COMMUNICATION METHOD, TERMINAL DEVICE, AND NETWORK DEVICE

Abstract

The embodiments of the present disclosure relate to a communication method, an apparatus, and a computer-readable medium. According to the embodiments of the present disclosure, an artificial intelligence / machine learning (AI / ML)-based positioning model is deployed in a terminal device or a network device. When the AI / ML-based positioning model is triggered, the location-related measurement information of the terminal device is determined based on the AI / ML-based positioning model. The core network device estimates the location of the terminal device based on the reported location-related measurement information. In this way, the terminal device can be more accurately positioned.

Description

[Technical field]

[0001] TECHNICAL FIELD Embodiments of the present disclosure relate generally to the field of telecommunications, and more particularly to communication methods, apparatus, and computer-readable media. [Background technology]

[0002] Several techniques have been proposed to improve communication performance. For example, a communication device may employ artificial intelligent / machine learning (AI / ML) models to improve communication quality. The AI / ML models can be applied to various scenarios to achieve better performance. Recent work items have been conducted in the third generation partner project (3GPP) for positioning support in New Radio (NR) systems. New reference signals for positioning have already been introduced in the downlink. For example, a terminal device may measure a reference signal time difference (RSTD) between positioning reference signals (PRS) from different transmission points to perform positioning. Alternatively or additionally, the terminal device may measure a receive-transmit (Rx-Tx) time difference, where the time difference is between two PRSs. Summary of the Invention [Problem to be solved by the invention]

[0003] Overall, the exemplary embodiments of the present disclosure provide a solution for communications. [Means for solving the problem]

[0004] In a first aspect, a communication method is provided, the method including: receiving, in a terminal device, a configuration of an artificial intelligence / machine learning (AI / ML) based positioning model from a network device, the configuration including a set of parameters for the AI / ML based positioning model and an instruction to initiate the AI / ML based positioning model, determining location-related measurement information of the terminal device based on the AI / ML based positioning model according to a determination that the AI / ML based positioning model is triggered, and transmitting the location-related measurement information.

[0005] In a second aspect, a communication method is provided, the method including: transmitting, in a network device, a configuration of an artificial intelligence / machine learning (AI / ML) based positioning model, the configuration including a set of parameters for the AI / ML based positioning model, and an instruction to initiate the AI / ML based positioning model to a terminal device.

[0006] In a third aspect, a method of communication is provided, the method including: sending, in a network device, an instruction to a terminal device to initiate an artificial intelligence / machine learning (AI / ML) based positioning model, determining location-related measurement information of the terminal device based on the AI / ML based positioning model according to a determination that the AI / ML based positioning model is triggered, and sending the location-related measurement information.

[0007] In a fourth aspect, a method of communications is provided, the method including receiving, at a terminal device, instructions from a network device to initiate an artificial intelligence / machine learning (AI / ML) based positioning model.

[0008] In a fifth aspect, there is provided a terminal device comprising a processing unit and a memory coupled to the processing unit storing instructions, the instructions being configured to, when executed by the processing unit, cause the terminal device to perform a method according to the first or fourth aspect.

[0009] In a sixth aspect, there is provided a network device comprising a processing unit and a memory coupled to the processing unit storing instructions which, when executed by the processing unit, cause the network device to perform a method according to the second or third aspect.

[0010] In a seventh aspect, there is provided a computer readable medium storing instructions which, when executed on at least one processor, cause the at least one processor to perform a method according to the first, second, third or fourth aspect.

[0011] Other features of the present disclosure will become readily apparent from the following description. [Brief description of the drawings]

[0012] The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description of several exemplary embodiments of the present disclosure in the accompanying drawings.

[0013] [Figure 1] FIG. 1 is a schematic diagram of a communication environment in which embodiments of the present disclosure can be implemented.

[0014] [Diagram 2] FIG. 2 illustrates a signaling flow for communication in accordance with some embodiments of the present disclosure.

[0015] [Diagram 3] FIG. 2 illustrates a signaling flow for communication in accordance with some embodiments of the present disclosure.

[0016] [Figure 4] FIG. 2 illustrates a signaling flow for communication in accordance with some embodiments of the present disclosure.

[0017] [Diagram 5] FIG. 2 illustrates a signaling flow for communication in accordance with some embodiments of the present disclosure.

[0018] [Figure 6] FIG. 1 is a schematic diagram of a positioning reference signal (PRS) resource.

[0019] [Figure 7] 1 is a flowchart of an exemplary method according to an embodiment of the present disclosure.

[0020] [Figure 8] 1 is a flowchart of an exemplary method according to an embodiment of the present disclosure.

[0021] [Figure 9] 1 is a flowchart of an exemplary method according to an embodiment of the present disclosure.

[0022] [Figure 10] 1 is a flowchart of an exemplary method according to an embodiment of the present disclosure.

[0023] [Figure 11] FIG. 1 is a schematic block diagram of an apparatus suitable for implementing embodiments of the present disclosure.

[0024] In the drawings, the same or similar reference numbers represent the same or similar elements. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0025] The principles of the present disclosure will now be described with reference to some exemplary embodiments. It should be understood that these embodiments are provided for illustrative purposes only, to assist those skilled in the art in understanding and implementing the present disclosure, and do not imply any limitations on the scope of the present disclosure. The disclosure described herein can be implemented in various ways different from those described below.

[0026] In the following description and claims, unless otherwise defined, 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 pertains.

[0027] As used herein, the term "terminal device" refers to any device having wireless or wired communication capabilities. Examples of terminal devices include User Equipment (UE), personal computers, desktops, mobile phones, cellular phones, smartphones, personal digital assistants (PDAs), portable computers, tablets, wearable devices, Internet of Things (IoT) devices, Ultra-Reliable Low Latency Communication (URLLC) devices, Internet of Everything (IoE) devices, machine type communication (MTC) devices, vehicle-mounted devices for V2X communications where X stands for pedestrian, vehicle, or infrastructure / network, devices for integrated access and integrated access and backhaul (IAB), satellite-based or airborne vehicles in a non-terrestrial network (NTN) including High Altitude Platforms (HAPs) including satellites and Unmanned Aircraft Systems (UASs), extended reality (XR) devices including different types of reality such as augmented reality (AR), mixed reality (MR), virtual reality (VR), unmanned aerial vehicles (UAVs), which are aircraft without a human pilot and commonly referred to as drones. "Terminal Device" includes, but is not limited to, a vehicle (V2X), a device on a high speed train (HST), or an image capture device such as a digital camera, a sensor game device, a music storage and playback device, or an Internet appliance that allows wireless or wired Internet access and browsing, etc. The "Terminal Device" may further have "multicast / broadcast" capabilities to support V2X applications, transparent IPv4 / IPv6 multicast distribution, IPTV, smart TV, wireless services, over-the-air software distribution, group communication, and IoT applications that are public safety and mission critical. It may also incorporate one or more Subscriber Identity Modules (SIMs), known as multi-SIMs.The term "terminal" may be used interchangeably with UE, mobile station, subscriber station, mobile terminal, user terminal, or wireless device. In the following description, the terms "terminal", "communications device", "terminal", "user equipment" and "UE" may be used interchangeably.

[0028] The terminal device or network device may have the capability of artificial intelligence (AI) or machine learning. Generally, it includes a model trained from a large amount of data collected for a specific function and can be used to predict some information.

[0029] The terminal device or network device may operate on several frequency ranges, such as FR1 (410 MHz-7125 MHz), FR2 (24.25 GHz-71 GHz), frequency bands greater than 100 GHz, and Tera Hertz (THz). It can also operate on licensed / unlicensed / shared spectrum. The terminal device may have two or more connections with the network device under Multi-Radio Dual Connectivity (MR-DC) application scenarios. The terminal device or network device can operate in full duplex, flexible duplex, and cross division duplex modes.

[0030] The term "network device" refers to a device capable of providing or hosting a cell or coverage in which a terminal device can communicate. Examples of network devices include, but are not limited to, a Node B (NodeB or NB), an evolved Node B (eNodeB or eNB), a next generation Node B (gNB), a transmit / receive point (TRP), a remote radio unit (RRU), a radio head (RH), a remote radio head (RRH), an IAB node, a femto node, a pico node, a low power node such as a reconfigurable intelligent surface (RIS), etc.

[0031] In one embodiment, the terminal device can connect to a first network device and a second network device. One of the first network device and the second network device may be a master 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 about the different RATs may be transmitted 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 transmitted from the first network device to the terminal device, and the second information may be transmitted from the second network device to the terminal device directly or via the first network device. In one embodiment, information about the configuration of the terminal device configured by the second network device may be transmitted from the second network device via the first network device. Information regarding the reconfiguration of the terminal device configured by the second network device may be transmitted from the second network device directly to the terminal device or via the first network device.

[0032] The communications described herein may conform to any suitable standard, including, but not limited to, New Radio Access (NR), Long Term Evolution (LTE), LTE-Evolution, LTE-Advanced (LTE-A), Wideband Code Division Multiple Access (WCDMA), Code Division Multiple Access (CDMA), cdma2000, Global System for Mobile Communications (GSM), and the like. Furthermore, the communications may be performed according to any generation of communication protocols now 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 techniques described herein may be used for the wireless networks and radio technologies mentioned above, as well as other wireless networks and radio technologies. The embodiments of the present disclosure may be performed according to any generation of communication protocols now 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.

[0033] The term "circuitry" as used herein may refer to a hardware circuit and / or a combination of a hardware circuit and software. For example, a circuit may be a combination of analog and / or digital hardware circuitry and software / firmware. As yet another example, a circuit may be any portion of a hardware processor having software, including a digital signal processor, software, and one or more memories, that cooperate to cause a device, such as a terminal device or a network device, to perform various functions. In yet another example, a circuit may be a hardware circuit and / or a processor, such as a microprocessor or a portion thereof, that requires software / firmware for operation, but the software may not be present if not required for operation. As used herein, the term "circuitry" also includes an implementation of only a hardware circuit or one or more processors, or a portion of a hardware circuit or one or more processors and its (or their) associated software and / or firmware.

[0034] As used herein, the singular forms "a," "an," and "said" include the plural forms unless the context clearly indicates otherwise. The term "comprises" and variations thereof 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." Terms such as "first," "second," and the like may refer to different or the same object. The following may include other explicit and implicit definitions.

[0035] In some instances, values, procedures, or devices are referred to as "best," "lowest," "highest," "minimum," "maximum," etc. It should be understood that such descriptions are intended to illustrate that selections can be made from among many functional alternatives used, and that such selections are not necessarily better, smaller, higher, or otherwise more preferred than other selections.

[0036] As mentioned above, this was done in 3GPP for positioning support in NR. Supporting various positioning methods to provide reliable and accurate UE location has long been one of the key features of 3GPP standards. Furthermore, AI / ML models can be applied to achieve better performance. Therefore, it is worth applying AI / ML models to terminal device positioning.

[0037] According to an embodiment of the present disclosure, an artificial intelligence / machine learning (AI / ML) based positioning model is implemented in a terminal device or a network device. When the AI / ML based positioning model is triggered, the location-related measurement information of the terminal device is determined based on the AI / ML based positioning model. The core network device estimates the location of the terminal device based on the location-related measurement information. In this way, the terminal device can be more accurately positioned.

[0038] 1 is a schematic diagram of a communication system in which embodiments of the present disclosure may be implemented. The communication system 100, which is part of a communication network, comprises terminal devices 110-1, 110-2, ..., 110-N, which may be collectively referred to as "terminal devices 110." The number N may be any suitable integer.

[0039] The communication system 100 further includes a network device 120. In the communication system 100, the network device 120 and the terminal device 110 can communicate data and control information with each other. The number of terminal devices shown in FIG. 1 is shown for illustration purposes only and does not imply any limitation. The communication system further includes a core network device 130. For example, the core network device 130 may be a location management function (LMF) entity.

[0040] Communications in the communications system 100 may be achieved according to any suitable communications protocol, including, but not limited to, cellular communications protocols, such as first generation (1G), second generation (2G), third generation (3G), fourth generation (4G), and fifth generation (5G), wireless local area network communications protocols, such as Institute for Electrical and Electronics Engineers (IEEE) 802.11, and / or any other protocols now known or developed in the future. Furthermore, communications may utilize any suitable wireless communications 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 now known or developed in the future.

[0041] The embodiments of the present disclosure may be applied to any suitable scenario. For example, the embodiments of the present disclosure may be implemented on a reduced capability NR device. Alternatively, the embodiments of the present disclosure may be implemented within one of NR multiple input multiple output (MIMO), NR sidelink enhancements, NR systems at frequencies higher than 52.6 GHz, enhanced NR operation up to 71 GHz, Narrowband Internet of Things (NB-IOT) / enhanced machine type communications (eMTC) over non-terrestrial based networks (NTN), NTN, UE power saving enhancements, NR coverage enhancements, NB-IOT and LTE-MTC, integrated access and backhaul (IAB), NR multicast and broadcast services, or multi-radio dual connectivity enhancements.

[0042] The term "slot" as used herein means a dynamic scheduling unit. A slot includes a predetermined number of symbols. The term "downlink (DL) subslot" may refer to a virtual subslot built on an uplink (UL) subslot. A DL subslot may include fewer symbols than a DL slot. A slot as used herein may refer to a normal slot including a predetermined number of symbols and a subslot including fewer symbols than the predetermined number of symbols.

[0043] In the following, an embodiment of the present disclosure in which an AI / ML-based processing model is implemented in a terminal device will be described in detail. First, reference will be made to FIG. 2, which illustrates a signaling diagram illustrating a process 200 between a terminal device and a network device according to some exemplary embodiments of the present disclosure. For illustrative purposes only, the process 200 will be described with reference to FIG. 1. The process 200 may involve the terminal device 110-1 and the network device 120 of FIG. 1.

[0044] The network device 120 may transmit a configuration of a positioning reference signal (PRS) to the terminal device 110-1. The configuration may indicate a set of occasions on which the PRS can be transmitted. The term "occasion" may occupy resources in the time domain and the frequency domain. The terminal device 110-1 may transmit a positioning request for downlink positioning to the network device 120 (2010).

[0045] Terminal device 110-1 may monitor the PRS on the occasion based on the configuration. Network device 120 may transmit the set of PRS to terminal device 110-1 (2020). Neighboring network devices (not shown) may also transmit the set of PRS to terminal device 110-1.

[0046] The terminal device 110-1 may perform measurements on the set of PRSs. For example, the measurements may be reference signal time difference (RSTD) measurements. Alternatively or additionally, the measurements may be reference signal received power (RSRP) measurements. In some other embodiments, the measurements may be receiving-transmitting (Rx-Tx) time difference measurements.

[0047] The terminal device 110-1 may transmit a measurement report to the core network device 130 (2030). For example, the measurement report may indicate a measured RSTD. Alternatively or additionally, the measurement report may indicate a measured RSRP. In some other embodiments, the measurement report may indicate a measured Rx-Tx time difference.

[0048] In some embodiments, the terminal device 110-1 may determine whether a condition for triggering the AI / ML-based positioning model is met. Alternatively, the network device 120 may determine whether a condition for triggering the AI / ML-based positioning model is met. In some embodiments, the condition for triggering the AI / ML-based positioning model may be the absence of a line of sight (LOS) path due to movement of the terminal device 110-1. Alternatively, the condition for triggering the AI / ML-based positioning model may be the absence of a LOS path due to a change in the propagation environment. In some other embodiments, the condition for triggering the AI / ML-based positioning model may be an unexpected synchronization error between the terminal device 110-1 and the network device 120 or between the network devices. In another embodiment, the condition for triggering the AI / ML-based positioning model may be the inaccurate reference position due to movement of the network device 120. In some embodiments, the condition for triggering the AI / ML-based positioning model may be poor DL ​​hearability from a neighboring cell to the terminal device 110-1. For example, if the link quality between the neighbor cell and the terminal device 110-1 is below a threshold, the AI / ML-based positioning model may be triggered. Alternatively or additionally, the condition for triggering the AI / ML-based positioning model may be a reduction in the overhead of the reference signal. Note that the condition for triggering the AI / ML-based positioning model may include any other condition not described.

[0049] In some embodiments, when the AI / ML-based positioning model is triggered by the network device 120, the network device 120 may notify the terminal device 110-1 to start the AI / ML-based positioning model. For example, the network device 120 may send an instruction to the terminal device 110-1 to start the AI / ML-based positioning model. In some embodiments, the instruction may be sent in downlink control information. Alternatively, the instruction may be sent in a medium access control (MAC) control element (CE). In an example embodiment, the terminal device 110-1 may feedback to the network device 120 that the AI-based positioning is not being performed for some reason, such as due to UE power saving.

[0050] Alternatively, if an AI / ML-based positioning model is triggered by terminal device 110-1, terminal device 110-1 may inform network device 120 to mute PRS resources during AI / ML-based positioning. Optionally, network device 120 may inform terminal device 110-1 that AI-based positioning is not being performed for some reason.

[0051] In some embodiments, if the AI / ML-based positioning model is triggered, the network device 120 may skip transmitting the PRS. In other words, the network device 120 may mute resources for PRS transmission. For example, as shown in FIG. 6, the PRS may be transmitted on resources 610-1, 610-2, 610-3, 610-4, 610-5, 610-6, and 610-7. The AI / ML-based positioning model may be triggered at time instance 620. The AI / ML-based positioning model may be running in time interval 630. In this case, resources 610-3, 610-4, and 610-5 in time interval 630 may be muted. In other words, the network device 120 may not transmit the PRS in time interval 630. After the AI / ML-based positioning model stops, the network device 120 may start transmitting the PRS.

[0052] The network device 120 may transmit the configuration of the AI / ML-based positioning model (2040). Additionally, the network device 120 may transmit an instruction to initiate the AI / ML-based positioning model to the terminal device 110-1. For example, the configuration of the AI / ML-based positioning model may be transmitted via RRC signaling. In some embodiments, the configuration of the AI / ML-based positioning model includes a set of parameters for the AI / ML-based positioning model. In some embodiments, the set of parameters may include an offset between a slot for an instruction to trigger the AI / ML-based positioning model and a start slot for the AI / ML-based positioning model.

[0053] Alternatively or additionally, the set of parameters may include a time interval parameter for the AI / ML based positioning model. In some embodiments, the time interval parameter may include a bitmap parameter set to a predetermined value. For example, a bitmap b 0 and b 1may be set to 0. Alternatively, the time interval parameter may include an on-time interval timer for the AI / ML based positioning model. For example, the AI / ML based positioning model may be used during the execution time of the on-time interval timer. In some other embodiments, the time interval parameter may include a parameter for disabling a higher layer parameter. For example, the parameter "NR-DL-PRS-PositioningFrequencyLayer" may be disabled. Alternatively, the parameter "NR-DL-PRS-ResourceSet" may be disabled.

[0054] If the AI / ML-based positioning model is triggered, the terminal device 110-1 may determine position-related measurement information of the terminal device 110-1 based on the AI / ML-based positioning model (2050). In some embodiments, the terminal device 110-1 may start the AI / ML-based position estimation based on the past position of the terminal device 110-1. Alternatively or additionally, the AI / ML-based position estimation may be started based on the moving speed and / or moving direction of the terminal device 110-1 or a user preference profile, for example, user habits, of the terminal device 110-1. It should be noted that the terminal device 110-1 may start the position estimation of the terminal device 110-1 using the positioning mode based on any suitable factor.

[0055] In some embodiments, the input of the AI / ML-based positioning model may include a set of previous RSTD measurements. Alternatively or additionally, the input of the AI / ML-based positioning model may include a set of past RSRP measurements. In some other embodiments, the input of the AI / ML-based positioning model may include a set of past round trip time (RTT) measurements. The past measurements may be obtained based on a previous PRS. The input of the AI / ML-based positioning model may also include a set of past location coordinates of the terminal device 110-1. For example, the location coordinates may be obtained from a global positioning system.

[0056] In some embodiments, the output of the AI / ML-based positioning model may include relative position information of the terminal device 110-1 with respect to a position determined based on the last reference signal. For example, this reference signal may be a PRS or a sounding reference signal. In some embodiments, the relative position information of the terminal device 110-1 may include position coordinates of the terminal device 110-1 with respect to a last position based on the last reference signal. Alternatively, the relative position information of the terminal device 110-1 may include an RSTD with respect to the last reference signal. In other embodiments, the relative position of the terminal device 110-1 may include an RSRP with respect to the last reference signal. The relative position of the terminal device 110-1 may include an RTT with respect to the last reference signal.

[0057] Alternatively or additionally, the output of the AI / ML-based positioning model may include absolute position information of the terminal device 110-1. In some embodiments, the absolute position information of the terminal device 110-1 may include position coordinates of the terminal device 110-1. Alternatively, the absolute position of the terminal device 110-1 may include RSTD. In other embodiments, the absolute position of the terminal device 110-1 may include RSRP. The absolute position of the terminal device 110-1 may include RTT.

[0058] The terminal device 110-1 may transmit (2060) the location-related measurement information to the core network device 130. In some embodiments, the terminal device 110-1 may transmit the location-related measurement information on a physical uplink shared channel (PUSCH) scheduled by the downlink control information. Alternatively, the location-related measurement information may be transmitted on a configured grant (CG) PUSCH. The core network device 130 may estimate (2070) the location of the terminal device 110-1 based on the location-related measurement information.

[0059] In some embodiments, if the output of the AI / ML-based positioning model is absolute position information of the terminal device 110-1, the position-related measurement information may include the absolute position of the terminal device 110-1. Alternatively, if the output of the AI / ML-based positioning model is relative position information of the terminal device 110-1, the position-related measurement information may include a combination of the relative position measurement result and the position information determined based on the last reference signal.

[0060] In some embodiments, in the case of observed time difference of arrival (OTDOA), the location-related measurement information is the sum of the last measured RSTD before the AI-based positioning and Δ RSTD and RSTD, where Δ RSTD is the timing difference converted from the output of the AI / ML-based model.

[0061] Alternatively or additionally, for DL ​​angle of departure (DL-AOD), the location related measurement information may be the last measured RSRP and Δ RSRP and RSRP, where Δ RSRP is the RSRP value converted from the output of the AI / ML based model. In some embodiments, for the UE Rx-TX time difference, the location related measurement information is the last measured RTT before the AI ​​based positioning and Δ RTT and RTT, where Δ RTT is the timing difference converted from the output of the AI / ML-based model.

[0062] According to the embodiment described with reference to Fig. 2, an AI / ML-based positioning model for DL ​​measurements can be implemented in the terminal device. In this way, the position of the terminal device can be measured more accurately. Also, power can be reduced since the terminal device does not need to monitor the PRS during positioning.

[0063] In the following, an embodiment of the present disclosure in which an AI / ML-based processing model is implemented in a terminal device will be described in detail. First, reference will be made to FIG. 3, which illustrates a signaling diagram illustrating a process 300 between a terminal device and a network device according to some exemplary embodiments of the present disclosure. For illustrative purposes only, the process 300 will be described with reference to FIG. 1. The process 300 may involve the terminal device 110-1 and the network device 120 of FIG. 1.

[0064] The network device 120 may transmit a configuration of a sounding reference signal (SRS) to the terminal device 110-1. In some embodiments, the configuration of the SRS may be received via RRC signaling. In some embodiments, the configuration of the SRS may include a plurality of physical resource blocks including a transmission periodicity allocated for the SRS. Alternatively or additionally, the configuration of the SRS may include a bandwidth parameter of the SRS. In some embodiments, the configuration of the SRS may indicate one or more SRS resources. Alternatively or additionally, the configuration of the SRS may indicate one or more SRS resource sets. The network device 120 may transmit a positioning request for uplink positioning to the terminal device 110-1 (3010).

[0065] The terminal device 110-1 may transmit a set of SRS to the network device 120 based on the SRS configuration (3020). The network device 120 may perform measurements on the set of SRS. For example, the measurements may be UL RSTD measurements. Alternatively or additionally, the measurements may be UL RSRP measurements. In some other embodiments, the measurements may be Rx-Tx time difference measurements. The measurements may be UL angle of arrival (AOA).

[0066] The network device 120 may send 3030 a measurement report to the core network device 130. For example, the measurement report may indicate a measured UL RSTD. Alternatively or additionally, the measurement report may indicate a measured UL RSRP. In some other embodiments, the measurement report may indicate a measured UL Rx-Tx time difference. In some embodiments, the measurement report may indicate a measured UL AOA.

[0067] In some embodiments, the terminal device 110-1 may determine whether a condition for triggering the AI / ML-based positioning model is met. Alternatively, the network device 120 may determine whether a condition for triggering the AI / ML-based positioning model is met. In some embodiments, the condition for triggering the AI / ML-based positioning model may be the absence of a line of sight (LOS) path due to movement of the terminal device 110-1. Alternatively, the condition for triggering the AI / ML-based positioning model may be the absence of a LOS path due to a change in the propagation environment. In some other embodiments, the condition for triggering the AI / ML-based positioning model may be an unexpected synchronization error between the terminal device 110-1 and the network device 120 or between the network devices. In another embodiment, the condition for triggering the AI / ML-based positioning model may be the inaccurate reference position due to movement of the network device 120. In some embodiments, the condition for triggering the AI / ML-based positioning model may be poor DL ​​hearability from a neighboring cell to the terminal device 110-1. For example, if the link quality between the neighbor cell and the terminal device 110-1 is below a threshold, the AI / ML-based positioning model may be triggered. Alternatively or additionally, the condition for triggering the AI / ML-based positioning model may be a reduction in the overhead of the reference signal. Note that the condition for triggering the AI / ML-based positioning model may include any other condition not described.

[0068] In some embodiments, when the AI / ML-based positioning model is triggered by the network device 120, the network device 120 may notify the terminal device 110-1 to start the AI / ML-based positioning model. For example, the network device 120 may send an instruction to the terminal device 110-1 to start the AI / ML-based positioning model. In some embodiments, the instruction may be sent in downlink control information. Alternatively, the instruction may be sent in a medium access control (MAC) control element (CE). In an example embodiment, the terminal device 110-1 may feedback to the network device 120 that the AI-based positioning is not being performed for some reason, such as due to UE power saving.

[0069] Alternatively, if an AI / ML-based positioning model is triggered by terminal device 110-1, terminal device 110-1 may inform network device 120 to mute PRS resources during AI / ML-based positioning. Optionally, network device 120 may inform terminal device 110-1 that AI-based positioning is not being performed for some reason.

[0070] In some embodiments, if the AI / ML-based positioning model is triggered, the network device 120 may skip transmitting the PRS. In other words, the network device 120 may mute resources for PRS transmission. For example, as shown in FIG. 6, the PRS may be transmitted on resources 610-1, 610-2, 610-3, 610-4, 610-5, 610-6, and 610-7. The AI / ML-based positioning model may be triggered at time instance 620. The AI / ML-based positioning model may be running in time interval 630. In this case, resources 610-3, 610-4, and 610-5 in time interval 630 may be muted. In other words, the network device 120 may not transmit the PRS in time interval 630. After the AI / ML-based positioning model stops, the network device 120 may start transmitting the PRS.

[0071] Alternatively, when the AI / ML-based positioning model is triggered, the terminal device 110-1 may skip transmitting the SRS, in other words, the terminal device 110-1 may mute resources for transmitting the SRS.

[0072] The network device 120 may transmit the configuration of the AI / ML-based positioning model to the terminal device 110-1 (3040). Additionally, the network device 120 may transmit an instruction to start the AI / ML-based positioning model to the terminal device 110-1. For example, the configuration of the AI / ML-based positioning model may be transmitted via RRC signaling. In some embodiments, the configuration of the AI / ML-based positioning model includes a set of parameters for the AI / ML-based positioning model. In some embodiments, the set of parameters may include an offset between a slot for an instruction to trigger the AI / ML-based positioning model and a start slot for the AI / ML-based positioning model.

[0073] Alternatively or additionally, the set of parameters may include a time interval parameter for the AI / ML based positioning model. In some embodiments, the time interval parameter may include a bitmap parameter set to a predetermined value. For example, a bitmap b 0 and b 1 may be set to 0. Alternatively, the time interval parameter may include an on-time interval timer for the AI / ML based positioning model. For example, the AI / ML based positioning model may be used during the execution time of the on-time interval timer. In some other embodiments, the time interval parameter may include a parameter for disabling a higher layer parameter. For example, the parameter "NR-DL-PRS-PositioningFrequencyLayer" may be disabled. Alternatively, the parameter "NR-DL-PRS-ResourceSet" may be disabled.

[0074] If the AI / ML-based positioning model is triggered, the terminal device 110-1 may determine position-related measurement information of the terminal device 110-1 based on the AI / ML-based positioning model (3050). In some embodiments, the terminal device 110-1 may start the AI / ML-based position estimation based on the past position of the terminal device 110-1. Alternatively or additionally, the AI / ML-based position estimation may be started based on the moving speed and / or moving direction of the terminal device 110-1 or a user preference profile, such as a user habit. It should be noted that the terminal device 110-1 may start the position estimation of the terminal device 110-1 using the positioning mode based on any suitable factor.

[0075] In some embodiments, the input of the AI / ML-based positioning model may include a set of previous RSTD measurements. Alternatively or additionally, the input of the AI / ML-based positioning model may include a set of past RSRP measurements. In some other embodiments, the input of the AI / ML-based positioning model may include a set of past round trip time (RTT) measurements. The past measurements may be obtained based on a previous PRS. The input of the AI / ML-based positioning model may also include a set of past location coordinates of the terminal device 110-1. For example, the location coordinates may be obtained from a global positioning system.

[0076] In some embodiments, the output of the AI / ML-based positioning model may include relative position information of the terminal device 110-1 with respect to a position determined based on the last reference signal. For example, this reference signal may be a PRS or a sounding reference signal. In some embodiments, the relative position information of the terminal device 110-1 may include position coordinates of the terminal device 110-1 with respect to a last position based on the last reference signal. Alternatively, the relative position information of the terminal device 110-1 may include an RSTD with respect to the last reference signal. In other embodiments, the relative position of the terminal device 110-1 may include an RSRP with respect to the last reference signal. The relative position of the terminal device 110-1 may include an RTT with respect to the last reference signal.

[0077] Alternatively or additionally, the output of the AI / ML-based positioning model may include absolute position information of the terminal device 110-1. In some embodiments, the absolute position information of the terminal device 110-1 may include position coordinates of the terminal device 110-1. Alternatively, the absolute position of the terminal device 110-1 may include RSTD. In other embodiments, the absolute position of the terminal device 110-1 may include RSRP. The absolute position of the terminal device 110-1 may include RTT.

[0078] Terminal device 110-1 may transmit (3060) the location-related measurement information to network device 120. In some embodiments, terminal device 110-1 may transmit the location-related measurement information on a physical uplink shared channel (PUSCH) scheduled by the downlink control information. Alternatively, the location-related measurement information may be transmitted on a CG PUSCH.

[0079] In some embodiments, the location-related measurement information may include an original output of an AI / ML-based measurement model. For example, if the output of the AI / ML-based positioning model is the absolute location of the terminal device 110-1, the location-related measurement information may include the absolute location of the terminal device 110-1.

[0080] In some other embodiments, the position related measurement information may include a relative position of the terminal device with respect to a position determined based on a last reference signal. Alternatively, the position related measurement information may include a linear distance with respect to a last measurement slot of the last reference signal. In some embodiments, the position related measurement information may include an azimuth angle with respect to the last measurement slot. In another embodiment, the position related measurement information may include an elevation angle with respect to the last measurement slot.

[0081] Alternatively, the location related measurement information may include a UL ROTA. In some embodiments, the location related measurement information may include a UL AOA. In other embodiments, the location related measurement information may include a UL RSRP. The location related measurement information may also include a gNB Rx-Tx time difference. The UL ROTA, UL AOA, UL RSRP, and gNB Rx-Tx time difference may be converted according to the location of the terminal device 110-1 predicted based on an AI / ML positioning model.

[0082] The network device 120 may transmit (3065) the location-related measurement information to the core network device 130. The core network device 130 may estimate (3070) the location of the terminal device 110-1 based on the location-related measurement information.

[0083] In some embodiments, the network device 120 may notify the core network device 130 of the absolute location of the terminal device 110-1. For example, one or more of the location coordinates, RTOA, AOA, RSRP, or RTT of the terminal device 110-1 may be transmitted to the core network device 130.

[0084] Alternatively, when the network device 120 receives the relative position of the terminal device 110-1, the network device 120 may combine the relative position of the terminal device 110-1 with the position determined based on the last reference signal. For example, in the case of observed time difference of arrival (OTDOA), the location related measurement information may be the last measured RSTD before the AI-based positioning and Δ RSTD and RSTD, where Δ RSTD is the timing difference converted from the output of the AI / ML-based model. Alternatively or additionally, for DL ​​angle of departure (DL-AOD), the location related measurement information is the last measured RSRP before the AI-based positioning and Δ RSRP and RSRP, where Δ RSRP is the RSRP value converted from the output of the AI / ML based model. In some embodiments, for the UE Rx-TX time difference, the location related measurement information is the last measured RTT before the AI ​​based positioning and Δ RTT and RTT, where Δ RTT is the timing difference converted from the output of the AI / ML-based model.

[0085] According to the embodiment described with reference to Fig. 3, an AI / ML based positioning model for UL measurements can be implemented in the terminal device, so that the position of the terminal device can be measured more accurately, and power can be saved since the terminal device does not need to monitor the PRS during positioning.

[0086] In the following, an embodiment of the present disclosure in which an AI / ML-based processing model is implemented in a network device will be described in detail. First, reference will be made to FIG. 4, which illustrates a signaling diagram illustrating a process 400 between a terminal device and a network device according to some exemplary embodiments of the present disclosure. For illustrative purposes only, the process 400 will be described with reference to FIG. 1. The process 400 may involve the terminal device 110-1 and the network device 120 of FIG. 1.

[0087] The network device 120 may transmit a configuration of the PRS to the terminal device 110-1. The configuration may indicate a set of occasions on which the PRS can be transmitted. The term "occasion" may occupy resources in the time domain and the frequency domain. The terminal device 110-1 may transmit a positioning request for downlink positioning to the network device 120 (4010).

[0088] The terminal device 110-1 may monitor the PRS on the occasion based on the setting. The network device 120 may transmit (4020) the set of PRS to the terminal device 110-1. A neighboring network device (not shown) may also transmit the set of PRS to the terminal device 110-1.

[0089] The terminal device 110-1 may perform measurements on the set of PRSs. For example, the measurements may be reference signal time difference (RSTD) measurements. Alternatively or additionally, the measurements may be RSRP measurements. In some other embodiments, the measurements may be Rx-Tx time difference measurements.

[0090] The terminal device 110-1 may transmit a measurement report to the core network device 130 (4030). For example, the measurement report may indicate a measured RSTD. Alternatively or additionally, the measurement report may indicate a measured RSRP. In some other embodiments, the measurement report may indicate a measured Rx-Tx time difference.

[0091] In some embodiments, the terminal device 110-1 may determine whether a condition for triggering the AI / ML-based positioning model is met. Alternatively, the network device 120 may determine whether a condition for triggering the AI / ML-based positioning model is met. In some embodiments, the condition for triggering the AI / ML-based positioning model may be the absence of a line of sight (LOS) path due to movement of the terminal device 110-1. Alternatively, the condition for triggering the AI / ML-based positioning model may be the absence of a LOS path due to a change in the propagation environment. In some other embodiments, the condition for triggering the AI / ML-based positioning model may be an unexpected synchronization error between the terminal device 110-1 and the network device 120 or between the network devices. In another embodiment, the condition for triggering the AI / ML-based positioning model may be the inaccurate reference position due to movement of the network device 120. In some embodiments, the condition for triggering the AI / ML-based positioning model may be poor DL ​​hearability from a neighboring cell to the terminal device 110-1. For example, if the link quality between the neighbor cell and the terminal device 110-1 is below a threshold, the AI / ML-based positioning model may be triggered. Alternatively or additionally, the condition for triggering the AI / ML-based positioning model may be a reduction in the overhead of the reference signal. Note that the condition for triggering the AI / ML-based positioning model may include any other condition not described.

[0092] In some embodiments, when the AI / ML-based positioning model is triggered by the network device 120, the network device 120 may inform the terminal device 110-1 to start the AI / ML-based measurement. In some embodiments, the instruction may be sent in the downlink control information. Alternatively, the instruction may be sent in the medium access control (MAC) control element (CE). In an example embodiment, the terminal device 110-1 may feedback to the network device 120 that the AI-based positioning is not being performed for some reason, e.g., due to UE power saving.

[0093] Alternatively, if an AI / ML-based positioning model is triggered by terminal device 110-1, terminal device 110-1 may inform network device 120 to mute PRS resources during AI / ML-based positioning. Optionally, network device 120 may inform terminal device 110-1 that AI-based positioning is not being performed for some reason.

[0094] In some embodiments, if the AI / ML-based positioning model is triggered, the network device 120 may skip transmitting the PRS. In other words, the network device 120 may mute resources for PRS transmission. For example, as shown in FIG. 6, the PRS may be transmitted on resources 610-1, 610-2, 610-3, 610-4, 610-5, 610-6, and 610-7. The AI / ML-based positioning model may be triggered at time instance 620. The AI / ML-based positioning model may be running in time interval 630. In this case, resources 610-3, 610-4, and 610-5 in time interval 630 may be muted. In other words, the network device 120 may not transmit the PRS in time interval 630. After the AI / ML-based positioning model stops, the network device 120 may start transmitting the PRS.

[0095] In some embodiments, after receiving an instruction to start the AI / ML-based positioning model, the terminal device 110-1 may not monitor the PRS on the occasion based on the setting. The terminal device 110-1 may also transmit (4040) location-related information to the network device 120. For example, the location-related information may indicate a moving speed of the terminal device 110-1. Alternatively or additionally, the location-related information may indicate a moving direction of the terminal device 110-1. The location-related information may be transmitted on a PUSCH.

[0096] If the AI / ML-based positioning model is triggered, the network device 120 may determine location-related measurement information of the terminal device 110-1 based on the AI / ML-based positioning model (4050). In some embodiments, the network device 120 may initiate an AI / ML-based location estimation based on the past location of the terminal device 110-1. Alternatively or additionally, the AI / ML-based location estimation may be initiated based on the moving speed and / or moving direction of the terminal device 110-1 or a user preference profile, such as user habits, of the terminal device 110-1. It should be noted that the network device 120 may initiate a location estimation of the terminal device 110-1 using a positioning mode based on any suitable factor.

[0097] In some embodiments, the input of the AI / ML-based positioning model may include a set of previous RSTD measurements. Alternatively or additionally, the input of the AI / ML-based positioning model may include a set of past RSRP measurements. In some other embodiments, the input of the AI / ML-based positioning model may include a set of past RTT measurements. The input of the AI / ML-based positioning model may also include a set of past location coordinates of the terminal device 110-1. For example, the location coordinates may be obtained from a global positioning system.

[0098] In some embodiments, the output of the AI / ML-based positioning model may include relative position information of the terminal device 110-1 with respect to a position determined based on the last reference signal. For example, this reference signal may be a PRS or a sounding reference signal. In some embodiments, the relative position information of the terminal device 110-1 may include position coordinates of the terminal device 110-1 with respect to a last position based on the last reference signal. Alternatively, the relative position of the terminal device 110-1 may include an RSTD with respect to the last reference signal. In other embodiments, the relative position of the terminal device 110-1 may include an RSRP with respect to the last reference signal. The relative position of the terminal device 110-1 may include an RTT with respect to the last reference signal.

[0099] Alternatively or additionally, the output of the AI / ML-based positioning model may include absolute position information of the terminal device 110-1. In some embodiments, the absolute position information of the terminal device 110-1 may include position coordinates of the terminal device 110-1. Alternatively, the absolute position of the terminal device 110-1 may include RSTD. In other embodiments, the absolute position of the terminal device 110-1 may include RSRP. The absolute position of the terminal device 110-1 may include RTT.

[0100] Network device 120 may transmit 4060 the location-related measurement information to terminal device 110-1. In some embodiments, network device 120 may transmit the location-related measurement information on a PDSCH.

[0101] In some embodiments, the location-related measurement information may include an original output of an AI / ML-based measurement model. For example, if the output of the AI / ML-based positioning model is the absolute location of the terminal device 110-1, the location-related measurement information may include the absolute location of the terminal device 110-1.

[0102] In some other embodiments, the position related measurement information may include relative position information of the terminal device with respect to a position determined based on a last reference signal. Alternatively, the position related measurement information may include a linear distance with respect to a last measurement slot of the last reference signal. In some embodiments, the position related measurement information may include an azimuth angle with respect to the last measurement slot. In another embodiment, the position related measurement information may include an elevation angle with respect to the last measurement slot.

[0103] Alternatively, the location related measurement information may include DL ROTA. In some embodiments, the location related measurement information may include DL AOA. In other embodiments, the location related measurement information may include DL RSRP. The location related measurement information may also include Rx-Tx time difference. DL ROTA, DL AOA, DL RSRP, and Rx-Tx time difference may be converted according to the location of the terminal device 110-1 predicted based on the AI / ML positioning model.

[0104] The terminal device 110-1 may transmit the location-related measurement information to the core network device 130 (4065). In some embodiments, the terminal device 110-1 may transmit the location-related measurement information on a PUSCH scheduled by the downlink control information. Alternatively, the location-related measurement information may be transmitted on a CG PUSCH. The core network device 130 may estimate the location of the terminal device 110-1 based on the location-related measurement information (4070).

[0105] In some embodiments, the terminal device 110-1 may inform the core network device 130 of the absolute location of the terminal device 110-1. For example, one or more of the location coordinates, RTOA, AOA, RSRP, or RTT of the terminal device 110-1 may be transmitted to the core network device 130.

[0106] Alternatively, when the terminal device 110-1 receives the relative position of the terminal device 110-1, the terminal device 110-1 may combine the relative position of the terminal device 110-1 with the position determined based on the last reference signal. For example, in the case of observed time difference of arrival (OTDOA), the location-related measurement information may be the last measured RSTD before the AI-based positioning and Δ RSTD and RSTD, where Δ RSTD is the timing difference converted from the output of the AI / ML-based model. Alternatively or additionally, for DL ​​angle of departure (DL-AOD), the location related measurement information is the last measured RSRP before the AI-based positioning and Δ RSRP and RSRP, where Δ RSRP is the RSRP value converted from the output of the AI / ML based model. In some embodiments, for the UE Rx-TX time difference, the location related measurement information is the last measured RTT before the AI ​​based positioning and Δ RTT and RTT, where Δ RTT is the timing difference converted from the output of the AI / ML-based model.

[0107] According to the embodiment described with reference to Fig. 4, an AI / ML-based positioning model for DL ​​measurements can be implemented in a network device, so that the position of the terminal device can be measured more accurately, and excess power consumption in the terminal device can be avoided.

[0108] In the following, an embodiment of the present disclosure in which an AI / ML-based processing model is implemented in a network device will be described in detail. First, reference will be made to FIG. 5, which illustrates a signaling diagram illustrating a process 500 between a terminal device and a network device according to some exemplary embodiments of the present disclosure. For illustrative purposes only, the process 500 will be described with reference to FIG. 1. The process 500 may involve the terminal device 110-1 and the network device 120 of FIG. 1.

[0109] The network device 120 may transmit the configuration of the SRS to the terminal device 110-1. In some embodiments, the configuration of the SRS may be received via RRC signaling. In some embodiments, the configuration of the SRS may include a plurality of physical resource blocks including a transmission period allocated for the SRS. Alternatively or additionally, the configuration of the SRS may include a bandwidth parameter of the SRS. In some embodiments, the configuration of the SRS may indicate one or more SRS resources. Alternatively or additionally, the configuration of the SRS may indicate one or more SRS resource sets. The network device 120 may transmit a positioning request for uplink positioning to the terminal device 110-1 (5010).

[0110] The terminal device 110-1 may transmit a set of SRS to the network device 120 based on the SRS configuration (5020). The network device 120 may perform measurements on the set of SRS. For example, the measurements may be UL RSTD measurements. Alternatively or additionally, the measurements may be UL RSRP measurements. In some other embodiments, the measurements may be Rx-Tx time difference measurements. The measurements may be UL AOA.

[0111] The network device 120 may send a measurement report to the core network device 130 (5030). For example, the measurement report may indicate a measured UL RSTD. Alternatively or additionally, the measurement report may indicate a measured UL RSRP. In some other embodiments, the measurement report may indicate a measured UL Rx-Tx time difference. In some embodiments, the measurement report may indicate a measured UL AOA.

[0112] In some embodiments, the terminal device 110-1 may determine whether a condition for triggering the AI / ML-based positioning model is met. Alternatively, the network device 120 may determine whether a condition for triggering the AI / ML-based positioning model is met. In some embodiments, the condition for triggering the AI / ML-based positioning model may be the absence of a line of sight (LOS) path due to movement of the terminal device 110-1. Alternatively, the condition for triggering the AI / ML-based positioning model may be the absence of a LOS path due to a change in the propagation environment. In some other embodiments, the condition for triggering the AI / ML-based positioning model may be an unexpected synchronization error between the terminal device 110-1 and the network device 120 or between the network devices. In another embodiment, the condition for triggering the AI / ML-based positioning model may be the inaccurate reference position due to movement of the network device 120. In some embodiments, the condition for triggering the AI / ML-based positioning model may be poor DL ​​hearability from a neighboring cell to the terminal device 110-1. For example, if the link quality between the neighbor cell and the terminal device 110-1 is below a threshold, the AI / ML-based positioning model may be triggered. Alternatively or additionally, the condition for triggering the AI / ML-based positioning model may be a reduction in the overhead of the reference signal. Note that the condition for triggering the AI / ML-based positioning model may include any other condition not described.

[0113] In some embodiments, if the AI / ML-based positioning model is triggered by the network device 120, the network device 120 may notify the terminal device 110-1 to start the AI / ML-based measurement. For example, the network device 120 may send an instruction to start the AI / ML-based positioning (5040). In some embodiments, the instruction may be sent in downlink control information. Alternatively, the instruction may be sent in a medium access control (MAC) control element (CE). In an example embodiment, the terminal device 110-1 may feedback to the network device 120 that the AI-based positioning is not being performed for some reason, e.g., due to UE power saving.

[0114] Alternatively, if an AI / ML-based positioning model is triggered by terminal device 110-1, terminal device 110-1 may inform network device 120 to mute PRS resources during AI / ML-based positioning. Optionally, network device 120 may inform terminal device 110-1 that AI-based positioning is not being performed for some reason.

[0115] In some embodiments, if the AI / ML-based positioning model is triggered, the network device 120 may skip transmitting the PRS. In other words, the network device 120 may mute resources for PRS transmission. For example, as shown in FIG. 6, the PRS may be transmitted on resources 610-1, 610-2, 610-3, 610-4, 610-5, 610-6, and 610-7. The AI / ML-based positioning model may be triggered at time instance 620. The AI / ML-based positioning model may be running in time interval 630. In this case, resources 610-3, 610-4, and 610-5 in time interval 630 may be muted. In other words, the network device 120 may not transmit the PRS in time interval 630. After the AI / ML-based positioning model stops, the network device 120 may start transmitting the PRS.

[0116] Alternatively, when the AI / ML-based positioning model is triggered, the terminal device 110-1 may skip transmitting the SRS, in other words, the terminal device 110-1 may mute resources for transmitting the SRS.

[0117] In some embodiments, after receiving an instruction to start the AI / ML-based positioning model, the terminal device 110-1 may not monitor the PRS on the occasion based on the setting. The terminal device 110-1 may also transmit (5050) location-related information to the network device 120. For example, the location-related information may indicate a moving speed of the terminal device 110-1. Alternatively or additionally, the location-related information may indicate a moving direction of the terminal device 110-1. The location-related information may be transmitted on a PUSCH.

[0118] If the AI / ML-based positioning model is triggered, the network device 120 may determine the location-related measurement information of the terminal device 110-1 based on the AI / ML-based positioning model (5060). In some embodiments, the network device 120 may initiate the AI / ML-based location estimation based on the past location of the terminal device 110-1. Alternatively or additionally, the AI / ML-based location estimation may be initiated based on the moving speed and / or moving direction of the terminal device 110-1 or a user preference profile, such as user habits, of the terminal device 110-1. It should be noted that the network device 120 may initiate the location estimation of the terminal device 110-1 using the positioning mode based on any suitable factor.

[0119] In some embodiments, the input of the AI / ML-based positioning model may include a set of previous RSTD measurements. Alternatively or additionally, the input of the AI / ML-based positioning model may include a set of past RSRP measurements. In some other embodiments, the input of the AI / ML-based positioning model may include a set of past RTT measurements. The input of the AI / ML-based positioning model may also include a set of past location coordinates of the terminal device 110-1. For example, the location coordinates may be obtained from a global positioning system.

[0120] In some embodiments, the output of the AI / ML-based positioning model may include relative position information of the terminal device 110-1 with respect to a position determined based on the last reference signal. For example, this reference signal may be a PRS or a sounding reference signal. In some embodiments, the relative position information of the terminal device 110-1 may include position coordinates of the terminal device 110-1 with respect to a last position based on the last reference signal. Alternatively, the relative position information of the terminal device 110-1 may include an RSTD with respect to the last reference signal. In other embodiments, the relative position of the terminal device 110-1 may include an RSRP with respect to the last reference signal. The relative position of the terminal device 110-1 may include an RTT with respect to the last reference signal.

[0121] Alternatively or additionally, the output of the AI / ML-based positioning model may include absolute position information of the terminal device 110-1. In some embodiments, the absolute position information of the terminal device 110-1 may include position coordinates of the terminal device 110-1. Alternatively, the absolute position of the terminal device 110-1 may include RSTD. In other embodiments, the absolute position of the terminal device 110-1 may include RSRP. The absolute position of the terminal device 110-1 may include RTT.

[0122] The network device 120 may transmit (5965) the location-related measurement information to the core network device 130. The core network device 130 may estimate (5070) the location of the terminal device 110-1 based on the location-related measurement information.

[0123] In some embodiments, the location-related measurement information may include an original output of an AI / ML-based measurement model. For example, if the output of the AI / ML-based positioning model is the absolute location of the terminal device 110-1, the location-related measurement information may include the absolute location of the terminal device 110-1.

[0124] In some other embodiments, the location-related measurement information may include a combination of the relative location of the terminal device 110-1 and the location determined based on the last reference signal. For example, in the case of uplink time difference of arrival (UTDOA), the location-related measurement information may include the last measured RTOA before the AI-based positioning and Δ RTOA and RTOA, where Δ RTOA is the timing difference converted from the output of the AI / ML-based model. Alternatively or additionally, in the case of UL-AOA, the location related measurement information is the last measured RSRP before the AI-based positioning and Δ RSRP and RSRP, where Δ RSRPis the RSRP value converted from the output of the AI / ML-based model. The location-related measurement information is the last measured AOA and Δ AOA and AOA, where Δ AOA is the AOA value converted from the output of the AI / ML-based model. In some embodiments, for gNB Rx-TX time difference, the location related measurement information is the last measured RTT before the AI-based positioning and Δ RTT and RTT, where Δ RTT is the timing difference converted from the output of the AI / ML-based model.

[0125] According to the embodiment described with reference to Fig. 5, an AI / ML-based positioning model for UL measurements can be implemented in a network device, so that the position of the terminal device can be measured more accurately, and excess power consumption in the terminal device can be avoided.

[0126] It should be noted that the embodiments described with reference to FIGS. 2 to 5 can be realized separately or collectively.

[0127] 7 is a flow chart of an example method 700 according to an embodiment of the present disclosure. The method 700 may be implemented in any suitable device. For illustrative purposes only, the method 700 may be implemented in a terminal device 110-1 as shown in FIG. 1.

[0128] In block 710, the terminal device 110-1 receives a configuration of an artificial intelligence / machine learning (AI / ML) based positioning model from the network device 120. The configuration of the AI / ML based positioning model includes a set of parameters for the AI / ML based positioning model.

[0129] In some embodiments, the set of parameters for the AI / ML-based positioning model includes at least one of: an offset between a slot for an instruction to trigger the AI / ML-based positioning model and a start slot for the AI / ML-based positioning model, or a time interval parameter of the AI / ML-based positioning model. For example, the time interval parameter includes one of: a bitmap parameter set to a predetermined value, an on-time interval timer of the AI / ML-based positioning model, or a parameter for disabling NR-DL-PRS-PositioningFrequencyLayer or NR-DL-PRS-ResourceSet.

[0130] In some embodiments, terminal device 110-1 may receive an instruction to initiate an AI / ML-based positioning model from network device 120. The instruction may be in downlink control information or in a media access control control element.

[0131] Alternatively or additionally, the terminal device 110-1 may send an instruction to the network device 120 to trigger AI-based positioning and request the network device 120 to mute the positioning reference signal resource within the time interval of the AI / ML-based positioning model. In this case, the terminal device 110-1 may also skip receiving the positioning reference signal.

[0132] In block 720, if the AI / ML-based positioning model is triggered, the terminal device 110-1 determines location-related measurement information of the terminal device based on the AI / ML-based positioning model.

[0133] In some embodiments, the input of the AI / ML-based positioning model may include at least one of a set of past reference signal time difference (RSTD) measurements, a set of past reference signal received power (RSRP) measurements, a set of past round trip time (RTT) measurements, or a set of past location coordinates of the terminal device.

[0134] Alternatively or additionally, the output of the AI / ML-based positioning model may include at least one of a relative position of the terminal device with respect to a position determined based on a reference signal, or an absolute position of the terminal device. In some embodiments, when the output of the AI / ML-based positioning model includes relative position information of the terminal device, the position-related measurement information may include a combination of a relative position measurement result and position information determined based on the last reference signal.

[0135] In some embodiments, the terminal device 110-1 may transmit a sounding reference signal to the network device 120. In this case, when the AI / ML-based positioning model is initiated, the terminal device 110-1 may not transmit a sounding reference signal. In some embodiments, the terminal device 110-1 may transmit location-related measurement information to the network device 120.

[0136] At block 730, the terminal device 110-1 transmits the location-related measurement information. In some embodiments, the location-related measurement information includes at least one of: a relative position of the terminal device with respect to a position determined based on a last reference signal, an absolute position of the terminal device, a linear distance with respect to a last measurement slot of the last reference signal, an azimuth angle with respect to the last measurement slot, an elevation angle with respect to the last measurement slot, an uplink relative time of arrival (ROTA), an uplink angle of arrival (AOA), an uplink RSRP, or a gNB round trip time.

[0137] 8 is a flow chart of an example method 800 according to an embodiment of the present disclosure. The method 800 may be implemented in any suitable device. For illustrative purposes only, the method 800 may be implemented in a network device 120 such as that shown in FIG.

[0138] In block 810, the network device 120 transmits a configuration of an artificial intelligence / machine learning (AI / ML) based positioning model to the terminal device 110-1. The configuration of the AI / ML based positioning model includes a set of parameters for the AI / ML based positioning model.

[0139] In some embodiments, the network device 120 may send an instruction to the terminal device 110-1 to initiate the AI / ML-based positioning model. In some embodiments, the instruction may be in the downlink control information. Alternatively, the instruction may be in the media access control control element.

[0140] In some embodiments, the network device 120 may receive an instruction from the terminal device 110-1 to start AI-based positioning and request to mute the positioning reference signal resource within the time interval of the AI / ML-based positioning model. In this case, the network device 120 may skip transmitting the positioning reference signal.

[0141] In some embodiments, in block 820, network device 120 may receive location-related measurement information of the terminal device from terminal device 110-1. The location-related measurement information may be determined based on an AI / ML-based measurement model. In this case, network device 120 may also transmit the location-related measurement information to a core network device.

[0142] In some embodiments, the location-related measurement information may include at least one of a relative position of the terminal device with respect to a position determined based on a last reference signal, an absolute position of the terminal device, a straight-line distance with respect to the last measurement slot of the last reference signal, an azimuth angle with respect to the last measurement slot, an elevation angle with respect to the last measurement slot, an uplink relative time of arrival (ROTA), an uplink angle of arrival (AOA), an uplink RSRP, or a gNB round trip time.

[0143] 9 is a flow chart of an example method 900 according to an embodiment of the present disclosure. The method 900 may be implemented in any suitable device. For illustrative purposes only, the method 900 may be implemented in a network device 120 such as that shown in FIG.

[0144] In block 910, the network device 120 transmits an instruction to the terminal device 110-1 to initiate an artificial intelligence / machine learning (AI / ML) based positioning model. In some embodiments, the network device 120 may transmit a configuration of the AI / ML based positioning model. For example, the configuration of the AI / ML based positioning model includes a set of parameters for the AI / ML based positioning model.

[0145] In some embodiments, the set of parameters for the AI / ML-based positioning model includes at least one of: an offset between a slot for an instruction to trigger the AI / ML-based positioning model and a start slot for the AI / ML-based positioning model, or a time interval parameter of the AI / ML-based positioning model. For example, the time interval parameter includes one of: a bitmap parameter set to a predetermined value, an on-time interval timer of the AI / ML-based positioning model, or a parameter for disabling NR-DL-PRS-PositioningFrequencyLayer or NR-DL-PRS-ResourceSet.

[0146] In some embodiments, terminal device 110-1 may receive an instruction to initiate an AI / ML-based positioning model from network device 120. The instruction may be in downlink control information or in a media access control control element.

[0147] Alternatively or additionally, the terminal device 110-1 may send an instruction to the network device 120 to trigger AI-based positioning and request the network device 120 to mute the positioning reference signal resource within the time interval of the AI / ML-based positioning model. In this case, the terminal device 110-1 may also skip receiving the positioning reference signal.

[0148] In block 920, if the AI / ML-based positioning model is triggered, the network device 120 determines location-related measurement information of the terminal device based on the AI / ML-based positioning model.

[0149] In some embodiments, the input of the AI / ML-based positioning model may include at least one of a set of past reference signal time difference (RSTD) measurements, a set of past reference signal received power (RSRP) measurements, a set of past round trip time (RTT) measurements, or a set of past location coordinates of the terminal device.

[0150] Alternatively or additionally, the output of the AI / ML-based positioning model may include at least one of a relative position of the terminal device with respect to a position determined based on a reference signal, or an absolute position of the terminal device. In some embodiments, if the output of the AI / ML-based positioning model includes relative position information of the terminal device, the location-related measurement information may include a combination of the relative position measurement result and the location information determined based on the last reference signal. In some embodiments, when the AI / ML-based positioning model is triggered, the network device 120 may skip transmitting a positioning reference signal.

[0151] At block 930, the network device 120 transmits the location-related measurement information. In some embodiments, the location-related measurement information may be transmitted to the terminal device 110-1. Alternatively, the location-related measurement information may be transmitted to a core network device. In this case, the location-related measurement information includes at least one of an uplink relative time of arrival (ROTA), an uplink angle of arrival (AOA), an uplink RSRP, or a receive-transmit time difference.

[0152] In some embodiments, network device 120 may receive from terminal device 110-1 at least one of a moving speed of the terminal device, or a moving direction of the terminal device.

[0153] 10 is a flowchart of an example method 1000 according to an embodiment of the present disclosure. The method 1000 may be implemented in any suitable device. For illustrative purposes only, the method 1000 may be implemented in a terminal device 110-1 as shown in FIG.

[0154] In block 1010, the terminal device 110-1 receives an instruction to initiate an artificial intelligence / machine learning (AI / ML) based positioning model from the network device 120. In some embodiments, the terminal device 110-1 may also receive a configuration of the artificial intelligence / machine learning (AI / ML) based positioning model, the configuration of the AI / ML based positioning model including a set of parameters for the AI / ML based positioning model.

[0155] In some embodiments, the set of parameters for the AI / ML-based positioning model includes at least one of: an offset between a slot for an instruction to trigger the AI / ML-based positioning model and a start slot for the AI / ML-based positioning model, or a time interval parameter for the AI / ML-based positioning model. For example, the time interval parameter includes one of: a bitmap parameter set to a predetermined value, an on-time interval timer for the AI / ML-based positioning model, or a parameter for disabling NR-DL-PRS-PositioningFrequencyLayer or NR-DL-PRS-ResourceSet.

[0156] In some embodiments, in block 1020, terminal device 110-1 may receive from network device 120 location-related measurement information of the terminal device determined based on the AI / ML-based positioning model. Terminal device 110-1 may transmit the location-related measurement information of the terminal device to a core network device. In some embodiments, the location-related measurement information may include an absolute location of the terminal device. Alternatively, the location-related measurement information may include a relative location of the terminal device and a location determined based on the last reference signal.

[0157] In some embodiments, if the AI / ML-based positioning model is triggered, the terminal device 110-1 may skip transmitting a sounding reference signal.

[0158] In some embodiments, terminal device 110-1 may transmit to network device 120 at least one of the terminal device's moving speed or the terminal device's moving direction.

[0159] In some embodiments, a terminal device comprises a circuit configured to receive from a network device a configuration of an artificial intelligence / machine learning (AI / ML) based positioning model, the configuration including a set of parameters for the AI / ML based positioning model and an instruction to initiate the AI / ML based positioning model; and, according to a determination that the AI / ML based positioning model is triggered, determine location-related measurement information of the terminal device based on the AI / ML based positioning model; and transmit the location-related measurement information.

[0160] In some embodiments, the set of parameters for the AI / ML-based positioning model includes at least one of an offset between a slot for an instruction to trigger the AI / ML-based positioning model and a start slot for the AI / ML-based positioning model, or a time interval parameter of the AI / ML-based positioning model.

[0161] In some embodiments, the time interval parameter includes one of a bitmap parameter set to a predetermined value, an on time interval timer for the AI / ML based positioning model, or a parameter for disabling NR-DL-PRS-PositioningFrequencyLayer or NR-DL-PRS-ResourceSet.

[0162] In some embodiments, the instruction to initiate the AI / ML based positioning model is received within downlink control information or within a media access control control element.

[0163] In some embodiments, the terminal device comprises a circuit, the circuit being configured to: send an instruction to the network device to trigger the AI-based positioning and request muting a positioning reference signal resource within a time interval of the AI / ML-based positioning model; and skip receiving a positioning reference signal.

[0164] In some embodiments, the input of the AI / ML-based positioning model includes at least one of a set of past reference signal time difference (RSTD) measurements, a set of past reference signal received power (RSRP) measurements, a set of past round trip time (RTT) measurements, or a set of past location coordinates of the terminal device.

[0165] In some embodiments, the output of the AI / ML-based positioning model includes at least one of a relative position of the terminal device with respect to a position determined based on a last reference signal, or an absolute position of the terminal device.

[0166] In some embodiments, if the output of the AI / ML-based positioning model includes a relative position of the terminal device, the position-related measurement information includes a combination of the relative position and a position determined based on the last reference signal.

[0167] In some embodiments, the terminal device comprises a circuit configured to: transmit a sounding reference signal to the network device; skip transmission of the sounding reference signal in accordance with a determination that the AI / ML based positioning model has been initiated; and transmit the location-related measurement information to the network device.

[0168] In some embodiments, the location-related measurement information includes at least one of a relative position of the terminal device with respect to a position determined based on a last reference signal, an absolute position of the terminal device, a straight-line distance with respect to the last measurement slot of the last reference signal, an azimuth angle with respect to the last measurement slot, an elevation angle with respect to the last measurement slot, an uplink relative time of arrival (ROTA), an uplink angle of arrival (AOA), an uplink RSRP, or a gNB round trip time.

[0169] In some embodiments, a network device comprises a circuit, the circuit being configured to execute, at the network device, sending to a terminal device a configuration of an artificial intelligence / machine learning (AI / ML) based positioning model and an instruction to initiate the AI / ML based positioning model, the configuration of the AI / ML based positioning model including a set of parameters for the AI / ML based positioning model.

[0170] In some embodiments, the network device comprises a circuit, the circuit configured to: send an instruction to the terminal device to initiate the AI / ML based positioning model, the instruction being within downlink control information or within a media access control control element.

[0171] In some embodiments, the network device comprises a circuit configured to: receive from the terminal device an instruction to initiate AI based positioning and request muting a positioning reference signal resource within a time interval of the AI / ML based positioning model; and skip transmission of a positioning reference signal.

[0172] In some embodiments, the network device comprises circuitry configured to: receive location-related measurement information of the terminal device from the terminal device, the location-related measurement information being determined based on the AI / ML based positioning model; and transmit the location-related measurement information to a core network device.

[0173] In some embodiments, the location-related measurement information includes at least one of a relative position of the terminal device with respect to a position determined based on a last reference signal, an absolute position of the terminal device, a straight-line distance with respect to a last measurement slot of the last reference signal, an azimuth angle with respect to the last measurement slot, an elevation angle with respect to the last measurement slot, an uplink relative time of arrival (ROTA), an uplink angle of arrival (AOA), an uplink RSRP, or a gNB round trip time.

[0174] In some embodiments, the network device comprises circuitry configured to: send, at the network device, an instruction to a terminal device to initiate an artificial intelligence / machine learning (AI / ML) based positioning model; determine location-related measurement information of the terminal device based on the AI / ML based positioning model in accordance with a determination that the AI / ML based positioning model has been triggered; and transmit the location-related measurement information.

[0175] In some embodiments, the network device comprises a circuit, the circuit being configured to: send a configuration of the AI / ML based positioning model to the terminal device, the configuration of the AI / ML based positioning model including a set of parameters for the AI / ML based positioning model.

[0176] In some embodiments, the set of parameters for the AI / ML based positioning model includes at least one of: an offset between a slot for an instruction to trigger the AI / ML based positioning model and a start slot for the AI / ML based positioning model, or a time interval parameter of the AI / ML based positioning model.

[0177] In some embodiments, the time interval parameter includes one of a bitmap parameter set to a predetermined value, an on time interval timer of the AI / ML based positioning model, or a parameter for disabling NR-DL-PRS-PositioningFrequencyLayer or NR-DL-PRS-ResourceSet.

[0178] In some embodiments, the input of the AI / ML-based positioning model includes at least one of a set of past reference signal time difference (RSTD) measurements, a set of past reference signal received power (RSRP) measurements, a set of past round trip time (RTT) measurements, or a set of past location coordinates of the terminal device.

[0179] In some embodiments, the output of the AI / ML-based positioning model includes at least one of a relative position of the terminal device with respect to a position determined based on a last reference signal, or an absolute position of the terminal device.

[0180] In some embodiments, if the output of the AI / ML-based positioning model includes a relative position of the terminal device, the position-related measurement information includes a combination of the relative position and a position determined based on the last reference signal.

[0181] In some embodiments, the network device comprises circuitry, the circuitry configured to: skip transmission of a positioning reference signal in accordance with a determination that the AI / ML based positioning model is triggered.

[0182] In some embodiments, the network device comprises circuitry, the circuitry configured to perform transmitting the location related measurement information by transmitting the location related measurement information to the terminal device.

[0183] In some embodiments, the network device comprises circuitry, the circuitry configured to: transmit the location related measurement information to the core network device; and transmit the location related measurement information.

[0184] In some embodiments, the location related measurement information includes at least one of an uplink relative time of arrival (ROTA), an uplink angle of arrival (AOA), an uplink RSRP, or a receive-transmit time difference.

[0185] In some embodiments, the network device comprises circuitry configured to receive from the terminal device at least one of a speed of movement of the terminal device, or a direction of movement of the terminal device.

[0186] In some embodiments, a terminal device comprises circuitry configured to: receive instructions from a network device to initiate an artificial intelligence / machine learning (AI / ML) based positioning model.

[0187] In some embodiments, the terminal device comprises a circuit, the circuit configured to perform: receiving a configuration of the AI / ML based positioning model from the network device, the configuration of the AI / ML based positioning model including a set of parameters for the AI / ML based positioning model.

[0188] In some embodiments, the set of parameters for the AI / ML based positioning model includes at least one of: an offset between a slot for an instruction to trigger the AI / ML based positioning model and a start slot for the AI / ML based positioning model, or a time interval parameter of the AI / ML based positioning model.

[0189] In some embodiments, the time interval parameter includes one of a bitmap parameter set to a predetermined value, an on time interval timer of the AI / ML based positioning model, or a parameter for disabling NR-DL-PRS-PositioningFrequencyLayer or NR-DL-PRS-ResourceSet.

[0190] In some embodiments, the terminal device comprises a circuit configured to receive, from the network device, location-related measurement information of the terminal device determined based on the AI / ML based positioning model, and to transmit the location-related measurement information of the terminal device to a core network device.

[0191] In some embodiments, the location-related measurement information includes an absolute position of the terminal device, or the location-related measurement information includes a relative position of the terminal device and a position determined based on the last reference signal.

[0192] In some embodiments, the terminal device comprises a circuit, the circuit configured to: skip transmission of a sounding reference signal in accordance with a determination that the AI / ML based positioning model is triggered.

[0193] In some embodiments, the terminal device comprises circuitry configured to perform the following: transmitting to the network device at least one of a moving speed of the terminal device or a moving direction of the terminal device.

[0194] Fig. 11 is a schematic block diagram of an apparatus 1100 suitable for implementing embodiments of the present disclosure. The apparatus 1100 may be considered as another exemplary implementation of the terminal device 110-1 or the network device 120 shown in Fig. 1. Thus, the apparatus 1100 may be implemented in the terminal device 110-1 or the network device 120, or as at least a part thereof.

[0195] As shown, the apparatus 1100 comprises a processor 1110, a memory 1120 coupled to the processor 1110, a suitable transmitter (TX) and receiver (RX) 1140 coupled to the processor 1110, and a communication interface coupled to the TX / RX 1140. The memory 1120 stores at least a portion of a program 1130. The TX / RX 1140 is used for bidirectional communication. The TX / RX 1140 has at least one antenna to facilitate communication, although the access nodes referred to herein may actually have multiple antennas. 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, an Un interface for communication between an eNB and a relay node (RN), or an Uu interface for communication between an eNB and a terminal device.

[0196] The program 1130 is assumed to include program instructions that, when executed by the associated processor 1110, enable the device 1100 to operate according to embodiments of the present disclosure, as described herein with reference to Figures 2-10. The embodiments of the present disclosure may be implemented by computer software executable by the processor 1110 of the device 1100, or by hardware, or by a combination of software and hardware. The processor 1110 may be configured to implement various embodiments of the present disclosure. Furthermore, the combination of the processor 1110 and the memory 1120 may form a processing means 1150 suitable for implementing various embodiments of the present disclosure.

[0197] The memory 1120 may be of any type suitable for a local technology network and may be implemented using any suitable data storage technology, such as, by way of non-limiting example, 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 1120 is shown in the device 1200, there may be several physically different memory modules in the device 1100. The processor 1110 may be of any type suitable for a local technology network and may include, by way of non-limiting example, 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 1100 may have multiple processors, for example application specific integrated circuit chips time-slaved to a clock that synchronizes the main processor.

[0198] Overall, various embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, logic, or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in 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 have been illustrated and described using block diagrams, flow charts, or some other pictorial representations, it should be understood that the blocks, devices, systems, techniques, or methods described herein may be implemented, by way of non-limiting examples, in hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controller or other computing device, or any combination thereof.

[0199] 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, that execute in a device on a target real or virtual processor to perform the process or method described above with reference to Figures 2-10. Generally, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In various embodiments, the functionality of the program modules may be combined or split between program modules as appropriate. The machine-executable instructions of the program modules may be executed in local or distributed devices. In distributed devices, the program modules may be located in both local and remote storage media.

[0200] Program codes for carrying out the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, a special purpose computer, or other programmable data processing device, and when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may run entirely on the machine, partially on the machine, as a separate software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0201] The above program code may be implemented on a machine-readable medium, which may be any tangible medium that can contain or store a program used by or associated with an instruction execution system, device, or apparatus. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the aforementioned media. More specific examples of the machine-readable storage medium may include an electrical connection having one or more wires, a portable computer 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the aforementioned.

[0202] It should be noted that although operations have been described in a particular order, it should not be understood that such operations need to be performed in the particular order shown, or in any sequential order, or that all of the operations described must be performed in order to achieve desired results. In some cases, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure, but rather as descriptions of features that may be specific to certain embodiments. Some features described in the context of individual embodiments may be realized in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may be implemented separately in multiple embodiments or in any suitable subcombination.

[0203] 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, as defined in the appended claims, is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

[0204] As used herein, the term "terminal device" refers to any device having wireless or wired communication capabilities. Examples of terminal devices include User Equipment (UE), personal computers, desktops, mobile phones, cellular phones, smartphones, personal digital assistants (PDAs), portable computers, tablets, wearable devices, Internet of Things (IoT) devices, Ultra-Reliable Low Latency Communication (URLLC) devices, Internet of Everything (IoE) devices, machine type communication (MTC) devices, vehicle-mounted devices for V2X communications where X means pedestrian, vehicle, or infrastructure / network, devices for integrated access and integrated access and backhaul (IAB), Small Data Transmission (SDT), Multicast and Broadcast Services (MBS), positioning, dynamic / flexible duplication in commercial networks, reduced capability (RedCap), High Altitude Platforms (HAP) including satellites and Unmanned Aircraft Systems (UAS). These include, but are not limited to, satellite-mounted vehicles or airborne vehicles in a non-terrestrial network (NTN) including the ICT Platform, 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), which are aircraft without a human pilot and are commonly referred to as drones, devices on high speed trains (HSTs), image acquisition devices such as digital cameras, sensor game devices, music storage and playback devices, or Internet appliances that enable wireless or wired Internet access and browsing.The "Terminal Equipment" may further have "Multicast / Broadcast" capabilities to support public safety and mission critical V2X applications, transparent IPv4 / IPv6 multicast distribution, IPTV, Smart TV, wireless services, over-the-air software distribution, group communication and IoT applications. It may also incorporate one or more Subscriber Identity Modules (SIMs), known as multi-SIMs. The term "Terminal Equipment" may be used interchangeably with UE, mobile station, subscriber station, mobile terminal, user terminal, or wireless device.

[0205] The term "network device" refers to a device capable of providing or hosting a cell or coverage in which a terminal device can communicate. Examples of network devices include, but are not limited to, a Node B (NodeB or NB), an evolved Node B (eNodeB or eNB), a next generation Node B (gNB), a transmit / receive point (TRP), a remote radio unit (RRU), a radio head (RH), a remote radio head (RRH), an IAB node, a femto node, a pico node, a reconfigurable intelligent surface (RIS), a low power node such as a network controlled repeater, etc.

[0206] The terminal device or network device may have the capability of artificial intelligence (AI) or machine learning. Generally, it includes a model trained from a large amount of data collected for a specific function and can be used to predict some information.

[0207] The terminal device or network device may operate on several frequency ranges, such as FR1 (410 MHz-7125 MHz), FR2 (24.25 GHz-71 GHz), frequency bands greater than 100 GHz, and Tera Hertz (THz). It can also operate on licensed / unlicensed / shared spectrum. The terminal device may have two or more connections with the network device under Multi-Radio Dual Connectivity (MR-DC) application scenarios. The terminal device or network device can operate in full duplex, flexible duplex, and cross division duplex modes.

[0208] The network device may have a function of network energy saving, Self-Organizing Networks (SON) / Minimization of Drive Test (MDT). The terminal may have a function of power saving.

[0209] Embodiments of the present disclosure may be implemented in test equipment, such as, for example, a signal generator, a signal analyzer, a spectrum analyzer, a network analyzer, a test terminal equipment, a test network equipment, a channel emulator, and the like.

[0210] Embodiments of the present disclosure may be performed in accordance with any generation of communication protocols now known or developed in the future, including, but 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.

Claims

1. 1. A method of communication comprising: receiving, at a terminal device, from a network device, a configuration of an artificial intelligence / machine learning (AI / ML) based positioning model, the configuration including a set of parameters for the AI / ML based positioning model, and an instruction to initiate the AI / ML based positioning model; determining location-related measurement information of the terminal device based on the AI / ML-based positioning model according to a determination that the AI / ML-based positioning model is triggered; transmitting said location related measurement information; The method includes:

2. The set of parameters for the AI / ML based positioning model may include: an offset between a slot for an instruction to trigger the AI / ML-based positioning model and a start slot for the AI / ML-based positioning model; or A time interval parameter of the AI / ML based positioning model; Contains at least one of The method of claim 1.

3. The time interval parameter is Bitmap parameters set to predetermined values, an on-interval timer for the AI / ML-based positioning model; or Parameters for disabling NR-DL-PRS-PositioningFrequencyLayer or NR-DL-PRS-ResourceSet, Contains one of The method of claim 2.

4. The instruction to initiate the AI / ML-based positioning model is received in a downlink control information or in a media access control control element. The method of claim 1.

5. sending an instruction to the network device to trigger the AI-based positioning and request the network device to mute positioning reference signal resources within a time interval of the AI / ML-based positioning model; skipping reception of a positioning reference signal; The method of claim 1 further comprising:

6. The input of the AI / ML-based positioning model is the terminal device, a set of past reference signal time difference (RSTD) measurements; a set of past reference signal received power (RSRP) measurements; A set of historical round trip time (RTT) measurements, or A set of past location coordinates, The method of claim 1 , comprising at least one of:

7. The output of the AI / ML based positioning model is: the relative position of the terminal device with respect to its position determined based on the last reference signal, or the absolute position of the terminal device; The method of claim 1 , comprising at least one of:

8. If the output of the AI / ML-based positioning model includes a relative position of the terminal device, the location-related measurement information includes a combination of the relative position and a position determined based on the last reference signal. The method according to claim 7.

9. transmitting a sounding reference signal to the network device; skipping transmission of a sounding reference signal in response to determining that the AI / ML based positioning model has been initiated; transmitting said location related measurement information to said network device; The method of claim 1 further comprising:

10. The location related measurement information is the relative position of the terminal device with respect to a position determined based on the last reference signal; the absolute position of the terminal device; the linear distance with respect to the last measurement slot of the last reference signal; an azimuth angle for said last measurement slot; an elevation angle relative to said last measurement slot; uplink relative time of arrival (ROTA); uplink angle of arrival (AOA), Uplink RSRP, or gNB round trip time, Contains at least one of 10. The method of claim 9.

11. 1. A method of communication comprising: sending, in a network device, to a terminal device, a configuration of an artificial intelligence / machine learning (AI / ML) based positioning model, the configuration including a set of parameters for the AI / ML based positioning model, and an instruction to initiate the AI / ML based positioning model; The method includes:

12. and sending an instruction to the terminal device to initiate the AI / ML-based positioning model, the instruction being in a downlink control information or in a media access control control element. The method of claim 11.

13. receiving, from the terminal device, an instruction to initiate AI-based positioning and request muting a positioning reference signal resource within a time interval of the AI / ML-based positioning model; skipping transmission of a positioning reference signal; The method of claim 11 further comprising:

14. receiving from the terminal device location-related measurement information of the terminal device, the location-related measurement information being determined based on the AI / ML based positioning model; transmitting the location related measurement information to a core network device; The method of claim 11 further comprising:

15. The location related measurement information is the relative position of the terminal device with respect to a position determined based on the last reference signal; the absolute position of the terminal device; the linear distance with respect to the last measurement slot of the last reference signal; an azimuth angle for said last measurement slot; an elevation angle relative to said last measurement slot; uplink relative time of arrival (ROTA); uplink angle of arrival (AOA), Uplink RSRP, or gNB round trip time, Contains at least one of The method of claim 14.

16. 1. A method of communication comprising: sending, in the network device, an instruction to a terminal device to initiate an artificial intelligence / machine learning (AI / ML) based positioning model; determining location-related measurement information of the terminal device based on the AI / ML-based positioning model according to a determination that the AI / ML-based positioning model is triggered; transmitting said location related measurement information; The method includes:

17. and transmitting to the terminal device a configuration of the AI / ML-based positioning model, the configuration of the AI / ML-based positioning model including a set of parameters for the AI / ML-based positioning model.

17. The method of claim 16.

18. The set of parameters for the AI / ML based positioning model may include: an offset between a slot for an instruction to trigger the AI / ML-based positioning model and a start slot for the AI / ML-based positioning model; or A time interval parameter of the AI / ML based positioning model; Contains at least one of 20. The method of claim 17.

19. The time interval parameter is Bitmap parameters set to predetermined values, an on-interval timer for the AI / ML-based positioning model; or Parameters for disabling NR-DL-PRS-PositioningFrequencyLayer or NR-DL-PRS-ResourceSet, Contains one of 20. The method of claim 18.

20. The input of the AI / ML-based positioning model is the terminal device, a set of past reference signal time difference (RSTD) measurements; a set of past reference signal received power (RSRP) measurements; A set of historical round trip time (RTT) measurements, or A set of past location coordinates, 17. The method of claim 16, comprising at least one of:

21. The output of the AI / ML based positioning model is: the relative position of the terminal device with respect to its position determined based on the last reference signal, or the absolute position of the terminal device; 17. The method of claim 16, comprising at least one of:

22. If the output of the AI / ML-based positioning model includes a relative position of the terminal device, the location-related measurement information includes a combination of the relative position and a position determined based on the last reference signal.

22. The method of claim 21.

23. skipping transmission of a positioning reference signal in response to determining that the AI / ML based positioning model is triggered; 20. The method of claim 16 further comprising:

24. Transmitting the location related measurement information further comprises: transmitting said location related measurement information to said terminal device; 17. The method of claim 16, comprising:

25. Transmitting the location related measurement information further comprises: transmitting said location related measurement information to said core network device; 17. The method of claim 16, comprising:

26. The location related measurement information is uplink relative time of arrival (ROTA); uplink angle of arrival (AOA), Uplink RSRP, or Reception-transmission time difference, Contains at least one of 26. The method of claim 25.

27. From the terminal device, The moving speed of the terminal device, or A moving direction of the terminal device; receiving at least one of:

20. The method of claim 16 further comprising:

28. 1. A method of communication comprising: receiving, at the terminal device, an instruction from the network device to initiate an artificial intelligence / machine learning (AI / ML) based positioning model; The method includes:

29. and receiving a configuration of the AI / ML-based positioning model from the network device, the configuration of the AI / ML-based positioning model including a set of parameters for the AI / ML-based positioning model.

30. The method of claim 28.

30. The set of parameters for the AI / ML based positioning model may include: an offset between a slot for an instruction to trigger the AI / ML-based positioning model and a start slot for the AI / ML-based positioning model; or A time interval parameter of the AI / ML based positioning model; Contains at least one of 30. The method of claim 29.

31. The time interval parameter is Bitmap parameters set to predetermined values, an on-interval timer for the AI / ML-based positioning model; or Parameters for disabling NR-DL-PRS-PositioningFrequencyLayer or NR-DL-PRS-ResourceSet, Contains one of 31. The method of claim 30.

32. receiving, from the network device, location-related measurement information of the terminal device determined based on the AI / ML-based positioning model; transmitting the location related measurement information of the terminal device to a core network device; 30. The method of claim 28, further comprising:

33. the location-related measurement information comprises the absolute location of the terminal device; or The location-related measurement information includes the relative location of the terminal device and a location determined based on the last reference signal.

33. The method of claim 32.

34. skipping transmission of a sounding reference signal in response to determining that the AI / ML-based positioning model is triggered; 30. The method of claim 28, further comprising:

35. The network device includes: The moving speed of the terminal device, or A moving direction of the terminal device; transmitting at least one of:

30. The method of claim 28, further comprising:

36. A terminal device comprising a processor and a memory coupled to the processor and storing instructions, The instructions, when executed by the processor, perform operations including a method according to any one of claims 1 to 10 or 28 to 35. Terminal device.

37. 1. A network device comprising: a processor; and a memory coupled to the processor and storing instructions, The instructions, when executed by the processor, perform operations including a method according to any one of claims 11 to 15 or 16 to 27. Network device.

38. storing instructions which, when executed on at least one processor, cause said at least one processor to perform a method according to any one of claims 1 to 10, or 11 to 15, or 16 to 27, or 28 to 35; Computer-readable medium.

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