Prediction-based localization in cellular systems

Abstract

Methods and apparatus for prediction-based user equipment (UE) positioning in cellular systems. A method for a UE to report information related to machine learning (ML)-based UE positioning includes: receiving, from a cell, first information related to reception of positioning reference signals (PRS) from one or more transmit receive points (TRPs) for measurement; receiving, from the cell, second information indicating one or more report amounts related to UE positioning; receiving, from the cell, third information related to transmitting the one or more report amounts; and receiving the PRS from the one or more TRPs based on the first information. The method further includes measuring a PRS; determining one or more report amounts indicated by the second information based on the measurement of the PRS; and transmitting a channel having one or more report amounts based on the third information.

Description

Technical Field

[0001] The present disclosure generally relates to wireless communication systems, and more particularly, the present disclosure relates to methods and apparatuses for predictive user equipment (UE) positioning in cellular systems. Background Art

[0002] Considering the development of wireless communication from generation to generation, technologies mainly for human-targeted services such as voice calls, multimedia services, and data services have been developed. After the commercialization of the 5G (5th generation) communication system, the number of connected devices is expected to increase exponentially. These devices will be increasingly connected to the communication network. Examples of connected things can include vehicles, robots, drones, household appliances, displays, smart sensors connected to various infrastructures, construction machinery, and factory equipment. Mobile devices are expected to evolve in various form factors such as augmented reality glasses, virtual reality headsets, and holographic devices. To provide various services by connecting hundreds of billions of devices and things in the 6G (6th generation) era, continuous efforts have been made to develop improved 6G communication systems. For these reasons, the 6G communication system is called the ultra 5G system.

[0003] The 6G communication system expected to be commercialized around 2030 will have a peak data rate of tera (1,000 giga) - level bps and a radio latency of less than 100 μsec (microseconds), and thus will be 50 times faster than the 5G communication system and have 1 / 10 of the radio latency of the 5G communication system.

[0004] To achieve such high data rates and ultra - low latency, implementing the 6G communication system in the terahertz frequency band (e.g., 95 GHz to 3 THz frequency band) has been considered. Since path loss and atmospheric absorption in the terahertz frequency band are more severe than those in the mmWave frequency band introduced in 5G, technologies capable of ensuring signal transmission distance (i.e., coverage) will become more critical. As the main technologies for ensuring coverage, it is necessary to develop radio frequency (RF) components, antennas, novel waveforms with better coverage than orthogonal frequency - division multiplexing (OFDM), beamforming, and massive multiple - input multiple - output (MIMO), full - dimensional MIMO (FD - MIMO), array antennas, and multi - antenna transmission technologies such as massive antennas. In addition, new technologies for improving the signal coverage in the terahertz frequency band, such as metasurface - based lenses and antennas, orbital angular momentum (OAM), and reconfigurable intelligent surfaces (RIS), have been under discussion.

[0005] In addition, to improve spectral efficiency and overall network performance, the following technologies have been developed for 6G communication systems: full-duplex technology that enables uplink and downlink transmissions to use the same frequency resources simultaneously at the same time; network technologies for integrally leveraging satellites, high-altitude platform stations (HAPS), etc.; improved network architectures for supporting mobile base stations, etc. and achieving optimized and automated network operations; dynamic spectrum sharing technology through conflict avoidance based on predicted spectrum usage; use of artificial intelligence (AI) in wireless communication to improve overall network operations and internalize end-to-end AI support functions by leveraging AI from the design phase of 6G development; and next-generation distributed computing technology to overcome the limitations of UE computing capabilities through reachable ultra-high-performance communication and computing resources (such as mobile edge computing (MEC), cloud, etc.) on the network. In addition, attempts to enhance connectivity between devices, optimize the network, promote the softwareization of network entities, and increase the openness of wireless communication are continuing by designing new protocols to be used in 6G communication systems, developing mechanisms for implementing hardware-based secure environments and secure use of data, and developing technologies for maintaining privacy.

[0006] Research and development of 6G communication systems in hyper-connectivity, including person-to-machine (P2M) and machine-to-machine (M2M), is expected to enable the next hyper-connectivity experience. In particular, it is expected that services such as truly immersive extended reality (XR), high-fidelity mobile holograms, and digital twins can be provided through 6G communication systems. In addition, services such as remote surgery, industrial automation, and emergency response for security and reliability enhancement will be provided through 6G communication systems, enabling these technologies to be applied to various fields such as industry, healthcare, automotive, and home appliances.

[0007] Wireless communication has been one of the most successful innovations in modern history. Recently, the number of subscribers to wireless communication services has exceeded 5 billion and continues to grow rapidly. Due to the increasing popularity of smart phones and other mobile data devices (such as tablets, "notepad" computers, netbooks, e-book readers, and machine-type devices) among consumers and enterprises, the demand for wireless data traffic is increasing rapidly. To meet the high growth of mobile data traffic and support new applications and deployments, improvement of radio interface efficiency and coverage is crucial. To meet the demand for continuously increasing wireless data traffic since the deployment of 4G communication systems and to enable various vertical applications, 5G communication systems have been developed and are currently being deployed. Summary of the Invention

[0008] Technical Problem

[0009] The present disclosure relates to prediction-based UE positioning in a cellular system.

[0010] Technical Solution

[0011] In an embodiment, a method for a UE to report information related to machine learning (ML)-based UE positioning is provided. The method includes: receiving, from a cell, first information related to reception of positioning reference signals (PRSs) from one or more transmit receive points (TRPs) for measurement; receiving, from the cell, second information indicating one or more reporting quantities related to UE positioning; receiving, from the cell, third information related to transmitting the one or more reporting quantities; and receiving the PRSs from the one or more TRPs based on the first information. The one or more reporting quantities are related to ML model-based UE positioning or life cycle management of the ML model. The method further includes measuring the PRSs; determining the one or more reporting quantities indicated by the second information based on the measurement of the PRSs; and transmitting a channel having the one or more reporting quantities based on the third information.

[0012] In another embodiment, a UE is provided. The UE includes: a transceiver configured to receive, from a cell, first information related to reception of PRSs from one or more TRPs for measurement; receive, from the cell, second information indicating one or more reporting quantities related to UE positioning; receive, from the cell, third information related to transmitting the one or more reporting quantities; and receive the PRSs from the one or more TRPs based on the first information. The one or more reporting quantities are related to ML model-based UE positioning or life cycle management of the ML model. The UE further includes a processor operatively coupled to the transceiver. The processor is configured to measure the PRSs and determine the one or more reporting quantities indicated by the second information based on the measurement of the PRSs. The transceiver is further configured to transmit a channel having the one or more reporting quantities based on the third information.

[0013] In yet another embodiment, a base station (BS) is provided. The BS includes a transceiver configured to transmit first information related to reception of PRSs from one or more TRPs for measurement, transmit second information indicating one or more reporting quantities related to UE positioning, transmit third information related to transmitting the one or more reporting quantities, wherein the PRSs are transmitted from the one or more TRPs based on the first information, and receive a channel having the one or more reporting quantities based on the third information, the one or more reporting quantities being based on the second information and the PRSs. The one or more reporting quantities are related to ML model-based UE positioning or life cycle management of the ML model.

[0014] According to the following drawings, description, and claims, other technical features may be apparent to those skilled in the art.

[0015] Before proceeding with the following detailed description, it may be advantageous to set forth definitions of certain words and phrases used throughout this patent document. The term "coupled" and its derivatives refer to any direct or indirect communication between two or more elements, whether or not those elements are in physical contact with each other. The terms "send," "receive," and "communicate" and their derivatives cover both direct and indirect communication. The terms "include" and "comprise" and their derivatives mean including but not limited to. The term "or" is inclusive and means and / or. The phrase "associated with" and its derivatives mean including, being included within, interconnected with, containing, being contained within, connected to or connected with, coupled to or coupled with, capable of communicating with, cooperating with, interlacing, juxtaposing, adjacent to, bound to or bound with, having, having the property of, having a relationship to or having a relationship with, and the like. The term "controller" means any device, system, or part thereof that controls at least one operation. Such a controller can be implemented in hardware or a combination of hardware and software and / or firmware. The functions associated with any particular controller can be centralized or distributed, whether local or remote. The phrase "at least one of" when used with a list of items means that different combinations of one or more of the listed items can be used and may only require one item from the list. For example, "at least one of A, B, and C" includes any of the following combinations: A, B, C, A and B, A and C, B and C, and A and B and C.

[0016] In addition, the various functions described below can be implemented or supported by one or more computer programs, each formed of computer-readable program code embodied in a computer-readable medium. The terms "application" and "program" refer to one or more computer programs, software components, instruction sets, procedures, functions, objects, classes, instances, related data, or a portion thereof that are adapted to be implemented in suitable computer-readable program code. The phrase "computer-readable program code" includes any type of computer code, including source code, object code, and executable code. The phrase "computer-readable medium" includes any type of medium that can be accessed by a computer, such as read-only memory (ROM), random access memory (RAM), hard disk drive, compact disc (CD), digital video disc (DVD), or any other type of memory. A "non-transitory" computer-readable medium excludes wired, wireless, optical, or other communication links that transmit transitory electrical or other signals. A non-transitory computer-readable medium includes media in which data can be stored permanently and media in which data can be stored and later rewritten, such as a rewritable optical disc or an erasable memory device.

[0017] Throughout this patent document, definitions of certain words and phrases are provided. Those of ordinary skill in the art should understand that in many, if not most, instances, such definitions apply to the prior as well as future use of the words and phrases so defined.

[0018] Beneficial effects

[0019] According to embodiments of the present disclosure, UE positioning can be performed.

[0020] Furthermore, according to embodiments of the present disclosure, the accuracy of predicting UE positioning can be improved. Brief Description of the Drawings

[0021] To more fully understand the present disclosure and its advantages, reference is now made to the following description taken in conjunction with the accompanying drawings, in which like reference numerals represent like parts:

[0022] Figure 1 An example wireless network according to embodiments of the present disclosure is shown;

[0023] Figure 2 An example gNodeB (gNB) according to embodiments of the present disclosure is shown;

[0024] Figure 3 An example user equipment (UE) according to embodiments of the present disclosure is shown;

[0025] Figure 4A An example of a wireless transmission path according to embodiments of the present disclosure is shown;

[0026] Figure 4B An example of a wireless reception path according to embodiments of the present disclosure is shown;

[0027] Figure 5 A diagram showing an example beam scan for downlink (DL) - angle of departure (AoD) measurement according to embodiments of the present disclosure;

[0028] Figure 6 A diagram showing DL - angle of departure (AoD) measurements from multiple TRPs according to embodiments of the present disclosure;

[0029] Figure 7 A diagram showing DL - time of flight (ToF) measurements according to embodiments of the present disclosure;

[0030] Figure 8 A flowchart showing an example process for a UE to send a prediction - based DL - AoD measurement report to assist positioning at a location management function (LMF) according to embodiments of the present disclosure;

[0031] Figure 9A diagram showing DL-AoD prediction from wide DL-PRS beam and sparse DL-PRS beam measurements according to an embodiment of the present disclosure;

[0032] Figure 10 A flowchart showing an example process for a UE to send a predicted DL reference signal time difference (RSTD) measurement report to assist positioning at the LMF according to an embodiment of the present disclosure;

[0033] Figure 11 A diagram showing an example DL-RSTD prediction from multiple TRPs according to an embodiment of the present disclosure; and

[0034] Figure 12 A flowchart showing an example process for a UE to send its predicted position to the LMF according to an embodiment of the present disclosure. Detailed Description

[0035] The following discussion Figures 1 to 12 and the various non-limiting embodiments used to describe the principles of the present disclosure in this patent document are merely illustrative and should not be construed in any way as limiting the scope of the present disclosure. Those skilled in the art will understand that the principles of the present disclosure can be implemented in any suitably arranged system or device.

[0036] To meet the increasing demand for wireless data traffic since the deployment of 4G communication systems and to enable various vertical applications, 5G / NR communication systems have been developed and are currently being deployed. The 5G / NR communication system is implemented in a higher frequency (mmWave) band (e.g., 28 GHz or 60 GHz band) to achieve higher data rates or in a lower frequency band (e.g., 6 GHz) to achieve robust coverage and mobility support. To reduce the propagation loss of radio waves and increase the transmission distance, beamforming, massive multiple-input multiple-output (MIMO), full-dimensional MIMO (FD-MIMO), array antennas, analog beamforming, and massive antenna technologies are discussed in 5G / NR communication systems.

[0037] In addition, in 5G / NR communication systems, system network improvement development is underway based on advanced small cells, cloud radio access network (RAN), ultra-dense networks, device-to-device (D2D) communication, wireless backhaul, mobile networks, cooperative communication, coordinated multi-point (CoMP), receiver-side interference cancellation, etc.

[0038] The discussion of 5G systems and the frequency bands associated therewith is for reference because certain embodiments of the present disclosure may be implemented in 5G systems. However, the present disclosure is not limited to 5G systems or the frequency bands associated therewith, and embodiments of the present disclosure may be used in conjunction with any frequency band. For example, aspects of the present disclosure may also be applied to 5G communication systems, 6G, or even later versions of deployments that may use the terahertz (THz) frequency band.

[0039] The following documents and standards are hereby incorporated by reference into this disclosure as if fully set forth herein: [1] 3GPP TS 38.211 v17.2.0, "NR; Physical channels and modulation"; [2] 3GPP TS 38.212 v17.2.0, "NR; Multiplexing and Channel coding"; [3] 3GPP TS 38.213 v17.2.0, "NR; Physical Layer Procedures for Control"; [4] 3GPP TS 38.214 v17.2.0, "NR; Physical Layer Procedures for Data"; [5] 3GPP TS 38.215 v17.1.0, "NR; Physical layer measurements"; [6] 3GPP TS 38.331 v17.1.0, "NR; Radio Resource Control (RRC) Protocol Specification"; [7] 3GPP TS 38.321 v17.1.0, "NR; Medium Access Control (MAC) protocol specification"; [8] 3GPP TS 38.133 v17.6.0, "NR; Requirements for support of radio resource management"; [9] 3GPP TS 38.300 v17.0.0, "NR; NR and NG-RAN Overall Description";

[10] 3GPP TS 38.305 v17.1.0, "NG Radio Access Network (NG-RAN); Stage 2 functional specification of User Equipment (UE) positioning in NG-RAN"; and

[11] 3GPP TS 38.455 v17.2.0, "NG-RAN; NR Positioning Protocol A (NRPPa)".

[0040] The following Figures 1 to 3 describes various embodiments implemented in a wireless communication system and using orthogonal frequency division multiplexing (OFDM) or orthogonal frequency division multiple access (OFDMA) communication techniques. Figures 1 to 3 The description does not imply physical or architectural limitations on how different embodiments can be implemented. Different embodiments of the present disclosure can be implemented in any suitably arranged communication system.

[0041] Figure 1 FIG. 11 shows an example wireless network 100 according to an embodiment of the present disclosure. Figure 1 The illustrated embodiment of the wireless network 100 is for illustrative purposes only. Other embodiments of the wireless network 100 can be used without departing from the scope of the present disclosure.

[0042] As Figure 1 shown, the wireless network 100 includes gNB 101 (e.g., a base station, BS), gNB 102, and gNB 103. gNB 101 communicates with gNB 102 and gNB 103. gNB 101 also communicates with at least one network 130 (such as the Internet, a proprietary Internet Protocol (IP) network, or other data networks).

[0043] gNB 102 provides wireless broadband access to the network 130 for a first plurality of user equipment (UEs) within the coverage area 120 of gNB 102. The first plurality of UEs includes UE 111, which can be located in a small business; UE 112, which can be located in an enterprise; UE 113, which can be a WiFi hotspot; UE 114, which can be located in a first residence; UE 115, which can be located in a second residence; and UE 116, which can be a mobile device such as a cellular phone, a wireless laptop computer, a wireless PDA, etc. gNB 103 provides wireless broadband access to the network 130 for a second plurality of UEs within the coverage area 125 of gNB 103. The second plurality of UEs includes UE 115 and UE 116. In some embodiments, one or more of gNB 101 to gNB 103 can communicate with each other and with UEs 111 to 116 using 5G / NR, Long Term Evolution (LTE), Long Term Evolution - Advanced (LTE - A), WiMAX, WiFi, or other wireless communication technologies.

[0044] Depending on the network type, the term "base station" or "BS" can refer to any component (or collection of components) configured to provide wireless access to a network, such as a transmission point (TP), a transmit-receive point (TRP), an enhanced base station (eNodeB or eNB), a 5G / NR base station (gNB), a macrocell, a femtocell, a Wi-Fi access point (AP), or other wireless-enabled devices. The base station can provide wireless access according to one or more wireless communication protocols (e.g., 5G / NR Third Generation Partnership Project (3GPP) NR, Long Term Evolution (LTE), LTE-Advanced (LTE-A), High Speed Packet Access (HSPA), Wi-Fi 802.11 a / b / g / n / ac, etc.). For convenience, the terms "BS" and "TRP" can be used interchangeably in this patent document to refer to the network infrastructure components that provide wireless access to remote terminals. Additionally, depending on the network type, the term "user equipment" or "UE" can refer to any component, such as a "mobile station", "subscriber station", "remote terminal", "wireless terminal", "reception point", or "user equipment". For convenience, the terms "user equipment" and "UE" are used in this patent document to refer to the remote wireless devices that wirelessly access the BS, whether the UE is a mobile device (such as a mobile phone or smartphone) or a device that is generally considered fixed (such as a desktop computer or a vending machine).

[0045] The dashed lines illustrate the approximate extent of coverage areas 120 and 125, which are shown as approximately circular for purposes of illustration and explanation only. It should be clearly understood that the coverage areas associated with a gNB (such as coverage areas 120 and 125) can have other shapes, including irregular shapes, depending on the configuration of the gNB and changes in the radio environment associated with natural and man-made obstacles.

[0046] As described in more detail below, one or more of UEs 111 to 116 include circuitry, programming, or a combination thereof for supporting prediction-based UE positioning in a cellular system. In certain embodiments, one or more of BSs 101 to 103 include circuitry, programming, or a combination thereof for leveraging prediction-based UE positioning in a cellular system.

[0047] Although Figure 1 an example of a wireless network is shown, it is possible to Figure 1Make various changes. For example, the wireless network 100 can include any number of gNBs and any number of UEs in any suitable arrangement. Additionally, gNB 101 can communicate directly with any number of UEs and provide those UEs with wireless broadband access to the network 130. Similarly, each of gNBs 102 to 103 can communicate directly with the network 130 and provide the UEs with direct wireless broadband access to the network 130. Further, gNB101, gNB 102, and / or gNB 103 can provide access to other or additional external networks, such as an external telephone network or other types of data networks.

[0048] Figure 2 An example gNB 102 according to an embodiment of the present disclosure is shown. Figure 2 The embodiment of the gNB 102 shown in Figure 1 and gNBs 101 and 103 can have the same or similar configurations. However, gNBs have a wide variety of configurations, and Figure 2 do not limit the scope of the present disclosure to any particular implementation of the gNB.

[0049] As Figure 2 shown, the gNB 102 includes a plurality of antennas 205a to 205n, a plurality of transceivers 210a to 210n, a controller / processor 225, a memory 230, and a backhaul or network interface 235.

[0050] The transceivers 210a to 210n receive incoming radio frequency (RF) signals from the antennas 205a to 205n, such as signals transmitted by UEs in the wireless network 100. The transceivers 210a to 210n down-convert the incoming RF signals to generate IF or baseband signals. The IF or baseband signals are processed by receive (RX) processing circuitry in the transceivers 210a to 210n and / or the controller / processor 225, and the RX processing circuitry generates a processed baseband signal by filtering, decoding, and / or digitizing the baseband or IF signal. The controller / processor 225 can further process the baseband signal.

[0051] The transmit (TX) processing circuitry in the transceivers 210a to 210n and / or the controller / processor 225 receives analog or digital data (such as voice data, web data, email, or interactive video game data) from the controller / processor 225. The TX processing circuitry encodes, multiplexes, and / or digitizes the outgoing baseband data to generate a processed baseband or IF signal. The transceivers 210a to 210n up-convert the baseband or IF signal to an RF signal transmitted via the antennas 205a to 205n.

[0052] The controller / processor 225 may include one or more processors or other processing devices that control the overall operation of the gNB 102. For example, the controller / processor 225 may control the transceiver 210a to 210n to receive uplink (UL) channel signals and transmit downlink (DL) channel signals according to well-known principles. The controller / processor 225 may also support additional functions, such as more advanced wireless communication functions. For example, the controller / processor 225 may support beamforming or directional routing operations, where the outgoing / incoming signals from / to multiple antennas 205a to 205n are weighted differently to effectively direct the outgoing signals in the desired direction. As another example, the controller / processor 225 may support methods for utilizing prediction-based UE positioning in a cellular system. The controller / processor 225 may support any of a variety of other functions in the gNB 102.

[0053] The controller / processor 225 is also capable of executing programs and other processes residing in the memory 230, such as processes for utilizing prediction-based UE positioning in a cellular system. The controller / processor 225 may move data into or out of the memory 230 as needed for the execution of the processes.

[0054] The controller / processor 225 is also coupled to a backhaul or network interface 235. The backhaul or network interface 235 allows the gNB102 to communicate with other devices or systems via a backhaul connection or via a network. The interface 235 may support communication via any suitable wired or wireless connection. For example, when the gNB 102 is implemented as part of a cellular communication system (such as a cellular communication system supporting 5G / NR, LTE, or LTE-A), the interface 235 may allow the gNB 102 to communicate with other gNBs via a wired or wireless backhaul connection. When the gNB 102 is implemented as an access point, the interface 235 may allow the gNB 102 to communicate with a larger network (such as the Internet) via a wired or wireless local area network or via a wired or wireless connection. The interface 235 includes any suitable structure that supports communication via a wired or wireless connection, such as Ethernet or a transceiver.

[0055] The memory 230 is coupled to the controller / processor 225. A portion of the memory 230 may include RAM, and another portion of the memory 230 may include flash memory or other ROM.

[0056] Although Figure 2 an example of the gNB 102 is shown, various changes may be made to Figure 2 it. For example, the gNB 102 may include any number of Figure 2 each of the components shown in Figure 2The various components in can be combined, further subdivided, or omitted, and additional components can be added according to specific needs.

[0057] Figure 3 An example UE 116 according to an embodiment of the present disclosure is shown. Figure 3 The embodiment of UE 116 shown in is for illustration only, and Figure 1 UEs 111 to 115 may have the same or similar configurations. However, UEs have a wide variety of configurations, and Figure 3 the scope of the present disclosure is not limited to any specific implementation of the UE.

[0058] As Figure 3 shown, UE 116 includes an antenna 305, a transceiver 310, and a microphone 320. UE 116 also includes a speaker 330, a processor 340, an input / output (I / O) interface (IF) 345, an input 350, a display 355, and a memory 360. The memory 360 includes an operating system (OS) 361 and one or more applications 362.

[0059] The transceiver 310 receives incoming RF signals transmitted by the gNB of the wireless network 100 from the antenna 305. The transceiver 310 down-converts the incoming RF signals to generate an intermediate frequency (IF) or baseband signal. The IF or baseband signal is processed by RX processing circuitry in the transceiver 310 and / or the processor 340, and the RX processing circuitry generates a processed baseband signal by filtering, decoding, and / or digitizing the baseband or IF signal. The RX processing circuitry sends the processed baseband signal to the speaker 330 (such as for voice data) or is processed by the processor 340 (such as for web browsing data).

[0060] The TX processing circuitry in the transceiver 310 and / or the processor 340 receives analog or digital voice data from the microphone 320, or other outgoing baseband data (such as web data, email, or interactive video game data) from the processor 340. The TX processing circuitry encodes, multiplexes, and / or digitizes the outgoing baseband data to generate a processed baseband or IF signal. The transceiver 310 up-converts the baseband or IF signal to an RF signal transmitted via the antenna 305.

[0061] The processor 340 may include one or more processors or other processing devices, and executes the OS 361 stored in the memory 360 to control the overall operation of the UE 116. For example, the processor 340 may control the transceiver 310 to receive DL channel signals and transmit UL channel signals according to well-known principles. In some embodiments, the processor 340 includes at least one microprocessor or microcontroller.

[0062] The processor 340 is also capable of executing other processes and programs resident in the memory 360. For example, the processor 340 may execute processes for supporting prediction-based UE positioning in a cellular system as described in embodiments of the present disclosure. The processor 340 may move data into or out of the memory 360 as needed for the execution process. In some embodiments, the processor 340 is configured to execute the application 362 based on the OS 361 or in response to signals received from the gNB or the operator. The processor 340 is also coupled to the I / O interface 345, which provides the UE 116 with the ability to connect to other devices such as laptop computers and handheld computers. The I / O interface 345 is a communication path between these accessories and the processor 340.

[0063] The processor 340 is also coupled to an input 350 and a display 355. The input 350 includes, for example, a touch screen, a keyboard, etc. The operator of the UE 116 may use the input 350 to input data into the UE 116. The display 355 may be a liquid crystal display, a light-emitting diode display, or other display capable of presenting text and / or at least limited graphics such as from a website.

[0064] The memory 360 is coupled to the processor 340. A portion of the memory 360 may include random access memory (RAM), and another portion of the memory 360 may include flash memory or other read-only memory (ROM).

[0065] Although Figure 3 an example of the UE 116 is shown, various changes may be made to Figure 3 it. For example, Figure 3 the various components in Figure 3 may be combined, further subdivided, or omitted, and additional components may be added according to specific needs. As a specific example, the processor 340 may be divided into multiple processors such as one or more central processing units (CPUs) and one or more graphics processing units (GPUs). In another example, the transceiver 310 may include any number of transceivers and signal processing chains and may be connected to any number of antennas. Additionally, although

[0066] Figure 4A and Figure 4BExamples of a wireless transmission path 400 and a wireless reception path 450 according to embodiments of the present disclosure are shown. For example, the transmission path 400 may be described as being implemented in a gNB (such as gNB 102), while the reception path 450 may be described as being implemented in a UE (such as UE 116). However, it should be understood that the reception path 450 may be implemented in a gNB, and the transmission path 400 may be implemented in a UE. In some embodiments, as described in embodiments of the present disclosure, the reception path 450 is configured to utilize prediction-based UE positioning in a cellular system.

[0067] As Figure 4A shown, the transmission path 400 includes a channel coding and modulation block 405, a serial-to-parallel (S-to-P) block 410, an inverse fast Fourier transform (IFFT) block 415 of size N, a parallel-to-serial (P-to-S) block 420, a cyclic prefix addition block 425, and an upconverter (UC) 430. The reception path 450 includes a downconverter (DC) 455, a cyclic prefix removal block 460, an S-to-P block 465, a fast Fourier transform (FFT) block 470 of size N, a parallel-to-serial (P-to-S) block 475, and a channel decoding and demodulation block 480.

[0068] In the transmission path 400, the channel coding and modulation block 405 receives a set of information bits, applies coding (such as low-density parity-check (LDPC) coding), and modulates the input bits (such as using quadrature phase shift keying (QPSK) or quadrature amplitude modulation (QAM)) to generate a sequence of frequency-domain modulation symbols. The serial-to-parallel block 410 converts (e.g., demultiplexes) the serial modulation symbols into parallel data to generate N parallel symbol streams, where N is the size of the IFFT / FFT used in gNB 102 and UE 116. The IFFT block 415 of size N performs an IFFT operation on the N parallel symbol streams to generate a time-domain output signal. The parallel-to-serial block 420 converts (such as multiplexes) the parallel time-domain output symbols from the IFFT block 415 of size N to generate a serial time-domain signal. The cyclic prefix addition block 425 inserts a cyclic prefix into the time-domain signal. The upconverter 430 modulates (such as upconverts) the output of the cyclic prefix addition block 425 to an RF frequency for transmission via a wireless channel. The signal may also be filtered at baseband before being converted to the RF frequency.

[0069] As Figure 4BAs shown, downconverter 455 downconverts the received signal to baseband frequency, and cyclic prefix removal block 460 removes the cyclic prefix to generate a serial time-domain baseband signal. Serial-to-parallel block 465 converts the time-domain baseband signal to a parallel time-domain signal. FFT block 470 of size N performs the FFT algorithm to generate N parallel frequency-domain signals. (P-to-S) block 475 converts the parallel frequency-domain signals to a sequence of modulated data symbols. Channel decoding and demodulation block 480 demodulates and decodes the modulated symbols to recover the original input data stream.

[0070] Each of gNBs 101 - 103 may implement a transmit path 400 similar to that for transmitting to UEs 111 to 116 in the downlink, and may implement a receive path 450 similar to that for receiving from UEs 111 to 116 in the uplink. Similarly, each of UEs 111 to 116 may implement a transmit path 400 for transmitting to gNBs 101 to 103 in the uplink, and may implement a receive path 450 for receiving from gNBs 101 to 103 in the downlink.

[0071] Figure 4A and Figure 4B Each component in may be implemented using only hardware or using a combination of hardware and software / firmware. As a specific example, Figure 4A and Figure 4B At least some components in may be implemented in software, while other components may be implemented by configurable hardware or a mix of software and configurable hardware. For example, FFT block 470 and IFFT block 415 may be implemented as configurable software algorithms, where the value of size N may be modified according to the implementation.

[0072] Furthermore, although described as using FFT and IFFT, this is by way of illustration only and should not be construed as limiting the scope of the present disclosure. Other types of transforms may be used, such as discrete Fourier transform (DFT) and inverse discrete Fourier transform (IDFT) functions. It should be understood that for DFT and IDFT functions, the value of variable N may be any integer (such as 1, 2, 3, 4, etc.), while for FFT and IFFT functions, the value of variable N may be any integer that is a power of two (such as 1, 2, 4, 8, 16, etc.).

[0073] Although Figure 4A and Figure 4B respectively show examples of wireless transmit path 400 and wireless receive path 450, various changes may be made to Figure 4A and Figure 4B For example, Figure 4A and Figure 4BThe various components therein can be combined, further subdivided, or omitted, and additional components can be added according to specific needs. Additionally, Figure 4A and Figure 4B are intended to show examples of the types of transmit and receive paths that can be used in a wireless network. Any other suitable architecture can be used to support wireless communication in a wireless network.

[0074] In the following, the italicized name of a parameter indicates that the parameter is provided by a higher layer.

[0075] DL transmission or UL transmission can be based on an OFDM waveform, which includes a variant using DFT precoding, which is referred to as DFT - spread OFDM and is generally applicable to UL transmission.

[0076] In the following, a sub - frame (SF) refers to the transmission time unit of the LTE RAT, and a time slot refers to the transmission time unit of the NR RAT. For example, the time slot duration can be a divisor of the SF duration. NR can use a DL or UL time slot structure different from that of the LTE SF structure. The differences can include the structure for transmitting the physical downlink control channel (PDCCH), the position and structure of the demodulation reference signal (DM - RS), the transmission duration, etc. Additionally, an eNB refers to the base station serving a UE operating in the LTE RAT, and a gNB refers to the base station serving a UE operating in the NR RAT. Exemplary embodiments consider the same set of parameters, which includes the sub - carrier spacing (SCS) configuration and the cyclic prefix (CP) length of OFDM symbols, for transmissions using the LTE RAT and using the NR RAT. In such a case, the OFDM symbols for the LTE RAT are the same as those for the NR RAT, the sub - frames are the same as the time slots, and for the sake of brevity, the term time slot is used subsequently in the remainder of this disclosure.

[0077] The unit for DL signaling or UL signaling on a cell is called a time slot and can include one or more symbols. The bandwidth (BW) unit is called a resource block (RB). One RB includes multiple sub - carriers (SC). For example, a time slot can have a duration of one millisecond, and an RB can have a bandwidth of 180 kHz and include 12 SCs with an SC - to - SC spacing of 15 kHz. The sub - carrier spacing (SCS) can be configured by determined as kHz. The unit of one sub - carrier on one symbol is called a resource element (RE). The unit of one RB on one symbol is called a physical RB (PRB).

[0078] The UE positioning function of NG-RAN provides mechanisms to support or assist in calculating the geographical location of the UE. UE location knowledge can be used, for example, to support radio resource management functions and for location-based services for operators, subscribers, and third-party service providers. Among the various positioning techniques supported in NR, several example positioning techniques are described in this document.

[0079] The multi-round-trip time (RTT) positioning method utilizes the UE Rx-Tx time difference measurements of the downlink signals received from multiple TRPs measured by the UE, the DL-PRS-received signal strength received power (RSRP) and DL-PRS-reference signal received path power (RSRPP), and the gNB Rx-Tx time difference measurements, UL-sounding reference signal (SRS)-RSRP, and UL-SRS-RSRPP measured at multiple TRPs of the uplink signals transmitted by the UE. The UE uses the assistance data received from the positioning server to measure the UE Rx-Tx time difference measurements (and optionally, the DL-PRS-RSRP and / or DL-PRS-RSRPP of the received signal). The TRP uses the assistance data received from the positioning server to measure the gNB Rx-Tx time difference measurements (and optionally, the UL-SRS-RSRP and / or UL-SRS-RSRPP of the received signal). The measurements are used to determine the RTT at the positioning server, and the RTT is used to estimate the location of the UE.

[0080] The DL-AoD positioning method utilizes the measured DL-PRS-RSRP and DL-PRS-RSRPP of the downlink signals received from multiple transmission points (TPs) at the UE. The UE uses the assistance data received from the positioning server to measure the DL-PRS-RSRP and DL-PRS-RSRPP of the received signal. The resulting measurement results, together with other configuration information, are used to locate the UE in relation to the neighboring TPs.

[0081] The DL time difference of arrival (TDOA) positioning method utilizes the DL RSTD (and optionally, the DL-PRS-RSRP and DL-PRS-RSRPP) of the downlink signals received from multiple TPs at the UE. The UE uses the assistance data received from the positioning server to measure the DL RSTD of the received signal (and optionally, the DL-PRS-RSRP and DL-PRS-RSRPP). The resulting measurement results, together with other configuration information, are used to locate the UE in relation to the neighboring TPs.

[0082] The UL-TDOA positioning method utilizes the UL relative time of arrival (RTOA) (and optionally, UL-SRS-RSRP and UL-SRS-RSRPP) at multiple receive points (RPs) of the uplink signal transmitted from the UE. The TRP uses the assistance data received from the positioning server to measure the UL-RTOA (and optionally, UL-SRS-RSRP and UL-SRS-RSRPP) of the received signal. The obtained measurement results are used together with other configuration information to estimate the location of the UE.

[0083] The UL-AoA positioning method utilizes the measured azimuth angle of arrival (A-AoA) and zenith angle of arrival (Z-AoA) at multiple RPs of the uplink signal transmitted from the UE. The RP uses the assistance data received from the positioning server to measure the A-AoA and Z-AoA (and optionally, UL-SRS-RSRPP) of the received signal. The obtained measurement results are used together with other configuration information to estimate the location of the UE.

[0084] In the current network, the application and standardization impact of AI / ML-based methods are mainly limited to the network layer. There have been standardization efforts related to AI / ML capabilities in the O-RAN Alliance and the Third Generation Partnership Project (3GPP). In particular, the O-RAN Alliance is developing a virtualized RAN with open interfaces and network intelligence, which has entities such as a non-real-time (RT) RAN intelligent controller (RIC) and a near-RT RIC. The non-RT RIC is a logical function that implements the non-real-time control and optimization of RAN elements and resources, and manages the overall AI / ML workflow of the O-RAN network, including model training, inference, and update. The near-RT RIC is a logical function that implements the near-real-time control and optimization of RAN elements and resources through fine-grained data collection and actions on the RAN interface. On the other hand, 3GPP has defined a network data analytics function (NWDAF) for network slice management in Rel-15, and has further enhanced it in Rel-16 and Rel-17. 3GPP has also defined a functional framework for RAN intelligence achieved through data collection.

[0085] It is expected that AI / ML methods will be applied to various cellular system air interface designs, including channel state information (CSI) compression / recovery, future CSI prediction, learning-based channel estimation, channel coding, and modulation, to name just a few. Based on simplified assumptions such as linear system models and additive white Gaussian noise (AWGN) channels, common physical layer algorithms have been derived. By leveraging AI / ML methods, optimized algorithms can be developed for more realistic system assumptions such as non-linear and fading channels.

[0086] It is also expected that, depending on the usage scenario, improvements can be made not only in system performance such as throughput, spectral efficiency, and latency, but also in complexity, reliability, overhead, etc. In addition, optimization can be carried out not only in a piecemeal manner for a given transmitter / receiver processing function, but also in an end-to-end manner that includes the entire transmitter / receiver processing chain. Therefore, it is expected that the scope of application of AI / ML in cellular systems will continue to expand.

[0087] For AI / ML-based positioning, both direct AI / ML-based positioning and AI / ML-assisted positioning are possible. In direct AI / ML-based positioning, the output of the AI / ML model is the UE location based on inputs such as DL-PRS measurements. The design of the AI / ML model for direct positioning can be neither based on any specific common positioning technique nor restricted by the specific DL-PRS resource configuration and transmission scheme associated with a specific positioning technique. The AI / ML model itself can be trained in a way that implicitly learns the underlying channel physical characteristics and directly estimates the UE location.

[0088] In AI / ML-assisted positioning, the output of the AI / ML model includes intermediate metrics such as Rx-Tx time difference, DL-RSTD, UL-RTOA, UL-AoA, DL-AoD, DL-PRS-RSRP, and / or DL-PRS-RSRPP, which can be utilized by common positioning techniques. If these intermediate metrics can be measured accurately, for example, in a line-of-sight (LOS) environment, it is expected that common positioning techniques based on multi-point positioning calculations will perform well. However, in a strong scattering non-line-of-sight (NLOS) environment, these intermediate metrics are difficult to measure accurately. With a sufficient dataset, the AI / ML model can estimate the true intermediate metrics from noisy and superimposed multipath DL-PRS signals. As long as these intermediate metrics can be estimated accurately, it is expected that the common positioning techniques will still work well as expected. The design of the AI / ML model for AI / ML-assisted positioning will be restricted by the specific common positioning technique assumed to be used by the network and the corresponding DL-PRS resource configuration and transmission scheme associated with the assumed positioning technique.

[0089] Figure 5 FIG. shows an example beam scan 500 for DL-AoD measurement according to an embodiment of the present disclosure. For example, the beam scan 500 for DL-AoD can be performed by Figure 1 any one of UEs 111 to 116. This example is for illustration only, and other embodiments can be used without departing from the scope of the present disclosure.

[0090] In DL-AoD-based positioning, a UE (e.g., UE 116) estimates the DL-AoD by measuring the RSRP of DL-PRS beams scanned by each TRP (e.g., BS 102 and / or antennas 205a to 205n).

[0091] Figure 6 FIG. shows DL-AoD measurements 600 from multiple TRPs according to an embodiment of the present disclosure. For example, the DL-AoD measurements 600 from a TRP can be performed by Figure 1 any of the UEs 111 to 116 to measure TRPs such as BS 102 to BS 103 and / or antennas 205a to 205n. This example is for illustration only, and other embodiments can be used without departing from the scope of the present disclosure.

[0092] The DL-AoD method involves a serving TRP and at least one neighboring TRP in the same or different cells.

[0093] Once DL-AoD measurements from multiple TRPs are obtained, angle multi-point positioning, i.e., UE-assisted positioning, is performed at the LMF, or angle multi-point positioning, i.e., UE-based positioning, is performed at the UE.

[0094] As an example of AI / ML-assisted positioning, an AI / ML model can improve DL-AoD measurements in challenging situations such as NLOS environments, estimate DL-AoD with fine granularity from sparse or wide DL-PRS beam scans, or predict DL-AoD for one or more future instances.

[0095] Figure 7 FIG. shows DL-Time of Flight (ToF) measurements 700 according to an embodiment of the present disclosure. For example, the DL-ToF measurements 700 can be performed by Figure 1 any of the UEs 111 to 116 to measure TRPs such as BS 102 to BS 103 and / or antennas 205a to 205n. This example is for illustration only, and other embodiments can be used without departing from the scope of the present disclosure.

[0096] In DL-TDOA-based positioning involving a serving TRP and at least two neighboring TRPs in the same or different cells, the UE measures the ToF by detecting the DL-PRS from each TRP. For a pair of TRPs, the UE calculates the RSTD (Reference Signal Time Difference) by computing the difference in ToF from the two TRPs. Once RSTD measurements from multiple pairs of TRPs are obtained, hyperbolic multi-point positioning, i.e., UE-assisted positioning, is performed at the LMF, or hyperbolic multi-point positioning, i.e., UE-based positioning, is performed at the UE.

[0097] As an example of AI / ML-assisted positioning, the AI / ML model can improve ToF or RSTD measurements in challenging situations such as NLOS environments, or predict ToF or RSTD for one or more future instances.

[0098] For both AI / ML-based positioning and AI / ML-assisted positioning, the DL-PRS overhead in the time domain and spatial domain can be reduced by timely predicting intermediate metrics or the UE location itself, or by predicting DL-PRS beams in the spatial domain from wide or sparse beam scans.

[0099] This disclosure is not limited to AI / ML-based positioning methods. Location prediction itself can be performed using AI / ML-based methods or non-AI / ML-based methods, such as using advanced signal processing techniques, e.g., interpolation, extrapolation, or extended Kalman filters, etc.

[0100] Embodiments of this disclosure recognize that the selection of an appropriate positioning model (AI / ML-assisted or AI / ML-based) can depend on the UE's channel environment and / or geographical location, and the model can be deployed at the UE or the network. Therefore, a set of signaling regarding the UE's channel environment and / or geographical location needs to be defined between the network and the UE to assist in the positioning model selection at the UE or the network.

[0101] For DL-AoD-based UE-assisted positioning, the DL-PRS beam set for measurement can be different from the beam set defining the beam space for prediction, e.g., sparser or wider. Therefore, a set of signaling from the network to the UE needs to be defined to inform the relationship between the first set of beams and the second set of beams. When predicting DL-PRS beams for more than one instance, the measurement report containing RSRP values for multiple instances also needs to be enhanced. A set of signaling from the UE to the network also needs to be defined to provide auxiliary information for RSRP prediction performance monitoring.

[0102] For DL-TDOA-based UE-assisted positioning, the network can request the UE to predict RSTD for more than one instance. Therefore, the measurement report containing RSTD values for multiple instances needs to be enhanced. A set of signaling from the UE to the network also needs to be defined to provide auxiliary information for RSTD prediction performance monitoring.

[0103] For UE-based positioning, the network can request the UE to predict its location for more than one instance. Therefore, the UE location report including UE coordinates for multiple instances needs to be enhanced. A set of signaling from the UE to the network also needs to be defined to provide auxiliary information for UE-based positioning performance monitoring.

[0104] The performance of currently used positioning models may degrade over time as the channel environment changes. Therefore, a set of signaling needs to be defined between the network and the UE at known locations to collect various data sets for positioning model performance monitoring and model updating / fine-tuning / re-training, etc. A set of signaling also needs to be defined to transfer those data sets collected from the UE at known locations to other UEs for model updating / fine-tuning / re-training, etc., or to instruct the UE to perform model switching or fallback to a common positioning method.

[0105] This disclosure relates to a communication system. This disclosure relates to defining functions and procedures for supporting prediction-based positioning (including UE-based positioning and UE-assisted positioning) in a cellular system.

[0106] This disclosure also relates to indicating the channel environment and / or geographical location of the UE to assist in positioning model selection at the UE or at the network.

[0107] This disclosure also relates to UE-assisted positioning based on DL-AoD, including signaling the relationship between the beamspace for DL-PRS measurement and the beamspace for DL-AoD prediction, enhancing the measurement report containing RSRP for multiple instances, and signaling the auxiliary information for DL-AoD prediction model performance monitoring.

[0108] This disclosure also relates to UE-assisted positioning based on DL-TDOA, including enhancing the measurement report containing RSTD for multiple instances and signaling the auxiliary information for DL-TDOA prediction model performance monitoring.

[0109] This disclosure also relates to UE-based positioning, including enhancing the UE location report for multiple instances and signaling the auxiliary information for UE-based positioning model performance monitoring.

[0110] This disclosure also relates to collecting data sets from the UE at known locations, transferring the data sets to other UEs for model updating / fine-tuning / re-training, and signaling a switch to another model or fallback to one of the common positioning techniques.

[0111] Embodiments of this disclosure for prediction-based positioning in a cellular system are further elaborated herein.

[0112] ● Methods and apparatuses for indicating the channel environment and / or geographical location of the UE to assist in positioning model selection at the UE or at the network.

[0113] ● Methods and apparatuses for signaling the relationship between the beamspace for DL-PRS measurement and the beamspace for DL-AoD prediction, enhancing the measurement report containing at least RSRP for multiple instances, and signaling the auxiliary information for DL-AoD prediction model performance monitoring.

[0114] ● Methods and apparatus for enhancing measurement reports and signaling notifications for RSTD for multiple instances, including auxiliary information for DL-TDOA prediction model performance monitoring.

[0115] ● Methods and apparatus for enhancing UE location reports and signaling notifications for multiple instances, including auxiliary information for UE-based positioning model performance monitoring.

[0116] ● Methods and apparatus for collecting a data set from a UE at a known location, transmitting the data set to other UEs for model update / fine-tuning / re-training, and signaling a switch to another model or fallback to one of the common positioning techniques.

[0117] This document provides a detailed description of systems and methods consistent with embodiments of the present disclosure. Although several embodiments are described, it should be understood that the present disclosure is not limited to any one embodiment, but includes many alternatives, modifications, and equivalents. Additionally, although many specific details are set forth in the following description to provide a thorough understanding of the embodiments disclosed herein, some or all of these details may be practiced without some of them. Further, certain technical materials known in the relevant art are not described in detail for clarity to avoid unnecessarily obscuring the present disclosure.

[0118] AI / ML-assisted or AI / ML-based positioning can be performed at the UE, at the network, or at both. When AI / ML-assisted or AI / ML-based positioning is performed at the UE, UE 116 sends intermediate metrics or its determined location as the output of the AI / ML model to network 130. AI / ML-assisted or AI / ML-based positioning can also be performed at the network based on UE measurement reports.

[0119] When AI / ML-assisted or AI / ML-based positioning is performed at the UE, UE 116 can have multiple positioning models designed and / or trained for specific scenarios and / or environments. In such a case, network 130 can provide UE 116 with auxiliary information regarding the validity conditions of a given model, and UE 116 can determine the appropriate model for a given environment based on this auxiliary information. The following validity conditions can be signaled to UE 116:

[0120] ● The validity conditions can include applicable areas / zones, which can be indicated by cell ID, tracking area ID, or zone ID within a cell, etc.

[0121] ● The validity conditions may include applicable scenarios / environments, which may indicate in terms of mobility aspects such as speed or classification in urban macrocell (UMa) / urban microcell (UMi) / indoor hotspot (InH) / rural, clutter / obstruction presence / density / severity, LOS / NLOS, indoor / outdoor, in-vehicle, pedestrian / vehicle / high-speed train, etc.

[0122] ● The validity conditions may be provided in terms of maximum or minimum Doppler shift and / or delay spread.

[0123] ● The validity conditions may be provided in terms of an effective time interval or a validity timer.

[0124] ● The validity conditions may be provided in terms of RSRP thresholds for the serving cell / neighboring cells. For example, the RSRP from the serving cell becomes less than / greater than a specific threshold, and the RSRP from the neighboring cell becomes greater than / less than a specific threshold.

[0125] ● The validity conditions may be provided in terms of the UE location. If the location of UE 116 deviates from the current location of UE 116 by more than a specific distance (e.g., in meters), the distance between UE 116 and a reference point (e.g., serving cell / TRP location) becomes greater than / less than a specific distance or greater than / less than a specific distance from the current distance, and the distance between UE 116 and another reference point (e.g., neighboring cell / TRP location) becomes less than / greater than a specific distance or less than / greater than a specific distance from the current distance.

[0126] When AI / ML-assisted positioning or AI / ML-based positioning is performed at the UE and UE 116 has multiple positioning models designed and / or trained for a specific scenario and / or environment, UE 116 provides, for example via a model ID, a set of models supported by UE 116 and associated information and / or model capabilities to network 130. Network 130 may indicate to UE 116 the model to be used by UE 116, for example via a model ID, based on the assistance information provided by UE 116. When network 130 signals a model ID to UE 116, network 130 may also provide validity conditions associated with the model, as disclosed herein. When AI / ML-assisted positioning or AI / ML-based positioning is performed at the network, network 130 may have multiple positioning models developed and / or trained for a scenario / site specifically. Network 130 may request UE 116 to provide assistance information for model selection / switching at network 130.

[0127] This text is an example of auxiliary information for appropriately positioning model selection or switching. If inference is performed at network 130, the auxiliary information can be provided by UE 116 to network 130, or if inference is performed at UE 116, the auxiliary information can be provided by network 130 to UE 116.

[0128] In one example, UE 116 provides the channel environment sensed by UE 116 to the serving cell, or the serving cell provides the channel environment of UE 116 sensed by network 130 (e.g., based on UL reference signal measurements) to UE 116, such as mobility in terms of UMa / UMi / InH / rural, clutter / blockage presence / density / severity, LOS / NLOS indication, indoor / outdoor indication, in-vehicle indication, in-building indication, speed (e.g., represented by an absolute value, a value range, or a mobility type), or speed classification, such as pedestrian / vehicle / high-speed train, etc.

[0129] In another example, UE 116 provides to the serving cell, or the serving cell provides to UE 116, a Doppler profile measured on the channel between UE 116 and the serving cell. The Doppler profile may include Doppler spread, Doppler shift, and relative Doppler shift.

[0130] In yet another example, UE 116 provides to the serving cell, or the serving cell provides to UE 116, a multipath delay profile measured on the channel between UE 116 and the serving cell. The multipath delay profile may include delay spread, per-path weight, delay, and Doppler values for each signal propagation path. For the case where UE 116 provides the multipath delay profile to the serving cell, the serving cell may provide a threshold of signal strength to UE 116 such that weights, delays, and Doppler values are reported to the serving cell for paths with an intensity greater than the threshold. The intensity can be represented by the amplitude or power of the signal. The intensity can be measured by averaging the values on the subcarriers and / or symbols carrying the reference signal, or taking the maximum value on the subcarriers and / or symbols carrying the reference signal.

[0131] In yet another example, UE 116 provides to the serving cell, or the serving cell provides to UE 116, the geographical location and / or scenario of UE 116 (which can be a zone ID or scenario ID from a set of predefined scenarios). The definition of the zone and the corresponding zone ID can be provided by the serving cell to UE 116. A zone may include one or more cells. If the zone includes a single cell, the zone ID may be the same as the cell ID. If the zone includes one or more cells, the zone ID may be the same as the tracking area ID.

[0132] In another example, the serving cell area is divided into multiple zones and is assigned a unique ID within the cell. A set of scenarios can be defined and signaled to the UE 116. It can be, for example, UMa / UMi / InH / rural scenarios, high / low clutter / blockage scenarios, LOS / NLOS scenarios, indoor / outdoor scenarios, in-vehicle scenarios, in-building scenarios, pedestrian / vehicle / high-speed train scenarios, etc.

[0133] Figure 8 A flowchart of an example process 800 for a UE to send predicted DL-AoD measurement reports for measurement according to an embodiment of the present disclosure is shown. For example, the process 800 for a UE to send predicted DL-AoD measurement reports to assist positioning at the LMF can be performed by Figure 3 the UE 116. This example is for illustration only, and other embodiments can be used without departing from the scope of the present disclosure.

[0134] Process 800 begins at 810, where information related to mapping a first set of DL-PRS beams to a second set of DL-PRS beams for each TRP transmitting DL-PRS to the UE 116 and one or more instances for DL-PRS RSRP beam prediction and reporting are provided from the LMF to the UE. In 820, the UE 116 performs DL-PRS measurements on the first set of beams from each TRP transmitting DL-PRS according to the resource configuration provided by the LMF. In 830, the UE 116 predicts the DL-PRS RSRP of the second set of beams based on the measurements of the first set of beams from each TRP transmitting DL-PRS for one or more instances indicated by the LMF. In 840, the UE 116 sends a report on its predicted DL-PRS RSRP to the LMF together with the auxiliary information.

[0135] Information related to mapping a first set of DL-PRS beams to a second set of DL-PRS beams for each TRP transmitting DL-PRS to the UE 116 is provided from the LMF.

[0136] Figure 9 A diagram of DL-AoD prediction 900 from wide DL-PRS beam and sparse DL-PRS beam measurements according to an embodiment of the present disclosure is shown. For example, the prediction 900 from wide DL-PRS beam and sparse DL-PRS beam measurements can be performed by Figure 1 any one of the UEs 111 to 116. This example is for illustration only, and other embodiments can be used without departing from the scope of the present disclosure.

[0137] In one example, the first set of beams may have a beam width wider than that of the second set of beams, which may be due to the limited spatial granularity resulting from the limited antenna array size at the TRP, or may be for reducing the DL-PRS beam scanning overhead. One wide beam in the first set may be mapped to multiple narrow beams in the second set. The serving cell notifies the UE 116 of how many narrow beams in the second set are associated with the beam in the first set and their mapping relationship relative to the beam in the first set, e.g., in terms of angular offset, 3-dB beam width, beam pattern, amplitude / power of the beam, etc.

[0138] In another example, the first set of beams may be sparser than the second set of beams. For example, the first set of beams is a subset of the second set of beams, which can reduce the DL-PRS beam scanning overhead. One beam in the first set may be mapped to multiple adjacent beams in the second set having the same characteristics (e.g., in terms of 3-dB beam width, etc.). The serving cell notifies the UE 116 of how many beams in the second set are associated with the beam in the first set and their mapping relationship relative to the beam in the first set (e.g., in terms of angular offset, etc.).

[0139] In yet another example, the first and second sets of beams are the same.

[0140] One or more instances for DL-PRS RSRP beam prediction and reporting are also provided from the serving cell to the UE 116. The one or more instances may include an instance of configuring DL-PRS beam measurement resources for the first set of beams. The one or more instances may also include future instances later than the instance of configuring the beam measurement resources.

[0141] In one example, the UE may be indicated by the serving cell a prediction window for which the UE 116 predicts the future DL-PRS RSRP / reference signal received quality (RSRQ) / signal-to-interference and noise ratio (SINR) from each TRP, and thus predicts the AoD from each TRP. The prediction window (e.g., valid time duration) may be indicated to the UE 116 by using the duration and the offset from the reference resource for the measurement of the first set of beams, e.g., {n ref +o,..., n ref +o+W p}, where W p is the prediction window duration, and o is the prediction start offset from the reference resource at n ref . Both W p and o can take zero or positive integer values.

[0142] In another example, the UE 116 may be instructed by the serving cell to predict a start offset o, a prediction interval I, and the number of instances K for prediction. Thus, the UE 116 predicts DL-PRS beams from a second set of beams for a set of instances {n ref +o, n ref +o+I, n ref +o+2·I, …, n ref +o+(K-1)·I}.

[0143] Alternatively, the UE 116 may be instructed by the network 130 to indicate a set of offset values that indicate future instances for prediction. For example, the network 130 may indicate to the UE 116 a set of offset values, such as {o 1 , o 2 , o 3}, and the UE 116 predicts DL-PRS beams for {n ref +o 1 , n ref +o 2 , n ref +o 3}.

[0144] When the UE 116 sends a DL-PRS measurement report to the serving cell, the UE 116 may be instructed by the serving cell whether the report should include the RSRP / RSRQ / SINR / RSRPP values of the top 1 strongest DL-PRS beam, the top K strongest DL-PRS beams, or all DL-PRS beams, together with the beam index or the DL-PRS measurement resource index. The UE 116 may also be instructed by the serving cell whether to report measurements from the first set of beams, the second set of beams, or from both the first set of beams and the second set of beams.

[0145] When the report includes more than one instance, report the RSRP / RSRQ / SINR / RSRPP value of the beam in the first instance, and report the differential RSRP / RSRQ / SINR / RSRPP from the first instance or the previous instance (i.e., the kth instance used as a reference for reporting the (k + 1)th instance) for the remaining instances. In this case, the differential RSRP / RSRQ / SINR / RSRPP may take positive or negative values.

[0146] For example, the sign of the differential RSRP / RSRQ / SINR / RSRPP may be indicated via a boolean value indication.

[0147] Alternatively, multiple reporting instances are sorted according to RSRP / RSRQ / SINR / RSRPP values, and the RSRP / RSRQ / SINR / RSRPP of the strongest instance is reported together with an index indicating the reporting instance, and the differential RSRP / RSRQ / SINR / RSRPP from the strongest instance or the next stronger instance (i.e., the differential RSRP / RSRQ / SINR / RSRPP of the n+1 strongest instance from the n strongest instance) is reported for the remaining instances together with an index indicating the reporting instance. When each reporting instance includes more than one DL-PRS beam of non-strongest DL-PRS beams, the differential RSRP / RSRQ / SINR / RSRPP can be reported according to the strongest or next stronger RSRP / RSRQ / SINR / RSRPP within the instance or the strongest RSRP / RSRQ / SINR / RSRPP of the first reported instance, where the first reported instance can be the earliest instance in time or the instance including the strongest RSRP / RSRQ / SINR / RSRPP value.

[0148] When UE 116 sends a DL-PRS measurement report to network 130, UE 116 can send auxiliary information to network 130 including the following:

[0149] ● The probability that the nth strongest predicted DL-PRS beam is within the N actual strongest DL-PRS beams at a predicted future instance. This information can be per TRP that sends DL-PRS to UE 116.

[0150] ● Spatial information of the receiving beam of the UE (e.g., receiving beam ID, beam direction, 3-dB beam width, spatial filter, etc.) for DL-PRS measurement. This information can be per TRP that sends DL-PRS to UE 116.

[0151] ● An indication of the need for more / less resources (i.e., more / less spatial beam scanning) for DL-PRS beam measurement to perform prediction. This information can be per TRP that sends DL-PRS to UE 116.

[0152] ● A preferred angular range of the DL-PRS beam direction for reference signal configuration. This information can be per TRP that sends DL-PRS to UE 116.

[0153] ● An indication of the need for DL-PRS beam measurement resources that are more / less frequent in time.

[0154] ● UE channel environment, e.g., UMa / UMi / InH / rural, clutter / blockage presence / density / severity, LOS / NLOS indication, indoor / outdoor indication, in-vehicle indication, in-building indication, mobility in terms of speed or speed class, e.g., pedestrian / vehicle / high-speed train, etc. Some information can be common to the TRPs, e.g., the indication of UMa / UMi / InH / rural, while some other information can be per-TRP for the DL-PRS sent to UE 116, e.g., the LOS / NLOS indication.

[0155] Figure 10 FIG. shows a flowchart of an example process 1000 for a UE to send a prediction-based DL-RSTD measurement report to assist positioning at the LMF according to an embodiment of the present disclosure. For example, the process 1000 for a UE to send a prediction-based DL-RSTD measurement report to assist positioning at the LMF can be performed by Figure 3 UE 116. This example is for illustration only, and other embodiments can be used without departing from the scope of the present disclosure.

[0156] The process starts at 1010, where the LMF provides the UE with information related to the DL-PRS resources from each TRP that sends the DL-PRS to UE 116 and one or more instances for DL-RSTD prediction and reporting. In 1020, UE 116 performs DL-PRS measurements from each TRP that sends the DL-PRS according to the resource configuration provided by the LMF. In 1030, UE 116 predicts the DL-RSTD based on the DL-PRS measurements of one or more instances indicated by the LMF. In 1040, UE 116 sends a report on its predicted DL-RSTD to the LMF together with the assisting information.

[0157] Figure 11 FIG. shows an example DL-RSTD prediction 1100 from multiple TRPs according to an embodiment of the present disclosure. For example, the DL-RSTD prediction 1100 from multiple TRPs can be performed by Figure 1 any one of UEs 111 to 116 to make measurements from TRPs such as BSs 102 to 103 and / or antennas 205a to 205n. This example is for illustration only, and other embodiments can be used without departing from the scope of the present disclosure.

[0158] One or more instances for RSTD prediction and reporting can be provided from the serving cell to UE 116. The one or more instances can include instances configuring DL-PRS measurement resources. The one or more instances can also include future instances later than the time of performing the measurement. UE 116 can be indicated by the serving cell with an indication method as disclosed herein for one or more instances for RSTD prediction. Due to UE mobility, the UE 116 location can change from one instance to another. For the one or more instances indicated, UE 116 can predict the ToF and the resulting RSTD values from each TRP by considering its moving speed, direction, and / or trajectory in a prediction model (AI / ML-based or non-AI / ML-based).

[0159] When the report for a given instance includes multiple RSTD values for multiple pairs of TRPs, UE 116 sends the RSTD value for the first pair of TRPs and the differential RSTD values for the remaining pairs of TRPs to the serving cell, where the differential is taken from the first pair of TRPs or the previous pair of TRPs for which the RSTD value is encoded in the report. The RSTD values can be reported in descending or ascending order of the RSTD values.

[0160] When UE 116 reports RSTD for more than one instance, the RSTD for each instance and a timestamp corresponding to the time of performing the prediction can be reported. For a given TRP pair, UE 116 sends the RSTD value for the first instance and the differential RSTD values for subsequent instances to the serving cell, where the difference is taken from the first reported instance or the previously reported instance.

[0161] Alternatively, for a given TRP pair, the multiple reported instances are sorted in descending or ascending order of the RSTD values, and the instance with the smallest or largest RSTD value is reported first, and the differential RSTD values are reported based on the first reported instance, the previously reported instance, and an index or timestamp indicating the reported instance.

[0162] When each reported instance includes more than one RSTD value for more than one pair of TRPs, the differential RSTD can be reported based on the first reported RSTD value within the instance or the first reported RSTD value within the first reported instance, where the first reported instance can be the earliest instance in time that contains the smallest or largest RSTD value after sorting in ascending or descending order.

[0163] When the UE 116 sends an RSTD measurement report to the network 130, the UE 116 may send auxiliary information to the network 130, as disclosed herein. In addition, the UE 116 may send, in a predicted future instance with a timestamp, the confidence in the predicted future RSTD values from each TRP and the estimated RSRP / RSRQ / received signal strength indicator (RSSI) / SINR / RSRPP values.

[0164] Figure 12 A flowchart of an example process 1200 for a UE to send its predicted location to the LMF in accordance with an embodiment of the present disclosure is shown. For example, the process 1200 for a UE to send its predicted location to the LMF may be performed by Figure 3 the UE 116. This example is for illustrative purposes only, and other embodiments may be used without departing from the scope of the present disclosure.

[0165] The process begins at 1210, where information related to the DL-PRS resources of each TRP for sending DL-PRS to the UE 116 and one or more instances for the UE 116 to send a report on its predicted location are provided to the UE from the LMF. In 1220, the UE 116 performs DL-PRS measurements from each TRP that sends DL-PRS according to the resource configuration provided by the LMF. In 1230, the UE 116 performs DL-PRS measurements from each TRP that sends DL-PRS according to the resource configuration provided by the LMF. In 1240, the UE 116 sends a report on its predicted location to the LMF together with auxiliary information.

[0166] The UE 116 may provide information about a set of models supported by the UE 116, as well as associated information and / or model capabilities, to the network 130 (e.g., via a model ID). The network 130 indicates to the UE 116 the model to be used by the UE 116 based on the auxiliary information provided by the UE 116, e.g., via a model ID.

[0167] Alternatively, the network 130 may provide auxiliary information to the UE 116 for appropriate model selection. Possible auxiliary information that may be exchanged between the UE 116 and the network 130 is as disclosed herein.

[0168] When the UE 116 reports its location for more than one instance, the corresponding timestamps can be used to report the UE 116 location for each instance. For the earliest instance in time, the UE reports its coordinates (e.g., according to Cartesian coordinates, polar coordinates, spherical coordinates), azimuth / elevation angle, latitude / longitude / altitude, possibly together with an uncertainty shape. For subsequent instances, the UE 116 sends differential coordinates from the earliest instance or from the previous instance before the current instance, i.e., the differential coordinates of the n+1th instance from the nth instance, where the instances are sorted in time. The UE 116 sends a report on its predicted location to the LMF together with auxiliary information, as disclosed herein.

[0169] The network 130 may request a UE at a known location to provide a data set to the network 130, such as DL-PRS measurements, channel impulse responses, or any relevant intermediate metrics, such as RSTD, RSRP / RSRQ / RSSI / SINR / RSRPP, UE Rx-Tx time difference, AoD, etc., or the network 130 requests a UE at a known location to send SRS for the TRP to measure the UL channel impulse response, AoA, etc. The network 130 may also request the UE 116 to provide the following information:

[0170] ● UE location coordinates.

[0171] ● UE mobility-related information, such as trajectory, direction of movement, and speed, etc.

[0172] ● Various UE-perceived channel environments, Doppler frequency shift, delay spread, as disclosed herein.

[0173] ● Statistics / distributions related to AI / ML model input data, such as DL-PRS measurements, channel impulse responses, or any relevant intermediate metrics, such as RSTD, RSRP / RSRQ / RSSI / SINR / RSRPP, UE Rx-Tx time difference, AoD, AoA, etc. Additionally, the UE 116 may be instructed by the serving cell to report the conditions for information related to the input data statistics / distributions, e.g., thresholds on the deviation of the input data statistics / distributions from the nominal statistics / distributions, etc.

[0174] ● Statistics / distributions related to AI / ML model output data, such as the determined UE location for UE-based positioning and intermediate metrics for UE-assisted positioning, e.g., RSTD, RSRP / RSRQ / RSSI / SINR / RSRPP, AoD, AoA, Rx-Tx time difference. Additionally, the UE 116 may be instructed by the serving cell to report the conditions for information related to the output data statistics / distributions, e.g., thresholds on the deviation of the output data statistics / distributions from the nominal statistics / distributions, etc.

[0175] ● Location / intermediate metric estimation accuracy. In one example, UE 116 may send to network 130 the difference between the UE location or any intermediate metric estimated using AI / ML, any advanced signal processing techniques and the known ground truth UE location or any known ground truth intermediate metric, i.e., to measure the effectiveness of the currently deployed model. Additionally, UE 116 may be indicated by the serving cell the conditions for reporting, e.g., a threshold on the deviation of the estimated UE location or any intermediate metric from the known ground truth UE location or any intermediate metric.

[0176] Based on the data collected from UEs at known locations, network 130 may determine the effectiveness of the currently deployed positioning model at other UEs. The UE may be provided by the serving cell with a data set collected by the serving cell from UEs at known locations, such as those disclosed for UE 116, to perform model updates, fine-tuning, and / or retraining.

[0177] Alternatively, the UE may be indicated by the serving cell to establish a D2D / sidelink connection with nearby UEs at known locations to obtain a data set directly transmitted from the UEs at known locations via D2D / sidelink.

[0178] Alternatively, UE 116 may be indicated by the serving cell the model ID of UE 116 to perform model switching. The effectiveness conditions for the provided model ID may also be provided to UE 116.

[0179] Alternatively, UE 116 may be indicated by the serving cell a common positioning technique, such as multi-RTT, UL TDOA, DL TDOA, UL AoA, DL AoD, to fallback together with DL-PRS or UL-SRS resource configuration.

[0180] The serving cell may also request UE 116 to compare its own input and / or output data statistics / distributions according to the provided data set and report the difference. For example, the difference may be measured according to mean, median, variance, standard deviation, distribution type, range, maximum / minimum, the difference between the reported value and the corresponding true value, etc. Additionally, UE 116 may be indicated by the serving cell the conditions for reporting, such as a threshold on the deviation of the input / output data statistics / distributions.

[0181] As an example, the threshold may be provided according to mean, median, variance, distribution, range, maximum / minimum, etc.

[0182] Based on data collected from a UE at a known location, network 130 can calculate location correction terms (e.g., based on latitude / longitude / altitude) to add to the location reported by UE 116. Correction terms can be calculated for intermediate metrics as disclosed herein and added to the intermediate metrics reported by the UE. These correction terms can be sent to the UE for the UE to apply the corrections before they send location reports.

[0183] Any of the above-described variant embodiments can be used independently or in combination with at least one other variant embodiment.

[0184] The above flowcharts illustrate example methods that can be implemented in accordance with the principles of the present disclosure, and various changes can be made to the methods illustrated in the flowcharts herein. For example, although shown as a series of steps, the various steps in each figure can overlap, occur in parallel, occur in a different order, or occur multiple times. In another example, steps can be omitted or replaced by other steps.

[0185] Although the drawings illustrate different examples of user equipment, various changes can be made to the drawings. For example, the user equipment can include any number of each component in any suitable arrangement. Generally, the drawings do not limit the scope of the present disclosure to any particular configuration. Additionally, although the drawings illustrate an operating environment in which various user equipment features disclosed in this patent document can be used, these features can be used in any other suitable system.

[0186] Although the present disclosure has been described with exemplary embodiments, various changes and modifications can be suggested to those skilled in the art. The present disclosure is intended to cover such changes and modifications that fall within the scope of the appended claims. None of the descriptions in this application should be construed as implying that any particular element, step, or function is an essential element that must be included within the scope of the claims. The scope of the patent subject matter is defined by the claims.

Claims

1. A method for a user equipment (UE) to report information related to UE positioning based on machine learning (ML), the method comprises: receiving, from a cell, first information related to reception of positioning reference signals (PRS) from one or more transmit-receive points (TRP) for measurement; receiving, from the cell, second information indicating one or more reporting quantities related to the UE positioning, wherein the one or more reporting quantities are related to the UE positioning based on an ML model or life cycle management of the ML model; receiving, from the cell, third information related to transmitting the one or more reporting quantities; and receiving the PRS from the one or more TRP based on the first information; measuring the PRS; determining the one or more reporting quantities indicated by the second information based on the measurement of the PRS; and transmitting, based on the third information, a channel having the one or more reporting quantities.

2. The method according to claim 1, wherein: the one or more reporting quantities indicated by the second information are related to one or more UE positions corresponding to one or more instances, and the one or more reporting quantities of an instance include: UE coordinates, a timestamp, or a parameter related to the confidence of the reported coordinates, and the one or more reporting quantities of an instance are reported relative to another instance or independently of other instances.

3. The method according to claim 1, wherein: the one or more reporting quantities indicated by the second information are related to the UE positioning of one or more instances, the one or more reporting quantities related to the UE positioning of an instance include: reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference-plus-noise ratio (SINR), angle of departure (AoD), reference signal timing difference (RSTD), a timestamp, or a parameter related to the confidence of the one or more reporting quantities, and the one or more reporting quantities related to the UE positioning of an instance are reported relative to another instance or independently of other instances.

4. The method according to claim 1, wherein: the one or more reporting quantities indicated by the second information are associated with data for training the ML model, and the one or more reporting quantities include: UE coordinates, parameters related to channel impulse response, reference signal received power (RSRP), reference signal received quality (RSRQ), received signal strength indicator (RSSI), signal-to-interference-plus-noise ratio (SINR), reference signal received path power (RSRPP), time difference between reception and transmission, angle of departure (AoD), a timestamp, parameters related to statistics of input data to the ML model, parameters related to statistics of output data from the ML model, or parameters related to the confidence of the reported one or more reporting quantities, including the difference between the determined one or more reporting quantities and the corresponding true values of the one or more reporting quantities.

5. The method according to claim 1, further comprises: receiving or transmitting information related to selecting the ML model Wherein, the information includes: parameters related to the Doppler profile, parameters related to the multipath delay profile, parameters related to the channel environment, parameters related to clutter or blockage, an indicator of line of sight (LOS) or non-line of sight (NLOS), an indicator of indoor or outdoor environment, an indicator of in-vehicle environment, an indicator of in-building environment, or the speed of the UE represented by an absolute value, a value range, or a movement type.

6. The method according to claim 1, further comprises: receiving information related to determining the validity of the ML model, wherein, the information includes: parameters related to the area, the effective time duration, one or more reference signal received power (RSRP) thresholds from one or more corresponding cells or TRPs, parameters according to the UE location, or parameters related to the channel environment.

7. The method according to claim 1, further comprises: transmitting a channel with information related to updating the reception of the PRS for measurement, wherein, the information indicates at least one of the following: the preferred direction of PRS reception, the preferred spatial granularity of PRS reception, and the preferred time frequency of PRS reception.

8. A user equipment (UE), comprising: a transceiver configured to: receive from a cell first information related to the reception of positioning reference signals (PRS) from one or more transmit-receive points (TRPs) for measurement, receive from the cell second information indicating one or more reported quantities related to UE positioning, wherein the one or more reported quantities are related to the UE positioning based on a machine learning (ML) model or the life cycle management of the ML model, receive from the cell third information related to transmitting the one or more reported quantities, and receive the PRS from the one or more TRPs based on the first information; and a processor operably coupled to the transceiver, the processor configured to: measure the PRS; and determine the one or more reported quantities indicated by the second information based on the measurement of the PRS, wherein the transceiver is further configured to transmit a channel with the one or more reported quantities based on the third information.

9. The UE according to claim 8, wherein: the transceiver is further configured to receive or transmit information related to selecting the ML model, and the information includes: parameters related to the Doppler profile, parameters related to the multipath delay profile, parameters related to the channel environment, parameters related to clutter or blockage, an indicator of line of sight (LOS) or non-line of sight (NLOS), an indicator of indoor or outdoor environment, an indicator of in-vehicle environment, an indicator of in-building environment, or the speed of the UE represented by an absolute value, a value range, or a movement type.

10. The UE according to claim 8, wherein: the transceiver is further configured to: receive first information related to determining the validity of the ML model, and transmit a channel with second information related to updating the reception of the PRS for measurement, the first information includes: parameters related to the area, the effective time duration, One or more reference signal received power (RSRP) thresholds from a respective one or more cells or TRPs, Parameters according to the UE location, or Parameters related to the channel environment, and The second information indicates at least one of the following: The preferred direction of PRS reception, The preferred spatial granularity of PRS reception, and The preferred time frequency of PRS reception.

11. A base station (BS), comprising: A transceiver configured to: Transmit first information related to the reception of positioning reference signals (PRSs) from one or more transmit-receive points (TRPs) for measurement, Transmit second information indicating one or more reporting quantities related to user equipment (UE) positioning, wherein the one or more reporting quantities are related to the UE positioning based on a machine learning (ML) model or the life cycle management of the ML model, Transmit third information related to transmitting the one or more reporting quantities, wherein the PRS is transmitted from the one or more TRPs based on the first information, and Receive, based on the third information, a channel with the one or more reporting quantities, the one or more reporting quantities being based on the second information and the PRS.

12. The BS according to claim 11, wherein: The one or more reporting quantities indicated by the second information are related to one or more UE positions corresponding to one or more instances, and The one or more reporting quantities of the instances include: UE coordinates, A timestamp, or A parameter related to the confidence of the reported coordinates, and The one or more reporting quantities of the instances are reported relative to another instance or independently of other instances.

13. The BS according to claim 11, wherein: The one or more reporting quantities indicated by the second information are related to the UE positioning of one or more instances, The one or more reporting quantities related to the UE positioning of the instances include: Reference signal received power (RSRP), Reference signal received quality (RSRQ), Signal-to-interference plus noise ratio (SINR), Angle of departure (AoD), Reference signal timing difference (RSTD), A timestamp, or A parameter related to the confidence of the one or more reporting quantities, and The one or more reporting quantities related to the UE positioning of the instances are reported relative to another instance or independently of other instances.

14. The BS according to claim 11, wherein: The one or more reporting quantities indicated by the second information are associated with data for training the ML model, and The one or more reporting quantities include: UE coordinates, Parameters related to the channel impulse response, Reference signal received power (RSRP), Reference signal received quality (RSRQ), Received signal strength indicator (RSSI), Signal-to-interference plus noise ratio (SINR), Reference signal received path power (RSRPP), Time difference between reception and transmission, Angle of departure (AoD), A timestamp, Parameters related to the statistics of the input data to the ML model, Parameters related to the statistics of the output data from the ML model, or A parameter related to the confidence level of the one or more reported amounts in the report, including the difference between the determined one or more reported amounts and the corresponding true values of the one or more reported amounts.

15. The BS according to claim 11, wherein: The transceiver is further configured to: Receive or transmit first information related to selecting the ML model, and Receive second information related to determining the effectiveness of the ML model, The first information includes: Parameters related to the Doppler profile, Parameters related to the multipath delay profile, Parameters related to the channel environment, Parameters related to clutter or blockage, An indicator of line of sight (LOS) or non-line of sight (NLOS), An indicator of indoor or outdoor environment, An indicator of in-vehicle environment, An indicator of in-building environment, or The speed of the UE represented by an absolute value, a value range, or a movement type, and The second information includes: Parameters related to the area, The effective time duration, One or more reference signal received power (RSRP) thresholds from the corresponding one or more cells or TRPs, Parameters according to the UE location, or Parameters related to the channel environment.

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