Communication methods, first communication device and second communication device

Abstract

The present disclosure provides communication methods, a first communication device and a second communication device. A method comprises: a first communication device receives first information, the first information being used for indicating at least one of the following: AI or ML positioning characteristics supported by a second communication device, and associated information of the AI or ML positioning characteristics supported by the second communication device. The present disclosure provides the method that allows, by means of the first information, the first communication device to obtain the AI or ML positioning characteristics which can be supported by the second communication device, so as to aid in configuration of subsequent positioning of the second communication device, helping to improve the accuracy of positioning the second communication device.

Description

Communication method, first communication device, and second communication device Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a communication method, a first communication device, and a second communication device. Background Art

[0002] In the research of wireless artificial intelligence (AI) or machine learning (ML), application cases of AI or ML include AI- or ML-based channel state information (CSI) enhancement, AI- or ML-based beam management, and AI- or ML-based positioning.

[0003] Summary of the Invention

[0004] The embodiments of the present disclosure provide a communication method, a first communication device, and a second communication device.

[0005] In a first aspect, an embodiment of the present disclosure provides a communication method, which is performed by a first communication device. The method includes:

[0006] The first communication device receives first information, where the first information is used to indicate at least one of the following:

[0007] AI or ML positioning features supported by the second communication device;

[0008] Association information of the AI ​​or ML positioning features supported by the second communication device.

[0009] In a second aspect, an embodiment of the present disclosure provides a communication method, which is performed by a second communication device. The method includes:

[0010] The first communication device sends first information, where the first information is used to indicate at least one of the following:

[0011] AI or ML positioning features supported by the second communication device;

[0012] Association information of the AI ​​or ML positioning features supported by the second communication device.

[0013] In a third aspect, an embodiment of the present disclosure provides a communication method applicable to a communication system, the method comprising:

[0014] The second communication device sends first information to the first communication device, where the first information is used to indicate at least one of the following:

[0015] AI or ML positioning features supported by the second communication device;

[0016] Association information of the AI ​​or ML positioning features supported by the second communication device.

[0017] In a fourth aspect, an embodiment of the present disclosure provides a first communication device, including:

[0018] The transceiver module is configured to receive first information, where the first information is configured to indicate at least one of the following:

[0019] AI or ML positioning features supported by the second communication device;

[0020] Association information of the AI ​​or ML positioning features supported by the second communication device.

[0021] In a fifth aspect, an embodiment of the present disclosure provides a second communication device, including:

[0022] The transceiver module is configured to send first information, where the first information is configured to indicate at least one of the following:

[0023] AI or ML positioning features supported by the second communication device;

[0024] Association information of the AI ​​or ML positioning features supported by the second communication device.

[0025] In a sixth aspect, an embodiment of the present disclosure provides a communication device, including:

[0026] one or more processors;

[0027] The processor is used to call instructions to enable the communication device to execute the communication method described in any one of the first aspect and the second aspect.

[0028] In the seventh aspect, an embodiment of the present disclosure proposes a communication system, characterized in that it includes a first communication device and a second communication device, wherein the first communication device is configured to implement the communication method described in the first aspect, and the second communication device is configured to implement the communication method described in the second aspect.

[0029] In an eighth aspect, an embodiment of the present disclosure proposes a storage medium storing instructions, wherein when the instructions are executed on a communication device, the communication device executes the communication method as described in any one of the first and second aspects.

[0030] In a ninth aspect, an embodiment of the present disclosure proposes a program product. When the computer program product is run on a communication device, the communication device executes the communication method as described in any one of the first and second aspects.

[0031] In a tenth aspect, an embodiment of the present disclosure provides a chip or a chip system, which includes a processing circuit configured to execute the method described in the optional implementation of the first and second aspects above.

[0032] In the above embodiment, the first communication device sends the first information to the second communication device. Through the first information, the second communication device can obtain the AI ​​or ML positioning characteristics currently applicable to the first communication device, thereby facilitating the subsequent positioning configuration of the first communication device and improving the accuracy of positioning.

[0033] It is understandable that the first communication device, the second communication device, the communication system, the storage medium, the program product, the chip, or the chip system are all used to perform the method proposed in the embodiment of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding method and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The above and / or additional aspects and advantages of the present disclosure will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0035] FIG1 is an exemplary schematic diagram of the architecture of a communication system provided according to an embodiment of the present disclosure.

[0036] 2A-2B are exemplary interaction diagrams of a communication method according to an embodiment of the present disclosure.

[0037] 3A-3B are flowcharts of a communication method according to an embodiment of the present disclosure.

[0038] 4A-4B are flowcharts of a communication method according to an embodiment of the present disclosure.

[0039] FIG5 is a schematic structural diagram of a communication device provided according to an embodiment of the present disclosure.

[0040] FIG6A is a schematic structural diagram of a communication device provided by an embodiment of the present disclosure;

[0041] FIG6B is a schematic structural diagram of a chip provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0042] The embodiments of the present disclosure provide a communication method, a first communication device, and a second communication device.

[0043] In a first aspect, an embodiment of the present disclosure provides a communication method applicable to a first communication device, the method comprising:

[0044] The first communication device receives first information, where the first information is used to indicate at least one of the following:

[0045] AI or ML positioning features supported by the second communication device;

[0046] Association information of the AI ​​or ML positioning features supported by the second communication device.

[0047] In the above embodiment, the first information can enable the first communication device to obtain the AI ​​or ML positioning characteristics supported by the second communication device, thereby facilitating the subsequent positioning configuration of the second communication device and improving the accuracy of positioning of the second communication device.

[0048] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0049] The first communication device sends second information, where the second information is used to query at least one of the following:

[0050] AI or ML positioning features supported by the second communication device;

[0051] Association information of the AL or ML positioning feature supported by the second communication device.

[0052] In the above embodiment, the first communication device can send the second information to the second communication device, and trigger the second communication device to report the supported AI or ML positioning characteristics, and / or the associated information of the AI ​​or ML positioning characteristics through the second information. There is no need for the second communication device to report periodically or in real time, which can avoid waste of communication resources.

[0053] In combination with some embodiments of the first aspect, in some embodiments, the first communication device is an LMF network element, the second communication device is a terminal device, the LMF network element sends the second information to the terminal device; the LMF network element receives the first information sent by the terminal device.

[0054] In the above embodiment, the LMF network element can trigger the terminal device to report the supported AI / ML positioning characteristics through the second information, which can avoid the waste of communication resources of the terminal device and enable the LMF network element to perceive the positioning capability of the terminal device, thereby facilitating the subsequent positioning configuration of the terminal device.

[0055] In combination with some embodiments of the first aspect, in some embodiments, the first communication network device is an LMF network element, the second communication device is a base station, and the LMF sends the second information to the base station device; the LMF network element receives the first information sent by the base station.

[0056] In the above embodiment, the LMF network element can trigger the base station to report the supported AI / ML positioning characteristics and / or associated information of the AI ​​or ML positioning characteristics through the second information, so that the LMF network element can perceive the positioning capability of the base station and thus configure the base station for subsequent positioning.

[0057] In combination with some embodiments of the first aspect, in some embodiments, the first communication network device is an AMF network element, the second communication device is an LMF network element, the AMF network element sends the second information to the LMF network element; the AMF network element receives the first information sent by the LMF network element.

[0058] In the above embodiment, the AMF network element can trigger the LMF network element to report the supported AI or ML positioning characteristics and / or associated information of the AI ​​or ML positioning characteristics through the second information, so that the AMF network element can perceive the positioning capability of the LMF network element and thus configure the LMF network element for subsequent positioning.

[0059] In combination with some embodiments of the first aspect, in some embodiments, the AI ​​or ML positioning features supported by the second communication device include one or more.

[0060] In conjunction with some embodiments of the first aspect, in some embodiments, different AI-based positioning characteristics differ in at least one of the following associated information:

[0061] The reference signal used for positioning;

[0062] Where the AI ​​or ML model is deployed;

[0063] The types of output information supported by the AI ​​or ML model.

[0064] In the above embodiment, by equipping the AI ​​or ML positioning characteristics with different associated information, the AI ​​positioning characteristics can be made more diverse, which is conducive to better positioning the second communication device.

[0065] In conjunction with some embodiments of the first aspect, in some embodiments, the second information is further used to query at least one of the following:

[0066] AI or ML positioning features: Whether AI or ML positioning functions are included;

[0067] Whether the AI ​​or ML positioning feature includes related information of the AI ​​or ML positioning function.

[0068] In the above embodiment, the second communication device can not only report its own positioning characteristics to the first communication device, but also send AI or ML positioning functions, and / or related information of the AI ​​or ML positioning functions to the first communication device, so that the first communication device can better understand the positioning capabilities of the second communication device, which is conducive to improving the accuracy of positioning.

[0069] In conjunction with some embodiments of the first aspect, in some embodiments, the first information is further used to indicate at least one of the following:

[0070] AI or ML positioning features: Whether AI or ML positioning functions are included;

[0071] Whether the AI ​​or ML positioning feature includes information related to the AI ​​or ML positioning function.

[0072] In the above embodiment, the first communication device can not only query the second communication device for the supported AI or ML positioning features, but also query the second communication device for the AI ​​or ML positioning function and / or related information of the AI ​​or ML positioning function, so that the first communication device can better understand the positioning capability of the second communication device, which is conducive to improving the accuracy of positioning.

[0073] In conjunction with some embodiments of the first aspect, in some embodiments, the AI ​​or ML positioning feature includes one or more AI or ML positioning functions.

[0074] In conjunction with some embodiments of the first aspect, in some embodiments, different AI or ML positioning functions differ in at least one of the following associated information:

[0075] Operating frequency band;

[0076] Configuration of reference signal RS;

[0077] Additional outputs from AI / ML models;

[0078] Type of measurement reported;

[0079] Application scenarios.

[0080] In the above embodiment, by equipping the AI ​​or ML positioning function with different associated information, the positioning function of AI / ML can be made more diverse, which is conducive to better positioning the second communication device.

[0081] In a second aspect, an embodiment of the present disclosure provides a communication method, which is performed by a second communication device. The method includes:

[0082] The second communication device sends first information, where the first information is used to indicate at least one of the following:

[0083] AI or ML positioning features supported by the second communication device;

[0084] Association information of the AI ​​or ML positioning features supported by the second communication device.

[0085] In the above embodiment, the first information enables the first communication device to obtain the AI ​​or ML positioning characteristics supported by the second communication device, thereby facilitating the subsequent positioning configuration of the second communication device and improving the accuracy of positioning the second communication device.

[0086] In conjunction with some embodiments of the first aspect, in some embodiments, the method further includes:

[0087] The second communication device receives second information, where the second information is used to query at least one of the following:

[0088] AI or ML positioning feature capabilities supported by the second communication device;

[0089] Association of AI or ML positioning features supported by the second communication device.

[0090] In combination with some embodiments of the second aspect, in some embodiments, the first communication device is an LMF network element, the second communication device is a terminal device, the terminal device receives the second information sent by the LMF network element; the terminal device sends the first information to the LMF network element.

[0091] In combination with some embodiments of the second aspect, in some embodiments, the first communication device is an LMF network element, the second communication device is a base station, the base station receives the second information sent by the LMF network element; the base station sends the first information to the LMF network element.

[0092] In combination with some embodiments of the second aspect, in some embodiments, the first communication device is an AMF network element, the second communication device is an LMF network element, the LMF network element receives the second information sent by the AMF; the LMF network element sends the first information to the AMF.

[0093] In combination with some embodiments of the first aspect, in some embodiments, the AI / ML positioning features supported by the second communication device include one or more.

[0094] In conjunction with some embodiments of the first aspect, in some embodiments, different AI or ML positioning characteristics differ in at least one of the following information items:

[0095] Reference signals used for positioning;

[0096] Where the AI / ML model is deployed;

[0097] The types of output information supported by the AI / ML model.

[0098] In conjunction with some embodiments of the first aspect, in some embodiments, the second information is further used to query at least one of the following:

[0099] AI or ML positioning features: Whether AI or ML positioning functions are included;

[0100] Whether the AI ​​or ML positioning feature includes related information of the AI ​​or ML positioning function.

[0101] In conjunction with some embodiments of the first aspect, in some embodiments, the first information is further used to indicate at least one of the following: whether the AI ​​or ML positioning feature includes an AI or ML positioning function;

[0102] Whether the AI ​​or ML positioning feature includes information related to the AI ​​or ML positioning function.

[0103] In conjunction with some embodiments of the first aspect, in some embodiments, the AI ​​or ML positioning feature includes one or more AI or ML positioning functions.

[0104] In conjunction with some embodiments of the first aspect, in some embodiments, different AI or ML positioning functions differ in at least one of the following information items:

[0105] Operating frequency band;

[0106] RS configuration;

[0107] Additional outputs from AI / ML models;

[0108] Type of measurement reported;

[0109] Application scenarios.

[0110] In a third aspect, an embodiment of the present disclosure provides a communication method applicable to a communication system, the method comprising:

[0111] The second communication device sends first information to the first communication device, where the first information is used to indicate at least one of the following:

[0112] AI or ML positioning features supported by the second communication device;

[0113] Association information of the AI ​​or ML positioning features supported by the second communication device.

[0114] In a fourth aspect, an embodiment of the present disclosure provides a first communication device, the first communication device comprising: a transceiver module;

[0115] The transceiver module is configured to receive first information, where the first information is configured to indicate at least one of the following:

[0116] AI or ML positioning features supported by the second communication device;

[0117] Association information of the AI ​​or ML positioning features supported by the second communication device.

[0118] In a fifth aspect, an embodiment of the present disclosure provides a second communication device, the second communication device including: a transceiver module;

[0119] The transceiver module is configured to send first information, where the first information is configured to indicate at least one of the following:

[0120] AI or ML positioning features supported by the second communication device;

[0121] Association information of the AI ​​or ML positioning features supported by the second communication device.

[0122] According to a sixth aspect of an embodiment of the present disclosure, a communication device is provided, including:

[0123] one or more processors;

[0124] The processor is used to call instructions to enable the communication device to execute the reporting method described in any one of the first aspect and the second aspect.

[0125] According to the seventh aspect of an embodiment of the present disclosure, a communication system is proposed, characterized in that it includes a first communication device and a second communication device, wherein the first communication device is configured to implement the determination method described in the first aspect, and the second communication device is configured to implement the determination method described in the second aspect.

[0126] According to an eighth aspect of an embodiment of the present disclosure, a storage medium is proposed, which stores instructions, and is characterized in that when the instructions are executed on a communication device, the communication device executes the determination method described in any one of the first and second aspects.

[0127] In a ninth aspect, an embodiment of the present disclosure proposes a computer program product, which, when executed on a communication device, enables the communication device to execute the communication method as described in any one of the first and second aspects.

[0128] In a tenth aspect, an embodiment of the present disclosure provides a chip or a chip system, which includes a processing circuit configured to execute the method described in the optional implementation of the first and second aspects above.

[0129] It is understandable that the first communication device, the second communication device, the communication system, the storage medium, the computer program product, the chip, or the chip system described above are all used to perform the methods proposed in the embodiments of the present disclosure. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding methods and will not be repeated here.

[0130] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0131] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular, such as "a", "an", "the", "above", "said", "the", "the", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun following the article may be understood as a singular expression or a plural expression.

[0132] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0133] In some embodiments, devices, etc. can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. Terms such as "device", "equipment", "device", "circuit", "network element", "node", "function", "unit", "section", "system", "network", "chip", "chip system", "entity", and "subject" can be used interchangeably.

[0134] In some embodiments, "network" can be interpreted as devices included in the network (eg, access network equipment, core network equipment, etc.).

[0135] In some embodiments, the terms "terminal", "terminal device", "user equipment (UE)", "user terminal", "mobile station (MS)", "mobile terminal (MT)", subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, etc. can be used interchangeably.

[0136] FIG1 is a schematic diagram of the architecture of a communication system according to an embodiment of the present disclosure. As shown in FIG1 , the communication system 100 may include a first communication device 101 and a second communication device 102. The second communication device may be a terminal, which may be a reference signal transmitting device or receiving device, and the second device may be a device for configuring reference signal resources. Optionally, the second communication device may also include at least one of an access network device (such as a base station and / or a transmission reception point (TRP)), a terminal, and a core network device (such as a location management function (LMF)). The first communication device may be a device that receives information sent by the first communication device, or may be a device that sends information to the second communication device. The first communication device may be, for example, a core network device, such as an LMF network element or an access and mobility management function (AMF) network element in the core network device.

[0137] In some embodiments, the terminal includes, for example, a mobile phone, a wearable device, an Internet of Things device, a car with communication function, a smart car, a tablet computer, a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control, a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in a smart grid, a wireless terminal device in transportation safety, a wireless terminal device in a smart city, and at least one of a wireless terminal device in a smart home, but is not limited thereto.

[0138] In some embodiments, the access network device is, for example, a node or device that accesses a terminal to a wireless network. The access network device may include an evolved NodeB (eNB), a next generation evolved NodeB (ng-eNB), a next generation NodeB (gNB), a node B (NB), a home node B (HNB), a home evolved nodeB (HeNB), a wireless backhaul device, a radio network controller (RNC), a base station controller (BSC), a base transceiver station (BTS), a base band unit (BBU), a mobile switching center, a base station in a 6G communication system, an open base station (Open RAN), a cloud base station (Cloud RAN), a base station in other communication systems, and at least one of an access node in a wireless fidelity (WiFi) system, but is not limited thereto.

[0139] In some embodiments, the technical solution of the present disclosure can be applied to the Open RAN architecture. In this case, the interfaces between or within the access network devices involved in the embodiments of the present disclosure can be transformed into internal interfaces of the Open RAN, and the processes and information interactions between these internal interfaces can be implemented through software or programs.

[0140] In some embodiments, the access network device can be composed of a centralized unit (CU) and a distributed unit (DU), where the CU can also be called a control unit. The CU-DU structure can be used to split the protocol layer of the access network device, with the functions of some protocol layers centrally controlled by the CU, and the functions of the remaining part or all of the protocol layers distributed in the DU, which is centrally controlled by the CU, but is not limited to this.

[0141] In some embodiments, the core network device may be a device including one or more network elements, or may be multiple devices or a group of devices, each including all or part of one or more network elements. The network element may be virtual or physical. The core network, for example, includes at least one of an Evolved Packet Core (EPC), a 5G Core Network (5GCN), and a Next Generation Core (NGC). Alternatively, the core network device may also be a location management function network element. Exemplarily, the location management function network element includes a location server (location server), which may be implemented as any one of the following: Location Management Function (LMF), Enhanced Serving Mobile Location Centre (E-SMLC), Secure User Plane Location (SUPL), and Secure User Plane Location Platform (SUPLLP).

[0142] It can be understood that the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution proposed in the embodiment of the present disclosure. Ordinary technicians in this field can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution proposed in the embodiment of the present disclosure is also applicable to similar technical problems.

[0143] The following embodiments of the present disclosure may be applied to the communication system 100 shown in Figure 1, or a portion thereof, but are not limited thereto. The entities shown in Figure 1 are illustrative only. The communication system may include all or part of the entities shown in Figure 1, or may include other entities outside of Figure 1. The number and form of the entities may be arbitrary. The connection relationship between the entities is illustrative only. The entities may be connected or disconnected, and the connection may be in any manner, including direct or indirect, wired or wireless.

[0144] The embodiments of the present disclosure can be applied to Long Term Evolution (LTE), LTE-Advanced (LTE-A), LTE-Beyond (LTE-B), SUPER 3G, IMT-Advanced, 4th generation mobile communication system (4G), 5th generation mobile communication system (5G), 5G new radio (NR), future radio access (FRA), new radio access technology (RAT), new radio (NR), new radio access (NX), future generation radio access (FX), Global System for Mobile Communications (GSM (registered trademark)), CDMA2000, Ultra Mobile Broadband (UMB), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20, Ultra-WideBand (UWB), Bluetooth (registered trademark), Public Land Mobile Network (PLMN) networks, Device-to-Device (D2D) systems, Machine-to-Machine (M2M) systems, Internet of Things (IoT) systems, Vehicle-to-Everything (V2X), systems utilizing other determination methods, and next-generation systems based on and extending these systems. Furthermore, a combination of multiple systems (e.g., a combination of LTE or LTE-A with 5G) may also be employed.

[0145] For artificial intelligence (AI) or machine learning (ML), concepts such as AI / ML features, AI / ML functionality, and AI / ML models have been proposed.

[0146] An AI / ML feature can be understood as a technical solution that uses certain technologies to achieve a specific purpose. Optionally, an AI or ML feature can include, but is not limited to, AI or ML positioning features, AI or ML beam management, and AI or ML CSI enhancement features. For example, an AI or ML positioning feature can be understood as a terminal device using AI / ML technology to perform PRS-based positioning, obtaining positioning assistance information or coordinate information for the terminal device.

[0147] Typically, an AI or ML feature can contain an AI or ML function and / or one or more AI or ML models.

[0148] AI or ML functions can be divided and determined by some key parameters of AI or ML characteristics. For example, the abstraction of the function of a set of AI or ML models can be implemented, which usually corresponds to supporting a specific parameter set. The AI ​​function can be, for example, the AI ​​function to be implemented when executing the corresponding AI use case. Optionally, the AI ​​function can correspond to a parameter set, and the parameter set can include specific parameters. The specific parameters are used to define the AI ​​function. For example, the specific parameters can be used to indicate what the AI ​​function specifically is. The specific parameters can be called, for example, an environment (condition). In addition, the parameters other than the specific parameters in the parameter set of the AI ​​function can be used to indicate the applicable scenario of the AI ​​function. Optionally, the applicable scenario can be understood as, for example, the parameters actually used when implementing the AI ​​function. The parameters other than the specific parameters in the parameter set of the AI ​​function can be called, for example, an additional environment (additional condition).

[0149] For example, under the AI ​​or ML positioning feature, the AI ​​or ML functionality can be defined to support the simultaneous reception of X TRP measurement signals. Optionally, these key parameters are included in the UE capability information.

[0150] An AI or ML model is an AI or ML algorithm, that is, an algorithm that obtains target output parameters through input parameters. For example, an AI or ML positioning model can input the measurement data of the positioning reference signal corresponding to the terminal device and ultimately output the positioning assistance information of the terminal device, or the positioning coordinates of the terminal device.

[0151] In the communication system, the following five cases are defined in AI / ML-based positioning. The five cases are divided according to different outputs and the deployment location of the model. Optionally, direct AI / ML positioning can directly output the positioning coordinates of the terminal device based on the input of the AI / ML model. Assisted positioning based on AI / ML can output intermediate parameters for positioning based on the input of the AI / ML model, which can be divided into many types. For example, arrival time, arrival angle, departure angle, line-of-sight (LOS) / non-line-of-sight (NLOS) indication information, etc.

[0152] Direct AI / ML positioning:

[0153] Case 1: The AI ​​or ML model deployed on the terminal device side can locate the terminal device based on the PRS and directly output the terminal device's positioning coordinates (UE-based positioning with UE-side model, direct AI / ML positioning);

[0154] Case 2b: The AI ​​or ML model deployed on the LMF side can perform UE-assisted / LMF-based positioning based on the PRS and directly output the terminal device's positioning coordinates (UE-assisted / LMF-based positioning with LMF-side model, direct AI / ML positioning).

[0155] Case 3b: The AI ​​or ML model deployed on the LMF side can perform assisted / LMF positioning of the terminal device based on the SRS and directly output the terminal device's positioning coordinates (NG-RAN node assisted positioning with LMF-side model, direct AI / ML positioning);

[0156] Assisted AI / ML positioning:

[0157] Case 2a: The AI ​​or ML model deployed on the terminal device side can output the predicted timing information of the terminal device based on the PRS (UE-assisted / LMF-based positioning with UE-side model, AI / ML assisted positioning, AI / ML-based timing information prediction);

[0158] Case 3a: The AI ​​or ML model deployed on the terminal device side can output the predicted time information of the terminal device based on the PRS (UE-assisted / LMF-based positioning with UE-side model, AI / ML assisted positioning, AI / ML-base angle information prediction).

[0159] In recent years, AI technology has achieved continuous breakthroughs in various fields. The continued development of fields such as intelligent voice and computer vision has not only brought a rich variety of applications to smart terminals, but has also found widespread application in education, transportation, home appliances, healthcare, retail, security, and other fields. This has not only brought convenience to people's lives but also promoted industrial upgrading across various industries. AI technology is also rapidly interpenetrating with other disciplines. Its development integrates knowledge from different disciplines while also providing new directions and methods for their development. In wireless AI research, AI application cases include AI- or ML-based CSI enhancement, AI- or ML-based beam management, and AI- or ML-based positioning. For AI- or ML-based positioning scenarios, how to report AI- or ML-based features has become a hot topic of research.

[0160] FIG2A is an interactive diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG2A , the embodiment of the present disclosure relates to a communication method, and the method includes:

[0161] Step S2101: The first communication device sends second information to the second communication device.

[0162] In some embodiments, the second communication device receives the second information.

[0163] In some embodiments, the second communication device may be a terminal device, a base station, or an LMF network element.

[0164] In some embodiments, the first communication device may be a LMF network element or an AMF network element.

[0165] In some embodiments, the first communication device is a LMF network element, and the second communication device is a terminal device, or a base station.

[0166] In some embodiments, the first communication device is an AMF network element and the second communication device is an LMF network element.

[0167] In some embodiments, the second information is used to obtain AI or ML positioning features supported by the second communication device.

[0168] In some embodiments, the second information is used to obtain information associated with AI or ML positioning features supported by the second communication device.

[0169] In some embodiments, the second information is used to obtain the AI ​​or ML positioning features supported by the second communication device and associated information of the AI ​​or ML positioning features.

[0170] In some embodiments, the second information is used to query the AI ​​or ML positioning features supported by the second communication device.

[0171] In some embodiments, the second information is used to query information associated with AI or ML positioning features supported by the second communication device.

[0172] In some embodiments, the second information is used to query the AI ​​or ML positioning features supported by the second communication device and associated information of the AI ​​or ML positioning features.

[0173] In some embodiments, the name of the second information is not limited, and may be, for example, "RequestCapabilities message", "query request", "capability query request", "acquisition request", "capability acquisition request", etc.

[0174] In some embodiments, the AI ​​or ML positioning feature can be understood as an AI / ML-based positioning feature.

[0175] In some embodiments, information associated with AI or ML positioning characteristics may include, but is not limited to:

[0176] Reference signals used for positioning, for example, may include but are not limited to positioning reference signals (PRS) and sounding reference signals (SRS). For example, a network device may locate a terminal device based on a PRS or based on an SRS.

[0177] The location where the AI / ML model is deployed. For example, the AI / ML model can be deployed on the terminal device side, on the base station side, or on the LMF side.

[0178] The types of output information supported by the AI / ML model. In some implementations, the AI / ML model can directly output the location coordinates of the second communication device (such as a terminal device). In other implementations, the AI / ML model can output the time information, angle information, and LOS / NLOS indication information used to locate the second communication device (terminal device).

[0179] In some embodiments, in some implementations, the AI ​​positioning features supported by the second communication device include one or more, for example, AI or ML positioning features may include but are not limited to AI / ML direct positioning and AI / ML-assisted positioning.

[0180] In some embodiments, in some implementations, different AI or ML positioning characteristics differ in at least one of the following associated information:

[0181] Reference signals used for positioning;

[0182] Where the AI / ML model is deployed;

[0183] The types of output information supported by the AI / ML model.

[0184] For example, in the AI ​​or ML positioning feature X-1, the reference signal that can be used can be a PRS. The type of output information supported by the AI / ML model is the direct output of the terminal device's positioning coordinates, and the deployment location can be on the terminal device side. In other words, the AI ​​or ML model deployed on the terminal device side can locate the terminal device based on the PRS and directly output the terminal device's positioning coordinates. In the embodiment of the present application, this situation can be defined as Case 1.

[0185] AI or ML positioning feature X-2: The reference signal that can be used can be PRS. The category of output information supported by the AI / ML model is to directly output the positioning coordinates of the terminal device, and the deployment location can be on the LMF side. In other words, the AI ​​or ML model deployed on the LMF side can perform auxiliary / LMF positioning of the terminal device based on PRS and directly output the positioning coordinates of the terminal device. In the embodiment of the present application, this situation can be defined as case 2b.

[0186] AI or ML positioning feature X-3: The reference signal that can be used can be SRS. The category of output information supported by the AI ​​or ML model is to directly output the positioning coordinates of the terminal device, and the deployment location can be on the LMF side. In other words, the AI ​​or ML model deployed on the LMF side can perform auxiliary / LMF positioning of the terminal device based on the SRS and directly output the positioning coordinates of the terminal device. In the embodiment of the present application, this situation can be defined as case 3b.

[0187] AI or ML positioning feature X-4: The reference signal that can be used can be a PRS. The category of output information supported by the AI / ML model is AI- or ML-based predicted time information for terminal device positioning, and the deployment location can be on the terminal device side. In other words, the AI ​​or ML model deployed on the terminal device side can output the predicted time information of the terminal device based on the PRS. In the embodiments of the present application, this situation can be defined as case 2a.

[0188] AI or ML positioning feature X-5: The reference signal that can be used can be PRS. The category of output information supported by the AI / ML model is the AI / ML-based angle information prediction for terminal device positioning, and the deployment location can be on the terminal device side. In other words, the AI ​​or ML model deployed on the terminal device side can output the predicted angle information of the terminal device based on PRS. This predicted angle information can assist in positioning the terminal device. In the embodiment of the present application, this situation can be defined as case 2a.

[0189] AI or ML positioning feature X-6: The reference signal that can be used can be SRS. The category of output information supported by the AI / ML model is the predicted time information for terminal device positioning based on AI or ML, and the deployment location can be on the base station (such as gNB) side. In other words, the AI ​​or ML model deployed on the base station side can output the predicted time information of the terminal device based on SRS. The predicted time information can assist in positioning the terminal device. In the embodiment of the present application, this situation can be defined as case 3a.

[0190] AI or ML positioning feature X-7, the reference signal that can be used can be SRS, and the category of output information supported by the AI / ML model is the predicted angle information for terminal device positioning based on AI or ML, and the deployment location can be on the base station side. In other words, the AI ​​or ML model deployed on the base station side can output the predicted angle information of the terminal device based on SRS, and the predicted angle information can assist in positioning the terminal device. In the embodiment of the present application, this situation can be defined as case 3a.

[0191] Table 1 shows examples of different AI or ML positioning features with at least one different item in the associated information, such as the reference signal used for positioning, the location where the AI / ML model is deployed, and the type of output information supported by the AI / ML model.

[0192] Table 1

[0193] It should be understood that each element in Table 1 exists independently. These elements are illustratively listed in the same table, but this does not necessarily mean that all elements in the table must be present simultaneously as shown. The value of each element is independent of the value of any other element in Table 1. Therefore, those skilled in the art will understand that the value of each element in Table 1 represents an independent embodiment.

[0194] In some embodiments, the second information may reuse existing information or may be newly added information.

[0195] In some embodiments, the second information is further used to query whether the AI ​​or ML positioning feature includes an AI or ML positioning function. The AI ​​or ML positioning function can be understood as AI / ML-based functionality. The AI / ML-based positioning function can be defined under each AI / ML positioning feature. For example, the AI ​​or ML positioning function can support PRS configuration measurement of up to X RBs under a specific AI / ML feature; for another example, the AI ​​or ML positioning function can be capable of simultaneously measuring PRSs from Y transmission and receiving points (TRPs).

[0196] In some embodiments, the second information is further used to query whether the AI ​​or ML positioning feature includes associated information of the AI ​​or ML positioning function.

[0197] In some embodiments, the second information is further used to query whether the AI ​​or ML positioning feature includes the AI ​​or ML positioning function and associated information of the AI ​​or ML positioning function.

[0198] In some embodiments, the second information is further used to obtain whether the AI ​​or ML positioning feature includes an AI or ML positioning function.

[0199] In some embodiments, the second information is further used to obtain whether the AI ​​or ML positioning feature includes associated information of the AI ​​or ML positioning function.

[0200] In some embodiments, the second information is further used to obtain whether the AI ​​or ML positioning characteristics include the AI ​​or ML positioning function and associated information of the AI ​​or ML positioning function.

[0201] In some embodiments, the second information includes second signaling. The second signaling is used to query whether the AI ​​or ML positioning feature includes the AI ​​or ML positioning function, and / or to query whether the AI ​​or ML positioning feature includes associated information of the AI ​​or ML positioning function. The second signaling is used to obtain whether the AI ​​or ML positioning feature includes the AI ​​or ML positioning function, and / or to obtain associated information of whether the AI ​​or ML positioning feature includes the AI ​​or ML positioning function. The second signaling is used to determine whether the AI ​​or ML positioning feature includes the AI ​​or ML positioning function, and / or to determine whether the AI ​​or ML positioning feature includes associated information of the AI ​​or ML positioning function.

[0202] In some embodiments, the name of the second signaling is not limited, and may be, for example, "function query information", "information query message", "function acquisition information", "information acquisition message", etc.

[0203] For example, the second signaling can be used to query whether a specific or certain AI or ML positioning characteristics include a specific or certain AI or ML positioning function. For example, it can be queried whether "AI or ML positioning characteristic X" includes "AI or ML positioning function Y"; another example can be queried whether "AI or ML positioning characteristic X" includes "AI or ML positioning function Y and positioning function Z"; another example can be queried whether "AI or ML positioning characteristics X and M" include "AI or ML positioning function Y and positioning function Z", etc.

[0204] The second signaling can be used to query whether a specific or certain AI or ML positioning characteristics include the associated information of a specific or certain AI or ML positioning function. For example, it can be queried whether "AI or ML positioning characteristic X" includes "the associated information of AI or ML positioning function Y"; for another example, it can be queried whether "AI or ML positioning characteristic X" includes "the associated information of AI or ML positioning function Y and positioning function Z"; for another example, it can be queried whether "AI or ML positioning characteristics X and M" include "the associated information of AI or ML positioning function Y and positioning function Z".

[0205] In some embodiments, the associated information of the AI ​​or ML positioning function may include but is not limited to:

[0206] Working frequency band, for example, the working frequency band may include FR1 and FR2.

[0207] Reference Signal (RS) configuration. For example, RS configuration can include different RS configurations or RS configurations sent by different base stations. For example, RS configuration can support PRS measurements from up to 9 TRPs; for example, RS configuration can support PRS measurements from up to 18 TRPs.

[0208] Additional outputs of the AI / ML model, for example, the additional outputs of the AI / ML model may be additional output reference signal received power (RSRP), additional output reference signal received quality (RSRQ), additional output signal to interference plus noise ratio (SINR), etc.

[0209] The type of measurement reporting, for example, periodic reporting, aperiodic reporting, or semi-periodic reporting, or the information included in the measurement reporting, etc.

[0210] Application scenarios of AI or ML positioning capabilities or AI / ML models. For example, AI or ML positioning capabilities or AI / ML models can be applied in macro cells or micro cells. For another example, AI or ML positioning capabilities or AI / ML models can be applied in indoor scenarios or outdoor scenarios.

[0211] In some embodiments, the AI ​​or ML positioning feature includes one or more AI or ML positioning functions.

[0212] In some embodiments, different AI or ML positioning capabilities differ in at least one of the following associated information:

[0213] Operating frequency band;

[0214] RS configuration;

[0215] Additional outputs from AI / ML models;

[0216] Type of measurement reported;

[0217] AI or ML positioning capabilities or application scenarios of AI / ML models.

[0218] Table 2 shows an example of different AI or ML positioning capabilities having at least one different item in related information such as the operating frequency band, RS configuration, additional output of the AI / ML model, type of measurement reporting, and application scenario of the AI ​​or ML positioning capability or AI / ML model.

[0219] Table 2

[0220] It should be understood that each element in Table 2 exists independently. These elements are illustratively listed in the same table, but this does not necessarily mean that all elements in the table must be present simultaneously as shown. The value of each element is independent of the value of any other element in Table 2. Therefore, those skilled in the art will understand that the value of each element in Table 2 represents an independent embodiment.

[0221] Step S2102: The second communication device sends first information to the first communication device.

[0222] In some embodiments, the first information is used to indicate AI or ML positioning features supported by the second communication device.

[0223] In some embodiments, the first information is used to report the AI ​​or ML positioning characteristics supported by the second communication device.

[0224] In some embodiments, the first information is used to indicate associated information of AI or ML positioning features supported by the second communication device.

[0225] In some embodiments, the first information is used to indicate associated information of AI or ML positioning features supported by the second communication device.

[0226] In some embodiments, the first information is used to indicate or report the AI ​​or ML positioning features supported by the second communication device and associated information of the AI ​​or ML positioning features.

[0227] In some embodiments, the name of the first information is not limited, and it can be, for example, "ProvideCapabilities", "first indication information", "characteristic indication information", "first reporting information", "characteristic reporting information", "first information indication message", "first information reporting message", etc.

[0228] In some embodiments, the first communication device receives the first information.

[0229] In some embodiments, the first communication device may be an LMF network element, the second communication device may be a terminal device, the LMF network element may send second information to the terminal device, and the LMF network element may receive first information sent by the terminal device.

[0230] In some embodiments, the first communication device may be a LMF network element, the second communication device may be a base station, the LMF network element sends the second information to the base station, and the LMF network element receives the first information sent by the base station.

[0231] In some embodiments, the first communication device may be an AMF network element, the second communication device may be an LMF network element, the AMF network element sends the second information to the LMF network element, and the AMF network element receives the first information sent by the LMF network element.

[0232] In some embodiments, the first information is further used to indicate whether the AI ​​or ML positioning feature includes an AI or ML positioning function.

[0233] In some embodiments, the first information is further used to indicate whether the AI ​​or ML positioning feature includes associated information of the AI ​​or ML positioning function.

[0234] In some embodiments, the first information is further used to report whether the AI ​​or ML positioning feature includes an AI or ML positioning function.

[0235] In some embodiments, the first information is further used to report whether the AI ​​or ML positioning feature includes associated information of the AI ​​or ML positioning function.

[0236] In some embodiments, the first information is further used to report whether the AI ​​or ML positioning feature includes the AI ​​or ML positioning function and associated information of the AI ​​or ML positioning function.

[0237] In some embodiments, the first information includes a first signaling. The first signaling is used to indicate whether the AI ​​or ML positioning feature includes the AI ​​or ML positioning function, and / or, to indicate whether the AI ​​or ML positioning feature includes associated information of the AI ​​or ML positioning function. The first signaling is used to report whether the AI ​​or ML positioning feature includes the AI ​​or ML positioning function, and / or, to report whether the AI ​​or ML positioning feature includes associated information of the AI ​​or ML positioning function.

[0238] In some embodiments, the name of the first signaling is not limited, and may be, for example, "second indication information", "function indication information", "second reporting information", "function reporting information", "second information indication message", "second information reporting message", etc.

[0239] For example, the first signaling may be used to indicate whether a particular AI or ML positioning feature or features include a particular AI or ML positioning function or functions. For example, it may indicate whether "AI or ML positioning feature X" includes "AI or ML positioning function Y"; another example may indicate whether "AI or ML positioning feature X" includes "AI or ML positioning function Y and positioning function Z"; another example may indicate whether "AI or ML positioning features X and M" include "AI or ML positioning function Y and positioning function Z", etc.

[0240] The first signaling may be used to indicate whether a specific AI or ML positioning feature or features include association information of a specific AI or ML positioning function or functions. For example, it may indicate whether "AI or ML positioning feature X" includes "association information of AI or ML positioning function Y"; another example may indicate whether "AI or ML positioning feature X" includes "association information of AI or ML positioning function Y and positioning function Z"; another example may indicate whether "AI or ML positioning features X and M" includes "association information of AI or ML positioning function Y and positioning function Z."

[0241] In some implementations, the AI ​​or ML positioning feature includes one or more AI or ML positioning functions.

[0242] In some implementations, information associated with AI or ML positioning capabilities includes, but is not limited to:

[0243] Operating frequency band;

[0244] RS configuration;

[0245] Additional outputs from AI or ML models;

[0246] Type of measurement reported;

[0247] AI or ML positioning capabilities or application scenarios of AI / ML models.

[0248] In some implementations, different AI or ML positioning capabilities have at least one different example in associated information such as the operating frequency band, RS configuration, additional output of the AI ​​or ML model, type of measurement reporting, AI or ML positioning capabilities, or application scenarios of the AI ​​or ML model, as shown in Table 2.

[0249] In some embodiments, the names of information, etc. are not limited to the names described in the embodiments, and terms such as "information", "message", "signal", "signaling", "report", "configuration", "indication", "instruction", "command", "channel", "parameter", "domain", "field", "symbol", "symbol", "codeword", "codebook", "codeword", "codepoint", "bit", "data", "program", and "chip" can be used interchangeably.

[0250] In some embodiments, the terms "downlink control information (DCI)", "downlink (DL) assignment", "DL DCI", "uplink (UL) grant", "UL DCI" and the like may be used interchangeably.

[0251] In some embodiments, the terms "radio", "wireless", "radio access network (RAN)", "access network (AN)", "RAN-based" and the like may be used interchangeably.

[0252] In some embodiments, terms such as "moment", "time point", "time", and "time position" can be replaced with each other, and terms such as "duration", "period", "time window", "window", and "time" can be replaced with each other.

[0253] In some embodiments, terms such as "resource block (RB)", "physical resource block (PRB)", "sub-carrier group (SCG)", "resource element group (REG)", "PRB pair", "RB pair", "resource element (RE)", and "sub-carrier" can be used interchangeably.

[0254] In some embodiments, terms such as wireless access scheme and waveform may be used interchangeably.

[0255] In some embodiments, "obtain", "get", "get", "receive", "transmit", "bidirectional transmission", "send and / or receive" can be interchangeable, and can be interpreted as receiving from other entities, obtaining from protocols, obtaining from higher layers, obtaining by self-processing, autonomous implementation, etc.

[0256] In some embodiments, terms such as "send", "transmit", "report", "download", "transmit", "bidirectional transmission", "send and / or receive" can be used interchangeably.

[0257] In some embodiments, terms such as "certain", "preset", "preset", "setting", "indicated", "a certain", "any", and "first" can be interchangeable. "Specific A", "preset A", "preset A", "setting A", "indicated A", "a certain A", "any A", and "first A" can be interpreted as A pre-specified in a protocol, etc., or as A obtained through setting, configuration, or indication, etc., or as specific A, a certain A, any A, or first A, etc., but not limited to this.

[0258] In some embodiments, the determination or judgment can be performed by a value represented by 1 bit (0 or 1), or by a true or false value (Boolean value) represented by true or false, or by comparison of numerical values ​​(for example, comparison with a predetermined value), but is not limited thereto.

[0259] In some embodiments, "not expecting to receive" can be interpreted as not receiving on time domain resources and / or frequency domain resources, or as not performing subsequent processing on the data after receiving it; "not expecting to send" can be interpreted as not sending, or as sending but not expecting the recipient to respond to the content sent.

[0260] The communication method involved in the embodiment of the present disclosure may include at least one of steps S2101 and S2102. For example, step S2102 may be implemented as an independent embodiment, and steps S2101 and S2102 may be implemented as independent embodiments, but are not limited thereto.

[0261] FIG2B is an interactive diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG2B , the embodiment of the present disclosure relates to a communication method, and the method includes:

[0262] Step S2201: The second communication device sends first information to the first communication device.

[0263] The optional implementation of step S2201 can refer to the optional implementation of steps S2101 and S2102 in Figure 2A, and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0264] FIG3A is an interactive diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG3A , the embodiment of the present disclosure relates to a communication method (on the first communication device side), the method comprising:

[0265] Step S3101: The first communication device sends second information.

[0266] The optional implementation of step S3101 can refer to the optional implementation of step S2101 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0267] Step S3102: The first communication device receives first information.

[0268] The optional implementation of step S3102 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0269] FIG3B is an interactive diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG3B , the embodiment of the present disclosure relates to a communication method (network device side), the method comprising:

[0270] Step S3201: The first communication device receives first information.

[0271] The optional implementation of step S3201 can refer to steps S2101 to S2102 in Figure 2A, the optional implementation of step S2201 in Figure 2B, and other related parts in the embodiments involved in Figures 2A and 2B, which will not be repeated here.

[0272] FIG4A is an interactive diagram of a communication method according to an embodiment of the present disclosure. As shown in FIG4A , the embodiment of the present disclosure relates to a communication method (on the second communication device side), the method comprising:

[0273] Step S4101: The second communication device receives second information.

[0274] The optional implementation of step S4101 can refer to the optional implementation of step S2101 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0275] Step S4102: The second communication device sends the first information.

[0276] The optional implementation of step S4102 can refer to the optional implementation of step S2102 in Figure 2A and other related parts in the embodiment involved in Figure 2A, which will not be repeated here.

[0277] FIG4B is a flow chart of a communication method according to an embodiment of the present disclosure. As shown in FIG4B , the embodiment of the present disclosure relates to a communication method (on the terminal device side), which includes:

[0278] Step S4201: The second communication device sends first information.

[0279] The optional implementation of step S4201 can refer to steps S2101 to S2102 in Figure 2A, the optional implementation of step S2201 in Figure 2B, and other related parts in the embodiments involved in Figures 2A and 2B, which will not be repeated here.

[0280] The following is an exemplary introduction to the above method.

[0281] In some implementations, the network device (LMF network element) sends second information to the terminal, where the second information includes a second signaling unit, and the second signaling unit is used to query whether the terminal side supports the first AI or ML positioning feature and / or associated information of the first AI or ML positioning feature.

[0282] In some implementations, the AI ​​or ML positioning features supported by the terminal may include one or more.

[0283] In some implementations, information related to different positioning characteristics includes, but is not limited to:

[0284] - Reference signal used for positioning, such as whether positioning is based on PRS or SRS

[0285] - The location where the AI / ML model is deployed, including AI / ML models deployed on the terminal side, AI / ML models deployed on the base station side, and AI / ML models deployed on the LMF side

[0286] -The output information categories supported by the AI / ML model include direct output of positioning coordinates, output of the time information used for positioning, output of the angle information required for positioning, and output of the LOS / NLOS information required for positioning.

[0287] In some implementations, different AI or ML positioning characteristics have at least one different example in associated information such as the reference signal used for positioning, the location where the AI / ML model is deployed, and the type of output information supported by the AI / ML model, as shown in Table 1.

[0288] In some implementations, the first signaling unit further includes one or more first signaling subunits, wherein the first signaling subunit is configured to query whether the first AI or ML positioning feature includes the first AI or ML positioning function and / or associated information of the included first AI or ML positioning function.

[0289] In some implementations, a first AI or ML positioning feature can include one or more first AI or ML positioning functions.

[0290] In some implementations, the associated information for the AI ​​or ML positioning function includes, but is not limited to:

[0291] - Different operating frequency bands: for example FR1, FR2;

[0292] - Different RS configurations may further include different RSs, RSs sent by different base stations;

[0293] -Different additional AI / ML model outputs, for example, the AI / ML model can also output RSRP information;

[0294] - Different measurement reporting types;

[0295] - Different application scenarios, such as application in macro cells, application in micro cells, application in indoor scenarios or outdoor scenarios.

[0296] In some implementations, different AI or ML positioning capabilities have at least one different example in associated information such as the operating frequency band, RS configuration, additional output of the AI / ML model, type of measurement reporting, application scenario of the AI ​​or ML positioning capability or AI / ML model, as shown in Table 2.

[0297] In some implementations, the terminal sends first information to the network device, where the first information includes a first signaling unit, and the first signaling unit is used to indicate one or more AI or ML positioning features supported by the terminal side.

[0298] In some implementations, in response to the terminal receiving the second signaling unit in the second information, the terminal sends the first information, where the first signaling unit includes the AI ​​or ML positioning characteristic information and / or associated information of the positioning characteristics queried by the first signaling unit.

[0299] In some implementations, the terminal proactively sends one or more of the supported AI or ML positioning features.

[0300] In some implementations, the first signaling unit further includes one or more first signaling subunits, where the first signaling subunits are used to report whether the first AI or ML positioning feature includes the first AI or ML positioning function and / or associated information of the included first AI or ML positioning function.

[0301] In some implementations, a first AI or ML positioning feature can include one or more first AI or ML positioning functions.

[0302] In some implementations, the network device may be a LMF, the second signaling may be a RequestCapabilities message, and the first signaling may be a ProvideCapabilities message.

[0303] In the above embodiment, when the first communication device is a network element in the core network and the second communication device is a terminal, the interaction between the network element in the core network and the terminal needs to be forwarded through the access network device (such as a base station).

[0304] In the embodiments of the present disclosure, some or all of the steps and their optional implementations may be arbitrarily combined with some or all of the steps in other embodiments, or may be arbitrarily combined with the optional implementations of other embodiments.

[0305] The embodiments of the present disclosure further provide an apparatus for implementing any of the above methods. For example, an apparatus is provided, comprising units or modules for implementing each step performed by a terminal in any of the above methods. For another example, another apparatus is provided, comprising units or modules for implementing each step performed by a network device (e.g., an access network device, a core network function node, a core network device, etc.) in any of the above methods.

[0306] It should be understood that the division of the various units or modules in the above device is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a physical entity, or they may be physically separated. In addition, the units or modules in the device may be implemented in the form of a processor calling software: for example, the device includes a processor, the processor is connected to a memory, and the memory stores instructions. The processor calls the instructions stored in the memory to implement any of the above methods or implement the functions of the various units or modules of the above device, wherein the processor is, for example, a general-purpose processor, such as a central processing unit (CPU) or a microprocessor, and the memory is a memory within the device or a memory outside the device. Alternatively, the units or modules in the device can be implemented in the form of hardware circuits, and the functions of some or all of the units or modules can be realized by designing the hardware circuits. The above-mentioned hardware circuits can be understood as one or more processors; for example, in one implementation, the above-mentioned hardware circuit is an application-specific integrated circuit (ASIC), which realizes the functions of some or all of the above units or modules by designing the logical relationship of the components in the circuit; for example, in another implementation, the above-mentioned hardware circuit can be realized by a programmable logic device (PLD). Taking a field programmable gate array (FPGA) as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured by configuring the configuration file, thereby realizing the functions of some or all of the above units or modules. All units or modules of the above devices can be realized in the form of software called by the processor, or in the form of hardware circuits, or in part by the form of software called by the processor, and the rest by hardware circuits.

[0307] In the embodiments of the present disclosure, the processor is a circuit with signal processing capabilities. In one implementation, the processor can be a circuit with instruction reading and execution capabilities, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor can implement certain functions through the logical relationship of the hardware circuit. The logical relationship of the above-mentioned hardware circuit is fixed or reconfigurable. For example, the processor is a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as an FPGA. In a reconfigurable hardware circuit, the process of the processor loading a configuration document and implementing the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units or modules. In addition, it can also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc.

[0308] FIG5 is a schematic diagram of the structure of a communication device according to an embodiment of the present disclosure. As shown in FIG5 , the communication device 5100 may include at least one of a transceiver module 5101 and a processing module 5102 .

[0309] In some embodiments, the communication device 5100 is a first communication device, and the transceiver module is configured to receive first information indicating AI or ML positioning features supported by the second communication device and / or associated information about the AI ​​or ML positioning features supported by the second communication device. Optionally, the transceiver module is configured to perform at least one of the communication steps, such as sending and / or receiving, performed by the first communication device in any of the above methods, which will not be further described herein.

[0310] In some embodiments, the communication device 5100 is a second communication device, and the transceiver module is used to send first information, where the first information is used to indicate the AI ​​or ML positioning feature supported by the terminal device. Optionally, the transceiver module is used to perform at least one of the communication steps of sending and / or receiving performed by the second communication device in any of the above methods, which will not be repeated here.

[0311] In some embodiments, the transceiver module may include a transmitting module and / or a receiving module, and the transmitting module and the receiving module may be separate or integrated. Optionally, the transceiver module may be interchangeable with the transceiver.

[0312] In some embodiments, the processing module can be a single module or can include multiple submodules. Optionally, the multiple submodules each execute all or part of the steps required to be executed by the processing module. Optionally, the processing module can be interchangeable with the processor.

[0313] Figure 6A is a schematic diagram of the structure of a communication device 6100 proposed in an embodiment of the present disclosure. Communication device 6100 can be a first communication device (e.g., a core network device), or a second communication device (e.g., an access network device, user equipment, a core network device), or a chip, chip system, or processor that supports the first communication device in implementing any of the above methods, or a chip, chip system, or processor that supports the second communication device in implementing any of the above methods. Communication device 6100 can be used to implement the methods described in the above method embodiments. For details, please refer to the description of the above method embodiments.

[0314] As shown in Figure 6A, the communication device 6100 includes one or more processors 6101. The processor 6101 can be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process the communication protocol and communication data, and the central processing unit can be used to control the communication device (such as a base station, a baseband chip, a terminal device, a terminal device chip, a DU or a CU, etc.), execute programs, and process program data. Optionally, the communication device 6100 is used to perform any of the above methods. Optionally, one or more processors 6101 are used to call instructions to enable the communication device 6100 to perform any of the above methods.

[0315] In some embodiments, the communication device 6100 further includes one or more transceivers 6102. When the communication device 6100 includes one or more transceivers 6102, the transceiver 6102 performs at least one of the communication steps, such as sending and / or receiving, in the above-described method, which will not be described in detail here. The processor 6101 performs the other steps. In an optional embodiment, the transceiver may include a receiver and / or a transmitter, and the receiver and transmitter may be separate or integrated. Optionally, the terms transceiver, transceiver unit, transceiver, transceiver circuit, interface circuit, and interface may be used interchangeably; the terms transmitter, transmitting unit, transmitter, and transmitting circuit may be used interchangeably; and the terms receiver, receiving unit, receiver, and receiving circuit may be used interchangeably.

[0316] In some embodiments, the communication device 6100 further includes one or more memories 6103 for storing data. Alternatively, all or part of the memories 6103 may be located outside the communication device 6100. In alternative embodiments, the communication device 6100 may include one or more interface circuits 6104. Optionally, the interface circuits 6104 are connected to the memory 6102 and may be configured to receive data from the memory 6102 or other devices, or to send data to the memory 6102 or other devices. For example, the interface circuits 6104 may read data stored in the memory 6102 and send the data to the processor 6101.

[0317] The communication device 6100 described in the above embodiment may be a network device or a terminal, but the scope of the communication device 6100 described in the present disclosure is not limited thereto, and the structure of the communication device 6100 may not be limited to FIG6A. The communication device may be an independent device or may be part of a larger device. For example, the communication device may be: 1) an independent integrated circuit IC, or a chip, or a chip system or subsystem; (2) a collection of one or more ICs, optionally, the above IC collection may also include a storage component for storing data and programs; (3) an ASIC, such as a modem; (4) a module that can be embedded in other devices; (5) a receiver, a terminal device, an intelligent terminal device, a cellular phone, a wireless device, a handheld device, a mobile unit, an in-vehicle device, a network device, a cloud device, an artificial intelligence device, etc.; (6) others, etc.

[0318] 6B is a schematic diagram of the structure of a chip 6200 according to an embodiment of the present disclosure. If the communication device 6100 can be a chip or a chip system, reference can be made to the schematic diagram of the structure of the chip 6200 shown in FIG6B , but the present disclosure is not limited thereto.

[0319] The chip 6200 includes one or more processors 6201. The chip 6200 is configured to execute any of the above methods.

[0320] In some embodiments, chip 6200 further includes one or more interface circuits 6202. Terms such as interface circuit, interface, and transceiver pins may be used interchangeably. In some embodiments, chip 6200 further includes one or more memories 6203 for storing data. Alternatively, all or part of memory 6203 may be located external to chip 6200. Optionally, interface circuit 6202 is connected to memory 6203 and may be used to receive data from memory 6203 or other devices, or may be used to send data to memory 6203 or other devices. For example, interface circuit 6202 may read data stored in memory 6203 and send the data to processor 6201.

[0321] In some embodiments, the interface circuit 6202 performs at least one of the communication steps, such as sending and / or receiving, in the above-described method, which will not be further described here. For example, the interface circuit 6202 performing the communication steps, such as sending and / or receiving, in the above-described method means that the interface circuit 6202 performs data exchange between the processor 6201, the chip 6200, the memory 6203, or the transceiver device. In some embodiments, the processor 6201 performs other steps.

[0322] The modules and / or devices described in various embodiments, such as virtual devices, physical devices, and chips, can be arbitrarily combined or separated according to circumstances. Optionally, some or all steps can also be performed collaboratively by multiple modules and / or devices, which is not limited here.

[0323] The present disclosure also proposes a storage medium having instructions stored thereon. When the instructions are executed on the communication device 6100, the communication device 6100 executes any of the above methods. Optionally, the storage medium is an electronic storage medium. Optionally, the storage medium is a computer-readable storage medium, but is not limited thereto and may also be a storage medium readable by other devices. Optionally, the storage medium may be a non-transitory storage medium, but is not limited thereto and may also be a transient storage medium.

[0324] The present disclosure also provides a program product, which, when executed by the communication device 6100, enables the communication device 6100 to perform any of the above methods. Optionally, the program product is a computer program product.

[0325] The present disclosure also proposes a computer program, which, when executed on a computer, causes the computer to perform any one of the above methods.

[0326] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer programs. When the computer program is loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present disclosure are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer program 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 program 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 accessed 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 high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0327] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this disclosure.

[0328] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0329] The above description is merely a specific embodiment of the present disclosure, but the scope of protection of the present disclosure 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 disclosure should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. A communication method, characterized in that: Executed by a first communication device, the method includes: The first communication device receives first information, where the first information is used to indicate at least one of the following: Artificial intelligence (AI) or machine learning (ML) positioning features supported by the second communication device; Association information of the AI or ML positioning features supported by the second communication device.

2. The method according to claim 1, characterized in that The method further comprises: The first communication device sends second information, where the second information is used to query at least one of the following: AI or ML positioning features supported by the second communication device; Association information of the AI or ML positioning features supported by the second communication device.

3. The method according to claim 2, characterized in that The method comprises: The first communication device is a LMF network element, the second communication device is a terminal device, and the LMF network element sends the second information to the terminal device; The LMF network element receives the first information sent by the terminal device.

4. The method according to claim 2, characterized in that The method comprises: The first communication network device is a LMF network element, the second communication device is a base station, and the LMF network element sends the second information to the base station device; The LMF network element receives the first information sent by the base station.

5. The method according to claim 2, characterized in that The method comprises: The first communication device is an AMF network element, the second communication device is an LMF network element, and the AMF network element sends the second information to the LMF network element; The AMF network element receives the first information sent by the LMF network element.

6. The method according to claim 1, characterized in that The AI / ML positioning features supported by the second communication device include one or more.

7. The method according to claim 6, characterized in that Different AI-based positioning features differ in at least one of the following related information: Reference signals used for positioning; Where the AI or ML model is deployed; The types of output information supported by the AI or ML model.

8. The method according to claim 7, characterized in that The second information includes second signaling, and the second signaling is used to query at least one of the following: Whether the AI or ML positioning feature includes AI or ML positioning functionality; Whether the AI or ML positioning characteristics include associated information of the AI or ML positioning function.

9. The method according to any one of claims 1 to 8, characterized in that The first information is further used to indicate at least one of the following: Whether the AI or ML positioning feature includes AI or ML positioning functionality; Whether the AI or ML positioning characteristics include AI or ML positioning function related information.

10. The method according to claim 9, characterized in that The AI or ML positioning feature includes one or more AI or ML positioning functions.

11. The method according to claim 10, characterized in that Different AI or ML positioning functions differ in at least one of the following related information: Operating frequency band; Configuration of reference signal RS; Additional outputs from AI / ML models; Type of measurement reported; Application scenarios.

12. A communication method, characterized in that: Executed by a second communication device, the method includes: The second communication device sends first information, where the first information is used to indicate at least one of the following: AI or ML positioning features supported by the second communication device; Association information of the AI or ML positioning features supported by the second communication device.

13. The method according to claim 12, characterized in that The method further comprises: The second communication device receives second information, where the second information is used to query at least one of the following: AI or ML positioning feature capabilities supported by the second communication device; Association information of the AI or ML positioning features supported by the second communication device.

14. The method according to claim 13, characterized in that The method comprises: The first communication device is a LMF network element, the second communication device is a terminal device, and the terminal device receives the second information sent by the LMF network element; The terminal device sends the first information to the LMF network element.

15. The method according to claim 13, characterized in that The method comprises: The first communication device is a LMF network element, the second communication device is a base station, and the base station receives the second information sent by the LMF network element; The base station sends the first information to the LMF network element.

16. The method according to claim 13, characterized in that The method comprises: The first communication device is an AMF network element, the second communication device is an LMF network element, and the LMF network element receives the second information sent by the AMF network element; The LMF network element sends the first information to the AMF network element.

17. The method according to claim 12, wherein: The second communication device supports one or more AI or ML positioning features.

18. The method according to claim 17, characterized in that Different AI or ML positioning features differ in at least one of the following information items: Reference signals used for positioning; Where the AI / ML model is deployed; The types of output information supported by the AI / ML model.

19. The method according to claim 13, wherein The second information is further used to query at least one of the following: Whether the AI or ML positioning feature includes AI or ML positioning functionality; Whether the AI or ML positioning characteristics include associated information of the AI or ML positioning function.

20. The method according to any one of claims 12 to 19, characterized in that The first information is further used to indicate at least one of the following: Whether the AI or ML positioning feature includes AI or ML positioning functionality; Whether the AI or ML positioning characteristics include AI or ML positioning function related information.

21. The method according to claim 20, characterized in that The AI or ML positioning feature includes one or more AI or ML positioning functions.

22. The method according to claim 21, characterized in that Different AI or ML positioning functions differ in at least one of the following information items: Operating frequency band; RS configuration; Additional outputs from AI / ML models; Type of measurement reported; Application scenarios.

23. A first communication device, characterized in that: include: The transceiver module is configured to send first information, where the first information is configured to indicate at least one of the following: AI or ML positioning features supported by the second communication device; Association information of the AI or ML positioning features supported by the second communication device.

24. A second communication device, characterized in that: include: The transceiver module is configured to receive first information, where the first information is configured to indicate at least one of the following: AI or ML positioning features supported by the second communication device; Association information of the AI or ML positioning features supported by the second communication device.

25. A communication system, characterized in that: The invention comprises a first communication device and a second communication device, wherein the first communication device is configured to implement the communication method according to any one of claims 1 to 11, and the second communication device is configured to implement the communication method according to any one of claims 12 to 22.

26. A storage medium storing instructions, characterized in that: When the instruction is executed on the first communication device, the first communication device is caused to execute the communication method according to any one of claims 1 to 11.

27. A storage medium storing instructions, characterized in that: When the instruction is executed on the second communication device, the second communication device is caused to execute the communication method according to any one of claims 12 to 22.

28. A program product, when said program product is run on a first communication device, causing said first communication device to execute the communication method according to any one of claims 1 to 11.

29. A program product, which, when executed on a second communication device, enables the second communication device to execute the communication method according to any one of claims 12 to 22.

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