Method, device and medium for communication

Abstract

The embodiment of the invention relates to a communication method, equipment and a medium. The terminal device receives an LPP message from the core network device, the LPP message informing the terminal device to activate AI-based positioning, and the AI-based positioning is used for determining position related information based on the output of the positioning model; and sending the position related information to the core network equipment. Furthermore, interaction between the access network device and the core network device via the NRPPa is triggered to activate AI-based positioning for determining location-related information based on the output of the positioning model, and sending the location-related information to the core network device. In this way, the process between the terminal device and the core network device is defined, and thus the positioning accuracy can be improved.

Description

Technical Field

[0001] Example embodiments of the present disclosure relate generally to the field of communication technology, and particularly to methods, devices, and computer-readable media for communication. Background Art

[0002] Several technologies have been proposed to improve communication performance. For example, communication devices can use artificial intelligence (AI) / machine learning (ML) models to improve communication quality. AI / ML models can be applied to different scenarios to achieve better performance.

[0003] There has been a recent work in the 3rd Generation Partnership Project (3GPP) for positioning support in New Radio (NR) systems. Support for AI / ML based positioning is proposed and can enhance positioning accuracy within the 3GPP framework. Summary of the invention

[0004] In general, the exemplary embodiments of the present disclosure provide a method, device, and computer storage medium for communication. Embodiments that do not fall within the scope of the claims, if any, will be construed as examples that help understand the various embodiments of the present disclosure.

[0005] In a first aspect, a communication method is provided. The method includes: receiving, at a terminal device, a Long Term Evolution Positioning Protocol (LPP) message from a core network device, the LPP message notifying the terminal device to activate AI-based positioning, the AI-based positioning being used to determine location-related information based on a positioning model; determining location-related information based on an output of the positioning model; and sending location-related information to the core network device.

[0006] In a second aspect, a communication method is provided. The method includes: at an access network device, sending a first NR Positioning Protocol A (NRPPa) message to a core network device, the first NRPPa message requesting activation of AI-based positioning; and receiving a second NRPPa message from the core network device, the second NRPPa message notifying the access network device whether to activate AI-based positioning, the AI-based positioning being used to determine location-related information of a terminal device based on a positioning model.

[0007] In a third aspect, a communication method is provided. The method includes: receiving, at a core network device, a first NRPPa message from an access network device, the first NRPPa message requesting activation of AI-based positioning; and sending a second NRPPa message to the access network device, the second NRPPa message notifying the access network device whether to activate AI-based positioning, the AI-based positioning being used to determine location-related information of a terminal device based on a positioning model.

[0008] In a fourth aspect, a communication method is provided. The method includes: at a core network device, sending an LPP message to a terminal device, the LPP message notifying the terminal device to activate AI-based positioning, the AI-based positioning is used to determine location-related information based on a positioning model; and receiving location-related information of the terminal device from the terminal device.

[0009] In a fifth aspect, a terminal device is provided. The terminal device includes a processor and a memory. The memory is coupled to the processor and has instructions stored thereon. When the instructions are executed by the processor, the terminal device executes the method according to the first aspect above.

[0010] In a sixth aspect, an access network device is provided. The access network device includes a processor and a memory. The memory is coupled to the processor and has instructions stored thereon. When the instructions are executed by the processor, the access network device executes the method according to the second aspect above.

[0011] In a seventh aspect, a core network device is provided. The core network device includes a processor and a memory. The memory is coupled to the processor and has instructions stored thereon. When the instructions are executed by the processor, the core device executes the method according to the third aspect or the fourth aspect.

[0012] In an eighth aspect, a computer-readable medium is provided, having instructions stored thereon, which, when executed on at least one processor, cause the at least one processor to perform a method according to the first aspect, the second aspect, the third aspect or the fourth aspect above.

[0013] It should be understood that the invention summary is not intended to identify the key or essential features of the embodiments of the present disclosure, nor is it intended to be used to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and other objects, features and advantages of the present disclosure will become more apparent through a more detailed description of some example embodiments of the present disclosure in the accompanying drawings, in which:

[0015] Figure 1 A schematic diagram illustrating an example system in which some embodiments of the present disclosure may be implemented;

[0016] Figure 2A An exemplary architecture of a 5G system (5GS) capable of locating a terminal device connected to a NG-RAN or an E-UTRAN according to some embodiments of the present disclosure is illustrated;

[0017] Figure 2B An exemplary process for locating a UE according to some embodiments of the present disclosure is illustrated;

[0018] Figure 2C illustrates an exemplary protocol layer for supporting LPP message transmission between an LMF and a UE according to some embodiments of the present disclosure;

[0019] Figure 2D illustrates an exemplary protocol layer to support NRPPaPDU transmission between LMF and NG-RAN node according to some embodiments of the present disclosure;

[0020] Figure 3A illustrates a schematic diagram illustrating an example process in which some embodiments of the present disclosure may be implemented;

[0021] Figure 3B illustrates a schematic diagram illustrating an example process in which some embodiments of the present disclosure may be implemented;

[0022] Figure 4A-4D A schematic diagram of an AI-based positioning model according to some embodiments of the present disclosure is illustrated;

[0023] Figure 5 A signaling diagram illustrating a process according to some example embodiments of the present disclosure is illustrated;

[0024] Figure 6A-6B A signaling diagram illustrating a process according to some example embodiments of the present disclosure is illustrated;

[0025] Figure 7 A signaling diagram illustrating a process according to some example embodiments of the present disclosure is illustrated;

[0026] Figure 8A-8E A signaling diagram illustrating a process according to some example embodiments of the present disclosure is illustrated;

[0027] Figure 8F-8G A signaling diagram illustrating a process according to some example embodiments of the present disclosure is illustrated;

[0028] Fig. 9 illustrates a flow chart of an example method according to some embodiments of the present disclosure;

[0029] Fig.10 illustrates a flow chart of an example method according to some embodiments of the present disclosure;

[0030] Fig.11 illustrates a flow chart of an example method according to some embodiments of the present disclosure;

[0031] Fig.12 A flowchart illustrating an example method according to some embodiments of the present disclosure; and

[0032] Fig.13A simplified block diagram of a device suitable for implementing embodiments of the present disclosure is illustrated.

[0033] Throughout the drawings, the same or similar reference numerals refer to the same or similar elements. DETAILED DESCRIPTION

[0034] The principles of the present disclosure will now be described with reference to some example embodiments. It should be understood that these embodiments are described only to illustrate and help those skilled in the art understand and implement the purpose of the present disclosure, without any limitation to the scope of the present disclosure. The embodiments described herein can be implemented in various other ways except for the way described below.

[0035] In the following description and claims, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0036] References in this disclosure to "one embodiment," "an embodiment," and "an example embodiment," etc., indicate that the described embodiment may include a particular feature, structure, or characteristic, but not every embodiment necessarily includes the particular feature, structure, or characteristic. Furthermore, such phrases do not necessarily refer to the same embodiment. Furthermore, when a particular feature, structure, or characteristic is described in conjunction with an embodiment, it is considered to be within the knowledge of those skilled in the art to affect such feature, structure, or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0037] It should be understood that although the terms "first" and "second" etc. may be used to describe various elements in this article, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, without departing from the scope of the exemplary embodiments, the first element may be referred to as the second element, and similarly, the second element may be referred to as the first element. As used in this article, the term "and / or" includes any and all combinations of one or more of the listed terms.

[0038] The terms used in this article are only used for the purpose of describing specific embodiments and are not intended to limit the exemplary embodiments. As used in this article, unless the context clearly indicates otherwise, the singular forms "a / an" and "the / said" are also intended to include the plural forms. It will be further understood that the terms "include / comprise", "have / have" and / or "contain / contain" when used in this article specify the presence of stated features, elements and / or components, etc., but do not exclude the presence or addition of one or more other features, elements, components and / or combinations thereof.

[0039] In some examples, values, processes, or devices are referred to as "optimal / best", "lowest", "highest", "minimum", "maximum", etc. It should be understood that such descriptions are intended to indicate that a selection can be made among many functional alternatives used, and that such a selection is not necessarily better, lesser, higher, or otherwise preferred over other selections.

[0040] As used herein, the term "communication network" refers to a network that follows any suitable communication standard (such as new radio (NR), long term evolution (LTE), advanced LTE (LTE-A), wideband code division multiple access (WCDMA), high speed packet access (HSPA), narrowband Internet of Things (NB-IoT), etc.). In addition, the communication between the terminal device and the network device in the communication network can be performed according to any suitable generation of communication protocol, including but not limited to the first generation (1G), second generation (2G), 2.5G, 2.75G, third generation (3G), fourth generation (4G), 4.5G, fifth generation (5G), 5.5G, 5G advanced network, or sixth generation (6G) communication protocol, and / or any other protocol currently known or to be developed in the future. The embodiments of the present disclosure can be applied to various communication systems. Considering the rapid development of communication, there will certainly be communication technologies and systems that can embody future types of the present disclosure. It should not be regarded as limiting the scope of the present disclosure to the above-mentioned system.

[0041] As used herein, the term "terminal device" refers to any device with wireless or wired communication capabilities. Examples of terminal devices include, but are not limited to, user equipment (UE), personal computers, desktops, mobile phones, cellular phones, smart phones, personal digital assistants (PDAs), portable computers, tablets, wearable devices, Internet of Things (IoT) devices, ultra-reliable low latency communications (URLLC) devices, Internet of Everything (IoE) devices, machine type communications (MTC) devices, vehicle-mounted devices for V2X communications (where X represents pedestrians, vehicles, or infrastructure / network), devices for integrated access and backhaul (IAB), space vehicles or air vehicles (including unmanned aerial systems (UAS)) in non-terrestrial networks (NTNs) (including satellites and high altitude platforms (HAPs)), extended reality (XR) devices (including different types of reality such as augmented reality (AR), mixed reality (MR), and virtual reality (VR)), unmanned aerial vehicles (UAVs) commonly referred to as drones (which are aircraft without any human pilots), devices on high-speed trains (HSTs), or image capture devices (such as digital cameras), sensors, gaming devices, music storage and playback devices, or Internet devices that enable wireless or wired Internet access and browsing, etc. The "terminal device" may also have "multicast / broadcast" features to support public safety and mission-critical, V2X applications, transparent IPv4 / IPv6 multicast delivery, IPTV, Smart TV, radio services, software delivery over the air, group communications, and IoT applications. It may also include one or more subscriber identity modules (SIMs) (referred to as multi-SIM). The term "terminal device" may be used interchangeably with UE, mobile station, subscriber station, mobile terminal, user terminal, or wireless device.

[0042] As used herein, the term "network equipment" refers to equipment that is capable of providing or hosting a cell or coverage area in which a terminal device can communicate. Examples of network equipment include, but are not limited to, satellites, unmanned aerial systems (UAS) platforms, Node Bs (NodeBs or NBs), evolved Node Bs (eNodeBs or eNBs), next generation Node Bs (gNBs), transmission reception points (TRPs), remote radio units (RRUs), radio heads (RHs), remote radio heads (RRHs), IAB nodes, low power nodes (such as femto nodes, pico nodes), reconfigurable smart surfaces (RISs), etc.

[0043] In one embodiment, the terminal device may be connected to a first network device and a second network device. One of the first network device and the second network device may be a primary node, and the other may be a secondary node. The first network device and the second network device may use different radio access technologies (RATs). In one embodiment, the first network device may be a first RAT device, and the second network device may be a second RAT device. In one embodiment, the first RAT device is an eNB, and the second RAT device is a gNB. Information related to different RATs may be sent from at least one of the first network device and the second network device to the terminal device. In one embodiment, the first information may be sent from the first network device to the terminal device, and the second information may be sent from the second network device directly or via the first network device to the terminal device. In one embodiment, information related to the configuration for the terminal device configured by the second network device may be sent from the second network device via the first network device. Information related to the reconfiguration of the terminal device configured by the second network device may be sent from the second network device directly or via the first network device to the terminal device.

[0044] The communication discussed in this article can meet any suitable standard, including but not limited to, new radio access (NR), long term evolution (LTE), LTE evolution, advanced LTE (LTE-A), wideband code division multiple access (WCDMA), code division multiple access (CDMA), cdma2000 and global system for mobile communications (GSM) etc. In addition, communication can be performed according to any generation of communication protocol currently known or developed in the future. Examples of communication protocols include but are not limited to first generation (1G), second generation (2G), 2.5G, 2.85G, third generation (3G), fourth generation (4G), 4.5G, fifth generation (5G) and sixth generation (6G) communication protocols. The technology described in this article can be used for the above-mentioned wireless network and radio technology and other wireless networks and radio technology. The embodiments of the present disclosure can be performed according to any generation of communication protocol currently known or developed in the future. Examples of communication protocols include, but are not limited to, first generation (1G), second generation (2G), 2.5G, 2.75G, third generation (3G), fourth generation (4G), 4.5G, fifth generation (5G) communication protocols, 5.5G, 5G advanced networks, or sixth generation (6G) networks.

[0045] The terminal device or network device may have artificial intelligence (AI) or machine learning capabilities. It usually includes a model that has been trained from a large amount of collected data for a specific function and can be used to predict certain information.

[0046] The terminal device or network device can operate on several frequency ranges, such as FR1 (410MHz-7125 MHz), FR2 (24.25GHz to 71GHz), frequency bands greater than 100GHz, and terahertz (THz). It can further operate on licensed / unlicensed / shared spectrum. In the multi-radio dual connection (MR-DC) application scenario, the terminal device can have more than one connection with the network device. The terminal device or network device can operate in full-duplex, flexible duplex, and cross-split duplex modes.

[0047] The embodiments of the present disclosure may be executed in a test device, such as a signal generator, a signal analyzer, a spectrum analyzer, a network analyzer, a test terminal device, a test network device, or a channel emulator.

[0048] Embodiments of the present disclosure may be performed according to any generation of communication protocols currently known or to be developed in the future. Examples of communication protocols include, but are not limited to, first generation (1G), second generation (2G), 2.5G, 2.75G, third generation (3G), fourth generation (4G), 4.5G, fifth generation (5G) communication protocols, 5.5G, 5G advanced networks, or sixth generation (6G) networks.

[0049] The term "circuitry" as used herein may refer to a combination of hardware circuits and / or hardware circuits and software. For example, a circuit system may be a combination of analog and / or digital hardware circuits and software / firmware. As another example, a circuit system may be any part of a hardware processor (including (multiple) digital signal processors), software, and (multiple) memory with software, which work together to enable devices such as terminal devices or network devices to perform various functions. In another example, a circuit system may be a hardware circuit and / or a processor, such as a microprocessor or a part of a microprocessor, which requires software / firmware to operate, but when the operation is not required, the software may not exist. As used herein, the term circuit system also encompasses the implementation of only a hardware circuit or (multiple) processors or a part of a hardware circuit or (multiple) processors and their accompanying software and / or firmware.

[0050] As used herein, the singular forms "a / an" and "the / said" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The term "including" and its variations should be understood as open-ended terms, meaning "including but not limited to". The term "based on" should be understood as "based at least in part on". The terms "one embodiment" and "embodiment" should be understood as "at least one embodiment". The term "another embodiment" should be understood as "at least one other embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may be included below.

[0051] In some examples, values, processes, or devices are referred to as "optimal / best", "lowest", "highest", "minimum", "maximum", etc. It should be understood that such descriptions are intended to indicate that a selection can be made among many functional alternatives used, and that such a selection is not necessarily better, lesser, higher, or otherwise preferred over other selections.

[0052] In the context of the present disclosure, the term "AI-based positioning" can be used interchangeably with AI / ML-based positioning, ML-based positioning, AI-based positioning, AI / ML-assisted positioning, etc. The term "positioning model" can be used interchangeably with AI positioning model, AI-based positioning model, AI-based model, AI / ML model, AI / ML positioning model, AI / ML-based positioning model, etc.

[0053] Supporting various positioning methods to provide reliable and accurate location of terminal devices has always been a key feature of 3GPP standards. In addition, in advanced 5G, it has been agreed to study the potential of AI / ML in the air interface to achieve better performance. As one of the use cases for Release 18, it is worthwhile to apply AI / ML models to the positioning of terminal devices, and AI / ML-based mechanisms can be used to improve positioning accuracy.

[0054] The current combination between AI / ML and positioning accuracy enhancement within the 3GPP framework focuses on methods that rely on radio access technology (RAT) to achieve positioning through the collaboration of terminal devices, access network devices, and core network devices such as location and mobility functions (LMF), so AI models can be deployed on any of these sides. In addition, AI model training can be performed on one side and AI model reasoning can be performed on the other side. For either case, the sequence of processes used to support AI-based positioning should be defined in the RAN specifications.

[0055] The embodiments of the present disclosure provide a communication solution. In this solution, a terminal device can receive an LPP message from a core network device to activate AI-based positioning. Therefore, the terminal device can determine its location-related information based on the output of the AI-based positioning model. In this way, the process between the terminal device and the core network device is defined, and the positioning accuracy can be improved.

[0056] The principles and implementations of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0057] Figure 11 is a schematic diagram of an example system 100 in which some embodiments of the present disclosure may be implemented. As part of a communication network, the system 100 includes a terminal device 110. The system 100 also includes an access network device 120-1, an access network device 120-2, and an access network device 120-3 (which may be collectively or individually referred to as "access network devices 120"). It should be noted that Figure 1 Only three access network devices are shown, but the number of access network devices may be more than three, and Figure 1 The access network device 120 in the figure is provided for the purpose of illustration and does not imply any limitation. In some embodiments, the access network devices 120-1 to 120-3 may be implemented as multiple transmission reception points (multi-TRPs). In some embodiments, the access network devices 120-1 to 120-3 may be implemented as gNBs, for example, may include a serving gNB and a neighboring gNB.

[0058] The system 100 also includes a core network device 130, which in some embodiments may include a location and mobility function (LMF). In addition, in some embodiments, the LMF may be referred to as a location management function, and is not limited herein.

[0059] In the system 100, the access network device 120 can transmit / send data and control information to the terminal device 110, and the terminal device 110 can also transmit / send data and control information to the access network device 120. The link from the access network device 120 to the terminal device 110 is called the downlink (DL), and the link from the terminal device 110 to the access network device 120 is called the uplink (UL). DL may include one or more logical channels, including but not limited to the physical downlink control channel (PDCCH) and the physical downlink shared channel (PDSCH). UL may include one or more logical channels, including but not limited to the physical uplink control channel (PUCCH) and the physical uplink shared channel (PUSCH). As used herein, the term "channel" may refer to a carrier or a portion of a carrier consisting of a group of consecutive resource blocks (RBs), wherein a channel access process is performed on these RBs in a shared spectrum.

[0060] The communication in the system 100 (e.g., the communication between the access network device 120 and the terminal device 110) can be implemented according to any appropriate (multiple) communication protocols, including but not limited to cellular communication protocols of the first generation (1G), the second generation (2G), the third generation (3G), the fourth generation (4G) and the fifth generation (5G), wireless local area network communication protocols (such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11, etc.), and / or any other protocol currently known or developed in the future. In addition, the communication can utilize any appropriate wireless communication technology, including but not limited to: code division multiple access (CDMA), frequency division multiple access (FDMA), time division multiple access (TDMA), frequency division duplexer (FDD), time division duplexer (TDD), multiple input multiple output (MIMO), orthogonal frequency division multiple access (OFDMA) and / or any other technology currently known or developed in the future.

[0061] Embodiments of the present disclosure may be applied to any suitable scenario. For example, embodiments of the present disclosure may be implemented at a reduced-capability NR device. Alternatively, embodiments of the present disclosure may be implemented in one of the following: NR multiple-input multiple-output (MIMO), NR sidechain enhancement, NR system with frequency above 52.6 GHz, extended NR operation up to 71 GHz, narrowband Internet of Things (NB-IOT) / enhanced machine type communication (eMTC) on non-terrestrial networks (NTN), NTN, UE energy saving enhancement, NR coverage enhancement, NB-IoT and LTE-MTC, integrated access and backhaul (IAB), NR multicast and broadcast services, or enhancement of multi-radio dual connectivity.

[0062] It should be understood that Figure 1 The number of devices (ie, terminal device 110 and access network device 120) shown in FIG. 1 and their connection relationships and types are for illustrative purposes only and do not represent any limitation. System 100 may include any suitable number of devices suitable for implementing the embodiments of the present disclosure.

[0063] Further references Figure 2A , which illustrates an exemplary architecture of a 5G system (5GS) 210 capable of locating a terminal device connected to a Next Generation Radio Access Network (NG-RAN) or an Evolved UMTS Terrestrial Radio Access Network (E-UTRAN) according to some embodiments of the present disclosure.

[0064] like Figure 2A As shown, UE 202 may be connected to NG-RAN 204, which may include a new generation evolved NB (ng-eNB) 121 and gNB 122. UE 202 may include a SET, ng-eNB 121 may include multiple transmission points (TPs), and gNB 122 may include multiple TRPs.

[0065] The NR-Uu interface is used to connect UE 202 to gNB 122 over the air and is used as one of several transmission links for NR positioning protocol(s) for target UEs with NR access to NG-RAN 204. The LTE-Uu interface is used to connect UE 202 to ng-eNB 121 over the air and is used as one of several transmission links for LTE positioning protocol(s) for target UEs with LTE access to NG-RAN 204.

[0066] The NG-C interface is used to connect gNB 122 and mobility management function (AMF) 132 or to connect ng-eNB 121 and AMF 132. NG-C is transparent to all UE positioning related procedures. It participates in these procedures only as a transmission link for (multiple) NR positioning protocols. For positioning procedures related to gNB 122, the NG-C interface transparently transfers positioning requests from LMF 131 to gNB 122 and positioning results from gNB 122 to LMF 131. For positioning procedures related to ng-eNB 121, the NG-C interface transparently transfers positioning requests from LMF 131 to ng-eNB 121 and positioning results from ng-eNB 121 to LMF 131.

[0067] The NL1 interface is used to connect LMF 131 and AMF 132 and is transparent to all UE-related positioning procedures, gNB 122-related positioning procedures, and ng-eNB 121-related positioning procedures. It is only used as a transport link for LTE Positioning Protocol (LPP) and NR Positioning Protocol A (NRPPa).

[0068] The LMF 131 is connected to an Enhanced Serving Mobile Location Center (E-SMLC) 133, and the E-SMLC 133 may enable the LMF 131 to access the E-UTRAN. The LMF 131 may be connected to a Secure User Plane Location (SUPL) Location Platform (SLP) 134. The LMF 131 may support and manage different location determination services for a target UE.

[0069] It is proposed that location information may be requested by a client (eg, an application) associated with the UE 202 or by a client attached to the core network, and reported to such a client.

[0070] Figure 2B An exemplary process 220 for locating a UE according to some embodiments of the present disclosure is illustrated. The location service request may be implemented through steps 1a, 1b or 1c.

[0071] In step 1a, the fifth generation core network (5GC) location service (LCS) entity 209 (such as the Gateway Mobile Location Center (GMLC)) sends a location service request for the target UE to the serving AMF 205. Alternatively, in step 1b, the serving AMF 205 of the target UE may determine the need for some location service, such as to locate the UE 201 for an emergency call. Alternatively, in step 1c, the UE 201 requests some location service (e.g., delivery of positioning or assistance data) from the serving AMF 205 at the non-access stratum (NAS) level.

[0072] In step 2, the AMF 205 may send a location service request to the LMF 207. And in step 3a, the LMF 207 may start a location procedure with the serving eNB and the serving gNB to obtain positioning data or positioning assistance data. Specifically, the LMF 207 initiates a location procedure with the serving and possible neighboring ng-eNBs or gNBs in the NG-RAN 203, for example, to obtain positioning measurements or assistance data. In addition to step 3a, or instead of step 3a, in step 3b the LMF 207 may initiate a location procedure for DL ​​positioning with the UE 201. Specifically, the LMF 207 initiates a location procedure with the serving and possible neighboring ng-eNBs or gNBs in the NG-RAN 203, for example, to obtain positioning measurements or assistance data.

[0073] In step 4, LMF 207 may provide a location service response to AMF 205. The location service response may include any desired results, such as a success or failure indication, and an estimated location for UE 201 (if requested and obtained).

[0074] Then, if step 1a is performed, the AMF 205 returns a location service response to the 5GC LCS entity 209, as shown in step 5a, and the location service response includes any required results, such as a location estimate for the UE 201.

[0075] If step 1b occurs, the AMF 205 uses the location service response received in step 4 to assist the service that triggered this in step 1b (e.g., a location estimate associated with an emergency call to the GMLC may be provided), as shown in step 5b.

[0076] If step 1c is executed, the AMF 205 returns a location service response to the UE 201 , as shown in step 5c , and the location service response includes any required results, such as a location estimate for the UE 201 .

[0077] Figure 2CAn exemplary protocol layer 230 to support LTE Positioning Protocol (LPP) message transmission between an LMF and a UE according to some embodiments of the present disclosure is illustrated.

[0078] The UE may include an L1 layer, a media access control (MAC) layer, a radio link control (RLC) layer, a packet data convergence protocol (PDCP) layer, a radio resource control (RRC) layer, a non-access stratum (NAS) layer, and an LPP layer. The NG-RAN node may include an L1 layer, a MAC layer, an RLC layer, a PDCP layer, an RRC layer, an Internet Protocol (IP) layer, a stream control transmission protocol (SCTP) layer, an NG application protocol (NGAP) layer, and a relay. The AMF may include an L1 layer, an L2 layer, an IP layer, an SCTP layer, an NGAP layer, a NAS layer, a transmission control protocol (TCP) layer, a transport layer security (TLS) layer, a hypertext transfer protocol (HTTP) / 2 layer, and a relay. The LMF may include an L1 layer, an L2 layer, an IP layer, a TCP layer, a TLS layer, an HTTP / 2 layer, and an LPP layer.

[0079] Figure 2D An exemplary protocol layer 240 to support NR Positioning Protocol A (NRPPa) PDU transmission between LMF and NG-RAN nodes according to some embodiments of the present disclosure is illustrated.

[0080] The NG-RAN node may include an L1 layer, an L2 layer, an IP layer, an SCTP layer, an NGAP layer, and an NRPPa layer. The AMF may include an L1 layer, an L2 layer, an IP layer, an SCTP layer, an NGAP layer, a TCP layer, a TLS layer, and an HTTP / 2 layer. The LMF may include an L1 layer, an L2 layer, an IP layer, a TCP layer, a TLS layer, an HTTP / 2 layer, and an NRPPa layer.

[0081] In some embodiments, the positioning procedures for signaling protocols specified in TS 38.305 interface the UE, gNB, AMF, and LMF through: NR-Uu connecting the UE to the gNB over the air, LTE-Uu connecting the UE to the ng-eNB over the air, NG-C between the gNB and the AMF and between the ng-eNB and the AMF, and NL1 between the LMF and the AMF. The LPP protocol operates between the UE and the LMF. In some embodiments, the NRPPa protocol operates between the gNB and the LMF.

[0082] It is proposed to introduce an AI-based positioning method to enhance positioning accuracy, therefore, a corresponding process should be specified to provide a signaling interface between the UE and the network. In some embodiments, for the LPP protocol and the NRPPa protocol, AI capabilities and trained AI models need to be exchanged between the UE, gNB, and LMF.

[0083] Figure 3A A schematic diagram illustrating an example process 310 in which some embodiments of the present disclosure may be implemented is illustrated. The process 310 involves a terminal device 110, an access network device 120, and a LMF 131, wherein the access network device 120 may include a serving gNB / TRP and one or more neighboring gNB / TRPs.

[0084] like Figure 3A As shown, in step 1, LMF 131 may request the positioning capability of the target terminal device 110 using the LPP capability transfer procedure. In step 2, LMF 131 sends an NRPPa TRP information request (NRPPa TRPINFORMATION REQUEST) message to gNB 120. The request includes an indication of which specific TRP configuration information is requested. In step 3, if available at gNB 120, gNB 120 provides the requested TRP information in an NRPPa TRP information response (NRPPa TRP INFORMATIONRESPONSE) message. In step 4, LMF 131 determines that auxiliary data needs to be provided to the terminal device 110, and sends an LPP Provide Assistanve Data (LPP Provide Assistanve Data) message to the terminal device 110.

[0085] In step 5, LMF 131 sends an LPP Request Location Information message to request a Reference Signal Time Difference (RSTD) measurement. In step 6, the terminal device 110 performs a Downlink Positioning Reference Signal (DL-PRS) measurement from all gNBs provided in the assistance data. In step 7, the terminal device 110 obtains a Downlink Time Difference of Arrival (DL-TDOA) measurement as requested in step 5. The terminal device 110 then sends an LPP Provide Location Information message to LMF 131.

[0086] Figure 3B A schematic diagram illustrating an example process 320 in which some embodiments of the present disclosure may be implemented is illustrated. The process 320 involves a terminal device 110, an access network device 120, and a LMF 131, wherein the access network device 120 may include a serving gNB / TRP and one or more neighboring gNB / TRPs.

[0087] like Figure 3BAs shown, in step 1, LMF 131 may request the positioning capabilities of terminal device 110 using the LPP capability transfer procedure. In step 2, LMF 131 sends an NRPPa TRP information request message to gNB 120. The request includes an indication of which specific TRP configuration information is requested. In step 3, serving gNB 120 determines the resources available for uplink sounding reference signal (UL-SRS) and configures terminal device 110 with a UL-SRS resource set in step 3a. In step 4, serving gNB 120 provides UL-SRS configuration information to LMF 131.

[0088] For semi-persistent or aperiodic SRS, LMF 131 may request activation of UE SRS transmission in step 5a, and then gNB 120 activates UE SRS transmission in step 5b and sends an NRPPa Positioning Activation Response message to LMF 131 in step 5c. In step 6, LMF 131 provides UL information in an NRPPa Measurement Request to the selected gNB 120. This message includes all information required to enable the gNB / TRP to perform UL measurements.

[0089] In step 7, LMF 131 sends an LPP Provide Assistance Data message to the terminal device 110. The message includes all information required to enable the gNB / TRP to perform UL measurements. In step 8, LMF 131 sends an LPP Request Location Information message to request multi-round trip time (multi-RTT) measurements. In steps 9a and 9b, the terminal device 110 and each gNB 120 perform DL-PRS measurements and UL SRS measurements.

[0090] It should be understood that Figure 3A-3B The process may be based on non-AI based positioning, and in some cases, the determined location may not be accurate.

[0091] The present disclosure provides a solution for determining location-related information of a terminal device based on an AI-based positioning model. The model can be trained in the model training stage and can be used to determine location-related information in the model reasoning stage. Figure 4A A schematic diagram 400 of an AI-based positioning model according to some embodiments of the present disclosure is illustrated. Figure 4A As shown, based on the input 402 of the AI-based positioning model 410, an output 412 can be obtained.

[0092] In some embodiments, input 402 may be based on a positioning reference signal, or may be based on other signals. In some example embodiments, input 402 of AI-based positioning mode 410 may include one or more of the following:

[0093] ●Time Difference of Arrival (TDOA) of different PRSs,

[0094] ● Angle of departure (AOD) of different PRS,

[0095] ●Time Difference of Arrival (TDOA) of different SRS,

[0096] ●Angle of arrival (AOA) of different SRS,

[0097] A first measurement result determined at the terminal device, indicating a receive-transmit (Rx-Tx) time difference between receiving a PRS from the access network device and sending an SRS to the access network device,

[0098] a second measurement result determined at the access network device, indicating an Rx-Tx time difference between receiving the SRS from the terminal device and sending the PRS to the terminal device,

[0099] The channel impulse response (CIR) value obtained from the channel state information reference signal (CSI-RS),

[0100] ● Path Reference Signal Received Power (RSRP) from CSI-RS or Synchronization Signal Block (SSB),

[0101] ●Historical location information of the terminal device, or

[0102] ●The speed of the terminal equipment.

[0103] In some embodiments, output 412 may include location-related information, such as the RSTD or the absolute location of the terminal device.

[0104] In some example embodiments, the AI-based positioning model 410 may be referred to as an AI model, the input 410 may be a CIR, and the output 412 may be the location of the terminal device, expressed as (X, Y), such as Figure 4B shown.

[0105] In some example embodiments, the AI-based positioning model 410 may be implemented as an AI network, the input 410 may be path RARPs and timings of size 1×18×256, and the output 412 may be the 2D position of the terminal device, such as Figure 4C shown.

[0106] In some example embodiments, the AI-based positioning model 410 may be implemented as an AI network including a convolutional neural network (CNN) layer and a fully connected layer, the input 410 may be a CIR and a L1-RSRP or a L1-RSRP, and the output 412 may be an estimated position of the terminal device, expressed as (X, Y), such as Figure 4D shown.

[0107] In some embodiments, the AI-based positioning model may be a one-sided model, also referred to as a one-sided AI / ML model. The one-sided model may be a UE-side (AI / ML) model or a network-side (AI / ML) model. In some examples, the UE-side (AI / ML) model may refer to an AI / ML model whose reasoning is performed entirely at the UE, and the network-side (AI / ML) model may refer to an AI / ML model whose reasoning is performed entirely at the network.

[0108] In some embodiments, some potential aspects of AI / ML methods for sub-use cases for AI / ML for positioning accuracy enhancement are studied and provided. In some examples, at the stage of AI / ML model training, training data type / size, training data source determination (e.g., UE / PRU / TRP), and auxiliary signaling and procedures for training data collection are proposed. In some examples, at the stage of AI / ML model indication / configuration, auxiliary signaling and procedures (e.g., for model configuration, model activation / deactivation, model recovery / termination, model selection) are proposed. In some examples, at the stage of AI / ML model monitoring and updating, auxiliary signaling and procedures (e.g., for model performance monitoring, model update / tuning) are proposed.

[0109] In some examples, at the stage of AI / ML model reasoning input, reporting / feedback of model input for reasoning (e.g., UE feedback as input for model reasoning on the network side), model input acquisition and preprocessing, and type / definition of model input are proposed. In some examples, at the stage of AI / ML model reasoning output, reporting / feedback of model reasoning output and post-processing of model reasoning output are proposed. In some examples, UE capabilities for (multiple) AI / ML models (e.g., for model training, model reasoning, and model monitoring) are proposed.

[0110] The following describes in detail embodiments of the present disclosure that perform AI-based positioning. Figure 5 , which illustrates a signaling diagram of a diagram process 500 according to some example embodiments of the present disclosure. For discussion purposes only, reference will be made to Figure 1 Process 500 is described. Process 500 may involve Figure 1 The terminal device 110 and the core network device 130 in the embodiment. In some embodiments, the core network device 130 includes LMF.

[0111] The core network device 130 sends 520 an LPP message 522 to the terminal device 110. In some embodiments, the LPP message 522 may include activation information notifying the terminal device 110 to activate AI-based positioning. In some embodiments, the LPP message 522 may be an LPP AI-assisted positioning activation measurement to trigger AI-based positioning.

[0112] In some example embodiments, core network device 130 (such as LMF) may trigger AI-based positioning based on changes in channel environment, fluctuating non-line-of-sight (NLOS) conditions, or synchronization errors, and the present disclosure is not limited in this regard.

[0113] In some example embodiments, the LPP message 522 may include one or more of the following: a duration for activating AI-based positioning, a type of input to a positioning model, a reporting period for an output, an offset indicating a gap between ending non-AI-based positioning and starting AI-based positioning, parameters for monitoring the positioning model, or resources for collecting a data set for the positioning model.

[0114] In some example embodiments, the LPP message 522 may include an information element (IE) "AIPositioningActication" and may be implemented as:

[0115]

[0116] In some example embodiments, "duration" may be used to configure the duration of an active AI model. For example, how many times the AI ​​model will be terminated; or how long the AI-based positioning will be performed. The time unit of the duration may be a symbol, a time slot, a subframe, or a frame. In some example embodiments, "offset" is used to configure the time gap between ending non-AI-based positioning and starting AI-based positioning. For example, the time to end non-AI-based positioning may be the time slot in which the last PRS is received, and the time to start AI-based positioning may be the time slot in which the AI-based positioning is started.

[0117] In some example embodiments, "model input (modelInput)" can be used to configure the type of input to the AI-based positioning model, such as the received PRS, RSRP, RSTD or CIR. In some example embodiments, "model output report (modelOutputReport)" can be used to configure the report of the output of the AI-based positioning model, such as a reporting period or reporting interval.

[0118] In some example embodiments, "model monitoring" is used to configure model monitoring parameters. For example, the parameter may include one or more of the monitoring frequency or the PRS resources used for monitoring. In some examples, the parameter may refer to the IE "modelMonitoring" in the LPP, and will not be repeated here. In some example embodiments, "monitoring dataset" is used to configure the PRS resources to collect a dataset for the monitoring model.

[0119] On the other side of the communication, the terminal device 110 receives 524 the LPP message 522. Therefore, the terminal device 110 can download the positioning model from the network side. In some embodiments, an AI model configuration transfer process can be performed. In some example embodiments, the terminal device 110 can download the positioning model based on its capability information.

[0120] In some embodiments, the positioning model may be stored in an access network device, a core network device (such as an LMF), or other device, and the terminal device 110 may download the positioning model from the device storing the positioning model. In some example embodiments, the downloaded positioning model may include AI model parameters, such as the type of network, the layer, the number of neurons in each layer, etc.

[0121] Alternatively or additionally, the terminal device 110 sends 510 capability information 512 to the core network device 130. On the other side of the communication, the core network device 130 receives 514 the capability information 512 of the terminal device 110. In some embodiments, the transmission of the capability information 512 may be unsolicited.

[0122] In some embodiments, the transmission of capability information 512 may be based on a request. In some examples, core network device 130 may send a capability request for capability information 512, and terminal device 110 may send capability information 512 to core network device 130 based on the capability request.

[0123] In some example embodiments, capability information 512 may indicate one or more of an expected positioning accuracy of terminal device 110, a computing power of terminal device 110, or a model size supported by terminal device 110. In some examples, the computing power may be characterized by floating point operations per second (FLOPs).

[0124] In some example embodiments, the capability request may be carried in an LPP request capability message and the capability information 512 may be carried in an LPP offer capability message, and the present disclosure is not limited in this respect.

[0125] It should be understood that the sending operation of capability information 512 is Figure 5520, but the present disclosure is not limited in this regard, for example, the terminal device 110 may send the capability information 512 after receiving the LPP message 522. In some examples, the capability information sent after operation 524 may be carried in a dedicated capability message.

[0126] The terminal device 110 determines 530 the location-related information of the terminal device 110. Specifically, the terminal device 110 may determine the location-related information based on the output of the downloaded positioning model. In some example embodiments, the terminal device 110 may perform model reasoning to determine (or obtain, predict) the location-related information. In some examples, the location-related information may indicate the geographic coordinates of the terminal device 110. In some examples, the location-related information may be an RTT value. In some examples, the location-related information may be in a standard format, for example, configured by an access network device or a core network device.

[0127] In some embodiments, the input of the positioning model may include the TDOA of different PRSs. Since the PRSs are sent from the access network device 120 to the terminal device 110, the TDOA of different PRSs may also be referred to as DL-TDOA. In some embodiments, the input of the positioning model may include the AOD of different PRSs, which may also be referred to as DL-AOD. In some example embodiments, non-AI-based positioning may be performed before process 500, for example, Figure 3A Steps 1-7 in may be performed before 520, or Figure 3A Steps 2-7 in can be performed between 510 and 520, which will be referred to below. Fig. 6A Give a description.

[0128] In some other embodiments, the input of the positioning model may include multi-RTT measurements, such as a first measurement result and / or a second measurement result. The first measurement result may be obtained by the terminal device 110, and the second measurement result may be obtained by the access network device 120. For example, the access network device 120 may determine the second measurement result and send it to the terminal device 110. In some examples, the first measurement result may indicate the Rx-Tx time difference between receiving the PRS and sending the SRS at the terminal device 110. In some examples, the second measurement result may indicate the Rx-Tx time difference between receiving the SRS and sending the PRS at the access network device 120. In some example embodiments, non-AI-based positioning may be performed before process 500, for example, Figure 3B Steps 1-9b in may be performed before 520, or Figure 3B Step 2-9b in can be performed between 510 and 520, which will be referred to below. Figure 6B Give a description.

[0129] In some other embodiments, the input of the positioning model may include one or more of the following: a CIR value obtained from the CSI-RS, a path RSRP from the CSI-RS or SSB, historical location information of the terminal device 110, or a speed of the terminal device 110. In this case, a positioning reference signal (such as a DL PRS or a UL SRS) is no longer required for the AI-based positioning model, and thus communication efficiency can be improved. However, in some example embodiments, the terminal device 110 may still send a UL SRS and / or receive a DL PRS to collect a data set during the model monitoring phase, and the present disclosure is not limited in this regard.

[0130] The terminal device 110 sends 540 location-related information 542 to the core network device 130. In some example embodiments, the location-related information 542 may be sent by providing reasoning information via an LPP including the output of a positioning model. Therefore, the terminal device 110 may report the result of the AI ​​model reasoning, and the result includes the location-related information 542. In some example embodiments, the location-related information may be used to assist in calculating the location of the terminal device 110 or to be used therein. In some example embodiments, the location-related information may be directly the location of the terminal device 110. In some examples, the location-related information may be an RTT value. In some example embodiments, the location-related information may indicate an additional type, such as a class UE coordinate.

[0131] On the other side of the communication, the core network device 130 (such as LMF) receives 544 the location-related information 542. Therefore, the core network device 130 can obtain the location-related information 542 of the terminal device 110, where the AI ​​model reasoning is performed.

[0132] Alternatively or additionally, the terminal device 110 may deactivate AI-based positioning. In some example embodiments, the terminal device 110 may fall back to non-AI-based positioning or ordinary positioning. In some embodiments, the deactivation information may also be referred to as AI-assisted positioning deactivation.

[0133] In some example embodiments, the terminal device 110 may receive the deactivation information from the core network device 130, and then the terminal device 110 may deactivate the AI-based positioning. In some embodiments, the deactivation information may be sent via the NRPPa message AI Positioning Deactivation.

[0134] In some example embodiments, the terminal device 110 may receive the deactivation information from the access network device 120, and then the terminal device 110 may deactivate the AI-based positioning. In some embodiments, the deactivation information may be sent via DCI. In some embodiments, the deactivation information may also be sent from the access network device 120 to the core network device 130 via the LPP message AI Positioning Deactivation.

[0135] In some example embodiments, the terminal device 110 may make a decision to deactivate the AI-based positioning by itself, and then the terminal device 110 may deactivate the AI-based positioning. In some embodiments, the deactivation information may also be sent from the terminal device 110 to the core network device 130 via the LPP message AI positioning deactivation.

[0136] In this way, any one of the terminal device 110, the access network device 120 or the core network device 130 may intend to terminate AI-based positioning during a period of a positioning model, AI-based positioning may be deactivated, and non-AI-based positioning may be resumed.

[0137] Fig. 6A A signaling diagram illustrating a process 610 according to some example embodiments of the present disclosure is illustrated. For discussion purposes only, reference will be made to Figure 1 Describe process 610. Process 610 may involve Figure 1 The terminal device 110, the access network device 120 and the core network device 130 in the embodiment of the present invention are shown in FIG. 1 . In some embodiments, the access network device 120 may include a serving gNB / TRP and a neighboring gNB / TRP. In some embodiments, the core network device 130 includes an LMF 131.

[0138] In step 1, the LPP capability transfer process is performed. In some embodiments, the terminal device 110 sends capability information to the core network device 130. In some embodiments, the core network device 130 sends a capability request to the terminal device 110, and the terminal device 110 sends the capability information based on the capability request. The following steps 2-7 can refer to the reference Figure 3A those described above and will not be repeated here.

[0139] In step 8, the core network device 130 sends an LPP message to the terminal device 110, which is similar to Figure 5 In step 9, the AI ​​model configuration transfer is performed. Specifically, the terminal device 110 can download the AI-based positioning model from the network side.

[0140] In step 10, the terminal device 110 determines the location-related information based on the output of the positioning model. In some embodiments, the input of the positioning model may include DL-TDOA and / or DL-AOD. In step 11, the terminal device 110 sends the location-related information to the core network device 130. A detailed description of the location-related information can be found in Figure 5 Operations 530 and 540 in .

[0141] In step 12, AI-based positioning deactivation may be performed. Specifically, any one of the terminal device 110, the access network device 120, or the core network device 130 may intend to terminate AI-based positioning during the period of the positioning model, the AI-based positioning may be deactivated, and the non-AI-based positioning may be restored.

[0142] Figure 6B A signaling diagram illustrating a process 620 according to some example embodiments of the present disclosure is illustrated. For discussion purposes only, reference will be made to Figure 1 Describe process 620. Process 620 may involve Figure 1 The terminal device 110, the access network device 120 and the core network device 130 in the embodiment of the present invention are shown in FIG. 1 . In some embodiments, the access network device 120 may include a serving gNB / TRP and a neighboring gNB / TRP. In some embodiments, the core network device 130 includes an LMF 131.

[0143] Figure 6B Step 1 in is similar to Fig. 6A Step 1 in Figure 6B Steps 2-9b in can be referred to Figure 3B Step 2-9b in , and Figure 6B Steps 10-11 can be referred to Fig. 6A , so they will not be repeated here.

[0144] In step 12, the access network device 120 sends the measurement result to the terminal device 110. In some embodiments, the access network device 120 may transmit a second measurement result to the terminal device 110, wherein the second measurement result indicates an Rx-Tx time difference between receiving the SRS and sending the PRS.

[0145] In step 13, the terminal device 110 determines the location-related information based on the output of the positioning model. In some embodiments, the input of the positioning model may include a first measurement result and a second measurement result. Figure 6B Steps 14-15 in can refer to Fig. 6A , so they will not be repeated here.

[0146] Further references Figure 7, which illustrates a signaling diagram of a diagram process 700 according to some example embodiments of the present disclosure. For discussion purposes only, reference will be made to Figure 1 Process 700 is described. Process 700 may involve Figure 1 The access network device 120 and the core network device 130 in the embodiment of the present invention. In some embodiments, the access network device 120 may include a serving gNB. In some embodiments, the core network device 130 includes an LMF.

[0147] Access network device 120 sends 710 a first NRPPa message 712 to core network device 130. In some example embodiments, first NRPPa message 712 may be a NRPPa AI positioning request. In some example embodiments, first NRPPa message 712 may be used to request AI-based positioning. In some examples, first NRPPa message 712 may request activation of AI-based positioning.

[0148] In some example embodiments, the first NRPPa message 712 may include one or more of the following: a type of positioning model, a type of input to the positioning model, a type of output of the positioning model, or a reporting period for the output.

[0149] On the other side of the communication, core network device 130 receives 714 first NRPPa message 712. In some example embodiments, core network device 130 may determine whether it can provide the requested information from access network device 120, and may also generate a second NRPPa message, such as a success or failure message.

[0150] The core network device 130 sends 720 a second NRPPa message 722 to the access network device 120. In some embodiments, the second NRPPa message 722 may be an AI positioning response indicating a successful response, or may be an AI positioning failure indicating a failed response.

[0151] In some example embodiments, the second NRPPa message 722 (e.g., AI positioning response) may include one or more of the following items: an identification (ID) of the terminal device 110, a type of positioning model, a type of input to the positioning model, or a type of output of the positioning model. For example, since the channel environment is specific to each UE, the second NRPPa message 722 may include the ID of the terminal device 110.

[0152] On the other side of the communication, the access network device 120 receives 724 the second NRPPa message 722. In some examples, the process 700 may be referred to as an AI model exchange process, and the process is initiated by the access network device 120.

[0153] Therefore, the access network device 120 (such as a serving gNB) is allowed to request the core network device 130 (such as an LMF) to provide detailed information of the positioning model hosted by other storage nodes. In some embodiments, the positioning model inference can also be performed at the access network device 120 or the core network device 130, which is respectively Figure 8A-8E Medium picture.

[0154] Fig. 8A A signaling diagram illustrating process 802 according to some example embodiments of the present disclosure is illustrated. Process 802 may involve Figure 1 The access network device 120 and the core network device 130 in the embodiment. In some embodiments, the core network device 130 includes LMF.

[0155] The access network device 120 sends 810 a first NRPPa message 812 to the core network device 130, and accordingly, the core network device 130 receives 814 the first NRPPa message 812. The core network device 130 sends 820 a second NRPPa message 822 to the access network device 120, and accordingly, the access network device 120 receives 824 the second NRPPa message 822. For example, the second NRPPa message 822 may be an AI positioning response indicating a successful response. It should be understood that the first NRPPa message 812 and the second NRPPa message 822 may refer to the above reference. Figure 7 those described above and will not be repeated here.

[0156] The access network device 120 determines 830 location-related information of the terminal device 110. Specifically, the access network device 120 may determine the location-related information based on the output of the downloaded positioning model. In some example embodiments, the access network device 120 may perform model reasoning to determine (or obtain, predict) location-related information. In some examples, the location-related information may indicate the geographic coordinates of the terminal device 110. In some examples, the location-related information may be an RTT value.

[0157] In some embodiments, the input to the positioning model may include the TDOA of different SRSs. Since the SRS is sent from the terminal device 110 to the access network device 120, the TDOA of different SRSs may also be referred to as UL-TDOA. In some embodiments, the input to the positioning model may include the AOA of different SRSs, which may also be referred to as UL-AOA. In some embodiments, the serving network device may receive measurement results from a neighboring network device. In some example embodiments, non-AI based positioning may be performed before process 802, for example, Figure 3A Steps 1-7 in can be performed before 810. In some embodiments, step 1 can be replaced by the above step 2 Figure 5 Similar steps as described at 510-514 in the Figure 8B Give a description.

[0158] In some other embodiments, the input of the positioning model may include multi-RTT measurements, such as a first measurement result and / or a second measurement result. The first measurement result may be obtained by the terminal device 110, and the second measurement result may be obtained by the access network device 120. For example, the terminal device 110 may determine the first measurement result and send it to the access network device 120. In some examples, the first measurement result may indicate the Rx-Tx time difference between receiving the PRS and sending the SRS at the terminal device 110. In some examples, the second measurement result may indicate the Rx-Tx time difference between receiving the SRS and sending the PRS at the access network device 120. In some embodiments, the serving network device may receive the measurement result from a neighboring network device. In some example embodiments, non-AI-based positioning may be performed before process 802, for example, Figure 3B Steps 1-9b in can be performed before 810. In some embodiments, step 1 can be replaced by the above step 1-9b in Figure 5 Similar steps as described at 510-514 in the Figure 8C Give a description.

[0159] In some other embodiments, the input of the positioning model may include one or more of the following items: CSI from the terminal device 110, historical location information of the terminal device 110, or the speed of the terminal device 110. In this case, a positioning reference signal (such as a DL PRS or a UL SRS) is no longer required for the AI-based positioning model, and thus communication efficiency can be improved. However, in some example embodiments, the access network device 120 may still send a DL PRS and / or receive a UL SRS to collect a data set during the model monitoring phase, and the present disclosure is not limited in this regard.

[0160] The access network device 120 sends 840 location-related information 842 to the core network device 130. In some example embodiments, the location-related information 842 may be sent via an NRPPa AI reasoning report including the output of the positioning model. Therefore, the access network device 120 may report the result of the AI ​​model reasoning, and the result includes the location-related information 842. In some example embodiments, the location-related information may be used to assist in calculating the location of the terminal device 110 or be used therein. In some example embodiments, the location-related information may be directly the location of the terminal device 110. In some examples, the location-related information may be an RTT value.

[0161] In some embodiments, the location-related information may be sent by the access network device 120 in a periodic manner, in other words, the message may be a periodic report. In some embodiments, an indication of a reporting period may be included in the first NRPPa message 812 .

[0162] On the other side of the communication, the core network device 130 (such as LMF) receives 844 the location-related information 842. Therefore, the core network device 130 can obtain the location-related information 842 of the terminal device 110, wherein the AI ​​model reasoning is performed at the access network device 120.

[0163] Alternatively or additionally, the access network device 120 may deactivate AI-based positioning. In some example embodiments, the access network device 120 may fall back to non-AI-based positioning or ordinary positioning. In some embodiments, the deactivation information may also be referred to as AI-assisted positioning deactivation.

[0164] In some example embodiments, access network device 120 may receive deactivation information from core network device 130, and then access network device 120 may deactivate AI-based positioning. In some embodiments, the deactivation information may be sent via an LPP message AI Positioning Deactivation.

[0165] In some example embodiments, the access network device 120 may receive the deactivation information from the terminal device 110, and then the access network device 120 may deactivate the AI-based positioning. In some embodiments, the deactivation information may also be sent from the terminal device 110 to the core network device 130 via the LPP message AI Positioning Deactivation.

[0166] In some example embodiments, the access network device 120 may make its own decision to deactivate the AI-based positioning, and then the access network device 120 may deactivate the AI-based positioning. In some embodiments, the deactivation information may also be sent from the access network device 120 to the core network device 130 via the LPP message AI positioning deactivation.

[0167] In this way, any one of the terminal device 110, the access network device 120 or the core network device 130 may intend to terminate AI-based positioning during a period of a positioning model, AI-based positioning may be deactivated, and non-AI-based positioning may be resumed.

[0168] Figure 8B A signaling diagram illustrating process 804 according to some example embodiments of the present disclosure is illustrated. Process 804 may involve Figure 1The terminal device 110, the access network device 120 and the core network device 130 in the embodiment of the present invention are shown in FIG. 1 . In some embodiments, the access network device 120 may include a serving gNB / TRP and a neighboring gNB / TRP. In some embodiments, the core network device 130 includes an LMF 131.

[0169] Steps 1-7 are similar to Fig. 6A Those shown, and the present disclosure will not be repeated here.

[0170] In step 8, the access network device 120 sends a first NRPPa message to the core network device 130, and in step 9, the core network device 130 sends a second NRPPa message to the access network device 120. A detailed description of the first NRPPa message and the second NRPPa message can be found in reference Figure 7-8A Those described.

[0171] In step 10, the neighboring gNB / TRP may pass the measurement results to the serving gNB / TRP, where the measurement results may be UL measurements regarding (multiple) SRS.

[0172] In step 11, the access network device 120 determines the location-related information based on the output of the positioning model. In some embodiments, the input of the positioning model may include UL-TDOA and / or UL-AOA. In step 12, the access network device 120 sends the location-related information to the core network device 130. A detailed description of the location-related information can be found in Fig. 8A Operations 830 and 840 in .

[0173] In step 13, AI-based positioning deactivation may be performed. Specifically, any one of the terminal device 110, the access network device 120, or the core network device 130 may intend to terminate AI-based positioning during the period of the positioning model, the AI-based positioning may be deactivated, and the non-AI-based positioning may be restored.

[0174] Figure 8C A signaling diagram illustrating process 806 according to some example embodiments of the present disclosure is illustrated. Process 806 may involve Figure 1 The terminal device 110, the access network device 120 and the core network device 130 in the embodiment of the present invention are shown in FIG. 1 . In some embodiments, the access network device 120 may include a serving gNB / TRP and a neighboring gNB / TRP. In some embodiments, the core network device 130 includes an LMF 131.

[0175] Figure 8C Steps 1-9b in are similar to Figure 3B Those shown, Figure 8C Steps 10-11 in are similar to Figure 8Band therefore will not be repeated here.

[0176] In step 12, the terminal device 110 and the neighboring gNB / TRP send measurement results (such as gNBRx-Tx timing difference and UE Rx-Tx timing difference) to the serving gNB / TRP.

[0177] In step 13, the access network device 120 determines the location-related information based on the output of the positioning model, and in step 14, the access network device 120 sends the location-related information to the core network device 130. Steps 13-14 may refer to Fig. 8A Operations 830 and 840 in .

[0178] In step 15, AI-based positioning deactivation may be performed, similar to Figure 8B Follow step 13 in the tutorial.

[0179] Fig.8D A signaling diagram illustrating process 808 according to some example embodiments of the present disclosure is illustrated. Process 808 may involve Figure 1 The access network device 120 and the core network device 130 in the embodiment. In some embodiments, the core network device 130 includes LMF.

[0180] The access network device 120 sends 810 a first NRPPa message 812 to the core network device 130, and accordingly, the core network device 130 receives 814 the first NRPPa message 812. The core network device 130 sends 820 a second NRPPa message 822 to the access network device 120, and accordingly, the access network device 120 receives 824 the second NRPPa message 822. For example, the second NRPPa message 822 may be an AI positioning response indicating a successful response. It should be understood that the first NRPPa message 812 and the second NRPPa message 822 may refer to those described above with reference to FIG. 6, and will not be repeated here.

[0181] The core network device 130 determines 850 location-related information of the terminal device 110. Specifically, if AI-based positioning is activated, the core network device 130 may determine the location-related information based on the output of the downloaded positioning model. In some example embodiments, the core network device 130 may perform model reasoning to determine (or obtain, predict) location-related information. In some examples, the location-related information may indicate the geographic coordinates of the terminal device 110. In some examples, the location-related information may be an RTT value.

[0182] In some other embodiments, the input of the positioning model may include multi-RTT measurements, such as a first measurement result and / or a second measurement result. The first measurement result may be obtained by the terminal device 110, and the second measurement result may be obtained by the access network device 120. For example, the terminal device 110 may determine the first measurement result and send it to the core network device 130. For example, the access network device 120 (e.g., including a serving gNB and a neighboring gNB) may determine the second measurement result and send it to the core network device 130. In some examples, the first measurement result may indicate an Rx-Tx time difference between receiving a PRS and sending an SRS at the terminal device 110. In some examples, the second measurement result may indicate an Rx-Tx time difference between receiving an SRS and sending an PRS at the access network device 120. In some example embodiments, non-AI-based positioning may be performed prior to process 800, for example, Figure 3B Steps 1-9b in may be performed before 810, which will be referred to below. Fig. 8E Give a description.

[0183] In some other embodiments, the input of the positioning model may include one or more of the following: a CIR value obtained from the CSI-RS, a path RSRP from the CSI-RS or SSB, historical location information of the terminal device 110, or a speed of the terminal device 110. In this case, a positioning reference signal (such as a DL PRS or a UL SRS) is no longer required for the AI-based positioning model, and thus communication efficiency can be improved. In some example embodiments, the second NRPPa message 822 may include information indicating that the positioning RS (PRS and / or SRS) configuration of the access network device 120 and / or the terminal device 110 is muted or canceled.

[0184] Alternatively or additionally, the core network device 130 may deactivate AI-based positioning. In some example embodiments, the core network device 130 may fall back to non-AI-based positioning or ordinary positioning. In some embodiments, the deactivation information may also be referred to as AI-assisted positioning deactivation.

[0185] In some example embodiments, core network device 130 may receive deactivation information from access network device 120, and then core network device 130 may deactivate AI-based positioning. In some embodiments, the deactivation information may be sent via an LPP message AI Positioning Deactivation.

[0186] In some example embodiments, the core network device 130 may receive the deactivation information from the terminal device 110, and then the core network device 130 may deactivate the AI-based positioning. In some embodiments, the deactivation information may also be sent via the LPP message AI Positioning Deactivation.

[0187] In some example embodiments, the core network device 130 may make its own decision to deactivate AI-based positioning, and the core network device 130 may then deactivate AI-based positioning.

[0188] In this way, any one of the terminal device 110, the access network device 120 or the core network device 130 may intend to terminate AI-based positioning during a period of a positioning model, AI-based positioning may be deactivated, and non-AI-based positioning may be resumed.

[0189] Fig. 8E A signaling diagram illustrating process 809 according to some example embodiments of the present disclosure is illustrated. Process 809 may involve Figure 1 The terminal device 110, the access network device 120 and the core network device 130 in the embodiment of the present invention are shown in FIG. 1 . In some embodiments, the access network device 120 may include a serving gNB / TRP and a neighboring gNB / TRP. In some embodiments, the core network device 130 includes an LMF 131.

[0190] Fig. 8E Steps 1-9b in are similar to Figure 3B Those shown, Fig. 8E Steps 10-11 can be referred to Fig.8D Operations 810-824 in , and therefore will not be repeated here.

[0191] In step 12, the terminal device 110 and the access network device 120 send the measurement results to the core network device 130. In some embodiments, the measurement results may include the gNB Rx-Tx timing difference, the UE Rx-Tx timing difference, or RSTD / RSRP. In some embodiments, the measurement results may include CIR and / or RSRP.

[0192] In step 13, the core network device 130 determines the location-related information based on the output of the positioning model. Fig.8D Operation 850 in step 14. In step 14, AI-based positioning deactivation may be performed. Specifically, any one of the terminal device 110, the access network device 120, or the core network device 130 may intend to terminate AI-based positioning during the period of the positioning model, the AI-based positioning may be deactivated, and the non-AI-based positioning may be restored.

[0193] The above various embodiments of the present disclosure may partially affect the current specification. For example, in view of the above various embodiments of the present disclosure, the current specification may be updated as follows (underlined). Regarding Section 8.10 in TS 38.305, Section 8.10.2.1 may include information that can be delivered from the LMF to the UE. Table 8.10.2.1-1 lists the information that can be delivered from the LMF to the UE.

[0194] Table 8.10.2.1-1: Assistance data that can be delivered from LMF to UE

[0195]

[0196]

[0197] With respect to clause 8.10 in TS 38.305, clause 8.10.2.2 may include information that may be passed from the UE to the LMF. The information that may be signaled from the UE to the LMF is listed in Table 8.10.2.2-1. Individual UE measurements are defined in TS 38.215.

[0198] Table 8.10.2.2-1: Measurement results that can be delivered from UE to LMF

[0199]

[0200] With regard to clause 8.10 in TS 38.305, clause 8.10.2.3 may include information that can be passed from the gNB to the LMF. The measurement results that can be signaled from the gNB to the LMF are listed in Table 8.10.2.3-3.

[0201] Table 8.10.2.3-3: Measurement results that can be delivered from gNB to LMF

[0202] Measurement results Measured NCGI and TRP IDs gNB Rx-Tx time difference measurement UL-SRS-RSRP UL arrival angle (azimuth and elevation) Timestamp of the measurement Quality for every measurement Measured beam information Reasoning of AI models

[0203] With regard to clause 8.10 in TS 38.305, clause 8.10.2.4 may include information that can be passed from the LMF to the gNB. Table 8.10.2.4-2 lists the TRP measurement request information that can be signaled from the LMF to the gNB.

[0204] Table 8.10.2.4-2: TRP measurement request information that can be passed from LMF to gNB

[0205]

[0206]

[0207] Regarding clause 8.12 in TS 38.305, clause 8.12.2 may include information to be passed between NG-RAN / 5GC elements, and clause 8.12.2.3 may include information to be passed from the gNB to the LMF. The assistance data that may be passed from the gNB to the LMF is listed in Table 8.12.2.3-1.

[0208] Table 8.12.2.3-1: Assistance data that can be delivered from the gNB to the LMF

[0209] information PCI, GCI, ARFCN, and TRP ID of the TRP served by the gNB Timing information of the TRP served by the gNB DL-PRS configuration for TRP served by gNB SSB information of TRP (time / frequency occupancy of SSB) Spatial direction information of DL-PRS resources of the TRP served by the gNB Geographic coordinate information of DL-PRS resources of the TRP served by the gNB

[0210] Table 8.12.2.3-2 lists the AI ​​model related data that can be passed from the gNB to the LMF.

[0211] Table 8.12.2.3-2: AI model related data that can be passed from gNB to LMF

[0212] information AI model types Model Output Model Input gNB reporting period

[0213] With regard to clause 8.2 of TS 38.455, a further clause 8.2.12 may be included as follows: 8.2.12 AI Model Type Information Exchange

[0214] 8.2.12.1 Overview

[0215] The purpose of the AI ​​model information exchange process is to allow the NG-RAN node to request the LMF node to provide AI model information hosted by the LMF node. The details for the AI ​​model are as follows. This procedure is only applicable when the NG-RAN node is a gNB.

[0216] 8.2.12.2 Successful Operation

[0217] Fig.8F You can refer to Figure 8.2.12.2-1: AI model information exchange process, successful operation of NG-RAN node by sending The process is initiated by sending an AI POSITIONING REQUEST message. LMF uses the AI ​​POSITIONING REQUEST message containing the requested AI model information. AIPOSITIONING RESPONSE message is used to respond.

[0218] 8.2.12.3 Unsuccessful operation

[0219] Figure 8G You can refer to Figure 8.2.12.2-2: AI model information exchange process, unsuccessful operation unsuccessful

[0220] If the LMF is unable to provide any of the information requested from the NG-RAN node, the LMF shall INFORMATION FAILURE message answer.

[0221] According to reference Figures 5 to 8E The described embodiments define the process between the terminal device and the core network device, and thereby can improve the positioning accuracy.

[0222] Fig. 9 FIG. 9 is a flowchart of an example method 900 implemented at a terminal device according to some embodiments of the present disclosure. For the purpose of discussion, reference will be made to Figure 1 The method 900 is described from the perspective of the terminal device 110 .

[0223] At block 910, the terminal device 110 receives an LPP message from the core network device 130, which notifies the terminal device 110 to activate AI-based positioning, which is used to determine location-related information based on the positioning model. At block 920, the terminal device 110 determines location-related information based on the output of the positioning model. At block 930, the terminal device 110 sends location-related information to the core network device 130.

[0224] In some example embodiments, the input to the positioning model includes one or more of the following items: TDOA of different PRSs, AOD of different PRSs, a first measurement result indicating an Rx-Tx time difference between receiving a PRS from an access network device and sending an SRS to the access network device 120, or a second measurement result received from the access network device 120 indicating an Rx-Tx time difference between receiving an SRS from a terminal device 110 and sending a PRS to the terminal device 110.

[0225] In some example embodiments, the input to the positioning model includes one or more of the following: a CIR value obtained from the CSI-RS, a path RSRP from the CSI-RS or SSB, historical location information of the terminal device 110 , or a speed of the terminal device 110 .

[0226] In some example embodiments, the terminal device 110 deactivates AI-based positioning based on one or more of the following: deactivation information from the core network device 130, which notifies the terminal device 110 to deactivate AI-based positioning; deactivation information from the access network device 120, or a decision made by the terminal device 110 to deactivate AI-based positioning.

[0227] In some example embodiments, the LPP message includes one or more of the following: a duration for activating AI-based positioning, a type of input to a positioning model, a reporting period for an output, an offset indicating an interval between ending non-AI-based positioning and starting AI-based positioning, parameters for monitoring the positioning model, or resources for collecting a data set for the positioning model.

[0228] In some example embodiments, terminal device 110 sends capability information of terminal device 110 to core network device 130 .

[0229] In some example embodiments, if terminal device 110 receives a capability request for capability information from core network device 130 , the capability information is sent to core network device 130 .

[0230] In some example embodiments, the capability information indicates one or more of: an expected positioning accuracy of the terminal device 110 , a computing power of the terminal device 110 , or a model size supported by the terminal device 110 .

[0231] Fig.10 FIG. 1 is a flowchart of an example method 1000 implemented at an access network device according to some embodiments of the present disclosure. For discussion purposes, reference will be made to Figure 1 The method 1000 is described from the perspective of the access network device 120 .

[0232] At block 1010, the access network device 120 sends a first NRPPa message to the core network device 130, the first NRPPa message requesting activation of AI-based positioning. At block 1020, the access network device 120 receives a second NRPPa message from the core network device 130, the second NRPPa message notifying the access network device 120 whether to activate AI-based positioning, the AI-based positioning being used to determine location-related information of the terminal device 110 based on the positioning model.

[0233] In some example embodiments, if the second NRPPa message notifies access network device 120 to activate AI-based positioning, access network device 120 determines location-related information of terminal device 110 based on the output of the positioning model, and sends the location-related information to core network device 130.

[0234] In some example embodiments, the input to the positioning model includes one or more of the following items: TDOA of different SRSs, AOA of different SRSs, a first measurement result received from the terminal device 110 indicating the Rx-Tx time difference between receiving the PRS from the access network device 120 and sending the SRS to the access network device 120, or a second measurement result indicating the Rx-Tx time difference between receiving the SRS from the terminal device 110 and sending the PRS to the terminal device 110.

[0235] In some example embodiments, the input of the positioning model includes one or more of the following: CSI from the terminal device 110 , historical location information of the terminal device 110 , or speed of the terminal device 110 .

[0236] In some example embodiments, the access network device 120 deactivates AI-based positioning based on one or more of the following: deactivation information from the core network device 130, which notifies the access network device 120 to deactivate AI-based positioning; deactivation information from the terminal device 110; or a decision made by the access network device 120 to deactivate AI-based positioning.

[0237] In some example embodiments, the first NRPPa message includes one or more of: a type of positioning model, a type of input to the positioning model, a type of output, or a reporting period for the output.

[0238] In some example embodiments, the second NRPPa message includes one or more of the following: an identification of the terminal device 110, a type of the positioning model, a type of an input to the positioning model, or a type of an output of the positioning model.

[0239] In some example embodiments, the second NRPPa message is a failure message.

[0240] Fig.11 FIG. 1 is a flowchart of an example method 1100 implemented at a core network device according to some embodiments of the present disclosure. For discussion purposes, reference will be made to Figure 1 The method 1100 is described from the perspective of the core network device 130 .

[0241] At block 1110, the core network device 130 receives a first NRPPa message from the access network device 120, the first NRPPa message requesting activation of AI-based positioning. At block 1120, the core network device 130 sends a second NRPPa message to the access network device 120, the second NRPPa message notifying the access network device 120 whether to activate AI-based positioning, the AI-based positioning being used to determine location-related information of the terminal device 110 based on the positioning model.

[0242] In some example embodiments, if AI-based positioning is activated, the core network device 130 determines location-related information based on the output of the positioning model.

[0243] In some example embodiments, the input to the positioning model includes: a first measurement result received from the terminal device 110 indicating the Rx-Tx time difference between receiving the PRS from the access network device and sending the SRS to the access network device 120, or a second measurement result received from the access network device 120 indicating the Rx-Tx time difference between receiving the SRS from the terminal device 110 and sending the PRS to the terminal device 110.

[0244] In some example embodiments, the input to the positioning model includes one or more of the following: a CIR value obtained from the CSI-RS, a path RSRP from the CSI-RS or SSB, historical location information of the terminal device 110 , or a speed of the terminal device 110 .

[0245] In some example embodiments, the core network device 130 deactivates AI-based positioning based on one or more of the following: deactivation information from the access network device 120, which notifies the core network device 130 to deactivate AI-based positioning; deactivation information from the terminal device 110; or a decision made by the core network device 130 to deactivate AI-based positioning.

[0246] In some example embodiments, the second NRPPa message is a failure message.

[0247] Fig.12 FIG. 1 is a flowchart of an example method 1200 implemented at a core network device according to some embodiments of the present disclosure. For discussion purposes, reference will be made to Figure 1 The method 2000 is described from the perspective of the core network device 130 .

[0248] At block 1210, the core network device 130 sends an LPP message to the terminal device 110, which notifies the terminal device 110 to activate AI-based positioning, which is used to determine location-related information based on the positioning model. At block 1220, the core network device 130 receives location-related information of the terminal device 110 from the terminal device 110.

[0249] In some example embodiments, the LPP message includes one or more of the following: a duration for activating AI-based positioning, a type of input to a positioning model, a reporting period for an output, an offset indicating an interval between ending non-AI-based positioning and starting AI-based positioning, parameters for monitoring the positioning model, or resources for collecting a data set for the positioning model.

[0250] In some example embodiments, the core network device 130 sends deactivation information to the terminal device 110 , which notifies the terminal device 110 to deactivate AI-based positioning.

[0251] In some example embodiments, core network device 130 receives capability information of terminal device 110 from terminal device 110 .

[0252] In some example embodiments, core network device 130 sends a capability request for capability information of terminal device 110 to terminal device 110 ; and receives capability information from terminal device 110 .

[0253] In some example embodiments, the capability information indicates one or more of: an expected positioning accuracy of the terminal device 110 , a computing power of the terminal device 110 , or a model size supported by the terminal device 110 .

[0254] Already referenced Figure 1-Figure 12Details of some embodiments according to the present disclosure are described. Example implementations of these devices will be discussed below.

[0255] In some example embodiments, a terminal device includes a circuit system configured to: receive an LPP message from a core network device, the LPP message notifying the terminal device to activate AI-based positioning, the AI-based positioning being used to determine location-related information based on a positioning model; determine location-related information based on an output of the positioning model; and send location-related information to the core network device.

[0256] In some example embodiments, the input to the positioning model includes one or more of the following items: TDOA of different PRSs, AOD of different PRSs, a first measurement result indicating an Rx-Tx time difference between receiving a PRS from an access network device and sending an SRS to the access network device, or a second measurement result received from the access network device indicating an Rx-Tx time difference between receiving an SRS from a terminal device and sending a PRS to the terminal device.

[0257] In some example embodiments, the input of the positioning model includes one or more of the following: a CIR value obtained from the CSI-RS, a path RSRP from the CSI-RS or SSB, historical location information of the terminal device, or a speed of the terminal device.

[0258] In some example embodiments, the terminal device includes a circuit system that is configured to: deactivate AI-based positioning based on at least one of the following items: deactivation information from a core network device that notifies the terminal device to deactivate AI-based positioning; deactivation information from an access network device, or a decision made by the terminal device to deactivate AI-based positioning.

[0259] In some example embodiments, the LPP message includes one or more of the following: a duration for activating AI-based positioning, a type of input to a positioning model, a reporting period for an output, an offset indicating an interval between ending non-AI-based positioning and starting AI-based positioning, parameters for monitoring the positioning model, or resources for collecting a data set for the positioning model.

[0260] In some example embodiments, the terminal device includes a circuit system configured to: send capability information of the terminal device to a core network device.

[0261] In some example embodiments, the terminal device includes circuitry configured to, in response to receiving a capability request for capability information from the core network device, send capability information to the core network device.

[0262] In some example embodiments, the capability information indicates one or more of: an expected positioning accuracy of the terminal device, a computing power of the terminal device, or a model size supported by the terminal device.

[0263] In some example embodiments, an access network device includes a circuit system configured to: send a first NRPPa message to a core network device, the first NRPPa message requesting activation of AI-based positioning; and receive a second NRPPa message from the core network device, the second NRPPa message notifying the access network device whether to activate AI-based positioning, the AI-based positioning being used to determine location-related information of a terminal device based on a positioning model.

[0264] In some example embodiments, the access network device includes a circuit system configured to: in response to a second NRPPa message notifying the access network device to activate AI-based positioning, determine location-related information of the terminal device based on an output of a positioning model; and send location-related information to a core network device.

[0265] In some example embodiments, the input to the positioning model includes one or more of the following: TDOA of different SRSs, AOA of different SRSs, a first measurement result received from a terminal device indicating an Rx-Tx time difference between receiving a PRS from an access network device and sending an SRS to the access network device, or a second measurement result indicating an Rx-Tx time difference between receiving an SRS from a terminal device and sending an SRS to the terminal device.

[0266] In some example embodiments, the input of the positioning model includes one or more of the following: CSI from the terminal device, historical location information of the terminal device, or speed of the terminal device.

[0267] In some example embodiments, an access network device includes a circuit system configured to: deactivate AI-based positioning based on one or more of the following: deactivation information from a core network device, which notifies the access network device to deactivate AI-based positioning; deactivation information from a terminal device; or a decision made by the access network device to deactivate AI-based positioning.

[0268] In some example embodiments, the first NRPPa message includes one or more of: a type of positioning model, a type of input to the positioning model, a type of output, or a reporting period for the output.

[0269] In some example embodiments, the second NRPPa message includes one or more of: an identification of the terminal device, a type of the positioning model, a type of an input to the positioning model, or a type of an output of the positioning model.

[0270] In some example embodiments, the second NRPPa message is a failure message.

[0271] In some example embodiments, a core network device includes a circuit system configured to: receive a first NRPPa message from an access network device, the first NRPPa message requesting activation of AI-based positioning; and send a second NRPPa message to the access network device, the second NRPPa message notifying the access network device whether to activate AI-based positioning, the AI-based positioning being used to determine location-related information of a terminal device based on a positioning model.

[0272] In some example embodiments, a core network device includes a circuit system configured to: determine location-related information based on an output of a positioning model in response to a determination that AI-based positioning is activated.

[0273] In some example embodiments, the input to the positioning model includes: a first measurement result received from the terminal device indicating an Rx-Tx time difference between receiving a PRS from an access network device and sending an SRS to the access network device, or a second measurement result received from the access network device indicating an Rx-Tx time difference between receiving an SRS from the terminal device and sending a PRS to the terminal device.

[0274] In some example embodiments, the input of the positioning model includes one or more of the following: a CIR value obtained from the CSI-RS, a path RSRP from the CSI-RS or SSB, historical location information of the terminal device, or a speed of the terminal device.

[0275] In some example embodiments, the core network device includes a circuit system configured to: deactivate AI-based positioning based on one or more of the following items: deactivation information from an access network device, which notifies the core network device to deactivate AI-based positioning; deactivation information from a terminal device; or a decision made by the core network device to deactivate AI-based positioning.

[0276] In some example embodiments, the second NRPPa message is a failure message.

[0277] In some example embodiments, a core network device includes a circuit system configured to: send an LPP message to a terminal device, the LPP message notifying the terminal device to activate AI-based positioning, the AI-based positioning being used to determine location-related information based on a positioning model; and receive location-related information of the terminal device from the terminal device.

[0278] In some example embodiments, the LPP message includes one or more of the following: a duration for activating AI-based positioning, a type of input to a positioning model, a reporting period for an output, an offset indicating an interval between ending non-AI-based positioning and starting AI-based positioning, parameters for monitoring the positioning model, or resources for collecting a data set for the positioning model.

[0279] In some example embodiments, the core network device includes a circuit system configured to: send deactivation information to the terminal device, the deactivation information notifying the terminal device to deactivate AI-based positioning.

[0280] In some example embodiments, a core network device includes circuitry configured to receive capability information of a terminal device from a terminal device.

[0281] In some example embodiments, a core network device includes circuitry configured to: send a capability request for capability information of a terminal device to a terminal device; and receive capability information from the terminal device.

[0282] In some example embodiments, the capability information indicates one or more of: an expected positioning accuracy of the terminal device, a computing power of the terminal device, or a model size supported by the terminal device.

[0283] Fig.13 1 shows a simplified block diagram of a device 1300 suitable for implementing an embodiment of the present disclosure. The device 1300 may be viewed as Figure 1 Another example implementation of the terminal device 110, the access network device 120, and / or the core network device 130 is shown. Therefore, the device 1300 can be implemented at the terminal device 110, the access network device 120, and / or the core network device 130 or as at least a part thereof.

[0284] As shown, the device 1300 includes a processor 1310, a memory 1320 coupled to the processor 1310, a suitable transmitter (TX) and a receiver (RX) 1340 coupled to the processor 1310, and a communication interface coupled to the TX / RX 1340. The memory 1310 stores at least a portion of the program 1330. The TX / RX 1340 is used for bidirectional communication. The TX / RX 1340 has at least one antenna to facilitate communication, but in fact, the access node mentioned in the present disclosure may have multiple. The communication interface may represent any interface required for communication with other network elements, such as an X2 interface for bidirectional communication between eNBs, an S1 interface for communication between a mobility management entity (MME) / serving gateway (S-GW) and an eNB, a Un interface for communication between an eNB and a relay node (RN), or a Uu interface for communication between an eNB and a terminal device.

[0285] Assume that the program 1330 includes program instructions that, when executed by the associated processor 1310, enable the device 1300 to operate according to the embodiments of the present disclosure, as discussed herein with reference to Figures 6 to 20. The embodiments herein may be implemented by computer software executable by the processor 1310 of the device 1300, or by hardware, or by a combination of software and hardware. The processor 1310 may be configured to implement various embodiments of the present disclosure. In addition, the combination of the processor 1310 and the memory 1320 may form a processing component 1350 suitable for implementing various embodiments of the present disclosure.

[0286] The memory 1320 may be of any type suitable for the local technology network and may be implemented using any suitable data storage technology, such as, as non-limiting examples, non-transitory computer-readable storage media, semiconductor-based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory, and removable memory. Although only one memory 1320 is shown in the device 1300, there may be several physically different memory modules in the device 1300. The processor 1310 may be of any type suitable for the local technology network and, as non-limiting examples, may include one or more of a general-purpose computer, a special-purpose computer, a microprocessor, a digital signal processor (DSP), and a processor based on a multi-core processor architecture. The device 1300 may have multiple processors, such as a dedicated integrated circuit chip that is time-slave to a clock synchronized with a main processor.

[0287] In summary, the embodiments of the present disclosure may provide the following solutions.

[0288] The present disclosure provides a communication method, comprising: at a terminal device, receiving a Long Term Evolution Positioning Protocol (LPP) message from a core network device, the LPP message notifying the terminal device to activate artificial intelligence (AI)-based positioning, the AI-based positioning being used to determine location-related information based on a positioning model; determining the location-related information based on an output of the positioning model; and sending the location-related information to the core network device.

[0289] In one embodiment, as in the above method, the input of the positioning model includes at least one of the following items: arrival time difference (TDOA) of different positioning reference signals (PRS), departure angle (AOD) of different PRS, a first measurement result indicating the receive-transmit (Rx-Tx) time difference between receiving PRS from an access network device and sending a sounding reference signal (SRS) to the access network device, or a second measurement result received from the access network device indicating the Rx-Tx time difference between receiving SRS from the terminal device and sending PRS to the terminal device.

[0290] In one embodiment, as in the above method, the input of the positioning model includes at least one of the following items: a channel impulse response (CIR) value obtained from a channel state information reference signal (CSI-RS), a path reference signal received power (RSRP) from the CSI-RS or a synchronization signal block (SSB), historical location information of the terminal device, or the speed of the terminal device.

[0291] In one embodiment, the above method also includes deactivating the AI-based positioning based on at least one of the following items: deactivation information from the core network device, the deactivation information notifying the terminal device to deactivate the AI-based positioning, the deactivation information from the access network device, or a decision determined by the terminal device to deactivate the AI-based positioning.

[0292] In one embodiment, as in the above method, the LPP message includes at least one of the following items: the duration for activating the AI-based positioning, the type of input to the positioning model, the reporting period of the output, an offset indicating the gap between the end of non-AI-based positioning and the start of the AI-based positioning, parameters for monitoring the positioning model, or resources for collecting a data set for the positioning model.

[0293] In one embodiment, the above method further includes: sending capability information of the terminal device to the core network device.

[0294] In one embodiment, in the above method, sending the capability information includes: in response to receiving a capability request for the capability information from the core network device, sending the capability information to the core network device.

[0295] In one embodiment, as in the above method, the capability information indicates at least one of the following items: the expected positioning accuracy of the terminal device, the computing power of the terminal device, or the model size supported by the terminal device.

[0296] The present disclosure provides a communication method, including: at an access network device, sending a first New Radio Positioning Protocol A (NRPPa) message to a core network device, the first NRPPa message requesting activation of artificial intelligence (AI)-based positioning; and receiving a second NRPPa message from the core network device, the second NRPPa message notifying the access network device whether to activate the AI-based positioning, the AI-based positioning being used to determine location-related information of a terminal device based on a positioning model.

[0297] In one embodiment, the above method also includes: in response to the second NRPPa message notifying the access network device to activate the AI-based positioning, determining the location-related information of the terminal device based on the output of the positioning model; and sending the location-related information to the core network device.

[0298] In one embodiment, as in the above method, the input of the positioning model includes at least one of the following items: arrival time difference (TDOA) of different sounding reference signals (SRS), AOA of different SRS, a first measurement result received from the terminal device, indicating the receive-send (Rx-Tx) time difference between receiving a positioning reference signal (PRS) from the access network device and sending the SRS to the access network device, or a second measurement result, indicating the Rx-Tx time difference between receiving the SRS from the terminal device and sending the PRS to the terminal device.

[0299] In one embodiment, as in the above method, the input of the positioning model includes at least one of the following items: channel state information (CSI) from the terminal device, historical location information of the terminal device, or speed of the terminal device.

[0300] In one embodiment, the above method also includes deactivating the AI-based positioning based on at least one of the following items: deactivation information from the core network device, the deactivation information notifying the access network device to deactivate the AI-based positioning, the deactivation information from the terminal device, or a decision determined by the access network device to deactivate the AI-based positioning.

[0301] In one embodiment, as in the above method, the first NRPPa message includes at least one of the following items: the type of the positioning model, the type of the input of the positioning model, the type of the output, or the reporting period of the output.

[0302] In one embodiment, as in the above method, the second NRPPa message includes at least one of the following items: an identifier of the terminal device, a type of the positioning model, a type of input of the positioning model, or a type of output of the positioning model.

[0303] In one embodiment, as in the above method, the second NRPPa message is a failure message.

[0304] The present disclosure provides a communication method, including: at a core network device, receiving a first New Radio Positioning Protocol A (NRPPa) message from an access network device, the first NRPPa message requesting activation of artificial intelligence (AI)-based positioning; and sending a second NRPPa message to the access network device, the second NRPPa message notifying the access network device whether to activate the AI-based positioning, the AI-based positioning being used to determine location-related information of a terminal device based on a positioning model.

[0305] In one embodiment, the above method further includes: determining the location-related information based on the output of the positioning model according to the determination that the AI-based positioning is activated.

[0306] In one embodiment, in the above method, the input of the positioning model includes: a first measurement result received from the terminal device, indicating a receive-send (Rx-Tx) time difference between receiving a positioning reference signal (PRS) from the access network device and sending a sounding reference signal (SRS) to the access network device, or a second measurement result received from the access network device, indicating an Rx-Tx time difference between receiving an SRS from the terminal device and sending a PRS to the terminal device.

[0307] In one embodiment, as in the above method, the input of the positioning model includes at least one of the following items: a channel impulse response (CIR) value obtained from a channel state information reference signal (CSI-RS), a path reference signal received power (RSRP) from the CSI-RS or a synchronization signal block (SSB), historical location information of the terminal device, or the speed of the terminal device.

[0308] In one embodiment, the above method also includes deactivating the AI-based positioning based on at least one of the following items: deactivation information from the access network device, the deactivation information notifying the core network device to deactivate the AI-based positioning, the deactivation information from the terminal device, or a decision determined by the core network device to deactivate the AI-based positioning.

[0309] In one embodiment, as in the above method, the second NRPPa message is a failure message.

[0310] The present disclosure provides a communication method, comprising: at a core network device, sending a Long Term Evolution Positioning Protocol (LPP) message to a terminal device, wherein the LPP message notifies the terminal device to activate artificial intelligence (AI)-based positioning, wherein the AI-based positioning is used to determine location-related information based on a positioning model; and receiving location-related information of the terminal device from the terminal device.

[0311] In one embodiment, as in the above method, the LPP message includes at least one of the following items: the duration for activating the AI-based positioning, the type of input to the positioning model, the reporting period of the output, an offset indicating the gap between the end of non-AI-based positioning and the start of the AI-based positioning, parameters for monitoring the positioning model, or resources for collecting a data set for the positioning model.

[0312] In one embodiment, the above method further includes: sending a deactivation message to the terminal device, wherein the deactivation message notifies the terminal device to deactivate the AI-based positioning.

[0313] In one embodiment, the above method further includes: receiving capability information of the terminal device from the terminal device.

[0314] In one embodiment, the above method further includes: sending a capability request for capability information of the terminal device to the terminal device; and receiving the capability information from the terminal device.

[0315] In one embodiment, as in the above method, the capability information indicates at least one of the following items: the expected positioning accuracy of the terminal device, the computing power of the terminal device, or the model size supported by the terminal device.

[0316] The present disclosure provides a terminal device, comprising: a processor; and a memory storing a computer program code; the memory and the computer program code are configured to work with the processor so that the terminal device executes the method implemented at the terminal device as discussed above.

[0317] The present disclosure provides an access network device, which includes: a processor; and a memory storing computer program code; the memory and the computer program code are configured to work with the processor to enable the access network device to execute the method implemented at the access network device as discussed above.

[0318] The present disclosure provides a core network device, which includes: a processor; and a memory storing computer program code; the memory and the computer program code are configured to work with the processor so that the core network device executes the method implemented at the core network device as discussed above.

[0319] Generally, various embodiments of the present disclosure may be implemented using hardware or dedicated circuits, software, logic, or any combination thereof. Some aspects may be implemented using hardware, while other aspects may be implemented using firmware or software that may be executed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of the present disclosure are illustrated and described as block diagrams, flow charts, or using some other graphical representations, it should be understood that, as non-limiting examples, the blocks, devices, systems, techniques, or methods described herein may be implemented using hardware, software, firmware, dedicated circuits or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0320] The present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer executable instructions, such as instructions included in a program module, which are executed in a device on a target real or virtual processor to perform the above referenced Figures 5 to 13 The process or method described. Typically, a program module includes routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of the program modules can be combined or split between program modules as needed. The machine executable instructions of the program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in both local and remote storage media.

[0321] The program code for executing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device so that the program code causes the function / operation specified in the flow chart and / or block diagram to be realized when executed by the processor or controller. The program code can be executed completely on the machine, partially on the machine, as an independent software package, partially on the machine and partially on a remote machine, or completely on a remote machine or server.

[0322] The above program code can be embodied in a machine-readable medium, which can be any tangible medium that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with it. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium will include an electrical connection with one or more wires, a portable computer floppy disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0323] In addition, although the operations are described in a particular order, this should not be understood as requiring such operations to be performed in the particular order shown or in order or performing all the operations shown to obtain the desired result. In some cases, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be interpreted as limitations on the scope of the present disclosure, but rather descriptions of features that may be specific to a particular embodiment. Certain features described in the context of a separate embodiment may also be implemented in combination in a single embodiment. On the contrary, the various features described in the context of a single embodiment may also be implemented in multiple embodiments individually or in any suitable sub-combination.

[0324] Although the present disclosure has been described in language specific to structural features and / or methodological acts, it should be understood that the present disclosure defined in the appended claims is not necessarily limited to the specific features or acts described above. Instead, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. A method of communication, comprising: At a terminal device, receiving a long term evolution positioning protocol (LPP) message from a core network device, the LPP message notifying the terminal device to activate artificial intelligence (AI)-based positioning, the AI-based positioning being used to determine location-related information based on a positioning model; Determine the location-related information based on the output of the positioning model; as well as Send the location-related information to the core network device.

2. The method according to claim 1, wherein the input of the positioning model comprises at least one of the following: The time difference of arrival (TDOA) of different positioning reference signals (PRS), Angle of departure (AOD) of different PRS, a first measurement result indicating a receive-transmit (Rx-Tx) time difference between receiving a PRS from an access network device and sending a sounding reference signal (SRS) to the access network device, or The second measurement result received from the access network device indicates an Rx-Tx time difference between receiving the SRS from the terminal device and sending the PRS to the terminal device.

3. The method according to claim 1, wherein the input of the positioning model comprises at least one of the following: The channel impulse response (CIR) value obtained from the channel state information reference signal (CSI-RS), a path reference signal received power (RSRP) from the CSI-RS or synchronization signal block (SSB), The historical location information of the terminal device, or The speed of the terminal device.

4. The method according to claim 1, further comprising: The AI-based positioning is disabled based on at least one of: deactivation information from the core network device, the deactivation information notifying the terminal device to deactivate the AI-based positioning, The deactivation information from the access network equipment, or A decision determined by the terminal device to deactivate the AI-based positioning.

5. The method of claim 1, wherein the LPP message comprises at least one of the following: the duration for activating the AI-based positioning, The type of input to the positioning model, the reporting period of the output, an offset indicating the gap between the end of a non-AI based positioning and the start of said AI based positioning, for monitoring parameters of the positioning model, or Resources for collecting datasets for the positioning model.

6. The method according to claim 1, further comprising: Sending capability information of the terminal device to the core network device, wherein the capability information indicates at least one of the following items: The expected positioning accuracy of the terminal device, the computing power of the terminal device, or Model sizes supported by the terminal device.

7. A method of communication, comprising: At the access network device, sending a first New Radio Positioning Protocol A (NRPPa) message to the core network device, the first NRPPa message requesting activation of artificial intelligence (AI)-based positioning; and A second NRPPa message is received from the core network device, and the second NRPPa message notifies the access network device whether to activate the AI-based positioning, and the AI-based positioning is used to determine the location-related information of the terminal device based on the positioning model.

8. The method according to claim 7, further comprising: In response to notifying the access network device of the second NRPPa message to activate the AI-based positioning, determining the location-related information of the terminal device based on the output of the positioning model; as well as Send the location-related information to the core network device.

9. The method of claim 8, wherein the input of the positioning model comprises at least one of the following: The time difference of arrival (TDOA) of different sounding reference signals (SRS), Angle of arrival (AOA) of different SRS, a first measurement result received from the terminal device, indicating a receive-transmit (Rx-Tx) time difference between receiving a positioning reference signal (PRS) from the access network device and sending an SRS to the access network device, or The second measurement result indicates the Rx-Tx time difference between receiving the SRS from the terminal device and sending the PRS to the terminal device.

10. The method of claim 8, wherein the input of the positioning model comprises at least one of the following: Channel State Information (CSI) from the terminal device, The historical location information of the terminal device, or The speed of the terminal device.

11. The method according to claim 8, further comprising: The AI-based positioning is disabled based on at least one of: deactivation information from the core network device, the deactivation information notifying the access network device to deactivate the AI-based positioning, the deactivation information from the terminal device, or A decision to deactivate the AI-based positioning is determined by the access network device.

12. The method of claim 7, wherein the first NRPPa message includes at least one of the following: The type of positioning model, The type of input to the positioning model, the type of output, or The reporting period for the output.

13. The method of claim 8, wherein the second NRPPa message includes at least one of the following: an identification of the terminal device, The type of positioning model, the type of input to the positioning model, or The type of output of the localization model.

14. The method of claim 7, wherein the second NRPPa message is a failure message.

15. A method of communication, comprising: At a core network device, receiving a first New Radio Positioning Protocol A (NRPPa) message from an access network device, the first NRPPa message requesting activation of artificial intelligence (AI) based positioning; as well as A second NRPPa message is sent to the access network device, and the second NRPPa message notifies the access network device whether to activate the AI-based positioning, and the AI-based positioning is used to determine the location-related information of the terminal device based on the positioning model.

16. The method according to claim 15, further comprising: Based on the determination that the AI-based positioning is activated, the location-related information is determined based on an output of the positioning model.

17. The method according to claim 16, wherein the input of the positioning model comprises: a first measurement result received from the terminal device, indicating a receive-transmit (Rx-Tx) time difference between receiving a positioning reference signal (PRS) from the access network device and sending a sounding reference signal (SRS) to the access network device, or The second measurement result received from the access network device indicates an Rx-Tx time difference between receiving the SRS from the terminal device and sending the PRS to the terminal device.

18. The method of claim 16, wherein the input of the positioning model comprises at least one of the following: The channel impulse response (CIR) value obtained from the channel state information reference signal (CSI-RS), a path reference signal received power (RSRP) from the CSI-RS or synchronization signal block (SSB), The historical location information of the terminal device, or The speed of the terminal device.

19. The method according to claim 16, further comprising: The AI-based positioning is disabled based on at least one of: deactivation information from the access network device, the deactivation information notifying the core network device to deactivate the AI-based positioning, the deactivation information from the terminal device, or A decision to deactivate the AI-based positioning is determined by the core network device.

20. The method of claim 15, wherein the second NRPPa message is a failure message.

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