Communication method, related equipment and system

Abstract

A communication method, a related device and a system can realize establishment of a communication connection of a communication management service based on first configuration information sent by an artificial intelligence (AI) training unit, the subsequent communication connection can be used for realizing transmission of service data of the communication management service, and the communication management service can be used for realizing communication management. Therefore, the management of the wireless access communication service is realized through the communication management service, so that the service quality of the wireless access communication service is improved. In the method, an AI training unit determines first configuration information, the first configuration information is used for configuring communication connection of data transmission, and the data transmission is transmission of service data of a communication management service performed between the AI training unit and an AI reasoning unit. The communication management service is used for managing a wireless access communication service between the terminal equipment and the wireless access equipment; the first configuration information comprises an identifier of the communication connection and identifiers of one or more QoS flows in the communication connection; and the AI training unit sends the first configuration information.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a communication method, related equipment and system. Background Art

[0002] Wireless communication can be a transmission communication between two or more communication nodes without propagating through conductors or cables. The communication nodes generally include terminal devices and wireless access devices.

[0003] At present, in a wireless communication system, a wireless access device can calculate and determine the transmission resources of a signal, for example, the transmission resources may include time domain resources and frequency domain resources for carrying the signal. Accordingly, the terminal device can send and receive signals on the transmission resources. In this way, the wireless access device can provide a wireless access communication service for network access for at least one terminal device.

[0004] With the development of communication technology, the concept of communication management service has emerged in wireless communication systems, which is used to manage wireless access communication services. However, there is no relevant record on how to establish a communication connection for the communication management service in the current public documents. Therefore, how to establish a communication connection for the communication management service in a wireless communication system is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present application provides a communication method, related equipment and system, which can realize the establishment and / or parameter configuration of a communication connection of a communication management service based on the first configuration information sent by an artificial intelligence (AI) training unit. Subsequently, the communication connection can be used to realize the transmission of business data of the communication management service, and then realize the management of the wireless access communication service through the communication management service to improve the service quality of the wireless access communication service.

[0006] The first aspect of the present application provides a communication method, which is performed by an artificial intelligence (AI) training unit, and the AI ​​training unit can be a communication device, or the AI ​​training unit can be a partial component of the communication device (such as a processor, a chip or a chip system, etc.), or the AI ​​training unit can be a logic module or software that implements all or part of the functions of the communication device. In the first aspect and its possible implementation, the method is described as an example of being performed by an AI training unit. In the method, the AI ​​training unit determines first configuration information, and the first configuration information is used to configure a communication connection for data transmission, and the data transmission is the transmission of business data of a communication management service between the AI ​​training unit and the AI ​​reasoning unit, and the communication management service is used to manage the wireless access communication service between the terminal device and the wireless access device; the first configuration information includes an identifier of the communication connection, and an identifier of one or more quality of service flows (quality of service flow, QoS flow) in the communication connection; the AI ​​training unit sends the first configuration information.

[0007] Based on the above technical solution, the communication management service is used to manage the wireless access communication service between the terminal device and the wireless access device. Among them, the first configuration information sent by the AI ​​training unit is used to configure the communication connection for data transmission, and the data transmission is the transmission of business data of the communication management service between the AI ​​training unit and the AI ​​reasoning unit. In other words, after the AI ​​reasoning unit receives the first configuration information, the AI ​​reasoning unit can transmit the business data of the communication management service based on the first configuration information. Thus, the first configuration information sent by the AI ​​training unit can realize the establishment of the communication connection of the communication management service, and the communication connection can be used to realize the transmission of business data of the communication management service, and then realize the management of the wireless access communication service through the communication management service, so as to improve the service quality of the wireless access communication service.

[0008] It should be understood that the AI ​​training unit may be a functional entity responsible for the training of the AI ​​model. Alternatively, the AI ​​training unit may also be a functional entity responsible for the control and management of the communication management service, and the functions of the functional entity include but are not limited to the training of the AI ​​model, the deployment of the AI ​​model, the management of the AI ​​connection, the quality of service (QoS) management of the service transmission in the communication management service, etc. Correspondingly, the AI ​​training unit may have other names, such as AI management unit, AI connection management unit, AI model training unit, etc. Exemplarily, the AI ​​training unit may be a wireless access device or a module involving AI processing in the wireless access device (such as a software module, a hardware module, a chip, etc.), or the AI ​​training unit may be a module external to the wireless access device.

[0009] Optionally, the AI ​​training unit may not be responsible for other functions in the control and management of the communication management service except the training of the AI ​​model, for example, the other functions may include the management of AI connections, the QoS management of service transmission in the communication management service, etc. Accordingly, the other functions may be implemented by other devices / other units, such as the AI ​​management unit.

[0010] It should be understood that the AI ​​reasoning unit can be a functional entity for business processing based on the AI ​​model, including functions of the functional entity including but not limited to reasoning of the AI ​​model, processing of AI business data, etc. Correspondingly, the AI ​​reasoning unit can have other names, such as AI model reasoning unit, AI data processing unit, etc. Exemplarily, the AI ​​reasoning unit can be a communication device or a module of the communication device (such as a software module, a hardware module, a chip, etc.) in a wireless access communication service. The communication device can be a terminal device or a wireless access device, and the wireless access device can include a wireless access network device, a centralized unit (CU) or a distributed unit (DU), etc.

[0011] It should be understood that the communication management service is used to manage the wireless access communication service, which can be understood as the communication management service is used to manage the communication parameters involved in the wireless access communication service in an intelligent manner (including at least one of configuration, update, and optimization), and the intelligent manner may include a manner enabled by technologies such as AI, neural network, and machine learning. Optionally, the terms AI, intelligence, neural network, and machine learning in this application can be replaced with each other.

[0012] In other words, the communication management service can be a service that improves the service quality of wireless access communication services in an intelligent manner, or the communication management service can be a service that intelligently manages the service quality of wireless access communication. Accordingly, the communication management service can also be replaced by a communication configuration service, a communication optimization service, etc.

[0013] For example, the communication parameters involved in the wireless access communication service may include channel state information (CSI). Accordingly, the communication management service may include intelligent management of CSI, which is also called an AI-enabled CSI feedback service or an AI-model-based CSI feedback service.

[0014] For example, the communication parameters involved in the wireless access communication service may include communication beams. Accordingly, the communication management service may include intelligent management of communication beams, which is also called AI-enabled beam management service or AI model-based beam management service.

[0015] Optionally, the service data of the communication management service includes an AI model corresponding to at least one of the above services to implement management of communication parameters corresponding to the at least one of the above services. For example, the AI ​​model included in the service data of the communication management service may include an AI model of an AI-based CSI feedback service to implement intelligent management of CSI through the AI ​​model; for example, the AI ​​model included in the service data of the communication management service may include an AI model of an AI-based beam management service to implement intelligent management of communication beams through the AI ​​model.

[0016] For example, the communication parameters involved in the wireless access communication service may include positioning information. Accordingly, the communication management service may include intelligent management of the positioning information, which is also called the communication management service as an AI-enabled location positioning service or an AI model-based location positioning service.

[0017] Optionally, the service data involved in the data transmission of the communication management service may include at least one of the AI ​​model information and the data required for AI model training. Among them, the AI ​​model information may include the parameter scale information of the AI ​​model, the structural information of the AI ​​model, and at least one of the intermediate results of the AI ​​model training (such as training hyperparameters). Among them, the AI ​​model can be used to calculate the communication parameters involved in the wireless access communication service (including at least one of CSI, communication beam, and positioning information) in an AI manner to obtain a calculation result, which can be used for the management of the wireless access communication service (including at least one of configuration, update, and optimization).

[0018] Further optionally, the AI ​​model can be replaced by a neural network model, a machine learning model, a deep learning model, an AI learning model, a machine learning model, a neural network, an AI algorithm, an AI algorithm model, etc.

[0019] It should be understood that the first configuration information includes the identifier of one or more QoS flows in the communication connection. That is, the one or more QoS flows belong to the communication connection. Among them, the one or more QoS flows can be the default QoS flow, the configuration information of the one or more QoS flows can be included in the first configuration information, or the configuration information of the one or more QoS flows can be pre-configured, or the configuration information of the one or more QoS flows can be sent to the AI ​​reasoning unit through the AI ​​training unit through other configuration information, which is not limited here.

[0020] Optionally, in the configuration information of a QoS flow (for example, one or more QoS flows mentioned above, or the N QoS flows appearing later, etc.), the configuration information of the QoS flow includes at least one of the following: the priority of data transmission of the QoS flow, the data scale of data transmission of the QoS flow, the packet loss rate requirement of data transmission of the QoS flow, or the delay requirement of data transmission of the QoS flow.

[0021] Optionally, the first configuration information also includes an identifier of the AI ​​training unit, so that a recipient of the first configuration information can clearly identify that the first configuration information comes from the AI ​​training unit based on the identifier.

[0022] In a possible implementation manner of the first aspect, the method further includes: the AI ​​training unit receiving first request information, where the first request information is used to request the first configuration information.

[0023] Based on the above technical solution, the AI ​​training unit can also receive first request information for requesting first configuration information, so that the AI ​​training unit triggers determining or sending the first configuration information based on the request, so that the solution can be applicable to scenarios where other devices (such as AI reasoning units) request first configuration information.

[0024] Optionally, the first request information includes at least one of the following: an identifier of an AI application scenario supported by the AI ​​reasoning unit, an identifier of a requested AI service type, a model identifier corresponding to a requested AI model, and an identifier of the AI ​​reasoning unit.

[0025] Optionally, the AI ​​scenario supported by the AI ​​reasoning unit satisfies any of the following:

[0026] The AI ​​inference unit is a DU or a module in the DU, and the AI ​​scenarios supported by the AI ​​inference unit include at least one of a single user medium access control (SU MAC) dictionary, a multi-user (MU) pairing dictionary, a multi-cell coordinated scheduling dictionary, and a low-speed channel map;

[0027] The AI ​​inference unit is a terminal device or a module in the terminal device, and the AI ​​scenarios supported by the AI ​​inference unit include at least one of an interference spectrum, a beam spectrum, and a smart channel state information (CSI);

[0028] The AI ​​reasoning unit is a CU or a module in the CU. The AI ​​scenarios supported by the AI ​​reasoning unit include at least one of smart grid, smart load balancing, and smart energy saving.

[0029] In a possible implementation of the first aspect, the AI ​​training unit determines the first configuration information, including: the AI ​​training unit determines the first configuration information based on at least one of the following items, including: an identifier of the AI ​​application scenario corresponding to the business data, and an identifier of the AI ​​service type corresponding to the communication management business.

[0030] Optionally, the basis for the AI ​​training unit to determine the first configuration information may include, in addition to at least one of the above items, a model identifier of the AI ​​model corresponding to the AI ​​model information contained in the business data.

[0031] Based on the above technical solution, the basis for determining the first configuration information by the AI ​​training unit may include at least one of the above items to enhance the flexibility of the implementation of the solution.

[0032] In a possible implementation of the first aspect, after the AI ​​training unit sends the first configuration information, the method also includes: the AI ​​training unit sends second configuration information, the second configuration information including configuration information of N QoS flows established in the communication connection, N is a positive integer; wherein the second configuration information includes at least one of the following: an identifier of the communication connection, N identifiers corresponding to the N QoS flows, QoS information of the N QoS flows, and an identifier of the AI ​​reasoning unit.

[0033] Based on the above technical solution, after the AI ​​training unit configures the communication connection between the AI ​​training unit and the AI ​​reasoning unit through the first configuration information, the AI ​​training unit may also send second configuration information including N QoS flows established in the communication connection, so that the recipient of the second configuration information can implement the configuration of the N QoS flows based on the second configuration information.

[0034] In addition, the N QoS flows may be different from one or more QoS flows in the first configuration information, so as to implement the update or addition of a QoS flow in the communication connection.

[0035] In a possible implementation manner of the first aspect, the method further includes: the AI ​​training unit receives second request information, where the second request information is used to request the second configuration information; wherein the second request information includes an identifier of the communication connection.

[0036] Based on the above technical solution, the AI ​​training unit can also receive second request information for requesting second configuration information, so that the AI ​​training unit triggers determination or sending of second configuration information based on the request, so that the solution can be applicable to scenarios where other devices (such as AI reasoning units) request second configuration information.

[0037] In a possible implementation manner of the first aspect, the method further includes: the AI ​​training unit sends first indication information, where the first indication information is used to indicate deletion of at least one QoS flow among the N QoS flows.

[0038] Based on the above technical solution, the AI ​​training unit can also send a first indication information so that the recipient of the first indication information can delete at least one QoS flow among the N QoS flows based on the indication of the first indication information to release the resources of the at least one QoS flow and avoid waste of resources caused by no business transmission on the at least one QoS flow. Alternatively, the resources of the at least one QoS flow can be used to transmit other high-priority services.

[0039] In the present application, deleting a QoS flow (for example, deleting at least one QoS flow among N QoS flows) can be replaced by releasing the QoS flow, or releasing the resources corresponding to the QoS flow, or terminating the resources corresponding to the QoS flow.

[0040] In a possible implementation manner of the first aspect, before the AI ​​training unit sends the first indication information, the method further includes: the AI ​​training unit receives third request information, where the third request information is used to request deletion of the at least one QoS flow.

[0041] Based on the above technical solution, the AI ​​training unit can also receive a third request information for requesting the deletion of at least one QoS flow, so that the AI ​​training unit triggers determination or sending of the first indication information based on the request, so that the solution can be applicable to scenarios where other devices (such as AI reasoning units) request the deletion of QoS flows.

[0042] In a possible implementation manner of the first aspect, the method further includes: the AI ​​training unit sending second indication information, where the second indication information is used to indicate the release of the communication connection.

[0043] Based on the above technical solution, the AI ​​training unit can also send a second indication information so that the recipient of the second indication information can release the communication connection based on the indication of the second indication information to release the resources of the communication connection and avoid waste of resources caused by no business transmission on the communication connection, or the resources of the communication connection can be used to transmit other high-priority services.

[0044] It can be understood that the AI ​​training unit or AI reasoning unit releases the communication connection, which can be understood as the AI ​​training unit or AI reasoning unit deleting (or releasing) the QoS flow of the communication connection.

[0045] In a possible implementation manner of the first aspect, the method further includes: the AI ​​training unit receiving fourth request information, where the fourth request information is used to request to release the communication connection.

[0046] Based on the above technical solution, the AI ​​training unit can also receive a fourth request information for requesting the release of the communication connection, so that the AI ​​training unit triggers determination or sending of second indication information based on the request, so that the solution can be applicable to scenarios where other devices (such as AI reasoning units) request to release the communication connection.

[0047] The second aspect of the present application provides a communication method, which is performed by an AI reasoning unit, and the AI ​​reasoning unit can be a communication device, or the AI ​​reasoning unit can be a partial component of the communication device (such as a processor, a chip or a chip system, etc.), or the AI ​​reasoning unit can be a logic module or software that implements all or part of the functions of the communication device. In the second aspect and its possible implementation, the method is described as an example of being performed by an AI reasoning unit. In the method, the AI ​​reasoning unit receives first configuration information, and the first configuration information is used to configure a communication connection for data transmission, and the data transmission is the transmission of business data of a communication management service between an AI training unit and an AI reasoning unit, and the communication management service is used to manage the wireless access communication service between a terminal device and a wireless access device; the first configuration information includes an identifier of the communication connection, and an identifier of one or more QoS flows in the communication connection; the AI ​​reasoning unit transmits the business data in the communication connection based on the first configuration information.

[0048] Based on the above technical solution, the communication management service is used to manage the wireless access communication service between the terminal device and the wireless access device. Among them, the first configuration information received by the AI ​​reasoning unit is used to configure the communication connection for data transmission, and the data transmission is the transmission of business data of the communication management service between the AI ​​training unit and the AI ​​reasoning unit. In other words, after the AI ​​reasoning unit receives the first configuration information, the AI ​​reasoning unit can transmit the business data of the communication management service based on the first configuration information. Thus, the first configuration information sent by the AI ​​training unit can realize the establishment of the communication connection of the communication management service, and the subsequent communication connection can be used to realize the transmission of business data of the communication management service, and then realize the management of the wireless access communication service through the communication management service, so as to improve the service quality of the wireless access communication service.

[0049] Optionally, the first configuration information also includes at least one of the following: quality of service (QoS) information of one or more QoS flows in the communication connection, and an identifier of the AI ​​training unit.

[0050] In a possible implementation manner of the second aspect, the method further includes: the AI ​​reasoning unit sends first request information, where the first request information is used to request the first configuration information.

[0051] Based on the above technical solution, the AI ​​reasoning unit can also send a first request information for requesting the first configuration information, so that the AI ​​training unit triggers determination or sending of the first configuration information based on the request, so that the solution can be applicable to the scenario where the AI ​​reasoning unit requests the first configuration information.

[0052] Optionally, the first request information includes at least one of the following: an identifier of an AI application scenario supported by the AI ​​reasoning unit, an identifier of a requested AI service type, a model identifier corresponding to a requested AI model, and an identifier of the AI ​​reasoning unit.

[0053] Optionally, the AI ​​scenario supported by the AI ​​reasoning unit satisfies any of the following:

[0054] The AI ​​inference unit is a distributed unit DU or a module in the DU, and the AI ​​scenarios supported by the AI ​​inference unit include at least one of a single-user medium access control SU MAC dictionary, a multi-user MU pairing dictionary, a multi-cell coordinated scheduling dictionary, and a low-speed channel map;

[0055] The AI ​​inference unit is a terminal device or a module in the terminal device, and the AI ​​scenarios supported by the AI ​​inference unit include at least one of an interference spectrum, a beam spectrum, and a smart channel state information CSI;

[0056] The AI ​​reasoning unit is a centralized unit CU or a module in the CU. The AI ​​scenarios supported by the AI ​​reasoning unit include at least one of smart grid, smart load balancing, and smart energy saving.

[0057] In a possible implementation of the second aspect, after the AI ​​reasoning unit receives the first configuration information, the method also includes: the AI ​​reasoning unit receives second configuration information, the second configuration information including configuration information of N QoS flows established in the communication connection, where N is a positive integer; wherein the second configuration information includes at least one of the following: an identifier of the communication connection, N identifiers corresponding to the N QoS flows, quality of service QoS information of the N QoS flows, and an identifier of the AI ​​reasoning unit.

[0058] Based on the above technical solution, after the AI ​​training unit configures the communication connection between the AI ​​training unit and the AI ​​reasoning unit through the first configuration information, the AI ​​reasoning unit can also receive second configuration information including N QoS flows established in the communication connection, so that the AI ​​reasoning unit can implement the configuration of the N QoS flows based on the second configuration information.

[0059] In addition, the N QoS flows may be different from one or more QoS flows in the first configuration information, so as to implement the update or addition of a QoS flow in the communication connection.

[0060] In a possible implementation manner of the second aspect, the method further includes: the AI ​​reasoning unit sends a second request information, where the second request information is used to request the second configuration information; wherein the second request information includes an identifier of the communication connection.

[0061] Based on the above technical solution, the AI ​​reasoning unit can also send a second request information for requesting the second configuration information, so that the AI ​​training unit triggers determination or sending of the second configuration information based on the request, so that the solution can be applicable to the scenario where the AI ​​reasoning unit requests the second configuration information.

[0062] In a possible implementation manner of the second aspect, the method further includes: the AI ​​reasoning unit receives first indication information, where the first indication information is used to indicate deletion of at least one QoS flow among the N QoS flows.

[0063] Based on the above technical solution, the AI ​​reasoning unit can also receive a first indication information, so that the AI ​​reasoning unit can delete at least one QoS flow among the N QoS flows based on the indication of the first indication information to release the resources of the at least one QoS flow and avoid resource waste caused by no business transmission on the at least one QoS flow, or the resources of the at least one QoS flow can be used to transmit other high-priority services.

[0064] In a possible implementation manner of the second aspect, before the AI ​​reasoning unit receives the first indication information, the method further includes: the AI ​​reasoning unit sends third request information, where the third request information is used to request deletion of the at least one QoS flow.

[0065] Based on the above technical solution, the AI ​​reasoning unit can also send a third request information for requesting the deletion of at least one QoS flow, so that the AI ​​training unit triggers determination or sends the first indication information based on the request, so that the solution can be applicable to the scenario where the AI ​​reasoning unit requests the deletion of the QoS flow.

[0066] In a possible implementation manner of the second aspect, the method further includes: the AI ​​reasoning unit receiving second indication information, where the second indication information is used to indicate the release of the communication connection.

[0067] Based on the above technical solution, the AI ​​reasoning unit can also receive a second indication information, so that the AI ​​reasoning unit can release the communication connection based on the indication of the second indication information to release the resources of the communication connection and avoid waste of resources caused by no business transmission on the communication connection, or the resources of the communication connection can be used to transmit other high-priority businesses.

[0068] It can be understood that the AI ​​training unit or AI reasoning unit releases the communication connection, which can be understood as the AI ​​training unit or AI reasoning unit deleting (or releasing) the QoS flow of the communication connection.

[0069] In a possible implementation manner of the second aspect, the method further includes: the AI ​​reasoning unit sends fourth request information, where the fourth request information is used to request to release the communication connection.

[0070] Based on the above technical solution, the AI ​​reasoning unit can also send a fourth request information for requesting the release of the communication connection, so that the AI ​​training unit triggers determination or sending of second indication information based on the request, so that the solution can be applicable to the scenario where the AI ​​reasoning unit requests the release of the communication connection.

[0071] The third aspect of the present application provides a communication device (e.g., a first communication device), which can implement the method in the above-mentioned first aspect and any possible implementation thereof, that is, the device can be an AI training unit in the first aspect and any possible implementation thereof. Wherein, the device includes a processing module and a transceiver module; the processing module is used to determine the first configuration information, the first configuration information is used to configure the communication connection for data transmission, the data transmission is the transmission of business data of the communication management service between the AI ​​training unit and the AI ​​reasoning unit, and the communication management service is used to manage the wireless access communication service between the terminal device and the wireless access device; the first configuration information includes the identifier of the communication connection, and the identifier of one or more quality of service flows QoS flow in the communication connection; the transceiver module is used to send the first configuration information.

[0072] In a possible implementation manner of the third aspect, the transceiver module is further used to receive first request information, where the first request information is used to request the first configuration information.

[0073] In a possible implementation manner of the third aspect, the first request information includes at least one of the following: an identifier of an AI application scenario supported by the AI ​​reasoning unit, an identifier of a requested AI service type, a model identifier corresponding to a requested AI model, and an identifier of the AI ​​reasoning unit.

[0074] In a possible implementation manner of the third aspect, the AI ​​scenario supported by the AI ​​reasoning unit satisfies any of the following:

[0075] The AI ​​inference unit is a distributed unit DU or a module in the DU, and the AI ​​scenarios supported by the AI ​​inference unit include at least one of a single-user medium access control SU MAC dictionary, a multi-user MU pairing dictionary, a multi-cell coordinated scheduling dictionary, and a low-speed channel map;

[0076] The AI ​​inference unit is a terminal device or a module in the terminal device, and the AI ​​scenarios supported by the AI ​​inference unit include at least one of an interference spectrum, a beam spectrum, and a smart channel state information CSI;

[0077] The AI ​​reasoning unit is a centralized unit CU or a module in the CU. The AI ​​scenarios supported by the AI ​​reasoning unit include at least one of smart grid, smart load balancing, and smart energy saving.

[0078] In a possible implementation manner of the third aspect, the first configuration information also includes at least one of the following: quality of service (QoS) information of one or more QoS flows in the communication connection, and an identifier of the AI ​​training unit.

[0079] In a possible implementation of the third aspect, the processing module is used to determine the first configuration information, including: the processing module is specifically used to determine the first configuration information based on at least one of the following items, including: an identifier of the AI ​​application scenario corresponding to the business data, an identifier of the AI ​​service type corresponding to the communication management business, and a model identifier of the AI ​​model corresponding to the AI ​​model information contained in the business data.

[0080] In a possible implementation of the third aspect, the transceiver module is also used to send second configuration information, the second configuration information including configuration information of N QoS flows established in the communication connection, where N is a positive integer; wherein the second configuration information includes at least one of the following: an identifier of the communication connection, N identifiers corresponding to the N QoS flows, quality of service QoS information of the N QoS flows, and an identifier of the AI ​​reasoning unit.

[0081] In a possible implementation manner of the third aspect, the method further includes: the transceiver module is further used to receive second request information, and the second request information is used to request the second configuration information; wherein the second request information includes an identifier of the communication connection.

[0082] In a possible implementation manner of the third aspect, the method further includes: the transceiver module is further used to send first indication information, where the first indication information is used to indicate deletion of at least one QoS flow among the N QoS flows.

[0083] In a possible implementation manner of the third aspect, the transceiver module is further used to receive third request information, where the third request information is used to request deletion of the at least one QoS flow.

[0084] In a possible implementation manner of the third aspect, the transceiver module is further used to send second indication information, where the second indication information is used to indicate the release of the communication connection.

[0085] In a possible implementation manner of the third aspect, the transceiver module is further used to receive fourth request information, where the fourth request information is used to request to release the communication connection.

[0086] The fourth aspect of the present application provides a communication device (e.g., a second communication device), which can implement the method in the above-mentioned second aspect and any possible implementation thereof, that is, the device can be an AI reasoning unit in the second aspect and any possible implementation thereof. Wherein, the device includes a processing module and a transceiver module; the transceiver module is used to receive first configuration information, and the first configuration information is used to configure a communication connection for data transmission, and the data transmission is the transmission of business data of a communication management service between an artificial intelligence AI training unit and an AI reasoning unit, and the communication management service is used to manage the wireless access communication service between a terminal device and a wireless access device; the first configuration information includes an identifier of the communication connection, and an identifier of one or more quality of service flows QoS flow in the communication connection; the processing module is used to transmit the business data in the communication connection based on the first configuration information.

[0087] In a possible implementation manner of the fourth aspect, the transceiver module is further used to send first request information, where the first request information is used to request the first configuration information.

[0088] In a possible implementation manner of the fourth aspect, the first request information includes at least one of the following: an identifier of an AI application scenario supported by the AI ​​reasoning unit, an identifier of a requested AI service type, a model identifier corresponding to a requested AI model, and an identifier of the AI ​​reasoning unit.

[0089] In a possible implementation manner of the fourth aspect, the AI ​​scenario supported by the AI ​​reasoning unit satisfies any of the following:

[0090] The AI ​​inference unit is a distributed unit DU or a module in the DU, and the AI ​​scenarios supported by the AI ​​inference unit include at least one of a single-user medium access control SU MAC dictionary, a multi-user MU pairing dictionary, a multi-cell coordinated scheduling dictionary, and a low-speed channel map;

[0091] The AI ​​inference unit is a terminal device or a module in the terminal device, and the AI ​​scenarios supported by the AI ​​inference unit include at least one of an interference spectrum, a beam spectrum, and a smart channel state information CSI;

[0092] The AI ​​reasoning unit is a centralized unit CU or a module in the CU. The AI ​​scenarios supported by the AI ​​reasoning unit include at least one of smart grid, smart load balancing, and smart energy saving.

[0093] In a possible implementation manner of the fourth aspect, the first configuration information also includes at least one of the following: quality of service QoS information of one or more QoS flows in the communication connection, and an identifier of the AI ​​training unit.

[0094] In a possible implementation of the fourth aspect, the transceiver module is also used to receive second configuration information, the second configuration information including configuration information of N QoS flows established in the communication connection, where N is a positive integer; wherein the second configuration information includes at least one of the following: an identifier of the communication connection, N identifiers corresponding to the N QoS flows, quality of service QoS information of the N QoS flows, and an identifier of the AI ​​reasoning unit.

[0095] In a possible implementation manner of the fourth aspect, the transceiver module is further used to send second request information, where the second request information is used to request the second configuration information; wherein the second request information includes an identifier of the communication connection.

[0096] In a possible implementation manner of the fourth aspect, the transceiver module is further used to receive first indication information, where the first indication information is used to indicate deletion of at least one QoS flow among the N QoS flows.

[0097] In a possible implementation manner of the fourth aspect, the transceiver module is further used to send third request information, where the third request information is used to request deletion of the at least one QoS flow.

[0098] In a possible implementation manner of the fourth aspect, the transceiver module is further used to receive second indication information, where the second indication information is used to indicate the release of the communication connection.

[0099] In a possible implementation manner of the fourth aspect, the transceiver module is further used to send fourth request information, where the fourth request information is used to request to release the communication connection.

[0100] In a fifth aspect, the present application provides a communication device, comprising at least one processor, which is used to execute programs or instructions in a memory so that the device implements the method described in the first aspect or any possible implementation of the first aspect, or enables the device to implement the method described in the second aspect or any possible implementation of the second aspect.

[0101] In a sixth aspect, the present application provides a communication device, comprising at least one logic circuit and an input / output interface; the logic circuit is used to execute the method described in the first aspect or any possible implementation of the first aspect, or to execute the method described in the second aspect or any possible implementation of the second aspect.

[0102] In a seventh aspect, the present application provides a computer-readable storage medium, which is used to store one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes a method as described in any possible implementation of any one of the first to second aspects above.

[0103] In an eighth aspect, the present application provides a computer program product. When a computer program in the computer program product is executed by a processor, the processor executes the method described in any possible implementation of any one of the first to second aspects above.

[0104] A ninth aspect of the present application provides a chip system, which includes at least one processor for supporting a communication device to implement functions involved in any possible implementation of any one of the first to second aspects.

[0105] In a possible design, the chip system may also include a memory for storing program instructions and data necessary for the communication device. The chip system may be composed of a chip, or may include a chip and other discrete devices. Optionally, the chip system also includes an interface circuit, which provides program instructions and / or data for the at least one processor.

[0106] The tenth aspect of the present application provides a communication system, which includes the AI ​​training unit and the AI ​​reasoning unit in any of the above aspects and any of its implementation methods.

[0107] Optionally, the AI ​​reasoning unit is a terminal device, or the communication system also includes a terminal device.

[0108] Among them, the technical effects brought about by any design method in the third aspect to the tenth aspect can refer to the technical effects brought about by the different design methods in the above-mentioned first aspect to the second aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] Figure 1 A schematic diagram of a communication system provided for this application;

[0110] Figure 2 A schematic diagram of a PDU session involved in this application;

[0111] Figure 3a A schematic diagram of the network architecture provided for this application;

[0112] Figure 3b Another schematic diagram of the network architecture provided for this application;

[0113] Figure 3c Another schematic diagram of the network architecture provided for this application;

[0114] Figure 3d Another schematic diagram of the network architecture provided for this application;

[0115] Figure 3e Another schematic diagram of the network architecture provided for this application;

[0116] Figure 4 A schematic diagram of the communication method provided by the present application;

[0117] Figure 5 Another schematic diagram of the communication method provided by the present application;

[0118] Figure 6 Another schematic diagram of the communication method provided by the present application;

[0119] Figure 7 Another schematic diagram of the communication method provided by the present application;

[0120] Figure 8 Another schematic diagram of the communication method provided by the present application;

[0121] Fig. 9 Another schematic diagram of the communication method provided by the present application;

[0122] Fig.10 Another schematic diagram of the communication method provided by the present application;

[0123] Fig.11 A schematic diagram of a communication device provided for this application;

[0124] Fig.12 Another schematic diagram of a communication device provided by the present application;

[0125] Fig.13 Another schematic diagram of a communication device provided by the present application;

[0126] Fig.14 Another schematic diagram of a communication device provided by the present application;

[0127] Fig.15 Another schematic diagram of a communication device provided by the present application. DETAILED DESCRIPTION

[0128] First, some terms in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0129] (1) Configuration and pre-configuration: In this application, configuration and pre-configuration will be used at the same time. Configuration refers to the base station or server and other network devices sending some configuration parameter information or parameter values ​​to the terminal through messages or signaling, so that the terminal can determine the communication parameters or resources during transmission based on these values ​​or information. Pre-configuration is similar to configuration. It can be a method in which a base station or server and other network devices send parameter information or values ​​to the terminal through a communication link or carrier; it can also be a definition of corresponding parameters or parameter values ​​given in the standard, or by setting the relevant parameters or values ​​in the terminal device in advance. This application does not limit this. Furthermore, these values ​​and parameters can be changed or updated.

[0130] (2) In this application, "used for indication" may include being used for direct indication and being used for indirect indication. When describing that a certain indication information is used for indicating A, it can be understood that the indication information carries A, directly indicates A, or indirectly indicates A.

[0131] In this application, the information indicated by the indication information is referred to as the information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated. For example, it can be implemented by direct indication, such as by indicating the information to be indicated itself or the index of the information to be indicated. It can also be implemented by indirectly indicating other information, wherein there is an association between the other information and the information to be indicated. It is also possible to indicate only a part of the information to be indicated, while the other parts of the information to be indicated are known or agreed in advance. For example, the indication of specific information can also be achieved with the help of the arrangement order of each information agreed in advance (for example, specified by the protocol), thereby reducing the indication overhead to a certain extent.

[0132] The information to be indicated can be sent as a whole, or divided into multiple sub-information and sent separately, and the sending period and / or sending time of these sub-information can be the same or different. The specific sending method is not limited in this application. Among them, the sending period and / or sending time of these sub-information can be pre-defined, for example, pre-defined according to the protocol, or can be configured by the transmitting device by sending configuration information to the receiving device. Among them, the configuration information can include, for example but not limited to, one or a combination of at least two of RRC signaling, medium access control (MAC) layer signaling and physical layer signaling. Among them, the MAC layer signaling, for example, includes a medium access control control element (MAC CE); the physical layer signaling, for example, includes downlink control information (DCI).

[0133] (3) The terms "system" and "network" in the embodiments of the present application can be used interchangeably. "At least one" means one or more, and "plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the objects associated with each other are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of A, B and C" includes A, B, C, AB, AC, BC or ABC. And, unless otherwise specified, the ordinal numbers such as "first" and "second" mentioned in the embodiments of the present application are used to distinguish multiple objects, and are not used to limit the order, timing, priority or importance of multiple objects.

[0134] (4) "Send" and "receive" in the embodiments of the present application indicate the direction of signal transmission. In the present application, when entity A sends information to entity B, it can be that A sends it directly to B, or that A sends it indirectly to B through other entities. Similarly, when entity B receives information from entity A, it can be that entity B directly receives the information sent by entity A, or that entity B indirectly receives the information sent by entity A through other entities. Entities A and B here can be RAN nodes or terminals, or modules inside RAN nodes or terminals. The sending and receiving of information can be information interaction between a RAN node and a terminal, for example, information interaction between a base station and a terminal; the sending and receiving of information can also be information interaction between two RAN nodes, for example, information interaction between a CU and a DU; the sending and receiving of information can also be information interaction between different modules inside a device, for example, information interaction between a terminal chip and other modules of the terminal, or information interaction between a base station chip and other modules in the base station. "Send" can also be understood as the "output" of a chip interface, and "receive" can also be understood as the "input" of a chip interface.

[0135] See also Figure 1 , is a schematic diagram of the architecture of a communication system 1000 used in an embodiment of the present application. Figure 1 As shown, the communication system includes a radio access network (RAN) 100 and a core network 200.

[0136] Optionally, the communication system 1000 may also include the Internet 300 .

[0137] exist Figure 1 In the example, RAN 100 includes at least one RAN node (e.g. Figure 1 110a and 110b in the figure, collectively referred to as 110), and may also include at least one terminal (such as Figure 1 120a-120j in the figure are collectively referred to as 120). The RAN node may also be referred to as a wireless access device, a network device, an access network device, etc.

[0138] exist Figure 1 In the example, RAN 100 may also include other RAN nodes, such as wireless relay equipment and / or wireless backhaul equipment ( Figure 1(not shown in the figure). The terminal 120 is connected to the RAN node 110 by wireless means, and the RAN node 110 is connected to the core network 200 by wireless or wired means. The core network device in the core network 200 and the RAN node 110 in the RAN 100 may be independent and different physical devices, or may be the same physical device that integrates the logical functions of the core network device and the logical functions of the RAN node. Terminals and terminals and RAN nodes and RAN nodes may be connected to each other by wired or wireless means.

[0139] RAN100 may be an evolved universal terrestrial radio access (E-UTRA) system, a new radio (NR) system, and a future radio access system defined in the 3rd generation partnership project (3GPP). RAN100 may also include two or more of the above-mentioned different radio access systems. RAN100 may also be an open RAN (O-RAN).

[0140] RAN node, also known as radio access network equipment, RAN entity or access node, is used to help terminals access the communication system wirelessly. In one application scenario, the RAN node can be a base station, an evolved NodeB (eNodeB), a transmission reception point (TRP), a next generation NodeB (gNB) in the fifth generation (5G) mobile communication system, a next generation NodeB in the sixth generation (6G) mobile communication system, or a base station in a future mobile communication system. RAN nodes can be macro base stations (such as Figure 1 110a), or a micro base station or an indoor station (such as Figure 1 110b) in the figure, it can also be a relay node or a donor node.

[0141] In another application scenario, the cooperation of multiple RAN nodes can be used to help the terminal achieve wireless access, and different RAN nodes respectively implement part of the functions of the base station. For example, the RAN node can be a centralized unit (CU), a distributed unit (DU) or a radio unit (RU). The CU here completes the functions of the radio resource control protocol and the packet data convergence protocol (PDCP) of the base station, and can also complete the function of the service data adaptation protocol (SDAP); the DU completes the functions of the radio link control layer and the MAC layer of the base station, and can also complete the functions of part of the physical layer or all of the physical layer. For the specific description of the above-mentioned various protocol layers, please refer to the relevant technical specifications of 3GPP. RU can be used to implement the transceiver function of radio frequency signals. CU and DU can be two independent RAN nodes, or they can be integrated in the same RAN node, such as integrated in a baseband unit (BBU). RU can be included in a radio frequency device, such as a remote radio unit (RRU) or an active antenna unit (AAU). The CU can be further divided into two types of RAN nodes: CU-control plane and CU-user plane.

[0142] In different systems, RAN nodes may have different names. For example, in an open radio access network (openRAN, O-RAN or ORAN) system, CU may also be called O-CU (open CU), DU may also be called O-DU, CU-CP may also be called O-CU-CP, CU-UP may also be called O-CU-UP, and RU may also be called O-RU. For the convenience of description, this application takes CU, CU-CP, CU-UP, DU and RU as examples for description. Any unit in the CU (or CU-CP, CU-UP), DU and RU in this application can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0143] The communication between the access network device and the terminal device follows a certain protocol layer structure. The protocol layer may include a control plane protocol layer and a user plane protocol layer. The control plane protocol layer may include at least one of the following: a radio resource control (RRC) layer, a packet data convergence protocol (PDCP) layer, a radio link control (RLC) layer, a media access control (MAC) layer, or a physical (PHY) layer. The user plane protocol layer may include at least one of the following: a service data adaptation protocol (SDAP) layer, a PDCP layer, an RLC layer, a MAC layer, or a physical layer.

[0144] For the correspondence between network elements in the ORAN system and their achievable protocol layer functions, refer to Table 1 below.

[0145] Table 1

[0146] ORAN network elements 3GPP protocol layer functions O-CU-CP RRC+PCDP-Control Plane (PDCP-C) O-CU-UP SDAP+PCDP-User Plane (PDCP-U) O-DU RLC+MAC+PHY-high O-RU PHY-low

[0147] The embodiments of the present application do not limit the specific technology and specific device form adopted by the RAN node. For the convenience of description, the following description takes a base station as an example of a RAN node.

[0148] A terminal is a device with wireless transceiver function, which can send signals to a base station or receive signals from a base station. A terminal can also be called a terminal device, user equipment (UE), a mobile station, a mobile terminal, etc. The terminal can be widely used in various scenarios, for example, device-to-device (D2D), vehicle-to-everything (V2X) communication, machine-type communication (MTC), Internet of Things (IOT), virtual reality, augmented reality, industrial control, automatic driving, telemedicine, smart grid, smart furniture, smart office, smart wear, smart transportation, smart city, etc. The terminal can be a mobile phone, a tablet computer, a computer with wireless transceiver function, a wearable device, a vehicle, an airplane, a ship, a robot, a mechanical arm, a smart home device, etc. The embodiments of the present application do not limit the specific technology and specific device form adopted by the terminal.

[0149] Base stations and terminals can be fixed or movable. Base stations and terminals can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on the water surface; they can also be deployed on airplanes, balloons, and artificial satellites. The embodiments of this application do not limit the application scenarios of base stations and terminals.

[0150] The roles of the base station and the terminal can be relative, for example, Figure 1 The helicopter or drone 120i in the figure can be configured as a mobile base station. For the terminal 120j that accesses the wireless access network 100 through 120i, the terminal 120i is a base station; but for the base station 110a, 120i is a terminal, that is, 110a and 120i communicate through the wireless air interface protocol. Of course, 110a and 120i can also communicate through the interface protocol between base stations. In this case, relative to 110a, 120i is also a base station. Therefore, base stations and terminals can be collectively referred to as communication devices. Figure 1 110a and 110b in the figure may be referred to as communication devices having base station functions. Figure 1 120a-120j in the figure can be called communication devices with terminal functions.

[0151] Base stations and terminals, base stations and base stations, and terminals and terminals can communicate through authorized spectrum, unauthorized spectrum, or both; they can communicate through spectrum below 6 gigahertz (GHz), spectrum above 6 GHz, or spectrum below 6 GHz and spectrum above 6 GHz. The embodiments of the present application do not limit the spectrum resources used for wireless communication.

[0152] In this application, the functions of the base station can also be performed by a module (such as a chip) in the base station, or by a control subsystem that includes the base station function. The control subsystem that includes the base station function here can be a control center in the above-mentioned application scenarios such as smart grid, industrial control, smart transportation, and smart city. The functions of the terminal can also be performed by a module (such as a chip or a modem) in the terminal, or by a device that includes the terminal function.

[0153] In wireless communication network systems, a data transmission guarantee mechanism based on packet data unit (PDU) session is defined for the transmission of users' common application service data (such as video, voice, game and other service data).

[0154] like Figure 2As shown, it is a schematic diagram of an implementation of a PDU session. Among them, the establishment of a PDU session depends on the participation of a core network device. Generally, a PDU session includes a wireless bearer between a terminal device and an access network device, and a next generation user plane (NG-U) tunnel between the access network device and the core network device. Optionally, in the case where the access network device includes a DU and a CU, the PDU session also includes an F1 bearer between the DU and the CU.

[0155] Exemplarily, the session management function (SMF) network element in the core network device can control the life cycle of the PDU session (including the creation, deletion, modification, etc. of the PDU session) based on the user's service type (the service type identifier is the data network name (DNN)) and its corresponding data transmission demand strategy (the strategy is stored in the service transmission QoS strategy, charging strategy, etc. of the core network network element policy control function (PCF)), and configure the parameters of the PDU session based on the service QoS strategy. In addition, during the establishment of the PDU session, the terminal device can initiate the establishment of the PDU session to the SMF network element based on the non-access stratum (NAS) message; thereafter, the SMF network element can execute the creation of the PDU session, determine the relevant configuration information, and guide the access network device to establish and configure the corresponding PDU session; accordingly, the access network device performs PDU Session related processing, including the creation or modification of the wireless bearer of the wireless air interface (for example, the data radio bearer (DRB)).

[0156] In addition, in a wireless communication system, a wireless access device can calculate and determine the transmission resources of a signal. For example, the transmission resources may include time domain resources and frequency domain resources for carrying signals. Accordingly, the terminal device can send and receive signals on the transmission resources. In this way, the wireless access device can provide the terminal device with a wireless access communication service for network access. However, in a communication network, how to improve data transmission performance is a technical problem that needs to be solved urgently.

[0157] In a possible implementation, the communication management service implemented by intelligent technology can intelligently manage the wireless access communication service (including optimization / configuration / update, etc.) to improve data transmission performance. The intelligent technology may include artificial intelligence technology, neural network technology, machine learning technology, etc.

[0158] It should be understood that the communication management service is used to manage the wireless access communication service, which can be understood as the communication management service is used to manage the communication parameters involved in the wireless access communication service in an intelligent manner (including at least one of configuration, update, and optimization), and the intelligent manner may include a manner enabled by technologies such as AI, neural network, and machine learning. Optionally, the terms AI, intelligence, neural network, and machine learning in this application can be replaced with each other.

[0159] In other words, the communication management service can be a service that improves the service quality of wireless access communication services in an intelligent manner, or the communication management service can be a service that intelligently manages the service quality of wireless access communication. Accordingly, the communication management service can also be replaced by a communication configuration service, a communication optimization service, etc.

[0160] For example, the communication parameters involved in the wireless access communication service may include channel state information (CSI). Accordingly, the communication management service may include intelligent management of CSI, which is also called an AI-enabled CSI feedback service or an AI-model-based CSI feedback service.

[0161] For example, the communication parameters involved in the wireless access communication service may include communication beams. Accordingly, the communication management service may include intelligent management of communication beams, which is also called AI-enabled beam management service or AI model-based beam management service.

[0162] Optionally, the service data of the communication management service includes an AI model corresponding to at least one of the above services to implement management of communication parameters corresponding to the at least one of the above services. For example, the AI ​​model included in the service data of the communication management service may include an AI model of an AI-based CSI feedback service to implement intelligent management of CSI through the AI ​​model; for example, the AI ​​model included in the service data of the communication management service may include an AI model of an AI-based beam management service to implement intelligent management of communication beams through the AI ​​model.

[0163] For example, the communication parameters involved in the wireless access communication service may include positioning information. Accordingly, the communication management service may include intelligent management of the positioning information, which is also called the communication management service as an AI-enabled location positioning service or an AI model-based location positioning service.

[0164] Generally, the service data involved in the communication management service is terminated in the terminal equipment and the access network equipment to realize the intelligent management of the wireless access communication service, such as improving the network performance or efficiency of the wireless access. If the traditional data transmission method is used, the service data involved in the communication management service can be transmitted through the PDU session. However, since the service data involved in the communication management service is terminated in the terminal equipment and the access network equipment, this leads to the way of transmitting service data through the PDU session established by the core network equipment, which will cause too many detours in the signaling process and waste network communication resources.

[0165] In order to solve the above technical problems, the present application provides a communication method, related equipment and system, which can establish a communication connection for a communication management service based on the first configuration information sent by the AI ​​training unit, and the communication connection can be used to transmit the service data of the communication management service, thereby managing the wireless access communication service through the communication management service to improve the service quality of the wireless access communication service. The following will be described in detail with reference to the accompanying drawings.

[0166] The following will be combined Figure 3a / Figure 3b / Figure 3c / Figure 3d / Figure 3e The example shown provides an exemplary description of the network architecture used by the communication method provided in this application.

[0167] In the following example, the AI ​​training unit may be a functional entity responsible for the training of the AI ​​model. Alternatively, the AI ​​training unit may also be a functional entity responsible for the control and management of the communication management service, and the functions of the functional entity include but are not limited to the training of the AI ​​model, the deployment of the AI ​​model, the management of the AI ​​connection, the quality of service (QoS) management of the service transmission in the communication management service, etc. Correspondingly, the AI ​​training unit may have other names, such as AI management unit, AI connection management unit, AI model training unit, etc. Exemplarily, the AI ​​training unit may be a wireless access device or a module involved in AI processing in the wireless access device (such as a software module, a hardware module, a chip, etc.), or the AI ​​training unit may be a module external to the wireless access device.

[0168] Optionally, the AI ​​training unit may not be responsible for other functions in the control and management of the communication management service except the training of the AI ​​model. For example, the other functions may include the management of AI connections, QoS management of service transmission in the communication management service, etc. Accordingly, the other functions can be implemented by other devices / other units, such as the AI ​​management unit. For ease of understanding, the following example takes the AI ​​training unit as an example of a functional entity responsible for the control and management of the communication management service.

[0169] In the following example, the AI ​​reasoning unit may be a functional entity for business processing based on an AI model, including functions of the functional entity including but not limited to reasoning of an AI model, processing of AI business data, etc. Correspondingly, the AI ​​reasoning unit may have other names, such as an AI model reasoning unit, an AI data processing unit, etc. Exemplarily, the AI ​​reasoning unit may be a communication device or a module of a communication device (such as a software module, a hardware module, a chip, etc.) in a wireless access communication service. The communication device may be a terminal device or a wireless access device, and the wireless access device may include a wireless access network device, a centralized unit (CU) or a distributed unit (DU), etc.

[0170] In the following example, CU is responsible for functions such as interface control and mobility management of terminal devices, and DU is responsible for functions such as access link management, wireless channel management and resource mapping of terminal devices.

[0171] Optionally, in Figure 3a / Figure 3b / Figure 3c / Figure 3d / Figure 3e In the example shown, the AI ​​training unit (or wireless access device, or CU, or DU) can be an ORAN network element (or a module in an ORAN network element), including but not limited to one or more of O-CU-CP, O-CU-UP, O-DU, and O-RU.

[0172] exist Figure 3a In the wireless access device, the wireless access device may include a CU and a DU, and the AI ​​training unit mentioned below may be a module in the wireless access device that is independent of the CU and the DU, wherein the AI ​​training unit may communicate with the CU via a G1 interface, and the AI ​​training unit may also communicate with the DU via a G2 interface. In addition, Figure 3a In the illustrated architecture, the AI ​​training unit may be referred to as a mobile intelligent function (MIF) entity, an AI entity, a RAN AI entity, etc. The AI ​​training unit may be deployed independently as a new network element entity or may be deployed in combination with a CU, but is independent of the CU in terms of logical function.

[0173] It should be understood that Figure 3a In the present invention, the CU, DU or terminal device can serve as an AI reasoning unit, that is, the AI ​​reasoning unit can be a CU, DU or terminal device, or the AI ​​reasoning unit can be a module in the CU, a module in the DU or a module in the terminal device.

[0174] exist Figure 3bIn the embodiment, the wireless access device may include a CU and a DU, and the AI ​​training unit mentioned below may be a module independent of the wireless access device, wherein the AI ​​training unit may communicate with the wireless access device via an E2 interface. In addition, Figure 3b The shown architecture can be applied to open RAN scenarios. For example, the AI ​​training unit can be a near-real-time intelligent controller (Near-RT RIC) functional entity of the radio access network, or the AI ​​training unit can be a basic platform function module (platform function module) of the Near-RT RIC.

[0175] It should be understood that Figure 3b In the present invention, the wireless access device, CU, DU or terminal device can serve as an AI reasoning unit, that is, the AI ​​reasoning unit can be a CU, DU or terminal device, or the AI ​​reasoning unit can be a module in the CU, a module in the DU or a module in the terminal device.

[0176] exist Figure 3c In the present invention, the wireless access device may include a CU and a DU, and the AI ​​training unit mentioned later may be a module built into the CU, wherein the AI ​​training unit may communicate with the DU through the F1 interface. In other words, by enhancing the CU function, that is, adding an AIQoS management function module to support the management of the QoS of the service transmission of the communication management service (including optimization / configuration / update), and enhancing the F1 interface between the CU and the DU, the interaction of the transmission configuration information mentioned later is supported.

[0177] It should be understood that Figure 3c In the present invention, the DU or the terminal device can serve as the AI ​​reasoning unit, that is, the AI ​​reasoning unit can be the DU or the terminal device, or the AI ​​reasoning unit can be a module in the DU or a module in the terminal device.

[0178] Optionally, in the above Figure 3a to Figure 3c In the embodiment, the scenario of separation of CU and DU in the wireless access network is taken as an example. However, in actual applications, the wireless access network can also be implemented in other ways.

[0179] For example, in Figure 3d In comparison with Figure 3a and Figure 3c In the architecture shown, the wireless access device may not distinguish between the CU and DU separation scenarios, that is, the AI ​​training unit may be a logical module / physical module in the wireless access device. Figure 3dIn the present invention, the terminal device can serve as an AI reasoning unit, that is, the AI ​​reasoning unit can be the terminal device, or the AI ​​reasoning unit can be a module in the terminal device.

[0180] For example, in Figure 3e In comparison with Figure 3b In the architecture shown, the wireless access device may not distinguish between the CU and DU separation scenarios, that is, the AI ​​training unit may be a logical module / physical module independent of the wireless access device. Figure 3e In the present invention, the wireless access device or the terminal device can serve as an AI reasoning unit, that is, the AI ​​reasoning unit can be a wireless access device or a terminal device, or the AI ​​reasoning unit can be a module in the wireless access device or a module in the terminal device.

[0181] It should be understood that the AI ​​reasoning unit mentioned below can be Figures 3a to 3e The specific implementation process of any one of the wireless access device, terminal device, CU or DU in the illustrated architecture is detailed in the description of the embodiments below.

[0182] See also Figure 4 , is a schematic diagram of the communication method provided in this application, and the method includes the following steps.

[0183] It should be noted that Figure 4 (and later Figures 8 to 10 ) is used as an example to illustrate the method by taking the AI ​​training unit and the AI ​​reasoning unit as the execution subjects of the interaction. The AI ​​training unit and the AI ​​reasoning unit can be Figures 3a to 3e The AI ​​training unit and AI reasoning unit in any of the figures, for example, the AI ​​training unit can be an internal module of the wireless access device, or a device / module independent of the wireless access device. For another example, the AI ​​reasoning unit can be a wireless access device, a CU, a DU, or a terminal device, or a module in a wireless access device, a module in a CU, a module in a DU, or a module in a terminal device.

[0184] It should be understood that Figure 4 (and later Figures 8 to 10 ), the AI ​​training unit may be a communication device, or the AI ​​training unit may be a partial component of a communication device (such as a processor, chip, or chip system, etc.), or the AI ​​training unit may be a logic module or software that implements all or part of the functions of the communication device. Similarly, the AI ​​reasoning unit may be a communication device, or the AI ​​reasoning unit may be a partial component of a communication device (such as a processor, chip, or chip system, etc.), or the AI ​​reasoning unit may be a logic module or software that implements all or part of the functions of the communication device.

[0185] Optionally, execute Figure 4(and later Figures 8 to 10 ) The AI ​​training unit (or wireless access device, or CU, or DU) of the method shown in the figure may be an ORAN network element (or a module in an ORAN network element), including but not limited to one or more of O-CU-CP, O-CU-UP, O-DU, and O-RU.

[0186] S401. The AI ​​training unit sends first configuration information. The first configuration information is used to configure a communication connection for data transmission, where the data transmission is the transmission of business data of a communication management service between the AI ​​training unit and the AI ​​reasoning unit, and the communication management service is used to manage the wireless access communication service between the terminal device and the wireless access device; the first configuration information includes an identifier of the communication connection, and an identifier of one or more quality of service flows (QoS flows) in the communication connection.

[0187] S402. The AI ​​reasoning unit and the AI ​​training unit transmit the first configuration information based on the first configuration information to transmit business data in the communication connection.

[0188] Optionally, the service data involved in the data transmission of the communication management service may include at least one of the AI ​​model information and the data required for AI model training. Among them, the AI ​​model information may include the parameter scale information of the AI ​​model, the structural information of the AI ​​model, and at least one of the intermediate results of the AI ​​model training (such as training hyperparameters). Among them, the AI ​​model can be used to calculate the communication parameters involved in the wireless access communication service (including at least one of CSI, communication beam, and positioning information) in an AI manner to obtain a calculation result, which can be used for the management of the wireless access communication service (including at least one of configuration, update, and optimization).

[0189] Further optionally, the AI ​​model can be replaced by a neural network model, a machine learning model, a learning model, an AI learning model, a machine learning model, a neural network, an AI network, etc.

[0190] In a possible implementation, after the AI ​​training unit and the AI ​​reasoning unit establish a communication connection for the communication management service based on the first configuration information in step S401, in step S402, the AI ​​training unit and the AI ​​reasoning unit can perform data transmission of the service data of the communication management service in the communication connection. As an example, Figure 5 An implementation example of the data format of the business data is given.

[0191] like Figure 5As shown, the service data may include a payload. Optionally, the service data may also include a common header for carrying relevant information of the payload, such as the size of the payload, the type of data carried by the payload, etc.

[0192] In addition, the encapsulation header corresponding to the service data can be used to carry the identifier of the communication connection and the identifier of the QoS flow.

[0193] Optionally, the service data may also be encapsulated through other headers of the transport layer, including but not limited to a layer 1 / layer 2 header (L1 / L2 header), an outer IP header (outer IP header), etc.

[0194] It should be understood that in step S401, the first configuration information sent by the AI ​​training unit includes the identifiers of one or more QoS flows in the communication connection. That is, the one or more QoS flows belong to the communication connection. Among them, the one or more QoS flows can be the default QoS flow, and the configuration information of the one or more QoS flows can be included in the first configuration information, or the configuration information of the one or more QoS flows can be pre-configured, or the configuration information of the one or more QoS flows can be sent to the AI ​​reasoning unit through the AI ​​training unit through other configuration information, which is not limited here.

[0195] Optionally, in the configuration information of a QoS flow (for example, one or more QoS flows mentioned above, or the N QoS flows appearing later, etc.), the configuration information of the QoS flow includes at least one of the following: the priority of data transmission of the QoS flow, the data scale of data transmission of the QoS flow, the packet loss rate requirement of data transmission of the QoS flow, or the delay requirement of data transmission of the QoS flow.

[0196] Optionally, the first configuration information also includes an identifier of the AI ​​training unit, so that a recipient of the first configuration information can clearly identify that the first configuration information comes from the AI ​​training unit based on the identifier.

[0197] In one possible implementation, Figure 4In the method shown, before step S401, the method may further include: the AI ​​training unit receives first request information, and the first request information is used to request the first configuration information. Specifically, the AI ​​training unit may also receive first request information for requesting the first configuration information, so that the AI ​​training unit triggers determination or sending of the first configuration information based on the request, so that the solution can be applied to scenarios where other devices (such as AI reasoning units) request the first configuration information.

[0198] Optionally, the first request information includes at least one of the following information A to information D.

[0199] Information A. Identification of AI application scenarios supported by the AI ​​reasoning unit.

[0200] Among them, information A is used to identify the wireless AI service feature scenarios that the AI ​​reasoning unit can support, such as intelligent CSI features, etc. The AI ​​training unit selects an AI model that can be used for the corresponding scenario based on the parameter and the capabilities of the AI ​​reasoning unit (such as convolutional neural networks (CNN), recurrent neural networks (RNN), deep neural networks (DNN) with different numbers of layers, etc.).

[0201] Optionally, in information A, the AI ​​scenario supported by the AI ​​reasoning unit satisfies any of the following:

[0202] The AI ​​inference unit is a DU or a module in the DU, and the AI ​​scenarios supported by the AI ​​inference unit include at least one of a single-user medium access control SU MAC dictionary, a multi-user MU pairing dictionary, a multi-cell coordinated scheduling dictionary, and a low-speed channel map;

[0203] The AI ​​inference unit is a terminal device or a module in the terminal device, and the AI ​​scenarios supported by the AI ​​inference unit include at least one of an interference spectrum, a beam spectrum, and a smart channel state information CSI;

[0204] The AI ​​reasoning unit is a CU or a module in the CU. The AI ​​scenarios supported by the AI ​​reasoning unit include at least one of smart grid, smart load balancing, and smart energy saving.

[0205] Information B. Identifier of the AI ​​service type requested.

[0206] Among them, the information B is used to identify the transmission data type of the communication connection requested to be established by the AI ​​reasoning unit, such as AI model, AI training data, AI training intermediate results, etc.

[0207] Information C. Model identifier corresponding to the requested AI model.

[0208] Among them, the information C is an optional parameter. For example, when the AI ​​service type identifier requested by information B includes the identifier of the AI ​​model, information C can be used to request the model identifier corresponding to the AI ​​model, such as CNN model 1, CNN model 2, etc.

[0209] Information D. Identification of the AI ​​reasoning unit.

[0210] Among them, information D is used to identify the AI ​​reasoning unit.

[0211] In an implementation example, taking the AI ​​reasoning unit as a terminal device or a module in a terminal device as an example, the information D can be a globally unique temporary identifier (GUTI), a radio network temporary identifier (RNTI), a temporary mobile subscriber identity (TMSI), or other identifier that can identify the terminal device.

[0212] In another implementation example, taking the AI ​​reasoning unit as a DU or a module in a DU as an example, the information D may be the IP address of the DU, the DU name, etc.

[0213] In another implementation example, taking the AI ​​reasoning unit as a CU or a module in the CU as an example, the information D may be the IP address of the CU, the CU name, etc.

[0214] In one possible implementation, the AI ​​training unit determines the first configuration information, including: the AI ​​training unit determines the first configuration information based on at least one of the following items, including: an identifier of the AI ​​application scenario corresponding to the business data, and an identifier of the AI ​​service type corresponding to the communication management business.

[0215] Specifically, the basis for determining the first configuration information by the AI ​​training unit may include at least one of the above items to enhance the flexibility of implementing the solution.

[0216] Optionally, the basis for the AI ​​training unit to determine the first configuration information may include, in addition to at least one of the above items, a model identifier of the AI ​​model corresponding to the AI ​​model information contained in the business data.

[0217] Optionally, if the AI ​​reasoning unit is a terminal device or a module in the terminal device, if the terminal device is in an idle (IDLE) state, then before the AI ​​training unit sends the first configuration information in step S401, the AI ​​training unit can page the terminal device through the CU / DU, so that after the terminal device is restored to the network, it enters a connected state (Connected), so that the terminal device can receive the first configuration information while in a connected state. Exemplarily, the AI ​​training unit can page the terminal device through the G1 interface, G2 interface, E2 interface, etc. between the CU / DU. The implementation of these interfaces can refer to the previous text. Figures 3a to 3e Description.

[0218] based on Figure 4 In the technical solution shown, the communication management service is used to manage the wireless access communication service between the terminal device and the wireless access device. Among them, the first configuration information sent by the AI ​​training unit in step S401 is used to configure the communication connection for data transmission, and the data transmission is the transmission of business data of the communication management service between the AI ​​training unit and the AI ​​reasoning unit. In other words, after the AI ​​reasoning unit receives the first configuration information, the AI ​​reasoning unit can transmit the business data of the communication management service based on the first configuration information. Thus, the first configuration information sent by the AI ​​training unit can realize the establishment of the communication connection of the communication management service, and the communication connection can be used to realize the transmission of business data of the communication management service, and then realize the management of the wireless access communication service through the communication management service, so as to improve the service quality of the wireless access communication service.

[0219] In addition, since the service data involved in the communication management service is terminated in the terminal equipment and access network equipment, Figure 2 The way of transmitting service data through PDU session established by core network equipment will cause too many detours in signaling process and waste network communication resources. Figure 4 In the technical scheme shown, the AI ​​training unit is a wireless access device or an external module of a wireless access device, and the AI ​​inference unit is a wireless access device or a terminal device. Therefore, the first configuration information sent by the AI ​​training unit can realize the transmission of business data of the communication management service. The transmission of business data of the communication management service can be realized without the participation of the core network equipment, and excessive signaling process detours can be avoided to save network communication resources.

[0220] In one possible implementation, Figure 4In the technical solution shown, after the AI ​​training unit sends the first configuration information in step S401, the method also includes: the AI ​​training unit sends second configuration information, the second configuration information includes configuration information of N QoS flows established in the communication connection, N is a positive integer; wherein the second configuration information includes at least one of the following: an identifier of the communication connection, N identifiers corresponding to the N QoS flows, QoS information of the N QoS flows, and an identifier of the AI ​​reasoning unit. Specifically, after the AI ​​training unit configures the communication connection between the AI ​​training unit and the AI ​​reasoning unit through the first configuration information, the AI ​​training unit may also send second configuration information including the N QoS flows established in the communication connection, so that the recipient of the second configuration information can implement the configuration of the N QoS flows based on the second configuration information.

[0221] In addition, the N QoS flows may be different from one or more QoS flows in the first configuration information, so as to implement the update or addition of a QoS flow in the communication connection.

[0222] Optionally, if the AI ​​reasoning unit is a terminal device or a module in the terminal device, if the terminal device is in an idle (IDLE) state, then before the AI ​​training unit sends the second configuration information, the AI ​​training unit can page the terminal device through the CU / DU, so that after the terminal device is restored to the network, it enters a connected state (Connected), so that the terminal device can receive the second configuration information while in a connected state. Exemplarily, the AI ​​training unit can page the terminal device through the G1 interface, G2 interface, E2 interface, etc. between the CU / DU. The implementation of these interfaces can refer to the above. Figures 3a to 3e Description.

[0223] In one possible implementation, the method further includes: the AI ​​training unit receives second request information, the second request information is used to request the second configuration information; wherein the second request information includes an identifier of the communication connection. Specifically, the AI ​​training unit may also receive second request information for requesting the second configuration information, so that the AI ​​training unit triggers determination or sending of the second configuration information based on the request, so that the solution can be applied to scenarios where other devices (such as AI reasoning units) request the second configuration information.

[0224] In a possible implementation, the method further includes: the AI ​​training unit sends a first indication message, and the first indication message is used to indicate the deletion of at least one QoS flow among the N QoS flows. Specifically, the AI ​​training unit may also send a first indication message so that the recipient of the first indication message can delete at least one QoS flow among the N QoS flows based on the indication of the first indication message to release the resources of the at least one QoS flow, avoid the waste of resources caused by no business transmission on the at least one QoS flow, or use the resources of the at least one QoS flow to transmit other high-priority services.

[0225] In the present application, deleting a QoS flow (for example, deleting at least one QoS flow among N QoS flows) can be replaced by releasing the QoS flow, or releasing the resources corresponding to the QoS flow.

[0226] In one possible implementation, before the AI ​​training unit sends the first indication information, the method further includes: the AI ​​training unit receives a third request information, and the third request information is used to request the deletion of the at least one QoS flow. Specifically, the AI ​​training unit may also receive a third request information for requesting the deletion of the at least one QoS flow, so that the AI ​​training unit triggers the determination or sending of the first indication information based on the request, so that the scheme can be applicable to the scenario where other devices (such as AI reasoning units) request the deletion of QoS flows.

[0227] In a possible implementation, the method further includes: the AI ​​training unit sends a second indication information, and the second indication information is used to indicate the release of the communication connection. Specifically, the AI ​​training unit can also send the second indication information so that the recipient of the second indication information can release the communication connection based on the indication of the second indication information to release the resources of the communication connection, avoid the waste of resources caused by no business transmission on the communication connection, or use the resources of the communication connection to transmit other high-priority businesses.

[0228] It can be understood that the AI ​​training unit or AI reasoning unit releases the communication connection, which can be understood as the AI ​​training unit or AI reasoning unit deleting (or releasing) the QoS flow of the communication connection.

[0229] In one possible implementation, the method further includes: the AI ​​training unit receives fourth request information, and the fourth request information is used to request the release of the communication connection. Specifically, the AI ​​training unit may also receive fourth request information for requesting the release of the communication connection, so that the AI ​​training unit triggers determination or sending of second indication information based on the request, so that the solution can be applied to scenarios where other devices (such as AI reasoning units) request the release of the communication connection.

[0230] As an application example, Figure 6 As shown, the AI ​​training unit can be Figure 3a The MIF entity shown in the figure can communicate with the CU through the G1 interface, and the MIF entity can communicate with the DU through the G2 interface.

[0231] exist Figure 4 In an implementation example of the technical solution shown, if the AI ​​reasoning unit is a CU, the communication connection established between the AI ​​training unit and the AI ​​reasoning unit can be the AI ​​connection (AIConnection) between the CU and the MIF shown in the figure, and the figure takes the AI ​​connection as an example including two AIQoS flows.

[0232] exist Figure 4 In another implementation example of the technical solution shown, if the AI ​​reasoning unit is a DU, the communication connection established between the AI ​​training unit and the AI ​​reasoning unit can be the AI ​​Connection between the DU and the MIF shown in the figure, and the figure takes the AI ​​connection as an example including two AIQoS flows.

[0233] exist Figure 4 In another implementation example of the technical solution shown, if the AI ​​reasoning unit is a terminal device (represented as UE in the figure), the communication connection established between the AI ​​training unit and the AI ​​reasoning unit can be the AI ​​Connection between the UE and the MIF shown in the figure, and the figure takes the AI ​​connection as an example including two AIQoS flows. It can be understood that the AI ​​Connection between the UE and the MIF includes two sections, one of which is the communication connection between the UE and the DU through the Uu interface, and the other is the communication connection between the DU and the MIF through the G2 interface.

[0234] Optionally, in the AI ​​Connection between the UE and the MIF, the communication connection between the UE and the DU implemented through the Uu interface may include a radio bearer (RB). The RB may be a data radio bearer (DRB) for transmitting service data of a wireless access communication service, or the RB may be a bearer other than the DRB. In the figure, the RB is illustrated as a computing radio bearer (CRB) different from the DRB.

[0235] As an application example, Figure 7 As shown, the AI ​​training unit can be Figure 3a The Near-RT RIC entity shown in FIG. The Near-RT RIC entity can communicate with the CU or DU through the E2 interface.

[0236] exist Figure 4 In an implementation example of the technical solution shown, if the AI ​​reasoning unit is a CU, the communication connection established between the AI ​​training unit and the AI ​​reasoning unit can be the AI ​​connection (AIConnection) between the CU and the Near-RT RIC shown in the figure, and the figure takes the AI ​​connection as an example including two AIQoS flows.

[0237] exist Figure 4 In another implementation example of the technical solution shown, if the AI ​​reasoning unit is a DU, the communication connection established between the AI ​​training unit and the AI ​​reasoning unit can be the AIConnection between the DU and the Near-RT RIC shown in the figure, and the figure takes the AI ​​connection as an example including two AIQoS flows.

[0238] exist Figure 4 In another implementation example of the technical solution shown, if the AI ​​reasoning unit is a terminal device (denoted as UE in the figure), the communication connection established between the AI ​​training unit and the AI ​​reasoning unit can be the AI ​​Connection between the UE and the Near-RTRIC shown in the figure, and the figure takes the AI ​​connection as an example including two AIQoS flows. It can be understood that the AI ​​Connection between the UE and the Near-RT RIC includes two sections, one of which is the communication connection between the UE and the DU through the Uu interface, and the other is the communication connection between the DU and the Near-RT RIC through the E2 interface.

[0239] Optionally, in the AI ​​Connection between the UE and the Near-RT RIC, the communication connection between the UE and the DU implemented through the Uu interface may include a radio bearer (RB). The RB may be a data radio bearer (DRB) for transmitting service data of a wireless access communication service, or the RB may be other bearers different from the DRB. In the figure, the RB is illustrated as a computing radio bearer (CRB) different from the DRB.

[0240] exist Figure 4 In the technical solution shown, the AI ​​training unit can implement the configuration of the communication connection through the first configuration information, implement the configuration of the QoS flow through the second configuration information, and can also delete or release the communication connection / QoS flow by deleting the indication information (for example, the first indication information or the second indication information).

[0241] The following will be Figures 8 to 10 The implementation examples shown in the figure are used to exemplify these implementation processes. Figures 8 to 10 In the example shown, the AI ​​training unit may be the MIF, Near-RT RIC and other entities mentioned above, and the AI ​​reasoning unit may be a CU, a DU or a terminal device (the terminal device is described as a UE). Figure 6 , Figure 7 The implementation example shown in the previous Figure 4 The communication connection established in the technical solution shown can be an AI Connection between the AI ​​training unit and the AI ​​reasoning unit. For this purpose, in the following Figures 8 to 10 In the example shown, the communication connection is AIConnection as an example for explanation.

[0242] See also Figure 8 ,for Figure 4 An implementation example of the technical solution shown in the figure, which involves the configuration of the communication connection implemented by the AI ​​training unit through the first configuration information. Figure 8 As shown, the implementation example includes the following steps.

[0243] It should be noted that in Figure 8 In the example shown, the transmission of the first configuration information involved in step 5, step 7, and step 9 is an implementation example of the aforementioned step S402.

[0244] Step 1: The terminal device sends a request message requesting configuration of a communication connection.

[0245] Specifically, if the UE supports AI-enabled air interface intelligent features (such as intelligent CSI feedback), it may need to request that an AI model be trained and sent to the UE during the service process, or the UE itself has the need to report AI model training data. Therefore, the UE can request the AI ​​training unit to establish an AI connection through step 1.

[0246] Optionally, the request information of step 1 may carry information such as an AI scenario identifier, a requested AI service type (Requested AI service) type, an AI model id, and a UE id (these four items of information are an implementation example of the above information A to information D), specifically:

[0247] AI scenario identification: used to identify the wireless AI service feature scenarios that can be supported on the UE side, such as intelligent CSI features, etc. The AI ​​training unit selects the AI ​​model that can be used for the corresponding scenario (such as CNN, RNN, DNN with different layers, etc.) based on this parameter and the UE's capabilities, and determines the QoS parameter configuration of the corresponding AI connection.

[0248] Requested AI service type: used to identify the type of transmission data that the UE requests to establish an AI Connection, such as AI model, AI training data, AI training intermediate results, etc.

[0249] AI model id: an optional parameter. If Requested AI service Type = AI model, this parameter is carried to identify the specific AI model requested for transmission (such as CNN model 1, CNN model 2, etc.).

[0250] UE id: an identifier used to identify the UE, such as GUTI (Globally Unique Temporary Identifier), RNTI (Radio Network Temporary Identifier), TMSI (Temporary Mobile Subscriber Identity), etc., which can identify the UE and are not limited in the present invention.

[0251] Step 2: DU sends a request message to CU requesting configuration of a communication connection.

[0252] Specifically, when the DU receives a request from the UE (i.e., step 1) or the DU itself has a demand for AI service data transmission, the DU can initiate an AI connection establishment to the AI ​​training unit through the CU.

[0253] Optionally, the F1 AI connection request message in step 2 carries information such as AI scenario identifier, Requested AI service type, AI model id, UE id, and DU id. Specifically:

[0254] AI scenario identifier: used to identify the wireless AI service feature scenarios that can be supported by the UE / DU side. Its functional description refers to step 1. For example, Table 2 provides an example of the correspondence between an AI scenario identifier and its DU / UE AI scenario.

[0255] Table 2

[0256]

[0257] Requested AI service type: Refer to the description in step 1.

[0258] AI model id: Refer to the description in step 1.

[0259] UE id: optional parameter. If the AI ​​connection establishment is triggered by the UE (step 1 occurs), this parameter is carried in step 2 and its value is the parameter value of step 1.

[0260] DU id: an identifier used to identify a DU, such as the DU's IP address, DU name, etc.

[0261] Step A1: The DU sends a request message to the AI ​​training unit requesting configuration of a communication connection.

[0262] Specifically, because there is a communication interface (such as G2 interface and E2 interface) between the DU and the AI ​​training unit, if the DU needs to initiate the AI ​​connection establishment process (whether it is triggered by the DU itself or the UE (step 1)), the DU can indirectly initiate a request to the AI ​​training unit through the CU (F1 interface) (ie, step 2), or it can directly initiate a request to the AI ​​training unit through step A1, that is, through the communication interface between the DU and the AI ​​training unit.

[0263] It should be noted that the request information of step A1 carries AI scenario identifier, Requested AI servicetype, AI model id, UE id, DU id and other information. For parameter explanation, please refer to the relevant description of step 2.

[0264] Step 3: The CU sends a request message to the AI ​​training unit requesting configuration of the communication connection.

[0265] Specifically, when the CU receives a request from the DU (i.e., step 2) or the CU itself has an AI service data transmission demand, the CU initiates the establishment of an AI connection to the AI ​​training unit through the CU. (If the DU's AI connection establishment process is directly requested to the AI ​​training unit through the G2 interface (i.e., step A1 is executed), then this step is triggered only by the CU's own AI service data transmission demand.)

[0266] It should be noted that the G1 AI connection request message in step 3 carries AI scenario identifier, Requested AI service type, AI model id, UE id, DU id, CU id and other information. Specifically:

[0267] AI scenario identifier: used to identify the wireless AI service feature scenarios that can be supported by the UE / DU / CU side. Its functional description can refer to the description of step 1. For example, Table 3 provides an example of the correspondence between an AI scenario identifier and its CU / DU / UE AI scenario.

[0268] Table 3

[0269]

[0270] Requested AI service type: Refer to the description in step 1.

[0271] AI model id: Refer to the description in step 1.

[0272] UE id: optional parameter. If the AI ​​connection establishment is triggered by the UE (step 1 occurs), this parameter is carried in step 2 and its value is the parameter value of step 1.

[0273] DU id: optional parameter. If the AI ​​connection establishment is triggered by UE / DU (step 2 occurs), this parameter is carried in step 1c and its value is the parameter value of step 2.

[0274] CU id: an identifier used to identify the CU, such as the CU's IP address, CU name, etc.

[0275] Step 4. The AI ​​training unit determines the first configuration information.

[0276] The implementation process of the first configuration information can refer to the previous Figure 4 and description of related implementation methods.

[0277] If the AI ​​training unit itself has AI business data transmission requirements (such as the AI ​​training unit has completed the training of an AI model used in a certain AI scenario or needs to collect data required for AI model training, etc.), the establishment of the AI ​​connection is triggered, that is, the AI ​​training unit is triggered to determine the first configuration information.

[0278] Optionally, if the AI ​​training unit's own AI service data transmission object is UE, then if the UE processes the IDLE state, the AI ​​training unit needs to page the UE through the CU / DU, so that after the UE restores the connection with the network, it enters the Connected state, and then the UE initiates step 1 to establish the corresponding AI connection. Therefore, it is necessary to add a Paging paging triggered by the AI ​​service on the G1 interface (that is, the AI ​​training unit sends a Paging message to the CU through G1, and the message carries the UE id and the cause value cause (whose value is the AI ​​service data transmission). The Paging processing method of the CU / DU and F1 interface can reuse the existing RAN triggered Paging process processing, which will not be repeated in this article).

[0279] Exemplarily, in step 4, the AI ​​training unit determines the first configuration information according to the request information received in the above step. For example, the Requested AI service type parameter and the AI ​​scenario identifier in the request information may correspond to the transmitted AI service data type and AI service scenario respectively, and the first configuration information determined by the AI ​​training unit includes the attribute description information of the newly created Connection, the assigned AI connection ID, etc. In addition, if the Connection needs to simultaneously support the transmission of different types of AI service data in the corresponding AI scenario, it is also necessary to determine the AIQoS flow ID and the corresponding QoS parameters for each AI service data transmission.

[0280] Step 5. The AI ​​training unit sends the first configuration information to the CU.

[0281] Specifically, if step 3 occurs, the AI ​​training unit executes step 5. For example, the AI ​​training unit sends the first configuration information to the CU through the G1 interface or the E2 interface. For example, the first configuration information may carry parameters such as AI Connection ID, AIQoS flow ID, AIQoS, UE id, DU id, and CU id.

[0282] Step A2. The AI ​​training unit sends first configuration information to the DU.

[0283] Specifically, if step A1 occurs, the AI ​​training unit executes step A2. For example, the AI ​​training unit sends the first configuration information to the DU through the G2 interface or the E2 interface. For example, the first configuration information may carry parameters such as AI Connection ID, AIQoS flow ID, AI QoS, UE id, and DU id.

[0284] Step 6. The CU performs configuration based on the first configuration information.

[0285] Specifically, if step 5 occurs, the CU executes step 6. For example, the CU builds a local AI connection context based on the first configuration information. Optionally, if the AI ​​connection in step 1 is triggered by a UE request, the CU establishes a CRB, configures AIQoS parameters, and completes the mapping relationship between the AIQoS flow and the CRB.

[0286] Step 7. CU sends the first configuration information to DU.

[0287] Specifically, if step 2 occurs, the CU executes step 7. For example, the CU sends an F1 AI connection establishment request message carrying the first configuration information to the DU through the F1 interface. Similarly, the first configuration information may carry parameters such as AI Connection ID, AIQoS flow ID, AIQoS, UE id, and DU id.

[0288] Step 8. The DU performs configuration based on the first configuration information.

[0289] Specifically, if step A2 or step 7 occurs, then execute step 8. The DU builds a local AI connection context.

[0290] Step 9. The DU sends first configuration information to the UE.

[0291] Specifically, if step 1 occurs, execute step 9. For example, the DU feeds back the AI ​​connection related parameters allocated by the AI ​​training unit to the UE. This step may use the RRC reconfiguration message of the Uu interface to send the first configuration information, and the first configuration information may carry AI Connection ID, AIQoS flow ID, AIQoS and other parameters.

[0292] Step 10. The UE performs configuration based on the first configuration information, for example, the UE performs parameter configuration of the corresponding wireless communication protocol layer.

[0293] Step 11. The UE sends feedback information, which is used to feedback whether the configuration is successful.

[0294] Step 12. DU sends feedback information to CU, where the feedback information is used to feedback whether the configuration is successful.

[0295] Specifically, if step 7 occurs, the DU may execute step 12. For example, after the DU completes the local AI connection processing or receives the feedback information from the UE in step 11, it replies to the CU with feedback information (or AI connection establishment response message), carrying the corresponding AI connection ID, AIQoS flow ID and creation execution result (whose value is success, failure, partial success, etc.).

[0296] Step A3. DU sends feedback information to the AI ​​training unit, which is used to feedback whether the configuration is successful.

[0297] Specifically, if step A2 occurs, the DU can execute step A3. For example, after the DU completes the local AI connection processing or receives the confirmation reply message from the UE, it directly replies to the AI ​​training unit through G2 with feedback information (or AI connection establishment response message), carrying the corresponding AI connection ID, AIQoS flow ID and creation execution result (whose value is success, failure and partial success).

[0298] Step 13. The CU sends feedback information to the AI ​​training unit, which is used to feedback whether the configuration is successful.

[0299] Specifically, if step 5 occurs, the CU can execute step 13. For example, after the CU completes the local AI connection processing or receives the confirmation reply message from the DU, it replies feedback information (or AI connection establishment response message) to the AI ​​training unit, carrying the corresponding AI connection ID, AIQoS flow ID and creation execution result (whose value is success, failure and partial success).

[0300] Thus, through Figure 8 The technical solution shown, when UE, CU, DU or AI training unit has AI service data transmission requirements (detailed triggering conditions are described above), triggers the establishment of AI connection to execute the service process of embodiment 1. In addition, the AI ​​training unit can effectively manage the life cycle of the connection for transmitting AI service data in combination with the transmission requirements of the AI ​​service, thereby reducing system signaling consumption.

[0301] See also Fig. 9,for Figure 4 An implementation example of the technical solution shown in the figure, which involves the AI ​​training unit implementing the configuration of the QoS flow through the second configuration information. Fig. 9 As shown, the implementation example includes the following steps.

[0302] It should be understood that Fig. 9 The business process of the implementation process shown is overall and Figure 8 The implementation process shown is similar. The main difference is that UE\CU\DU can request AIQoS flow of AI transmission from AI training unit based on the actual AI service data transmission requirements in the same AI scenario. AI training unit evaluates whether the QoS configuration of the current AIQoS flow in AI connection meets the new requirements. If not, it executes the creation of a new AIQoS flow, that is, executes Fig. 9 The flow of the implementation process is shown.

[0303] Step 1. UE sends a request message requesting the configuration of a QoS flow.

[0304] Specifically, in the same AI scenario, if the UE's AI service transmission requirements change, it is possible that the currently established AI QoS flow cannot meet the requirements. In this case, it is necessary to request the AI ​​training unit to create a corresponding AI QoS flow in the AI ​​connection through step 1.

[0305] Optionally, the request information in step 1 may carry information such as AI Connection ID, Requested AI servicetype, AI model id, UE id, etc. Specifically:

[0306] AI Connection ID: used to identify the AIconnection for business data transmission that supports AI business scenarios on the UE side.

[0307] Requested AI service type: Reference Figure 8 Description of the implementation process shown.

[0308] AI model id: reference Figure 8 Description of the implementation process shown.

[0309] UE id: reference Figure 8 Description of the implementation process shown.

[0310] Step 2. DU sends a request message to CU requesting configuration of QoS flow.

[0311] Specifically, when the DU receives a request from the UE (i.e., step 1) or the DU itself has a new AI service data transmission request, the DU carries the AI ​​Connection ID, Requested AI service type, AI modelid, UE id, DU id and other information through the request information of step 2. For parameter explanation, please refer to the previous description.

[0312] Step B1. DU sends a request message to the AI ​​training unit requesting configuration of QoS flow.

[0313] Specifically, because there is a G2 interface or E2 interface between the DU and the AI ​​training unit, if the DU needs to initiate the AI ​​connection modification process (whether it is triggered by the DU itself or the UE (step 1)), the DU can indirectly initiate a request to the AI ​​training unit through the CU (F1 interface) (ie, step 2), or it can directly send a request message carrying AI Connection ID, Requested AI service type, AI model id, UEid, DU id, etc. to the AI ​​training unit through the G2 interface or E2 interface through step B1. For parameter explanation, refer to the previous description.

[0314] Step 3. The CU sends a request message to the AI ​​training unit requesting the configuration of the QoS flow.

[0315] Specifically, when the CU receives a request from the DU (i.e., step 2) or the CU itself has a new AI service data transmission request, the CU initiates an AI connection modification to the AI ​​training unit. (If the DU's AI connection modification process is directly requested to the AI ​​training unit through the G2 interface (i.e., step B1 is executed), the triggering of step 3 can be triggered by the CU's own new AI service data transmission request.)

[0316] In addition, the request information sent by the CU in step 3 may carry information such as AI Connection ID, Requested AIservice type, AI model id, UE id, DU id, CU id, etc. For parameter explanation, refer to the previous description.

[0317] Step 4. The AI ​​training unit determines the second configuration information.

[0318] The implementation of the second configuration information can refer to the previous Figure 4 And a description of the related implementation process.

[0319] Specifically, if the AI ​​training unit itself has new AI business data transmission requirements (such as the AI ​​training unit has completed the training of a new type of AI model used in a certain AI scenario, etc.), it can trigger the AI ​​connection modification, that is, trigger the AI ​​training unit to determine the second configuration information.

[0320] Optionally, if the AI ​​training unit's own AI service data transmission object is UE, then if the UE processes the IDLE state, the AI ​​training unit needs to page the UE through the CU / DU, so that after the UE restores the connection with the network, it enters the Connected state, and then the UE initiates step 1 to implement the corresponding AI connection modification. Therefore, it is necessary to add a Paging paging triggered by the AI ​​service on the G1 interface (that is, the AI ​​training unit sends a Paging message to the CU through G1, and the message carries the UE id and the cause value cause (whose value is the AI ​​service data transmission connection modification). The Paging processing method of the CU / DU and F1 interface can reuse the existing RAN-triggered Paging process, which will not be repeated in this article).

[0321] In addition, the AI ​​training unit can determine the second configuration information based on the parameters in the request information obtained in the above steps. For example, the AI ​​training unit determines the QoS parameters of the requested AI service data transmission based on the Requested AI service type parameter, and evaluates that the existing AIQoS flows in the AI ​​connection identified by the AI ​​connection ID cannot meet the QoS requirements of the new AI service, then decides to create a new AIQoS flow, allocate an AIQoS flow ID, update the Connection context, and other functional processing.

[0322] Step 5. The AI ​​training unit sends the second configuration information to the CU.

[0323] Specifically, if step 3 occurs, execute step 5. For example, the AI ​​training unit sends the second configuration information to the CU through the G1 interface or the E2 interface, and the second configuration information may carry AI Connection ID, new AIQoS flowID, AIQoS, UE id, DUid, CU id and other parameters.

[0324] Step B2. The AI ​​training unit sends the second configuration information to the DU.

[0325] Specifically, if step B1 occurs, step B2 is executed. For example, the AI ​​training unit sends the second configuration information to the DU through the G2 interface or the E2 interface, and the second configuration information may carry parameters such as AI Connection ID, new AIQoS flowID, AIQoS, UE id, and DU id.

[0326] Step 6. CU performs configuration.

[0327] Specifically, if step 5 occurs, the CU may execute step 6. For example, the CU updates the local AI connection context. Optionally, if the AI ​​connection in step 1 is modified by the UE request, the CU creates or modifies the CRB, configures the AI ​​QoS parameters, and completes the mapping relationship between the AI ​​QoS flow and the CRB.

[0328] Step 7. CU sends the second configuration information to DU.

[0329] Specifically, if step 2 occurs, the CU can execute step 7. For example, the CU sends an addF1 AI QoS flow message to the DU through the F1 interface, and the message carries AI Connection ID, new AIQoS flow ID, AIQoS, UE id, DUid and other parameters.

[0330] Step 8.DU performs configuration.

[0331] Specifically, if step B2 or step 7 occurs, then execute step 8. The DU updates the local AI connection context.

[0332] Step 9. The DU sends the second configuration information to the UE.

[0333] Specifically, if step 1 occurs, the DU can execute step 9. For example, the DU feeds back the AIQoSflow-related parameters allocated by the AI ​​training unit to the UE. This step can use the RRC reconfiguration message of the Uu interface to send AIConnection ID, new AIQoS flow ID, AIQoS and other parameters.

[0334] Step 10. UE performs configuration.

[0335] Step 11. The UE sends feedback information to the DU to indicate whether the configuration is successful.

[0336] Step 12. DU sends feedback information to CU to feedback whether the configuration is successful.

[0337] Specifically, if step 7 occurs, the DU can execute step 12. For example, after the DU completes the local AI connection modification process or receives the confirmation reply message from the UE, it replies to the CU with an AIQoS flow add response message, carrying the corresponding AIconnection ID, AIQoS flow ID and creation execution result (its value is success, failure and partial success).

[0338] Step B3. DU sends feedback information to the AI ​​training unit to feedback whether the configuration is successful.

[0339] Specifically, if step B2 occurs, the DU can execute step A3. For example, after the DU completes the local AI connection modification process or receives the confirmation reply message from the UE, it directly replies to the AI ​​training unit through G2 with an AIQoS flow add response message, carrying the corresponding AI connection ID, AIQoS flow ID and creation execution result (whose values ​​are success, failure and partial success).

[0340] Step 13. The CU sends feedback information to the AI ​​training unit to indicate whether the configuration is successful.

[0341] Specifically, if step 5 occurs, the CU can execute step 13. For example, after the CU completes the local AI connection modification process or receives the confirmation reply message from the DU, it replies to the AI ​​training unit with an AIQoS flow add response message, carrying the corresponding AI connection ID, AIQoS flow ID and creation execution result (whose values ​​are success, failure and partial success).

[0342] based on Fig. 9 In the implementation process shown in the figure, when UE, CU, DU or AI training unit has AI service data transmission requirements and the current AIQoS flow cannot meet the QoS requirements of the corresponding AI service, Fig. 9 The implementation process shown creates one or more AIQoS flows in the created AIconnection. In addition, the AI ​​training unit can timely update the connection for transmitting AI service data based on the latest transmission requirements of the AI ​​service to ensure reasonable allocation of resources.

[0343] See also Fig.10 ,for Figure 4 An implementation example of the technical solution shown in the figure, which involves the AI ​​training unit deleting the communication connection or the QoS flow by deleting the indication information. Fig.10 As shown, the implementation example includes the following steps.

[0344] It should be understood that Fig.10 The business process of the implementation process shown is overall and Figure 8 The implementation process shown is similar, the main difference is that Fig.10 The implementation process shown describes that when the AI ​​features supported by the UE / CU / DU change, or there is no AI service data transmission or no data transmission of a specific AI service type for a long time (such as 1 day, 7 days), Fig.10 The implementation process shown releases the corresponding AI connection or releases the AIQoS flow therein.

[0345] Step 1. The UE sends a request message requesting to delete the configuration.

[0346] Specifically, if the UE no longer needs to transmit AI services, or no longer needs to transmit data of one or more AI service types in the AI ​​scenario, the UE requests the AI ​​training unit to delete the complete AI connection through step 1, or only deletes one or more AIQoS flows in the AI ​​connection.

[0347] Optionally, the request information in step 1 may carry information such as AI Connection ID, AIQoS flow ID, UE id, etc. Specifically:

[0348] AI Connection ID: used to identify the AI ​​connection that the UE wants to delete.

[0349] AIQoS flow ID: Optional parameter. If the UE needs to terminate the entire AI connection ID, step 1 only includes the AI ​​connection ID and does not carry the AIQoS flow ID parameter. If the UE only deletes the AIQoS flow specified in the AI ​​connection, the AIQoS flow ID parameter must be carried.

[0350] UE id: refer to the previous description.

[0351] Step 2. DU sends a request message to CU to request configuration deletion.

[0352] Specifically, when the DU receives a request from the UE (ie, step 1) or the DU itself no longer needs to transmit AI services, or no longer needs to transmit data of one or more AI service types in the AI ​​scenario, the DU carries AI Connection ID, AIQoS flow ID, UE id, DU id and other information through the F1 AI connection delete request message in step 2. For parameter explanation, refer to the previous description.

[0353] Step C1. DU sends a request message to the AI ​​training unit requesting to delete the configuration.

[0354] Specifically, because there is a G2 interface or E2 interface between the DU and the AI ​​training unit, if the DU needs to initiate the AI ​​connection deletion process (whether it is triggered by the DU itself or the UE (step 1)), the DU can indirectly initiate a request to the AI ​​training unit through the CU (F1 interface) (ie, step 2), or it can directly send a request message carrying AI Connection ID, AIQoS flow ID, UE id, DU id and other information to the AI ​​training unit through step C1, that is, through the G2 interface or E2 interface. For parameter explanation, refer to the previous description.

[0355] Step 3. The CU sends a request message to the AI ​​training unit requesting configuration deletion.

[0356] Specifically, when the CU receives a request from the DU (i.e., step 2) or the CU no longer needs to transmit AI services, or no longer needs to transmit data of one or more AI service types in the AI ​​scenario, the CU initiates AI connection termination to the AI ​​training unit through the CU. (If the DU's AI connection deletion process is directly requested to the AI ​​training unit through the G2 interface or the E2 interface (i.e., step C1 is executed), this step is triggered only because the CU itself no longer has the demand for AI service data transmission.)

[0357] Optionally, in the request information carrying AI Connection ID, AIQoS flow ID, UE id, DU id, CU id and other information in step 3, the explanation of each parameter refers to the previous description.

[0358] Step 4. The AI ​​training unit determines the deletion instruction information.

[0359] The implementation process of deleting the indication information may refer to the implementation process of the first indication information or the second indication information mentioned above.

[0360] Specifically, if the AI ​​training unit has no AI business data transmission for a long time, the implicit AI connection deletion process is triggered.

[0361] Optionally, if the AI ​​training unit's own AI service data transmission object is UE, then if the UE processes the IDLE state, the AI ​​training unit needs to page the UE through the CU / DU, so that after the UE restores the connection with the network, it enters the Connected state, and then the UE initiates step 1 to achieve the corresponding AI connection deletion. Therefore, it is necessary to add a Paging paging triggered by the AI ​​service on the G1 interface (that is, the AI ​​training unit sends a Paging message to the CU through G1, and the message carries the UE id and the cause value cause (whose value is the deletion of the AI ​​service data transmission connection). The Paging processing method of the CU / DU and F1 interface can reuse the existing RAN-triggered Paging process, which will not be repeated in this article).

[0362] Exemplarily, in the process of the AI ​​training unit determining to delete the indication information, the AI ​​training unit can determine the corresponding processing according to the parameters carried by the request information received in the above steps. For example, if the message carries both the AIconnection ID and the AIQoS flow ID, only the AIQoS flow identified by the AIQoS flow ID is deleted; if the message carries only the AI ​​connection ID, the entire AI connection is terminated.

[0363] Step 5. The AI ​​training unit sends a deletion indication message to the CU.

[0364] Specifically, if step 3 occurs, the AI ​​training unit executes step 5.

[0365] For example, if the AI ​​training unit decides to terminate the entire AI connection in step 4, it sends a G1 AI connection termination request message carrying deletion indication information to the CU through the G1 interface or the E2 interface. The message carries AI Connection ID, new AIQoS flow ID, UE id, DU id, CU id and other parameters.

[0366] For example, if the AI ​​training unit decides to delete the AIQoS flow specified in the AI ​​connection in step 4, it sends a delete G1 AIQoS flow request message carrying deletion indication information to the CU through the G1 interface or the E2 interface. The message carries AI Connection ID, AIQoS flow ID, UE id, DU id, CU id and other parameters.

[0367] Step C2. The AI ​​training unit sends deletion indication information to the DU.

[0368] Specifically, if step C1 occurs, step C2 is executed.

[0369] For example, if the AI ​​training unit decides to terminate the entire AI connection in step 4, it sends a G2 AI connection termination request message carrying deletion indication information to the DU through the G2 interface or the E2 interface. The message carries AI Connection ID, new AIQoS flow ID, UE id, DU id, CU id and other parameters.

[0370] For example, if the AI ​​training unit decides to delete the AIQoS flow specified in the AI ​​connection in step 4, it sends a delete G2 AIQoS flow request message carrying deletion indication information to the DU through the G2 interface or the E2 interface. The message carries parameters such as AI Connection ID, AIQoS flow ID, UE id, DU id, and CU id.

[0371] Step 6. CU performs deletion.

[0372] Specifically, if step 5 occurs, the CU executes step 6. For example, the CU deletes the AI ​​connection context identified by the AI ​​connection ID, or deletes the AI ​​QoS flow in the AI ​​connection identified by the AIQoS flow id, according to the deletion indication information of step 5, and releases the corresponding CRB and wireless resources.

[0373] Step 7. CU sends deletion indication information to DU.

[0374] Specifically, if step 2 occurs, step 7 is executed.

[0375] Step 8. DU performs deletion.

[0376] Specifically, if step C2 or step 7 occurs, execute step 8. For example, the DU deletes the AI ​​connection context identified by the AI ​​connection ID, or deletes the AI ​​QoS flow in the AI ​​connection identified by the AIQoS flow id, according to the request in step 8, and releases the corresponding CRB and radio resources.

[0377] Step 9. DU sends deletion indication information to UE.

[0378] Specifically, if step 1 occurs, execute step 9. For example, the DU feeds back the AI ​​connection or AI QoS flow information deleted by the AI ​​training unit to the UE. This step can use the RRC reconfiguration message of the Uu interface to send deletion indication information, and the deletion indication information can carry parameters such as AI Connection ID and AIQoS flow ID.

[0379] Step 10. UE performs deletion.

[0380] Step 11. The UE sends feedback information to the DU to indicate whether to delete the data.

[0381] Step 12: DU sends feedback information to CU to inform whether to delete.

[0382] Specifically, if step 7 occurs, execute step 12. For example, after the DU completes the local AI connection deletion process, or receives the feedback information from the UE that the feedback has been deleted in step 11, the DU replies to the CU with an AI connection termination / AIQoS flow deletion response message, carrying the corresponding AI connection ID, AIQoS flow ID and the execution result of the deletion (whose value is success, failure and partial success).

[0383] Step C3. DU sends feedback information to the AI ​​training unit to inform whether to delete.

[0384] Specifically, if step C2 occurs, the DU executes step C3. For example, after the DU completes the local AI connection deletion process or receives the confirmation reply message from the UE, it directly replies to the AI ​​training unit through the G2 interface or the E2 interface with an AI connection termination / AI QoS flow deletion response message, carrying the corresponding AI connection ID, AI QoS flow ID and the execution result of the deletion (whose values ​​are success, failure and partial success).

[0385] Step 13. The CU sends feedback information to the AI ​​training unit to inform whether to delete it.

[0386] Specifically, if step 9, the CU executes step 13. For example, after the CU completes the local AI connection deletion process or receives the confirmation reply message from the DU, it replies to the AI ​​training unit with an AI connection termination / AIQoS flow deletion response message, carrying the corresponding AI connection ID, AIQoS flow ID and the execution result of the deletion (whose values ​​are success, failure and partial success).

[0387] based on Fig.10 In the implementation process shown in the figure, when the AI ​​features supported by the UE / CU / DU change, or there is no AI service data transmission or no data transmission of a specific AI service type for a long time (such as 1 day, 7 days), the Fig.10 The implementation process shown releases the corresponding AI connection or releases the AIQoS flow therein. In addition, the AI ​​training unit can timely recycle resources for the connection that transmits AI service data based on the status of the AI ​​service, thereby improving resource utilization.

[0388] See also Fig.11 The present application embodiment provides a communication device 1100, which can implement the functions of the communication device in the above method embodiment, and thus can also achieve the beneficial effects of the above method embodiment. The communication device can be the AI ​​training unit or AI reasoning unit described in the above embodiment.

[0389] In addition, the communication device 1100 may be a communication device, or an integrated circuit or component inside the communication device, such as a chip.

[0390] In one possible implementation, when the device 1100 (for example, a first communication device) is used to execute the method executed by the AI ​​training unit in any of the aforementioned embodiments, the device 1100 includes a processing module 1101 and a transceiver module 1102; the processing module 1101 is used to determine first configuration information, and the first configuration information is used to configure a communication connection for data transmission, and the data transmission is the transmission of business data of a communication management service between the AI ​​training unit and the AI ​​reasoning unit, and the communication management service is used to manage the wireless access communication service between the terminal device and the wireless access device; the first configuration information includes an identifier of the communication connection, and an identifier of one or more quality of service flows QoS flow in the communication connection; the transceiver module 1102 is used to send the first configuration information.

[0391] In a possible implementation, the transceiver module 1102 is further configured to receive first request information, where the first request information is used to request the first configuration information.

[0392] In one possible implementation, the first request information includes at least one of the following: an identifier of an AI application scenario supported by the AI ​​reasoning unit, an identifier of a requested AI service type, a model identifier corresponding to a requested AI model, and an identifier of the AI ​​reasoning unit.

[0393] In a possible implementation, the AI ​​scenario supported by the AI ​​reasoning unit satisfies any of the following:

[0394] The AI ​​inference unit is a distributed unit DU or a module in the DU, and the AI ​​scenarios supported by the AI ​​inference unit include at least one of a single-user medium access control SU MAC dictionary, a multi-user MU pairing dictionary, a multi-cell coordinated scheduling dictionary, and a low-speed channel map;

[0395] The AI ​​inference unit is a terminal device or a module in the terminal device, and the AI ​​scenarios supported by the AI ​​inference unit include at least one of an interference spectrum, a beam spectrum, and a smart channel state information CSI;

[0396] The AI ​​reasoning unit is a centralized unit CU or a module in the CU. The AI ​​scenarios supported by the AI ​​reasoning unit include at least one of smart grid, smart load balancing, and smart energy saving.

[0397] In a possible implementation, the first configuration information also includes at least one of the following: quality of service (QoS) information of one or more QoS flows in the communication connection, and an identifier of the AI ​​training unit.

[0398] In one possible implementation, the processing module 1101 is used to determine the first configuration information, including: the processing module 1101 is specifically used to determine the first configuration information based on at least one of the following items, including: an identifier of the AI ​​application scenario corresponding to the business data, an identifier of the AI ​​service type corresponding to the communication management business, and a model identifier of the AI ​​model corresponding to the AI ​​model information contained in the business data.

[0399] In one possible implementation, the transceiver module 1102 is also used to send second configuration information, which includes configuration information of N QoS flows established in the communication connection, where N is a positive integer; wherein the second configuration information includes at least one of the following: an identifier of the communication connection, N identifiers corresponding to the N QoS flows, quality of service QoS information of the N QoS flows, and an identifier of the AI ​​reasoning unit.

[0400] In a possible implementation, the method further includes: the transceiver module 1102 is further used to receive second request information, where the second request information is used to request the second configuration information; wherein the second request information includes an identifier of the communication connection.

[0401] In a possible implementation, the method further includes: the transceiver module 1102 is further configured to send first indication information, where the first indication information is configured to indicate deletion of at least one QoS flow among the N QoS flows.

[0402] In a possible implementation manner, the transceiver module 1102 is further used to receive third request information, where the third request information is used to request deletion of the at least one QoS flow.

[0403] In a possible implementation, the transceiver module 1102 is further configured to send second indication information, where the second indication information is used to indicate the release of the communication connection.

[0404] In a possible implementation, the transceiver module 1102 is further configured to receive fourth request information, where the fourth request information is used to request to release the communication connection.

[0405] In one possible implementation, when the device 1100 (for example, a second communication device) is used to execute the method executed by the AI ​​reasoning unit in any of the aforementioned embodiments, the device 1100 includes a processing module 1101 and a transceiver module 1102; the transceiver module 1102 is used to receive first configuration information, and the first configuration information is used to configure a communication connection for data transmission, and the data transmission is the transmission of business data of a communication management service between an artificial intelligence AI training unit and an AI reasoning unit, and the communication management service is used to manage a wireless access communication service between a terminal device and a wireless access device; the first configuration information includes an identifier of the communication connection, and an identifier of one or more quality of service flows QoSflow in the communication connection; the processing module 1101 is used to transmit the business data in the communication connection based on the first configuration information.

[0406] In a possible implementation, the transceiver module 1102 is further configured to send first request information, where the first request information is used to request the first configuration information.

[0407] In one possible implementation, the first request information includes at least one of the following: an identifier of an AI application scenario supported by the AI ​​reasoning unit, an identifier of a requested AI service type, a model identifier corresponding to a requested AI model, and an identifier of the AI ​​reasoning unit.

[0408] In a possible implementation, the AI ​​scenario supported by the AI ​​reasoning unit satisfies any of the following:

[0409] The AI ​​inference unit is a distributed unit DU or a module in the DU, and the AI ​​scenarios supported by the AI ​​inference unit include at least one of a single-user medium access control SU MAC dictionary, a multi-user MU pairing dictionary, a multi-cell coordinated scheduling dictionary, and a low-speed channel map;

[0410] The AI ​​inference unit is a terminal device or a module in the terminal device, and the AI ​​scenarios supported by the AI ​​inference unit include at least one of an interference spectrum, a beam spectrum, and a smart channel state information CSI;

[0411] The AI ​​reasoning unit is a centralized unit CU or a module in the CU. The AI ​​scenarios supported by the AI ​​reasoning unit include at least one of smart grid, smart load balancing, and smart energy saving.

[0412] In a possible implementation, the first configuration information also includes at least one of the following: quality of service (QoS) information of one or more QoS flows in the communication connection, and an identifier of the AI ​​training unit.

[0413] In one possible implementation, the transceiver module 1102 is also used to receive second configuration information, which includes configuration information of N QoS flows established in the communication connection, where N is a positive integer; wherein the second configuration information includes at least one of the following: an identifier of the communication connection, N identifiers corresponding to the N QoS flows, quality of service QoS information of the N QoS flows, and an identifier of the AI ​​reasoning unit.

[0414] In a possible implementation, the transceiver module 1102 is further used to send second request information, where the second request information is used to request the second configuration information; wherein the second request information includes an identifier of the communication connection.

[0415] In a possible implementation manner, the transceiver module 1102 is further used to receive first indication information, where the first indication information is used to indicate to delete at least one QoS flow among the N QoS flows.

[0416] In a possible implementation, the transceiver module 1102 is further configured to send third request information, where the third request information is used to request deletion of the at least one QoS flow.

[0417] In a possible implementation, the transceiver module 1102 is further configured to receive second indication information, where the second indication information is used to indicate releasing the communication connection.

[0418] In a possible implementation, the transceiver module 1102 is further configured to send fourth request information, where the fourth request information is used to request to release the communication connection.

[0419] It should be noted that the information execution process and other contents of the units of the above-mentioned communication device 1100 can be specifically referred to the description in the method embodiment shown in the above-mentioned application, and will not be repeated here.

[0420] See also Fig.12 , is another schematic structural diagram of the communication device 1200 provided in the present application, the communication device 1200 includes a logic circuit 1201 and an input / output interface 1202. The communication device 1200 may be a chip or an integrated circuit.

[0421] in, Fig.11 The transceiver module 1102 shown may be a communication interface, which may be Fig.12 The input / output interface 1202 in the communication interface may include an input interface and an output interface. Alternatively, the communication interface may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0422] Optionally, the logic circuit 1201 is used to determine first configuration information, where the first configuration information is used to configure a communication connection for data transmission, where the data transmission is the transmission of business data of a communication management service between an AI training unit and an AI reasoning unit, where the communication management service is used to manage a wireless access communication service between a terminal device and a wireless access device; the first configuration information includes an identifier of the communication connection, and an identifier of one or more quality of service flows QoS flows in the communication connection; the input-output interface 1202 is used to send the first configuration information.

[0423] Optionally, the input-output interface 1202 is used to receive first configuration information, and the first configuration information is used to configure a communication connection for data transmission, where the data transmission is the transmission of business data of a communication management service between an artificial intelligence AI training unit and an AI reasoning unit, and the communication management service is used to manage a wireless access communication service between a terminal device and a wireless access device; the first configuration information includes an identifier of the communication connection, and an identifier of one or more quality of service flows QoS flows in the communication connection; the logic circuit 1201 is used to transmit the business data in the communication connection based on the first configuration information.

[0424] Among them, the logic circuit 1201 and the input-output interface 1202 can also execute other steps performed by the AI ​​training unit or the AI ​​reasoning unit in any embodiment and achieve corresponding beneficial effects, which will not be repeated here.

[0425] In one possible implementation, Fig.11 The processing module 1101 shown can be Fig.12 The logic circuit 1201 in.

[0426] Optionally, the logic circuit 1201 may be a processing device, and the functions of the processing device may be partially or completely implemented by software. The functions of the processing device may be partially or completely implemented by software.

[0427] Optionally, the processing device may include a memory and a processor, wherein the memory is used to store a computer program, and the processor reads and executes the computer program stored in the memory to perform corresponding processing and / or steps in any one of the method embodiments.

[0428] Alternatively, the processing device may include only a processor. A memory for storing a computer program is located outside the processing device, and the processor is connected to the memory via a circuit / wire to read and execute the computer program stored in the memory. The memory and the processor may be integrated together, or may be physically independent of each other.

[0429] Optionally, the processing device may be one or more chips, or one or more integrated circuits. For example, the processing device may be one or more field-programmable gate arrays (FPGA), application specific integrated circuits (ASIC), system on chip (SoC), central processor unit (CPU), network processor (NP), digital signal processor (DSP), microcontroller unit (MCU), programmable logic device (PLD) or other integrated chips, or any combination of the above chips or processors.

[0430] See also Fig.13 , is the communication device 1300 involved in the above-mentioned embodiments provided by the embodiments of the present application, and the communication device 1300 can specifically be the communication device as a terminal device in the above-mentioned embodiments.

[0431] Among them, a possible logical structure diagram of the communication device 1300 is shown, and the communication device 1300 may include but is not limited to at least one processor 1301 and a communication port 1302.

[0432] in, Fig.11 The transceiver module 1102 shown may be a communication interface, which may be Fig.13 The communication port 1302 in the embodiment may include an input interface and an output interface. Alternatively, the communication port 1302 may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0433] Further optionally, the device may also include at least one of a memory 1303 and a bus 1304 . In an embodiment of the present application, the at least one processor 1301 is used to control and process the actions of the communication device 1300 .

[0434] In addition, the processor 1301 can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It can implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements a computing function, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, and the like. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0435] It should be noted that Fig.13 The communication device 1300 shown can be specifically used to implement the steps implemented by the terminal device in the aforementioned method embodiment, and achieve the corresponding technical effects of the terminal device. Fig.13 The specific implementation methods of the communication device shown can all refer to the description in the aforementioned method embodiment, and will not be described in detail here.

[0436] See also Fig.14 , is a schematic diagram of the structure of the communication device 1400 involved in the above embodiments provided in the embodiments of the present application. The communication device 1400 may specifically be a communication device as the first communication device or the wireless access device in the above embodiments. The structure of the communication device may refer to Fig.14 The structure shown.

[0437] The communication device 1400 includes at least one processor 1411 and at least one network interface 1414. Further optionally, the communication device also includes at least one memory 1412, at least one transceiver 1413 and one or more antennas 1415. The processor 1411, the memory 1412, the transceiver 1413 and the network interface 1414 are connected, for example, through a bus. In an embodiment of the present application, the connection may include various interfaces, transmission lines or buses, etc., which are not limited in this embodiment. The antenna 1415 is connected to the transceiver 1413. The network interface 1414 is used to enable the communication device to communicate with other communication devices through a communication link. For example, the network interface 1414 may include a network interface between the communication device and the core network device, such as an S1 interface, and the network interface may include a network interface between the communication device and other communication devices (such as other network devices or core network devices), such as an X2 or Xn interface.

[0438] in, Fig.11 The transceiver module 1102 shown may be a communication interface, which may be Fig.14 The network interface 1414 in the embodiment may include an input interface and an output interface. Alternatively, the network interface 1414 may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0439] The processor 1411 is mainly used to process the communication protocol and communication data, and to control the entire communication device, execute the software program, and process the data of the software program, for example, to support the communication device to perform the actions described in the embodiment. The communication device may include a baseband processor and a central processing unit. The baseband processor is mainly used to process the communication protocol and communication data, and the central processing unit is mainly used to control the entire terminal device, execute the software program, and process the data of the software program. Fig.14 The processor 1411 in the embodiment can integrate the functions of the baseband processor and the central processor. It can be understood by those skilled in the art that the baseband processor and the central processor can also be independent processors, which are interconnected through technologies such as buses. It can be understood by those skilled in the art that the terminal device can include multiple baseband processors to adapt to different network formats, and the terminal device can include multiple central processors to enhance its processing capabilities. The various components of the terminal device can be connected through various buses. The baseband processor can also be described as a baseband processing circuit or a baseband processing chip. The central processor can also be described as a central processing circuit or a central processing chip. The function of processing the communication protocol and the communication data can be built into the processor, or it can be stored in the memory in the form of a software program, and the processor executes the software program to realize the baseband processing function.

[0440] The memory is mainly used to store software programs and data. The memory 1412 can be independent and connected to the processor 1411. Optionally, the memory 1412 can be integrated with the processor 1411, for example, integrated into a chip. Among them, the memory 1412 can store program codes for executing the technical solutions of the embodiments of the present application, and the execution is controlled by the processor 1411. The various types of computer program codes executed can also be regarded as drivers of the processor 1411.

[0441] Fig.14 Only one memory and one processor are shown. In an actual terminal device, there may be multiple processors and multiple memories. The memory may also be referred to as a storage medium or a storage device, etc. The memory may be a storage element on the same chip as the processor, i.e., an on-chip storage element, or an independent storage element, which is not limited in the embodiments of the present application.

[0442] The transceiver 1413 can be used to support the reception or transmission of radio frequency signals between the communication device and the terminal, and the transceiver 1413 can be connected to the antenna 1415. The transceiver 1413 includes a transmitter Tx and a receiver Rx. Specifically, one or more antennas 1415 can receive radio frequency signals, and the receiver Rx of the transceiver 1413 is used to receive the radio frequency signal from the antenna, and convert the radio frequency signal into a digital baseband signal or a digital intermediate frequency signal, and provide the digital baseband signal or the digital intermediate frequency signal to the processor 1411, so that the processor 1411 further processes the digital baseband signal or the digital intermediate frequency signal, such as demodulation and decoding. In addition, the transmitter Tx in the transceiver 1413 is also used to receive a modulated digital baseband signal or a digital intermediate frequency signal from the processor 1411, and convert the modulated digital baseband signal or the digital intermediate frequency signal into a radio frequency signal, and send the radio frequency signal through one or more antennas 1415. Specifically, the receiver Rx can selectively perform one or more stages of down-mixing and analog-to-digital conversion processing on the RF signal to obtain a digital baseband signal or a digital intermediate frequency signal, and the order of the down-mixing and analog-to-digital conversion processing is adjustable. The transmitter Tx can selectively perform one or more stages of up-mixing and digital-to-analog conversion processing on the modulated digital baseband signal or digital intermediate frequency signal to obtain a RF signal, and the order of the up-mixing and digital-to-analog conversion processing is adjustable. The digital baseband signal and the digital intermediate frequency signal can be collectively referred to as a digital signal.

[0443] The transceiver 1413 may also be referred to as a transceiver module, a transceiver, a transceiver device, etc. Optionally, a device in the transceiver module for implementing a receiving function may be regarded as a receiving unit, and a device in the transceiver module for implementing a sending function may be regarded as a sending unit, that is, the transceiver module includes a receiving unit and a sending unit, the receiving unit may also be referred to as a receiver, an input port, a receiving circuit, etc., and the sending unit may be referred to as a transmitter, a transmitter, or a transmitting circuit, etc.

[0444] It should be noted that Fig.14 The communication device 1400 shown can be specifically used to implement the steps implemented by the first communication device or the wireless access device in the aforementioned method embodiment, and achieve the corresponding technical effects of the network device. Fig.14 The specific implementation methods of the communication device 1400 shown can all refer to the description in the aforementioned method embodiment, and will not be described in detail here.

[0445] See also Fig.15 , which is a structural diagram of the communication device involved in the above-mentioned embodiments provided in the embodiments of the present application.

[0446] It is understandable that the communication device 150 includes, for example, modules, units, elements, circuits, or interfaces, etc., which are appropriately configured together to perform the technical solutions provided in this application. The communication device 150 may be the AI ​​training unit or AI reasoning unit described above, or it may be a component (such as a chip) in these devices to implement the method described in the following method embodiment. The communication device 150 includes one or more processors 151. The processor 151 may be a general-purpose processor or a dedicated processor, etc. For example, it may be a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication device (such as a RAN node, a terminal, or a chip, etc.), execute software programs, and process data of software programs.

[0447] Optionally, in one design, the processor 151 may include a program 153 (sometimes also referred to as code or instruction), and the program 153 may be executed on the processor 151 so that the communication device 150 performs the method described in the following embodiments. In another possible design, the communication device 150 includes a circuit ( Fig.15 not shown).

[0448] Optionally, the communication device 150 may include one or more memories 152 on which a program 154 (sometimes also referred to as code or instruction) is stored. The program 154 may be executed on the processor 151 so that the communication device 150 executes the method described in the above method embodiment.

[0449] Optionally, the processor 151 and / or the memory 152 may include an AI module 157, 1511, and the AI ​​module is used to implement AI-related functions. The AI ​​module may be implemented by software, hardware, or a combination of software and hardware. For example, the AI ​​module may include a radio intelligence control (RIC) module. For example, the AI ​​module may be a near real-time RIC or a non-real-time RIC.

[0450] Optionally, data may also be stored in the processor 151 and / or the memory 152. The processor and the memory may be provided separately or integrated together.

[0451] Optionally, the communication device 150 may further include a transceiver 155 and / or an antenna 156. The processor 151 may also be sometimes referred to as a processing module, which controls the communication device (e.g., a RAN node or a terminal). The transceiver 155 may also be sometimes referred to as a transceiver module, a transceiver, a transceiver circuit, or a transceiver, etc., which is used to implement the transceiver function of the communication device through the antenna 156.

[0452] in, Fig.11 The transceiver module 1102 shown may be a communication interface, which may be Fig.15 The transceiver 155 in the embodiment may include an input interface and an output interface. Alternatively, the transceiver 155 may also be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0453] An embodiment of the present application also provides a computer-readable storage medium, which is used to store one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the method described in the possible implementation methods of the AI ​​training unit or the AI ​​reasoning unit in the aforementioned embodiments.

[0454] An embodiment of the present application also provides a computer program product (or computer program). When the computer program product is executed by the processor, the processor executes the method of possible implementation of the above-mentioned AI training unit or AI reasoning unit.

[0455] The embodiment of the present application also provides a chip system, which includes at least one processor for supporting the communication device to implement the functions involved in the above-mentioned implementation method. Optionally, the chip system also includes an interface circuit, which provides program instructions and / or data for the at least one processor. In one possible design, the chip system may also include a memory, which is used to store the necessary program instructions and data for the communication device. The chip system can be composed of chips, or it can include chips and other discrete devices, wherein the communication device can specifically be an AI training unit or an AI reasoning unit in the aforementioned method embodiment.

[0456] An embodiment of the present application also provides a communication system, which includes the AI ​​training unit and the AI ​​reasoning unit in any of the above embodiments.

[0457] Optionally, the communication system also includes a terminal device, or the AI ​​reasoning unit may be a terminal device.

[0458] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0459] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0460] In addition, each functional unit in each embodiment of the present application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

Claims

1. A communication method, characterized in that: include: Determine first configuration information, where the first configuration information is used to configure a communication connection for data transmission, where the data transmission is the transmission of service data of a communication management service between an artificial intelligence AI training unit and an AI reasoning unit, and the communication management service is used to manage a wireless access communication service between a terminal device and a wireless access device; the first configuration information includes an identifier of the communication connection, and identifiers of one or more quality of service flows QoS flow in the communication connection; Send the first configuration information.

2. The method according to claim 1, characterized in that: The method further comprises: First request information is received, where the first request information is used to request the first configuration information.

3. The method according to claim 2, characterized in that The first request information includes at least one of the following: The identifier of the AI ​​application scenario supported by the AI ​​reasoning unit, the requested AI service type identifier, the model identifier corresponding to the requested AI model, and the identifier of the AI ​​reasoning unit.

4. The method according to claim 3, characterized in that The AI ​​scenarios supported by the AI ​​inference unit meet any of the following conditions: The AI ​​reasoning unit is a distributed unit DU or a module in the DU, and the AI ​​scenarios supported by the AI ​​reasoning unit include at least one of a single-user medium access control SU MAC dictionary, a multi-user MU pairing dictionary, a multi-cell coordinated scheduling dictionary, and a low-speed channel map; The AI ​​inference unit is a terminal device or a module in the terminal device, and the AI ​​scenarios supported by the AI ​​inference unit include at least one of an interference spectrum, a beam spectrum, and a smart channel state information CSI; The AI ​​reasoning unit is a centralized unit CU or a module in the CU, and the AI ​​scenarios supported by the AI ​​reasoning unit include at least one of smart grid, smart load balancing, and smart energy saving.

5. The method according to any one of claims 1 to 4, characterized in that: The first configuration information also includes at least one of the following: Quality of service (QoS) information of one or more QoS flows in the communication connection, and an identifier of the AI ​​training unit.

6. The method according to any one of claims 1 to 5, characterized in that: The determining of the first configuration information comprises: The first configuration information is determined based on at least one of the following: The identifier of the AI ​​application scenario corresponding to the business data, the identifier of the AI ​​service type corresponding to the communication management business, and the model identifier of the AI ​​model corresponding to the AI ​​model information contained in the business data.

7. The method according to any one of claims 1 to 6, characterized in that: After sending the first configuration information, the method further includes: Sending second configuration information, wherein the second configuration information includes configuration information of N QoS flows established in the communication connection, where N is a positive integer; wherein the second configuration information includes at least one of the following: The identifier of the communication connection, the N identifiers corresponding to the N QoS flows, the quality of service QoS information of the N QoS flows, and the identifier of the AI ​​reasoning unit.

8. The method according to claim 7, characterized in that The method further comprises: Second request information is received, where the second request information is used to request the second configuration information; wherein the second request information includes an identifier of the communication connection.

9. The method according to claim 7 or 8, characterized in that: The method further comprises: Send first indication information, where the first indication information is used to indicate to delete at least one QoS flow among the N QoS flows.

10. The method according to claim 9, characterized in that Before sending the first indication information, the method further includes: A third request information is received, where the third request information is used to request deletion of the at least one QoS flow.

11. The method according to any one of claims 1 to 10, characterized in that: The method further comprises: Sending second indication information, where the second indication information is used to indicate releasing the communication connection.

12. The method according to claim 11, characterized in that The method further comprises: Fourth request information is received, where the fourth request information is used to request to release the communication connection.

13. A communication method, characterized in that: include: Receive first configuration information, where the first configuration information is used to configure a communication connection for data transmission, where the data transmission is the transmission of service data of a communication management service between an artificial intelligence (AI) training unit and an AI reasoning unit, where the communication management service is used to manage a wireless access communication service between a terminal device and a wireless access device; the first configuration information includes an identifier of the communication connection, and identifiers of one or more quality of service flows (QoS flows) in the communication connection; The service data is transmitted in the communication connection based on the first configuration information.

14. The method according to claim 13, characterized in that The method further comprises: Send first request information, where the first request information is used to request the first configuration information.

15. The method according to claim 14, characterized in that The first request information includes at least one of the following: The identifier of the AI ​​application scenario supported by the AI ​​reasoning unit, the requested AI service type identifier, the model identifier corresponding to the requested AI model, and the identifier of the AI ​​reasoning unit.

16. The method according to claim 15, characterized in that The AI ​​scenarios supported by the AI ​​inference unit meet any of the following conditions: The AI ​​reasoning unit is a distributed unit DU or a module in the DU, and the AI ​​scenarios supported by the AI ​​reasoning unit include at least one of a single-user medium access control SU MAC dictionary, a multi-user MU pairing dictionary, a multi-cell coordinated scheduling dictionary, and a low-speed channel map; The AI ​​inference unit is a terminal device or a module in the terminal device, and the AI ​​scenarios supported by the AI ​​inference unit include at least one of an interference spectrum, a beam spectrum, and a smart channel state information CSI; The AI ​​reasoning unit is a centralized unit CU or a module in the CU, and the AI ​​scenarios supported by the AI ​​reasoning unit include at least one of smart grid, smart load balancing, and smart energy saving.

17. The method according to any one of claims 13 to 16, characterized in that The first configuration information also includes at least one of the following: Quality of service (QoS) information of one or more QoS flows in the communication connection, and an identifier of the AI ​​training unit.

18. The method according to any one of claims 13 to 17, characterized in that After receiving the first configuration information, the method further includes: Receive second configuration information, where the second configuration information includes configuration information of N QoS flows established in the communication connection, where N is a positive integer; wherein the second configuration information includes at least one of the following: The identifier of the communication connection, the N identifiers corresponding to the N QoS flows, the quality of service QoS information of the N QoS flows, and the identifier of the AI ​​reasoning unit.

19. The method according to claim 18, characterized in that The method further comprises: Sending second request information, where the second request information is used to request the second configuration information; wherein the second request information includes an identifier of the communication connection.

20. The method according to claim 18 or 19, characterized in that The method further comprises: First indication information is received, where the first indication information is used to indicate to delete at least one QoS flow among the N QoS flows.

21. The method according to claim 20, characterized in that Before receiving the first indication information, the method further includes: Sending third request information, where the third request information is used to request deletion of the at least one QoS flow.

22. The method according to any one of claims 13 to 21, characterized in that The method further comprises: Second indication information is received, where the second indication information is used to instruct to release the communication connection.

23. The method according to claim 22, characterized in that The method further comprises: Sending fourth request information, where the fourth request information is used to request to release the communication connection.

24. A communication method, characterized in that: include: The artificial intelligence AI training unit sends first configuration information, where the first configuration information is used to configure a communication connection for data transmission, where the data transmission is the transmission of service data of a communication management service between the AI ​​training unit and the AI ​​reasoning unit, and the communication management service is used to manage a wireless access communication service between a terminal device and a wireless access device; the first configuration information includes an identifier of the communication connection, and an identifier of one or more quality of service flows QoS flow in the communication connection; The AI ​​reasoning unit receives the first configuration information; The business data is transmitted between the AI ​​training unit and the AI ​​reasoning unit in the communication connection based on the first configuration information.

25. The method according to claim 24, characterized in that The method further comprises: The AI ​​reasoning unit sends a first request message to the AI ​​training unit, where the first request message is used to request the first configuration information; wherein the first request message includes at least one of the following: The identifier of the AI ​​application scenario supported by the AI ​​reasoning unit, the requested AI service type identifier, the model identifier corresponding to the requested AI model, and the identifier of the AI ​​reasoning unit.

26. The method according to claim 25, characterized in that The AI ​​scenarios supported by the AI ​​inference unit meet any of the following conditions: The AI ​​reasoning unit is a distributed unit DU or a module in the DU, and the AI ​​scenarios supported by the AI ​​reasoning unit include at least one of a single-user medium access control SU MAC dictionary, a multi-user MU pairing dictionary, a multi-cell coordinated scheduling dictionary, and a low-speed channel map; The AI ​​inference unit is a terminal device or a module in the terminal device, and the AI ​​scenarios supported by the AI ​​inference unit include at least one of an interference spectrum, a beam spectrum, and a smart channel state information CSI; The AI ​​reasoning unit is a centralized unit CU or a module in the CU, and the AI ​​scenarios supported by the AI ​​reasoning unit include at least one of smart grid, smart load balancing, and smart energy saving.

27. The method according to any one of claims 24 to 26, characterized in that The first configuration information also includes at least one of the following: Quality of service (QoS) information of one or more QoS flows in the communication connection, and an identifier of the AI ​​training unit.

28. A communication device, characterized in that: Comprising means for performing the method as claimed in any one of claims 1 to 23.

29. A communication device, characterized in that: The method comprises at least one processor coupled to a memory; the at least one processor is configured to execute the method according to any one of claims 1 to 23.

30. A communication system, characterized in that: including a first communication device and a second communication device; The first communication device is used to execute the method according to any one of claims 1 to 12, and the second communication device is used to execute the method according to any one of claims 13 to 23.

31. The communication system according to claim 30, characterized in that: The second communication device is a terminal device, or the communication system also includes a terminal device.

32. A readable storage medium, characterized in that: The storage medium stores a computer program or an instruction. When the computer program or the instruction is executed by the communication device, the communication device implements the method according to any one of claims 1 to 27.

33. A computer program product, characterized in that When the computer program in the computer program product is executed by a communication device, the communication device is caused to implement the method according to any one of claims 1 to 27.

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