0057] Based on this method, terminal devices can flexibly adjust the transmission method and / or transmission latency, reducing the impact of data transmission on the terminal device's operational services. Furthermore, this method also facilitates timely adjustment of the scheduled data volume on the network side, preventing data collection from failing to meet the latency and data volume requirements of training entities.

[0058] Sixthly, this application also provides a communication method applicable to a network-side device, which may be an access network device, a core network device, an operation, administration and maintenance (OAM) network element, a chip or chip module within the network-side device, or a module or unit capable of realizing all or part of the functions of the network-side device. Taking a network device as an example, in this method: the network device receives first information, which indicates the transmission method and / or transmission delay used by the terminal for data transmission; the data is used to train an AI model; based on the first information, the network device sends second information, which indicates the amount of data M2 that the terminal needs to return for data collection.

[0059] Based on this method, the network side schedules the amount of data collected by the terminal device according to the data transmission method and / or transmission latency of the terminal device, which helps to meet the latency requirements of data collection for AI model training and / or the data volume requirements of AI model training.

[0060] Optionally, the data volume M2 is less than or equal to the total amount of data collected by the terminal.

[0061] Based on this method, the network side can adjust the amount of data M2 that the terminal device needs to feed back, taking into account the transmission method and / or transmission delay of the terminal device.

[0062] In one possible implementation, the first information indicates the transmission delay T of the terminal device for data transmission.

[0063] Based on this method, the network side learns the transmission delay T of the terminal device for data transmission, which is beneficial for scheduling the corresponding terminal to provide data feedback or scheduling the terminal to provide a certain amount of data feedback, thus avoiding the situation where data collection cannot meet the latency and data volume requirements of the training entity.

[0064] In an optional implementation, the method further includes: the network device sending a first message, the first message being used to query the transmission method and / or transmission delay of the terminal device for data transmission.

[0065] Based on this method, the network side can proactively obtain the transmission method and / or transmission latency used by the terminal for data transmission, which facilitates the scheduling of the corresponding data volume for the terminal.

[0066] Optionally, the first message can be capability signaling, used to query the terminal's capability information for data transmission, such as the transmission method and / or transmission latency. This approach is suitable for situations where the amount of data returned by the terminal device is static. Alternatively, the first message can be other information, rather than capability information, suitable for situations where the amount of data returned by the terminal device is dynamically changing.

[0067] In one possible implementation, the transmission method is associated with at least one of the following: the amount of data transmitted, the AI ​​model used for training, or the scenario; and / or, the transmission latency is associated with at least one of the following: the amount of data transmitted, the AI ​​model used for training, or the scenario.

[0068] Based on this method, the network side can adjust the amount of data scheduled in a timely manner based on the transmission method and / or transmission latency of the terminal feedback data, so as to avoid the data collection being unable to meet the latency and data volume requirements of the training entities.

[0069] Seventhly, embodiments of this application also provide a communication device. This communication device is the terminal device described in the foregoing aspects, or a device capable of being used in conjunction with a terminal device. In one possible implementation, the communication device includes a functional module, which is either hardware circuitry, software, or a combination of hardware circuitry and software.

[0070] In one possible embodiment, the communication device includes one or more functional units, such as a processing unit and a communication unit, wherein the processing unit is used to determine first information, the first information being used to instruct the terminal device to collect data for training the model within a time period T, and the amount of data M1 that can be fed back; the communication unit is used to send the first information.

[0071] Optionally, possible implementations of the communication device can be found in the relevant description in the first aspect, and will not be detailed here.

[0072] In another possible embodiment, in the communication device, the communication unit is used to receive first information, which indicates that the terminal needs to return M2 of the data collected for training the model; based on the first information, the communication unit sends second information, which indicates that the data volume M2 is greater than the remaining data volume that the terminal can return for data collection. Optionally, the processing unit is used to determine the second information based on the first information.

[0073] Optionally, possible implementations of the communication device can be found in the relevant description in the third aspect, which will not be detailed here.

[0074] In another possible embodiment, in the communication device, the processing unit is used to determine first information, the first information indicating the transmission method and / or transmission delay of the terminal device for data transmission; the data is used to train a neural network AI model; and the communication unit is used to send the first information.

[0075] Optionally, possible implementations of the communication device can be found in the relevant description in the fifth aspect, which will not be detailed here.

[0076] Eighthly, embodiments of this application also provide a communication device. This communication device is a network device as described in the foregoing aspects, or a device compatible with a network device. In one possible implementation, the network device includes functional modules, which are hardware circuits, software, or a combination of hardware circuits and software.

[0077] In one possible embodiment, the communication device includes one or more functional units, such as a communication unit and a processing unit. The communication unit is configured to receive first information, which indicates the amount of data M1 that the terminal device can return during time T for model training. The communication unit is also configured to send second information based on the first information, which indicates the amount of data M2 that the terminal device needs to return. Optionally, the processing unit is configured to determine the second information based on the first information.

[0078] Possible implementations of this communication device can be found in the relevant description in the second aspect, and will not be detailed here.

[0079] In another possible embodiment, in the communication device, the communication unit is used to send first information, which indicates that the terminal device needs to return M2 of the data collected for training the model; the communication unit is also used to receive second information, which indicates that the data amount M2 is greater than the remaining data that the terminal device can return for data collection. Optionally, the processing unit is used to determine the first information.

[0080] Possible implementations of this communication device can be found in the relevant description in the fourth aspect, and will not be detailed here.

[0081] In another possible embodiment, in this communication device, the communication unit is used to receive first information, which indicates the transmission method and / or transmission delay for data transmission by the terminal; the data is used to train an AI model; the communication unit is also used to send second information based on the first information, which indicates the amount of data M2 that the terminal needs to return for data collection. Optionally, the processing unit determines the second information based on the first information.

[0082] Possible implementations of this communication device can be found in the relevant description in Section VI, and will not be detailed here.

[0083] For aspects seven and eight, as examples, the processing unit can be a processing unit or can be embodied as a processing circuit or logic circuit; the communication unit can be an input / output interface, interface circuit, output circuit, input circuit, pin or related circuit on the chip or chip system.

[0084] In implementation, the processor can be used for, but is not limited to, baseband-related processing, and the transceiver or communication interface can be used for, but is not limited to, radio frequency transceiver. These devices can be disposed on separate chips, or at least partially or entirely on the same chip. For example, the processor can be further divided into analog baseband processors and digital baseband processors. The analog baseband processor can be integrated with the transceiver (or communication interface) on the same chip, while the digital baseband processor can be disposed on a separate chip. With the continuous development of integrated circuit technology, more and more devices can be integrated on the same chip. For example, a digital baseband processor can be integrated with multiple application processors (e.g., but not limited to graphics processors, multimedia processors, etc.) on the same chip. Such a chip can be called a System on a Chip (SoC). Whether the devices are disposed independently on different chips or integrated on one or more chips often depends on the needs of the product design. This application does not limit the implementation form of the above-mentioned devices.

[0085] Ninthly, this application provides a communication device that may include a processing circuit and a transceiver circuit connected together. The transceiver circuit is used for exchanging (or sending / receiving or inputting / outputting) information or data, and the processing circuit is used for executing program instructions to cause the communication device to perform the methods described in any one of the first to sixth aspects or any possible embodiments thereof. The transceiver circuit may be a communication interface, an input / output interface, or a transceiver. The transceiver may be a radio frequency module in the communication device, or a combination of a radio frequency module and an antenna. The transceiver circuit may be an input / output interface of a chip or circuit.

[0086] In a tenth aspect, this application provides a communication device including a processor for executing the method shown in any one of the first to sixth aspects or any possible implementation thereof. Alternatively, the processor is configured to execute a program stored in a memory, wherein when the program is executed, the method described in any one of the first to sixth aspects or any possible implementation thereof is executed.

[0087] In one possible implementation, the memory is located outside the aforementioned communication device.

[0088] In one possible implementation, the memory is located within the aforementioned communication device.

[0089] In one possible implementation, the processor and memory can also be integrated into a single device; that is, the processor and memory can be integrated together. For example, the communication device can be a chip or a chip system.

[0090] In one possible implementation, the communication device further includes a transceiver for receiving or transmitting first information. Exemplarily, the transceiver can also be used to receive or transmit second information. Exemplarily, the communication device can be a terminal device or a network device.

[0091] In one aspect, this application provides a computer-readable storage medium storing program instructions that, when executed on a computer, cause the computer to perform the method described in any one of the first to sixth aspects or any possible implementation thereof.

[0092] In a twelfth aspect, this application provides a program product containing program instructions that, when executed, cause the method described in any one of the first to sixth aspects or any possible implementation thereof to be performed.

[0093] In a thirteenth aspect, this application provides an apparatus, which can be implemented as a chip or as a device, including a processing circuit. The processing circuit reads and executes a program stored in a memory to perform one or more of the communication methods provided in any of the first to sixth aspects or any possible embodiments thereof. Optionally, the apparatus further includes a memory connected to the processing circuit via a circuit. Further optionally, the apparatus includes a communication interface to which the processing circuit is connected. The communication interface receives information to be processed, the processing circuit obtains the information from the communication interface, processes the information, and outputs the processing result through the communication interface. The communication interface can be an input / output interface.

[0094] Optionally, the aforementioned processing circuitry and memory can be physically independent units, or the memory can be integrated with the processing circuitry.

[0095] In a fourteenth aspect, this application provides a communication system comprising one or more terminal devices and a network device; the terminal devices are configured to perform the methods described in any possible implementation of the first, third, fifth, or any of the above-described aspects, and the second communication device is configured to perform the methods described in any possible implementation of the second, fourth, or sixth, or any of the above-described aspects. Attached Figure Description

[0096] Figure 1 This is a schematic diagram of a communication system;

[0097] Figure 2 This is a schematic diagram of another communication system;

[0098] Figure 3 This is a schematic diagram of a possible application framework in a communication system;

[0099] Figure 4 This is a schematic diagram of another possible application framework in a communication system;

[0100] Figure 5 This is a flowchart illustrating a communication method provided in an embodiment of this application;

[0101] Figure 6 This is a flowchart illustrating another communication method provided in an embodiment of this application;

[0102] Figure 7 This is a flowchart illustrating another communication method provided in an embodiment of this application;

[0103] Figure 8 This is a flowchart illustrating another communication method provided in an embodiment of this application;

[0104] Figure 9 This is a schematic diagram of the structure of a communication device provided in an embodiment of this application;

[0105] Figure 10 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application. Detailed Implementation

[0106] To facilitate a clear description of the technical solutions of the embodiments of this application, the following points will be explained before introducing the solutions of this application.

[0107] (1) "And / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone, where A and B can be singular or plural. In the description of this application, unless otherwise stated, "multiple" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural 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.

[0108] (2) “Instruction” can include direct instruction, indirect instruction, explicit instruction, and implicit instruction. When describing a certain instruction information or when an instruction is used to instruct A, it can be understood that the instruction information carries A, directly instructs A, or indirectly instructs A.

[0109] The instruction information, or the information that the instruction indicates, is called the instruction-to-instruction information. In practical implementation, there are many ways to instruct the instruction-to-instruction information, such as, but not limited to, directly instructing the instruction-to-instruction information itself or its index. It can also indirectly instruct the instruction-to-instruction information by instructing other information, where there is a correlation between the other information and the instruction-to-instruction information. Furthermore, it can instruct only a part of the instruction-to-instruction information, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. In addition, the instruction-to-instruction information can be sent as a whole or divided into multiple sub-information pieces, and the sending period and / or timing of these sub-information pieces can be the same or different.

[0110] (3) "Send" and "receive" indicate the direction of signal transmission. For example, "send information to XX" can be understood as the destination of the information being XX, which can include sending directly through the air interface or sending indirectly through the air interface by other units or modules. "receive information from YY" can be understood as the source of the information being YY, which can include receiving directly from YY through the air interface or receiving indirectly from YY through the air interface by other units or modules.

[0111] "Sending" can also be understood as the "output" of a chip interface, and "receiving" can be understood as the "input" of a chip interface. In other words, sending and receiving can occur between devices, such as between network devices and terminal devices, or within a device, such as between components, modules, chips, software modules, or hardware modules within the device via buses, traces, or interfaces. Furthermore, unless otherwise specified, "transmission" includes receiving and / or sending. For example, transmitting signals can include receiving signals and / or sending signals.

[0112] For example, in this application, "sending information" can be understood as one device sending information to another device, or it can also be understood as one logical module within a device sending information to another logical module. For example, "terminal sending information" can be understood as a terminal sending information to another device (such as an access network device), or it can be understood as logical module 1 in the terminal sending information to logical module 2 in the terminal.

[0113] For example, in this application, "receiving information" can be understood as one device receiving information from another device, or it can also be understood as a logical module within a device receiving information from another logical module. For example, "terminal device receiving information" can be understood as a terminal device receiving information from another device (such as a network device), or it can be understood as logical module 1 in the terminal receiving information from logical module 2 in the network device.

[0114] In this application, phrases such as "sending information to... (e.g., a terminal)" or related illustrations in the accompanying drawings can be understood as indicating that the destination of the information is an access network device. This can include sending information directly or indirectly to an access network device. Similarly, phrases such as "receiving information from... (e.g., a network device)," "receiving information from... (e.g., a network device)," or "receiving information sent by (e.g., a network device)," or related illustrations in the accompanying drawings, can be understood as indicating that the source of the information is a network device. This can include receiving information directly or indirectly from a network device. Information may undergo necessary processing between the source and destination, such as format changes, but the destination can understand the valid information from the source. Similar expressions in this application can be interpreted similarly and will not be elaborated further here.

[0115] (4) Information C is used to determine information D, which includes both information D being determined solely based on information C and information D being determined based on information C and other information. In addition, information C can also be used to determine information D indirectly, for example, information D is determined based on information E, and information E is determined based on information C.

[0116] (5) "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product or device.

[0117] (6) In the various embodiments of this application, unless otherwise specified or in case of logical conflict, the terms and / or descriptions of different embodiments are consistent and can be referenced by each other. The technical features of different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0118] (7) In this application, "first" and "second" are used for convenience of description to distinguish objects and are not intended to limit the scope of the embodiments of this application, nor are they used to describe the order or sequence of features. It should be understood that the objects described in this way can be interchanged where appropriate so as to describe solutions other than those in the embodiments of this application.

[0119] (8) The words “exemplary” or “for example” are used to indicate that they are 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 designs. Specifically, the use of the words “exemplary” or “for example” is intended to present the relevant concepts in a specific manner.

[0120] (9) "Information", such as first information, can be a message or the content of a message.

[0121] (10) “Network device sends to terminal”, correspondingly, “terminal receives from network device” or “terminal receives from network device”; similarly, “network device receives from terminal”, correspondingly, “terminal sends to network device” or “network device receives from terminal”, which will not be elaborated here.

[0122] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0123] The technical solutions provided in this application can be applied to various communication systems, such as: 5th generation (5G) or new radio (NR) systems, long term evolution (LTE) systems, LTE frequency division duplex (FDD) systems, LTE time division duplex (TDD) systems, wireless local area network (WLAN) systems, satellite communication systems, future communication systems, or integrated systems of multiple systems. The technical solutions provided in this application can also be applied to device-to-device (D2D) communication, vehicle-to-everything (V2X) communication, machine-to-machine (M2M) communication, machine-type communication (MTC), and Internet of Things (IoT) communication systems or other communication systems.

[0124] In a communication system, one device can send signals to or receive signals from another device. These signals can include information, signaling, or data. The device can also be replaced by an entity, network entity, equipment, communication device, communication module, node, communication node, etc. This application describes terminal devices and network devices as examples. For instance, a terminal device is a terminal equipment, a chip or chip module within a terminal equipment, or a module or unit applied to achieve all or part of the functions of a terminal equipment; a network device is an access network device, or a core network device, or an operation, administration, and maintenance (OAM) network element, or a chip or chip module applicable to a network-side device, or a module or unit applied to achieve all or part of the functions of a network-side device, etc. A communication system can include at least one terminal device and at least one network device.

[0125] In wireless communication networks, such as mobile communication networks, the services supported by the networks are becoming increasingly diverse, and the requirements they need to meet are also becoming more varied. For example, networks need to support ultra-high speeds, ultra-low latency, and / or massive connectivity. These requirements make network planning, network configuration, and / or resource scheduling increasingly complex. For example, service implementation, network planning, configuration, and resource scheduling may involve modulation, coding, transmitters, receivers, multi-antenna technology, or positioning technologies in wireless communication systems. To meet this challenge, 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 into the network.

[0126] For example, Figure 1 This is a schematic diagram of a communication system. For example... Figure 1 As shown, the communication system may include at least one network device, such as Figure 1 The network device 110 shown; the communication system may also include at least one terminal device, such as Figure 1 The terminal devices 120 and 130 are shown. Network device 110 can communicate with the terminal devices (such as terminal devices 120 and 130) via a wireless link. Communication devices in this communication system, for example, network device 110 and terminal device 120, can communicate via multi-antenna technology.

[0127] As an example, a terminal device is a device that provides voice or data and has wireless connectivity. A terminal device can be called a terminal, user equipment (UE), mobile station (MS), mobile terminal (MT), etc., and can be a device with wireless transceiver capabilities. It can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; it can also be deployed on water (such as on ships); and it can also be deployed in the air (e.g., on airplanes, balloons, and satellites). Terminal devices can be used to connect people, objects, and machines. Terminal devices can be widely used in various scenarios, such as cellular communication, device-to-device (D2D), vehicle-to-everything (V2X), peer-to-peer (P2P), machine-to-machine (M2M), machine-type communication (MTC), Internet of Things (IoT), virtual reality (VR), augmented reality (AR), industrial control, autonomous driving, telemedicine, smart grids, smart furniture, smart offices, smart wearables, smart transportation, smart cities, smart homes, remote sensing, passive sensing, positioning, navigation, autonomous delivery and mobility, etc.

[0128] As examples, terminal devices can be UEs conforming to the 3rd Generation Partnership Project (3GPP) standards, fixed devices, mobile devices, handheld devices, wearable devices, cellular phones, smartphones, Session Initiation Protocol (SIP) phones, tablets, laptops, PDAs, personal computers, mobile internet devices (MIDs), VR devices, AR devices, smart books, vehicles, satellites, Global Positioning System (GPS) devices, drones, robots, helicopters, aircraft, ships, remote control devices, wireless terminals or industrial equipment in industrial control, wireless terminals in self-driving vehicles, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals or smart home devices in smart homes, cellular phones, cordless phones, SIP phones, and wireless local loops. Terminal devices can also be communication devices in future wireless communication systems.

[0129] As an example, 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 only hardware devices but can 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 application function and require the use of other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0130] Optionally, the device used to implement the terminal's functions can be the terminal itself; it can also be a device capable of supporting the terminal in implementing these functions, such as a chip system, a communication module, or a modem, which can be installed in the terminal. In this embodiment, the chip system can be composed of chips or may include chips and other discrete devices. This embodiment does not limit the specific technology or device form used in the terminal device. In one possible implementation, the UE can act as a base station. For example, the UE can act as a scheduling entity, providing sidelink signals between UEs in V2X, D2D, or P2P, without relaying communication signals through a base station. In another possible implementation, the UE can also act as a relay node. For example, the UE can act as a relay device or an integrated access and backhaul (IAB) node to provide wireless backhaul services to the terminal device.

[0131] As an example, a network device is a device used to communicate with a terminal device, an entity on the network side used to transmit or receive signals, such as a base station (BS). A network device can also be a radio access network (RAN) node (or device) through which a terminal device accesses a wireless network. A BS can be a device deployed in a RAN capable of wireless communication with a terminal. Base stations can take many forms, such as macro base stations, micro base stations, relay stations, and access points. Exemplarily, the base station involved in this application embodiment can be a base station in 5G, a base station in a 6th generation (6G) mobile communication system, an access network device or module of an access network device in an open radio access network (O-RAN) system, a base station in a future mobile communication system or an access node in a WiFi system, or an evolved node B (eNB) in LTE, etc. Among these, a base station in 5G can also be called a transmission reception point (TRP) or a 5G base station (next-generation node B, gNB). Base stations can also be replaced by the following names, such as: wireless access point, node B, transmitting point (TP), master MeNB, secondary SeNB, multi-standard radio (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), centralized unit (CU), distributed unit (DU), location node, IAB donor, etc. Base stations can also be mobile switching centers and devices that perform base station functions in D2D, V2X, and M2M communications. Base stations can support networks using the same or different access technologies. Optionally, RAN nodes can also be servers, wearable devices, vehicles, or in-vehicle equipment. For example, in V2X technology, RAN equipment can be a roadside unit (RSU).

[0132] 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.

[0133] In some deployments, the network devices mentioned in the embodiments of this application may be devices including CU, DU, or CU and DU, or devices with control plane CU nodes (central unit-control plane (CU-CP)) and user plane CU nodes (central unit-user plane (CU-UP)) and DU nodes. For example, the network devices may include gNB-CU-CP, gNB-CU-UP, and gNB-DU.

[0134] In some deployments, multiple radio access network (RAN) nodes collaborate to assist terminals in achieving radio access, with different RAN nodes implementing some of the base station's functions. For example, RAN nodes can be CUs, DUs, CU-CPs, CU-UPs, or RUs. CUs and DUs can be configured separately or included in the same network element, such as a BBU. RUs can be included in radio equipment or radio unit units, such as RRUs, AAUs, or RRHs.

[0135] RAN nodes can support one or more types of fronthaul interfaces, each corresponding to a DU and RU with different functions. If the fronthaul interface between the DU and RU is a common public radio interface (CPRI), the DU is configured to implement one or more baseband functions, and the RU is configured to implement one or more radio frequency functions. If the fronthaul interface between the DU and RU is another type of interface, relative to CPRI, some downlink and / or uplink baseband functions, such as, for downlink, precoding, digital beamforming (BF), or one or more of inverse fast Fourier transform (IFFT) / cyclic prefix addition (CP), are moved from the DU to the RU; and for uplink, digital beamforming (BF), or one or more of fast Fourier transform (FFT) / cyclic prefix removal (CP), are moved from the DU to the RU. In one possible implementation, the interface can be an enhanced common public radio interface (eCPRI). Under the eCPRI architecture, the segmentation between DU and RU differs, corresponding to different categories (Cat) of eCPRI, such as eCPRI Cat A, B, C, D, E, F.

[0136] Taking eCPRI Cat A as an example, for downlink transmission, the DU is configured to implement one or more functions preceding and following layer mapping (i.e., coding, rate matching, scrambling, modulation, and layer mapping), while other functions following layer mapping (e.g., resource element (RE) mapping, digital beamforming (BF), or one or more functions following inverse fast Fourier transform (IFFT) / cyclic prefix (CP) addition) are moved to the RU. For uplink transmission, the DU is configured to implement one or more functions preceding and following de-mapping (i.e., decoding, rate matching de-mapping, descrambling, demodulation, inverse discrete Fourier transform (IDFT), channel equalization, and de-RE mapping), while other functions following de-mapping (e.g., digital BF or fast Fourier transform (FFT) / CP removal) are moved to the RU. It is understandable that the functional descriptions of the DU and RU corresponding to various types of eCPRI can be found in the eCPRI protocol, and will not be elaborated here.

[0137] In one possible design, the processing unit in the BBU used to implement baseband functions is called the baseband high (BBH) unit, and the processing unit in the RRU / AAU / RRH used to implement baseband functions is called the baseband low (BBL) unit.

[0138] 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 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 O-RU. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software modules and hardware modules.

[0139] In this embodiment, the device for implementing the functions of the network device can be the network device itself; or it can be a device capable of supporting the network device in implementing the functions, such as a chip system, hardware circuit, software module, or hardware circuit plus software module. This device can be installed in the network device or used in conjunction with the network device.

[0140] For example, Figure 2 This is a schematic diagram of another communication system. Compared to... Figure 1 Regarding the communication system shown, Figure 2 The communication system shown also includes an AI network element 140. The AI ​​network element 140 is used to perform AI-related operations, such as building training datasets or training AI models.

[0141] In one possible implementation, network device 110 can send data related to the training of the AI ​​model to AI network element 140, which then constructs a training dataset and trains the AI ​​model. For example, the data related to the training of the AI ​​model may include data reported by the terminal device. AI network element 140 can send the results of operations related to the AI ​​model to network device 110, which then forwards them to the terminal device. For example, the results of operations related to the AI ​​model may include at least one of the following: a trained AI model, model evaluation results, or test results. Exemplarily, a portion of the trained AI model may be deployed on network device 110, and another portion on the terminal device. Alternatively, the trained AI model may be deployed on network device 110. Or, the trained AI model may be deployed on the terminal device.

[0142] Figure 2 Taking the direct connection between AI network element 140 and network device 110 as an example, in other scenarios, AI network element 140 can also be connected to a terminal device. Alternatively, AI network element 140 can be connected to both network device 110 and a terminal device simultaneously. Alternatively, AI network element 140 can also be connected to network device 110 through a third-party network element. This application embodiment does not limit the connection relationship between AI network elements and other network elements. It is understood that this application embodiment does not limit the number of AI network elements. For example, when there are multiple AI network elements, these multiple AI network elements can be divided based on function, such as different AI network elements being responsible for different functions.

[0143] Optionally, the AI ​​network element 140 can be an AI node or an AI module.

[0144] AI network element 140 can be an independent device, or it can be integrated into the same device to implement different functions. Alternatively, it can be a network element in a hardware device, a software function running on dedicated hardware, or a virtualization function instantiated on a platform (e.g., a cloud platform). This application does not limit the specific form of the AI ​​network element 140.

[0145] For example, the AI ​​network element 140 can also be configured as a module in network devices and / or terminal devices, for example, configured in Figure 1The AI ​​network element 140 can be deployed as a module in the core network equipment of the communication system, or in a location other than the terminal equipment, network equipment, and core network equipment, such as in the host or cloud server of an over-the-top (OTT) system. The AI ​​network element 140 can communicate with other devices in the communication system, which may be one or more of the following: network equipment, terminal equipment, or core network elements.

[0146] It should be noted that, Figure 1 and Figure 2 This is a simplified illustration for ease of understanding only. For example, the communication system may also include other devices, such as wireless relay devices and / or wireless backhaul devices. Figure 1 and Figure 2 The figures are not shown. 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] Figure 3 This is a schematic diagram of a possible application framework in a communication system. For example... Figure 3 As shown, network elements in a communication system are connected via interfaces (e.g., NG, Xn) or air interfaces. These network element nodes, such as core network equipment, access network nodes (RAN nodes), terminals, or one or more devices in the operation, administration, and maintenance (OAM) systems, are equipped with one or more AI modules (for clarity, ...). Figure 3 (Only one is shown in the image). The access network node can be a single RAN node or can include multiple RAN nodes, such as CU and DU. The CU and / or DU can also be configured with one or more AI modules. Optionally, the CU can also be split into CU-CP and CU-UP. One or more AI models are configured in CU-CP and / or CU-UP.

[0148] The AI ​​module is 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 bias in the activation function), input parameters (e.g., type and / or dimension of input parameters), or output parameters (e.g., type and / or dimension of output parameters). The bias in the activation function can also be referred to as the neural network bias.

[0149] In one example, the neural network mentioned above can be a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), or a generative adversarial network (GAN).

[0150] Deep Neural Networks (DNNs) are artificial neural network architectures with multiple layers of nonlinear transformation units stacked in a hierarchical structure to form deep computational models. Compared to shallow neural networks, deep neural networks have more hidden layers, allowing the network model to capture more complex data structures and higher-level abstract features.

[0151] A CNN is a deep neural network with a convolutional structure. A CNN contains a feature extractor consisting of convolutional layers and subsampling layers. This feature extractor can be viewed as a filter, and the convolution process can be seen as performing convolution between a trainable filter and an input image or a convolutional feature map.

[0152] RNN is a type of recursive neural network that takes sequence data as input, recursively moves along the direction of sequence evolution, and connects all nodes (recurrent units) in a chain-like manner.

[0153] GAN is a deep learning model. It consists of a generator and a discriminator, and is trained through adversarial learning. Its purpose is to estimate the potential distribution of data samples and generate new data samples.

[0154] 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.

[0155] Figure 4 This is a schematic diagram illustrating another possible application framework in a communication system. For example... Figure 4 As shown, the communication system includes a RAN intelligent controller (RIC). For example, the RIC can be... Figure 3 The AI ​​modules 117 and 118 shown are used to implement AI-related functions. The RIC includes near-real-time RIC (near-RT RIC) and non-real-time RIC (non-RT RIC). Non-real-time RIC primarily processes non-real-time information, such as data that is not sensitive to latency, with latency in the order of seconds. Real-time RIC primarily processes near-real-time information, such as data that is relatively sensitive to latency, with latency in the order of tens of milliseconds.

[0156] 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 RAN nodes (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 RAN nodes 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 a DU, which then forwards them to an RU.

[0157] 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 RAN nodes (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 RAN nodes 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 inference results to a DU, which then forwards them to an RU.

[0158] Near real-time RICs and non-real-time RICs can also be configured as separate network elements. Optionally, the near real-time RICs and non-real-time RICs can also be part of other devices. For example, the near real-time RIC can be set in a RAN node (e.g., in a CU or DU), while the non-real-time RIC can be set in an OAM, a cloud server, a core network device, or other network devices.

[0159] For example, a network device can be Figure 3 The core network equipment, access network node (RAN node), or one or more devices in the OAM are shown. For example, the AI ​​module can be... Figure 4 The RICs shown are such as near real-time RICs or non-real-time RICs. For example, near real-time RICs are set in RAN nodes (e.g., CU, DU), while non-real-time RICs are set in OAM, cloud servers, core network devices, or other network devices. Exemplarily, near real-time RICs and non-real-time RICs can also be set up as separate network elements, and the network device can be either a near real-time RIC or a non-real-time RIC.

[0160] It should be noted that, Figures 1 to 4 This is a simplified illustration for ease of understanding only. For example, the communication system may also include other devices, such as wireless relay devices and / or wireless backhaul devices. Figures 1 to 4 The figures are not shown. 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.

[0161] AI can endow machines with human-like intelligence; for example, it allows machines to use computer hardware and software to simulate certain intelligent human behaviors. Generally, artificial intelligence refers to the technology of using ordinary computer programs to exhibit human intelligence. Artificial intelligence can be defined as a machine or computer that imitates humans and possesses cognitive functions related to human thinking, such as learning and problem-solving. Artificial intelligence can learn from past experiences, make rational decisions, and respond quickly.

[0162] Machine learning (ML) is an important technological approach to achieving artificial intelligence, using machine learning to solve problems within artificial intelligence. Machine learning theory primarily involves designing and analyzing algorithms that allow computers to automatically "learn." Machine learning algorithms are a class of algorithms that automatically analyze data to obtain patterns and use these patterns to predict unknown data. Neural networks (NNs) are a specific implementation of machine learning; they are mathematical models that mimic the behavioral characteristics of animal neural networks to process information.

[0163] To train an AI model deployed on the terminal, data needs to be collected from the terminal. Data collection includes control plane data collection and user plane data collection. The communication method involved in this application is applicable to user plane data collection.

[0164] Research indicates that user-side data collection methods need to fully consider the data volume and / or transmission latency of different terminals to determine the strategy for scheduling terminal data transmission, thereby meeting the data volume and dataset transmission latency requirements of cloud servers. However, current solutions do not consider the data volume and / or transmission latency of terminals, and cannot determine data collection strategies based on the data volume and / or transmission latency of different terminals for AI data transmission, such as, but not limited to, terminal selection and allocation of data transmission volume between terminals.

[0165] The communication method provided in this application allows the network device to schedule the terminal device to transmit a certain amount of data based on the total amount of data or the remaining amount of data used by the terminal for data collection. This avoids situations where scheduling exceeds the amount of data that the terminal device can return, resulting in the inability to obtain data or the amount of returned data not meeting the indicators required for model training, thus affecting model performance or increasing model training latency.

[0166] AI / ML technology refers to training models using relevant data to achieve specific objectives. The objectives a model can achieve are related to the data used during training. For example, models or scenarios trained using data include, but are not limited to, service scenarios based on AI / ML such as channel state information (CSI) feedback, beam management (BM), and positioning.

[0167] Taking AI / ML-based CSI feedback as an example, this scenario includes AI / ML-based CSI compression and AI / ML-based CSI prediction. AI / ML-based CSI compression refers to the terminal compressing the downlink CSI (measured by the terminal) using AI / ML technology. The terminal then transmits the compressed CSI to the network device over the air interface. The network device then restores (decompresses) the CSI using AI / ML. Compared to traditional compression algorithms, AI / ML-based compression algorithms have higher compression ratios and better CSI restoration capabilities. Therefore, the terminal can feed back more CSI with less air interface overhead, enabling the network device to perform more accurate downlink precoding. AI / ML-based CSI prediction refers to the network device using AI / ML technology to predict the downlink CSI for future times based on the current / historical downlink CSI, and then performing precoding according to the predicted CSI. This scheme predicts CSI that better matches the channel state when downlink data is scheduled, thus overcoming the channel aging problem and achieving more accurate downlink precoding. The training data for models involved in business scenarios based on AI / ML CSI feedback includes CSI.

[0168] Taking a business scenario based on AI / ML (Beam Builder) as an example, network devices and terminal devices can use AI / ML technology to predict transmit and / or receive beams. For instance, AI / ML can be used to infer the optimal beam from a small number of beam scan results. Compared to traditional solutions that require scanning a large number of beams to obtain the optimal beam, AI / ML-based beam prediction reduces the processing overhead of beam scanning. For example, a terminal can scan a small number of beams and then use an AI / ML model to predict the optimal beam from a large number of candidate beams, thus avoiding the need to scan all candidate beams and reducing overhead. The small number of beams scanned by the terminal can be sparse or wide beams, while the candidate beams can be dense or narrow beams. The terminal can input the beam information scanned at the current / historical time into the model to predict the optimal beam at future time points, thus avoiding the need to perform beam scanning again at future time points and improving beam scanning efficiency. In AI / ML-based business scenarios involving BM, the training data for the models includes beam information, such as beam ID and / or the reference signal receiving power (RSRP) of the corresponding beam.

[0169] Taking AI / ML-based positioning as an example, the communication device inputs channel information into the AI / ML model to infer the intermediate parameters required for positioning, or directly obtains the location coordinates. Compared to traditional positioning algorithms, the intermediate parameters or positioning coordinates obtained based on AI / ML are more accurate. The training data for the model in AI / ML-based positioning scenarios includes channel information and / or location information. Channel information includes power information, phase information, delay information, distance information, velocity information, channel scattering information, and channel-related information such as line-of-sight (LOS) / non-line-of-sight (NLOS) information.

[0170] This application's embodiments illustrate interaction between a network device and a terminal device, but can also involve interaction between a terminal device, an access network device, and a core network device. For ease of explanation, this application uses the interaction between a network device and a terminal device as an example to describe various communication methods. Optionally, the network device can be an access network device, a core network device, or a network-side device such as an operations, administration, and maintenance (OAM) network element. Alternatively, the network device can be a chip or chip module within a network-side device, or a module or unit capable of implementing all or part of the functions of the network-side device. The terminal device can be a chip or chip module within a network-side device, or a module or unit capable of implementing all or part of the functions of the network-side device.

[0171] Please see Figure 5 , Figure 5 This is a flowchart illustrating a communication method provided in an embodiment of this application. For example... Figure 5 As shown, the communication method may include, but is not limited to, the following steps:

[0172] 101. The terminal device determines first information, which is used to instruct the terminal device to collect data for training the model within time T, and the amount of data M1 that can be fed back.

[0173] 102. The terminal device sends first information, and the network device receives the first information accordingly.

[0174] Optionally, the network device may send a first message to query the amount of data M1 that the terminal device can return; the terminal device responds to the first message, confirms, and sends the aforementioned first information. Based on this implementation, the network side can proactively query the amount of data that the terminal device can return, so as to schedule the terminal to return the corresponding amount of data.

[0175] In one approach, the first message is a capability signaling message used to query the terminal's data collection capabilities, such as the amount of data that can be fed back and / or the transmission latency of the fed-back data. This approach is suitable for situations where the amount of data fed back by the terminal device is static. In another approach, the first message is other information, rather than capability information, which is suitable for situations where the amount of data fed back by the terminal device is dynamically changing.

[0176] Optionally, after receiving the first information, the network device may further include the following steps:

[0177] 103. The network device sends second information based on the first information, the second information being used to indicate the amount of data M2 that the terminal device needs to return; accordingly, the terminal device receives the second information.

[0178] 104. The terminal device collects and transmits data of amount M2 based on the second information.

[0179] Where M2 is less than or equal to M1.

[0180] Based on this method, the terminal device can report the amount of data M1 available for data collection within a time period T. The network side then schedules the terminal device to collect a certain amount of data M2 based on this data amount M1. This avoids the situation where, due to an unknown amount of data M1 available for data collection by the terminal device, the scheduled amount exceeds the amount of data M1 that the terminal device can report, resulting in data failure and the need to schedule other terminals for data collection, thus reducing signaling and latency overhead. Furthermore, based on this method, the network side schedules the amount of data collected by the terminal device based on this data amount M1, avoiding the situation where scheduling exceeds the amount of data M1 that the terminal device can report, resulting in data failure or insufficient data to meet the requirements for model training, thus affecting model performance or increasing model training latency.

[0181] In one optional implementation, in step 101, the first information is used to indicate the total amount of data collected by the terminal device within time T. For example, the first information indicates that the total amount of data collected by the terminal device each month is 2M. For example, in step 103, the network device determines the remaining amount of data for the terminal device based on the 2M supported per month and the amount of data already allocated to the terminal device, and allocates the subsequent amount of data for the terminal device, thereby determining the second information to indicate the amount of data M2 that the terminal device needs to report.

[0182] In another optional implementation, in step 101, the first information is used to indicate the amount of data remaining for data collection currently used by the terminal device. For example, the first information is used to indicate that the amount of data remaining for data collection currently used by the terminal device is 1M. For example, in step 103, the network device allocates subsequent data based on the amount of data remaining on the terminal device to determine the amount of data M2 indicated by the second information.

[0183] In another optional implementation, in step 101, the first information is used to indicate that the amount of remaining data currently available for data collection by the terminal device is time-related. For example, the first information indicates the amount of remaining data available for data collection by the terminal device within time T, such as 1MB per month or 1MB of remaining data for the current month. For example, in step 103, the network device allocates subsequent data based on the remaining data amount of the terminal device to determine the data amount M2 indicated by the second information.

[0184] In another optional implementation, the value of data volume M1 is related to the model or scenario used for training. Alternatively, the value of data volume M1 is related to the entity used for training. Or, the value of data volume M1 is related to the use case. For example, the data volume indicated by the first information is the data volume used for AI-CSI, such as 2M / month for AI-CSI. For example, in step 103, the network device allocates subsequent data volume based on the data volume of 2M / month used by the terminal device for AI-CSI, and determines the data volume M2 indicated by the second information.

[0185] In this embodiment of the application, there may be one or more terminal devices. If there are multiple terminal devices, each terminal device can perform the operation corresponding to the terminal device in the communication method. The first information indicates the amount of data M1 that can be fed back. The network device selects the corresponding terminal device and indicates the corresponding amount of data M2 that needs to be fed back based on the amount of data M1 fed back by each terminal device.

[0186] Based on this method, the network device schedules the amount of data M2 that the terminal device needs to return according to the amount of data M1. This avoids the situation where the amount of data M1 that the terminal device can use for data collection is unknown, and the scheduling exceeds the amount of data M1 that the terminal can return, resulting in the inability to obtain data. This would require scheduling other terminals again for data collection, thus avoiding the signaling overhead and latency overhead.

[0187] Please see Figure 6 , Figure 6 This is a flowchart illustrating another communication method provided in an embodiment of this application. For example... Figure 6 As shown, the communication method may include, but is not limited to, the following steps:

[0188] 201. The network device sends first information, and correspondingly, the terminal device receives the first information. The first information indicates that the terminal needs to collect data for training the model and that the amount of data to be fed back is M2.

[0189] 202. Based on the first information, the terminal device sends the second information, which indicates that the amount of data M2 is greater than the amount of remaining data that the terminal can return for data collection.

[0190] Optionally, the second information sent by the terminal may include at least one of the following: a negative response (NACK); information indicating the amount of remaining data that the terminal has for data collection; or, information indicating the validity period T of the remaining data.

[0191] In one embodiment, the terminal device receives first information indicating the amount of data M2 to be collected for training the model; the terminal device sends a negative acknowledgment (NACK). For example, the network device sends first information indicating the amount of data to be collected is 1M; the terminal device may respond to the first information by returning NACK, indicating that the 1M indicated in the first information exceeds the remaining amount of data that the terminal device is using for data collection.

[0192] Based on this embodiment, when the data volume M2 is greater than the amount of data that the terminal can collect and feedback for training the model, the terminal device sends a NACK, so that the network side can know in time that the scheduled data volume exceeds the amount of data that the terminal can feedback, thereby adjusting the scheduled data volume in a timely manner, and avoiding the impact on model performance or the increase in model training latency due to the inability to obtain data or the amount of feedback data not meeting the indicators required for model training.

[0193] In another embodiment, the terminal device receives first information indicating that the amount of data M2 to be collected for training the model; the terminal device then sends information indicating the remaining amount of data to be collected. For example, the network device sends first information indicating that 1M of data will be collected; the terminal device may respond to the first information by sending information indicating that 0.5M of data remains to be collected.

[0194] Based on this embodiment, when the amount of data M2 is greater than the amount of data that the terminal can collect and feedback for training the model, the terminal device sends information indicating the remaining amount of data that the terminal can collect. This allows the network side to adjust the amount of data scheduled in a timely manner based on the remaining amount of data, thus avoiding the impact on model performance or the increase in model training latency due to the inability to obtain data or the amount of data fed back not meeting the indicators required for model training.

[0195] In another embodiment, the terminal device receives first information indicating that the terminal needs to collect data for training the model, and that the amount of data to be fed back is M2. The terminal device then sends an effective time T for data collection or an effective time T for the remaining amount of data that can be fed back. For example, the network device sends first information indicating that the amount of data to be collected is 1M. The terminal device may respond to the first information by sending an effective time T for data collection.

[0196] Based on this embodiment, when the data volume M2 is greater than the amount of data collected by the terminal for training the model and can be fed back, the terminal device sends an indication of the effective time T of the remaining data volume. This allows the network side to adjust the scheduled data volume in a timely manner based on the remaining data volume, or to reschedule the terminal to collect the corresponding data volume after time T. This avoids affecting the model's performance or increasing the model training latency due to the inability to obtain data or the amount of data fed back not meeting the indicators required for model training.

[0197] Optional, Figure 6 The method further includes the following steps:

[0198] 203. Based on the second information, the network device sends a third information, which is used to indicate the amount of data M3 that the terminal device needs to return; accordingly, the terminal device receives the third information.

[0199] 204. The terminal device collects and transmits data of volume M3 based on third-party information.

[0200] Optionally, M3 is less than or equal to the amount of data M1 that the terminal can report back during data collection. Alternatively, M3 is less than or equal to the amount of data M1 that the terminal can report back during time T of data collection. For example, the amount of data that the terminal can report back is reset after time T, so that the network side determines that the remaining amount of data used by the terminal for data collection can be M1. Or, M3 is less than M2.

[0201] Based on this method, the network device adjusts the amount of data scheduled based on the second information, thereby avoiding the impact on model performance or the increase in model training latency due to the inability to obtain data or the insufficient amount of feedback data to meet the indicators required for model training.

[0202] Please see Figure 7 , Figure 7 This is a flowchart illustrating another communication method provided in an embodiment of this application. For example... Figure 7 As shown, the communication method may include, but is not limited to, the following steps:

[0203] 301. The terminal device determines first information, the first information indicating the transmission method and / or transmission delay used by the terminal device for data transmission; the data is used to train an AI model.

[0204] In one possible implementation, the network device may send a first message to the terminal device, the first message being used to query the data transmission method and / or transmission latency used by the terminal. Based on this method, the terminal can determine first information upon receiving the first message.

[0205] Optionally, the first message can be capability signaling, used to query the terminal's capability information for data transmission, such as the transmission method and / or transmission latency. This approach is suitable for situations where the amount of data returned by the terminal device is static. Alternatively, the first message can be other information, rather than capability information, suitable for situations where the amount of data returned by the terminal device is dynamically changing.

[0206] For example, if the terminal device indicated by the first information uses a wireless local area network (WLAN) for data transmission, such as in a wireless-fidelity (WiFi) scenario, it means that the terminal device can only upload data when connected to WiFi.

[0207] Optionally, the data transmission method used by the terminal device is associated with at least one of the following: the amount of data transmitted, the AI ​​model used for training, or the scenario. For example, when the amount of data transmitted is greater than 0.5M, the data transmission method used is WiFi. In this case, the terminal device needs to connect to WiFi to upload data when it needs to transmit data larger than 0.5M. As another example, when the transmitted data is used for AI-CSI, the data transmission method used is WiFi. In this case, the terminal device needs to connect to WiFi to upload data when it needs to transmit data used for AI-CSI.

[0208] The first information indicates that the data transmission delay of the terminal device is time T, meaning that the terminal device can only upload data at intervals of time T. For example, if time T is after midnight, then the terminal device can only upload data after midnight every day.

[0209] Optionally, the data transmission delay of the terminal device indicated by the first information is a period T, meaning that the terminal device can only upload data after a period T. For example, the period T is 6 hours (h), meaning that the terminal device can only upload data every 6 hours.

[0210] Optionally, the transmission latency of the terminal device for data transmission is associated with at least one of the following: the amount of data transmitted, or the AI ​​model or scenario used for training. For example, the first information indicates that the transmission latency for 0.5M of data is 1 day, and the transmission latency for 10M of data is 1 month. In this way, the terminal device can transmit a maximum of 0.5M of data per day and 10M of data per month. As another example, the first information indicates that the transmission latency for AI-CSI data transmission is 1 month, and the terminal device can transmit AI-CSI data at 1-month intervals.

[0211] 302. The terminal device sends first information, and the network device receives the first information accordingly.

[0212] In one possible implementation, the terminal device can indicate the transmission method and transmission delay for data transmission through different information.

[0213] In an optional implementation, the method further includes:

[0214] 303. The network device determines second information based on the first information, and the second information indicates the amount of data M2 that the terminal device needs to return for data collection.

[0215] Optionally, step 303 includes: after receiving the first information, the network device can determine the amount of remaining data that the terminal device has used for data collection, so as to determine the second information.

[0216] Optionally, step 303 includes: after receiving the first information, the network device can determine the time when the terminal device uses the data collection to determine the second information.

[0217] 304. The network device sends a second message, and the terminal device receives the second message accordingly. The second message indicates the amount of data M2 that the terminal device needs to return for data collection.

[0218] 305. The terminal device collects and transmits data of amount M2 based on the second information.

[0219] Optionally, after time T, the network device may instruct the terminal device to upload the corresponding amount of data again.

[0220] Based on this method, the terminal device reports the transmission method and / or transmission latency used for data transmission. This allows the network side to schedule the amount of data collected by the terminal device according to the transmission method and / or transmission latency, which helps meet the latency requirements of data collection for AI model training. This method avoids the situation where the network side instructs the terminal device to collect data, and the terminal device can only provide data feedback in specific scenarios, which would prevent the network side from meeting the latency requirements of data collection for training entities.

[0221] Please see Figure 8 , Figure 8 This is a flowchart illustrating another communication method provided in an embodiment of this application. For example... Figure 7 As shown, the communication method may include, but is not limited to, the following steps:

[0222] 401. The terminal device sends first information, and the network device receives the first information accordingly. The first information is used to indicate the transmission delay T of the terminal device for data transmission.

[0223] 402. Based on the first information, the network device sends the second information, which is used to indicate the total amount of data that the terminal needs to return for data collection; accordingly, the terminal device receives the second information.

[0224] 403. The terminal device collects and transmits data of the first data volume based on the second information.

[0225] The first data volume may be less than or equal to the total data volume indicated by the second information. Optionally, if the first data volume is less than the total data volume, the network device may, based on the data volume already allocated to the terminal and combined with the transmission delay T, reschedule the terminal to upload the corresponding data volume. For example, if the network device indicates through the second information that the total data volume for data collection by the terminal is 0.5M, and the terminal uploads 0.3M, the network device may request the terminal to upload another 0.2M after time T. Figure 8 As shown, the terminal device can upload data of 0.2M.

[0226] Based on this method, the network side can obtain the transmission delay T of each terminal device for data collection, and determine the amount of data to be collected by the scheduled terminal according to the different delays of different data volumes, so as to meet the delay requirements of data collection.

[0227] This application considers how to determine the amount of data a terminal uses for data collection, and provides a solution to this problem. Figure 5 The method involves the terminal device reporting the total amount of data used for data collection within a time period T. For example, the network side determines the remaining data amount for the terminal based on the total data amount supported by the terminal within time period T and the allocated data amount; then, it allocates subsequent data amounts to the terminal based on the remaining data amount. Alternatively, the terminal device reports the remaining data amount currently used for data collection. For example, the network side allocates data amounts to the terminal based on the remaining data amount. Optionally, this remaining data amount is associated with time T. Optionally, this remaining data amount is related to the scenario or model.

[0228] In addition, if T is periodic, the maximum amount of data that the network can collect after time T is the total amount of data reported by the terminal device; if T is non-periodic, the network will query the maximum amount of data that the terminal device can collect again after time T.

[0229] As can be seen, the method provided in this application allows the network side to determine the amount of data to be collected by the terminal device based on the total or remaining data volume of the terminal device. This avoids the signaling and latency overhead caused by having to re-schedule other terminal devices for data collection due to unknown remaining data volume of the terminal device. Furthermore, when the amount of data collected by the terminal device exceeds the terminal's capacity, the terminal may be unable to report the amount of data collected or the reported amount may be insufficient to meet the requirements for model training. This affects the model's ability to collect enough data, thereby impacting model performance or increasing training latency.

[0230] This application provides Figure 6 The method described above allows the terminal to return a NACK (Not Accepted Acknowledgement) to the network side when the amount of data scheduled by the network exceeds the remaining data available for collection. Alternatively, the terminal can return the remaining data amount or the validity period T of the remaining data, meaning that after time T, the amount of data that can be collected needs to be re-determined. The network side then determines the remaining data amount based on the information returned by the terminal. Based on this determined remaining data amount, the network side schedules the terminal to collect data that is less than or equal to the remaining data amount.

[0231] This application considers how to determine the latency used for data collection. To address this issue, it provides... Figure 7 The method described allows the terminal device to report data transmission latency. For example, the terminal reports the data transmission latency, such as time T, with a period of 6 hours, or after midnight. Optionally, the data transmission latency is related to the amount of data and / or to the scenario or model to which the dataset is applicable. For example, the transmission latency for 0.5MB of data is 1 day, and the transmission latency for 10MB of data is 1 month. As another example, the transmission latency for data used in AI-CSI is 1 month.

[0232] Figure 7 In the method described, the terminal device can also report the data transmission method. For example, the terminal can only upload data when connected to WiFi. Optionally, the data transmission method is also related to the data volume; for example, if the data volume is above 0.5M, data can only be uploaded when connected to WiFi, otherwise there is no restriction. Optionally, the data transmission method is related to the scenario or model to which the dataset is applicable. For example, the transmission method for data used in AI-CSI can only be WiFi. In this method, the terminal uses capability signaling or other signaling to report information for data collection, such as data transmission latency and / or data transmission method. The network side determines the latency required for data collection by the terminal based on the capability information. The network side determines the latency requirement for data collection and schedules the terminal to collect and transmit data that meets the latency requirement.

[0233] Figure 8In the method described, the terminal can report latency information for data transmission in scenario 1 or model A. Optionally, the latency information is related to the amount of data. The network side queries and indicates the total amount of data that the terminal can collect or the remaining amount of data, and the terminal transmits data to collect the corresponding amount of data.

[0234] One solution involves collecting user plane data from the network side or a third-party cloud server. Considering data billing, the cloud server collects data from the terminal, with the end user paying for the data, and the monthly data limit is capped. In some scenarios, cloud server data collection requires data transmission over WLAN, such as WiFi. Another solution involves collecting user plane data from the network side or a third-party cloud server, considering caching and transmission. The cloud server collects data from the terminal during the day and transmits it to the cloud server at night. The time interval between each data transmission may be greater than 24 hours, or in 6-hour or 12-hour cycles. The dataset transmission latency (or transfer latency) is the total time required to transmit the dataset, which is related to the amount of data required for AI model training. The effective duration of the dataset refers to the time range required for AI model training based on changes in the environment. The model deployment cycle refers to the training cycle under different scenarios. Different terminals have different amounts of data available for transmission and / or different transmission latencies. As can be seen, the communication method provided in this application can determine the data collection strategy, such as the selection of the terminal and the allocation of the amount of data transmitted by the terminal, based on the amount of data, transmission latency and / or transmission method of different terminals used for model training data transmission, so as to meet the data volume requirements and dataset transmission latency requirements of the model training entity, such as the cloud server.

[0235] The above combination Figures 5 to 8 The communication method provided in the embodiments of this application is explained. In the various embodiments of this application, unless otherwise specified or logically conflicting, the terms and / or descriptions between the various embodiments can be referenced to each other, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0236] The following is combined Figures 9 to 10 This application describes the communication device provided in the embodiments. It should be understood that the description of the device embodiments corresponds to the description of the method embodiments. Therefore, for content not described in detail, please refer to the above method embodiments. For the sake of brevity, it will not be repeated here.

[0237] For example, Figure 9 A possible exemplary block diagram of the communication device involved in an embodiment of this application is shown. For example... Figure 9As shown, the communication device may include modules or units for implementing the methods described in the embodiments above. In one possible design, the communication device includes a communication unit 501 and a processing unit 502. Optionally, the communication device may further include a storage unit 503 for storing device program code and / or data.

[0238] The communication device can be the terminal device in the above embodiments, such as a terminal or a communication module in a terminal, or a circuit or chip in a terminal responsible for communication functions. Optionally, the communication device can perform the relevant operations of the terminal device in the above method embodiments, which will not be described in detail here.

[0239] In one possible design, when the communication device is a terminal or a communication module within a terminal, the functionality of the processing unit 502 can be implemented by one or more processors. Specifically, the processor may include a modem chip, or a system-on-a-chip (SoC) or SIP chip containing a modem core. The functionality of the communication unit 501 can be implemented by transceiver circuitry.

[0240] In one possible design, when the communication device is a circuit or chip in a terminal responsible for communication functions, such as a modem chip or a system-on-a-chip (SoC) or SIP chip containing a modem core, the function of the processing unit 502 can be implemented by a circuit system in the aforementioned chip that includes one or more processors or processor cores. The function of the communication unit 501 can be implemented by an interface circuit or data transceiver circuit on the aforementioned chip.

[0241] In one possible design, when the communication device is a terminal or a processing module within a terminal, the functionality of the processing unit 502 can be implemented by one or more processors. Specifically, the processor may include a GPU, or a system-on-a-chip (SoC) or SIP chip containing a GPU. The functionality of the communication unit 501 can be implemented by transceiver circuitry.

[0242] In one possible design, when the communication device is a circuit or chip in the terminal responsible for processing functions, such as a GPU or a system-on-a-chip (SoC) or SIP chip containing a GPU, the function of the processing unit 502 can be implemented by a circuit system in the aforementioned chip that includes one or more processors or processor cores. The function of the communication unit 501 can be implemented by interface circuitry or data transceiver circuitry on the aforementioned chip.

[0243] The communication device can be a network-side device as described in the above embodiments, such as a network device or network apparatus. This communication device can perform the relevant operations of the network apparatus in the above method embodiments, which will not be detailed here.

[0244] It is understood that the division of units in the above-described device is a logical functional division. One function can correspond to one functional unit, or two or more functions can be integrated into one functional unit. In actual implementation, all or some units can be integrated into one physical entity, or distributed across different physical entities. Furthermore, the aforementioned functional units can be implemented in hardware, software, or a combination of both. Whether a function is executed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for specific applications, but such implementations should not be considered beyond the scope of this application.

[0245] In one example, the functional unit in any of the above devices may be one or more integrated circuits configured to implement the above methods, such as: one or more application-specific integrated circuits (ASICs), or one or more central processing units (CPUs), one or more microcontroller units (MCUs), one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0246] In one example, storage unit 503 may include random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory and / or registers, etc.

[0247] For example, Figure 10 This is a schematic diagram of the structure of a terminal provided in an embodiment of this application. The terminal can correspond to... Figures 1 to 8 The terminal or terminal device shown is used to implement the operation of the terminal or terminal device in the above embodiments. For example... Figure 10 As shown, the terminal includes: one or more antennas 610, a radio frequency processing system 620, and a processor system 630.

[0248] In the downlink or sidelink direction, the RF processing system 620 receives RF signals through the antenna 610 and sends the RF-processed signals to the processor system 630 for further processing. In the uplink or sidelink direction, the processor system 630 processes the terminal-side information and sends it to the RF processing system 620, which then processes the signal and transmits it through the antenna 610.

[0249] In one example, the radio frequency (RF) processing system 620 serves as the communication interface for external communication of the terminal and may include a radio frequency front end (RFFE) 621 and a radio frequency transceiver 622. The RFFE 621 is primarily used for one or more processing operations, such as shaping, passband selection, or gain adjustment, on the RF signals received by the antenna or the RF signals to be transmitted through the antenna. It may include one or more components such as RF switches, duplexers, filters, power amplifiers, antenna tuners, and low-noise amplifiers. The RFFE 621 can be a circuit system composed of multiple discrete components or integrated into one or more chips. The RF transceiver 622 processes the RF signals received by the RFFE 621 into baseband / IF signals for further processing by the processor system 630, and processes the baseband / IF signals provided by the processor system 630 into RF signals for transmission to the RFFE 621. The baseband / IF signals transmitted between the RF transceiver 622 and the processor system 630 can be digital or analog signals. The radio frequency transceiver 622 can be implemented by one or more chips, which are usually referred to as radio frequency integrated circuits (RFICs).

[0250] In one example, processor system 630 may include one or more processors for processing signals and executing one or more communication protocols. Optionally, processor system 630 may also include memory 636. In one example, the one or more processors include at least one baseband processor 631 (also known as a modem processor). Memory 636 is used to store data and / or computer program instructions. Optionally, processor system 630 may also include one or more application processors 632 for implementing processing of the terminal operating system and application layer. Application processor 632 may include, for example, a GPU. Optionally, processor system 630 may also include one or more of a voice subsystem 633, a multimedia subsystem 634, or an interface circuit 635. The voice subsystem 633 is used to process voice signals, the multimedia subsystem 634 is used to handle multimedia-related operations, such as video encoding / decoding, image processing, etc., and the interface circuit 635 is used to enable communication with other terminal components, such as display 640, input device 650, memory 660, etc. The above-mentioned components in processor system 630 can communicate with each other via a bus or communication interface circuit.

[0251] In one example, the processor system 630 can be packaged as a single processor chip, such as a SoC chip or a SIP chip. In another example, the processor system 630 can be a system composed of multiple chips; for example, the baseband processor 631 can be packaged as a single chip, or packaged with part or all of the circuitry of the radio frequency processing system into a single chip.

[0252] In one example, memory 636 can be on-chip memory, i.e., located on the processor system 630 chip. In another example, memory 660 can be off-chip memory, i.e. located outside the processor system 630 chip.

[0253] In one example, the baseband processor 631 may include one or more processor cores 6311 and interface circuitry 6314. The one or more processor cores 6311 are used to process signals and execute one or more communication protocols. Optionally, the baseband processor 631 may also include a memory 6312 for storing at least a portion of the corresponding computer program instructions and / or data. In one example, the one or more processor cores 6311 execute the computer program instructions stored in the memory 6312 to implement the relevant operations (such as generating and sending first information) in the above method embodiments. In this application, the memory 6312 storing the corresponding computer program instructions and / or data may mean that the memory 6312 stores all the corresponding computer program instructions and / or data for the processor core 6311 to execute; or it may mean that the memory 6312 stores a portion of the corresponding computer program instructions and / or data, which includes the computer program instructions and / or data that the processor core 6311 currently needs to execute. The memory 6312 can store different portions of computer program instructions and / or data multiple times for the processor core 6311 to execute in order to implement the relevant operations in the above method embodiments. Interface circuit 6314 serves as a communication interface for communication with other components, such as transmitting signals with RF processing system 620, communicating with other subsystems and related components of processor system 630 via bus, such as transmitting data control signals with application processor 632, and transmitting data or computer program instructions with memory 636 or memory 660. Optionally, to reduce the load on the processor core, baseband signal processing circuit 6313 can also be provided to perform at least some baseband signal processing, including one or more of signal demodulation, modulation, encoding, or decoding.

[0254] The aforementioned processors, processor systems, application processors, baseband processors, processor circuits, or processor cores can be collectively referred to as processors. These processors may include one or more combinations of a central processing unit (CPU), a digital signal processor (DSP), a microprocessor unit (MPU), a microcontroller unit (MCU), a graphics processing unit (GPU), a field-programmable gate array (FPGA), an artificial intelligence processor (AI processor), or a neural network processing unit (NPU). Some or all steps of the communication method in the embodiments of this application can be implemented by a GPU or NPU, or by a GPU or NPU in conjunction with other processors.

[0255] The aforementioned memory may include one or more of the following storage media: random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), phase-change memory (PCM), resistive random access memory (RERAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), cache, register, read-only memory (ROM), flash memory, erasable programmable read-only memory (EPROM), hard disk, etc. In one example, computer program instructions for executing the above embodiments may be stored in non-volatile memory, such as at least a portion of the aforementioned memory 660 (e.g., one or more of ROM, flash memory, EPROM, or hard disk). When the terminal is running, the corresponding computer program instructions may be partially or wholly loaded onto a memory with a faster transfer speed than the processor, such as at least a portion of memory 636 and / or memory 6312 (e.g., one or more of RAM, SRAM, DRAM, PCM, RERAM, MRAM, FRAM, cache, or register), for the processor to execute in order to implement the steps in the above method embodiments.

[0256] In one example, the RF transceiver 622 and the RF front-end 621 can also be packaged in a single chip. In another example, the RF transceiver 622, the RF front-end 621, and the baseband processor 631 can also be packaged in a single chip.

[0257] The terms "system" and "network" in the embodiments of this application may be used interchangeably. "At least one" means one or more, and "multiple" means two or more.

[0258] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, etc.) containing computer-usable program code.

[0259] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0260] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0261] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0262] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A communication method, characterized in that, The method includes: Determine the first information, which indicates that the terminal device can return M1 of the amount of data M1 used for training the model within time T. Send the first message.

2. The method of claim 1, wherein, The method further includes: Receive a first message, which is used to query the amount of data M1 that the terminal device can return.

3. The method according to claim 1 or 2, characterized in that, The method further includes: Receive second information, which indicates the amount of data M2 that the terminal needs to return; Based on the second information, data of the data quantity M2 is collected and transmitted, wherein M2 is less than or equal to M1.

4. The method according to any one of claims 1 to 3, characterized in that, The data volume M1 is the total or remaining data volume that the terminal device can report during time T for training the model; and / or, The value of the data volume M1 is related to the model or scenario used for training.

5. A communication method characterized by comprising: The method includes: Receive first information, the first information instructs the terminal device to collect data for training the model, the amount of data to be fed back is M2; Based on the first information, a second information is sent, which indicates that the data volume M2 is greater than the remaining data volume that the terminal device can return for data collection.

6. The method of claim 5, wherein, The second information includes one or more of the following: A negative response (NACK) is given. Information indicating the amount of remaining data the terminal device is used for data collection; or, The effective duration T of the remaining data amount is indicated.

7. The method according to claim 5 or 6, characterized in that, The method further includes: Receive third information, the third information being used to indicate the amount of data M3 that the terminal device needs to feed back; Based on the third information, data of the data quantity M3 is collected and transmitted, where M3 is less than M2.

8. A communication method characterized by comprising: The method includes: First information is determined, which indicates the transmission method and / or transmission delay used by the terminal device for data transmission; the data is used to train a neural network AI model. Send the first message.

9. The method according to claim 8, characterized in that, The method further includes: Receive a second message, which instructs the terminal to collect data and specify the amount of data M2 to be returned. Based on the second information, the data of the data volume M2 is collected and transmitted.

10. The method according to claim 8 or 9, characterized in that, The method further includes: Receive a first message, which is used to query the data transmission method and / or transmission delay of the terminal.

11. The method according to any one of claims 8 to 10, characterized in that, The transmission method is associated with at least one of the following: the amount of data transmitted, or the AI ​​model or scenario used for training; and / or, The transmission delay is associated with at least one of the following: the amount of data transmitted, or the AI ​​model or scenario used for training.

12. The method of claim 8 or 9, wherein, The first information indicates the transmission delay T used by the terminal for data transmission.

13. A method of communication, comprising: The method includes: Receive first information, which is used to instruct the terminal device to collect data for training the model within time T, and the amount of data that can be fed back is M1. Based on the first information, a second information is sent, which indicates the amount of data M2 that the terminal device needs to return; wherein, M2 is less than or equal to M1.

14. The method of claim 13, wherein, The method further includes: Send a first message, which is used to query the amount of data that the terminal device can return.

15. The method according to claim 13 or 14, characterized in that, The data volume M1 is the total or remaining data volume that the terminal device can report during time T for training the model; and / or, The value of the data volume M1 is related to the model or scenario used for training.

16. A method of communication, comprising: The method includes: Send a first message, which is used to instruct the terminal device to collect data for training the model, and the amount of data M2 that needs to be returned; Receive second information, which indicates that the data volume M2 is greater than the remaining data volume that the terminal device can return for data collection.

17. The method of claim 16, wherein, The second information includes one or more of the following: A negative response (NACK) is given. Information indicating the amount of remaining data the terminal is used for data collection; or, The effective duration T of the remaining data amount.

18. The method according to claim 16 or 17, characterized in that The method further includes: Based on the second information, a third information is sent, which indicates the amount of data M3 that the terminal device needs to return, and M3 is less than M2.

19. A method of communication, comprising: The method includes: Receive first information, the first information indicating the transmission method and / or transmission delay used by the terminal for data transmission; the data is used to train a neural network AI model; Based on the first information, a second information is sent, which is used to indicate the amount of data M2 that the terminal needs to return for data collection.

20. The method of claim 19, wherein, The method further includes: Send a first message, which is used to query the data transmission method and / or transmission delay of the terminal.

21. The method according to any one of claims 19 to 20, characterized in that, The transmission method is associated with at least one of the following: the amount of data transmitted, or the AI ​​model or scenario used for training; and / or, The transmission delay is associated with at least one of the following: the amount of data transmitted, or the AI ​​model or scenario used for training.

22. The method of claim 19 or 20, wherein, The first information indicates the transmission delay T used by the terminal for data transmission.

23. A communications device, characterized by Includes units or modules for implementing the method as described in any one of claims 1 to 22.

24. A communications device, characterized by The communication device includes at least one processor; the at least one processor is configured to enable the communication device to implement the method as described in any one of claims 1 to 22.

25. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed, cause the computer to perform the method as described in any one of claims 1 to 22.

26. A computer program product, characterised in that, The computer program product includes: computer program code, which, when executed by a computer, causes the computer to perform the method as described in any one of claims 1 to 22.

27. A communications device, characterized by The communication device includes logic circuitry and an interface, the interface being used for inputting and / or outputting information, and the logic circuitry being used to cause the communication device to perform the method as described in any one of claims 1 to 22.

28. A chip, characterized by It includes at least one processor, the processor being configured to execute instructions to cause a communication device including the chip to perform the method as described in any one of claims 1 to 22.

29. The chip of claim 28, wherein, The chip further comprises an interface circuit for receiving the executed instructions and transmitting to the processor, or outputting information from the processor.

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