Wireless communication method executed by means of authorized entity, and communication device

Abstract

Provided are a wireless communication method executed by means of an authorized entity, and a communication device. The method comprises: acquiring authorization information of a terminal device; determining, on the basis of the authorization information, whether to authorize an AI / ML service request related to the terminal device, wherein the AI / ML service request is used for requesting from the terminal device the transmission of information associated with the AI / ML service, and / or, the AI / ML service request is used for requesting that information of the terminal device is acquired to perform an AI / ML operation. In the present application, an authorization entity determines, on the basis of authorization information of a terminal device, whether to authorize an AI / ML service request related to a terminal device, such that an authorization mechanism related to the AI / ML service request is specified, thereby facilitating the prevention of an attacker from acquiring information related to an AI / ML service.

Description

Wireless communication method and communication device performed by authorized entity Technical Field

[0001] The present application relates to the field of communication technology, and more particularly to a wireless communication method and communication device performed by an authorized entity. Background Art

[0002] With the advancement of technology, the introduction of artificial intelligence / machine learning (AI / ML) technologies is a growing trend in communication systems. The application of AI / ML technologies in communication systems may involve the transmission of AI / ML-related information between different communication devices. However, AI / ML-related information is a critical asset for AI / ML technology providers, so the legitimacy of those who access it must be ensured. Therefore, establishing an authorization mechanism for AI / ML-related information is crucial.

[0003] Summary of the Invention

[0004] The present application provides a wireless communication method and a communication device executed by an authorized entity. The following introduces various aspects involved in the present application.

[0005] In a first aspect, a wireless communication method performed by an authorization entity is provided, the method comprising: obtaining authorization information of a terminal device; and determining whether to authorize an AI / ML service request related to the terminal device based on the authorization information, the AI / ML service request being used to request transmission of information associated with the AI / ML service to the terminal device, and / or requesting acquisition of information of the terminal device for performing an AI / ML operation.

[0006] In a second aspect, an authorization entity is provided, comprising: an acquisition module for acquiring authorization information of a terminal device; an authorization module for determining whether to authorize an AI / ML service request related to the terminal device based on the authorization information, wherein the AI / ML service request is used to request transmission of information associated with the AI / ML service to the terminal device, and / or the AI / ML service request is used to request acquisition of information of the terminal device for performing an AI / ML operation.

[0007] According to a third aspect, a wireless communication system is provided, including: a first network element, configured to obtain authorization information of a terminal device based on an AI / ML service request related to the terminal device, and determine whether to authorize the AI / ML service request based on the authorization information; wherein the AI / ML service request is used to request transmission of information associated with the AI / ML service to the terminal device, and / or the AI / ML service request is used to request acquisition of information of the terminal device for performing AI / ML operations.

[0008] In a fourth aspect, a communication device is provided, comprising a memory and a processor, wherein the memory is used to store a program, and the processor is used to call the program in the memory so that the communication device executes the method described in the first aspect.

[0009] In this application, the authorization entity determines whether to authorize AI / ML service requests related to the terminal device based on the authorization information of the terminal device, clarifies the authorization mechanism related to AI / ML service requests, and helps prevent information related to AI / ML services from being obtained by attackers. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] FIG1 is a schematic structural diagram of a wireless communication system to which an embodiment of the present application is applicable.

[0011] Figure 2 is a schematic flowchart of the transmission of AI / ML models within the same operator network.

[0012] Figure 3 is a schematic flowchart of the transmission of AI / ML models in different operator networks.

[0013] FIG4 is a schematic flowchart of a wireless communication method performed by an authorization entity according to an embodiment of the present application.

[0014] FIG5 is an example diagram of a wireless communication method performed by an authorization entity according to an embodiment of the present application.

[0015] FIG6 is a schematic flowchart of a wireless communication method performed by an authorization entity according to another embodiment of the present application.

[0016] FIG7 is a schematic flowchart of a wireless communication method performed by an authorization entity according to another embodiment of the present application.

[0017] FIG8 is a schematic flowchart of a wireless communication method performed by an authorization entity according to another embodiment of the present application.

[0018] 9A-9B are schematic flow charts of a wireless communication method performed by an authorization entity according to another embodiment of the present application.

[0019] FIG10 is a schematic flowchart of a wireless communication method performed by an authorization entity according to another embodiment of the present application.

[0020] FIG11 is a schematic diagram of the structure of the authorization entity provided in an embodiment of the present application.

[0021] FIG12 is a schematic structural diagram of a wireless communication system provided in an embodiment of the present application.

[0022] FIG13 is a schematic diagram of the structure of a communication device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The technical solution in this application will be described below with reference to the accompanying drawings.

[0024] Communication system architecture

[0025] Figure 1 is a schematic diagram of a communication system architecture applicable to an embodiment of the present application. The network architecture may include terminal equipment, access network (AN) network elements, and core network network elements.

[0026] It should be understood that the technical solutions of the embodiments of the present application can be applied to various communication systems, such as: fifth generation (5G) system or new radio (NR), long term evolution (LTE) system, LTE frequency division duplex (FDD) system, LTE time division duplex (TDD), etc. The technical solutions provided in this application can also be applied to future communication systems, such as the sixth generation mobile communication system, satellite communication system, etc.

[0027] The terminal device in the embodiments of the present application may also be referred to as user equipment (UE), access terminal, user unit, user station, mobile station, mobile station (MS), mobile terminal (MT), remote station, remote terminal, mobile device, user terminal, terminal, wireless core network element, user agent or user device. The terminal device in the embodiments of the present application may refer to a device that provides voice and / or data connectivity to a user and can be used to connect people, objects and machines, such as a handheld device with wireless connection function, a vehicle-mounted device, etc. The terminal device in the embodiments of the present application can be a mobile phone, a tablet computer, a laptop computer, a PDA, a mobile internet device (MID), a wearable device, a virtual reality (VR) device, an augmented reality (AR) device, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical surgery, a wireless terminal in a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, etc. Optionally, the terminal device can be used to act as a base station. For example, the terminal device can act as a dispatching entity that provides sidelink signals between terminal devices in vehicle-to-everything (V2X) or device-to-device (D2D). For example, a cellular phone and a car communicate with each other using sidelink signals. The cellular phone and smart home devices communicate without relaying the communication signal through a base station.

[0028] An access network element can be an access network device. This device is used by terminals to wirelessly access the network architecture and is primarily responsible for radio resource management, quality of service (QoS) management, data compression, and encryption on the air interface side. An access network device can also be referred to as a radio access network (RAN) device. For example, an access network device can be a base station. A base station may broadly cover various names as follows, or be replaced with the following names, such as: NodeB, evolved NodeB (eNB), next generation NodeB (gNB), relay station, access point, transmitting and receiving point (TRP), transmitting point (TP), master eNB (MeNB), secondary eNB (SeNB), multi-standard radio (MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), positioning node, etc. A base station may be a macro base station, a micro base station, a relay node, a donor node, or the like, or a combination thereof. A base station may also refer to a communication module, modem, or chip used to be set in the aforementioned device or apparatus. A base station may also be a mobile switching center and a device that performs base station functions in D2D, V2X, and machine-to-machine (M2M) communications, a network-side device in a 6G network, or a device that performs base station functions in future communication systems. A base station may support networks with the same or different access technologies. The embodiments of this application do not limit the specific technology and specific device form used by the access network device.

[0029] Base stations can be fixed or mobile. For example, a helicopter or drone can be configured to act as a mobile base station, and one or more cells can move based on the location of the mobile base station. In other examples, a helicopter or drone can be configured to act as a device that communicates with another base station.

[0030] In some deployments, the access network device in the embodiments of the present application may refer to a CU or a DU, or the access network device may include a CU and a DU. The gNB may also include an AAU.

[0031] The types of core network elements may include user plane function (UPF) network elements, access and mobility management function (AMF) network elements, session management function (SMF) network elements, policy control function (PCF) network elements, application function (AF), data network (DN), network slice selection function (NSSF), authentication server function (AUSF), unified data management function (UDM), network exposure function (NEF), network repository function (NRF), and network slice-specific authentication and authorization function (NSSAAF). In addition, some networks (such as 5G networks) have added a network data analytics function (NWDAF) to the core network. NWDAF can be further divided into analytics logical function (AnLF) and model training logical function (MTLF). In some communication systems (such as 5G systems), core network elements may also be referred to as network functions (NFs).

[0032] The network elements in Figure 1 can be network elements in hardware devices, software functions running on dedicated hardware, or virtualized functions implemented on a platform (e.g., a cloud platform). It should be noted that the network architecture shown in the above figure is only an example of the network elements included in the entire network architecture. In the embodiments of the present application, the network elements included in the entire network architecture are not limited.

[0033] Those skilled in the art will appreciate that the network architecture shown in FIG1 does not limit the network architecture. In a specific implementation, the network architecture may include more or fewer network elements than shown, or may combine certain network elements. It should be understood that in FIG1 , the AN or RAN is represented by (R)AN.

[0034] In some scenarios, network devices and terminal devices can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; they can also be deployed in the air on aircraft, balloons, and satellites. The embodiments of this application do not limit the scenarios in which network devices and terminal devices are located.

[0035] AI / ML Services in Communication Systems

[0036] In a communication system, AI / ML services may include AI / ML services that enhance the application layer and AI / ML services that enhance communication capabilities (also referred to as "AI enhancement"). The communication system provides some auxiliary capabilities to support AI / ML operations at the application layer, such as separation of AI / ML operations between AI / ML endpoints, distribution and sharing of AI / ML models / data, and distributed / federated learning. In order to enhance the AI / ML services at the application layer, consideration may be given to aspects such as how to design QoS to meet transmission requirements and how to select UEs to participate in federated learning. The main application cases of AI enhancement may include channel state information (CSI) feedback enhancement, beam management enhancement, and positioning accuracy enhancement.

[0037] CSI feedback enhancement mainly refers to the use of AI / ML models on both sides to perform CSI compression in the spatial frequency domain. The AI / ML models on both sides can be two-sided models jointly trained on a single side (terminal device side or network side). Joint training refers to the forward and backward propagation training of the generation model and the reconstruction model in the same loop. Joint training can be performed on a single node or across multiple nodes through gradient exchange between nodes. Joint training can include: joint training of the two-sided models on the network side and the terminal device side respectively; separate training of the network side and the terminal device side, with the terminal device side training the CSI generation part and the network side training the CSI reconstruction part.

[0038] Enhanced beam management primarily involves using AI / ML models for spatial or temporal domain beam prediction. The training and derivation of these AI / ML models can be performed on both the network side and the terminal device side.

[0039] Positioning accuracy enhancement can include the use of AI / ML models for direct positioning and assisted positioning.

[0040] Transmission of AI / ML models in communication systems

[0041] In the above-mentioned AI / ML services, the transmission of AI / ML model-related information (including the AI / ML model itself and its related information) between different devices may be involved. Generally, AI / ML model-related information can be transmitted in the operator network. As shown in Figure 2, model-related information can be transmitted in the network of the same operator. In step S210, the NWDAF service consumer can subscribe to / unsubscribe the ML model from another NWDAF (including the NWDAF of MTLF) through relevant service requests, such as NWDAF_ML Model Provision_Subscribe (Nnwdaf_MLModelProvision_Subscribe) request and NWDAF_ML Model Provision_Unsubscribe (Nnwdaf_MLModelProvision_Unsubscribe) request. In step S220, the NWDAF can respond to the above request through relevant messages, such as NWDAF_ML Model Provision_Notify (Nnwdaf_MLModelProvision_Notify) message. Among them, the NWDAF service consumer can carry the analytics identity (ID) in the service request, and the NWDAF can notify the NWDAF service consumer of the ML model information (such as a unique ML model tag (model identifier)) in the service request response. As shown in Figure 3, model-related information can also be transmitted in the networks of different operators. In step S310, the NWDAF service consumer can also subscribe / unsubscribe the ML model to another NWDAF (including the NWDAF of MTLF) through the Nnwdaf_MLModelProvision_Subscribe request and the Nnwdaf_MLModelProvision_Unsubscribe request. In step S320, the NWDAF can respond to the above request through the Nnwdaf_MLModelProvision_Notify message. Among them, the NWDAF service consumer can carry ML model interoperability information (model interoperability information) in the service request to support the transmission of models between multiple operators. This information can be operator-specific information (such as the requested model format, model execution environment, etc.).

[0042] With the development of AI / ML services, terminal devices may also participate in AI / ML model training. Therefore, AI / ML model-related information may also be transmitted between the network and terminal devices. For example, in CSI feedback enhancement or beam management enhancement, AI / ML model-related information may be transmitted between the terminal device and gNB, the terminal device and the network side, or the terminal device and the server application (server applicable). For another example, in positioning accuracy enhancement, AI / ML model-related information may be transmitted between the terminal device and the location management function (LMF) of the positioning network element on the network side, or between the terminal device and the server applicable.

[0043] Furthermore, to assist terminal devices in performing AI / ML operations, whether they are AI / ML services that enhance the application layer or AI / ML services that enhance communication capabilities, the network may also transmit some auxiliary information related to the AI / ML services to the terminal devices. For example, the network may provide the network status of each terminal device (such as bit rate, latency, reliability, and network performance analysis) to the AF and the terminal device client to assist in AI / ML operations.

[0044] However, AI / ML model-related information and network-related information are important assets for model providers (such as operators). Therefore, ensuring the legitimacy of the terminal devices that access this information is crucial. Otherwise, attackers may be able to steal the AI / ML model and its related information, or network-related information, on the network side.

[0045] Based on the above problems, the embodiments of the present application are introduced in detail below.

[0046] As shown in Figure 4, an embodiment of the present application provides a wireless communication method performed by an authorized entity. The authorized entity is an entity on the network side. The authorized entity can be a network element in the core network described above, such as an NWDAF, a gateway mobile location center (GMLC), a LMF, an AMF, etc. Alternatively, the authorized entity can also be a network element in the access network described above, such as a base station, etc. Alternatively, the authorized entity can also be an operation administration and maintenance (OAM) network element.

[0047] The method shown in FIG. 4 may include steps S410 to S420 .

[0048] In step S410, the authorization entity obtains authorization information for the terminal device. The authorization information may be stored locally in the authorization entity, in which case the authorization entity may perform a local search to obtain the authorization information. Alternatively, the authorization information may be stored in an entity other than the authorization entity (hereinafter referred to as the "authorization information storage entity"), in which case the authorization entity may request the authorization information from the authorization information storage entity. As an example, the authorization information may be stored in the core network's UDM or unified data repository (UDR).

[0049] In step S420, the authorization entity determines whether to authorize an AI / ML service request related to the terminal device based on the authorization information. The AI / ML service request is used to request the transmission of information associated with the AI / ML service to the terminal device, and / or the AI / ML service request is used to request the acquisition of information of the terminal device to perform an AI / ML operation. The AI / ML operation may refer to an operation related to an AI / ML model, such as performing AI / ML model training. The authorization information may be determined based on the contract data of the terminal device, and / or the authorization information may also be determined based on an instruction from the terminal device.

[0050] AI / ML services may refer to services or operations involving AI / ML models, and may include the aforementioned AI / ML services for enhancing the application layer or AI / ML services for enhancing communication capabilities. AI / ML services for enhancing communication capabilities may include positioning services based on AI / ML models, CSI feedback services based on AI / ML models, and beam management services based on AI / ML models.

[0051] Information associated with AI / ML services may include AI / ML model-related information and / or network-assisted information. AI / ML model-related information may include AI / ML models and model-related information, etc. Network-assisted information can be used to assist AI / ML services and may include the network status of the terminal device, network performance analysis, and information that assists AI / ML model training. For example, network-assisted information may include communication link quality, QoS analysis, NWDAF prediction information, etc. Information associated with AI / ML services may be stored in the authorization entity or in another entity other than the authorization entity (hereinafter referred to as the "AI / ML service information storage entity"). For example, the AI / ML service information storage entity may be a network element such as the NWDAF, LMF, or OAM. If the authorization entity does not store information associated with the AI / ML service, the authorization entity may request the AI / ML service information storage entity to obtain information associated with the AI / ML service. It is worth noting that, based on the request of the authorization entity, the AI / ML service information storage entity may also determine whether to authorize the AI / ML service request based on the authorization information.

[0052] An AI / ML service request can be triggered by an authorized entity, which can trigger an AI / ML service request within an AI / ML service. For example, in a positioning service based on an AI / ML model, the authorized entity can trigger an AI / ML service request on its own to obtain information about the terminal device for AI / ML operations. Alternatively, an AI / ML service request can be triggered by an entity other than the authorized entity (hereinafter referred to as the "AI / ML service requesting entity"). The AI / ML service requesting entity can be a network element in the core network, a terminal device, an external server, an external client, etc. For example, a terminal device can trigger an AI / ML service request through registration. For another example, in a positioning service based on an AI / ML model, a terminal device can initiate an AI / ML service request to the authorized entity, requesting the acquisition of an AI / ML model for positioning services. Alternatively, in a positioning service based on an AI / ML model, a positioning client or a core network element such as an NWDAF can also initiate an AI / ML service request to the authorized entity, requesting the transmission of an AI / ML model to the terminal device to obtain the terminal device's location information. For example, in a CSI feedback service based on an AI / ML model or a beam management service based on an AI / ML model, the terminal device can trigger an AI / ML service request by reporting AI / ML capability information to the authorization entity to request information related to the AI / ML model and network auxiliary information. For another example, a network element storing an AI / ML model (such as a gNB, OAM, or NWDAF) can also initiate an AI / ML service request to the authorization entity to request information about the terminal device (such as QoS, terminal mobility information, terminal IP address, etc.) for training the AI / ML model.

[0053] In this application, the authorization entity determines whether to authorize AI / ML service requests related to the terminal device based on the authorization information of the terminal device, clarifies the authorization mechanism related to AI / ML service requests, and helps prevent information related to AI / ML services from being obtained by attackers.

[0054] The following describes the content of the authorization information in detail. The authorization information may include the AI / ML subscription data of the terminal device and / or the AI / ML configuration information of the terminal device.

[0055] The AI / ML subscription data of the terminal device may refer to the subscription data of the terminal device for the AI / ML service. The AI / ML subscription data may include information related to the public land mobile network (PLMN) that allows the terminal device to use the AI / ML service, and / or information related to the AI / ML service that the terminal device is allowed to use.

[0056] The PLMN that the terminal device is allowed to use AI / ML services may refer to the PLMN that the terminal device is authorized to use AI / ML services, or the PLMN that the terminal device is authorized to use specific AI / ML services. Information related to the PLMNs allowed to be used by the terminal device can be represented by a PLMN set or a PLMN list.

[0057] The information related to the AI / ML services allowed to be used by the terminal device may include at least one of the following: information used to indicate that the terminal device is allowed to use the AI / ML service; AI / ML model information; and the service type to which the AI / ML model is applicable. The information used to indicate that the terminal device is allowed to use the AI / ML service may be information indicating that the terminal device is authorized to obtain the AI / ML model. The AI / ML model information may include the storage location of the AI / ML model, the model ID of the AI / ML model, the type of the AI / ML model, the version of the AI / ML model, etc. The service types to which the AI / ML model is applicable may include positioning services based on the AI / ML model, CSI services based on the AI / ML model, beam management services based on the AI / ML model, energy-saving services based on the AI / ML model, mobility optimization services based on the AI / ML model, etc.

[0058] AI / ML subscription data can be determined based on the device's subscription data. For example, the carrier may generate and maintain AI / ML subscription data based on the subscriber's subscription data. Therefore, the device or application function AF cannot directly modify AI / ML subscription data, but it can be modified by updating the subscription data. For example, the device can change AI / ML subscription data by changing the carrier's plan.

[0059] The AI / ML configuration information of a terminal device, also known as an AI / ML profile, can include information indicating whether the terminal device allows AI / ML model transmission and / or information indicating whether the terminal device allows AI / ML model training. Generally, the model types supported by a terminal device are related to the terminal device's capabilities, computing power, and storage. However, the terminal device's status may change, so the models supported by the terminal device are not static. Furthermore, due to the mobility of the terminal device, the applicable area for training models may also change. Alternatively, in some cases, the terminal device may not be able to support high-precision models. Therefore, when determining whether to transmit information associated with AI / ML services to a terminal device, the authorized entity may also determine whether the terminal device supports training and / or transmitting AI / ML models based on the terminal device's status information. However, because certain terminal device status information (such as battery level, computing power, and storage size) is considered private information and cannot be accessed by the network, the network cannot determine whether the terminal device is available for model transmission and / or model training at a certain time. Therefore, using AI / ML configuration information allows the network to clearly determine whether the terminal device is available for model transmission and / or model training, helping to protect the privacy of the terminal device.

[0060] The AI / ML configuration information can be determined based on the indication of the terminal device. For example, the terminal device can trigger the update of the AI / ML configuration information through a non-access stratum (NAS) message. For another example, the authorized AF updates the AI / ML configuration information for the terminal device through the NEF. Furthermore, when the terminal device prohibits model transmission or model training, the terminal device can report an indication of prohibiting model transmission or prohibiting model training to the network side, and update the authorization information of the terminal device by updating the AI / ML configuration information of the terminal device.

[0061] It is worth noting that in some implementations, the AI / ML configuration information can also be determined based on the location service (LCS) privacy configuration information. For example, when the AI / ML service described in the previous article is a positioning service based on an AI / ML model, the authorization entity can determine the AI / ML configuration information of the terminal device based on the LCS privacy configuration information of the terminal device. Among them, the LCS privacy configuration information of the terminal device can be obtained through the LCS privacy profile of the terminal device. Furthermore, the AI / ML configuration information may include a location privacy indication (LPI) in the LCS privacy configuration information, and the LPI can be used to indicate whether the terminal device is allowed to perform positioning operations based on the AI / ML model. The LPI can be an AI based location privacy indication (AILPI) specifically for the AI / ML service, or it can be the original LPI in the LCS privacy profile.

[0062] The following is an example of the wireless communication method performed by the authorized entity in an embodiment of the present application, with reference to Figure 5. For example, in the method shown in Figure 5, the authorization information and the information associated with the AI / ML service are stored in an entity other than the authorized entity, and the AI / ML service request is initiated by an entity other than the authorized entity. Among them, the authorization information is stored in the authorization information storage entity, the information associated with the AI / ML service is stored in the AI / ML service information storage entity, and the AI / ML service request is initiated by the AI / ML service request entity. Of course, the authorization information and the information associated with the AI / ML service can also be stored in the authorized entity, and the AI / ML service request can also be initiated by the authorized entity, which is not limited in this application. As shown in Figure 5, the method may include steps S510 to S580.

[0063] In step S510, the AI / ML service request entity initiates an AI / ML service request to the authorization entity. The AI / ML service request can be used to request the transmission of information associated with the AI / ML service to the terminal device. Alternatively, the AI / ML service request can also be used to request the acquisition of terminal device information for AI / ML operations. The AI / ML service request can carry the ID of the terminal device, such as a user permanent identifier (SUPI), a user hidden identifier (SUCI), or a generic public subscription identifier (GPSI), to indicate the terminal device to which the AI / ML service request is directed.

[0064] In step S520, the authorization entity sends an authorization information request message to the authorization information storage entity. The authorization information request message may carry the ID of the terminal device to indicate the terminal device to which the request message is directed.

[0065] In step S530, the authorization information storage entity retrieves the authorization information of the terminal device.

[0066] In step S540, the authorization information storage entity sends the authorization information of the terminal device to the authorization entity.

[0067] In step S550, the authorization entity checks the authorization information to determine whether to authorize the AI / ML service request. If the authorization entity determines to authorize the AI / ML service request, the process proceeds to step S560.

[0068] In step S560, the authorization entity sends a request message for information associated with the AI / ML service / terminal device to the AI / ML service information storage entity. The request message may carry the ID of the terminal device to indicate the terminal device to which the request is directed.

[0069] In step S570 , the AI / ML service information storage entity sends information associated with the AI / ML service / information of the terminal device to the authorization entity.

[0070] In step S580, the authorization entity sends information associated with the AI / ML service / terminal device information to the AI / ML service requesting entity. It is worth noting that the AI / ML service information storage entity can also directly transmit information associated with the AI / ML service to the terminal device (not shown in the figure).

[0071] Based on the method shown in Figure 5, the authorization entity determines the authorization mechanism for transmitting information associated with the AI / ML service to the terminal device, or transmitting the terminal device's information to the AI / ML service requesting entity based on the authorization information of the terminal device, which helps to ensure the security of information associated with the AI / ML service and the terminal device's information.

[0072] The following examples, in combination with Examples 1 to 4, illustrate how to apply the methods of the embodiments of the present application in different scenarios. In different scenarios, the AI / ML service information storage entity is different. In Examples 1 to 3, the AI / ML service information storage entity is a network element in the core network. In Example 4, the AI / ML service information storage entity is an entity (such as a base station) or OAM in the access network. For ease of understanding, in the following description, the authorization entity may also be referred to as an "authorization node."

[0073] Example 1

[0074] The scenario applicable to the first embodiment is that the UE acts as a consumer of network-side models (the model itself, model information), data used to assist UE in training models, or network auxiliary information (such as communication link quality, QoS analysis, NWDAF prediction information, etc.), and needs to obtain relevant information from the NWDAF. Exemplarily, the AI / ML service information storage entity in the first embodiment is the NWDAF, the authorization node is the NF in the core network, such as AMF, PCF, LMF, NWDAF, or UDM, the authorization information storage entity is the UDM, and the AI / ML service request entity is the UE. As shown in Figure 6, the method in the first embodiment may include steps S610 to S680.

[0075] In step S610, the UE initiates a registration request or an AI / ML service request (AIML service Request) to the NF to request information related to the AI / ML model, data used to assist the UE in training the model, or network assistance information. The UE can carry its own ID and capability information in the request.

[0076] In step S620, the NF sends an Authorization Request message or a Nudm_SDM_Request message to the UDM to request the authorization information of the UE. The NF may carry the ID of the UE in the request.

[0077] In step S630, the UDM retrieves the authorization information of the UE.

[0078] In step S640, the UDM sends an authorization response (Authorization Response) message or a UDM_SDM_Response (Nudm_SDM_Response) message to the NF. The response message may carry the authorization information of the UE.

[0079] In step S650, the NF checks the UE's authorization information to determine whether the UE is authorized to obtain AI / ML model-related information, data used to assist the UE in training the model, or network assistance information. If the NF determines that the UE's AI / ML service request is authorized based on the authorization information, the process proceeds to step S660.

[0080] In step S660, the NF sends an NWDAF_model (Nnwdaf_model) message or an analytics_Request (analytics_Request) message to the NWDAF to request the transmission of AI / ML model-related information, data for assisting the UE in training the model, or network assistance information to the terminal device.

[0081] In step S670, the NWDAF sends an Nnwdaf_model message or an analytics_Response message to the NF. The NWDAF transmits AI / ML model related information, data for assisting the UE in training the model, or network assistance information to the UE (not shown in the figure). The NWDAF may transmit AI / ML model related information, data for assisting the UE in training the model, or network assistance information to the UE through control plane signaling, for example, through the NWDAF-AMF-UE method. Alternatively, considering the size of the AI / ML model, the NWDAF may also transmit AI / ML model related information, data for assisting the UE in training the model, or network assistance information to the UE through user plane data, for example, by establishing a specific protocol data unit (PDU) session through the UPF.

[0082] In step S680, the NF sends an AI / ML service response message or a registration response message to the UE.

[0083] Based on the method of embodiment 1, when the network transmits AI / ML model-related information in the NWDAF, data for assisting the UE in training the model, or network-assisted information to the UE, the legitimacy of the UE can be determined based on the UE's authorization information, which helps to ensure the security of AI / ML model-related information, data for assisting the UE in training the model, and network-assisted information.

[0084] Example 2

[0085] The scenario applicable to the second embodiment may be a scenario in which the UE, as a consumer of the network-side model (the model itself, model information) and network auxiliary information (such as communication link quality, QoS analysis, NWDAF prediction information, etc.), needs to obtain relevant information from the LMF. Exemplarily, the second embodiment may be applicable to the sidelink mobile originating location request ((SL)-mobile originating-location request, (SL)-MO-LR) process in the positioning service. For AI / ML-based positioning services, the AI / ML model may be stored in the LMF, and the authorization node on the network side can determine whether the relevant information can be transmitted to the UE. Alternatively, the LMF can obtain the model through the NWDAF, and the LMF can act as an authorization node to determine whether the relevant information can be transmitted to the UE. In conjunction with Figure 7, the method of the second embodiment is described below, taking the AI / ML service information storage entity as the LMF, the authorization node as the NF in the core network, such as the AMF, LMF, LMF, or UDM, the authorization information storage node as the UDM, and the AI / ML service request entity as the UE as an example. As shown in FIG. 7 , the method of the embodiment of the present application in the second embodiment may include steps S710 to S780 .

[0086] In step S710, the UE initiates a registration request or an AIML service request to the NF to request information related to the AI / ML model, data used to assist the UE in training the model, or network assistance information. The UE can carry the UE ID and capability information in the request.

[0087] In step S720, the NF sends an Authorization Request message or a Nudm_SDM_Request message to the UDM to request the UE's authorization information. The NF may carry the UE's ID in the request to clearly identify the terminal device to which the request is directed.

[0088] In step S730 , the UDM retrieves the authorization information of the UE.

[0089] In step S740, the UDM sends an Authorization Response message or a Nudm_SDM_Response message to the NF. The response message may carry the authorization information of the UE.

[0090] In step S750, the NF checks the UE's authorization information to determine whether the UE is authorized to obtain AI / ML model-related information, data used to assist the UE in training the model, or network assistance information. If the NF determines to authorize the UE's AI / ML service request based on the authorization information, see step S760.

[0091] In step S760, the NF sends an LMF_model_request (Nlmf_model_Request) message to the LMF. The NF may also include the UE ID in the request to specify the terminal device to which the request is directed.

[0092] In step S770, the LMF sends an LMF_model_response (Nlmf_model_Response) message to the NF. The LMF sends AI / ML model related information, data for assisting the UE in training the model, or network assistance information (not shown in the figure) to the UE.

[0093] In step S780, the NF sends an AIML service Response message or a registration response message to the UE.

[0094] Based on the method of embodiment 2, when the network transmits AI / ML model-related information in the LMF, data for assisting the UE in training the model, or network-assisted information to the UE, it can determine the legitimacy of the UE based on the UE's authorization information, which helps to ensure the security of AI / ML model-related information, data for assisting the UE in training the model, and network-assisted information.

[0095] Example 3

[0096] The scenario applicable to the third embodiment may be a scenario in which an external client or a core network element initiates a service exposure request to the network to request that AI / ML related information on the network side be transmitted to the UE. For example, in the process of mobile terminating-location request (MT-LR), the LCS client, AF or NF (such as NWDAF) may initiate a service exposure request to the network to request the location information of the target UE. In this scenario, the network may transmit information related to the AI / ML model to improve positioning accuracy, data for assisting the UE in training the model, or network assistance information to the target UE, so that the target UE can calculate its own location information based on the AI / ML model. And the GMLC on the network side may determine the legitimacy of the target UE based on the authorization information of the target UE. In conjunction with Figure 8, the method of the third embodiment is introduced below, taking the AI / ML service information storage entity as LMF, the authorization node as GMLC in the core network, the authorization information storage node as UDM, and the AI / ML service request entity as LCS client / AF / NF as an example. As shown in Figure 8, the method in the third embodiment may include steps S810 to S880.

[0097] In step S810, the LCS client / AF / NF initiates a service exposure request to the GMLC to request the location information of the target UE. The request may carry the ID of the target UE.

[0098] In step S820, the GMLC sends an Authorization Request message or a Nudm_SDM_Request message to the UDM to request authorization information of the target UE. The GMLC may carry the ID of the target UE in the request to clearly identify the terminal device to which the request is directed.

[0099] In step S830, the UDM retrieves authorization information of the target UE.

[0100] In step S840, the UDM sends an Authorization Response message or a Nudm_SDM_Response message to the GMLC. The response message may carry authorization information of the target UE.

[0101] In step S850, the GMLC checks the authorization information of the target UE to determine whether the target UE is authorized to obtain AI / ML model-related information, data used to assist the UE in training the model, or network assistance information for positioning operations. If the GMLC determines that the target UE is authorized to obtain AI / ML model-related information, data used to assist the UE in training the model, or network assistance information, the procedure proceeds to step S860.

[0102] In step S860, the GMLC sends an Nlmf_model_Request message to the LMF to request the transmission of AI / ML model-related information, data used to assist the UE in training the model, or network assistance information to the target UE. The GMLC may also include the target UE ID in the request to clearly identify the terminal device for which the request is intended.

[0103] In step S870, the LMF sends an Nlmf_model_Response message to the GMLC. The LMF sends AI / ML model-related information, data for assisting the UE in training the model, or network assistance information (not shown in the figure) to the target UE.

[0104] In step S880, the GMLC sends a service request response message to the LCS client / AF / NF.

[0105] It is worth noting that, in the (SL)-MT-LR process, the above-mentioned process of GMLC checking the target UE authorization information can be performed together with the process of GMLC checking the target UE subscription data.

[0106] In addition, in sidelink positioning, the LMF can determine whether the server UE calculates the positioning result. Therefore, in AI-based positioning enhancement, the network can also transmit information related to the AI / ML model that improves positioning accuracy, data used to assist the UE in training the model, or network assistance information to the server UE, so that the server UE can calculate the target UE's location information based on the AI / ML model. Therefore, in the method of embodiment 3, the network-side GMLC can also determine the legitimacy of the server UE based on the server UE's authorization information.

[0107] Based on the method of embodiment three, during the 5GC-MT-LR process, when the network transmits AI / ML model-related information, data used to assist the UE in training the model, or network-assisted information to the UE, the network can determine the legitimacy of the UE based on the UE's authorization information, which helps to ensure the security of AI / ML model-related information, data used to assist the UE in training the model, and network-assisted information.

[0108] Example 4

[0109] The fourth embodiment is applicable to scenarios where AI / ML model-related information is stored in the access network or OAM. For example, for beam management or CSI feedback processes, the RAN already has AI / ML capabilities. That is, if the RAN already has the relevant models, the RAN can transmit AI / ML model-related information, data used to assist the UE in model training, or network assistance information to the UE. Alternatively, the AI / ML model may be stored in the OAM. The AI / ML model-related information, data used to assist the UE in model training, or network assistance information can be transmitted to the RAN on the radio access side via OAM, and then transmitted from the RAN to the UE. Therefore, the gNB or OAM on the RAN side can act as an authorization node and determine whether to authorize the transmission of AI / ML model-related information, data used to assist the UE in model training, or network assistance information to the UE based on UE authorization information stored locally or obtained through the UDM or AMF of the core network. Alternatively, if the gNB or OAM is not an authorization node, the gNB or OAM can send an authorization request to the network. After authorization, the network-side NF (e.g., AMF, LMF) returns the authorization result to the gNB or OAM. The gNB or OAM then determines whether to transmit the AI / ML model, data used to assist UE model training, or network assistance information to the UE based on the authorization result. Furthermore, the gNB can also determine whether to transmit the AI / ML model, data used to assist UE model training, or network assistance information to the UE based on the OAM authorization result.

[0110] The method of the fourth embodiment is described in detail below with reference to Figures 9 and 10. For example, in the fourth embodiment, the AI / ML service information storage entity is the OAM, the authorization node is the gNB in ​​the access network or the core network NF or the OAM, the authorization information storage node is the UDM, and the AI / ML service requesting entity is the UE.

[0111] The interaction process of each entity in the fourth embodiment may be as shown in FIG. 9A , including steps S910 to S970 .

[0112] In step S910 , the UE initiates an AI / ML service request to the RAN side to request an AI / ML model.

[0113] In step S920, the RAN sends an authorization information request to the UDM of the core network.

[0114] In step S930, the UDM of the core network returns UE authorization information to the RAN.

[0115] In step S940, the RAN checks the UE authorization information to determine whether to authorize the UE's AI / ML service request. If the RAN determines to authorize the UE's AI / ML service request, the process proceeds to step S950.

[0116] At step S950 , the RAN sends an AI / ML model request to the OAM.

[0117] In step S960 , the OAM returns the AI / ML model to the RAN.

[0118] In step S970 , the RAN returns the AI / ML model to the UE.

[0119] The interaction process of each entity in the fourth embodiment may also be as shown in FIG. 9B , including steps S910 to S970 .

[0120] In step S910 , the UE initiates an AI / ML service request to the RAN side to request an AI / ML model.

[0121] In step S920, the RAN sends an authorization check request to the NF of the core network (e.g., AMF, LMF).

[0122] In step S930, the NF (eg, AMF, LMF) checks the UE authorization information.

[0123] In step S940, the NF of the core network returns an authorization check response to the RAN.

[0124] At step S950 , the RAN sends an AI / ML model request to the OAM.

[0125] In step S960 , the OAM returns the AI / ML model to the RAN.

[0126] In step S970 , the RAN returns the AI / ML model to the UE.

[0127] It is worth noting that the RAN in the method shown in FIG. 9 may also be replaced by OAM, and the corresponding method may not include step S950 and step S960.

[0128] Furthermore, the detailed process of the method of the fourth embodiment may be as shown in FIG. 10 , and may include steps S1010 to S1070 .

[0129] In step S1010, the UE initiates a UE capability report to the gNB. This UE capability report can trigger an AI / ML service request to obtain an AI / ML model. The network-side authorization process for a UE-triggered AI / ML service request can include the following three scenarios.

[0130] If the gNB is the authorized node and the gNB has the UE's authorization information stored locally, the gNB checks the UE's authorization information in step S1020. Based on the authorization result, the gNB determines whether to transmit the AI / ML model to the UE in step S1060. In step S1070, the gNB transmits the AI / ML model to the UE.

[0131] If the gNB is the authorizing node and the gNB does not store the UE's authorization information locally, then, as in step S1030, the gNB sends an authorization information request to the UDM in the core network. In step S1040, the UDM retrieves the UE's authorization information. As in step S1050, the UDM sends an authorization information response to the gNB. The gNB then checks the UE's authorization information and, in step S1070, transmits the AI / ML model to the UE.

[0132] If the gNB is not the authorized node, referring to step S1030, the gNB sends an authorization check request to the core network's NF or OAM. In step S1040, the core network's NF or OAM checks the UE's authorization information. Referring to step S1050, the core network's NF or OAM returns an authorization check response to the gNB. Based on the authorization result, the gNB decides in step S1060 whether to transmit the AI / ML model to the UE. In step S1070, the gNB transmits the AI / ML model to the UE.

[0133] It is worth noting that the gNB in ​​the method shown in Figure 10 can also be replaced by OAM. In the case where OAM is not an authorized node, in step S1030, OAM sends an authorization check request to NF.

[0134] Based on the method of embodiment 4, when the gNB or OAM transmits the AI / ML model to the UE, it can determine the legitimacy of the UE based on the UE's authorization information, which helps ensure the security of the AI / ML model.

[0135] The method embodiment of the present application is described in detail above in conjunction with Figures 1 to 10. The device embodiment of the present application is described in detail below in conjunction with Figures 11 to 13. It should be understood that the description of the method embodiment corresponds to the description of the device embodiment. Therefore, for parts not described in detail, reference can be made to the above method embodiment.

[0136] Figure 11 is a schematic diagram of the structure of an authorization entity provided in an embodiment of the present application. Authorization entity 1100 in Figure 11 includes an acquisition module 1110 and an authorization module 1120. Acquisition module 1110 is used to obtain authorization information for a terminal device, and authorization module 1120 is used to determine whether to authorize an AI / ML service request related to the terminal device based on the authorization information. An AI / ML service request is used to request the transmission of information associated with an AI / ML service to a terminal device, and / or to request the acquisition of terminal device information for AI / ML operations.

[0137] In some implementations, the authorization information includes AI / ML subscription data of the terminal device and / or AI / ML configuration information of the terminal device.

[0138] In some implementations, the AI / ML subscription data includes at least one of the following: information indicating that the terminal device is allowed to use the AI / ML service; a set of public land mobile networks (PLMNs) that the terminal device is allowed to use the AI / ML service; AI / ML model information; and a service type to which the AI / ML model is applicable.

[0139] In some implementations, the AI / ML configuration information includes at least one of the following: information indicating whether the terminal device allows AI / ML model transmission; information indicating whether the terminal device allows AI / ML model training.

[0140] In some implementations, the AI / ML configuration information is determined based on location service (LCS) privacy configuration information.

[0141] In some implementations, the AI / ML configuration information includes a location privacy indication LPI in the LCS privacy configuration information, where the LPI is used to indicate whether the terminal device is allowed to perform positioning operations based on the AI / ML model.

[0142] In some implementations, the information associated with the AI / ML service includes at least one of the following: AI / ML model-related information; network assistance information used to assist the AI / ML service.

[0143] In some implementations, the authorization information is determined based on subscription data of the terminal device, and / or the authorization information is determined based on an indication of the terminal device.

[0144] Figure 12 is a schematic diagram of the structure of a wireless communication system provided by an embodiment of the present application. The wireless communication system 1200 in Figure 12 includes a first network element 1210. The first network element 1210 is used to obtain authorization information of the terminal device based on an artificial intelligence AI / machine learning ML service request related to the terminal device, and determine whether to authorize the AI / ML service request based on the authorization information. The AI / ML service request is used to request the transmission of information associated with the AI / ML service to the terminal device, and / or the AI / ML service request is used to request the acquisition of information of the terminal device for AI / ML operations.

[0145] In some implementations, the system further includes a second network element 1220 , which is configured to initiate an AI / ML service request to the first network element 1210 .

[0146] In some implementations, the system further includes a third network element 1230, which is configured to store information associated with the AI / ML service and transmit the information associated with the AI / ML service to the terminal device based on the authorization of the AI / ML service request by the first network element 1210.

[0147] In some implementations, the system further includes a fourth network element 1240 , which is configured to store the authorization information and transmit the authorization information to the first network element 1210 based on a request of the first network element 1210 .

[0148] In some implementations, the authorization information includes AI / ML subscription data of the terminal device and / or AI / ML configuration information of the terminal device.

[0149] In some implementations, the AI / ML subscription data includes at least one of the following: information indicating that the terminal device is allowed to use the AI / ML service; a set of public land mobile networks (PLMNs) that the terminal device is allowed to use the AI / ML service; AI / ML model information; and a service type to which the AI / ML model is applicable.

[0150] In some implementations, the AI / ML configuration information includes at least one of the following: information indicating whether the terminal device allows AI / ML model transmission; information indicating whether the terminal device allows AI / ML model training.

[0151] In some implementations, the AI / ML configuration information is determined based on location service (LCS) privacy configuration information.

[0152] In some implementations, the AI / ML configuration information includes a location privacy indication LPI in the LCS privacy configuration information, where the LPI is used to indicate whether the terminal device is allowed to perform positioning operations based on the AI / ML model.

[0153] In some implementations, the information associated with the AI / ML service includes at least one of the following: AI / ML model-related information; network assistance information used to assist the AI / ML service.

[0154] In some implementations, the authorization information is determined based on subscription data of the terminal device, and / or the authorization information is determined based on an indication of the terminal device.

[0155] Figure 13 is a schematic diagram of the structure of a communication device provided in an embodiment of the present application. The communication device 1300 in Figure 13 can be used to implement the method described in the above method embodiment. The device 1300 can be a chip, a terminal device, or a base station.

[0156] The communication device 1300 may include one or more processors 1310. The processor 1310 may support the device 1300 to implement the method described in the above method embodiment. The processor 1310 may be a general-purpose processor or a special-purpose processor. For example, the processor may be a central processing unit (CPU). Alternatively, the processor may be another general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0157] The communication device 1300 may further include one or more memories 1320. The memories 1320 store programs that can be executed by the processor 1310, causing the processor 1310 to perform the methods described in the above method embodiments. The memories 1320 may be independent of the processor 1310 or integrated into the processor 1310.

[0158] The communication device 1300 may further include a transceiver 1330. The processor 1310 may communicate with other devices or chips via the transceiver 1330. For example, the processor 1310 may transmit and receive data with other devices or chips via the transceiver 1330.

[0159] It should be understood that in the embodiment of the present application, the processor 1310 can adopt a general central processing unit (CPU), a microprocessor, an application specific integrated circuit (ASIC), or one or more integrated circuits to execute relevant programs to implement the technical solutions provided in the embodiment of the present application.

[0160] The memory 1320 may include a read-only memory and a random access memory, and provides instructions and data to the processor 1310. A portion of the processor 1310 may also include a non-volatile random access memory. For example, the processor 1310 may also store information about the device type.

[0161] During implementation, each step of the above method can be completed by an integrated logic circuit of the hardware in the processor 1310 or by instructions in the form of software. The method for requesting uplink transmission resources disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 1320, and the processor 1310 reads the information in the memory 1320 and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0162] It should be understood that in the embodiment of the present application, the processor 1310 may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0163] The present application also provides a computer-readable storage medium for storing a program. The computer-readable storage medium can be applied to a terminal device or network device provided in the present application, and the program enables a computer to execute the wireless communication method performed by the authorized entity in each embodiment of the present application.

[0164] The present application also provides a computer program product. The computer program product includes a program. The computer program product can be applied to a terminal device or network device provided in the present application, and the program causes a computer to execute the wireless communication method performed by the authorized entity in each embodiment of the present application.

[0165] The present application also provides a computer program that can be applied to a terminal device or a network device provided in the present application, and enables a computer to execute the wireless communication method performed by the authorized entity in each embodiment of the present application.

[0166] It should be understood that all or part of the functions of the communication device in this application can also be implemented through software functions running on hardware, or through virtualization functions instantiated on a platform (such as a cloud platform).

[0167] It should be understood that the terms "system" and "network" in this application can be used interchangeably. In addition, the terms used in this application are only used to explain the specific embodiments of this application and are not intended to limit this application. The terms "first", "second", "third", and "fourth" in the specification and claims of this application and the accompanying drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0168] In the embodiments of this application, the term "indication" may refer to a direct indication, an indirect indication, or an indication of an association. For example, "A indicates B" may refer to a direct indication of B, e.g., B can obtain information through A; it may refer to an indirect indication of B, e.g., A indicates C, e.g., B can obtain information through C; or it may refer to an association between A and B.

[0169] In the embodiments of this application, the term "include" can refer to direct inclusion or indirect inclusion. Alternatively, the term "include" in the embodiments of this application can be replaced with "indicates" or "is used to determine." For example, "A includes B" can be replaced with "A indicates B" or "A is used to determine B."

[0170] In various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0171] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0172] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0173] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0174] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be read by a computer or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital versatile disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0175] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A wireless communication method performed by an authorized entity, the method comprising: Obtaining authorization information of a terminal device; Determining whether to authorize an AI / Machine Learning (ML) service request related to the terminal device based on the authorization information, where the AI / ML service request is for requesting to transmit information associated with the AI / ML service to the terminal device, and / or the AI / ML service request is for requesting to obtain information of the terminal device for AI / ML operations.

2. The method according to claim 1, wherein the authorization information includes the AI / ML subscription data of the terminal device and / or the AI / ML configuration information of the terminal device.

3. The method according to claim 2, wherein the AI / ML subscription data includes at least one of the following: Information indicating that the terminal device is allowed to use the AI / ML service; A set of Public Land Mobile Networks (PLMNs) that allow the terminal device to use the AI / ML service; AI / ML model information; The service type applicable to the AI / ML model.

4. The method according to claim 2, wherein the AI / ML configuration information includes at least one of the following: Information indicating whether the terminal device allows the transmission of the AI / ML model; Information indicating whether the terminal device allows the training of the AI / ML model.

5. The method according to claim 2, wherein the AI / ML configuration information is determined based on Location Service (LCS) privacy configuration information.

6. The method according to claim 5, wherein the AI / ML configuration information includes a Location Privacy Indicator (LPI) in the LCS privacy configuration information, and the LPI is used to indicate whether to allow the terminal device to perform positioning operations based on the AI / ML model.

7. The method according to claim 1, wherein the information associated with the AI / ML service includes at least one of the following: AI / ML model-related information; Network assistance information for assisting the AI / ML service.

8. The method according to claim 1, wherein the authorization information is determined based on the subscription data of the terminal device, and / or the authorization information is determined based on an indication of the terminal device.

9. An authorized entity, the authorized entity comprising: An obtaining module for obtaining authorization information of a terminal device; An authorization module for determining whether to authorize an AI / ML service request related to the terminal device based on the authorization information, where the AI / ML service request is for requesting to transmit information associated with the AI / ML service to the terminal device, and / or the AI / ML service request is for requesting to obtain information of the terminal device for AI / ML operations.

10. The authorized entity according to claim 9, wherein the authorization information includes the AI / ML subscription data of the terminal device and / or the AI / ML configuration information of the terminal device.

11. The authorized entity according to claim 10, wherein the AI / ML subscription data includes at least one of the following: Information indicating that the terminal device is allowed to use the AI / ML service; A set of Public Land Mobile Networks (PLMNs) that allow the terminal device to use the AI / ML service; AI / ML model information; Service types applicable to the AI / ML model.

12. The authorized entity according to claim 10, wherein the AI / ML configuration information includes at least one of the following: Information for indicating whether the terminal device allows the transmission of the AI / ML model; Information for indicating whether the terminal device allows the training of the AI / ML model.

13. The authorized entity according to claim 10, wherein the AI / ML configuration information is determined based on location service (LCS) privacy configuration information.

14. The authorized entity according to claim 13, wherein the AI / ML configuration information includes a location privacy indication (LPI) in the LCS privacy configuration information, and the LPI is used to indicate whether to allow the terminal device to perform a positioning operation based on the AI / ML model.

15. The authorized entity according to claim 9, wherein the information associated with the AI / ML service includes at least one of the following: AI / ML model-related information; Network assistance information for assisting the AI / ML service.

16. The authorized entity according to claim 9, wherein the authorization information is determined based on the subscription data of the terminal device, and / or the authorization information is determined based on an indication of the terminal device.

17. A wireless communication system, comprising: A first network element, configured to obtain authorization information of the terminal device based on an artificial intelligence (AI) / machine learning (ML) service request related to the terminal device, and determine whether to authorize the AI / ML service request based on the authorization information; wherein the AI / ML service request is used to request to transmit information associated with the AI / ML service to the terminal device, and / or the AI / ML service request is used to request to obtain information of the terminal device for AI / ML operations.

18. The wireless communication system according to claim 17, wherein the system further comprises: A second network element, configured to initiate the AI / ML service request to the first network element.

19. The wireless communication system according to claim 17, wherein the system further comprises: A third network element, configured to store the information associated with the AI / ML service, and transmit the information associated with the AI / ML service to the terminal device based on the authorization of the first network element for the AI / ML service request.

20. The wireless communication system according to claim 17, wherein the system further comprises: A fourth network element, configured to store the authorization information, and transmit the authorization information to the first network element based on a request of the first network element.

21. The wireless communication system according to claim 17, wherein the authorization information includes the AI / ML subscription data of the terminal device and / or the AI / ML configuration information of the terminal device.

22. The wireless communication system according to claim 21, wherein the AI / ML subscription data includes at least one of the following: Information for indicating that the terminal device is allowed to use the AI / ML service; A public land mobile network (PLMN) set that allows the terminal device to use the AI / ML service; AI / ML model information; Service types applicable to the AI / ML model.

23. The wireless communication system according to claim 21, wherein the AI / ML configuration information includes at least one of the following: Information for indicating whether the terminal device allows the transmission of an AI / ML model; Information for indicating whether the terminal device allows the training of an AI / ML model.

24. The wireless communication system according to claim 21, wherein the AI / ML configuration information is determined based on location service (LCS) privacy configuration information.

25. The wireless communication system according to claim 21, wherein the AI / ML configuration information includes a location privacy indication (LPI) in the LCS privacy configuration information, and the LPI is used to indicate whether to allow the terminal device to perform a positioning operation based on an AI / ML model.

26. The wireless communication system according to claim 17, wherein the information associated with the AI / ML service includes at least one of the following: AI / ML model-related information; Network assistance information for assisting the AI / ML service.

27. The wireless communication system according to claim 17, wherein the authorization information is determined based on the subscription data of the terminal device, and / or the authorization information is determined based on an indication of the terminal device.

28. A communication device, characterized in that, Comprising a memory and a processor, the memory is used for storing programs, and the processor is used for calling the programs in the memory to enable the communication device to execute the method according to any one of claims 1-8.

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