Signaling and reporting for UE-side ML displacement positioning

Abstract

Aspects presented herein may enable a UE or a location server to estimate the displacement of the UE based on measuring wireless signals, such that the UE may accurately perform accurate displacement estimation without using sensors (e.g., IMUs). In one aspect, a UE receives a request from a network entity to report information associated with ML-based displacement positioning. The UE sends information associated with ML-based displacement positioning for the network entity based on the request. The UE receives a configuration for ML-based displacement positioning from the network entity based on the information. The UE receives, from the at least one network node, a set of RSs associated with ML-based displacement positioning based on the configuration.

Description

[0001] Cross-references to related applications

[0002] This application claims the benefit of U.S. Nonprovisional Patent Application Serial No. 18 / 181,400, filed March 9, 2023, entitled “SIGNALING AND REPORTING FOR UE-SIDE ML DISPLACEMENT POSITIONING”, the entire contents of which are expressly incorporated herein by reference. Technical Field

[0003] This disclosure relates generally to communication systems, and more specifically to wireless communication relating to positioning. Background Technology

[0004] Wireless communication systems are widely deployed to provide a variety of telecommunications services, such as telephone, video, data, messaging, and broadcasting. Typical wireless communication systems employ multiple access technologies that enable communication with multiple users by sharing available system resources. Examples of such multiple access technologies include Code Division Multiple Access (CDMA) systems, Time Division Multiple Access (TDMA) systems, Frequency Division Multiple Access (FDMA) systems, Orthogonal Frequency Division Multiple Access (OFDMA) systems, Single Carrier Frequency Division Multiple Access (SC-FDMA) systems, and Time Division Synchronous Code Division Multiple Access (TD-SCDMA) systems.

[0005] These multiple access technologies have been adopted in various telecommunications standards to provide a common protocol that enables different wireless devices to communicate at the city, country, region, and even global levels. An example telecommunications standard is 5G New Radio (NR). 5G NR is part of the Continuous Evolution of Mobile Broadband (CEM) program issued by the 3rd Generation Partnership Project (3GPP) to meet new requirements associated with latency, reliability, security, scalability (e.g., with the Internet of Things (IoT)), and other requirements. 5G NR includes services associated with enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). Some aspects of 5G NR can be based on the 4G Long Term Evolution (LTE) standard. Further improvements to 5G NR technology are needed. These improvements can also be applied to other multiple access technologies and telecommunications standards that adopt these technologies. Summary of the Invention

[0006] The following is a simplified overview of one or more aspects to provide a basic understanding of these aspects. This invention is not a comprehensive overview of all conceived aspects. It neither identifies key or essential elements of all aspects nor describes the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed descriptions that follow.

[0007] In one aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus receives, from a network entity, a request to report information associated with a machine learning (ML)-based displacement positioning. The apparatus transmits, to the network entity based on the request, the information associated with the ML-based displacement positioning. The apparatus receives, from the network entity based on the information, a configuration for the ML-based displacement positioning. The apparatus receives, from at least one network node based on the configuration, a set of reference signals (RSs) associated with the ML-based displacement positioning.

[0008] In one aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus transmits, to a user equipment (UE), a request to report information associated with a ML-based displacement positioning. The apparatus receives, from the UE based on the request, the information associated with the ML-based displacement positioning. The apparatus transmits, to the UE based on the information, a configuration for the ML-based displacement positioning. The apparatus receives, from the UE based on the configuration, a set of displacement radio frequency fingerprinting positioning (RFFP) measurements or an estimated displacement of the UE.

[0009] To the accomplishment of the foregoing and related aspects, one or more aspects can include the features recited in the following description and the claims, which are not limited to the aspects summarized below. The following description and the annexed drawings set forth in detail certain illustrative aspects of one or more aspects. These aspects are indicative, however, of but a few of the various ways in which principles of various aspects can be employed. BRIEF DESCRIPTION OF DRAWINGS

[0010] Figure 1 is a diagram illustrating an example of a wireless communications system and an access network.

[0011] Figure 2A is a diagram illustrating an example of a first frame in accordance with various aspects of the present disclosure.

[0012] Figure 2B is a diagram illustrating an example of a downlink (DL) channel within a subframe in accordance with various aspects of the present disclosure.

[0013] Figure 2C is a diagram illustrating an example of a second frame in accordance with various aspects of the present disclosure.

[0014] Figure 2D is a diagram illustrating an example of an uplink (UL) channel within a subframe in accordance with various aspects of the present disclosure.

[0015] Figure 3 is a diagram illustrating an example of a base station and user equipment (UE) in an access network.

[0016] Figure 4is a diagram illustrating an example of UE positioning based on reference signal measurements.

[0017] Figure 5 is a diagram illustrating an example of a UE estimating / calculating a dead reckoning (DR) trajectory using an inertial measurement unit (IMU) in accordance with various aspects of the present disclosure.

[0018] Figure 6 is a diagram illustrating an example of displacement positioning in accordance with various aspects of the present disclosure.

[0019] Figure 7A is a diagram illustrating an example of UE-assisted / network-based machine learning (ML) displacement positioning in accordance with various aspects of the present disclosure.

[0020] Figure 7B is a diagram illustrating an example of UE-based ML displacement positioning in accordance with various aspects of the present disclosure.

[0021] Figure 8 is a diagram illustrating an example of artificial intelligence (AI) / ML positioning training and inference for estimating displacement of a UE based on radio frequency fingerprinting positioning (RFFP) in accordance with various aspects of the present disclosure.

[0022] Figure 9 is a communication flow for AI / ML-based displacement positioning in accordance with various aspects of the present disclosure.

[0023] Figure 10 is a flow diagram of a method of wireless communication.

[0024] Figure 11 is a flow diagram of a method of wireless communication.

[0025] Figure 12 is a diagram illustrating an example of a hardware implementation for an example apparatus and / or network entity.

[0026] Figure 13 is a flow diagram of a method of wireless communication.

[0027] Figure 14 is a diagram illustrating an example of a hardware implementation for an example network entity. DETAILED DESCRIPTION

[0028] Aspects presented herein can enable a UE to estimate its displacement based on measuring wireless signals (which can be referred to as displacement positioning), such that a UE without sensors (e.g., inertial measurement units (IMUs)) can also be able to accurately estimate its displacement (e.g., to some specified accuracy). Aspects presented herein also provide signaling and reporting between a UE and a network entity (e.g., a base station, a location server, a location management function (LMF), etc.) that enables artificial intelligence (AI) / machine learning (ML) (AI / ML) based displacement positioning at the UE side or AI / ML based displacement positioning at the network side. Thus, aspects presented herein can be applicable to both UE-assisted / LMF-based AI / ML displacement positioning and UE-based AI / ML displacement positioning.

[0029] By enabling displacement of a UE to be measured based on wireless signals, the UE can not be designated to report measurements or compute two separate positions (e.g., one at an initial position and the other at a displacement position) for two separate positioning occasions, which can reduce signaling overhead between the UE and a network entity (e.g., a location server, an LMF, etc.). Aspects presented herein can also enable training data to be generated for AI / ML models that can be used to perform displacement positioning (which can be referred to as AI / ML based displacement positioning). Moreover, the generation of training data can also be applicable to other types of AI / ML positioning methods, such that different positioning label types (e.g., displacement and anchor) can be directed. For example, an enhanced AI / ML estimated displacement can be utilized to train other AI / ML positioning methods. Aspects presented herein can also enable a UE (or its sensors) to provide enhanced sensor outputs by fusing radio frequency fingerprinting (RFFP) estimated displacement with sensor outputs.

[0030] The detailed description set forth below, in connection with the appended drawings, is a description of various configurations and does not represent the only configurations in which the concepts described herein can be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of various concepts. However, it will be apparent to those skilled in the art that these concepts can be practiced without these specific details. In some instances, well-known structures and components are shown in block diagram form, in order to avoid obscuring such concepts.

[0031] Several aspects of telecommunication systems are presented with reference to various apparatus and methods. These apparatus and methods are described in the following detailed description and illustrated in the accompanying drawings by various blocks, components, circuits, processes, algorithms, etc. (collectively referred to as “elements”). These elements can be implemented with electronic hardware, computer software, or any combination thereof. Whether such elements are implemented as hardware or software depends on the particular application and design constraints imposed on the overall system.

[0032] As an example, an element, or any portion of an element, or any combination of elements can be implemented as a “processing system” that includes one or more processors. Examples of processors include microprocessors, microcontrollers, graphics processing units (GPUs), central processing units (CPUs), application processors, digital signal processors (DSPs), reduced instruction set computing (RISC) processors, systems on a chip (SoC), baseband processors, field programmable gate arrays (FPGAs), programmable logic devices (PLDs), state machines, gated logic, discrete hardware circuits, and other suitable hardware configured to perform the various functionality described throughout this disclosure. One or more processors in the processing system can execute software. Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software components, applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, functions, or any combination of

[0033] Accordingly, in one or more example aspects, implementations, and / or use cases, the described features can be implemented in hardware, software, or any combination thereof. If implemented in software, the functions can be stored on or encoded as one or more instructions or code on a computer-readable medium. Computer-readable media includes computer storage media. Storage media can be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise a random-access memory (RAM), a read-only memory (ROM), an electrically erasable programmable ROM (EEPROM), optical disk storage, magnetic disk storage, other magnetic storage devices, combinations of the aforementioned types of computer-readable media, or any other medium that can be used to store computer executable code in the form of instructions or data structures that can be accessed by a computer.

[0034] While aspects, implementations and / or use cases described herein can be described in the context of 5G NR technology, aspects, implementations and / or use cases described herein can be implemented in the context of any suitable wireless communication technology, such as any NR technology, 3GPP UMTS Terrestrial Radio Access (UTRA), 4G Long Term Evolution (LTE), 5G NR, etc. NR is often associated with 5G technology, but it is noted that the aspects, implementations and / or use cases described herein can be implemented in the context of other technologies, including 4G and / or 3G technologies, as well as next generation technologies. While aspects, implementations and / or use cases described herein can be described in the context of some examples, additional or different aspects, implementations and / or use cases can be generated in many different ways. The aspects, implementations and / or use cases described herein can be implemented across many different platforms, devices, systems, shapes, sizes, and packaging arrangements. For example, aspects, implementations and / or use cases can be realized in integrated chip implementations and other non-module-component-based devices (e.g., end-user devices, vehicles, communication devices, computing devices, industrial equipment, retail / purchasing devices, medical devices, artificial intelligence (AI)-enabled devices, etc.). While some examples can or can not be specifically directed to use cases or applications, a wide range of applicability of the described examples can result. Aspects, implementations and / or use cases can range in scope across chip-level or modular components to non-modular, non-chip-level implementations, and further to aggregate, distributed, or original equipment manufacturer (OEM) devices or systems incorporating one or more technologies described herein. In some practical settings, devices incorporating aspects and features described can also include additional components and features for implementation and practice of claimed and described aspects. For example, transmission and reception of wireless signals necessarily includes a variety of components, hardware, and software (e.g., hardware components including antennas, RF chains, power amplifiers, modulators, buffers, processors, interleavers, adders / summers, etc.). The techniques described herein can be practiced in a wide variety of devices, chip-level components, systems, distributed arrangements, aggregate or disaggregate components, end-user devices, etc. of varying sizes, shapes, and constitutions.

[0035] Deployments of communication systems, such as 5G NR systems, can be arranged in a variety of ways with various components or constituent parts. In a 5G NR system or network, a network node, network entity, mobility element of a network, radio access network (RAN) node, core network node, network element, or network equipment, such as a base station (BS) or one or more elements (or one or more components) performing base station functions (such as a Node B (NB), an evolved NB (eNB), a NR BS, a 5G NB, an access point (AP), a transmission reception point (TRP), or a cell, etc.) can be implemented in an aggregated or disaggregated architecture. For example, a BS, such as a node B (NB), an evolved NB (eNB), a NR BS, a 5G NB, an access point (AP), a transmission reception point (TRP), or a cell, etc. can be implemented as an aggregated base station (also referred to as a standalone BS or a monolithic BS) or a disaggregated base station.

[0036] A disaggregated base station can be configured to utilize a radio protocol stack that is physically or logically integrated within a single RAN node. A disaggregated base station can be configured to utilize a protocol stack that is distributed physically or logically across two or more units, such as one or more central or centralized units (CUs), one or more distributed units (DUs), or one or more radio units (RUs). In some aspects, a CU can be implemented within a RAN node, and one or more DUs can be co-located with the CU, or alternatively, can be geographically or virtually distributed in one or more other RAN nodes. The DUs can be implemented to be in communication with one or more RUs. Each of the CU, DU, and RU can be implemented as virtual units, a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU).

[0037] Base station operations or network designs can take into account the disaggregated nature of base station functionality. For example, a disaggregated base station can be utilized in an integrated access backhaul (IAB) network, an open radio access network (O-RAN (such as network configurations initiated by the O-RAN Alliance)), or a virtualized radio access network (vRAN, also referred to as a cloud radio access network (C-RAN)). Disaggregation can include distributing functionality across two or more units at various physical locations, as well as virtually distributing functionality of at least one unit, which can enable flexibility in network design. The various units of a disaggregated base station or disaggregated RAN architecture can be configured for wired or wireless communication with at least one other unit.

[0038] Figure 1 is a diagram 100 illustrating examples of wireless communication systems and access networks. The illustrated wireless communication systems include a disaggregated base station architecture. The disaggregated base station architecture can include one or more CUs 110 that can communicate directly with a core network 120 via a backhaul link, or indirectly with the core network 120 through one or more disaggregated base station units, such as a near real-time (near-RT) RAN intelligent controller (RIC) 125 via an E2 link, or a non-real-time (non-RT) RIC 115 associated with a service management and orchestration (SMO) framework 105, or both. The CUs 110 can communicate with one or more DUs 130 via respective fronthaul links, such as an Fl interface. The DUs 130 can communicate with one or more RUs 140 via respective front-haul links. The RUs 140 can communicate with respective UEs 104 via one or more radio frequency (RF) access links. In some implementations, a UE 104 can be simultaneously served by multiple RUs 140.

[0039] Each of the units (i.e., CU 110, DU 130, RU 140, and near-RT RIC 125, non-RT RIC 115, and SMO framework 105) can include or be coupled to one or more interfaces configured to receive or transmit signals, data, or information (collectively, signals) via a wired or wireless transmission medium. Each of the units, or an associated processor or controller providing instructions to the communication interfaces of these units, can be configured to communicate with one or more of the other units via the transmission medium. For example, the units can include a wired interface configured to receive signals from or transmit signals to one or more of the other units over a wired transmission medium. Additionally, the units can include a wireless interface that can include a receiver, a transmitter, or a transceiver (such as an RF transceiver) configured to receive and / or transmit signals to one or more of the other units over a wireless transmission medium.

[0040] In some aspects, CU 110 can host one or more higher layer control functions. Such control functions can include radio resource control (RRC), packet data convergence protocol (PDCP), service data adaptation protocol (SDAP), etc. Each control function can be implemented with an interface configured to communicate signals with other control functions hosted by CU 110. CU 110 can be configured to handle user plane functionality (i.e., central unit-user plane (CU-UP)), control plane functionality (i.e., central unit-control plane (CU-CP)), or a combination thereof. In some implementations, CU 110 can be logically split into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, CU-UP units can communicate bi-directionally with CU-CP units via an interface such as an El interface. CU 110 can be implemented to communicate with DU 130 as needed for network control and signaling.

[0041] DU 130 can correspond to a logical unit that includes one or more base station functions for controlling operation of one or more RUs 140. In some aspects, DU 130 can host one or more of a radio link control (RLC) layer, a medium access control (MAC) layer, and one or more high physical (PHY) layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation and demodulation, etc.) in accordance with a functional split, such as those defined by 3GPP. In some aspects, DU 130 can also host one or more low PHY layers. Each layer (or module) can be implemented with an interface configured to communicate signals with other layers (and modules) hosted by DU 130 or with control functions hosted by CU 110.

[0042] Lower-layer functionality can be implemented by one or more RU 140s. In some deployments, an RU140 controlled by a DU 130 may correspond to a logical node that hosts RF processing functions or low-PHY layer functions (such as performing Fast Fourier Transform (FFT), Inverse FFT (iFFT), digital beamforming, Physical Random Access Channel (PRACH) extraction and filtering, or both, at least in part based on functional decomposition (such as lower-layer functional decomposition). In this architecture, the RU 140 may be implemented to handle over-the-air (OTA) communications with one or more UEs 104. In some specific implementations, the real-time and non-real-time aspects of control plane and user plane communications with the RU 140 may be controlled by the corresponding DU 130. In some scenarios, this configuration enables the implementation of the DU 130 and CU 110 in a cloud-based RAN architecture (such as a vRAN architecture).

[0043] SMO framework 105 can be configured to support RAN deployment and provisioning of both non-virtualized and virtualized network elements. For non-virtualized network elements, SMO framework 105 can be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which can be managed via operation and maintenance interfaces such as the O1 interface. For virtualized network elements, SMO framework 105 can be configured to interact with a cloud computing platform such as Open Cloud (O-Cloud) 190 to perform network element lifecycle management (such as instantiating virtualized network elements) via a cloud computing platform interface such as the O2 interface. Such virtualized network elements may include, but are not limited to, CU 110, DU 130, RU 140, and near-RT RIC 125. In some implementations, SMO framework 105 can communicate with the hardware aspects of the 4G RAN (such as Open eNB (O-eNB) 111) via the O1 interface. Additionally, in some implementations, SMO framework 105 can communicate directly with one or more RU 140s via the O1 interface. SMO framework 105 may also include a non-RT RIC 115 configured to support the functionality of SMO framework 105.

[0044] The non-RT RIC 115 can be configured to include logical functions that enable non-real-time control and optimization of RAN elements and resources, artificial intelligence (Al) / machine learning (ML) (Al / ML) workflows including model training and updating, or policy-based steering of applications / features in the near-RT RIC 125. The non-RT RIC 115 can be coupled to, or communicate with, the near-RT RIC 125, such as via an Al interface. The near-RT RIC 125 can be configured to include logical functions that enable near-real-time control and optimization of RAN elements and resources via data collection and actions via an interface, such as via an E2 interface, that connects one or more CUs 110, one or more DUs 130, or both, and an O-eNB with the near-RT RIC 125.

[0045] In some implementations, to generate Al / ML models to be deployed in the near-RT RIC 125, the non-RT RIC 115 can receive parameters or external enrichment information from an external server. Such information can be utilized by the near-RT RIC 125 and can be received at the SMO framework 105 or the non-RT RIC 115 from non-network data sources or from network functions. In some examples, the non-RT RIC 115 or the near-RT RIC 125 can be configured to tune RAN behavior or performance. For example, the non-RT RIC 115 can monitor long-term trends and patterns of performance and employ Al / ML models to perform corrective actions through the SMO framework 105, such as via reconfiguration of Ol, or via creation of RAN management policies, such as Al policies.

[0046] At least one of the CUs 110, the DUs 130, and the RUs 140 can be referred to as a base station 102. Thus, the base station 102 can include one or more of the CUs 110, the DUs 130, and the RUs 140 (each component is indicated in dashed lines to represent that each component can or can not be included in the base station 102). The base station 102 provides wireless access to the core network 120 for the UEs 104. A base station 102 can include a macro cell (high power cellular base station) and / or a small cell (low power cellular base station). Small cells include femto cells, pico cells, and micro cells. A network that includes both small cells and macro cells can be known as a heterogeneous network. A heterogeneous network can also include home evolved node Bs (eNBs) (HeNBs), which can provide service to a restricted group known as a closed subscriber group (CSG). The communication links 120 between the RUs 140 and the UEs 104 can include uplink (UL) (also referred to as reverse link) transmissions from a UE 104 to a RU 140 and / or downlink (DL) (also referred to as forward link) transmissions from a RU 140 to a UE 104. The communication links 120 can use multiple input multiple output (MIMO) antenna technology, including spatial multiplexing, beamforming, and / or transmit diversity. The communication links 120 can be through one or more carriers. The base station 102 / UE 104 can use spectrum up to Y MHz (e.g., 5 MHz, 10 MHz, 15 MHz, 20 MHz, 100 MHz, 400 MHz, etc.) bandwidth per carrier allocated in a carrier aggregation of up to a total of Yx MHz (x component carriers) in some cases. The carriers can or can not be adjacent to each other. The allocation of carriers can be asymmetric about the DL and UL (e.g., there can be more carriers allocated to the DL than to the UL).

[0047] Some UEs 104 can communicate with each other using device-to-device (D2D) communication link 158. The D2D communication link 158 can use the DL / UL WWAN spectrum. The D2D communication link 158 can use one or more sidelink channels, such as a physical sidelink broadcast channel (PSBCH), a physical sidelink discovery channel (PSDCH), a physical sidelink shared channel (PSSCH), and a physical sidelink control channel (PSCCH). D2D communication can be through a variety of wireless D2D communications systems, such as for example, Bluetooth, Wi-Fi based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard, LTE, or NR.

[0048] The wireless communications system can also include a Wi-Fi AP 150 in communication with UEs 104 (also referred to as Wi-Fi stations (STAs)) via communication links 154, e.g., in a 5 GHz unlicensed frequency spectrum or the like. When communicating in an unlicensed frequency spectrum, the UEs 104 / AP 150 can perform a clear channel assessment (CCA) prior to communicating in order to determine whether the channel is available.

[0049] The electromagnetic spectrum is often subdivided based on frequency / wavelength into various classes, bands, channels, etc. In 5G NR, two initial operating bands have been identified as frequency range designations FR1 (410 MHz - 7.125 GHz) and FR2 (24.25 GHz - 52.6 GHz). Despite a portion of FR1 being greater than 6 GHz, FR1 is often referred to (interchangeably) as a “Sub- 6 GHz” band in various documents and articles. A similar naming confusion occurs with regard to FR2, which is often referred to (interchangeably) as a “millimeter wave” band in documents and articles, despite being different from the extremely high frequency (EHF) band (30 GHz - 300 GHz) which is designated as a “millimeter wave” band by the International Telecommunications Union (ITU).

[0050] The frequencies between FR1 and FR2 are often referred to as mid-band frequencies. Recent 5G NR studies have identified operating bands for these mid-band frequencies as frequency range designation FR3 (7.125 GHz - 24.25 GHz). Bands falling within FR3 can inherit FR1 characteristics and / or FR2 characteristics, and thus can effectively extend features of FR1 and / or FR2 to mid-band frequencies. Moreover, even higher bands are currently under exploration to extend 5G NR operation beyond 52.6 GHz. For example, three higher operating bands have been identified as frequency range designations FR2-2 (52.6 GHz - 71 GHz), FR4 (71 GHz - 114.25 GHz), and FR5 (114.25 GHz - 300 GHz). Each of these higher bands fall within the EHF band.

[0051] With the above in mind, unless specifically stated otherwise, if the term “Sub-6 GHz” or the like is used herein, this can broadly represent frequencies that can be less than 6 GHz, can be within FR1, or can include mid-band frequencies. Further, unless specifically stated otherwise, if the term “millimeter wave” or the like is used herein, this can broadly represent frequencies that can include mid-band frequencies, can be within FR2, FR4, FR2-2, and / or FR5, or can be within the EHF band.

[0052] The base stations 102 and the UEs 104 can each include multiple antennas (such as antenna elements, antenna panels, and / or antenna arrays) to facilitate beamforming. The base stations 102 can transmit to UEs 104 in one or more transmit directions using beamforming. The UEs 104 can receive from the base stations 102 in one or more receive directions using beamforming. The UEs 104 can also transmit to the base stations 102 in one or more transmit directions using beamforming. The base stations 102 can receive from the UEs 104 in one or more receive directions using beamforming. The base station 102 / UE 104 can perform beam training to determine the best receive and transmit directions for each of the base station 102 / UE 104. The transmit and receive directions for the base station 102 can or can not be the same. The transmit and receive directions for the UE 104 can or can not be the same.

[0053] The base stations 102 can include and / or be referred to as gNBs, NodeBs, eNBs, access points, transceiver base stations, radio base stations, radio transceivers, transceiver functions, basic service sets (BSSs), extended service sets (ESSs), TRPs, network nodes, network entities, network equipment, or some other suitable terminology. The base stations 102 can be implemented as integrated access and backhaul (IAB) nodes, relay nodes, sidelink nodes, aggregated (monolithic) base stations with baseband units (BBUs) including CUs and DUs and RUs, or as disaggregated base stations including one or more of CUs, DUs, and / or RUs. A collection of base stations that can include disaggregated and / or aggregated base stations can be referred to as a next generation (NG) RAN (NG-RAN).

[0054] The core network 120 can include an access and mobility management function (AMF) 161, a session management function (SMF) 162, a user plane function (UPF) 163, a unified data management (UDM) 164, one or more location servers 168, and other functional entities. The AMF 161 is a control node that handles signaling between the UEs 104 and the core network 120. The AMF 161 supports registration management, connection management, mobility management, and other functions. The SMF 162 supports session management and other functions. The UPF 163 supports packet routing, packet forwarding, and other functions. The UDM 164 supports generation of authentication and key agreement (AKA) credentials, user identification handling, access authorization, and subscription management. The one or more location servers 168 are illustrated as including a gateway mobile location center (GMLC) 165 and a location management function (LMF) 166. However, generally speaking, the one or more location servers 168 can include one or more location / determination servers, which can include one or more of a GMLC 165, an LMF 166, a positioning determination entity (PDE), a serving mobile location center (SMLC), a mobile positioning center (MPC), and the like. The GMLC 165 and the LMF 166 support UE location services. The GMLC 165 provides an interface for clients / applications (e.g., emergency services) to access UE positioning information. The LMF 166 receives measurements and assistance information from the NG-RAN and the UE 104 via the AMF 161 to compute a position of the UE 104. The NG-RAN can utilize one or more positioning methods to determine a position of the UE 104. Positioning the UE 104 can involve signal measurements, position estimation, and optional velocity calculations based on these measurements. The signal measurements can be made by the UE 104 and / or the base stations 102 serving the UE 104. The measured signals can be based on one or more of a satellite positioning system (SPS) 170 (e.g., Global Navigation Satellite System (GNSS), Global Positioning System (GPS), Non-Terrestrial Network (NTN), or other satellite positioning / location system), LTE signals, Wireless Local Area Network (WLAN) signals, Bluetooth signals, Terrestrial Beacon System (TBS), sensor-based information (e.g., barometric pressure sensors, motion sensors), NR Enhanced Cell ID (NR E-CID) methods, NR signals (e.g., multi-round trip time (multi-RTT), DL angle of departure (DL-AoD), DL time difference of arrival (DL-TDOA), UL time difference of arrival (UL-TDOA), and UL angle of arrival (UL-AoA) positioning), and / or other systems / signals / sensors.

[0055] Examples of a UE 104 include a cellular phone, a smart phone, a session initiation protocol (SIP) phone, a laptop, a personal digital assistant (PDA), a satellite radio, a global positioning system, a multimedia device, a video device, a digital audio player (e.g., MP3 player), a camera, a game console, a tablet, a smart device, a wearable device, a vehicle, an electric meter, a gas pump, a large or small kitchen appliance, a healthcare device, an implant, a sensor / actuator, a display, or any other similar functional device. Some of the UEs 104 can be referred to as IoT devices (e.g., a parking meter, gas pump, toaster, vehicle, heart monitor, etc.). The UE 104 can also be referred to as a station, a mobile station, a subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a mobile device, a wireless device, a wireless communication device, a remote device, a mobile subscriber station, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handset, a user agent, a mobile client, a client, or some other suitable terminology. In some scenarios, the term UE can also apply to one or more companion devices, such as in a device constellation arrangement. One or more of these devices can collectively or individually access a network.

[0056] Referring again to Figure 1 In certain aspects, the UE 104 can include an ML-based displacement positioning component 198 that can be configured to receive, from a network entity, a request to report information associated with ML-based displacement positioning, transmit, to the network entity, the information associated with ML-based displacement positioning based on the request, receive, from the network entity, a configuration for ML-based displacement positioning based on the information, and receive, from at least one network node, a set of RSs associated with ML-based displacement positioning based on the configuration.

[0057] In certain aspects, one or more location servers 168 can have an ML-based displacement positioning configuration component 199 that can be configured to transmit, to a UE, a request to report information associated with ML-based displacement positioning, receive, from the UE, the information associated with ML-based displacement positioning based on the request, transmit, to the UE, a configuration for ML-based displacement positioning based on the information, and receive, from the UE, a set of RFFP measurements or an estimated displacement of the UE based on the configuration.

[0058] Figure 2A FIG. 200 is a diagram 200 illustrating an example of a first subframe within a 5G NR frame structure. Figure 2B FIG. 230 is a diagram 230 illustrating an example of DL channels within a 5G NR subframe. Figure 2C FIG. 250 is a diagram 250 illustrating an example of a second subframe within a 5G NR frame structure. Figure 2Dis a diagram 280 illustrating an example of UL channels within the 5G NR subframe. The 5G NR frame structure can be frequency division duplex (FDD) in which for a particular set of subcarriers (carrier system bandwidth), subframes are dedicated for DL or UL, or can be time division duplex (TDD) in which for a particular set of subcarriers (carrier system bandwidth), subframes are dedicated for both DL and UL. In Figure 2A , Figure 2C In the examples provided, the 5G NR frame structure is assumed to be TDD with subframe 4 configured with slot format 28 (with most as DL), where D is DL, U is UL, and F is flexible that can be used between DL / UL, and subframe 3 configured with slot format 1 (with all as UL). While subframes 3, 4 are shown with slot formats 1, 28, respectively, any particular subframe can be configured with any of the various available slot formats 0-61. Slot formats 0, 1 are all DL, UL, respectively. Other slot formats 2-61 include a mix of DL, UL, and flexible symbols. UEs are configured with the slot format by receiving a slot format indicator (SFI) (dynamically configured by DL control information (DCI) or semi-statically / statically configured by radio resource control (RRC) signaling). Note that the following description also applies for 5G NR frame structures that are TDD.

[0059] Figures 2A-2D The frame structure is illustrated, and aspects of the present disclosure can be applicable to other wireless communication technologies that can have different frame structures and / or different channels. One frame (10 ms) can be divided into 10 equally sized subframes (1 ms). Each subframe can include one or more slots. A subframe can also include mini-slots, which can contain 7, 4, or 2 symbols. Each slot can include 14 or 12 symbols depending on whether a cyclic prefix (CP) is normal or extended, respectively. For a normal CP, each slot can contain 14 symbols, and for an extended CP, each slot can contain 12 symbols. Symbols on the DL can be CP-orthogonal frequency division multiplexing (OFDM) (CP-OFDM) symbols. Symbols on the UL can be CP-OFDM symbols (for high throughput scenarios) or discrete Fourier transform (DFT) spread OFDM (DFT-s-OFDM) symbols (for power limited scenarios; limited to single stream transmission). The number of slots within a subframe is dependent on the CP and the numerology. The numerology defines the subcarrier spacing (SCS) (see TABLE 1). The symbol length / duration can be scaled with 1 / SCS.

[0060]

[0061]

[0062] Table 1: Numerology, SCS, and CP

[0063] For normal CP (14 symbols / slot), different numerologies μ0to 4allow for 1, 2, 4, 8, and 16 slots per subframe, respectively. For extended CP, numerology 2allows for 4 slots per subframe. Thus, for normal CP and numerology μ, there are 14 symbols / slot and 2 μ slots / subframe. The subcarrier spacing can equal 2 μ * 15 kHz, where μ is the numerology 0 to 4. Thus, the subcarrier spacing for numerology μ = 0 is 15 kHz, and the subcarrier spacing for numerology μ = 4 is 240 kHz. The symbol length / duration is inversely related to the subcarrier spacing. Figures 2A-2D An example of normal CP with 14 symbols per slot and numerology μ = 2 with 4 slots per subframe is provided. The slot duration is 0.25 ms, the subcarrier spacing is 60 kHz, and the symbol duration is approximately 16.67 μβ. Within a frame collection, there can be one or more different bandwidth parts (BWPs) that are frequency division multiplexed (see Figure 2B ). Each BWP can have a particular numerology and CP (normal or extended).

[0064] A resource grid can be used to represent the frame structure. Each time slot includes a resource block (RB) (also referred to as a physical RB (PRB)) that extends 12 consecutive subcarriers. The resource grid is divided into multiple resource elements (REs). The number of bits carried by each RE depends on the modulation scheme.

[0065] As illustrated in Figure 2A , some of the REs carry reference (pilot) signals (RS) for the UE. The RS can include demodulation RS (DM-RS) (indicated as R for one particular configuration, but other DM-RS configurations are possible) and channel state information reference signals (CSI-RS) for channel estimation at the UE. The RS can also include beam measurement RS (BRS), beam refinement RS (BRRS), and phase tracking RS (PT-RS).

[0066] Figure 2BExamples of various DL channels are illustrated. The physical downlink control channel (PDCCH) carries DCI within one or more control channel elements (CCEs) (e.g., 1, 2, 4, 8, or 16 CCEs), each CCE including six RE groups (REGs), each REG including 12 consecutive REs in an OFDM symbol of an RB. A PDCCH within one BWP can be referred to as a control resource set (CORESET). A UE is configured to monitor PDCCH search space (e.g., common search space, UE-specific search space) for PDCCH candidates during PDCCH monitoring occasions on the CORESET, where PDCCH candidates have different DCI formats and different aggregation levels. Additional BWPs can be located at higher and / or lower frequencies on the channel bandwidth. A primary synchronization signal (PSS) can be within symbol 2 of particular subframes of a frame. The PSS is used by a UE 104 to determine subframe / symbol timing and a physical layer identity. A secondary synchronization signal (SSS) can be within symbol 4 of particular subframes of a frame. The SSS is used by a UE to determine a physical layer cell identity group number and radio frame timing. Based on the physical layer identity and the physical layer cell identity group number, the UE can determine a physical cell identifier (PCI). Based on the PCI, the UE can determine the locations of DM-RS. The physical broadcast channel (PBCH), which carries a master information block (MIB) that provides system bandwidth together with the SFN, can be logically grouped with the PSS and SSS to form a synchronization signal (SS) / PBCH block (also referred to as SS block (SSB)). The MIB provides a number of RBs in the system bandwidth and a system frame number (SFN). The physical downlink shared channel (PDSCH) carries user data, broadcast system information such as system information blocks (SIBs), and paging messages.

[0067] As Figure 2C illustrated, some of the REs carry DM-RS (indicated as R for one particular configuration, but other DM-RS configurations are possible) for channel estimation at the base station. The UE can transmit DM-RS for channel estimation at the base station. The UE can transmit a physical uplink control channel (PUCCH) DM-RS and a physical uplink shared channel (PUSCH) DM-RS. The PUSCH DM-RS can be transmitted in the first one or two symbols of a slot. The PUCCH DM-RS can be transmitted on different configurations depending on whether short or long PUCCHs are transmitted and depending on the particular PUCCH format used. The UE can transmit a sounding reference signal (SRS). The SRS can be transmitted in the last symbol of a slot. The SRS can have a comb-structure, and a UE can transmit an SRS on one of the combs. The SRS can be used by a base station for channel quality estimation to enable frequency-dependent scheduling for the UL.

[0068] Figure 2DExamples of various UL channels are illustrated. The PUCCH can be located as indicated in one configuration. The PUCCH carries uplink control information (UCI), such as scheduling requests, a channel quality indicator (CQI), a precoding matrix indicator (PMI), a rank indicator (RI), and hybrid automatic repeat request (HARQ) acknowledgment (ACK) (HARQ-ACK) feedback (i.e., one or more HARQ ACK bits indicating one or more ACKs and / or negative ACKs (NACKs)). The PUSCH carries data, and can additionally be used to carry buffer status reports (BSRs), power headroom reports (PHRs), and / or UCI.

[0069] Figure 3 is a block diagram of the base station 310 and the UE 350 in an access network. In the DL, Internet Protocol (IP) packets from the controller / processor 375 can be provided to the scheduling entity. The scheduling entity implements the functions of the layer 3 and layer 2. Layer 3 includes a radio resource control (RRC) layer, and layer 2 includes a service data adaptation protocol (SDAP) layer, a packet data convergence protocol (PDCP) layer, a radio link control (RLC) layer, and a medium access control (MAC) layer. The controller / processor 375 provides RRC layer functionality that includes broadcast of system information (e.g., MIB, SIBs), RRC connection control (e.g., RRC connection paging, RRC connection establishment, RRC connection modification, and RRC connection release), inter-radio access technology (RAT) mobility, and measurement configuration and measurement reporting for UE measurement reporting; PDCP layer functionality related to header compression / decompression, security (ciphering, deciphering, integrity protection, integrity verification), and handover support functions; RLC layer functionality related to the transfer of upper layer packet data units (PDUs), error correction through ARQ, concatenation, segmentation, and reassembly of RLC service data units (SDUs), re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality related to mapping between logical channels and transport channels, multiplexing / de-multiplexing of MAC SDUs onto / from transport blocks (TBs), scheduling information reporting, error correction through HARQ, priority handling, and logical channel prioritization.

[0070] The transmit (TX) processor 316 and the receive (RX) processor 370 implement layer 1 functionality associated with various signal processing functions. Layer 1, which includes a physical (PHY) layer, can include error detection on the transport channels, forward error correction (FEC) coding / decoding of the transport channels, interleaving, rate matching, mapping to physical channels, modulation / demodulation of physical channels, and MIMO antenna processing. The TX processor 316 handles mapping to signal constellations based on various modulation schemes (e.g., binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), M-phase-shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The coded and modulated symbols can then be split into parallel streams. Each stream can then be mapped to an OFDM subcarrier, multiplexed with a reference signal (e.g., pilot) in the time and / or frequency domain, and then combined together using an inverse fast Fourier transform (IFFT) to produce a physical channel carrying a time domain OFDM symbol stream. The OFDM stream is spatially precoded to produce multiple spatial streams if multiple spatial streams are

[0071] At the UE 350, each receiver 354Rx receives a signal through its respective antenna 352. Each receiver 354Rx recovers information modulated onto an RF carrier and provides the information to the receive (RX) processor 356. The TX processor 368 and the RX processor 356 implement layer 1 functionality associated with various signal processing functions. The RX processor 356 can perform spatial processing on the information to recover any spatial streams destined for the UE 350. If multiple spatial streams are destined for the UE 350, they can be combined by the RX processor 356 into a single OFDM symbol stream. The RX processor 356 then converts the OFDM symbol stream from the time domain to the frequency domain using a fast Fourier transform (FFT). The frequency domain signal includes a separate OFDM symbol stream for each subcarrier of the OFDM signal. The symbols on each subcarrier, and the reference signal, are recovered and demodulated by determining the most likely signal constellation points transmitted by the base station 310. These soft decisions can be based on channel estimates computed by the channel estimator 358. The soft decisions are then decoded and deinterleaved to recover the data and control signals that were originally transmitted by the base station 310 on the physical channel. The data and control signals are then provided to the controller / processor 359, which implements layer 3 and layer 2 functionality.

[0072] The controller / processor 359 can be associated with a memory 360 that stores program codes and data. The memory 360 can be referred to as a computer-readable medium. In the UL, the controller / processor 359 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, and control signal processing to recover IP packets from the EPC 190. The controller / processor 359 is also responsible for error detection using an ACK and / or NACK protocol to support HARQ operations.

[0073] Similar to the functionality described in connection with the DL transmission by the base station 310, the controller / processor 359 provides RRC layer functionality associated with system information (e.g., MIB, SIBs) acquisition, RRC connections, and measurement reporting; PDCP layer functionality associated with header compression / decompression, and security (ciphering, deciphering, integrity protection, integrity verification); RLC layer functionality associated with the transfer of upper layer PDUs, error correction through ARQ, concatenation, segmentation, and reassembly of RLC SDUs, re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs onto TBs, demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction through HARQ, priority handling, and logical channel prioritization.

[0074] Channel estimates derived by the channel estimator 358 from a reference signal or feedback transmitted by the base station 310 can be used by the TX processor 368 to select the appropriate coding and modulation schemes, and to facilitate spatial processing. The spatial streams generated by the TX processor 368 can be provided to different antenna 352 via separate transmitters 354. Each transmitter 354 can modulate an RF carrier with a respective spatial stream for transmission.

[0075] The UL transmission is processed at the base station 310 in a manner similar to that described in connection with the receiver function at the UE 350. Each receiver 318 receives a signal through its respective antenna 320. Each receiver 318 recovers information modulated onto an RF carrier and provides the information to a RX processor 370.

[0076] The controller / processor 375 can be associated with a memory 376 that stores program codes and data. The memory 376 can be referred to as a computer-readable medium. In the UL, the controller / processor 375 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover IP packets from the EPC 190. The controller / processor 375 is also responsible for error detection using an ACK and / or NACK protocol to support HARQ operations.

[0077] At least one of the TX processor 368, the RX processor 356, and the controller / processor 359 can be configured to perform aspects related to the ML-based displacement positioning component 198. Figure 1

[0078] At least one of the TX processor 316, the RX processor 370, and the controller / processor 375 can be configured to perform aspects related to the ML-based displacement positioning configuration component 199. Figure 1

[0079] Figure 4 FIG. 400 is a diagram 400 illustrating an example of reference signal measurement based UE positioning (which can also be referred to as “network-based positioning”) in accordance with various aspects of the present disclosure. A UE 404 can transmit an UL-SRS 412 at a time T SRS_TX -T PRS_RX receive a DL positioning reference signal (PRS) (DL-PRS) 410 at a time T SRS_RX -T PRS_TX transmit the DL-PRS 410. The UE 404 can receive the DL-PRS 410 before transmitting the UL-SRS 412, or can transmit the UL-SRS 412 before receiving the DL-PRS 410. In both cases, a positioning server (e.g., location server 168) or the UE 404 can determine an RTT 414 based on ||T SRS_RX -T PRS_TX -T SRS_TX -T PRS_RX ||. Thus, multi-RTT positioning can utilize UE Rx-Tx time difference measurements (i.e., |T SRS_TX -T PRS_RX -T SRS_RX -T PRS_TX ​​|) and UL-SRS-RSRP. The UE 404 measures the UE Rx-Tx time difference measurements (and optionally the DL-PRS-RSRP of the received signals) using assistance data received from the location server, and the TRPs 402, 406 measure the gNB Rx-Tx time difference measurements (and optionally the UL-SRS-RSRP of the transmitted signals) using assistance data received from the location server. These measurements can be used at the location server or the UE 404 to determine RTTs, which are used to estimate the location of the UE 404. Other methods for determining RTTs are possible, such as for example using DL-TDOA and / or UL-TDOA measurements.

[0080] PRS can be defined for network-based positioning (e.g., NR positioning) to enable a UE to detect and measure more neighbor transmission and reception points (TRPs) with support for multiple configurations to enable various deployments (e.g., indoor, outdoor, sub-6, mmW, etc.). To support PRS beam operations, beam sweeping can also be configured for PRS. UL positioning reference signals can be based on sounding reference signals (SRS) with enhancements / adjustments for positioning purposes. In some examples, UL-PRS can be referred to as “SRS for positioning,” and new information elements (IEs) can be configured for SRS for positioning in RRC signaling.

[0081] DL PRS-RSRP can be defined as the linear average of the power contributions (in [W]) of resource elements carrying the antenna ports of DL PRS reference signals configured for RSRP measurement over the considered measurement frequency bandwidth. In some examples, for FR1, the reference point for DL PRS-RSRP can be the UE’s antenna connectors. For FR2, DL PRS-RSRP can be measured based on the combined signal from the antenna elements corresponding to a given receiver branch. For FR1 and FR2, if the UE uses receiver diversity, the reported DL PRS-RSRP value can not be lower than the corresponding DL PRS-RSRP of any of the individual receiver branches. Similarly, UL SRS-RSRP can be defined as the linear average of the power contributions (in [W]) of resource elements carrying sounding reference signals (SRS). UL SRS-RSRP can be measured over the considered measurement frequency bandwidth, in the configured measurement occasions, by the configured resource elements. In some examples, for FR1, the reference point for UL SRS-RSRP can be the base station’s (e.g., gNB’s) antenna connectors. For FR2, UL SRS-RSRP can be measured based on the combined signal from the antenna elements corresponding to a given receiver branch. For FR1 and FR2, if the base station uses receiver diversity, the reported UL SRS-RSRP value can not be lower than the corresponding UL SRS-RSRP of any of the individual receiver branches.

[0082] PRS-path RSRP (PRS-RSRPP) can be defined as the linear average of the power of the channel response at the i-th path delay of resource elements carrying the DL PRS signals configured for measurement, where the DL PRS-RSRPP of the 1st path delay is the power contribution corresponding to the first detected path in time. In some examples, a PRS path phase measurement can refer to a phase associated with the i-th path of the channel derived using PRS resources.

[0083] DL-AoD positioning can utilize measured DL-PRS-RSRP of downlink signals received at the UE 404 from multiple TRPs 402, 406. The UE 404 measures the DL-PRS-RSRP of the received signals using assistance data received from a positioning server, and the resulting measurements are used along with the azimuth angle of departure (A-AoD), zenith angle of departure (Z-AoD), and other configuration information to position the UE 404 relative to the neighboring TRPs 402, 406.

[0084] DL-TDOA positioning can utilize the DL Reference Signal Time Difference (RSTD) (and optionally DL-PRS-RSRP) of downlink signals received at the UE 404 from multiple TRPs 402, 406. The UE 404 measures the DL RSTD (and optionally DL-PRS-RSRP) of the received signals using assistance data received from a positioning server, and the resulting measurements are used along with other configuration information to position the UE 404 relative to the neighboring TRPs 402, 406.

[0085] UL-TDOA positioning can utilize the UL Relative Time of Arrival (RTOA) (and optionally UL-SRS-RSRP) of uplink signals sent from the UE 404 at multiple TRPs 402, 406. The TRPs 402, 406 measure the UL-RTOA (and optionally UL-SRS-RSRP) of the received signals using assistance data received from a positioning server, and the resulting measurements are used along with other configuration information to estimate the location of the UE 404.

[0086] UL-AoA positioning can utilize the measured Azimuth of Arrival (A-AoA) and Zenith of Arrival (Z-AoA) of uplink signals sent from the UE 404 at multiple TRPs 402, 406. The TRPs 402, 406 measure the A-AoA and Z-AoA of the received signals using assistance data received from a positioning server, and the resulting measurements are used along with other configuration information to estimate the location of the UE 404. For the purposes of this disclosure, positioning operations in which the UE provides measurements to a base station / positioning entity / server for use in computing UE positioning can be described as “UE-assisted,” “UE-assisted positioning,” and / or “UE-assisted positioning computation,” while positioning operations in which the UE measures and computes its own position can be described as “UE-based,” “UE-based positioning,” and / or “UE-based positioning computation.”

[0087] Additional positioning methods can be used to estimate the location of the UE 404, such as, for example, UE-side UL-AoD and / or DL-AoA. Note that data / measurement from various techniques can be combined in various ways to increase accuracy, determine and / or enhance certainty, supplement / complement measurements, and / or replace / provide missing information. For example, some UE positioning mechanisms can be radio access technology (RAT)-dependent (e.g., the positioning of the UE is based on the RAT), such as downlink positioning (e.g., measurements of observed time difference of arrival (OTDOA)), uplink positioning (e.g., measurements of uplink time difference of arrival (UTDOA)), and / or combined DL and UL based positioning (e.g., measurements of RTT with respect to neighboring cells), etc. Some wireless communications systems can also support enhanced cell ID (E-CID) positioning procedures based on radio resource management (RRM) measurements. On the other hand, some UE positioning mechanisms can be RAT-independent (e.g., the positioning of the UE is not dependent on the RAT), such as enhanced GNSS, and / or WLAN, Bluetooth, terrestrial beacon system (TBS) based positioning techniques, and / or sensor (e.g., barometric pressure sensor, motion sensor) based positioning techniques, etc. Some UE positioning mechanisms can be based on a hybrid model, where multiple positioning methods are used, which can include both RAT-dependent and RAT-independent positioning techniques (e.g., GNSS with OTDOA hybrid positioning).

[0088] Note that the terms “positioning reference signal” and “PRS” generally refer to specific reference signals used for positioning in NR and LTE systems. However, as used herein, the terms “positioning reference signal” and “PRS” can also refer to any type of reference signal that can be used for positioning, such as, but not limited to: PRS as defined in LTE and NR, TRS, PTRS, CRS, CSI-RS, DMRS, PSS, SSS, SSB, SRS, UL-PRS, etc. Moreover, the terms “positioning reference signal” and “PRS” can refer to downlink or uplink positioning reference signals, unless otherwise indicated by the context. To further distinguish between types of PRS, downlink positioning reference signals can be referred to as “DL PRS,” and uplink positioning reference signals (e.g., SRS, PTRS for positioning) can be referred to as “UL-PRS.” Moreover, for signals that can be transmitted in both uplink and downlink (e.g., DMRS, PTRS), these signals can be prepended with “UL” or “DL” to distinguish the direction. For example, “UL-DMRS” can be distinguished from “DL-DMRS.”

[0089] In some scenarios, in addition to GNSS-based positioning (e.g., performing positioning based on measurements of GNSS signals from GNSS satellites) and / or network-based positioning (e.g., as described in connection with FIG. 2), the UE 404 can also perform positioning based on WLAN signals (e.g., based on measurements of WLAN signals from one or more WLAN access points). For example, the UE 404 can perform WLAN-based positioning based on measurements of received signal strength indicator (RSSI), round trip signal arrival time (RTT), and / or time of arrival (TOA) of signals transmitted by the UE 404 and received by one or more WLAN access points.Figure 4 In addition to the described, a UE can also estimate or compute its position or relative position (e.g., between two points in time) by measuring its displacement. For example, a UE can be able to generate and record its displacement using one or more sensors such as an inertial measurement unit (IMU). An inertial measurement unit (IMU) is a device / apparatus that is capable of measuring and reporting the specific force and / or angular rate of the object (e.g., UE) to which it is attached. In some examples, an IMU can include a gyroscope (e.g., to measure angular rate), an accelerometer (e.g., to measure force / acceleration), and / or a magnetometer (e.g., to measure magnetic field), among others. An accelerometer (e.g., a three-axis accelerometer) can be used to measure the speed of movement of an electronic device in linear directions. In some examples, an accelerometer can include the ability to detect gravity as a static acceleration as well as detect dynamic acceleration applied to the electronic device. A gyroscope (e.g., a three-axis gyroscope) can be used to determine the angular motion of an electronic device. For example, a gyroscope can determine whether an electronic device is twisted in any direction by measuring the angular velocity and / or rotational force around the three axes (e.g., X-axis, Y-axis, and Z-axis) of the gyroscope. A magnetometer (which can also be referred to as a compass) can be used to sense a magnetic field, such as detecting the movement of a compass in the direction of magnetic north relative to the Earth. In some examples, a magnetometer can also be used to assist in the calibration of a gyroscope, such as to determine a gyroscope or gyroscope bias to calibrate the gyroscope. Thus, the displacement and / or absolute orientation of an electronic device can be detected by a combination of an accelerometer, a gyroscope, and a magnetometer, which can also be used in a navigation system of the electronic device.

[0090] In some examples, a series of displacements can be referred to as a dead reckoning (DR) trajectory. Under navigation or position tracking, DR can refer to a process of calculating a current position of a moving object (e.g., a user holding / wearing an electronic device that performs the DR trajectory) based on the speed, heading, and / or course of the moving object from an initial position (e.g., a previously determined position or fixed point) over a period of time.

[0091] Figure 5 is a diagram 500 illustrating an example of a UE estimating / computing its DR trajectory using an IMU in accordance with various aspects of the present disclosure. A UE 502 with at least one IMU 504 (which can include an accelerometer, a gyroscope, and / or a magnetometer) can have the ability to generate a DR trajectory as the UE 502 moves. In one example, the UE 502 can be a smartphone or a wearable device with the ability to track the travel path of a user (e.g., a pedestrian), and / or the UE 502 can be a GNSS / tracking device installed on a car with the ability to track the travel path of the car, among others. Then, based on the speed, heading, and / or course of the UE 502 over a period of time from an initial position, the UE 502 can be able to generate a DR trajectory of the UE 502.

[0092] For example, as shown at 506, the UE 502 can move in alphabetical order from point A to point J. While the UE 502 is moving, the UE 502 can use an accelerometer of the IMU 504 to measure the moving speed of the UE 502, use a gyroscope of the IMU 504 to detect the angular motion of the UE 502, such as detecting turns at points B, C, D, E, etc., and use a magnetometer of the IMU 504 to track the heading direction of the UE. Then, as shown at 508, based on the data collected from the IMU 504, the UE 502 (or a processor of the UE 502) can generate a DR trajectory of the UE 502 from point A to point J.

[0093] While the displacement of a UE can be measured using sensors (e.g., using an IMU), certain UEs (e.g., RedCap UEs) can not have sensors and / or can have noisy sensor outputs (e.g., the sensors do not provide accurate outputs), which can result in these UEs being unable to measure / estimate their displacement. Moreover, most networks can not support reporting measurements for artificial intelligence (AI) and / or machine learning (ML) displacement learning with UE assistance.

[0094] Aspects presented herein can enable a UE to estimate its displacement based on measuring wireless signals (which can be referred to as displacement positioning), such that UEs without sensors (e.g., IMUs) can also be able to accurately perform displacement estimation (e.g., to certain specified accuracy). Aspects presented herein also provide signaling and reporting between a UE and a network entity (e.g., a base station, a location server, an LMF, etc.) that enables AI / ML based displacement positioning at the UE side or AI / ML based displacement positioning at the network side (e.g., for UE assisted positioning). Thus, aspects presented herein can be applicable to both UE assisted / LMF based AI / ML displacement positioning and UE based AI / ML displacement positioning.

[0095] By enabling measurements of the UE’s displacement based on wireless signals, the UE can not be designated to report measurements or compute two separate positions (e.g., one at an initial position and another at a displacement position) for two separate positioning occasions, which can reduce signaling overhead between the UE and network entities (e.g., location server, LMF, etc.). Aspects presented herein can also enable generation of training data for AI / ML models that can be used to perform displacement positioning. Further, the generation of training data can also be applicable to other types of AI / ML positioning methods, which can lead to different positioning label types (e.g., displacement and anchor). For example, the enhanced AI / ML estimated displacement can be utilized to train other AI / ML positioning methods. Aspects presented herein can also enable a UE (or its sensors) to provide enhanced sensor outputs by fusing a radio frequency fingerprinting (RFFP) estimated displacement with sensor outputs.

[0096] For purposes of the present disclosure, AI / ML based displacement positioning can refer to displacement positioning performed using at least an AI / ML model. For example, an AI / ML model can be configured to estimate a displacement of a UE. Configuration for AI / ML based displacement positioning can include one or more parameters associated with displacement positioning and / or for running an AI / ML model for AI / ML based displacement positioning.

[0097] Figure 6 FIG. 600 is a diagram 600 illustrating examples of displacement positioning in accordance with various aspects of the present disclosure. For purposes of the present disclosure, displacement positioning can refer to determining / estimating a displacement (e.g., a difference in position of a UE) of a UE at two points in time (or at multiple points in time). For example, as shown at 608, when a UE 602 is located at a first position (e.g., at a fixed point A < x A ,y A > at a first point in time (T1), the UE 602 can receive and measure a positioning reference signal (PRS) transmitted from a first TRP 604 (TRP 1) (e.g., PRS1 at T1) and a PRS transmitted from a second TRP 606 (TRP 2) (e.g., PRS2 at T1). Then, as shown at 610, when the UE 602 is located at a second position (e.g., at a fixed point B < x B ,y BWhen the first and second time points are the same (e.g., Tl = T2), the UE 602 can receive and measure another PRS transmitted from the first TRP 604 (e.g., PRS 1 at T2) and another PRS transmitted from the second TRP 606 (e.g., PRS 2 at T2). In some examples, the measurements of the PRS transmitted from the first TRP 604 and the second TRP 606 can be referred to as a radio frequency (RF) fingerprint. In one aspect of the disclosure, a displacement of the UE 602 can be estimated based on an aggregation / composition or a difference of the measurements of the PRS transmitted from the first TRP 604 and the second TRP 606 at the first and second time points, which can be referred to as a “displacement measurement” and / or a “displacement RF FP measurement” (discussed in detail below). For the purposes of the present disclosure, the term “aggregation” can be used interchangeably with the term “composition.” Thus, the displacement of the UE 602 can include: Dx AB which can refer to a displacement of the UE 602 in the x-axis between the first and second positions; and Dy AB which can refer to a displacement of the UE 602 in the y-axis between the first and second positions. While Figure 6 the examples in Eqs. (1) and (2) illustrate a displacement of the UE 602 based on a two-dimensional (2D) scenario (e.g., in the x-axis and the y-axis), the displacement can also apply to a three-dimensional (3D) scenario, which can include an additional z-axis. Thus, the displacement of the UE 602 can also include Dz AB which can refer to a displacement of the UE 602 in the z-axis between the first and second positions.

[0098] Figure 7A is a diagram 700A illustrating an example of UE-assisted / network-based ML displacement positioning according to various aspects of the present disclosure. For the purposes of the present disclosure, UE-assisted or network-based (e.g., LMF-based) ML displacement positioning can refer to a UE performing positioning-related measurements (e.g., measuring positioning reference signals) and transmitting the positioning-related measurements to a network entity (e.g., a location server, an LMF, etc.). The network entity can then use an ML / AI model to estimate a position of the UE (e.g., an absolute position of the UE, a relative position of the UE, a displacement position of the UE, a true position of the UE, etc.) based on the positioning-related measurements. For example, a UE 702 can receive and measure PRS transmitted from base stations 704 (e.g., via a first TRP and a second TRP as discussed in connection with FIG. 6A), and the UE 702 can transmit the PRS measurements to a location server 706 (e.g., an LMF). Based on the PRS measurements, an AI / ML model 708 associated with the location server 706 can be configured to determine a displacement of the UE 702, such as based on a radio frequency fingerprint positioning (RFFP) (discussed in detail below). Figure 6

[0099] Figure 7B ​is a diagram 700B illustrating examples of UE-based ML displacement positioning in accordance with various aspects of the present disclosure. On the other hand, UE-based ML displacement positioning can refer to a UE performing positioning-related measurements and estimating its position based on the positioning-related measurements. For example, a UE 702 can receive and measure PRS transmitted from base stations 704 (e.g., via beams as discussed in connection with Figure 6 the first and second TRPs discussed above), and the UE 702 can determine its displacement based on the PRS measurements, such as using an AI / ML model 708 based on RFFP (discussed in detail below). In some examples, the UE 702 can also transmit its displacement information to a location server 706. In some examples, for UE-based positioning, the UE can also receive positioning-related measurements from other network entities, such as from base stations / TRPs and / or a location server, etc.

[0100] In some scenarios, as described in connection with Figure 6 , Figure 7A and Figure 7B , a position of a UE can be determined based on radio frequency (RF) fingerprints. In some scenarios, RF (e.g., wireless signals) transmitted from a wireless device (e.g., a UE, a TRP, a wireless access point (AP), etc.) or from a set of wireless devices can contain one or more features / patterns that are different from another wireless device or another set of wireless devices. In other words, the RF transmitted from a wireless device or from a set of wireless devices can be unique, and the unique features / patterns associated with this RF can be referred to as RF fingerprints. For the purposes of the present disclosure, techniques associated with RF fingerprints can be referred to as “RF fingerprinting” and / or “RFFP,” and positioning methods associated with RF fingerprinting can be referred to as “RF fingerprinting-based positioning,” “RFFP-based positioning,” and / or “RFFP positioning.” RF fingerprinting aims to develop a unique RF fingerprint of a wireless device that can be used as an identity, which can operate in a similar manner to a biological fingerprint. For example, an RF fingerprint can include radio measurements, such as received signal strength (RSS), path loss, and / or channel impulse response measurements, etc., from multiple APs / TRPs to provide a fingerprint of the radio conditions at a particular location (e.g., at fixed point A and fixed point B in Figure 6 . Then, the location of the fingerprint can be estimated using known locations of similar fingerprints recorded previously. For example, RF fingerprints from different locations can be captured and recorded in a database. Thus, if the RF fingerprint captured by a wireless device matches an RF fingerprint stored in the database, the location of the wireless device can be estimated.

[0101] In some examples, the RF fingerprint database can be created and / or maintained based on ML, and RF FP positioning with ML can be referred to as ML RFFP positioning. For ML RFFP positioning, the RF fingerprints and their associated positions can be used as features and labels, respectively, for training a ML module (e.g., AI / ML model 708) in a supervised manner. After the ML module is trained, the ML module can be used to estimate the position by passing a newly captured RF fingerprint to it. In other words, the ML module can be configured to collect RF fingerprints and their associated locations, and the process can be referred to as ML positioning training or simply ML training. After the ML module is trained (e.g., RF fingerprints stored in the database are associated with known locations with a certain level of certainty / accuracy), the ML module can be used to estimate the location of a detected / captured RF fingerprint, and the process can be referred to as ML positioning inference or simply ML inference. In some examples, the ML positioning training / inference can be applied at the UE side and / or the network side. The RFFP ML training and inference can also be based on millimeter wave (mmW or mmwave), sub-THz, and / or THz frequencies.

[0102] Figure 8 is a diagram 800 illustrating an example of AI / ML positioning training and inference for estimating the displacement of a UE based on RFFP, in accordance with various aspects of the present disclosure. During an offline phase 802 (which can also be referred to as a training phase), a database can be configured to collect RF fingerprints (such as channel frequency responses (CFRs) and / or channel impulse responses (CIRs)) and their associated locations (e.g., where the CFR / CIR was captured). Thus, each RF fingerprint in the database can be associated to a known location, such as corresponding to local coordinates (x, y) or (x, y, z), global coordinates (e.g., latitude and longitude coordinates), a real-world location, an address, and / or a label, etc. The collection of RF fingerprints can be based on field investigation, crowd-sourcing, or by other means. After the RF fingerprints and their associated locations are collected, the database can process them and create a mapping between the RF fingerprints and their associated locations. The mapping can be accessed by a neural network (NN) or an AI / ML module associated with the NN. This training process can be performed at the UE, at a network entity (e.g., location server, LMF, etc.), or at a third-party entity.

[0103] During an online phase 804 (which can also be referred to as an operational phase or a training phase), the NN or AI / ML module can receive an RF fingerprint captured by a wireless device with an unknown location. The NN can compare the RF fingerprint with the unknown location to the mapping, select one or more RF fingerprints from the mapping that are similar to the RF fingerprint with the unknown location, and compute an estimated position of the wireless device. Throughout the process, the NN or AI / ML module can continue to update the mapping to improve the accuracy of the position estimation.

[0104] Since the location of the UE can be determined based on the RF FP, the displacement of the UE can also be estimated based on the RF fingerprints captured by the UE at its current location and / or based on the aggregation or difference (e.g., based on displacement RF FP measurements) between the RF fingerprints captured at a first location (e.g., at fixed point A) and the RF fingerprints captured at a second location (e.g., at fixed point B). For example, as shown in diagram 800, at online phase 804, the NN or AI / ML module can receive the RF fingerprints (e.g., CFR / CIR) captured by the wireless device at two time instances (e.g., at T1 and T2). The NN can compare the RF fingerprints captured at the two time instances to the map, select one or more RF fingerprints from the map that are similar to the RF fingerprints captured at the two time instances, determine the location of the wireless device at the two time instances, and compute the estimated displacement information of the wireless device for the two time instances (e.g., the displacement (delta (D) X, D Y, D Z) of the wireless device between T1 and T2). Throughout the process, the NN or AI / ML module can continue to update the map to improve the accuracy of the displacement estimation.

[0105] In one example, the labels for training the AI / ML model can be obtained based on at least one of the following options: (1) a mobile reference positioning unit equipped with highly accurate sensors that provide displacement information; (2) a mobile reference positioning unit that can be configured to access known fixed locations, where displacement labels can be derived based on these known locations; (3) a set of fixed reference positioning units with known fixed locations, where displacement labels can be derived based on these known locations; and / or (4) running other positioning methods (e.g., GNSS-based positioning, network-based positioning, etc.) to estimate the positioning at different fixed points and derive displacement information based on these estimates.

[0106] Figure 9 is a communication flow 900 illustrating signaling and reporting for AI / ML based displacement positioning in accordance with various aspects of the present disclosure. The numbering associated with communication flow 900 does not specify a particular timing sequence, and is used only as a reference for communication flow 900. In one example, a UE 902 can be configured by an LMF 908 (or location server) to perform AI / ML based displacement positioning, which can include receiving PRS from a serving base station 904 and / or one or more neighboring base stations 906.

[0107] In one aspect, the LMF 908 and the UE 902 can exchange signaling to use AI / ML methods / modules to infer target (e.g., UE 902) displacement information, e.g., as part of LTE Positioning Protocol (LPP) Annex (LPPa) signaling. For example, at 910, the LMF 908 can request the UE 902 to indicate its capability to support or perform AI / ML based displacement positioning, and / or its capability to report intermediate information (e.g., reference signal measurements, captured RF fingerprints, etc.) that can be used to perform AI / ML based displacement positioning at the LMF 908.

[0108] In one example, the LMF 908 can send a request to the UE 902 using a Request Capabilities message (RequestCapabilities) associated with a LPP capabilities transfer procedure. For example, during the LPP capabilities transfer procedure, the LMF 908 can request the UE 902 to report its capabilities (e.g., as part of LPPa signaling) for two modes for ML displacement positioning. A first mode (Mode 1) can be associated with UE-assisted / LMF-based displacement positioning, where the UE 902 can be configured to report PRS-based intermediate quantities (e.g., displacement RFFP) that can be used to perform AI / ML based displacement positioning at the LMF 908. In one example, the PRS-based intermediate quantities can correspond to an aggregation of PRS measurements from multiple base stations / TRPs. For example, referring back to FIG. 7, the PRS-based intermediate quantities can be an aggregated measurement of PRSs (e.g., PRS1 and PRS2) transmitted from the first TRP and the second TRP of the base station 704. The displacement RFFP measurement (or displacement RFFP) can refer to an aggregation (or difference) of PRS measurements (e.g., PRS1 and PRS2) captured at two different locations (e.g., at two fixed points). A second mode (Mode 2) can be associated with UE-based displacement positioning, where the UE 902 can perform PRS-based ML displacement positioning and report the displacement results (e.g., displacement information of the UE 902) back to the LMF 908. Figure 7A

[0109] In one example, the displacement results / information can correspond to at least one of the following options: (1) a radial distance (e.g., Ar) between a first location (e.g., a first fixed point, i.e., fixed point A) and a second location (e.g., a second fixed point, i.e., fixed point B); (2) angular information (e.g., elevation angle DQ, azimuth angle Dp) relative to a known reference location between the first location and the second location; (3) radial and angular information (e.g., Ar, DQ, Dp) between the first location and the second location; (4) distances (e.g., Dx, Dy, Dz) between the first location and the second location (e.g., as discussed in connection with FIG. 8). Figure 6 ​(5) the latitude and longitude difference between the first and second locations (e.g., Delta lat, Delta long).

[0110] Based on the request, the UE 902 can respond to the LMF 908 with a UE capability messaging indicating the UE’s 902 capability to support or perform AI / ML based displacement positioning, and / or the UE’s 902 capability to report intermediate information that can be used to perform AI / ML based displacement positioning at the LMF 908 (collectively referred to as capability to perform / assist ML based displacement positioning). The UE 902 can send the response (e.g., UE capabilities) to the LMF 908 using a Provide Capabilities message (ProvideCapabilities) that is also associated with the LPP capability transfer procedure. For example, the capability message can include whether the UE 902 supports one of the two modes (e.g., Mode 1 and Mode 2 described above) for ML displacement positioning. The capability message can also include the UE’s 902 bandwidth capability, the UE’s 902 buffering capability related to observing multiple PRS resources (e.g., the UE’s 902 ability to measure PRSs from multiple base stations / TRPs simultaneously, which can be used by the LMF to configure time gaps for the UE 902), supported displacement RFFP types that the UE 902 is capable of constructing / performing and / or reporting (e.g., to the LMF 908), measurement gap specification (e.g., time to measure PRS at the first location (fixed point A) and the second location (fixed point B)), or a combination thereof.

[0111] At 912, based on the response from the UE 902, the LMF 908 can configure the UE assisted ML displacement positioning or UE based ML displacement positioning for the UE 902. For example, the LMF 908 can provide configuration / parameters associated with AI / ML based displacement positioning and / or UE assisted ML displacement positioning to the UE 902 via assistance data (AD) (e.g., via a Provide Assistance Data message) during the LPP AD transfer / exchange procedure. The configuration can also include parameters associated with the AI / ML model used for AI / ML based displacement positioning.

[0112] In one example, the configuration messaging (e.g., assistance data) can include a time granularity of PRS resources that the UE 902 is designated to consider for obtaining displacement information or for reporting displacement RFFP measurements (e.g., the UE 902 is designated to measure the frequency of PRS, such as every 50 ms, every 10 seconds, every minute, etc.). In another example, the configuration messaging can include a periodicity in which the UE 902 is designated to report its displacement information or displacement RFFP measurements. In another example, the configuration messaging can include a possibility / capability of the UE 902 to adjust the time granularity of PRS resources for obtaining displacement information or for reporting displacement RFFP measurements based on the UE’s 902 estimated speed (e.g., if the UE 902 is moving at a speed higher than a speed threshold, the UE 902 can not be capable of adjusting the time granularity of PRS resources). For example, the UE 902 can estimate its speed and skip some PRS resources for obtaining displacement information or for reporting displacement RFFP measurements. In another example, the UE 902 can be configured to measure PRS resources more frequently if the UE 902 is moving at a higher speed, and / or the UE 902 can be configured to measure PRS resources less frequently if the UE 902 is moving at a lower speed, etc.

[0113] At 914, for UE-assisted ML displacement positioning, as described in connection with Figure 7B the UE 902 can observe, receive, and measure PRS (or other types of reference signals) transmitted on two (or multiple) time resources (e.g., from the serving base station 904 and / or one or more neighboring base stations 906). Then, the UE 902 can derive displacement RFFP measurements (e.g., an aggregation / difference of RFFP measurements between the two (or multiple) time resources) based on the PRS and report the derived displacement RFFP measurements to the LMF 908. In one example, the UE 902 can report the derived displacement RFFP measurements to the LMF 908 based on an LPP Request / Provide Location Information procedure, such as via a Provide Location Information message. In response, the LMF 908 can utilize the reported displacement RFFP measurements to infer displacement information of the UE 902, such as using an LMF-side AI / ML module 916 that is trained / configured to infer displacement of a UE based on displacement RFFP measurements (e.g., as described in connection with Figure 8 In some examples, along with the displacement RFFP measurements, the UE 902 can also be configured to report the use of non-radio access technology (non-RAT) methods (e.g., using a GNSS receiver, a camera, a barometer, etc.) for obtaining displacement information, as described in connection with Figure 5The displacement RFFP measurements can also be referred to as radio access technology (RAT)-based displacement RFFP measurements, where the RFFP measurements are based on measuring reference signals transmitted from one or more base stations / TRPs.

[0114] On the other hand, for UE-based ML displacement positioning, as described in connection with Figure 7A The UE 902 can observe, receive, and measure PRS (or other types of reference signals) transmitted on two (or multiple) time resources (e.g., from the serving base station 904 and / or one or more neighboring base stations 906). The UE 902 can then derive displacement RFFP measurements (e.g., an aggregation / difference of RFFP measurements between the two (or multiple) time resources) based on the PRS, and the UE 902 can make an inference of the UE’s 902 displacement information, such as using the AI / ML module 918 on the UE side that is trained / configured to infer the UE’s displacement based on the displacement RFFP measurements (e.g., as described in connection with Figure 8 In some examples, the UE 902 can also be configured to report its displacement information to the LMF 908 via a Provide Location Information (ProvideLocationlnformation) message based on, such as, an LPP Request / Provide Location Information procedure. Similarly, along with the RAT-based displacement information, the UE 902 can also be configured to report other displacement information obtained using non-RAT methods (e.g., using IMUs, motion sensors, etc. as described in connection with Figure 5 The RAT-based displacement information can refer to the UE’s 902 displacement information obtained based on measuring reference signals (e.g., RFFP measurements) transmitted from one or more base stations / TRPs. In one example, along with the RAT-based displacement information, the UE 902 can also be configured to fuse other displacement information obtained using non-RAT methods (e.g., IMUs, motion sensors). For example, the UE 902 (or the positioning engine of the UE 902) can do this fusion in a similar way as sensor fusion. This can reduce the reporting overhead of the UE 902. In another example, the UE 902 can also be configured to report soft information of the ML RAT-based displacement information. The soft information can refer to a probability / indication of how accurate the displacement information can be (e.g., variance, expectation, and / or bias, etc.). For example, in addition to the displacement information, the ML module 918 can also provide soft information of its estimated displacement .

[0115] In one aspect of the disclosure, for UE-based displacement positioning performed with an AI / ML module (e.g., AI / ML module 918), an AI / ML-based positioning displacement estimation method can be configured to run on a target UE (e.g., UE 902), where the output of the AI / ML module can be the displacement information of the target UE, and the input can be the radio fingerprints constructed from the first and second RF measurements. The first RF measurements can correspond to a first set of signals transmitted by a first set of TRPs and observed by the target UE when the target UE is at a first fixed point, and the second RF measurements can correspond to a second set of signals transmitted by a second set of TRPs and observed by the target UE when the target UE is at a second fixed point, such as Figure 6 illustrated.

[0116] On the other hand, for UE-assisted displacement positioning performed with an AI / ML module (e.g., AI / ML module 916), an ML-based positioning displacement estimation method can be configured to run on a location server (e.g., LMF 908), where the output of the AI / ML module can be the displacement information of a target UE (e.g., UE 902), and the input can be the displacement radio fingerprints reported by the target UE and constructed from the first and second RF measurements. Similarly, the first RF measurements can correspond to a first set of signals transmitted by a first set of TRPs and observed by the target UE when the target UE is at a first fixed point, and the second RF measurements can correspond to a second set of signals transmitted by a second set of TRPs and observed by the target UE when the target UE is at a second fixed point, such as Figure 6 illustrated.

[0117] In one example, the first set of signals and the second set of signals can be downlink (DL) reference signals (e.g., positioning reference signals (PRSs), channel state information reference signals (CSI-RSs), synchronization signal blocks (SSBs), etc.). In another example, the first set of TRPs can be the same as the second set of TRPs, or they can be different or partially different from each other (e.g., with some TRPs being overlapping). In some implementations, if the target UE has sensors (e.g., IMU, motion sensors, etc.) available, the target UE can be configured to record sensor information whose timing falls between (including) the timing of the first set of signals and the timing of the second set of signals, where the recorded sensor information can be utilized to obtain labels for training the AI / ML module. As Figure 8As described, training of the AI / ML module can be performed at the target UE (e.g., UE 902), a location server (e.g., LMF 908), and / or a third-party server (e.g., an over-the-top (OTT) server run by a vendor of the target UE).

[0118] In another example, the displacement RFFP can be an aggregation / consolidation of the first RF measurement and the second RF measurement, which can include: a channel impulse response (CIR) measurement, a channel frequency response (CFR) measurement, a reference signal received quality (RSRQ) measurement, a reference signal received power (RSRP) measurement, a delay spread measurement, an angular spread measurement, an angle of arrival (AoA) measurement, an angle of departure (AoD) measurement, a Doppler spread measurement, or a combination thereof. The aggregation / consolidation of the RF measurements can reduce reporting overhead of the target UE.

[0119] In conjunction with Figure 9The aspects discussed provide various signaling and reporting between a UE and a network entity (e.g., LMF) for displacement positioning. For example, in one aspect, in response to a request from the LMF, the UE can respond with its capability for ML-based displacement positioning (UE-based) or assisted ML-based displacement positioning (UE-assisted). The LMF can then configure the UE for ML-based displacement positioning. In another aspect, the UE’s capability response can also include the UE’s bandwidth capability, the UE’s buffering capability for multiple PRS resources, the UE’s supported displacement RFFP type, and / or the UE’s measurement gap specification. In another aspect, the LMF’s configuration message for the UE can be transmitted as part of the assistance data signaling and can include: a time granularity of PRS resources that the UE should consider, a specified periodicity for reporting RFFP measurements or displacement measurements, a possibility for the UE to adjust the time granularity and periodicity. In another aspect, for UE-assisted displacement positioning, the UE can observe PRS signals transmitted on two (or multiple) time resources and derive and report displacement RFFP measurements to the LMF. The LMF can utilize the reported displacement RFFP measurements to infer target displacement information. In another aspect, the UE can also report other measurements obtained using non-RAT methods (e.g., motion sensors). In another aspect, the UE can also report soft information for the ML-based RAT’s displacement information. In another aspect, for UE-based displacement positioning, the UE can observe PRS signals transmitted on two (or multiple) time resources, derive displacement RFFP measurements, make an inference of displacement information based on a UE-side ML model, and report the displacement information to the LMF. In another aspect, in making the inference of displacement information, the UE can also take into account measurements obtained using non-RAT methods (e.g., motion sensors). In another aspect, the UE can also report soft information for the ML-based RAT’s displacement information. In another aspect, the ML-based positioning displacement estimation method can run on a target UE, where the output of the ML model is the target UE’s displacement information, and the input of the ML model is a radio fingerprint constructed from first measurements and second measurements, where the first measurements can correspond to a first set of signals transmitted by a first set of TRPs and observed by the target UE when the target UE is at a first fixed point, and the second measurements can correspond to a second set of signals transmitted by a second set of TRPs and observed by the target UE when the target UE is at a second fixed point.In another aspect, the ML-based positioning displacement estimation method runs on the LMF, where the output of the ML model is the displacement information of the target UE, and the input of the ML model is the displacement radio fingerprints constructed from the first measurements and the second measurements, where the first measurements can correspond to a first set of signals transmitted by a first set of TRPs and observed by the target UE when the target UE is located at a first fixed point, and the second measurements can correspond to a second set of signals transmitted by a second set of TRPs and observed by the target UE when the target UE is located at a second fixed point. In another aspect, the first set of signals and the second set of signals are DL reference signals (e.g., PRS, CSI-RS, SSB, etc.). In another aspect, the first set of TRPs and the second set of TRPs are the same. In another aspect, the ML model can be trained using labels generated based on information from the target recording sensors, the timing of which falls between the timing of the first set of signals and the timing of the second set of signals. In another aspect, the ML model can be trained at the target UE, the LMF, or a third-party server.

[0120] Figure 10 FIG. 10 is a flow diagram of a method of wireless communication. The method can be performed by a UE (e.g., the UE 104, the UE 404, the UE 502, the UE 602, the UE 702, the UE 902; the apparatus 1204). The method can enable the UE or a location server to estimate a displacement of the UE based on measuring wireless signals, such that the UE can accurately perform accurate displacement estimation without using a sensor (e.g., an IMU).

[0121] At 1002, the UE can receive, from a network entity, a request to report information associated with machine learning (ML)-based displacement positioning, such as described in connection with Figure 9 For example, at 910, the UE 902 can receive, from the LMF 908, a request indicating a capability of the UE 902 to support or perform AI / ML-based displacement positioning, and / or a capability of the UE 902 to report intermediate information (e.g., reference signal measurements, captured RF fingerprints, etc.) that can be used to perform AI / ML-based displacement positioning at the LMF 908. The reception of the request for information associated with ML-based displacement positioning can be performed by, for example, Figure 12 the ML-based displacement positioning component 198 of the apparatus 1204 in FIG. 13, the application processor 1206, the cellular baseband processor 1224, and / or the transceiver 1222.

[0122] At 1004, the UE can transmit, to the network entity based on the request, information associated with ML-based displacement positioning, such as described in connection with Figure 9Described. For example, at 910, based on the request, the UE 902 can respond to the LMF 908 with a UE capability messaging indicating a capability of the UE 902 to support or perform AI / ML-based displacement positioning, and / or a capability of the UE to report intermediate information that can be used to perform AI / ML-based displacement positioning at the LMF 908. The transmission of information associated with ML-based displacement positioning can be performed by, for example Figure 12 the ML-based displacement positioning component 198 of the apparatus 1204 in FIG. 13, the application processor 1206, the cellular baseband processor 1224, and / or the transceiver 1222.

[0123] At 1006, the UE can receive, from the network entity, a configuration for ML-based displacement positioning based on the information, such as described in connection with Figure 9 Described. For example, at 912, the UE 902 can receive, from the LMF 908, a configuration for UE-assisted ML displacement positioning or UE-based ML displacement positioning. The reception of the configuration for ML-based displacement positioning can be performed by, for example Figure 12 the ML-based displacement positioning component 198 of the apparatus 1204 in FIG. 13, the application processor 1206, the cellular baseband processor 1224, and / or the transceiver 1222.

[0124] In one example, the configuration for ML-based displacement positioning can be received in assistance data. In some implementations, the configuration can include a time granularity of a set of RSs, a periodicity of reporting UE’s displacement or a set of displacement RFFP measurements, a capability of the UE to adjust the time granularity of the set of RSs based on the UE’s speed, or a combination thereof.

[0125] At 1008, the UE can receive, from at least one network node, a set of reference signals (RSs) associated with ML-based displacement positioning based on the configuration, such as described in connection with Figure 9 Described. For example, at 914, the UE 902 can observe, receive, and measure PRSs (or other types of reference signals) transmitted on two (or multiple) time resources (e.g., from the serving base station 904 and / or one or more neighboring base stations 906). Then, the UE 902 can derive displacement RFFP measurements (e.g., a difference of RFFP measurements between the two (or multiple) time resources) based on the PRSs and report the derived displacement RFFP measurements to the LMF 908. The reception of the set of RSs associated with ML-based displacement positioning can be performed by, for example Figure 12 the ML-based displacement positioning component 198 of the apparatus 1204 in FIG. 13, the application processor 1206, the cellular baseband processor 1224, and / or the transceiver 1222.

[0126] In one example, the ML-based displacement positioning can be UE-assisted displacement positioning initiated by a network entity. The UE can derive a set of displacement radio frequency fingerprinting positioning (RFFP) measurements based on a set of RSs, and the UE can transmit the set of displacement RFFP measurements to the network entity. In some implementations, the information can include a capability of the UE to assist in the ML-based displacement positioning and a list of displacement RFFPs that can be used for the ML-based displacement positioning at the network entity. In some implementations, a displacement RFFP measurement of the set of displacement RFFP measurements can correspond to a composition of first measurements of first reference signals (RSs) transmitted from at least one transmission reception point (TRP) when the UE is at a first location, and a second RFFP measurement of the set of displacement RFFP measurements corresponds to second measurements of second RSs transmitted from the at least one TRP when the UE is at a second location.

[0127] In another example, the ML-based displacement positioning can be performed by the UE. The UE can estimate a displacement of the UE based on a set of RSs using an ML model, and the UE can transmit the estimated displacement of the UE to the network entity. In some implementations, the information associated with the ML-based displacement positioning can include a capability of the UE to perform the ML-based displacement positioning. In some implementations, the information associated with the ML-based displacement positioning can include a bandwidth capability of the UE, a buffer capability of the UE to observe a plurality of reference signal (RS) resources, one or more supported displacement RFFP types that the UE is capable of constructing and reporting to the network entity, a measurement gap specification, or a combination thereof. In some implementations, the estimated displacement of the UE can include soft information of the estimated displacement. In some implementations, to estimate the displacement of the UE based on the set of RSs using the ML model, the UE can transmit a set of displacement RFFP measurements to the ML model based on the set of RSs, where a first displacement RFFP measurement of the set of displacement RFFP measurements can correspond to first measurements of first reference signals (RSs) transmitted from at least one TRP when the UE is at a first location, and a second displacement RFFP measurement of the set of displacement RFFP measurements can correspond to second measurements of second RSs transmitted from the at least one TRP when the UE is at a second location, and the UE can receive the displacement of the UE from the ML model based on the set of displacement RFFP measurements.

[0128] In another example, the ML-based displacement positioning can be performed by the UE, and the UE can estimate a first displacement of the UE based on a set of RSs using an ML model, estimate a second displacement of the UE using a sensor, fuse the first displacement and the second displacement to obtain a fused displacement, and then transmit the fused displacement to the network entity.

[0129] In another example, the set of RSs can include: one or more positioning reference signals (PRSs), one or more channel state information reference signals (CSI-RSs), one or more synchronization signal blocks (SSBs), or a combination thereof.

[0130] In another example, to receive the set of RSs associated with the ML-based displacement positioning based on the configuration, the UE can receive, when the UE is at a first location, a first subset of RSs of the set of RSs from a first set of TRPs; and receive, when the UE is at a second location, a second subset of RSs of the set of RSs from a second set of TRPs. In some implementations, the first set of TRPs and the second set of TRPs include at least one same TRP.

[0131] In another example, at 1010, the UE can estimate a displacement of the UE using at least one sensor; and transmit, to the network entity, the estimated displacement of the UE, such as described in connection with Figure 9 For example, along with RAT-based displacement information, the UE 902 can also be configured to report other displacement information obtained using non-RAT methods (e.g., using an IMU, motion sensors, etc., as described in connection with Figure 5 FIG. 19). The estimation of the displacement and / or the transmission of the estimated displacement can be performed by, for example, the ML-based displacement positioning component 198 of the apparatus 1204 in Figure 12 FIG. 19), the application processor 1206, the cellular baseband processor 1224, the one or more sensor modules 1218, and / or the transceiver 1222.

[0132] In another example, at 1012, the UE can train an ML model to perform ML-based displacement positioning based on displacement RFFP measurements, such as described in connection with Figure 8 For example, the UE can be configured to perform AI / ML positioning training and inference for RFFP. The training of the ML model can be performed by, for example, the ML-based displacement positioning component 198 of the apparatus 1204 in Figure 12 FIG. 19), the application processor 1206, the cellular baseband processor 1224, and / or the transceiver 1222. In some implementations, the UE can record second information from the at least one sensor during the reception of the set of RSs, where the ML model is further trained based on the second information.

[0133] In another example, at 1014, the UE can derive a set of displacement radio frequency fingerprinting positioning (RFFP) measurements based on the set of RSs, where the set of RFFP measurements correspond to a composition of a plurality of measurements including: CIR measurements, CFR measurements, RSRQ measurements, RSRP measurements, delay spread measurements, angular spread measurements, AoA measurements, AoD measurements, Doppler spread measurements, or a combination thereof, such as described in connection withFigure 9 The displacement RFFP can be a composition of first and second RF measurements, which can include: CIR measurements, CFR measurements, RSRQ measurements, RSRP measurements, delay spread measurements, angular spread measurements, AoA measurements, AoD measurements, Doppler spread measurements, or combinations thereof, as described. Derivation of the displacement RFFP measurement set can be performed by, for example Figure 12 The ML-based displacement positioning component 198 of the apparatus 1204 in FIG. 13, the application processor 1206, the cellular baseband processor 1224, and / or the transceiver 1222 can perform the receiving of the request for reporting information associated with the ML-based displacement positioning, as described.

[0134] Figure 12 is a flowchart 1100 of a method of wireless communication. The method can be performed by a UE (e.g., the UE 104, the UE 404, the UE 502, the UE 602, the UE 702, the UE 902; the apparatus 1204). The method can enable the UE or a location server to estimate a displacement of the UE based on measuring wireless signals, such that the UE can accurately perform accurate displacement estimation without using sensors (e.g., IMUs).

[0135] At 1102, the UE can receive, from a network entity, a request to report information associated with machine learning (ML)-based displacement positioning, such as described in connection with Figure 3 For example, at 910, the UE 902 can receive, from the LMF 908, a request indicating a capability of the UE 902 to support or perform AI / ML-based displacement positioning, and / or a capability of the UE 902 to report intermediate information (e.g., reference signal measurements, captured RF fingerprints, etc.) that can be used to perform AI / ML-based displacement positioning at the LMF 908. The receiving of the request for information associated with the ML-based displacement positioning can be performed by, for example Figure 9 The ML-based displacement positioning component 198 of the apparatus 1204 in FIG. 13, the application processor 1206, the cellular baseband processor 1224, and / or the transceiver 1222 can perform the receiving of the request for reporting information associated with the ML-based displacement positioning, as described.

[0136] At 1104, the UE can transmit, to the network entity, information associated with the ML-based displacement positioning based on the request, such as described in connection with Figure 12 For example, at 910, based on the request, the UE 902 can respond to the LMF 908 with a UE capability messaging indicating a capability of the UE 902 to support or perform AI / ML-based displacement positioning, and / or a capability of the UE to report intermediate information that can be used to perform AI / ML-based displacement positioning at the LMF 908. The transmitting of the information associated with the ML-based displacement positioning can be performed by, for example Figure 13the ML-based displacement positioning component 198 of the apparatus 1204 in FIG. 12, the application processor 1206, the cellular baseband processor 1224, and / or the transceiver 1222.

[0137] At 1106, the UE can receive, from the network entity, a configuration for ML-based displacement positioning based on the information, such as described in connection with Figure 9 described. For example, at 912, the UE 902 can receive, from the LMF 908, a configuration for UE-assisted ML displacement positioning or UE-based ML displacement positioning. The reception of the configuration for ML-based displacement positioning can be performed by, for example, Figure 14 the ML-based displacement positioning component 198 of the apparatus 1204 in FIG. 12, the application processor 1206, the cellular baseband processor 1224, and / or the transceiver 1222.

[0138] In one example, the configuration for ML-based displacement positioning can be received in assistance data. In some implementations, the configuration can include a time granularity of a set of RSs, a periodicity of reporting a UE’s displacement or a set of displacement radio frequency fingerprinting (RFFP) measurements, a capability of the UE to adjust the time granularity of the set of RSs based on a speed of the UE, or a combination thereof.

[0139] At 1108, the UE can receive, from at least one network node, a set of reference signals (RSs) associated with the ML-based displacement positioning based on the configuration, such as described in connection with Figure 9 described. For example, at 914, the UE 902 can observe, receive, and measure PRSs (or other types of reference signals) transmitted on two (or more) time resources (e.g., from the serving base station 904 and / or one or more neighboring base stations 906). The UE 902 can then derive displacement RFFP measurements (e.g., a difference in RFFP measurements between the two (or more) time resources) based on the PRSs and report the derived displacement RFFP measurements to the LMF 908. The reception of the set of RSs associated with the ML-based displacement positioning can be performed by, for example, Figure 14 the ML-based displacement positioning component 198 of the apparatus 1204 in FIG. 12, the application processor 1206, the cellular baseband processor 1224, and / or the transceiver 1222.

[0140] In one example, the ML-based displacement positioning can be UE-assisted displacement positioning initiated by a network entity. The UE can derive a set of displacement radio frequency fingerprinting positioning (RFFP) measurements based on the set of RSs, and the UE can transmit the set of displacement RFFP measurements to the network entity. In some implementations, the information can include a capability of the UE to assist in the ML-based displacement positioning and a list of displacement RFFPs that can be used for the ML-based displacement positioning at the network entity. In some implementations, a displacement RFFP measurement of the set of displacement RFFP measurements can correspond to a composition of first measurements of first RSs transmitted from the at least one TRP when the UE is at a first location, and a second RFFP measurement of the set of displacement RFFP measurements corresponds to second measurements of second RSs transmitted from the at least one TRP when the UE is at a second location.

[0141] In another example, the ML-based displacement positioning can be performed by the UE. The UE can estimate a displacement of the UE based on the set of RSs using a ML model, and the UE can transmit the estimated displacement of the UE to the network entity. In some implementations, the information associated with the ML-based displacement positioning can include a capability of the UE to perform the ML-based displacement positioning. In some implementations, the information associated with the ML-based displacement positioning can include a bandwidth capability of the UE, a buffering capability of the UE to observe multiple RS resources, one or more supported displacement RFFP types that the UE can construct and report to the network entity, a measurement gap specification, or a combination thereof. In some implementations, the estimated displacement of the UE can include soft information of the estimated displacement. In some implementations, to estimate the displacement of the UE based on the set of RSs using the ML model, the UE can transmit a set of displacement RFFP measurements to the ML model based on the set of RSs, where a first displacement RFFP measurement of the set of displacement RFFP measurements can correspond to first measurements of first RSs transmitted from the at least one TRP when the UE is at a first location, and a second displacement RFFP measurement of the set of displacement RFFP measurements can correspond to second measurements of second RSs transmitted from the at least one TRP when the UE is at a second location, and the UE can receive the displacement of the UE from the ML model based on the set of displacement RFFP measurements.

[0142] In another example, the ML-based displacement positioning can be performed by the UE, and the UE can estimate a first displacement of the UE based on the set of RSs using a ML model, estimate a second displacement of the UE using a sensor, fuse the first displacement and the second displacement to obtain a fused displacement, and then transmit the fused displacement to the network entity.

[0143] In another example, the set of RSs can include one or more PRSs, one or more CSI-RSs, one or more SSBs, or a combination thereof.

[0144] In another example, to receive the set of RSs associated with the ML-based displacement positioning based on the configuration, the UE can receive, when the UE is at a first location, a first subset of RSs of the set of RSs from a first set of TRPs; and receive, when the UE is at a second location, a second subset of RSs of the set of RSs from a second set of TRPs. In some implementations, the first set of TRPs and the second set of TRPs include at least one same TRP.

[0145] In another example, the UE can estimate a displacement of the UE using at least one sensor; and transmit, to the network entity, the estimated displacement of the UE, such as described in connection with Figure 9 For example, along with RAT-based displacement information, the UE 902 can also be configured to report other displacement information obtained using non-RAT methods (e.g., using an IMU, motion sensors, etc., as described in connection with Figure 14 FIG. 1). The estimation of displacement and / or the transmission of the estimated displacement can be performed by, for example, the ML-based displacement positioning component 198 of the apparatus 1204 in Figure 9 FIG. 1). The estimation of displacement and / or the transmission of the estimated displacement can be performed by, for example, the ML-based displacement positioning component 198 of the apparatus 1204 in

[0146] In another example, the UE can train a ML model to perform ML-based displacement positioning based on displacement RFFP measurements, such as described in connection with Figure 14 For example, the UE can be configured to perform AI / ML positioning training and inference for RFFP. The training of the ML model can be performed by, for example, the ML-based displacement positioning component 198 of the apparatus 1204 in Figure 14 For example, the UE can be configured to perform AI / ML positioning training and inference for RFFP. The training of the ML model can be performed by, for example, the ML-based displacement positioning component 198 of the apparatus 1204 in

[0147] In another example, the UE can derive a set of displacement radio frequency fingerprint positioning (RFFP) measurements based on the set of RSs, where the set of RFFP measurements correspond to a composition of a plurality of measurements including: a CIR measurement, a CFR measurement, a RSRQ measurement, a RSRP measurement, a delay spread measurement, an angular spread measurement, an AoA measurement, an AoD measurement, a Doppler spread measurement, or a combination thereof, such as described in connection with ​Described. For example, the displacement RFFP can be a composition of first and second RF measurements, which can include: CIR measurements, CFR measurements, RSRQ measurements, RSRP measurements, delay spread measurements, angular spread measurements, AoA measurements, AoD measurements, Doppler spread measurements, or combinations thereof. Derivation of the displacement RFFP measurement set can be performed by, for example ​ the ML-based displacement positioning component 198 of the apparatus 1204 in FIG. 13, the application processor 1206, the cellular baseband processor 1224, and / or the transceiver 1222.

[0148] ​is a diagram 1200 illustrating an example of a hardware implementation for an apparatus 1204. The apparatus 1204 can be a UE, a component of a UE, or can implement UE functionality. In some aspects, the apparatus 1204 can include a cellular baseband processor 1224 (also referred to as a modem) coupled with one or more transceivers 1222 (e.g., cellular RF transceivers). The cellular baseband processor 1224 can include on-chip memory 1224'. In some aspects, the apparatus 1204 can further include one or more Subscriber Identity Modules (SIM) cards 1220, and an application processor 1206 coupled with a secure digital (SD) card 1208 and a screen 1210. The application processor 1206 can include on-chip memory 1206'. In some aspects, the apparatus 1204 can further include a Bluetooth module 1212, a WLAN module 1214, a SPS module 1216 (e.g., a GNSS module), one or more sensor modules 1218 (e.g., a barometric pressure sensor / altimeter; a motion sensor such as an inertial measurement unit (IMU), a gyroscope, and / or an accelerometer; a light detection and ranging (LIDAR), radio detection and ranging (RADAR), sound navigation and ranging (SONAR), a magnetometer, audio for positioning, and / or other technologies), an additional memory module 1226, a power source 1230, and / or a camera 1232. The Bluetooth module 1212, the WLAN module 1214, and the SPS module 1216 can include on-chip transceivers (TRXs) (or in some cases, just receivers (RXs)). The Bluetooth module 1212, the WLAN module 1214, and the SPS module 1216 can include their own dedicated antennas and / or communicate using the antennas 1280. The cellular baseband processor 1224 communicates with the UE 104 and / or with a RU associated with the network entity 1202 via the one or more antennas 1280 through the transceiver 1222. The cellular baseband processor 1224 and the application processor 1206 can each include computer-readable media / memory 1224', 1206', respectively. The additional memory module 1226 can also be considered a computer-readable medium / memory. Each computer-readable medium / memory 1224', 1206', 1226 can be non-transitory. The cellular baseband processor 1224 and the application processor 1206 each are responsible for general processing, including the execution of software stored on the computer-readable media / memory. The software, when executed by the cellular baseband processor 1224 / application processor 1206, causes the cellular baseband processor 1224 / application processor 1206 to perform the various functions described supra. The computer-readable media / memory can also be used for storing data manipulated by the cellular baseband processor 1224 / application processor 1206 when executing software.The cellular baseband processor 1224 / application processor 1206 can be a component of the UE 350 and can include the memory 360, and / or at least one of the TX processor 368, the RX processor 356, and the controller / processor 359. In one configuration, the apparatus 1204 can be a processor chip (modem and / or application) and only include the cellular baseband processor 1224 and / or the application processor 1206, and in another configuration, the apparatus 1204 can be an entire UE (e.g., see FIG. 1) and include the apparatus 1204 along with the antenna 352, the RF transceiver 370, and the one or more user interface 384 and / or peripheral interfaces 386. ​ The apparatus 1204 can be a component of the UE 350 and include additional modules not shown in FIG. 12. For example, if the apparatus 1204 is a modem chip or a modem modem chip, the apparatus 1204 can further include a memory controller 380, a power supply controller 382, a power supply 370, an audio codec 388, a speaker 390, a microphone 392, etc.

[0149] As discussed above, the ML-based displacement positioning component 198 can be configured to receive, from a network entity, a request to report information associated with ML-based displacement positioning. The ML-based displacement positioning component 198 can be further configured to transmit, to the network entity, information associated with ML-based displacement positioning based on the request. The ML-based displacement positioning component 198 can be further configured to receive, from the network entity, a configuration for ML-based displacement positioning based on the information. The ML-based displacement positioning component 198 can be further configured to receive, from at least one network node, a set of RSs associated with ML-based displacement positioning based on the configuration. The ML-based displacement positioning component 198 can be within the cellular baseband processor 1224, the application processor 1206, or both the cellular baseband processor 1224 and the application processor 1206. The ML-based displacement positioning component 198 can be one or more hardware components specifically configured to carry out the stated processes / algorithm, implemented by one or more processors configured with specific computer readable firmware or software, stored in memory that is read and then executed by the one or more processors, or some combination thereof. As illustrated, the apparatus 1204 can include various components configured for various functions. In one configuration, the apparatus 1204, and in particular the cellular baseband processor 1224 and / or the application processor 1206, can include means for receiving, from a network entity, a request to report information associated with ML-based displacement positioning. The apparatus 1204 can further include means for transmitting, to the network entity, information associated with ML-based displacement positioning based on the request. The apparatus 1204 can further include means for receiving, from the network entity, a configuration for ML-based displacement positioning based on the information. The apparatus 1204 can further include means for receiving, from at least one network node, a set of RSs associated with ML-based displacement positioning based on the configuration.

[0150] In one configuration, a configuration for ML-based displacement positioning can be received in assistance data. In some implementations, the configuration can include a time granularity of a set of RSs, a periodicity of reporting a UE’s displacement or a set of displacement RFFP measurements, a capability of the UE to adjust the time granularity of the set of RSs based on a speed of the UE, or a combination thereof.

[0151] In another configuration, the ML-based displacement positioning can be UE-assisted displacement positioning initiated by a network entity. The apparatus 1204 can also include means for deriving a set of displacement RFFP measurements based on a set of RSs, and means for transmitting the set of displacement RFFP measurements to the network entity. In some implementations, the information can include a capability of the apparatus 1204 to assist in ML-based displacement positioning and a list of displacement RFFPs that can be used for ML-based displacement positioning at the network entity. In some implementations, a displacement RFFP measurement in the set of displacement RFFP measurements can correspond to a first measurement of a first RS transmitted from at least one TRP when the apparatus 1204 is at a first location, and a second RFFP measurement in the set of displacement RFFP measurements corresponds to a second measurement of a second RS transmitted from the at least one TRP when the apparatus 1204 is at a second location.

[0152] In another configuration, the ML-based displacement positioning can be performed by the apparatus 1204. The apparatus 1204 can also include means for estimating a displacement of the apparatus 1204 based on a set of RSs using a ML model, and means for transmitting the estimated displacement of the apparatus 1204 to the network entity. In some implementations, the information associated with the ML-based displacement positioning can include a capability of the apparatus 1204 to perform ML-based displacement positioning. In some implementations, the information associated with the ML-based displacement positioning can include a bandwidth capability of the apparatus 1204, a buffering capability of the apparatus 1204 to observe a plurality of RS resources, one or more supported displacement RFFP types that the apparatus 1204 is capable of constructing and reporting to the network entity, a measurement gap specification, or a combination thereof. In some implementations, the estimated displacement of the apparatus 1204 can include soft information of the estimated displacement. In some implementations, the means for estimating a displacement of the apparatus 1204 based on a set of RSs using a ML model can include configuring the apparatus 1204 to transmit a set of displacement RFFP measurements to the ML model based on the set of RSs, where a first displacement RFFP measurement in the set of displacement RFFP measurements can correspond to a first measurement of a first RS transmitted from at least one TRP when the apparatus 1204 is at a first location, and a second displacement RFFP measurement in the set of displacement RFFP measurements can correspond to a second measurement of a second RS transmitted from the at least one TRP when the apparatus 1204 is at a second location; and receive a displacement of the apparatus 1204 from the ML model based on the set of displacement RFFP measurements.

[0153] In another configuration, the ML-based displacement positioning can be performed by the apparatus 1204, and the apparatus 1204 can further include means for estimating a first displacement of the apparatus 1204 based on a set of RSs using a ML model, means for estimating a second displacement of the apparatus 1204 using a sensor, means for fusing the first displacement and the second displacement to obtain a fused displacement, and means for transmitting the fused displacement to a network entity.

[0154] In another configuration, the set of RSs can include one or more PRSs, one or more CSI-RSs, one or more SSBs, or a combination thereof.

[0155] In another configuration, the means for receiving, based on the configuration, the set of RSs associated with the ML-based displacement positioning can include means for configuring the apparatus 1204 to receive a first subset of RSs in the set of RSs from a first set of TRPs when the apparatus 1204 is located at a first location, and receive a second subset of RSs in the set of RSs from a second set of TRPs when the apparatus 1204 is located at a second location. In some implementations, the first set of TRPs and the second set of TRPs include at least one same TRP.

[0156] In another configuration, the apparatus 1204 can further include means for estimating a displacement of the apparatus 1204 using at least one sensor, and means for transmitting the estimated displacement of the apparatus 1204 to a network entity.

[0157] In another configuration, the apparatus 1204 can further include means for training a ML model to perform the ML-based displacement positioning based on displacement RFFP measurements. In some implementations, the apparatus 1204 can further include means for recording second information from at least one sensor during the reception of the set of RSs, where the ML model is further trained based on the second information.

[0158] In another configuration, the apparatus 1204 can further include means for deriving a set of displacement RFFP measurements based on the set of RSs, where the set of RFFP measurements corresponds to a composition of a plurality of measurements including: a CIR measurement, a CFR measurement, a RSRQ measurement, a RSRP measurement, a delay spread measurement, an angular spread measurement, an AoA measurement, an AoD measurement, a Doppler spread measurement, or a combination thereof, such as described in connection with ​ For the configuration, the displacement RFFP can be a composition of a first RF measurement and a second RF measurement, which can include: a CIR measurement, a CFR measurement, a RSRQ measurement, a RSRP measurement, a delay spread measurement, an angular spread measurement, an AoA measurement, an AoD measurement, a Doppler spread measurement, or a combination thereof. The derivation of the set of displacement RFFP measurements can be performed by, for example,​ The ML-based displacement positioning component 198 of the apparatus 1204 in FIG. 13.00 can perform the operations of the method 1300. In some aspects, the method 1300 can be performed by the ML-based displacement positioning component 198 of the apparatus 1204 in FIG. 13.00, the application processor 1206, the cellular baseband processor 1224, and / or the transceiver 1222.

[0159] The means can be the ML-based displacement positioning component 198 of the apparatus 1204 configured to perform the functions recited by the means. As described above, the apparatus 1204 can include the TX processor 368, the RX processor 356, and the controller / processor 359. Accordingly, in one configuration, the means can be the TX processor 368, the RX processor 356, and / or the controller / processor 359 configured to perform the functions recited by the means.

[0160] ​ is a flow diagram of a method of wireless communication. The method can be performed by a network entity (e.g., one or more location servers 168; location server 706; LMF 908; network entity 1460). The method can enable the network entity to configure a UE to estimate a displacement of the UE based on measuring wireless signals, or to send, to the network entity, measurements associated with estimating the displacement of the UE.

[0161] At 1302, the network entity can transmit, for a UE, a request to report information associated with machine learning (ML)-based displacement positioning, such as described in connection with ​ For example, at 910, the LMF 908 can request the UE 902 to indicate its capability to support or perform AI / ML-based displacement positioning, and / or its capability to report intermediate information (e.g., reference signal measurements, captured RF fingerprints, etc.) that can be used to perform AI / ML-based displacement positioning at the LMF 908. The transmission of the request to report information associated with ML-based displacement positioning can be performed by, for example, ​ The ML-based displacement positioning configuration component 199 of the network entity 1460 in FIG. 14, the network processor 1412, and / or the network interface 1480 can perform the operations of the method 1400. In some aspects, the method 1400 can be performed by the ML-based displacement positioning configuration component 199 of the network entity 1460 in FIG. 14, the network processor 1412, and / or the network interface 1480.

[0162] At 1304, the network entity can receive, from the UE based on the request, information associated with ML-based displacement positioning, such as described in connection with ​ For example, at 910, based on the request, the LMF 908 can receive, from the UE 902, UE capability messaging indicating the UE’s 902 capability to support or perform AI / ML-based displacement positioning, and / or the UE’s 902 capability to report intermediate information that can be used to perform AI / ML-based displacement positioning at the LMF 908. The reception of the information associated with ML-based displacement positioning can be performed by, for example, ​ ​the AI / ML-based displacement positioning configuration component 199 of the network entity 1460 in FIG. 15, the network processor 1412, and / or the network interface 1480.

[0163] At 1306, the network entity can transmit, for the UE, a configuration for ML-based displacement positioning based on the information, such as described in connection with ​ described. For example, at 912, the LMF 908 can transmit, to the UE 902, a configuration for UE-assisted ML displacement positioning or UE-based ML displacement positioning. The transmitting of the configuration for ML-based displacement positioning can be performed by, for example, ​ the AI / ML-based displacement positioning configuration component 199 of the network entity 1460 in FIG. 15, the network processor 1412, and / or the network interface 1480.

[0164] At 1308, the network entity can receive, from the UE, a set of displacement radio frequency fingerprint positioning (RFFP) measurements or an estimated displacement of the UE based on the configuration, such as described in connection with ​ described. For example, at 914, for UE-assisted ML displacement positioning, the UE 902 can derive displacement RFFP measurements based on PRSs, and report the derived displacement RFFP measurements to the LMF 908. On the other hand, for UE-based ML displacement positioning, the UE 902 can make an inference of the displacement information of the UE 902, and the UE 902 can report its displacement information to the LMF 908. The receiving of the set of displacement RFFP measurements or the estimated displacement of the UE can be performed by, for example, ​ the AI / ML-based displacement positioning configuration component 199 of the network entity 1460 in FIG. 15, the network processor 1412, and / or the network interface 1480.

[0165] In one example, ML-based displacement positioning can be performed by a network entity, and the network entity can receive a set of displacement RFFP measurements. The network can use a ML model to estimate a displacement of the UE based on the set of displacement RFFP measurements. In some implementations, the information can include a capability of the UE-assisted ML-based displacement positioning and a list of displacement RFFPs that can be used for ML-based displacement positioning at the network entity. In some implementations, a displacement RFFP measurement in the set of displacement RFFP measurements can correspond to a composition of a first measurement of a first RS transmitted from at least one TRP when the UE is at a first location, and a second RFFP measurement in the set of displacement RFFP measurements can correspond to a second RS measurement transmitted from the at least one TRP when the UE is at a second location.

[0166] In another example, ML-based displacement positioning can be initiated by a UE, and a network entity can receive an estimated displacement of the UE. In some implementations, information associated with ML-based displacement positioning can include a capability of the UE to perform ML-based displacement positioning. In some implementations, information associated with ML-based displacement positioning can include a bandwidth capability of the UE, a buffer capability of the UE to observe multiple RS resources, one or more supported displacement RFFP types that the UE is capable of constructing and reporting to the network entity, a measurement gap specification, or a combination thereof. In some implementations, the estimated displacement of the UE can include soft information of the estimated displacement.

[0167] In another example, a network entity can receive a set of RFFP measurements from a UE, and the network entity can send the set of displacement RFFP measurements to an ML model, where a first displacement RFFP measurement in the set of displacement RFFP measurements can correspond to a first measurement of a first RS transmitted from at least one TRP when the UE is at a first location, and a second displacement RFFP measurement in the set of displacement RFFP measurements can correspond to a second measurement of a second RS transmitted from the at least one TRP when the UE is at a second location, and the network entity can receive a computed displacement of the UE from the ML model based on the set of displacement RFFP measurements.

[0168] In another example, a configuration for ML-based displacement positioning can be sent in assistance data, and where the configuration can include a time granularity of a set of RS for the UE to use to obtain displacement information, a periodicity for the UE to report a second set of displacement or displacement radio frequency fingerprinting positioning (RFFP) measurements, a capability of the UE to adjust the time granularity of the set of RS based on a speed of the UE, or a combination thereof. In some implementations, the set of RS can include one or more PRS, one or more CSI-RS, one or more SSB, or a combination thereof.

[0169] In another example, a network entity can send a first set of RS for a UE via a first set of TRPs when the UE is at a first location, and a second set of RS for the UE via a second set of TRPs when the UE is at a second location. In some implementations, the first set of TRPs and the second set of TRPs include at least one same TRP.

[0170] In another example, a network entity can train an ML model to perform ML-based displacement positioning based on a second set of displacement RFFP measurements.

[0171] In another example, the set of RFFP measurements can correspond to a composition of multiple measurements including: a CIR measurement, a CFR measurement, a RSRQ measurement, a RSRP measurement, a delay spread measurement, an angular spread measurement, an AoD measurement, a Doppler spread measurement, or a combination thereof.

[0172] ​ is a diagram 1400 illustrating an example of a hardware implementation for a network entity 1460. In one example, the network entity 1460 can be located within the core network 120. The network entity 1460 can include a network processor 1412. The network processor 1412 can include on-chip memory 1412’. In some aspects, the network entity 1460 can also include an additional memory module 1414. The network entity 1460 communicates directly (e.g., a backhaul link) or indirectly (e.g., through a RIC) with the CU 1402 via a network interface 1480. The on-chip memory 1412’ and the additional memory module 1414 can each be considered a computer- readable medium / memory. Each computer-readable medium / memory can be non-transitory. The processor 1412 is responsible for general processing, including the execution of software stored on the computer-readable medium / memory. The software, when executed by the corresponding processor, causes the processor to perform the various functions described supra. The computer-readable medium / memory can also be used for storing data that is manipulated by the processor when executing software.

[0173] As discussed supra, the ML-based displacement positioning configuration component 199 can be configured to transmit, for a UE, a request to report information associated with ML-based displacement positioning. The ML-based displacement positioning configuration component 199 can be further configured to receive, from the UE, the information associated with ML-based displacement positioning based on the request. The ML-based displacement positioning configuration component 199 can be further configured to transmit, for the UE, a configuration for ML-based displacement positioning based on the information. The ML-based displacement positioning configuration component 199 can be further configured to receive, from the UE, a set of displacement RFFP measurements or an estimated displacement of the UE based on the configuration.

[0174] The ML-based displacement positioning configuration component 199 can be within the processor 1412. The ML-based displacement positioning configuration component 199 can be one or more hardware components specifically configured to perform the stated processes / algorithm, implemented by one or more processors configured to perform the stated processes / algorithm, stored within a computer-readable medium for implementation by one or more processors, or some combination thereof. The network entity 1460 can include a variety of components configured for various functions. In one configuration, the network entity 1460 can include a means for transmitting, for a UE, a request for reporting information associated with ML-based displacement positioning. The network entity 1460 can also include a means for receiving, from the UE, the information associated with ML-based displacement positioning based on the request. The network entity 1460 can also include a means for transmitting, for the UE, a configuration for ML-based displacement positioning based on the information. The network entity 1460 can also include a means for receiving, from the UE, a set of displacement RFFP measurements or an estimated displacement of the UE based on the configuration.

[0175] In one configuration, the ML-based displacement positioning can be performed by the network entity, and the network entity 1460 can also include a means for receiving a set of displacement RFFP measurements. The network entity 1460 can also include a means for estimating a displacement of the UE based on the set of displacement RFFP measurements using a ML model. In some implementations, the information can include a capability of the UE to assist in ML-based displacement positioning and a list of displacement RFFPs that can be used for ML-based displacement positioning at the network entity. In some implementations, a displacement RFFP measurement of the set of displacement RFFP measurements can correspond to a first measurement of a first RS transmitted from at least one TRP when the UE is at a first location, and a second RFFP measurement of the set of displacement RFFP measurements can correspond to a second measurement of a second RS transmitted from the at least one TRP when the UE is at a second location.

[0176] In another configuration, the ML-based displacement positioning can be initiated by the UE, and the network entity 1460 can also include a means for receiving an estimated displacement of the UE. In some implementations, the information associated with ML-based displacement positioning can include a capability of the UE to perform ML-based displacement positioning. In some implementations, the information associated with ML-based displacement positioning can include a bandwidth capability of the UE, a buffering capability of the UE to observe a plurality of RS resources, one or more supported displacement RFFP types that the UE can construct and report to the network entity, a measurement gap specification, or a combination thereof. In some implementations, the estimated displacement of the UE can include soft information of the estimated displacement.

[0177] In another configuration, the network entity 1460 can also include means for receiving, from the UE, a set of RFFP measurements; and means for transmitting, for the ML model, a set of displaced RFFP measurements, where a first displaced RFFP measurement of the set of displaced RFFP measurements can correspond to a first measurement of a first RS transmitted from the at least one TRP when the UE is at a first location, and a second displaced RFFP measurement of the set of displaced RFFP measurements can correspond to a second measurement of a second RS transmitted from the at least one TRP when the UE is at a second location; and means for receiving, from the ML model, a calculated displacement of the UE based on the set of displaced RFFP measurements.

[0178] In another configuration, the configuration for ML-based displacement positioning can be transmitted in assistance data, and where the configuration can include a time granularity of a set of RSs for the UE to use to obtain displacement information, a periodicity for the UE to report a displacement of the UE or a second set of displacement RFFP measurements, a capability of the UE to adjust the time granularity of the set of RSs based on a speed of the UE, or a combination thereof. In some implementations, the set of RSs can include one or more PRSs, one or more CSI-RSs, one or more SSBs, or a combination thereof.

[0179] In another configuration, the network entity 1460 can also include means for transmitting, for the UE, a first set of RSs via a first set of TRPs when the UE is at a first location, and means for transmitting, for the UE, a second set of RSs via a second set of TRPs when the UE is at a second location. In some implementations, the first set of TRPs and the second set of TRPs include at least one same TRP.

[0180] In another configuration, the network entity 1460 can also include means for training the ML model to perform ML-based displacement positioning based on the second set of displacement RFFP measurements.

[0181] In another configuration, the set of RFFP measurements can correspond to a composition of a plurality of measurements including a CIR measurement, a CFR measurement, an RSRQ measurement, an RSRP measurement, a delay spread measurement, an angular spread measurement, an AoD measurement, a Doppler spread measurement, or a combination thereof.

[0182] The means can be a ML-based displacement positioning configuration component 199 of the network entity 1460 configured to perform the functions recited by the means.

[0183] It should be understood that the particular order or hierarchy of steps in the processes / flow diagrams disclosed are merely examples. It should be appreciated that a particular order or hierarchy of steps in the processes / flow diagrams can be re-arranged based on design choices. Furthermore, some steps can be optional. The accompanying method claims present elements of the various steps in a sample order, and are not limited to the specific order or hierarchy presented.

[0184] The preceding description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other aspects. Thus, the claims are not to be limited to the aspects described herein, but are to be given the full scope defined by the language of the claims. Unless otherwise defined, a reference to a singular element includes “one or more” thereof. Terms such as “if,” “when,” and “while” do not imply direct temporal relationships or reactions. That is, the phrases “when...”, “if...”, and “while” do not mean that the action occurs immediately upon the occurrence of the condition or during the occurrence of the condition, but simply that the action will occur if the condition is met, without a specific or immediate temporal limitation on the occurrence of the action. The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects. Unless otherwise indicated, the term “some” refers to one or more. Combinations such as “at least one of A, B, or C”, “one or more of A, B, or C”, “at least one of the group consisting of A, B, and C”, “one or more of the group consisting of A, B, and C”, and “A, B, and / or C or any combination thereof” include number one of A, number two of A, number one of B, number two of B, number one of C, number two of C, or any combination thereof. In other words, use of the term “combination” in the context of the foregoing examples should not be interpreted as limiting the combination to one of the foregoing combinations, but rather, the term “combination” is intended to encompass any of the foregoing examples, as well as any combination of one or more of A, B, and / or C. Sets should be interpreted as a collection of elements, where the number of elements is one or more. Thus, for a set of X, X will include one or more elements. If a first device receives data from a second device or transmits data to a second device, the data can be received / transmitted directly between the first and second devices, or indirectly via a set of devices between the first and second devices. A device configured to “output” data (such as a transmission, signal, or message) may, for example, transmit the data with a transceiver, or can transfer the data to a device that transmits the data. A device configured to “obtain” data (such as a transmission, signal, or message) may, for example, receive the data with a transceiver, or can obtain the data from a device that receives the data. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims.Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly identified as being "dedicated to the public." Neither is the word "module" nor "mechanism" to be interpreted as a means-plus-function limitation only. Therefore, the claims are not to be limited to the elements recited in the claims, but rather are to be interpreted as encompassing the full and broad scope of equivalents thereof. It is to be understood that any reference to an element in a claim is not a limitation to a means-plus-function limitation unless the claim specifically recites a "means for" or "step for" step-plus-function limitation.

[0185] As used herein, the phrase "based on" is not meant to be limiting and does not imply a closed set of conditions. In other words, the phrase "based on" should not be interpreted as "based only on" or "based solely on." Rather, the phrase "based on" should be interpreted as "based, at least in part, on."

[0186] The following aspects are merely exemplary and are not limited to the disclosed aspects or features.

[0187] Aspect 1 is a method of wireless communication at a user equipment (UE), the method comprising: receiving, from a network entity, a request to report information associated with a machine learning (ML)-based displacement positioning; transmitting, based on the request, the information associated with the ML-based displacement positioning to the network entity; receiving, from the network entity based on the information, a configuration for the ML-based displacement positioning; and receiving, based on the configuration, a set of reference signals (RSs) associated with the ML-based displacement positioning from at least one network node.

[0188] Aspect 2 is the method of aspect 1, wherein the ML-based displacement positioning is a UE- assisted displacement positioning initiated by the network entity, the method further comprising: deriving, based on the set of RSs, a set of displacement radio frequency fingerprint positioning (RFFP) measurements; and transmitting, to the network entity, the set of displacement RFFP measurements.

[0189] Aspect 3 is the method of aspect 2, wherein the information comprises a capability of the UE to assist the ML-based displacement positioning and a list of displacement RFFPs that can be used for the ML-based displacement positioning at the network entity.

[0190] Aspect 4 is the method of aspect 2, wherein a displacement RFFP measurement of the set of displacement RFFP measurements corresponds to a composition of a first measurement of a first reference signal (RS) transmitted from at least one transmission reception point (TRP) when the UE is at a first location and a second RFFP measurement of the set of displacement RFFP measurements corresponds to a second measurement of a second RS transmitted from the at least one TRP when the UE is at a second location.

[0191] Aspect 5 is the method of any of aspects 1-4, wherein the ML-based displacement positioning is performed by the UE, the method further comprising: estimating a displacement of the UE based on the set of RSs using a ML model; and transmitting the estimated displacement of the UE to the network entity.

[0192] Aspect 6 is the method of aspect 5, wherein the information associated with the ML- based displacement positioning comprises a capability of the UE to perform the ML-based displacement positioning.

[0193] Aspect 7 is the method of aspect 5, wherein the information associated with the ML- based displacement positioning comprises: a bandwidth capability of the UE, a buffer capability of the UE to observe a plurality of reference signal (RS) resources, one or more supported displacement RF fingerprinting (RFFP) types that the UE is capable of constructing and reporting to the network entity, a measurement gap specification, or a combination thereof.

[0194] Aspect 8 is the method of aspect 5, wherein the estimated displacement of the UE comprises soft information of the estimated displacement.

[0195] Aspect 9 is the method of aspect 5, wherein estimating the displacement of the UE based on the set of RSs using the ML model comprises: transmitting a set of displacement radio frequency fingerprinting (RFFP) measurements for the ML model based on the set of RSs, wherein a first displacement RFFP measurement of the set of displacement RFFP measurements corresponds to a first measurement of a first reference signal (RS) transmitted from at least one transmission reception point (TRP) when the UE is at a first location and a second displacement RFFP measurement of the set of displacement RFFP measurements corresponds to a second measurement of a second RS transmitted from the at least one TRP when the UE is at a second location; and receiving the displacement of the UE from the ML model based on the set of displacement RFFP measurements.

[0196] Aspect 10 is the method of any of aspects 1-9, wherein the configuration for the ML- based displacement positioning is received in assistance data, and wherein the configuration comprises: a time granularity of the set of RSs, a periodicity of reporting a displacement or a set of displacement radio frequency fingerprinting (RFFP) measurements of the UE, a capability of the UE to adjust the time granularity of the set of RSs based on a velocity of the UE, or a combination thereof.

[0197] Aspect 11 is the method of any of aspects 1-10, the method further comprising: estimating a displacement of the UE using at least one sensor; and transmitting the estimated displacement of the UE to the network entity.

[0198] Aspect 12 is the method of any of aspects 1-11, wherein the ML-based displacement positioning is performed by the UE, the method further comprising: estimating a first displacement of the UE based on the set of RSs using a ML model; estimating a second displacement of the UE using a sensor; fusing the first displacement and the second displacement to obtain a fused displacement; and transmitting the fused displacement to the network entity.

[0199] Aspect 13 is the method of any of aspects 1-12, wherein the set of RSs comprises: one or more positioning reference signals (PRSs), one or more channel state information reference signals (CSI-RSs), one or more synchronization signal blocks (SSBs), or a combination thereof.

[0200] Aspect 14 is the method of any of aspects 1-12, wherein receiving the set of RSs associated with the ML-based displacement positioning based on the configuration comprises: receiving a first subset of RSs in the set of RSs from a first set of transmission reception points (TRPs) when the UE is at a first location; and receiving a second subset of RSs in the set of RSs from a second set of TRPs when the UE is at a second location.

[0201] Aspect 15 is the method of aspect 14, wherein the first set of TRPs and the second set of TRPs comprise at least one same TRP.

[0202] Aspect 16 is the method of any of aspects 1-15, the method further comprising: training a ML model to perform the ML-based displacement positioning based on displacement radio frequency fingerprinting positioning (RFFP) measurements.

[0203] Aspect 17 is the method of aspect 16, the method further comprising: recording second information from at least one sensor during reception of the set of RSs, wherein the ML model is further trained based on the second information.

[0204] Aspect 18 is the method of any of aspects 1-17, the method further comprising: deriving a set of displacement RFFP measurements based on the set of RSs, wherein the set of radio frequency fingerprinting positioning (RFFP) measurements correspond to a plurality of measured configurations, the plurality of measurements comprising: channel impulse response (CIR) measurements, channel frequency response (CFR) measurements, reference signal received quality (RSRQ) measurements, reference signal received power (RSRP) measurements, delay spread measurements, angular spread measurements, angle of arrival (AoA) measurements, angle of departure (AoD) measurements, Doppler spread measurements, or a combination thereof.

[0205] Aspect 19 is an apparatus for wireless communication at a user equipment (UE), comprising: a memory; and at least one processor coupled to the memory and configured to implement any of aspects 1 through 18 based at least in part on information stored in the memory.

[0206] Aspect 20 is the apparatus of aspect 19, further comprising at least one of a transceiver or an antenna coupled to the at least one processor.

[0207] Aspect 21 is an apparatus for wireless communication, comprising means for implementing any of aspects 1 through 18.

[0208] Aspect 22 is a computer-readable medium (for example, a non-transitory computer- readable medium) storing computer executable code, where the code when executed by a processor causes the processor to implement any of aspects 1 through 18.

[0209] Aspect 23 is a method of wireless communication at a network entity, the method comprising: transmitting, for a user equipment (UE), a request to report information associated with a machine learning (ML) based displacement positioning; receiving, from the UE based on the request, the information associated with the ML based displacement positioning; transmitting, for the UE based on the information, a configuration for the ML based displacement positioning; and receiving, from the UE based on the configuration, a set of displacement radio frequency fingerprint positioning (RFFP) measurements or an estimated displacement of the UE.

[0210] Aspect 24 is the method of aspect 23, wherein the ML based displacement positioning is performed by the network entity, and wherein the network entity receives the set of displacement RFFP measurements, the method further comprising: estimating, using a ML model, the displacement of the UE based on the set of displacement RFFP measurements.

[0211] Aspect 25 is the method of aspect 24, wherein the information comprises a capability of the UE to assist the ML based displacement positioning and a list of displacement RFFPs that can be used for the ML based displacement positioning at the network entity.

[0212] Aspect 26 is the method of aspect 24, wherein a displacement RFFP measurement of the set of displacement RFFP measurements corresponds to a composition of first measurements of first reference signals (RSs) transmitted from at least one transmission reception point (TRP) when the UE is at a first location, and a second RFFP measurement of the set of displacement RFFP measurements corresponds to second measurements of second RSs transmitted from at least one TRP when the UE is at a second location.

[0213] Aspect 27 is the method of any of aspects 23-26, wherein the ML-based displacement positioning is initiated by the UE, and wherein the network entity receives an estimated displacement of the UE.

[0214] Aspect 28 is the method of aspect 27, wherein the information associated with the ML- based displacement positioning comprises a capability of the UE to perform the ML-based displacement positioning.

[0215] Aspect 29 is the method of aspect 27, wherein the information associated with the ML- based displacement positioning comprises: a bandwidth capability of the UE, a buffering capability of the UE to observe a plurality of reference signal (RS) resources, one or more supported displacement RFFP types that the UE is capable of constructing and reporting to the network entity, a measurement gap specification, or a combination thereof.

[0216] Aspect 30 is the method of aspect 27, wherein the estimated displacement of the UE comprises soft information of the estimated displacement.

[0217] Aspect 31 is the method of any of aspects 23-30, wherein the network entity receives the set of RFFP measurements from the UE, the method further comprising: sending the set of displacement RFFP measurements to a ML model, wherein a first displacement RFFP measurement of the set of displacement RFFP measurements corresponds to a first measurement of a first reference signal (RS) transmitted from at least one transmission reception point (TRP) when the UE is at a first location, and a second displacement RFFP measurement of the set of displacement RFFP measurements corresponds to a second measurement of a second RS transmitted from the at least one TRP when the UE is at a second location; and receiving a computed displacement of the UE from the ML model based on the set of displacement RFFP measurements.

[0218] Aspect 32 is the method of any of aspects 23-31, wherein the configuration for the ML- based displacement positioning is sent in assistance data, and wherein the configuration comprises: a time granularity of a set of reference signals (RSs) for the UE to obtain displacement information, a periodicity for the UE to report a second set of displacement or displacement radio frequency fingerprinting positioning (RFFP) measurements, a capability of the UE to adjust the time granularity of the set of RSs based on a velocity of the UE, or a combination thereof.

[0219] Aspect 33 is the method of aspect 32, wherein the set of RSs comprises: one or more positioning reference signals (PRSs), one or more channel state information reference signals (CSI-RSs), one or more synchronization signal blocks (SSBs), or a combination thereof.

[0220] Aspect 34 is the method of any of aspects 23-33, further comprising: transmitting, via a first set of transmission reception points (TRPs), a first set of reference signals (RSs) for the UE when the UE is at a first location; and transmitting, via a second set of TRPs, a second set of RSs for the UE when the UE is at a second location.

[0221] Aspect 35 is the method of aspect 34, wherein the first set of TRPs and the second set of TRPs comprise at least one same TRP.

[0222] Aspect 36 is the method of any of aspects 23-35, further comprising: training the ML model to perform the ML-based displacement positioning based on a second set of displacement radio frequency fingerprinting (RFFP) measurements.

[0223] Aspect 37 is the method of any of aspects 23-36, wherein the set of RFFP measurements corresponds to a composition of multiple measurements comprising: channel impulse response (CIR) measurements, channel frequency response (CFR) measurements, reference signal received quality (RSRQ) measurements, reference signal received power (RSRP) measurements, delay spread measurements, angular spread measurements, angle of departure (AoD) measurements, Doppler spread measurements, or a combination thereof.

[0224] Aspect 38 is an apparatus for wireless communication at a network entity, comprising: a memory; and at least one processor coupled to the memory and configured to implement any of aspects 23-37 based at least in part on information stored in the memory.

[0225] Aspect 39 is the apparatus of aspect 38, further comprising at least one of a transceiver or an antenna coupled to the at least one processor.

[0226] Aspect 40 is an apparatus for wireless communication, comprising means for implementing any of aspects 23-37.

[0227] Aspect 41 is a computer-readable medium (for example, a non-transitory computer- readable medium) storing computer executable code, where the code when executed by a processor causes the processor to implement any of aspects 23-37.

Claims

1. An apparatus for wireless communication at a user equipment (UE), the apparatus comprising: Memory; and At least one processor, coupled to the memory, and configured, based at least in part on first information stored in the memory, to: Requests to receive reports from network entities that are associated with machine learning (ML)-based displacement localization; Based on the request, the information associated with the ML-based displacement localization is sent to the network entity; Based on the information, receive the configuration for the ML-based displacement positioning from the network entity; as well as Based on the configuration, a set of reference signals (RS) associated with the ML-based displacement positioning is received from at least one network node.

2. The apparatus of claim 1, wherein the ML-based displacement positioning is UE-assisted displacement positioning initiated by the network entity, and wherein the at least one processor is further configured to: Based on the RS set, a displacement radio frequency fingerprinting (RFFP) measurement set is derived; and The displacement RFFP measurement set is sent to the network entity.

3. The apparatus of claim 2, wherein the information includes the UE's ability to assist in the ML-based displacement positioning and a list of displacement RFFPs that can be used to perform the ML-based displacement positioning at the network entity.

4. The apparatus of claim 2, wherein the displacement RFFP measurement in the displacement RFFP measurement set corresponds to a configuration of a first measurement of a first RS transmitted from at least one transmit-receive point (TRP) when the UE is located at a first position, and the second RFFP measurement in the displacement RFFP measurement set corresponds to a second measurement of a second RS transmitted from the at least one TRP when the UE is located at a second position.

5. The apparatus of claim 1, wherein the ML-based displacement positioning is performed by the UE, and wherein the at least one processor is further configured to: The displacement of the UE is estimated using an ML model based on the RS set; and The estimated displacement of the UE is transmitted to the network entity.

6. The apparatus of claim 5, wherein the information associated with the ML-based displacement positioning includes the UE's ability to perform the ML-based displacement positioning.

7. The apparatus of claim 5, wherein the information associated with the ML-based displacement positioning includes: The bandwidth capability of the UE The UE observes the buffering capacity of multiple RS resources. The UE is configured to build and report one or more supported displacement RFFP types to the network entity. Measurement gap specifications, or Their combination.

8. The apparatus of claim 5, wherein the estimated displacement of the UE includes soft information of the estimated displacement.

9. The apparatus of claim 5, wherein, in order to estimate the displacement of the UE based on the RS set using the ML model, the at least one processor is configured to: Based on the RS set, a displacement radio frequency fingerprinting (RFFP) measurement set is transmitted for the ML model, wherein a first displacement RFFP measurement in the displacement RFFP measurement set corresponds to a first measurement of a first RS transmitted from at least one transmit-receive point (TRP) when the UE is located at a first location, and a second displacement RFFP measurement in the displacement RFFP measurement set corresponds to a second measurement of a second RS transmitted from the at least one TRP when the UE is located at a second location; and The displacement of the UE is received from the ML model based on the displacement RFFP measurement set.

10. The apparatus of claim 1, wherein the at least one processor is configured to receive the ML-based displacement localization in auxiliary data, and wherein the auxiliary data includes: The time granularity of the RS set The report describes the periodicity of the UE's displacement or displacement radio frequency fingerprint (RFFP) measurement set. The ability of the UE to adjust the time granularity of the RS set based on the UE's speed, or Their combination.

11. The apparatus of claim 1, wherein the at least one processor is further configured to: The displacement of the UE is estimated using at least one sensor; and The estimated displacement of the UE is transmitted to the network entity.

12. The apparatus of claim 1, wherein the ML-based displacement positioning is performed by the UE, and wherein the at least one processor is further configured to: The first displacement of the UE is estimated using an ML model based on the RS set; The second displacement of the UE is estimated using a sensor; The first displacement and the second displacement are merged to obtain a merged displacement; as well as The fused displacement is sent to the network entity.

13. The apparatus of claim 1, wherein the RS set comprises: One or more positioning reference signals (PRS), One or more Channel State Information Reference Signals (CSI-RS), One or more synchronization signal blocks (SSBs), or Their combination.

14. The apparatus of claim 1, wherein, in order to receive the RS set associated with the ML-based displacement positioning based on the configuration, the at least one processor is configured to: When the UE is located at the first position, it receives a first subset of RSs in the RS set from a first set of Transmit Receive Points (TRPs); and When the UE is located at the second position, it receives a second subset of RSs in the RS set from the second set of TRP.

15. The apparatus of claim 14, wherein the first set of TRPs and the second set of TRPs comprise at least one identical TRP.

16. The apparatus of claim 1, wherein the at least one processor is further configured to: The ML model is trained to perform the ML-based displacement localization using displacement-based radio frequency fingerprint (RFFP) measurements.

17. The apparatus of claim 16, wherein the at least one processor is further configured to: Second information from at least one sensor is recorded during the reception of the RS set, wherein the ML model is further trained based on the second information.

18. The apparatus of claim 1, wherein the at least one processor is further configured to: Based on the RS set, a displacement radio frequency fingerprinting (RFFP) measurement set is derived, wherein the RFFP measurement set corresponds to a combination of multiple measurements, including: Channel impulse response (CIR) measurement, Channel frequency response (CFR) measurement, Reference signal reception quality (RSRQ) measurement, Reference signal received power (RSRP) measurement, Delay spread measurement, Angle extension measurement, Angle of arrival (AoA) measurement, Departure angle (AoD) measurement, Doppler extended measurement, or Their combination.

19. A method for conducting wireless communication at a user equipment (UE), the method comprising: Requests to receive reports from network entities that are associated with machine learning (ML)-based displacement localization; Based on the request, the information associated with the ML-based displacement localization is sent to the network entity; Based on the information, receive the configuration for the ML-based displacement positioning from the network entity; as well as Based on the configuration, a set of reference signals (RS) associated with the ML-based displacement positioning is received from at least one network node.

20. An apparatus for wireless communication at a network entity, the apparatus comprising: Memory; and At least one processor, coupled to the memory, and configured, based at least in part on first information stored in the memory, to: Requests are sent to user equipment (UE) to report information associated with machine learning (ML)-based displacement localization. Based on the request, receive the information associated with the ML-based displacement positioning from the UE; Based on the information, a configuration for the ML-based displacement positioning is sent to the UE; as well as Based on the configuration, the UE receives a set of displacement radio frequency fingerprint (RFFP) measurements or an estimated displacement from the UE.

21. The apparatus of claim 20, wherein the ML-based displacement localization is performed by the network entity, and based on the reception of the displacement RFFP measurement set, the at least one processor is further configured to: The displacement of the UE is estimated using an ML model based on the displacement RFFP measurement set.

22. The apparatus of claim 20, wherein the ML-based displacement positioning is initiated by the UE, and the at least one processor is configured to receive the estimated displacement from the UE.

23. The apparatus of claim 22, wherein the information associated with the ML-based displacement positioning includes: The bandwidth capability of the UE The UE observes the buffering capacity of multiple reference signal (RS) resources. The UE is configured to build and report one or more supported displacement RFFP types to the network entity. Measurement gap specifications, or Their combination.

24. The apparatus of claim 20, wherein, in order to receive the RFFP measurement set, the at least one processor is configured to receive the RFFP measurement set from the UE, and wherein the at least one processor is further configured to: The displacement RFFP measurement set is transmitted for the ML model, wherein a first displacement RFFP measurement in the displacement RFFP measurement set corresponds to a first measurement of a first reference signal (RS) transmitted from at least one transmit-receive point (TRP) when the UE is located at a first position, and a second displacement RFFP measurement in the displacement RFFP measurement set corresponds to a second measurement of a second RS transmitted from the at least one TRP when the UE is located at a second position; and The displacement of the UE is received from the ML model based on the displacement RFFP measurement set.

25. The apparatus of claim 20, wherein the at least one processor is configured to transmit the configuration for the ML-based displacement positioning in auxiliary data, and wherein the configuration includes: The time granularity of the reference signal (RS) set used by the UE to obtain displacement information. The report describes the periodicity of the second set of displacement or displacement RFFP measurements of the UE. The ability of the UE to adjust the time granularity of the RS set based on the UE's speed, or Their combination.

26. The apparatus of claim 20, wherein the at least one processor is further configured to: When the UE is located at a first position, a first set of reference signals (RS) is transmitted to the UE via a first set of transmit-receive points (TRPs); and When the UE is in the second location, the second set of RS is sent to the UE via the second set of TRP.

27. The apparatus of claim 26, wherein the first set of TRPs and the second set of TRPs comprise at least one identical TRP.

28. The apparatus of claim 20, wherein the at least one processor is further configured to: The ML model is trained to perform the ML-based displacement localization using a second set of displacement-based radio frequency fingerprint (RFFP) measurements.

29. The apparatus of claim 20, wherein the RFFP measurement set corresponds to a configuration of a plurality of measurements, the plurality of measurements including: Channel impulse response (CIR) measurement, Channel frequency response (CFR) measurement, Reference signal reception quality (RSRQ) measurement, Reference signal received power (RSRP) measurement, Delay spread measurement, Angle extension measurement, Departure angle (AoD) measurement, Doppler extended measurement, or Their combination.

30. A method for wireless communication at a network entity, the method comprising: Requests are sent to user equipment (UE) to report information associated with machine learning (ML)-based displacement localization. Based on the request, receive the information associated with the ML-based displacement positioning from the UE; Based on the information, a configuration for the ML-based displacement positioning is sent to the UE; as well as Based on the configuration, the UE receives a set of displacement radio frequency fingerprint (RFFP) measurements or an estimated displacement from the UE.

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