Communication method and apparatus, and storage medium

Abstract

The present disclosure relates to the technical field of communications, and provides a communication method and apparatus, and a storage medium. The method comprises: a second functional entity sending first AI / ML related information to a third functional entity, the first AI / ML related information comprising at least one of the following: first data information used for AI / ML model training or analysis, first AI / ML model information, and first analysis information; receiving index information of the first AI / ML related information sent by the third functional entity; and a first functional entity acquiring the index information of the first AI / ML related information of the second functional entity, acquiring the first AI / ML related information on the basis of the index information of the first AI / ML related information, and using the first AI / ML related information to perform communication processing with the second functional entity. The accuracy and timeliness of communication processing between the first functional entity and the second functional entity are improved.

Description

Communication method, device and storage medium

[0001] This disclosure claims priority to the Chinese patent application filed with the China Patent Office on September 15, 2023, with application number 202311199143.5 and application name “A Communication Method, Device and Storage Medium,” the entire contents of which are incorporated by reference into this disclosure. Technical Field

[0002] The present disclosure relates to the field of communication technologies, and in particular to a communication method, device, and storage medium. Background Art

[0003] In current 5G mobile communication systems, the access network's data collection, model training, and inference process can be illustrated in Figure 1: The operation administration and maintenance (OAM) entity can obtain input data for artificial intelligence (AI) / machine learning (ML) model training from access network (AN) devices. This input data includes data from the access network devices themselves and measurement data from terminals. The OAM entity uses the input data to train an AI / ML model (e.g., an AI / ML model for terminal mobility optimization) and sends the AI / ML model to the access network device. The access network device can use the AI / ML model and input data to perform inference (or prediction), outputting inference (or prediction) results, such as terminal trajectory prediction, target cell prediction, and target 5G radio access network node prediction. Based on the inference (or prediction) results, the access network device initiates a terminal mobility optimization process, such as handing the terminal off from access network device 1 to access network device 2.

[0004] Since OAM entities are mainly used for configuration scheduling and performance maintenance management of network resources, and do not directly participate in the control of terminals and networks (such as the control of terminal mobility and sessions), the terminal and network control-related models trained and provided by OAM may not meet real-time requirements.

[0005] Summary of the Invention

[0006] The present disclosure provides a communication method, device and storage medium, which solve the technical problem that OAM training, provision of terminal and network control related models may not meet real-time requirements.

[0007] In a first aspect, an embodiment of the present disclosure provides a communication method, applied to a first functional entity, the method comprising:

[0008] Obtain index information of first artificial intelligence (AI) / machine learning (ML) related information of the second functional entity, where the index information of the first AI / ML related information is used to indicate the first AI / ML related information stored by the second functional entity in the third functional entity, where the first AI / ML related information includes at least one of the following: first data information for AI / ML model training or analysis, first AI / ML model information, and first analysis information;

[0009] acquiring the first AI / ML related information according to the index information of the first AI / ML related information;

[0010] Use the first AI / ML related information to communicate with the second functional entity.

[0011] In one embodiment, obtaining index information of the first AI / ML related information of the second functional entity includes:

[0012] A first message sent by the second functional entity is received, where the first message carries index information of the first AI / ML related information.

[0013] In one embodiment, before receiving the first message sent by the second functional entity, the method further includes:

[0014] Send a second message to the second functional entity, where the second message is used to request the first AI / ML related information.

[0015] In one implementation, the sending the second message to the second functional entity includes:

[0016] Sending the second message to the second functional entity based on any one of the following triggering conditions:

[0017] Determining that data or an AI / ML model of the second functional entity is required for AI / ML model training or analysis;

[0018] receiving a third message sent by the second functional entity, where the third message is used to request processing by the access layer, the non-access layer, or the application layer;

[0019] A fourth message sent by a fourth functional entity is received, where the fourth message is used to request access layer, non-access layer, or application layer control of the second functional entity.

[0020] In one embodiment, obtaining the first AI / ML related information according to the index information of the first AI / ML related information includes:

[0021] Obtaining the first AI / ML related information from a third functional entity using index information of the first AI / ML related information; or

[0022] Determine the first AI / ML related information based on locally stored AI / ML related information and index information of the first AI / ML related information, where the locally stored AI / ML related information is AI / ML related information obtained and stored locally by the first functional entity from the third functional entity, and the locally stored AI / ML related information includes index information of the first AI / ML related information.

[0023] In one embodiment, obtaining index information of the first AI / ML related information of the second functional entity includes:

[0024] receiving a fifth message sent by a fifth functional entity, where the fifth message carries index information of the first AI / ML related information and / or information of the third functional entity, and the fifth message is used to request handover or to provide terminal context;

[0025] The acquiring the first AI / ML related information according to the index information of the first AI / ML related information includes:

[0026] Obtaining the first AI / ML related information from the third functional entity using the index information of the first AI / ML related information and / or the information of the third functional entity;

[0027] The second functional entity is a second terminal, the first functional entity is a functional entity currently serving the second functional entity, and the fifth functional entity is a functional entity that previously served the second functional entity.

[0028] In one embodiment, the method further comprises:

[0029] Sending a sixth message to the third functional entity, where the sixth message is used to request a subscription to the first AI / ML related information of the second functional entity;

[0030] Receive index information of the first AI / ML related information sent by the third functional entity.

[0031] In one embodiment, before using the first AI / ML related information to communicate with the second functional entity, the method further includes:

[0032] Send index information of the first AI / ML related information to the second functional entity.

[0033] In one embodiment, the second functional entity performs communication processing, including:

[0034] Using the first data information to train an AI / ML model to obtain a second AI / ML model; or using the first data information to perform analysis to obtain second analysis information;

[0035] Use the second AI / ML model or the second analysis information to communicate with the second functional entity.

[0036] In one embodiment, the first AI / ML related information is first AI / ML model information, and the using the first AI / ML related information to communicate with the second functional entity includes:

[0037] determining a first AI / ML model based on the first AI / ML model information;

[0038] Use the first AI / ML model to communicate with the second functional entity.

[0039] In one embodiment, the performing communication processing with the second functional entity includes:

[0040] Performing access layer processing with the second functional entity;

[0041] The first functional entity and the second functional entity meet one of the following conditions:

[0042] The first functional entity is a first access network device, and the second functional entity is a second terminal;

[0043] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0044] The first functional entity is a first terminal, and the second functional entity is a second access network device.

[0045] In one embodiment, the performing communication processing with the second functional entity includes:

[0046] Performing non-access layer or application layer processing with the second functional entity;

[0047] The first functional entity and the second functional entity meet one of the following conditions:

[0048] The first functional entity is a first core network functional entity, and the second functional entity is a second terminal;

[0049] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0050] The first functional entity is a first terminal, and the second functional entity is a second core network functional entity.

[0051] In a second aspect, an embodiment of the present disclosure provides a communication method, applied to a second functional entity, the method comprising:

[0052] Sending first artificial intelligence (AI) / machine learning (ML) related information to the third functional entity, where the first AI / ML related information includes at least one of the following: first data information for AI / ML model training or analysis, first AI / ML model information, and first analysis information;

[0053] receiving index information of the first AI / ML related information sent by the third functional entity, where the index information of the first AI / ML related information is used to indicate the first AI / ML related information stored in the third functional entity by the second functional entity;

[0054] Use the first AI / ML related information to communicate with the first functional entity.

[0055] In one embodiment, after receiving the index information of the first AI / ML related information sent by the third functional entity and before using the first AI / ML related information to communicate with the first functional entity, the method further includes:

[0056] A first message is sent to the first functional entity, where the first message carries index information of the first AI / ML related information.

[0057] In one embodiment, the performing communication processing with the first functional entity includes:

[0058] Performing access layer processing with the first functional entity;

[0059] The first functional entity and the second functional entity meet one of the following conditions:

[0060] The first functional entity is a first access network device, and the second functional entity is a second terminal;

[0061] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0062] The first functional entity is a first terminal, and the second functional entity is a second access network device.

[0063] In one embodiment, the performing communication processing with the first functional entity includes:

[0064] Performing non-access layer or application layer processing with the first functional entity;

[0065] The first functional entity and the second functional entity meet one of the following conditions:

[0066] The first functional entity is a first core network functional entity, and the second functional entity is a second terminal;

[0067] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0068] The first functional entity is a first terminal, and the second functional entity is a second core network functional entity.

[0069] In a third aspect, an embodiment of the present disclosure provides a communication device, applied to a first functional entity, the device including:

[0070] a first acquiring unit, configured to acquire index information of first artificial intelligence (AI) / machine learning (ML) related information of a second functional entity, where the index information of the first AI / ML related information is used to indicate the first AI / ML related information stored by the second functional entity in the third functional entity, where the first AI / ML related information includes at least one of the following: first data information for AI / ML model training or analysis, first AI / ML model information, and first analysis information;

[0071] a second acquiring unit, configured to acquire the first AI / ML related information according to the index information of the first AI / ML related information;

[0072] A processing unit is configured to perform communication processing with the second functional entity using the first AI / ML related information.

[0073] In one embodiment, the first acquiring unit is specifically configured to:

[0074] A first message sent by the second functional entity is received, where the first message carries index information of the first AI / ML related information.

[0075] In one embodiment, the apparatus further includes a first sending unit, wherein the first sending unit is configured to:

[0076] Send a second message to the second functional entity, where the second message is used to request the first AI / ML related information.

[0077] In one embodiment, the first sending unit is specifically configured to:

[0078] Sending the second message to the second functional entity based on any one of the following triggering conditions:

[0079] Determining that data or an AI / ML model of the second functional entity is required for AI / ML model training or analysis;

[0080] receiving a third message sent by the second functional entity, where the third message is used to request processing by the access layer, the non-access layer, or the application layer;

[0081] A fourth message sent by a fourth functional entity is received, where the fourth message is used to request access layer, non-access layer, or application layer control of the second functional entity.

[0082] In one embodiment, the second acquiring unit is specifically configured to:

[0083] Obtaining the first AI / ML related information from a third functional entity using index information of the first AI / ML related information; or

[0084] Determine the first AI / ML related information based on locally stored AI / ML related information and index information of the first AI / ML related information, where the locally stored AI / ML related information is AI / ML related information obtained and stored locally by the first functional entity from the third functional entity, and the locally stored AI / ML related information includes index information of the first AI / ML related information.

[0085] In one embodiment, the first acquiring unit is specifically configured to:

[0086] receiving a fifth message sent by a fifth functional entity, where the fifth message carries index information of the first AI / ML related information and / or information of the third functional entity, and the fifth message is used to request handover or to provide terminal context;

[0087] The second acquiring unit is specifically configured to:

[0088] Obtaining the first AI / ML related information from the third functional entity using the index information of the first AI / ML related information and / or the information of the third functional entity;

[0089] The second functional entity is a second terminal, the first functional entity is a functional entity currently serving the second functional entity, and the fifth functional entity is a functional entity that previously served the second functional entity.

[0090] In one embodiment, the device further includes a second sending unit and a receiving unit, wherein:

[0091] The second sending unit is configured to send a sixth message to the third functional entity, where the sixth message is used to request a subscription to the first AI / ML related information of the second functional entity;

[0092] The receiving unit is configured to receive index information of the first AI / ML related information sent by the third functional entity.

[0093] In one embodiment, the first sending unit is further configured to:

[0094] Send index information of the first AI / ML related information to the second functional entity.

[0095] In one embodiment, the first AI / ML related information is the first data information, and the processing unit is specifically configured to:

[0096] Using the first data information to train an AI / ML model to obtain a second AI / ML model; or using the first data information to perform analysis to obtain second analysis information;

[0097] Use the second AI / ML model or the second analysis information to communicate with the second functional entity.

[0098] In one embodiment, the first AI / ML related information is first AI / ML model information, and the processing unit is specifically configured to:

[0099] determining a first AI / ML model based on the first AI / ML model information;

[0100] Use the first AI / ML model to communicate with the second functional entity.

[0101] In one embodiment, the processing unit is specifically configured to:

[0102] Performing access layer processing with the second functional entity;

[0103] The first functional entity and the second functional entity meet one of the following conditions:

[0104] The first functional entity is a first access network device, and the second functional entity is a second terminal;

[0105] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0106] The first functional entity is a first terminal, and the second functional entity is a second access network device.

[0107] In one embodiment, the processing unit is specifically configured to:

[0108] Performing non-access layer or application layer processing with the second functional entity;

[0109] The first functional entity and the second functional entity meet one of the following conditions:

[0110] The first functional entity is a first core network functional entity, and the second functional entity is a second terminal;

[0111] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0112] The first functional entity is a first terminal, and the second functional entity is a second core network functional entity.

[0113] In a fourth aspect, an embodiment of the present disclosure provides a communication device, applied to a second functional entity, the device including:

[0114] A sending unit, configured to send first artificial intelligence (AI) / machine learning (ML) related information to a third functional entity, where the first AI / ML related information includes at least one of the following: first data information for AI / ML model training or analysis, first AI / ML model information, and first analysis information;

[0115] a receiving unit, configured to receive index information of the first AI / ML related information sent by the third functional entity, where the index information of the first AI / ML related information is used to indicate the first AI / ML related information stored in the third functional entity by the second functional entity;

[0116] A processing unit is configured to perform communication processing with a first functional entity using the first AI / ML related information.

[0117] In one embodiment, the sending unit is further configured to:

[0118] A first message is sent to the first functional entity, where the first message carries index information of the first AI / ML related information.

[0119] In one embodiment, the processing unit is specifically configured to:

[0120] Performing access layer processing with the first functional entity;

[0121] The first functional entity and the second functional entity meet one of the following conditions:

[0122] The first functional entity is a first access network device, and the second functional entity is a second terminal;

[0123] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0124] The first functional entity is a first terminal, and the second functional entity is a second access network device.

[0125] In one embodiment, the processing unit is specifically configured to:

[0126] Performing non-access layer or application layer processing with the first functional entity;

[0127] The first functional entity and the second functional entity meet one of the following conditions:

[0128] The first functional entity is a first core network functional entity, and the second functional entity is a second terminal;

[0129] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0130] The first functional entity is a first terminal, and the second functional entity is a second core network functional entity.

[0131] In a fifth aspect, an embodiment of the present disclosure provides a communication device, applied to a first functional entity, the device including a memory, a transceiver, a processor,

[0132] The memory is used to store a computer program; the transceiver is used to send and receive data under the control of the processor; and the processor is used to read the computer program in the memory and perform the following operations:

[0133] Obtain index information of first artificial intelligence (AI) / machine learning (ML) related information of the second functional entity, where the index information of the first AI / ML related information is used to indicate the first AI / ML related information stored by the second functional entity in the third functional entity, where the first AI / ML related information includes at least one of the following: first data information for AI / ML model training or analysis, first AI / ML model information, and first analysis information;

[0134] acquiring the first AI / ML related information according to the index information of the first AI / ML related information;

[0135] Use the first AI / ML related information to communicate with the second functional entity.

[0136] In one embodiment, the processor is specifically configured to perform the following operations:

[0137] A first message sent by the second functional entity is received, where the first message carries index information of the first AI / ML related information.

[0138] In one embodiment, the processor is further configured to perform the following operations:

[0139] Send a second message to the second functional entity, where the second message is used to request the first AI / ML related information.

[0140] In one embodiment, the processor is specifically configured to perform the following operations:

[0141] Sending the second message to the second functional entity based on any one of the following triggering conditions:

[0142] Determining that data or an AI / ML model of the second functional entity is required for AI / ML model training or analysis;

[0143] receiving a third message sent by the second functional entity, where the third message is used to request processing by the access layer, the non-access layer, or the application layer;

[0144] A fourth message sent by a fourth functional entity is received, where the fourth message is used to request access layer, non-access layer, or application layer control of the second functional entity.

[0145] In one embodiment, the processor is specifically configured to perform the following operations:

[0146] Obtaining the first AI / ML related information from a third functional entity using index information of the first AI / ML related information; or

[0147] Determine the first AI / ML related information based on locally stored AI / ML related information and index information of the first AI / ML related information, where the locally stored AI / ML related information is AI / ML related information obtained and stored locally by the first functional entity from the third functional entity, and the locally stored AI / ML related information includes index information of the first AI / ML related information.

[0148] In one embodiment, the processor is specifically configured to perform the following operations:

[0149] receiving a fifth message sent by a fifth functional entity, where the fifth message carries index information of the first AI / ML related information and / or information of the third functional entity, and the fifth message is used to request handover or to provide terminal context;

[0150] Obtaining the first AI / ML related information from the third functional entity using the index information of the first AI / ML related information and / or the information of the third functional entity;

[0151] The second functional entity is a second terminal, the first functional entity is a functional entity currently serving the second functional entity, and the fifth functional entity is a functional entity that previously served the second functional entity.

[0152] In one embodiment, the processor is further configured to perform the following operations:

[0153] Sending a sixth message to the third functional entity, where the sixth message is used to request a subscription to the first AI / ML related information of the second functional entity;

[0154] Receive index information of the first AI / ML related information sent by the third functional entity.

[0155] In one embodiment, the processor is further configured to perform the following operations:

[0156] Send index information of the first AI / ML related information to the second functional entity.

[0157] In one embodiment, the first AI / ML related information is the first data information, and the processor is specifically configured to perform the following operations:

[0158] Using the first data information to train an AI / ML model to obtain a second AI / ML model; or using the first data information to perform analysis to obtain second analysis information;

[0159] Use the second AI / ML model or the second analysis information to communicate with the second functional entity.

[0160] In one embodiment, the first AI / ML related information is first AI / ML model information, and the processor is specifically configured to perform the following operations:

[0161] determining a first AI / ML model based on the first AI / ML model information;

[0162] Use the first AI / ML model to communicate with the second functional entity.

[0163] In one embodiment, the processor is specifically configured to perform the following operations:

[0164] Performing access layer processing with the second functional entity;

[0165] The first functional entity and the second functional entity meet one of the following conditions:

[0166] The first functional entity is a first access network device, and the second functional entity is a second terminal;

[0167] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0168] The first functional entity is a first terminal, and the second functional entity is a second access network device.

[0169] In one embodiment, the processor is specifically configured to perform the following operations:

[0170] Performing non-access layer or application layer processing with the second functional entity;

[0171] The first functional entity and the second functional entity meet one of the following conditions:

[0172] The first functional entity is a first core network functional entity, and the second functional entity is a second terminal;

[0173] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0174] The first functional entity is a first terminal, and the second functional entity is a second core network functional entity.

[0175] In a sixth aspect, an embodiment of the present disclosure provides a communication device, applied to a second functional entity, the device including a memory, a transceiver, a processor,

[0176] The memory is used to store a computer program; the transceiver is used to send and receive data under the control of the processor; and the processor is used to read the computer program in the memory and perform the following operations:

[0177] Sending first artificial intelligence (AI) / machine learning (ML) related information to the third functional entity, where the first AI / ML related information includes at least one of the following: first data information for AI / ML model training or analysis, first AI / ML model information, and first analysis information;

[0178] receiving index information of the first AI / ML related information sent by the third functional entity, where the index information of the first AI / ML related information is used to indicate the first AI / ML related information stored in the third functional entity by the second functional entity;

[0179] Use the first AI / ML related information to communicate with the first functional entity.

[0180] In one embodiment, the processor is further configured to perform the following operations:

[0181] A first message is sent to the first functional entity, where the first message carries index information of the first AI / ML related information.

[0182] In one embodiment, the processor is specifically configured to perform the following operations:

[0183] Performing access layer processing with the first functional entity;

[0184] The first functional entity and the second functional entity meet one of the following conditions:

[0185] The first functional entity is a first access network device, and the second functional entity is a second terminal;

[0186] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0187] The first functional entity is a first terminal, and the second functional entity is a second access network device.

[0188] In one embodiment, the processor is specifically configured to perform the following operations:

[0189] Performing non-access layer or application layer processing with the first functional entity;

[0190] The first functional entity and the second functional entity meet one of the following conditions:

[0191] The first functional entity is a first core network functional entity, and the second functional entity is a second terminal;

[0192] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0193] The first functional entity is a first terminal, and the second functional entity is a second core network functional entity.

[0194] In a seventh aspect, an embodiment of the present disclosure provides a non-transitory readable storage medium, wherein the non-transitory readable storage medium stores a computer program, and the computer program is used to enable a processor to execute the method described in the first aspect.

[0195] In an eighth aspect, an embodiment of the present disclosure provides a non-transitory readable storage medium, wherein the non-transitory readable storage medium stores a computer program, and the computer program is used to enable a processor to execute the method described in the second aspect.

[0196] The embodiments of the present disclosure provide a communication method, device, and storage medium, in which a second functional entity sends first AI / ML related information to a third functional entity, where the first AI / ML related information includes at least one of the following: first data information, first AI / ML model information, and first analysis information for AI / ML model training or analysis; receives index information of the first AI / ML related information sent by the third functional entity, where the index information of the first AI / ML related information is used to indicate the first AI / ML related information stored in the third functional entity by the second functional entity; the first functional entity obtains the index information of the first AI / ML related information of the second functional entity, obtains the first AI / ML related information based on the index information of the first AI / ML related information, and uses the first AI / ML related information to communicate with the second functional entity. By interacting with the second and third functional entities, the first functional entity can quickly obtain the first AI / ML related information, thereby improving the accuracy and real-time performance of its communication with the second functional entity.

[0197] It should be understood that the contents described in the above summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easier to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0198] In order to more clearly illustrate the technical solutions in the present disclosure or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0199] FIG1 is a schematic diagram of the data collection, model training and inference process of the access network in the related art;

[0200] FIG2 is a schematic diagram of an architecture of a communication system provided by an embodiment of the present disclosure;

[0201] FIG3 is another schematic diagram of the architecture of a communication system provided by an embodiment of the present disclosure;

[0202] FIG4 is a flow chart of a communication method according to an embodiment of the present disclosure;

[0203] FIG5 is a second flow chart of the communication method provided in an embodiment of the present disclosure;

[0204] FIG6 is a third flow chart of the communication method provided in an embodiment of the present disclosure;

[0205] FIG7 is a fourth flow chart of a communication method according to an embodiment of the present disclosure;

[0206] FIG8 is a fifth flow chart of a communication method according to an embodiment of the present disclosure;

[0207] FIG9 is a sixth flow chart of a communication method according to an embodiment of the present disclosure;

[0208] FIG10 is a seventh flow chart of a communication method according to an embodiment of the present disclosure;

[0209] FIG11 is a flow chart eight of a communication method according to an embodiment of the present disclosure;

[0210] FIG12 is a ninth flowchart of a communication method according to an embodiment of the present disclosure;

[0211] FIG13 is a schematic structural diagram of a communication device 1300 provided in an embodiment of the present disclosure;

[0212] FIG14 is a schematic structural diagram of a communication device 1400 provided in an embodiment of the present disclosure;

[0213] FIG15 is a schematic structural diagram of a communication device 1500 provided in an embodiment of the present disclosure;

[0214] FIG16 is a schematic structural diagram of a communication device 1600 provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0215] In the embodiments of the present disclosure, the term "and / or" describes the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0216] In the embodiments of the present disclosure, the term "plurality" refers to two or more than two, and other quantifiers are similar thereto.

[0217] The terms "first" and "second" in the embodiments of the present disclosure are only used to illustrate and distinguish the described objects. There is no order, nor does it indicate any special limitation on the number of objects in the embodiments of the present disclosure, and cannot constitute any limitation on the embodiments of the present disclosure.

[0218] The following will be combined with the accompanying drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the embodiments described are only part of the embodiments of the present disclosure and not all of the embodiments. Based on the embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present disclosure.

[0219] Embodiments of the present disclosure provide a communication method, apparatus, and storage medium for improving the accuracy and real-time performance of communication processing between a first functional entity and a second functional entity.

[0220] Among them, the method and the device are based on the same application concept. Since the principles of solving problems by the method and the device are similar, the implementation of the device and the method can refer to each other, and the repeated parts will not be repeated.

[0221] The technical solution provided by the embodiments of the present disclosure can be applicable to a variety of systems, especially 5G systems. For example, applicable systems may be global system of mobile communication (GSM) systems, code division multiple access (CDMA) systems, wideband code division multiple access (WCDMA) general packet radio service (GPRS) systems, long term evolution (LTE) systems, LTE frequency division duplex (FDD) systems, LTE time division duplex (TDD) systems, long term evolution advanced (LTE-A) systems, universal mobile telecommunication systems (UMTS), worldwide interoperability for microwave access (WiMAX) systems, 5G new radio (NR) systems, etc. These various systems include terminal devices and network devices. The system may also include a core network part, such as an evolved packet system (EPS), a 5G system (5GS), etc.

[0222] The terminal involved in the embodiments of the present disclosure may be a device that provides voice and / or data connectivity to a user, a handheld device with wireless connection capabilities, or other processing devices connected to a wireless modem. In different systems, the name of the terminal may also be different. For example, in a 5G system, the terminal may be called user equipment (UE). A wireless terminal can communicate with one or more core networks (CN) via a radio access network (RAN). A wireless terminal device may be a mobile terminal device, such as a mobile phone (or "cellular" phone) and a computer with a mobile terminal device. For example, it may be a portable, pocket-sized, handheld, computer-built-in, or vehicle-mounted mobile device that exchanges voice and / or data with a radio access network. For example, personal communication service (PCS) phones, cordless phones, session initiated protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), and other devices. The wireless terminal device may also be referred to as a system, a subscriber unit, a subscriber station, a mobile station, a mobile station, a remote station, an access point, a remote terminal device, an access terminal device, a user terminal device, a user agent, or a user device, but is not limited in the embodiments of the present disclosure.

[0223] The access network equipment involved in the embodiments of the present disclosure may also be referred to as a wireless access network equipment, or an access network functional entity, which can provide authorized users in a specific area with the function of accessing a communication network. Specifically, it may include wireless network equipment in a 3rd Generation Partnership Project (3GPP) network, or it may include an access point in a non-3GPP network. The access network equipment can be responsible for functions such as wireless resource management, quality of service (QoS) management, data compression and encryption on the air interface side. The access network equipment provides access services for the terminal, and then completes the forwarding of control signals and user data between the terminal and the core network. The access network device involved in the embodiments of the present disclosure may be a base transceiver station (BTS) in the global system for mobile communications (GSM) or code division multiple access (CDMA), a NodeB in wide-band code division multiple access (WCDMA), an evolutionary Node B (eNB or e-NodeB) in the long term evolution (LTE) system, a 5G base station (gNB) in the 5G next generation system, a home evolved node B (HeNB), a relay node, a femto, a pico, etc., and is not limited in the embodiments of the present disclosure. In some network structures, the access network device may include a centralized unit (CU) node and a distributed unit (DU) node, and the centralized unit and the distributed unit may also be geographically separated.

[0224] The communication scenario of the present disclosure is described below with reference to Figure 2. Figure 2 is a schematic diagram of an architecture of a communication system provided by an embodiment of the present disclosure.

[0225] As shown in Figure 2, the architecture can include terminals, access network equipment, core network functional entities, and data networks (DNs). Terminals, access network equipment, and core network functional entities are the main components of the architecture. Logically, they can be divided into two parts: the user plane and the control plane. The control plane is responsible for mobile network management, and the user plane is responsible for service data transmission.

[0226] Core network functional entity, also known as core network network element, or core network device. As an example, the core network functional entity may include any one of the following: network data analytics function (NWDAF) entity, policy control function (PCF) entity, application function (AF) entity, access and mobility management function (AMF) entity, session management function (SMF) entity, network exposure function (NEF) entity, user plane function (UPF) entity, unified data repository (UDR) entity, network slice selection function (NSSF) entity, authentication server function (AUSF) entity, unified data management (UDM) entity, network repository function (NRF) entity, OAM entity.

[0227] The NWDAF entity can use the data provided by the core network functional entities (such as AMF entity, SMF entity, UPF entity, PCF entity, NEF entity, AF entity) and the data of the terminal application as input data to train the AI / ML model, and use the input data and AI / ML model to perform network data analysis to obtain analysis information (including statistical and / or predictive information) on the behavior or performance of the terminal or core network. The core network functional entity controls the core network and the terminal based on the analysis information (including statistical and / or predictive information) provided by the NWDAF entity, for example, determining the service quality of the session data flow, selecting the core network functional entity for the service and the terminal, etc.

[0228] The NWDAF entity and the data collection function (DCCF) entity can store input data, AI / ML models and / or analysis information to the analytics data repository function (ADRF) entity, and request input data, AI / ML models and / or analysis information from the ADRF entity.

[0229] FIG3 is another schematic diagram of the architecture of the communication system provided by the embodiment of the present disclosure. FIG3 is a detailed architecture determined based on FIG2.

[0230] In related technologies, the data collection, model training, and inference process for access networks can be illustrated in Figure 1. Since OAM entities are primarily responsible for configuring and scheduling network resources and maintaining performance, and do not directly control terminals and networks (for example, terminal mobility and session control), training and providing terminal and network control-related models by OAM may not meet real-time requirements and is inconsistent with the existing core network data analysis architecture. Furthermore, the interface between OAM entities and access network equipment is not standardized, making the process shown in Figure 1 difficult to implement.

[0231] Based on the problems in the existing technology, the present disclosure proposes the following technical concept: the first functional entity improves the accuracy and real-time performance of its communication processing with the second functional entity by quickly obtaining first AI / ML related information.

[0232] The communication method provided by the present disclosure is introduced below in conjunction with specific embodiments.

[0233] FIG4 is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4 , the method includes:

[0234] S401. The second functional entity sends first AI / ML related information to the third functional entity.

[0235] In other words, the third functional entity receives the first AI / ML related information sent by the second functional entity.

[0236] The first AI / ML related information is AI / ML related information of the second functional entity.

[0237] The first AI / ML related information includes at least one of the following: first data information used for AI / ML model training or analysis, first AI / ML model information, and first analysis information.

[0238] The first data information may include data used by the second functional entity for AI / ML model training or analysis (referred to as first data).

[0239] The first AI / ML model information may include information of the AI / ML model of the second functional entity.

[0240] The first analysis information may include analysis information on the behavior or performance of the second functional entity obtained by the second functional entity performing network data analysis using input data and an AI / ML model.

[0241] The first analysis information may include statistical information and / or prediction information.

[0242] The second functional entity may be a terminal, an access network device or a core network functional entity.

[0243] The third functional entity may be an ADRF entity.

[0244] When the second functional entity is a terminal, it can send the first AI / ML related information to the third functional entity through a core network functional entity (e.g., AF entity, AMF entity) and / or an access network device.

[0245] For example, the terminal may send its first AI / ML related information to the AF entity through the user plane (application layer), and the AF entity then sends the first AI / ML related information to the third functional entity.

[0246] As another example, the terminal may send the first AI / ML related information to the AMF entity through a non-access stratum (NAS) message, and the AMF entity may then send the first AI / ML related information to the third functional entity.

[0247] In another example, the terminal may send the first AI / ML related information to the third functional entity through the access network device.

[0248] In another example, the terminal may send the first AI / ML related information to the AMF entity through the access network device, and the AMF entity may then send the first AI / ML related information to the third functional entity.

[0249] When the second functional entity is an access network device or a core network functional entity (such as an AMF entity, an SMF entity, a UPF entity, a PCF entity, a NEF entity, an AF entity, a NWDAF entity, or a DCCF entity), the second functional entity can directly send its first AI / ML related information to the third functional entity.

[0250] In one possible implementation, when the second functional entity sends the first AI / ML-related information to the third functional entity, it may also send first descriptive information to the third functional entity. The first descriptive information includes at least one of the following: descriptive information of the first data, descriptive information of the first AI / ML model, and first analysis descriptive information.

[0251] The description information of the first data may include at least one of the following: data collection time (segment), sampling frequency, and data accuracy.

[0252] The model description information of the first AI / ML model may include at least one of the following: model generation time, model identifier, model type (indicating what function / feature the model is used for, such as a model of terminal mobility characteristics or mobility trajectories, a model of terminal communication characteristics or communication performance, a terminal energy consumption or energy efficiency model), and model accuracy (characterizing the accuracy of the analysis results obtained using the model).

[0253] The first analysis description information may include at least one of the following: generation time of the first analysis information, statistics or predictions, analysis identification (indicating what kind of function / characteristic of the analysis information, such as analysis information of terminal movement characteristics or movement trajectory, analysis information of terminal communication characteristics or communication performance, terminal energy consumption or energy efficiency analysis information), confidence level (applicable to prediction information), applicable duration, usage area, etc.

[0254] If the second functional entity simultaneously sends the first AI / ML related information and the first description information to the third functional entity, the first AI / ML related information includes the first data information, and the first description information includes the description information of the first data; if the first AI / ML related information includes the first AI / ML model information, the first description information includes the model description information of the first AI / ML model; if the first AI / ML related information includes the first analysis information, the first description information includes the first analysis description information.

[0255] After receiving the first AI / ML related information, the third functional entity stores the first AI / ML related information, determines index information of the first AI / ML related information, and executes S402.

[0256] S402. The third functional entity sends index information of the first AI / ML related information to the second functional entity.

[0257] In other words, the second functional entity receives index information of the first AI / ML related information sent by the third functional entity.

[0258] When the second functional entity is a terminal, the third functional entity may send index information of the first AI / ML related information to the terminal through a core network functional entity (e.g., an AF entity, an AMF entity) or an access network device.

[0259] For example, if the terminal sends the first AI / ML related information to the third functional entity through the AF entity, the third functional entity can send the index information of the first AI / ML related information to the AF entity, and the AF entity sends the index information of the first AI / ML related information to the terminal through the user plane (application layer).

[0260] As another example, if the terminal sends the first AI / ML related information to the third functional entity through the AMF entity, the third functional entity can send the index information of the first AI / ML related information to the AMF entity, and the AMF entity sends the index information of the first AI / ML related information to the terminal through a NAS message.

[0261] As another example, if the terminal sends the first AI / ML related information to the third functional entity through the access network device, the third functional entity can send the index information of the first AI / ML related information to the access network device, and the access network device sends the index information of the first AI / ML related information to the terminal.

[0262] As another example, if the terminal sends the first AI / ML related information to the third functional entity through the access network device and the AMF entity, the third functional entity can send the index information of the first AI / ML related information to the access network device through the AMF entity, and the access network device sends the index information of the first AI / ML related information to the terminal.

[0263] When the second functional entity is an access network device or a core network functional entity (such as an AMF entity, an SMF entity, a UPF entity, a PCF entity, a NEF entity, an AF entity, a NWDAF entity, or a DCCF entity), the third functional entity may directly send the index information of its first AI / ML related information to the second functional entity.

[0264] A reference point or service-based interface may be defined between the access network device and the third functional entity so that the access network device and the third functional entity can communicate directly.

[0265] The index information of the first AI / ML related information may include an index value or identifier of each item of information in the first AI / ML related information.

[0266] Exemplarily, if the first AI / ML related information is the first data information, the index information is the index value of the first data information; if the first AI / ML related information is the first data information and the first AI / ML model information, the index information is the index identifier of the first data information and the index value of the first AI / ML model information; if the first is the first data information, the first AI / ML model information and the first analysis information, the index information is the index value of the first data information, the index value of the first AI / ML model information and the index value of the first analysis information.

[0267] S403: The first functional entity obtains index information of first AI / ML related information.

[0268] The index information of the first AI / ML related information may be used to indicate that the second functional entity stores the first AI / ML related information in the third functional entity.

[0269] The second functional entity may be a terminal, an access network device or a core network functional entity.

[0270] The first functional entity and the second functional entity meet one of the following conditions:

[0271] When the first functional entity is a first access network device, the second functional entity may be a second terminal;

[0272] When the first functional entity is a first terminal, the second functional entity may be a second terminal;

[0273] When the first functional entity is a first terminal, the second functional entity may be a second access network device;

[0274] When the first functional entity is a first core network functional entity, the second functional entity may be a second terminal;

[0275] When the first functional entity is a first terminal, the second functional entity may be a second core network functional entity.

[0276] S404: The first functional entity obtains the first AI / ML related information according to the index information of the first AI / ML related information.

[0277] In one possible implementation, if the first functional entity does not store the first AI / ML related information locally, the first functional entity may use the index information of the first AI / ML related information to obtain the first AI / ML related information from the third functional entity; if the AI / ML related information stored locally by the first functional entity includes the first AI / ML related information and its index information, the first functional entity may determine the first AI / ML related information based on the locally stored AI / ML related information and the index information of the first AI / ML related information; wherein, the locally stored AI / ML related information is the AI / ML related information obtained by the first functional entity from the third functional entity and stored locally.

[0278] In one possible implementation, the first AI / ML related information may be obtained from the third functional entity using index information of the first AI / ML related information in the following manner:

[0279] The first functional entity sends a request message to the third functional entity, where the request message carries index information of the first AI / ML related information. The third functional entity sends a reply message to the first functional entity based on the index information of the first AI / ML related information, where the reply message carries the file address of the first AI / ML related information. The first functional entity obtains the first AI / ML related information based on the file address of the first AI / ML related information.

[0280] The first functional entity can use the first AI / ML related information to perform local AI calculation, processing and / or decision-making, for example, access and mobility management based on terminal mobility characteristics (such as paging, switching, redirection, guiding the terminal to perform cell reselection, roaming guidance, slice reselection, etc.); data bearer resource scheduling, load balancing and / or energy efficiency control based on terminal communication characteristics; selecting target cells and / or target access network equipment based on access network performance or load or energy consumption characteristics, selecting core network functional entities (such as AMF entities, SMF entities) or slice instances based on core network performance or load or energy consumption characteristics, etc.

[0281] S405: The first functional entity uses the first AI / ML related information to communicate with the second functional entity.

[0282] In one possible implementation, when the first AI / ML related information is first data information, the first functional entity may use the first data information to perform AI / ML model training to obtain a second AI / ML model; or, use the first data information for analysis to obtain second analysis information; and use the second AI / ML model or the second analysis information to communicate and process with the second functional entity.

[0283] In one possible implementation, when the first AI / ML related information is first AI / ML model information, the first functional entity may determine the first AI / ML model based on the first AI / ML model information, and use the first AI / ML model to perform communication processing with the second functional entity.

[0284] In a possible implementation, when the first AI / ML related information is first analysis information, the first functional entity directly uses the first analysis information to perform communication processing with the second functional entity.

[0285] Communication processing may include access layer processing (such as physical layer processing, radio resource control, etc.), non-access layer processing (such as terminal access and mobility management, session management, policy control, etc.) or application layer processing (such as establishing, modifying or releasing application layer connections or sessions with terminals, adjusting terminal applications or service parameters, etc.).

[0286] When the first functional entity and the second functional entity perform access layer processing, the first functional entity and the second functional entity meet one of the following conditions:

[0287] The first functional entity is a first access network device, and the second functional entity is a second terminal;

[0288] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0289] The first functional entity is a first terminal, and the second functional entity is a second access network device.

[0290] Specifically, the first functional entity performs access layer communication processing with the second functional entity through the first AI / ML related information, including: for example, when the first AI / ML related information is the physical layer codec model information of the second functional entity, the first functional entity uses the physical layer codec model to perform physical layer encoding on the data sent to the second functional entity, and the second functional entity uses the physical layer codec model to perform physical layer decoding on the data received from the first functional entity. For another example, when the first AI / ML related information is the communication service related data of the second functional entity (such as the data transmission rate and transmission interval of the second terminal at a specific time or in a specific area) or related analysis information (such as prediction information of the data transmission rate and transmission interval of the second terminal in the future at a specific time or in a specific area), the first functional entity can determine the resource allocation and scheduling strategy for communication with the second functional entity based on the first AI / ML related information, thereby allocating and configuring wireless resources based on this strategy to communicate with the second functional entity.

[0291] When the first functional entity and the second functional entity perform non-access layer or application layer processing, the first functional entity and the second functional entity meet one of the following conditions:

[0292] The first functional entity is a first core network functional entity, and the second functional entity is a second terminal;

[0293] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0294] The first functional entity is a first terminal, and the second functional entity is a second core network functional entity.

[0295] Specifically, the first functional entity performs non-access layer or application layer communication processing with the second functional entity through the first AI / ML related information, including: for example, when the first AI / ML related information is data, models or analysis information related to the mobility (such as moving range, moving trajectory, moving speed) of the second functional entity, the first functional entity can determine the preferred network and / or access technology (such as a mobile communication network using satellite access, or proximity service communication (i.e., direct or relay communication between adjacent terminals, not through the network)) when communicating with the second functional entity based on the information, and use the preferred network and / or access technology to communicate with the second terminal. For another example, when the first AI / ML related information is data, models or analysis information related to the communication (such as idle, busy or congested time periods) of the second functional entity, the first functional entity can determine the time period for establishing a connection or session with the second functional entity based on the information, thereby avoiding connection or service establishment failure and improving service experience and performance.

[0296] In the embodiment shown in FIG4 , the second functional entity sends first AI / ML-related information to the third functional entity, where the first AI / ML-related information includes at least one of the following: first data information, first AI / ML model information, and first analysis information used for AI / ML model training or analysis; receives index information of the first AI / ML-related information sent by the third functional entity, where the index information of the first AI / ML-related information is used to indicate the first AI / ML-related information stored by the second functional entity in the third functional entity; the first functional entity obtains the index information of the first AI / ML-related information of the second functional entity, obtains the first AI / ML-related information based on the index information of the first AI / ML-related information, and uses the first AI / ML-related information to communicate with the second functional entity. By interacting with the second and third functional entities, the first functional entity can quickly obtain the first AI / ML-related information, thereby improving the accuracy and real-time performance of its communication with the second functional entity.

[0297] Based on the embodiment shown in FIG4 , the following describes in detail how functional entities interact to obtain index information of the first AI / ML related information according to different situations.

[0298] FIG5 is a second flow chart of a communication method provided by an embodiment of the present disclosure. As shown in FIG5 , the method includes:

[0299] S501. A second functional entity sends first AI / ML related information to a third functional entity.

[0300] S502: The third functional entity determines index information of the first AI / ML related information.

[0301] S503: The third functional entity sends index information of the first AI / ML related information to the second functional entity.

[0302] It should be noted that the execution process of S501 to S503 can refer to the execution process of S401 to S402, and will not be repeated here.

[0303] S504. The second functional entity sends a first message to the first functional entity. The first message carries index information of the first AI / ML related information.

[0304] In other words, the first functional entity receives the first message sent by the second functional entity.

[0305] The first message may be used to request the first functional entity to perform communication processing with the second functional entity using the first AI / ML related information corresponding to the index information.

[0306] In a possible implementation, the first message may be a reply message or a request message.

[0307] Exemplarily, the first may be an air interface reply message or an air interface request message sent by the access network device to the terminal, or a reply or request message related to terminal access and mobility management sent by the access network device to the AMF entity, or a reply or request message related to terminal session management sent by the access network device to the SMF entity, or a reply or request message related to terminal policy control sent by the access network device to the PCF entity.

[0308] If the first functional entity has previously sent a request message or command message related to the first AI / ML related information to the second functional entity, the second functional entity may send a reply message (first message) to the first functional entity, so that the first functional entity uses the first AI / ML related information to communicate with the second functional entity.

[0309] If the first functional entity has not previously sent a request message or command message related to the first AI / ML related information to the second functional entity, the second functional entity may send a request message (first message) to the first functional entity, where the request message carries the index information of the first AI / ML related information; if the first functional entity confirms that the first AI / ML related information corresponding to the index information will be used for communication processing, a confirmation message or a reply message may be sent to the second functional entity, where the confirmation message or the reply message carries the index information of the first AI / ML related information, to confirm to the second functional entity that the first AI / ML related information will be used for communication processing.

[0310] S505: The first functional entity obtains the first AI / ML related information according to the index information of the first AI / ML related information.

[0311] It should be noted that the execution process of S505 can refer to the execution process of S404, and will not be repeated here.

[0312] S506: The first functional entity sends index information of the first AI / ML related information to the second functional entity.

[0313] In other words, the second functional entity receives index information of the first AI / ML related information sent by the first functional entity.

[0314] The first functional entity may send the index information of the first AI / ML related information to the second functional entity in order to confirm to the second functional entity that it will use the first AI / ML related information corresponding to the index information for communication processing.

[0315] S507 : The first functional entity uses the first AI / ML related information to communicate with the second functional entity.

[0316] It should be noted that the execution process of S507 can refer to the execution process of S403, and will not be repeated here.

[0317] In the embodiment shown in Figure 5, the first functional entity obtains the first AI / ML related information based on the request of the second functional entity, and communicates and processes with the second functional entity based on the first AI / ML related information, thereby improving the accuracy and real-time performance of the communication and processing between the first functional entity and the second functional entity.

[0318] FIG6 is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG6 , the method includes:

[0319] S601. The second functional entity sends first AI / ML related information to the third functional entity.

[0320] S602: The third functional entity determines index information of the first AI / ML related information.

[0321] S603: The third functional entity sends index information of the first AI / ML related information to the second functional entity.

[0322] It should be noted that the execution process of S601 to S603 can refer to the execution process of S401 to S402, and will not be repeated here.

[0323] S604: The first functional entity sends a second message to the second functional entity, where the second message is used to request first AI / ML related information.

[0324] In other words, the second functional entity receives the second message sent by the first functional entity.

[0325] The second message may carry the index information of the first AI / ML related information, or may not carry the index information of the first AI / ML related information.

[0326] S605. The second functional entity sends a first message to the first functional entity. The first message carries index information of the first AI / ML related information.

[0327] S606: The first functional entity obtains the first AI / ML related information according to the index information of the first AI / ML related information.

[0328] S607: The first functional entity sends index information of the first AI / ML related information to the second functional entity.

[0329] S608: The first functional entity uses the first AI / ML related information to communicate with the second functional entity.

[0330] It should be noted that the execution process of S605 to S608 can refer to the execution process of S504 to S507, and will not be repeated here.

[0331] In the embodiment shown in FIG6 , the first functional entity actively requests to obtain first AI / ML related information, and performs communication processing with the second functional entity based on the first AI / ML related information, thereby improving the accuracy and real-time performance of the communication processing between the first functional entity and the second functional entity.

[0332] FIG7 is a fourth flow chart of a communication method provided by an embodiment of the present disclosure. As shown in FIG7 , the method includes:

[0333] S701. The second functional entity sends first AI / ML related information to the third functional entity.

[0334] S702: The third functional entity determines index information of the first AI / ML related information.

[0335] S703: The third functional entity sends index information of the first AI / ML related information to the second functional entity.

[0336] It should be noted that the execution process of S701 to S703 can refer to the execution process of S401 to S402, and will not be repeated here.

[0337] S704: The first functional entity sends a second message to the second functional entity based on the trigger condition.

[0338] In one possible implementation, the trigger condition may include any of the following:

[0339] 1) Determine whether the data or AI / ML model of the second functional entity is required for AI / ML model training or analysis.

[0340] That is, the first functional entity determines that the data of the second functional entity is needed to train the AI / ML model, or the first functional entity determines that the data of the second functional entity is needed for analysis, or the first functional entity determines that the AI / ML model of the second functional entity is needed to train the AI / ML model local to the first functional entity (such as federated learning, transfer learning), or the first functional entity determines that the AI / ML model of the second functional entity is needed for analysis.

[0341] 2) A third message sent by the second functional entity is received, where the third message is used to request processing by the access layer, non-access layer, or application layer.

[0342] The third message may be an air interface message, such as an access request message or a measurement report message.

[0343] 3) A fourth message sent by a fourth functional entity is received, where the fourth message is used to request access layer, non-access layer, or application layer control of the second functional entity.

[0344] For example, the fourth message may be a request or command message related to terminal access and mobility management sent by an AMF entity; it may also be a request or command message related to terminal session management sent by an SMF entity; it may also be a request or command message related to terminal policy (such as proximity service-related policy) control sent by a PCF entity.

[0345] S705. The second functional entity sends a first message to the first functional entity. The first message carries index information of the first AI / ML related information.

[0346] S706. The first functional entity obtains the first AI / ML related information according to the index information of the first AI / ML related information.

[0347] S707: The first functional entity sends index information of the first AI / ML related information to the second functional entity.

[0348] S708: The first functional entity uses the first AI / ML related information to communicate with the second functional entity.

[0349] It should be noted that the execution process of S705 to S708 can refer to the execution process of S504 to S507, and will not be repeated here.

[0350] In the embodiment shown in FIG7 , the first functional entity actively requests to obtain first AI / ML related information based on a trigger condition, and performs communication processing with the second functional entity based on the first AI / ML related information, thereby improving the accuracy and real-time performance of the communication processing between the first functional entity and the second functional entity.

[0351] FIG8 is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG8 , the method includes:

[0352] S801. A first functional entity sends a sixth message to a third functional entity, where the sixth message is used to request a subscription to first AI / ML related information of a second functional entity.

[0353] In other words, the third functional entity receives the sixth message sent by the first functional entity.

[0354] In a possible implementation, the sixth message may carry at least one of the following information:

[0355] 1) Identification information of the second functional entity.

[0356] When the second functional entity is a terminal, the identification information of the second functional entity may be the identification of one or more terminals, the terminal group identification or the information of a terminal that meets certain conditions, wherein the information of the terminal that meets certain conditions may be slice information or slice instance information (indicating all / any terminals served by a specified slice or slice instance), or area information (indicating all / any terminals within a specified area, and the specified area may be a tracking area, a cell area or a geographic area).

[0357] For example, if the identification information of the second functional entity is information of cell 1, it means that the first functional entity requests the third functional entity to subscribe to the first AI / ML related information of all terminals in cell 1.

[0358] 2) Subscription type.

[0359] The subscription type may be used to indicate the type of AI / ML related information subscription requested.

[0360] Subscription types can be categorized into model data type, analysis data type, model type, and analysis information type.

[0361] For example, when the subscription type is model data type, it indicates a request to subscribe to data information used for AI / ML model training.

[0362] 3) Description of the AI / ML-related information requested for subscription.

[0363] The description information may include at least one of the following:

[0364] Data type (indicating the data related to the terminal's functions or characteristics, such as data on terminal mobility characteristics or movement trajectory, data on terminal communication characteristics or communication performance, or data on terminal energy consumption or energy efficiency), data collection time (segment), data collection area, and data accuracy;

[0365] AI / ML model type (indicating the function / feature the model is used for, such as a model of terminal mobility characteristics or trajectory, a model of terminal communication characteristics or performance, or a model of terminal energy consumption or energy efficiency), and model accuracy (indicating the accuracy of the analysis results obtained using the model);

[0366] Analysis identification (indicating the analysis information of which function / characteristic, such as analysis information of terminal mobility characteristics or mobility trajectory, analysis information of terminal communication characteristics or communication performance, terminal energy consumption or energy efficiency analysis information), analysis information confidence level (applicable to prediction information), applicable time of analysis information, applicable area of ​​analysis information, etc.

[0367] 4) Information of the first functional entity.

[0368] For example, the information of the first functional entity may include an identifier or address of the first functional entity, a subscription association identifier, an address for receiving subscription information (or notification / report), and the like.

[0369] S802. The second functional entity sends first AI / ML related information to the third functional entity.

[0370] S803: The third functional entity determines index information of the first AI / ML related information.

[0371] S804. The third functional entity sends index information of the first AI / ML related information to the second functional entity.

[0372] It should be noted that the execution process of S902 to S904 can refer to the execution process of S401 to S402, and will not be repeated here.

[0373] S805. The third functional entity sends index information of the first AI / ML related information to the first functional entity.

[0374] In other words, the first functional entity receives index information of the first AI / ML related information sent by the third functional entity.

[0375] The third functional entity may send index information of the latest stored first AI / ML related information to the first functional entity.

[0376] In one possible implementation, when the third functional entity sends index information of the first AI / ML-related information to the first functional entity, it may also send first description information to the first functional entity. For the relevant description of the first description information, refer to S401 and will not be repeated here.

[0377] S806. The first functional entity sends a second message to the second functional entity. The second message carries index information of the first AI / ML related information.

[0378] The second message may be used to instruct or request the second functional entity to use the first AI / ML related information corresponding to the index information to perform communication processing.

[0379] The first functional entity may send the second message to the second functional entity based on the triggering condition. For the description of the triggering condition, refer to S704 and will not be repeated here.

[0380] S807. The second functional entity sends a first message to the first functional entity. The first message carries index information of the first AI / ML related information.

[0381] The first message may be used to confirm that it will use the first AI / ML related information corresponding to the index information for communication processing.

[0382] S808. The first functional entity obtains the first AI / ML related information according to the index information of the first AI / ML related information.

[0383] S809. The first functional entity sends index information of the first AI / ML related information to the second functional entity.

[0384] S810: The first functional entity uses the first AI / ML related information to communicate with the second functional entity.

[0385] It should be noted that the execution process of S808 to S810 can refer to the execution process of S505 to S507, and will not be repeated here.

[0386] In the embodiment shown in FIG8 , the first functional entity proactively requests to obtain first AI / ML related information, and performs communication processing with the second functional entity based on the first AI / ML related information, thereby improving the accuracy and real-time performance of the communication processing between the first functional entity and the second functional entity.

[0387] FIG9 is a sixth flow chart of a communication method provided by an embodiment of the present disclosure. As shown in FIG9 , taking the second functional entity as a terminal and the first functional entity as an access network device as an example, the method includes:

[0388] S901. The terminal sends first AI / ML related information to a third functional entity.

[0389] S902. The third functional entity determines index information of the first AI / ML related information.

[0390] S903. The third functional entity sends index information of the first AI / ML related information to the terminal.

[0391] It should be noted that the execution process of S901 to S903 can refer to the execution process of S401 to S402, and will not be repeated here.

[0392] S904. The fifth functional entity sends a fifth message to the first functional entity. The fifth message carries index information of the first AI / ML related information and / or information of the third functional entity. The fifth message is used to request switching of the access network device.

[0393] The fifth functional entity is the access network device that previously served the terminal, and may also be referred to as a source access network device.

[0394] The first functional entity is an access network device that will serve the terminal, and may also be referred to as a target access network device.

[0395] The fifth functional entity may determine to initiate handover according to the measurement report of the terminal and analysis information of the mobility characteristics of the terminal.

[0396] In addition to carrying handover-related information such as handover target cell information and wireless resource information of the terminal in the fifth functional entity, the fifth message also carries first description information. For the relevant description of the first description information, please refer to S401 and will not be repeated here.

[0397] The information of the third functional entity may include an identifier, an address, and routing information (eg, a fully qualified domain name (FQDN) or a uniform resource identifier (URI)) of the third functional entity.

[0398] S905: The first functional entity sends a handover request confirmation message to the fifth functional entity.

[0399] S906. The terminal switches from the fifth functional entity to the first functional entity.

[0400] S907: The first functional entity uses the index information of the first AI / ML related information and / or the information of the third functional entity to obtain the first AI / ML related information from the third functional entity.

[0401] If the fifth message carries information about the third functional entity, the first functional entity can determine the third functional entity based on the information about the third functional entity, and then use the index information of the first AI / ML related information to obtain the first AI / ML related information from the third functional entity. The acquisition method can be found in the relevant description in S404 and will not be repeated here.

[0402] If the fifth message does not carry the information of the third functional entity, the first functional entity may determine the third functional entity through other means.

[0403] S908. The first functional entity sends index information of the first AI / ML related information to the terminal.

[0404] S909. The first functional entity uses the first AI / ML related information to perform communication processing with the terminal.

[0405] It should be noted that the execution process of S908 to S909 can refer to the execution process of S506 to S507, and will not be repeated here.

[0406] In the embodiment shown in Figure 9, when the terminal switches the access network device, the source access network device provides the target access network device with the index information of the first AI / ML related information and / or the information of the third functional entity. The target access network device quickly obtains the first AI / ML related information from the third functional entity according to the index information of the first AI / ML related information, and communicates and processes with the terminal according to the first AI / ML related information, thereby improving the accuracy and real-time performance of the communication processing between the access network device and the terminal.

[0407] FIG10 is a flow chart of the communication method according to an embodiment of the present disclosure. As shown in FIG10 , taking the second functional entity as a terminal and the first functional entity as an access network device as an example, the method includes:

[0408] S1001. The terminal sends first AI / ML related information to a third functional entity.

[0409] S1002. The third functional entity determines index information of the first AI / ML related information.

[0410] S1003. The third functional entity sends index information of the first AI / ML related information to the terminal.

[0411] S1004. The fifth functional entity sends a fifth message to the first functional entity. The fifth message carries index information of the first AI / ML related information and / or information of the third functional entity. The fifth message is used to request switching of the access network device.

[0412] S1005. The first functional entity sends a handover request confirmation message to the fifth functional entity.

[0413] S1006. The terminal switches from the fifth functional entity to the first functional entity.

[0414] It should be noted that the execution process of S1001 to S1006 can refer to the execution process of S901 to S906, which will not be repeated here.

[0415] S1007. The first functional entity sends a sixth message to the third functional entity, where the sixth message is used to request subscription to first AI / ML related information.

[0416] If the fifth message carries information of the third functional entity, the first functional entity may determine the third functional entity according to the information of the third functional entity, and send the sixth message to the third functional entity.

[0417] If the fifth message does not carry the information of the third functional entity, the first functional entity may determine the third functional entity through other means.

[0418] The content carried by the sixth message can be found in the description of S801 and will not be repeated here.

[0419] S1008. The third functional entity sends index information of the first AI / ML related information to the first functional entity.

[0420] The third functional entity may send index information of the latest stored first AI / ML related information to the first functional entity.

[0421] That is, the index information of the first AI / ML related information sent by the third functional entity to the first functional entity may be the same as or different from the index information of the first AI / ML related information carried by the fifth message; when the two are different, the index information of the first AI / ML related information sent by the third functional entity to the first functional entity may be used.

[0422] S1009: The first functional entity uses index information of the first AI / ML related information to obtain the first AI / ML related information from the third functional entity.

[0423] S1010. A first functional entity sends index information of first AI / ML related information to a terminal.

[0424] S1011. The first functional entity uses the first AI / ML related information to communicate with the terminal.

[0425] It should be noted that the execution process of S1009 to S1011 can refer to the execution process of S907 to S909, and will not be repeated here.

[0426] In the embodiment shown in Figure 10, when the terminal switches the access network device, the source access network device provides the target access network device with the index information of the first AI / ML related information and / or the information of the third functional entity. The target access network device can subscribe to the first AI / ML related information from the third functional entity based on the information of the third functional entity, and can quickly obtain the latest first AI / ML related information from the third functional entity based on the index information of the latest first AI / ML related information fed back by the third functional entity, and communicate and process with the terminal based on the latest first AI / ML related information, thereby improving the accuracy and real-time performance of communication and processing between the access network device and the terminal.

[0427] FIG11 is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG11 , taking the second functional entity as a terminal and the first functional entity as an access network device as an example, the method includes:

[0428] S1101. The terminal sends first AI / ML related information to a third functional entity.

[0429] S1102. The third functional entity determines index information of the first AI / ML related information.

[0430] S1103. The third functional entity sends index information of the first AI / ML related information to the terminal.

[0431] It should be noted that the execution process of S1101 to S1103 can refer to the execution process of S401 to S402, and will not be repeated here.

[0432] S1104. The terminal sends a connection recovery request message to the first functional entity.

[0433] The terminal is in a radio resource control (RRC) inactive state.

[0434] S1105: The first functional entity sends a request message for obtaining the terminal context to the fifth functional entity.

[0435] The fifth functional entity is the access network device that previously served the terminal, and may also be referred to as a source access network device.

[0436] The first functional entity is an access network device currently serving the terminal, and may also be referred to as a target access network device.

[0437] S1106. The fifth functional entity sends a fifth message to the first functional entity. The fifth message carries index information of the first AI / ML related information and / or information of the third functional entity. The fifth message is used to provide terminal context.

[0438] In addition to carrying the context of the terminal (terminal capabilities, information about sessions established by the terminal, and other information), the fifth message also carries first description information. For relevant descriptions of the first description information, see S401 and will not be repeated here.

[0439] S1107. The first functional entity sends a connection recovery confirmation message to the terminal.

[0440] S1108. The first functional entity uses index information of the first AI / ML related information and / or information of the third functional entity to obtain the first AI / ML related information from the third functional entity.

[0441] S1109. The first functional entity sends index information of first AI / ML related information to the terminal.

[0442] S1110. The first functional entity uses first AI / ML related information to communicate with the terminal.

[0443] It should be noted that the execution process of S1108 to S1110 can refer to the execution process of S907 to S909, and will not be repeated here.

[0444] In the embodiment shown in Figure 11, during the connection recovery process, the source access network device provides the target access network device with index information of the first AI / ML related information and / or information of the third functional entity. The target access network device quickly obtains the first AI / ML related information from the third functional entity based on the index information of the first AI / ML related information, and communicates and processes with the terminal based on the first AI / ML related information, thereby improving the accuracy and real-time performance of communication processing between the access network device and the terminal.

[0445] FIG12 is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG12 , taking the second functional entity as a terminal and the first functional entity as an access network device as an example, the method includes:

[0446] S1201. The terminal sends first AI / ML related information to a third functional entity.

[0447] S1202. The third functional entity determines index information of the first AI / ML related information.

[0448] S1203. The third functional entity sends index information of the first AI / ML related information to the terminal.

[0449] S1204. The terminal sends a connection recovery request message to the first functional entity.

[0450] S1205: The first functional entity sends a request message for obtaining the terminal context to the fifth functional entity.

[0451] S1206. The fifth functional entity sends a fifth message to the first functional entity. The fifth message carries index information of the first AI / ML related information and / or information of the third functional entity. The fifth message is used to provide terminal context.

[0452] S1207. The first functional entity sends a connection recovery confirmation message to the terminal.

[0453] It should be noted that the execution process of S1201 to S1207 can refer to the execution process of S1101 to S1107, and will not be repeated here.

[0454] S1208. The first functional entity sends a sixth message to the third functional entity, where the sixth message is used to request subscription to the first AI / ML related information of the second functional entity.

[0455] S1209. The third functional entity sends index information of the first AI / ML related information to the first functional entity.

[0456] S1210: The first functional entity uses index information of the first AI / ML related information to obtain the first AI / ML related information from the third functional entity.

[0457] S1211. The first functional entity sends index information of first AI / ML related information to the terminal.

[0458] S1212. The first functional entity uses the first AI / ML related information to perform communication processing with the terminal.

[0459] It should be noted that the execution process of S1208 to S1212 can refer to the execution process of S1007 to S1011, and will not be repeated here.

[0460] In the embodiment shown in Figure 12, during the connection recovery process, the source access network device provides the target access network device with index information of the first AI / ML related information and / or information of the third functional entity. The target access network device can subscribe to the first AI / ML related information from the third functional entity based on the information of the third functional entity, and can quickly obtain the latest first AI / ML related information from the third functional entity based on the index information of the latest first AI / ML related information fed back by the third functional entity, and communicate and process with the terminal based on the latest first AI / ML related information, thereby improving the accuracy and real-time performance of communication and processing between the access network device and the terminal.

[0461] To facilitate understanding, several specific examples are given below to illustrate the various steps in the communication method disclosed herein.

[0462] Example 1: Take the access network device as the first functional entity, the terminal as the second functional entity, and the ADRF as the third functional entity as an example.

[0463] S1. The access network device sends a sixth message to the ADRF entity. The sixth message is used to request subscription to first AI / ML related information of the terminal. The first AI / ML related information includes at least one of the following: terminal data, terminal AI / ML model, and terminal analysis information.

[0464] The sixth message carries the following information:

[0465] a) Terminal identification, which may include: identification of one or more terminals, terminal group identification, information of terminals that meet certain conditions (such as slice or slice instance information, indicating all / any terminals served by a specified slice or slice instance; or area information (such as tracking area, cell or geographic area), indicating all / any terminals in a specified area).

[0466] b) a subscription type, indicating a request to subscribe to at least one of the terminal's data, the terminal's AI / ML model, and the terminal's analysis information;

[0467] c) Description or filtering information of the first AI / ML related information of the terminal requesting subscription, for example:

[0468] - Data type (indicating what function / feature of the terminal the data is related to, such as data on terminal mobility characteristics or movement trajectory, data on terminal communication characteristics or communication performance, terminal energy consumption or energy efficiency data), data collection time (segment), data collection area, and data accuracy;

[0469] - AI / ML model type (indicating the function / feature the model is used for, such as a model of terminal mobility characteristics or movement trajectory, a model of terminal communication characteristics or communication performance, or a model of terminal energy consumption or energy efficiency), and model accuracy (indicating the accuracy of the analysis results obtained using the model);

[0470] -Analysis identification (indicating the analysis information of which function / characteristic, such as analysis information of terminal mobility characteristics or mobility trajectory, analysis information of terminal communication characteristics or communication performance, terminal energy consumption or energy efficiency analysis information), confidence level of analysis information (applicable to prediction information), applicable duration of analysis information, applicable area of ​​analysis information, etc.

[0471] d) Information about the access network device, such as the ID or address of the access network device, subscription association identifier, address for receiving subscription information ( / notification or report), etc.

[0472] S2. The terminal sends the first AI / ML related information of the terminal to the AF entity through the user plane (application layer) (or sends it to the AMF entity through the NAS layer message).

[0473] Optionally, the message sending the first AI / ML related information of the terminal may also include descriptive information corresponding to the first AI / ML related information, and the descriptive information includes at least one of the following: terminal data description information, terminal model description information, and terminal analysis description information. Specifically, the terminal data description information may include the following information: data collection time (segment), sampling frequency, and data accuracy. The terminal model description information may include the following information: model generation time, model identifier, model type (indicating what function / feature the model is used for, such as a model of terminal mobility characteristics or mobility trajectory, a model of terminal communication characteristics or communication performance, or a terminal energy consumption or energy efficiency model), and model accuracy (characterizing the accuracy of the analysis results obtained using the model). The terminal analysis description information may include the following information: generation time of the analysis information, statistics or predictions, analysis identifier (indicating what function / feature the analysis information is for, such as analysis information of terminal mobility characteristics or mobility trajectory, analysis information of UE communication characteristics or communication performance, or terminal energy consumption or energy efficiency analysis information), confidence (applicable to prediction information), applicable duration, usage area, etc.

[0474] S3. The AF entity (or AMF entity) sends the first AI / ML related information (and corresponding description information) of the terminal to the ADRF entity. The ADRF stores the first AI / ML related information (and corresponding description information) of the terminal and assigns different index values ​​to different first AI / ML related information. For example, the terminal data is assigned a terminal data index value (UE data index, UDI), the terminal AI / ML model is assigned a terminal model index value (UE model index, UMI), and the terminal analysis information is assigned a terminal analysis index value (UE analytics index, UAI).

[0475] S4. The ADRF entity sends a reply message to the AF entity (or AMF entity), which contains at least one of UDI, UMI and UAI.

[0476] Optionally, the AF entity sends a reply message to the terminal through the user plane (application layer) or the AMF entity, which includes at least one of UDI, UMI and UAI.

[0477] S5. The ADRF entity sends a message to the access network device, notifying the access network device of the latest stored first AI / ML related information of the terminal. The message carries at least one of the UDI, UMI, and UAI, as well as corresponding description information.

[0478] The access network device (directly or through AMF) requests the ADRF entity of the core network to subscribe to the first AI / ML related information of at least one of the terminal, access network device and core network device, and receives an update notification of the first AI / ML related information of at least one of the terminal, access network device and core network device from the ADRF entity.

[0479] Example 2: Take the access network device / terminal as the first functional entity, the terminal / access network device / core network functional entity as the second functional entity, and the ADRF as the third functional entity as an example.

[0480] In this example, if the first functional entity is access network device 2 and the second functional entity is access network device 1, access network device 1 and access network device 2 can be the centralized control unit (for example, gNB-CU) and distributed unit (for example, gNB-DU) of the access network, respectively, or access network device 1 and access network device 2 are two access network devices accessed by the terminal through dual access, or access network device 1 and access network device 2 are two adjacent access network devices.

[0481] In this example, if the first functional entity is terminal 2 and the second functional entity is terminal 1, terminal 1 and terminal 2 can be two different terminals. Terminal 2 can only obtain the first AI / ML-related information of terminal 1 if terminal 1 authorizes terminal 2 to use the first AI / ML-related information of terminal 1. In particular, terminal 1 and terminal 2 can be different terminals of the same user (e.g., a mobile phone, computer, IoT device, etc.).

[0482] S1. Access network device 2 (or terminal 2) may send a sixth message to the ADRF entity.

[0483] (1) The sixth message may be used to request a subscription to first AI / ML related information of terminal 1, where the first AI / ML related information includes at least one of the following: data of terminal 1, an AI / ML model of terminal 1, and analysis information of terminal 1.

[0484] (2) The sixth message may also be used to request a subscription to first AI / ML related information of access network device 1, where the first AI / ML related information includes at least one of the following: data of access network device 1, an AI / ML model of access network device 1, and analysis information of access network device 1.

[0485] At this time, the sixth message may carry the following information:

[0486] a) Terminal identification, which may include: identification of one or more terminals, terminal group identification, information of terminals that meet certain conditions (such as slice or slice instance information, indicating all / any terminals served by a specified slice or slice instance; or area information (such as tracking area, cell or geographic area), indicating all / any terminals in a specified area).

[0487] b) a subscription type, used to indicate a request to subscribe to at least one of the data of access network device 1, the AI / ML model of access network device 1, and the analysis information of access network device 1;

[0488] c) Description or filtering information of the first AI / ML related information of the access network device 1 requested for subscription, for example:

[0489] - Data type (indicating what function / feature the data is related to, such as data on terminal mobility characteristics or movement trajectory, terminal communication characteristics or communication performance data, access network equipment performance data, access network equipment load or resource usage data, access network equipment energy consumption or energy efficiency), data collection time (segment), data collection area, and data accuracy;

[0490] - AI / ML model type (indicating the function / feature the model is used for, such as a model of terminal mobility characteristics or trajectory, a model of terminal communication characteristics or communication performance, a model of access network equipment performance, a model of access network equipment load or resource usage, or a model of access network equipment energy consumption or energy efficiency), and model accuracy (indicating the accuracy of the analysis results obtained using the model);

[0491] -Analysis identification (indicating the analysis information of which function / characteristic, such as analysis information of terminal mobility characteristics or mobility trajectory, analysis information of terminal communication characteristics or communication performance, access network equipment performance analysis information, access network equipment load or resource usage analysis information, access network equipment energy consumption or energy efficiency analysis information), analysis information confidence level (applicable to prediction information), analysis information applicability duration, analysis information applicability area, etc.

[0492] d) Information of the access network device 2, such as the ID or address of the access network device 2, subscription association identifier, address for receiving subscription information ( / notification or report), etc.

[0493] (3) The sixth message may also be used to request a subscription to first AI / ML related information of a core network function entity, where the first AI / ML related information includes at least one of the following: data of the core network function entity, an AI / ML model of the core network function entity, and analysis information of the core network function entity.

[0494] At this time, the sixth message may carry the following information:

[0495] a) Terminal identification, which may include: identification of one or more terminals, terminal group identification, information of terminals that meet certain conditions (such as slice or slice instance information, indicating all / any terminals served by a specified slice or slice instance; or area information (such as tracking area, cell or geographic area), indicating all / any terminals in a specified area).

[0496] b) a subscription type, used to indicate a request to subscribe to at least one of the core network function entity's data, the core network function entity's AI / ML model, and the core network function entity's analysis information;

[0497] c) Description or filtering information of the first AI / ML related information of the core network functional entity requested for subscription, for example:

[0498] - Data type (indicating what function / feature the data is related to, such as data on terminal mobility characteristics or movement trajectory, terminal communication characteristics or communication performance data, core network performance data, core network load or resource usage data, core network energy consumption or energy efficiency), data collection time (segment), data collection area, and data accuracy;

[0499] - AI / ML model type (indicating the function / feature the model is used for, such as a model of terminal mobility characteristics or trajectory, a model of terminal communication characteristics or communication performance, a core network performance model, a core network load or resource usage model, or a core network energy consumption or energy efficiency model), and model accuracy (indicating the accuracy of the analysis results obtained using the model);

[0500] -Analysis identification (indicating the analysis information of which function / characteristic, such as analysis information of terminal mobility characteristics or mobility trajectory, analysis information of terminal communication characteristics or communication performance, core network performance analysis information, core network load or resource usage analysis information, core network energy consumption or energy efficiency analysis information), analysis information confidence level (applicable to prediction information), analysis information applicability duration, analysis information applicable area, etc.

[0501] d) Information of the access network device 2, such as the ID or address of the access network device 2, subscription association identifier, address for receiving subscription information ( / notification or report), etc.

[0502] S2. Terminal 1 sends the first AI / ML-related information of terminal 1 to access network device 1 via an access layer message. Optionally, the access layer message may also include descriptive information corresponding to the first AI / ML-related information of terminal 1. The descriptive information includes at least one of the following: terminal 1 data description information, terminal 1 model description information, and terminal 1 analysis description information. For details of the descriptive information, see step S1 of Example 1.

[0503] S3. Access network device 1 sends the first AI / ML related information (and corresponding description information) of terminal 1 to the ADRF entity. The ADRF entity stores the first AI / ML related information (and corresponding description information) of terminal 1 and assigns index values ​​to different first AI / ML related information. For example, a UDI is assigned to the data of terminal 1, a UMI is assigned to the AI / ML model of terminal 1, and a UAI is assigned to the analysis information of terminal 1.

[0504] S4. The ADRF entity sends a reply message to the access network device 1, which includes at least one of the UDI, UMI and UAI.

[0505] Note: The ADRF entity can send messages to or receive messages from the access network device through the AMF entity, or directly send messages to or receive messages from the access network device (for example: define a reference point or service interface between the access network device and the ADRF entity so that the access network device and the ADRF entity can communicate directly).

[0506] S5. Optionally, the access network device 1 sends a reply message to the terminal 1, which includes at least one of UDI, UMI and UAI.

[0507] S6. The ADRF entity sends a message to access network device 2 (or terminal 2), notifying access network device 2 (or terminal 2) of the latest stored first AI / ML related information of terminal 1. The message carries at least one of UDI, UMI, and UAI, as well as corresponding description information.

[0508] S7. Access network device 1 sends the first AI / ML-related information of access network device 1 and descriptive information corresponding to the first AI / ML-related information of access network device 1 to the ADRF entity. The descriptive information includes at least one of the following: data description information of access network device 1, model description information of access network device 1, and analysis description information of access network device 1. For details of the descriptive information, see step S1 of Example 1.

[0509] ADRF stores the first AI / ML related information (and corresponding description information) of access network device 1, and assigns index values ​​to different first AI / ML related information, for example, assigning an access network data index value (AN data index, ADI) to the data of access network device 1, assigning an access network model index value (AN model index, AMI) to the AI / ML model of access network device 1, and assigning an access network analysis index value (AN analytics index, AAI) to the analysis information of access network device 1.

[0510] S8. The ADRF entity sends a reply message to the access network device 1, which includes at least one of ADI, AMI and AAI.

[0511] S9. The ADRF entity sends a message to access network device 2 (or terminal 2), notifying access network device 2 (or terminal 2) of the latest stored information related to the first AI / ML of access network device 1. The message carries at least one of ADI, AMI, and AAI, as well as corresponding description information.

[0512] S10. A core network function entity (e.g., an AMF entity, an SMF entity, an UPF entity, a PCF entity, a NEF entity, an AF entity, an NWDAF entity, or a DCCF entity) sends first AI / ML-related information and corresponding description information of the core network function entity to an ADRF entity; the description information includes at least one of the following: core network function entity data description information, core network function entity model description information, and core network function entity analysis description information. For specific content of the description information, see step S1 of Example 1.

[0513] ADRF stores the first AI / ML related information (and corresponding description information) of the core network functional entity, and assigns index values ​​to different first AI / ML related information, for example, assigning a core network data index value (CN data index, CDI) to the data of the core network functional entity, assigning a core network model index value (CN model index, CMI) to the AI / ML model of the core network functional entity, and assigning a core network analysis index value (CN analytics index, CAI) to the analysis information of the core network functional entity.

[0514] Optionally, the ADRF entity sends a reply message to the core network function entity, which includes at least one of CDI, CMI and CAI.

[0515] S11. The ADRF entity sends a message to access network device 2 (or terminal 2), notifying access network device 2 (or terminal 2) of the newly stored first AI / ML-related information of the core network function entity. The message carries at least one of CDI, CMI, and CAI, as well as corresponding description information.

[0516] S12. Access network device 2 (or terminal 2) may subsequently send a request message to the ADRF entity, requesting first AI / ML related information of at least one of terminal 1, access network device 1, and core network function entity 1. The request message may carry at least one of UDI, UMI, UAI, ADI, AMI, AAI, CDI, CMI, and CAI.

[0517] S13. The ADRF entity sends a reply message to access network device 2 (or terminal 2), providing access network device 2 (or terminal 2) with first AI / ML-related information of at least one of terminal 1, access network device 1, and core network function entity 1. The reply message carries the file address of the first AI / ML-related information of at least one of terminal 1, access network device 1, and core network function entity 1.

[0518] S14. Access network device 2 (or terminal 2) obtains first AI / ML related information of at least one of terminal 1, access network device 1 and core network function entity 1 based on the received file address of the first AI / ML related information of at least one of terminal 1, access network device 1 and core network function entity 1.

[0519] S15. Access network device 2 (or terminal 2) uses the first AI / ML related information of at least one of terminal 1, access network device 1 and core network functional entity 1 to perform local AI calculation, processing and / or decision-making, such as access and mobility management based on terminal mobility characteristics (such as paging, switching, redirection, guiding the terminal to perform cell reselection, roaming guidance, slice reselection, etc.), data bearer resource scheduling based on terminal communication characteristics, load balancing and / or energy efficiency control, target cell and / or target access network device selection based on access network device performance or load or energy consumption characteristics, core network functional entity (such as AMF, SMF) selection or slice instance selection based on core network performance or load or energy consumption characteristics, etc.

[0520] The access network device or terminal uses the first AI / ML related information of at least one of the terminal, the access network device and the core network functional entity to perform local AI calculation, processing and / or decision-making.

[0521] Example 3: Take the access network device as the first functional entity, the terminal as the second functional entity, and the ADRF as the third functional entity as an example.

[0522] S1. The access network device determines, based on a trigger condition, that first AI / ML-related information of a terminal needs to be obtained. The first AI / ML-related information includes at least one of the following: data of the terminal, an AI / ML model of the terminal, and analysis information of the terminal.

[0523] Trigger conditions include any of the following:

[0524] 1a. The access network device receives a request or command message related to terminal access and mobility management sent by the AMF entity;

[0525] 1b. The access network device receives a request or command message related to terminal session management sent by the SMF entity;

[0526] 1c. The access network device receives a terminal policy (eg, proximity service-related policy) control-related request or command message sent by the PCF entity;

[0527] 1d. The access network device receives an air interface message sent by the terminal, such as an access request or measurement report;

[0528] 1e. The access network device determines that it needs the terminal's data to train the AI / ML model;

[0529] 1f. The access network device determines that the terminal's AI / ML model is needed to train the access network device's local AI / ML model (e.g., federated learning, transfer learning).

[0530] In the above cases 1a-1d, the access network device determines that it needs analysis information of the terminal to determine the terminal behavior and assist in decision-making (eg, access or resource management of the terminal).

[0531] S2. The access network device sends a request message to the ADRF entity, requesting first AI / ML-related information of the terminal. The request message carries an index value of the first AI / ML-related information of the terminal requested by the access network device, for example, at least one of UDI, UMI, and UAI.

[0532] S3. The ADRF entity sends a reply message to the access network device, providing the access network device with the first AI / ML-related information of the terminal. The reply message carries the file address of the first AI / ML-related information of the terminal requested by the access network device, for example, at least one of the file address of the terminal data, the file address of the terminal AI / ML model, and the file address of the terminal analysis information.

[0533] S4. The access network device obtains the first AI / ML related information of the terminal according to the file address of the first AI / ML related information of the terminal in the reply message.

[0534] The access network device uses the first AI / ML related information of the terminal to perform local AI calculation, processing and / or decision-making, such as access and mobility management based on terminal mobility characteristics (such as paging, handover, redirection, guiding the terminal to perform cell reselection, roaming guidance, slice reselection, etc.), data bearer resource scheduling based on terminal communication characteristics, load balancing and / or energy efficiency control, etc.

[0535] If the access network device determines to send an air interface message to the terminal based on step S4 to perform access layer control on the terminal, the following steps S5 to S7 are executed:

[0536] S5. The access network device sends an air interface reply or request message to the terminal, which carries the index value of the first AI / ML related information of the terminal. For example, if it carries the UMI, it instructs the terminal and the access network device to use the AI / ML model corresponding to the UMI for access layer processing (e.g., encoding and decoding, channel control, and resource configuration).

[0537] S6. The terminal sends an air interface confirmation or reply message to the access network device, which carries the UMI, to confirm to the access network device that the AI / ML model corresponding to the UMI will be used for access layer processing.

[0538] S7. The AI / ML model corresponding to the UMI is used between the terminal and the access network device for access layer processing.

[0539] If the access network device determines to send a terminal-related reply or request to the core network based on step 4 or step 7, it performs one of the following operations:

[0540] S8a. The access network device sends a reply or request message related to terminal access and mobility management to the AMF entity;

[0541] S8b, the access network device sends a reply or request message related to terminal session management to the SMF entity;

[0542] S8c: The access network device sends a terminal policy (eg, proximity service-related policy) control-related reply or request message to the PCF entity.

[0543] Based on the trigger condition, the access network device requests the ADRF entity to obtain first AI / ML related information of at least one of the terminal, the access network device, and the core network device.

[0544] Example 4: Take the access network device as the first functional entity, the terminal as the second functional entity, and the ADRF as the third functional entity as an example.

[0545] S1. The terminal sends an air interface message to the access network device, which carries the UMI, requesting that the AI / ML model corresponding to the UMI be used between the terminal and the access network device for access layer processing (such as encoding and decoding, channel control, and resource configuration).

[0546] S2. After the access network device receives the air interface message, if the access network device does not locally store the UMI or the AI / ML model of the terminal corresponding to the UMI, the access network device decides to request the AI / ML model of the terminal from the ADRF entity ( / other core network functional entity).

[0547] S3. The access network device sends a request message to the ADRF entity, carrying the UMI, to request the AI / ML model of the terminal corresponding to the UMI.

[0548] S4. The ADRF entity sends a reply message to the access network device. The reply message carries the file address of the AI / ML model of the terminal corresponding to the UMI.

[0549] S5. The access network device obtains the AI / ML model corresponding to the UMI based on the file address of the terminal's AI / ML model in the reply message. The access network device sends an air interface reply message to the terminal, which carries the UMI, confirming to the terminal that the AI / ML model corresponding to the UMI will be used for access layer processing.

[0550] S6. The AI / ML model corresponding to the UMI is used between the terminal and the access network device for access layer processing.

[0551] The terminal and the access network device use the information (index / identification) of the AI / ML model stored in the ADRF entity to negotiate the AI / ML model to be used.

[0552] The embodiments of the present disclosure provide a method for an access network device to obtain first AI / ML-related information of at least one of a terminal, an access network device, and a core network device through an ADRF entity, thereby improving the efficiency of the access network device in obtaining input data, models, and analysis information from the terminal and other network elements, improving the accuracy and real-time performance of local AI calculations, processing, or decision-making by the access network device, and realizing AI-based access layer processing using a unified model between the access network device and the terminal.

[0553] FIG13 is a schematic diagram of the structure of a communication device 1300 provided in an embodiment of the present disclosure. As shown in FIG13 , the device 1300 includes: a memory 1310 , a transceiver 1320 , and a processor 1330 .

[0554] The memory 1310 is used to store computer programs. The transceiver 1320 is used to send and receive data under the control of the processor 1330. The processor 1330 is used to read the computer program stored in the memory 1310 and perform the following operations:

[0555] Obtain index information of first artificial intelligence (AI) / machine learning (ML) related information of the second functional entity, where the index information of the first AI / ML related information is used to indicate the first AI / ML related information stored by the second functional entity in the third functional entity, where the first AI / ML related information includes at least one of the following: first data information used for AI / ML model training or analysis, first AI / ML model information, and first analysis information;

[0556] Obtaining first AI / ML related information according to index information of the first AI / ML related information;

[0557] Use the first AI / ML related information to communicate with the second functional entity.

[0558] In one embodiment, the processor 1330 is specifically configured to perform the following operations:

[0559] A first message sent by the second functional entity is received, where the first message carries index information of first AI / ML related information.

[0560] In one embodiment, the processor 1330 is further configured to perform the following operations:

[0561] A second message is sent to the second functional entity, where the second message is used to request the first AI / ML related information.

[0562] In one embodiment, the processor 1330 is specifically configured to perform the following operations:

[0563] Based on any one of the following triggering conditions, a second message is sent to the second functional entity:

[0564] Determine that the data or AI / ML model of the second functional entity is required for AI / ML model training or analysis;

[0565] receiving a third message sent by the second functional entity, where the third message is used to request processing by the access layer, the non-access layer, or the application layer;

[0566] A fourth message sent by a fourth functional entity is received, where the fourth message is used to request access layer, non-access layer, or application layer control of the second functional entity.

[0567] In one embodiment, the processor 1330 is specifically configured to perform the following operations:

[0568] Obtain the first AI / ML related information from the third functional entity using the index information of the first AI / ML related information; or

[0569] The first AI / ML related information is determined based on the locally stored AI / ML related information and the index information of the first AI / ML related information. The locally stored AI / ML related information is the AI / ML related information obtained and stored locally by the first functional entity from the third functional entity. The locally stored AI / ML related information includes the index information of the first AI / ML related information.

[0570] In one embodiment, the processor 1330 is specifically configured to perform the following operations:

[0571] receiving a fifth message sent by the fifth functional entity, where the fifth message carries index information of the first AI / ML related information and / or information of the third functional entity, and the fifth message is used to request handover or to provide terminal context;

[0572] Obtain the first AI / ML related information from the third functional entity using the index information of the first AI / ML related information and / or the information of the third functional entity;

[0573] The second functional entity is the second terminal, the first functional entity is the functional entity currently serving the second functional entity, and the fifth functional entity is the functional entity that previously served the second functional entity.

[0574] In one embodiment, the processor 1330 is further configured to perform the following operations:

[0575] Send a sixth message to the third functional entity, where the sixth message is used to request a subscription to the first AI / ML related information of the second functional entity;

[0576] Receive index information of the first AI / ML related information sent by the third functional entity.

[0577] In one embodiment, the processor 1330 is further configured to perform the following operations:

[0578] Send index information of the first AI / ML related information to the second functional entity.

[0579] In one embodiment, the first AI / ML related information is first data information, and the processor 1330 is specifically configured to perform the following operations:

[0580] Using the first data information to train an AI / ML model to obtain a second AI / ML model; or using the first data information to perform analysis to obtain second analysis information;

[0581] Use the second AI / ML model or the second analysis information to communicate with the second functional entity.

[0582] In one embodiment, the first AI / ML related information is first AI / ML model information, and the processor 1330 is specifically configured to perform the following operations:

[0583] determining a first AI / ML model based on the first AI / ML model information;

[0584] The first AI / ML model is used to communicate with the second functional entity.

[0585] In one embodiment, the processor 1330 is specifically configured to perform the following operations:

[0586] Perform access layer processing with the second functional entity;

[0587] The first functional entity and the second functional entity meet one of the following conditions:

[0588] The first functional entity is a first access network device, and the second functional entity is a second terminal;

[0589] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0590] The first functional entity is a first terminal, and the second functional entity is a second access network device.

[0591] In one embodiment, the processor 1330 is specifically configured to perform the following operations:

[0592] Performing non-access layer or application layer processing with the second functional entity;

[0593] The first functional entity and the second functional entity meet one of the following conditions:

[0594] The first functional entity is a first core network functional entity, and the second functional entity is a second terminal;

[0595] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0596] The first functional entity is a first terminal, and the second functional entity is a second core network functional entity.

[0597] In FIG13 , the bus architecture may include any number of interconnected buses and bridges, specifically one or more processors represented by processor 1330 and various circuits of memory represented by memory 1310. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface. The transceiver 1320 may be a plurality of components, i.e., a transmitter and a receiver, providing a unit for communicating with various other devices over a transmission medium, such as a wireless channel, a wired channel, an optical cable, and the like. The processor 1330 is responsible for managing the bus architecture and general processing, and the memory 1310 may store data used by the processor 1330 when performing operations.

[0598] Optionally, the processor 1330 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a complex programmable logic device (CPLD), and the processor may also adopt a multi-core architecture.

[0599] When the communication device 1300 is a terminal, it may also include a user interface. For different terminals, the user interface may also be an interface capable of connecting external or internal devices. The connected devices include but are not limited to a keypad, display, speaker, microphone, joystick, etc.

[0600] The processor 1330 is configured to execute all method steps of the first functional entity of the embodiment of the present disclosure according to the obtained executable instructions by calling the computer program stored in the memory 1310. The processor 1330 and the memory 1310 can also be arranged physically separately.

[0601] It should be noted here that the above-mentioned communication device 1300 provided by the present disclosure can implement all the method steps implemented by the first functional entity in the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.

[0602] FIG14 is a schematic diagram of the structure of a communication device 1400 provided in an embodiment of the present disclosure. As shown in FIG14 , the device 1400 includes: a memory 1410 , a transceiver 1420 , and a processor 1430 .

[0603] The memory 1410 is used to store computer programs. The transceiver 1420 is used to send and receive data under the control of the processor 1430. The processor 1430 is used to read the computer program stored in the memory 1410 and perform the following operations:

[0604] Sending first artificial intelligence (AI) / machine learning (ML) related information to the third functional entity, where the first AI / ML related information includes at least one of the following: first data information for AI / ML model training or analysis, first AI / ML model information, and first analysis information;

[0605] receiving index information of the first AI / ML related information sent by the third functional entity, where the index information of the first AI / ML related information is used to indicate the first AI / ML related information stored in the third functional entity by the second functional entity;

[0606] Use the first AI / ML related information to communicate with the first functional entity.

[0607] In one embodiment, the processor 1430 is further configured to perform the following operations:

[0608] A first message is sent to the first functional entity, where the first message carries index information of the first AI / ML related information.

[0609] In one embodiment, the processor 1430 is specifically configured to perform the following operations:

[0610] Performing access layer processing with the first functional entity;

[0611] The first functional entity and the second functional entity meet one of the following conditions:

[0612] The first functional entity is a first access network device, and the second functional entity is a second terminal;

[0613] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0614] The first functional entity is a first terminal, and the second functional entity is a second access network device.

[0615] In one embodiment, the processor 1430 is specifically configured to perform the following operations:

[0616] Performing non-access layer or application layer processing with the first functional entity;

[0617] The first functional entity and the second functional entity meet one of the following conditions:

[0618] The first functional entity is a first core network functional entity, and the second functional entity is a second terminal;

[0619] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0620] The first functional entity is a first terminal, and the second functional entity is a second core network functional entity.

[0621] In FIG14 , the bus architecture may include any number of interconnected buses and bridges, specifically one or more processors represented by processor 1430 and various circuits of memory represented by memory 1410. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface. The transceiver 1420 may be a plurality of components, i.e., a transmitter and a receiver, providing a unit for communicating with various other devices on a transmission medium, such as a wireless channel, a wired channel, an optical cable, and the like. The processor 1430 is responsible for managing the bus architecture and general processing, and the memory 1410 may store data used by the processor 1430 when performing operations.

[0622] Optionally, the processor 1430 may be a CPU, an ASIC, an FPGA, or a CPLD, and the processor may also adopt a multi-core architecture.

[0623] When the communication device 1400 is a terminal, it may also include a user interface. For different terminals, the user interface may also be an interface that can connect to required external or internal devices. The connected devices include but are not limited to a keypad, display, speaker, microphone, joystick, etc.

[0624] The processor 1430 is configured to execute all method steps of the second functional entity of the embodiment of the present disclosure according to the obtained executable instructions by calling the computer program stored in the memory 1410. The processor 1430 and the memory 1410 may also be arranged physically separately.

[0625] It should be noted here that the above-mentioned communication device 1400 provided by the present disclosure can implement all the method steps implemented by the second functional entity in the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.

[0626] FIG15 is a schematic diagram of the structure of a communication device 1500 provided in an embodiment of the present disclosure. As shown in FIG15 , the device 1500 includes:

[0627] A first acquiring unit 1510 is configured to acquire index information of first artificial intelligence (AI) / machine learning (ML) related information of a second functional entity, where the index information of the first AI / ML related information indicates the first AI / ML related information stored by the second functional entity in a third functional entity, where the first AI / ML related information includes at least one of the following: first data information used for AI / ML model training or analysis, first AI / ML model information, and first analysis information;

[0628] The second acquiring unit 1520 is configured to acquire the first AI / ML related information according to the index information of the first AI / ML related information;

[0629] The processing unit 1530 is configured to perform communication processing with the second functional entity using the first AI / ML related information.

[0630] In one embodiment, the first acquiring unit 1510 is specifically configured to:

[0631] A first message sent by the second functional entity is received, where the first message carries index information of first AI / ML related information.

[0632] In one embodiment, the apparatus 1500 further includes a first sending unit, which is configured to:

[0633] A second message is sent to the second functional entity, where the second message is used to request the first AI / ML related information.

[0634] In one embodiment, the first sending unit is specifically configured to:

[0635] Based on any one of the following triggering conditions, a second message is sent to the second functional entity:

[0636] Determine that the data or AI / ML model of the second functional entity is required for AI / ML model training or analysis;

[0637] receiving a third message sent by the second functional entity, where the third message is used to request processing by the access layer, the non-access layer, or the application layer;

[0638] A fourth message sent by a fourth functional entity is received, where the fourth message is used to request access layer, non-access layer, or application layer control of the second functional entity.

[0639] In one embodiment, the second obtaining unit 1520 is specifically configured to:

[0640] Obtain the first AI / ML related information from the third functional entity using the index information of the first AI / ML related information; or

[0641] The first AI / ML related information is determined based on the locally stored AI / ML related information and the index information of the first AI / ML related information. The locally stored AI / ML related information is the AI / ML related information obtained and stored locally by the first functional entity from the third functional entity. The locally stored AI / ML related information includes the index information of the first AI / ML related information.

[0642] In one embodiment, the first acquiring unit 1510 is specifically configured to:

[0643] receiving a fifth message sent by the fifth functional entity, where the fifth message carries index information of the first AI / ML related information and / or information of the third functional entity, and the fifth message is used to request handover or to provide terminal context;

[0644] The second acquiring unit 1520 is specifically configured to:

[0645] Obtain the first AI / ML related information from the third functional entity using the index information of the first AI / ML related information and / or the information of the third functional entity;

[0646] The second functional entity is the second terminal, the first functional entity is the functional entity currently serving the second functional entity, and the fifth functional entity is the functional entity that previously served the second functional entity.

[0647] In one embodiment, the apparatus 1500 further includes a second sending unit and a receiving unit, wherein:

[0648] The second sending unit is configured to send a sixth message to the third functional entity, where the sixth message is used to request a subscription to the first AI / ML related information of the second functional entity;

[0649] The receiving unit is configured to receive index information of the first AI / ML related information sent by the third functional entity.

[0650] In one embodiment, the first sending unit is further configured to:

[0651] Send index information of the first AI / ML related information to the second functional entity.

[0652] In one embodiment, the first AI / ML related information is first data information, and the processing unit 1530 is specifically configured to:

[0653] Using the first data information to train an AI / ML model to obtain a second AI / ML model; or using the first data information to perform analysis to obtain second analysis information;

[0654] Use the second AI / ML model or the second analysis information to communicate with the second functional entity.

[0655] In one embodiment, the first AI / ML related information is first AI / ML model information, and the processing unit 1530 is specifically configured to:

[0656] determining a first AI / ML model based on the first AI / ML model information;

[0657] The first AI / ML model is used to communicate with the second functional entity.

[0658] In one embodiment, the processing unit 1530 is specifically configured to:

[0659] Perform access layer processing with the second functional entity;

[0660] The first functional entity and the second functional entity meet one of the following conditions:

[0661] The first functional entity is a first access network device, and the second functional entity is a second terminal;

[0662] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0663] The first functional entity is a first terminal, and the second functional entity is a second access network device.

[0664] In one embodiment, the processing unit 1530 is specifically configured to:

[0665] Performing non-access layer or application layer processing with the second functional entity;

[0666] The first functional entity and the second functional entity meet one of the following conditions:

[0667] The first functional entity is a first core network functional entity, and the second functional entity is a second terminal;

[0668] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0669] The first functional entity is a first terminal, and the second functional entity is a second core network functional entity.

[0670] It should be noted here that the above-mentioned communication device 1500 provided by the present disclosure can implement all the method steps implemented by the first functional entity in the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.

[0671] FIG16 is a schematic diagram of the structure of a communication device 1600 provided in an embodiment of the present disclosure. As shown in FIG16 , the device 1600 includes:

[0672] A sending unit 1610 is configured to send first artificial intelligence (AI) / machine learning (ML) related information to a third functional entity, where the first AI / ML related information includes at least one of the following: first data information for AI / ML model training or analysis, first AI / ML model information, and first analysis information;

[0673] The receiving unit 1620 is configured to receive index information of the first AI / ML related information sent by the third functional entity, where the index information of the first AI / ML related information is used to indicate the first AI / ML related information stored in the third functional entity by the second functional entity;

[0674] The processing unit 1630 is configured to perform communication processing with the first functional entity using the first AI / ML related information.

[0675] In one implementation, the sending unit 1610 is further configured to:

[0676] A first message is sent to the first functional entity, where the first message carries index information of the first AI / ML related information.

[0677] In one embodiment, the processing unit 1630 is specifically configured to:

[0678] Performing access layer processing with the first functional entity;

[0679] The first functional entity and the second functional entity meet one of the following conditions:

[0680] The first functional entity is a first access network device, and the second functional entity is a second terminal;

[0681] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0682] The first functional entity is a first terminal, and the second functional entity is a second access network device.

[0683] In one embodiment, the processing unit 1630 is specifically configured to:

[0684] Performing non-access layer or application layer processing with the first functional entity;

[0685] The first functional entity and the second functional entity meet one of the following conditions:

[0686] The first functional entity is a first core network functional entity, and the second functional entity is a second terminal;

[0687] The first functional entity is a first terminal, and the second functional entity is a second terminal;

[0688] The first functional entity is a first terminal, and the second functional entity is a second core network functional entity.

[0689] It should be noted here that the above-mentioned communication device 1600 provided by the present disclosure can implement all the method steps implemented by the second functional entity in the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.

[0690] It should be noted that the division of units in the embodiments of the present disclosure is schematic and is merely a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional units in the various embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0691] 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 processor-readable storage medium. Based on this understanding, the technical solution of the present disclosure is essentially or the part that contributes to the prior art 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 a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present disclosure. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0692] An embodiment of the present disclosure further provides a non-transitory readable storage medium, which stores a computer program. The computer program is used to enable a processor to execute all method steps of the first functional entity in the above method embodiment.

[0693] An embodiment of the present disclosure further provides a non-transitory readable storage medium, which stores a computer program. The computer program is used to enable a processor to execute all method steps of the second functional entity in the above method embodiment.

[0694] Non-transitory readable storage media can be any available media or data storage devices that can be accessed by a computer, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO)), optical storage (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (such as ROMs, EPROMs, EEPROMs, non-volatile memories (NAND FLASH), solid-state drives (SSDs)), etc.

[0695] The embodiments of the present disclosure further provide a computer program product, including a computer program, which implements the method described in any one of the above method embodiments when the computer program is executed by a processor.

[0696] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, systems, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) containing computer-usable program code.

[0697] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer-executable instructions. These computer-executable instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0698] These processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the processor-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0699] These processor-executable instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0700] Obviously, those skilled in the art may make various changes and modifications to the present disclosure without departing from the spirit and scope of the present disclosure. Thus, if these modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is intended to include these modifications and variations.

Claims

1. A communication method, characterized in that: Applied to the first functional entity, the method comprises: Obtain index information of first artificial intelligence AI / machine learning ML related information of the second functional entity, where the index information of the first AI / ML related information is used to indicate the first AI / ML related information stored by the second functional entity in the third functional entity, where the first AI / ML related information includes at least one of the following: first data information for AI / ML model training or analysis, first AI / ML model information, and first analysis information; Acquire the first AI / ML related information according to the index information of the first AI / ML related information; Use the first AI / ML related information to communicate with the second functional entity.

2. The method according to claim 1, characterized in that The obtaining of index information of the first AI / ML related information of the second functional entity includes: A first message sent by the second functional entity is received, where the first message carries index information of the first AI / ML related information.

3. The method according to claim 2, characterized in that Before the receiving the first message sent by the second functional entity, the method further includes: Send a second message to the second functional entity, where the second message is used to request the first AI / ML related information.

4. The method according to claim 3, characterized in that The sending the second message to the second functional entity comprises: Sending the second message to the second function entity based on any one of the following triggering conditions: Determining that data or an AI / ML model of the second functional entity is required for AI / ML model training or analysis; receiving a third message sent by the second functional entity, where the third message is used to request access layer, non-access layer or application layer processing; A fourth message sent by a fourth functional entity is received, where the fourth message is used to request access layer, non-access layer, or application layer control of the second functional entity.

5. The method according to any one of claims 1 to 4, characterized in that: The acquiring the first AI / ML related information according to the index information of the first AI / ML related information includes: Using the index information of the first AI / ML related information, obtaining the first AI / ML related information from a third functional entity; or, The first AI / ML related information is determined according to locally stored AI / ML related information and index information of the first AI / ML related information, where the locally stored AI / ML related information is AI / ML related information obtained by the first functional entity from the third functional entity and stored locally, and the locally stored AI / ML related information includes index information of the first AI / ML related information.

6. The method according to claim 1, characterized in that The obtaining of index information of the first AI / ML related information of the second functional entity includes: receiving a fifth message sent by a fifth functional entity, where the fifth message carries index information of the first AI / ML related information and / or information of the third functional entity, and the fifth message is used to request switching or to provide terminal context; The acquiring the first AI / ML related information according to the index information of the first AI / ML related information includes: Using the index information of the first AI / ML related information and / or the information of the third functional entity, from the third functional entity The entity can obtain the first AI / ML related information; The second functional entity is a second terminal, the first functional entity is a functional entity currently serving the second functional entity, and the fifth functional entity is a functional entity that previously served the second functional entity.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: Sending a sixth message to the third functional entity, where the sixth message is used to request a subscription to the first AI / ML related information of the second functional entity; Receive index information of the first AI / ML related information sent by the third functional entity.

8. The method according to claim 1, characterized in that Before using the first AI / ML related information to communicate with the second functional entity, the method further includes: Send index information of the first AI / ML related information to the second functional entity.

9. The method according to claim 1, characterized in that: The first AI / ML related information is the first data information, and the using the first AI / ML related information to perform communication processing with the second functional entity includes: Using the first data information to perform AI / ML model training to obtain a second AI / ML model; or using the first data information to perform analysis to obtain second analysis information; Use the second AI / ML model or the second analysis information to communicate with the second functional entity.

10. The method according to claim 1, characterized in that The first AI / ML related information is first AI / ML model information, and the using the first AI / ML related information to perform communication processing with the second functional entity includes: Determining a first AI / ML model according to the first AI / ML model information; Use the first AI / ML model to communicate with the second functional entity.

11. The method according to any one of claims 1 to 10, characterized in that: The performing communication processing with the second functional entity includes: performing access layer processing with the second functional entity; The first functional entity and the second functional entity meet one of the following conditions: The first functional entity is a first access network device, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second access network device.

12. The method according to any one of claims 1 to 10, characterized in that: The performing communication processing with the second functional entity includes: Performing non-access layer or application layer processing with the second functional entity; The first functional entity and the second functional entity meet one of the following conditions: The first functional entity is a first core network functional entity, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second core network functional entity.

13. A communication method, characterized in that: Applied to the second functional entity, the method comprises: Sending first artificial intelligence AI / machine learning ML related information to the third functional entity, where the first AI / ML related information includes at least one of the following: first data information for AI / ML model training or analysis, first AI / ML model information, and first analysis information; receiving index information of the first AI / ML related information sent by the third functional entity, where the index information of the first AI / ML related information is used to indicate the first AI / ML related information stored in the third functional entity by the second functional entity; Use the first AI / ML related information to communicate with the first functional entity.

14. The method according to claim 13, characterized in that After receiving the index information of the first AI / ML related information sent by the third functional entity, and before using the first AI / ML related information to perform communication processing with the first functional entity, the method further includes: A first message is sent to the first functional entity, where the first message carries index information of the first AI / ML related information.

15. The method according to claim 13 or 14, characterized in that The communication processing with the first functional entity includes: Performing access layer processing with the first functional entity; The first functional entity and the second functional entity meet one of the following conditions: The first functional entity is a first access network device, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second access network device.

16. The method according to claim 13 or 14, characterized in that The communication processing with the first functional entity includes: Performing non-access layer or application layer processing with the first functional entity; The first functional entity and the second functional entity meet one of the following conditions: The first functional entity is a first core network functional entity, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second core network functional entity.

17. A communication device, characterized in that: Applied to a first functional entity, the device comprises: A first acquisition unit is used to acquire index information of first artificial intelligence AI / machine learning ML related information of a second functional entity, where the index information of the first AI / ML related information is used to indicate the first AI / ML related information stored by the second functional entity in a third functional entity, where the first AI / ML related information includes at least one of the following: first data information for AI / ML model training or analysis, first AI / ML model information, and first analysis information; a second acquiring unit, configured to acquire the first AI / ML related information according to the index information of the first AI / ML related information; A processing unit is configured to perform communication processing with the second functional entity using the first AI / ML related information.

18. The device according to claim 17, characterized in that The first acquisition unit is specifically used to: A first message sent by the second functional entity is received, where the first message carries index information of the first AI / ML related information.

19. The device according to claim 18, characterized in that The device further includes a first sending unit, wherein the first sending unit is configured to: Send a second message to the second functional entity, where the second message is used to request the first AI / ML related information.

20. The device according to claim 19, characterized in that The first sending unit is specifically configured to: Sending the second message to the second function entity based on any one of the following triggering conditions: Determining that data or an AI / ML model of the second functional entity is required for AI / ML model training or analysis; receiving a third message sent by the second functional entity, where the third message is used to request access layer, non-access layer or application layer processing; A fourth message sent by a fourth functional entity is received, where the fourth message is used to request access layer, non-access layer, or application layer control of the second functional entity.

21. The device according to any one of claims 17 to 20, characterized in that The second acquisition unit is specifically used to: Using the index information of the first AI / ML related information, obtaining the first AI / ML related information from a third functional entity; or, The first AI / ML related information is determined according to locally stored AI / ML related information and index information of the first AI / ML related information, where the locally stored AI / ML related information is AI / ML related information obtained by the first functional entity from the third functional entity and stored locally, and the locally stored AI / ML related information includes index information of the first AI / ML related information.

22. The device according to claim 17, characterized in that The first acquisition unit is specifically used to: receiving a fifth message sent by a fifth functional entity, where the fifth message carries index information of the first AI / ML related information and / or information of the third functional entity, and the fifth message is used to request switching or to provide terminal context; The second acquisition unit is specifically used to: obtaining the first AI / ML related information from the third functional entity using the index information of the first AI / ML related information and / or the information of the third functional entity; The second functional entity is a second terminal, the first functional entity is a functional entity currently serving the second functional entity, and the fifth functional entity is a functional entity that previously served the second functional entity.

23. The device according to any one of claims 17 to 22, characterized in that The device further comprises a second sending unit and a receiving unit, wherein: The second sending unit is configured to send a sixth message to the third functional entity, where the sixth message is used to request a subscription to the first AI / ML related information of the second functional entity; The receiving unit is configured to receive index information of the first AI / ML related information sent by the third functional entity.

24. The device according to claim 17, characterized in that The first sending unit is further used for: Send index information of the first AI / ML related information to the second functional entity.

25. The device according to claim 17, characterized in that The first AI / ML related information is the first data information, and the processing unit is specifically configured to: Using the first data information to perform AI / ML model training to obtain a second AI / ML model; or using the first data information to perform analysis to obtain second analysis information; Use the second AI / ML model or the second analysis information to communicate with the second functional entity.

26. The device according to claim 17, characterized in that The first AI / ML related information is first AI / ML model information, and the processing unit is specifically configured to: Determining a first AI / ML model according to the first AI / ML model information; Use the first AI / ML model to communicate with the second functional entity.

27. The device according to any one of claims 17 to 26, characterized in that The processing unit is specifically used for: performing access layer processing with the second functional entity; The first functional entity and the second functional entity meet one of the following conditions: The first functional entity is a first access network device, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second access network device.

28. The device according to any one of claims 17 to 26, characterized in that The processing unit is specifically used for: Performing non-access layer or application layer processing with the second functional entity; The first functional entity and the second functional entity meet one of the following conditions: The first functional entity is a first core network functional entity, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second core network functional entity.

29. A communication device, characterized in that: Applied to the second functional entity, the device includes: A sending unit, configured to send first artificial intelligence AI / machine learning ML related information to a third functional entity, where the first AI / ML related information includes at least one of the following: first data information for AI / ML model training or analysis, first AI / ML model information, and first analysis information; a receiving unit, configured to receive index information of the first AI / ML related information sent by the third functional entity, where the index information of the first AI / ML related information is used to indicate the first AI / ML related information stored in the third functional entity by the second functional entity; A processing unit is configured to perform communication processing with a first functional entity using the first AI / ML related information.

30. The device according to claim 29, characterized in that The sending unit is further used for: A first message is sent to the first functional entity, where the first message carries index information of the first AI / ML related information.

31. The device according to claim 29 or 30, characterized in that The processing unit is specifically used for: Performing access layer processing with the first functional entity; The first functional entity and the second functional entity meet one of the following conditions: The first functional entity is a first access network device, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second access network device.

32. The device according to claim 29 or 30, characterized in that The processing unit is specifically used for: Performing non-access layer or application layer processing with the first functional entity; The first functional entity and the second functional entity meet one of the following conditions: The first functional entity is a first core network functional entity, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second core network functional entity.

33. A communication device, characterized in that: Applied to the first functional entity, the device includes a memory, a transceiver, a processor, The memory is used to store a computer program; the transceiver is used to send and receive data under the control of the processor; the processor is used to read the computer program in the memory and perform the following operations: Obtain index information of first artificial intelligence AI / machine learning ML related information of the second functional entity, where the index information of the first AI / ML related information is used to indicate the first AI / ML related information stored by the second functional entity in the third functional entity, where the first AI / ML related information includes at least one of the following: first data information for AI / ML model training or analysis, first AI / ML model information, and first analysis information; Acquire the first AI / ML related information according to the index information of the first AI / ML related information; Use the first AI / ML related information to communicate with the second functional entity.

34. The device according to claim 33, characterized in that The processor is specifically configured to perform the following operations: A first message sent by the second functional entity is received, where the first message carries index information of the first AI / ML related information.

35. The device according to claim 34, characterized in that The processor is further configured to perform the following operations: Send a second message to the second functional entity, where the second message is used to request the first AI / ML related information.

36. The device according to claim 35, characterized in that The processor is specifically configured to perform the following operations: Sending the second message to the second function entity based on any one of the following triggering conditions: Determining that data or an AI / ML model of the second functional entity is required for AI / ML model training or analysis; receiving a third message sent by the second functional entity, where the third message is used to request access layer, non-access layer or application layer processing; A fourth message sent by a fourth functional entity is received, where the fourth message is used to request access layer, non-access layer, or application layer control of the second functional entity.

37. The device according to any one of claims 33 to 36, characterized in that The processor is specifically configured to perform the following operations: Using the index information of the first AI / ML related information, obtaining the first AI / ML related information from a third functional entity; or, The first AI / ML related information is determined according to locally stored AI / ML related information and index information of the first AI / ML related information, where the locally stored AI / ML related information is AI / ML related information obtained by the first functional entity from the third functional entity and stored locally, and the locally stored AI / ML related information includes index information of the first AI / ML related information.

38. The device according to claim 33, characterized in that The processor is specifically configured to perform the following operations: receiving a fifth message sent by a fifth functional entity, where the fifth message carries index information of the first AI / ML related information and / or information of the third functional entity, and the fifth message is used to request switching or to provide terminal context; obtaining the first AI / ML related information from the third functional entity using the index information of the first AI / ML related information and / or the information of the third functional entity; The second functional entity is a second terminal, the first functional entity is a functional entity currently serving the second functional entity, and the fifth functional entity is a functional entity that previously served the second functional entity.

39. The device according to any one of claims 33 to 38, characterized in that The processor is further configured to perform the following operations: Sending a sixth message to the third functional entity, where the sixth message is used to request a subscription to the first AI / ML related information of the second functional entity; Receive index information of the first AI / ML related information sent by the third functional entity.

40. The device according to claim 33, characterized in that The processor is further configured to perform the following operations: Send index information of the first AI / ML related information to the second functional entity.

41. The device according to claim 33, characterized in that The first AI / ML related information is the first data information, and the processor is specifically configured to perform the following operations: Using the first data information to perform AI / ML model training to obtain a second AI / ML model; or using the first data information to perform analysis to obtain second analysis information; Use the second AI / ML model or the second analysis information to communicate with the second functional entity.

42. The device according to claim 33, characterized in that The first AI / ML related information is first AI / ML model information, and the processor is specifically configured to perform the following operations: Determining a first AI / ML model according to the first AI / ML model information; Use the first AI / ML model to communicate with the second functional entity.

43. The device according to any one of claims 33 to 42, characterized in that The processor is specifically configured to perform the following operations: performing access layer processing with the second functional entity; The first functional entity and the second functional entity meet one of the following conditions: The first functional entity is a first access network device, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second access network device.

44. The device according to any one of claims 33 to 42, characterized in that The processor is specifically configured to perform the following operations: Performing non-access layer or application layer processing with the second functional entity; The first functional entity and the second functional entity meet one of the following conditions: The first functional entity is a first core network functional entity, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second core network functional entity.

45. A communication device, characterized in that: Applied to the second functional entity, the device includes a memory, a transceiver, a processor, The memory is used to store a computer program; the transceiver is used to send and receive data under the control of the processor; the processor is used to read the computer program in the memory and perform the following operations: Sending first artificial intelligence AI / machine learning ML related information to the third functional entity, where the first AI / ML related information includes at least one of the following: first data information for AI / ML model training or analysis, first AI / ML model information, and first analysis information; receiving index information of the first AI / ML related information sent by the third functional entity, where the index information of the first AI / ML related information is used to indicate the first AI / ML related information stored in the third functional entity by the second functional entity; Use the first AI / ML related information to communicate with the first functional entity.

46. ​​The device according to claim 45, characterized in that The processor is further configured to perform the following operations: A first message is sent to the first functional entity, where the first message carries index information of the first AI / ML related information.

47. The device according to claim 45 or 46, characterized in that The processor is specifically configured to perform the following operations: Performing access layer processing with the first functional entity; The first functional entity and the second functional entity meet one of the following conditions: The first functional entity is a first access network device, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second access network device.

48. The device according to claim 45 or 46, characterized in that The processor is specifically configured to perform the following operations: Performing non-access layer or application layer processing with the first functional entity; The first functional entity and the second functional entity meet one of the following conditions: The first functional entity is a first core network functional entity, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second terminal; The first functional entity is a first terminal, and the second functional entity is a second core network functional entity.

49. A non-transitory readable storage medium, characterized in that The non-transitory readable storage medium stores a computer program, and the computer program is used to enable a processor to execute the method according to any one of claims 1 to 12, or the method according to any one of claims 13 to 16.

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