User equipment-based (UE-based) positioning based on self-radio frequency fingerprints (self-RFFP)

Abstract

In an aspect, a UE may transmit one or more reference signals. The UE may obtain one or more self-radio frequency fingerprint (self-RFFP) measurements based on reflections of one or more reference signals transmitted by the UE. The UE may determine a location of the UE based on applying a machine learning model to the one or more self-RFFP measurements.

Description

Technical Field

[0001]

[0013] Generally speaking, aspects of the present disclosure relate to wireless communications. Background Art

[0002] Wireless communication systems have evolved over several generations, including first generation analog wireless telephone service (1G), second generation (2G) digital wireless telephone service (including temporary 2.5G and 2.75G networks), third generation (3G) high speed data, Internet-enabled wireless services, and fourth generation (4G) services (e.g., Long Term Evolution (LTE) or WiMax). Currently, many different types of wireless communication systems are used, including cellular and personal communications service (PCS) systems. Examples of known cellular systems include cellular analog Advanced Mobile Phone System (AMPS) and digital cellular systems based on code division multiple access (CDMA), frequency division multiple access (FDMA), time division multiple access (TDMA), Global System for Mobile Communications (GSM), etc.

[0003] The fifth generation (5G) wireless standard, referred to as New Radio (NR), enables higher data transmission speeds, a greater number of connections, and better coverage, among other improvements. According to the Next Generation Mobile Networks Alliance, the 5G standard is designed to provide higher data rates, more accurate positioning (e.g., based on reference signals for positioning (RS-P), such as downlink, uplink, or sidelink positioning reference signals (PRS)), and other technical enhancements compared to previous standards. These enhancements, along with the use of higher frequency bands, advances in PRS procedures and technologies, and high-density deployments of 5G enable highly accurate 5G-based positioning. Summary of the invention

[0004] A simplified overview of one or more aspects disclosed herein is given below. Therefore, the overview below should not be considered an exhaustive review of all expected aspects, and the overview below should neither be considered to identify key or important elements related to all expected aspects, nor should it be considered to describe the scope associated with any particular aspect. Accordingly, the sole purpose of the overview below is to present certain concepts related to one or more aspects of the mechanisms disclosed herein in a simplified form as a prelude to the detailed description given below.

[0005] In one aspect, a method of operating a user equipment (UE) includes: sending one or more reference signals; obtaining one or more self-radio frequency fingerprint (self-RFFP) measurements based on reflections of the one or more reference signals sent by the UE; and determining a location of the UE based on applying a machine learning model to the one or more self-RFFP measurements.

[0006] In one aspect, a method of operating a user equipment (UE) includes: receiving one or more self-radio frequency fingerprint (self-RFFP) measurements from a second UE obtained by the second UE based on reflections of one or more reference signals sent by the second UE; obtaining one or more sidelink RFFP (SL-RFFP) measurements based on one or more sidelink reference signals between the UE and the second UE; and determining the position of the second UE based on applying a machine learning model to the one or more self-RFFP measurements and the one or more SL-RFFP measurements.

[0007] In one aspect, a user equipment (UE) includes: a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver. The at least one processor is configured to: transmit one or more reference signals via the at least one transceiver; obtain one or more self-RFF fingerprint (self-RFFP) measurements based on reflections of the one or more reference signals transmitted by the UE; and determine a location of the UE based on applying a machine learning model to the one or more self-RFFP measurements.

[0008] In one aspect, a user equipment (UE) includes: a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver. The at least one processor is configured to: receive from a second UE via the at least one transceiver one or more self-RFFP (self-RFFP) measurements obtained by the second UE based on reflections of one or more reference signals sent by the second UE; obtain one or more sidelink RFFP (SL-RFFP) measurements based on one or more sidelink reference signals between the UE and the second UE; and determine a location of the second UE based on applying a machine learning model to the one or more self-RFFP measurements and the one or more SL-RFFP measurements.

[0009] In one aspect, a user equipment (UE) includes: a unit for sending one or more reference signals; a unit for obtaining one or more self-radio frequency fingerprint (self-RFFP) measurements based on reflections of the one or more reference signals sent by the UE; and a unit for determining the location of the UE based on applying a machine learning model to the one or more self-RFFP measurements.

[0010] In one aspect, a user equipment (UE) includes: a unit for receiving one or more self-radio frequency fingerprint (self-RFFP) measurements obtained by a second UE based on reflections of one or more reference signals sent by the second UE; a unit for obtaining one or more sidelink RFFP (SL-RFFP) measurements based on one or more sidelink reference signals between the UE and the second UE; and a unit for determining the position of the second UE based on applying a machine learning model to the one or more self-RFFP measurements and the one or more SL-RFFP measurements.

[0011] In one aspect, a non-transitory computer-readable medium stores computer-executable instructions that, when executed by a user equipment (UE), cause the UE to: send one or more reference signals; obtain one or more self-radio frequency fingerprint (self-RFFP) measurements based on reflections of the one or more reference signals sent by the UE; and determine a location of the UE based on applying a machine learning model to the one or more self-RFFP measurements.

[0012] In one aspect, a non-temporary computer-readable medium stores computer-executable instructions that, when executed by a user equipment (UE), cause the UE to: receive from a second UE one or more self-RF fingerprint (self-RFFP) measurements obtained by the second UE based on reflections of one or more reference signals sent by the second UE; obtain one or more sidelink RFFP (SL-RFFP) measurements based on one or more sidelink reference signals between the UE and the second UE; and determine the position of the second UE based on applying a machine learning model to the one or more self-RFFP measurements and the one or more SL-RFFP measurements.

[0013] Other objects and advantages associated with the various aspects disclosed herein will be apparent to those skilled in the art based on the accompanying drawings and detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The accompanying drawings are presented to aid in describing various aspects of the present disclosure and are provided solely for illustration of the aspects and not limitation thereof.

[0015] Figure 1 An example wireless communication system in accordance with aspects of the present disclosure is shown.

[0016] Figure 2A , 2B 2C illustrate example wireless network structures according to aspects of the present disclosure.

[0017] Figure 3A , 3B 3C are simplified block diagrams of several example aspects of components that may be employed in a user equipment (UE), a base station, and a network entity, respectively, and configured to support communications as taught herein.

[0018] Figure 4 Examples of various positioning methods supported in New Radio (NR) according to aspects of the present disclosure are shown.

[0019] Figure 5 is a diagram representing example radio frequency (RF) channel estimation in accordance with aspects of the present disclosure.

[0020] Figure 6 An example neural network in accordance with aspects of the present disclosure is shown.

[0021] Fig. 7A A mobile device is shown arranged in an ambient environment in accordance with aspects of the present disclosure.

[0022] Figure 7B It means that according to various aspects of the present disclosure, Fig. 7A Graph of the self-RFF fingerprint (self-RFFP) measurement of the reflection shown in .

[0023] Fig. 8A Examples of UE-based positioning operations based on self-RFFP measurements in accordance with aspects of the present disclosure are shown.

[0024] Figure 8B It is a diagram showing various aspects of the present disclosure. Fig. 8A A diagram of the operations performed by the various entities in .

[0025] Fig. 9 is a signaling and event diagram illustrating various operations during the position inference phase in accordance with aspects of the present disclosure.

[0026] Fig.10 is a signaling and event diagram illustrating various operations during a model training phase in accordance with aspects of the present disclosure.

[0027] Fig.11 Example methods of operating a UE during a position inference phase in accordance with aspects of the present disclosure are shown.

[0028] Fig.12 Example methods of operating a UE during a position inference phase in accordance with aspects of the present disclosure are shown.

[0029] Fig.13 Example methods of operating a network entity or UE during a model training phase in accordance with aspects of the present disclosure are shown. DETAILED DESCRIPTION

[0030] Various aspects of the present disclosure are provided in the following description and related drawings relating to various examples provided for illustrative purposes. Alternative aspects may be designed without departing from the scope of the present disclosure. In addition, well-known elements of the present disclosure will not be described in detail or will be omitted to avoid obscuring the relevant details of the present disclosure.

[0031] The words "exemplary" and / or "example" are used herein to mean "serving as an example, instance, or illustration." Any aspect described herein as "exemplary" and / or "example" is not necessarily to be construed as preferred or advantageous over other aspects. Likewise, the term "aspects of the disclosure" does not require that all aspects of the disclosure include the discussed feature, advantage, or mode of operation.

[0032] Those skilled in the art will recognize that the information and signals described below may be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be mentioned throughout the following description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof, which depends in part on the specific application, in part on the desired design, in part on the corresponding technology, and so on.

[0033] In addition, many aspects are described in terms of a sequence of actions to be performed by, for example, elements of a computing device. It will be appreciated that the various actions described herein may be performed by a specific circuit (e.g., an application specific integrated circuit (ASIC)), by program instructions executed by one or more processors, or by a combination of the two. In addition, the sequence of actions described herein may be considered to be fully embodied in any form of non-transitory computer-readable storage medium having a set of corresponding computer instructions stored therein, which, when executed, will cause or instruct the associated processor of the device to perform the functions described herein. Therefore, various aspects of the present disclosure may be embodied in a variety of different forms, all of which are contemplated to be within the scope of the claimed subject matter. In addition, for each of the various aspects described herein, the corresponding form of any such aspect may be described herein as, for example, "logic configured to perform the described actions."

[0034] As used herein, unless otherwise noted, the terms "user equipment" (UE) and "base station" are not intended to be specific to or otherwise limited to any particular radio access technology (RAT). In general, a UE may be any wireless communication device (e.g., a mobile phone, a router, a tablet computer, a laptop computer, a consumer asset location device, a wearable device (e.g., a smart watch, glasses, augmented reality (AR) / virtual reality (VR) headsets, etc.), a vehicle (e.g., a car, a motorcycle, a bicycle, etc.), an Internet of Things (IoT) device, etc.) used by a user to communicate on a wireless communication network. A UE may be mobile or (e.g., at certain times) may be fixed and may communicate with a radio access network (RAN). As used herein, the term "UE" may be interchangeably referred to as an "access terminal" or "AT", "client device", "wireless device", "subscriber device", "subscriber terminal", "subscriber station", "user terminal" or UT, "mobile device", "mobile terminal", "mobile station" or variations thereof. In general, a UE may communicate with a core network via a RAN, and the core network may connect the UE to an external network such as the Internet and to other UEs. Of course, other mechanisms are also possible for the UE to connect to the core network and / or the Internet, such as over a wired access network, a wireless local area network (WLAN) network (e.g., based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11 specification, etc.), and so on.

[0035] The base station may operate according to one of several RATs in communication with the UE, depending on the network in which the base station is deployed, and the base station may be alternatively referred to as an access point (AP), a network node, a node B, an evolved node B (eNB), a next generation eNB (ng-eNB), a new radio (NR) node B (also referred to as a gNB or gNodeB), etc. The base station may be primarily used to support wireless access for the UE, including supporting data, voice and / or signaling connections for the supported UE. In some systems, the base station may provide pure edge node signaling functions, while in other systems, it may provide additional control and / or network management functions. The communication link through which the UE can send a signal to the base station is called an uplink (UL) channel (e.g., a reverse traffic channel, a reverse control channel, an access channel, etc.). The communication link through which the base station can send a signal to the UE is called a downlink (DL) or forward link channel (e.g., a paging channel, a control channel, a broadcast channel, a forward traffic channel, etc.). As used herein, the term traffic channel (TCH) may refer to an uplink / reverse traffic channel or a downlink / forward traffic channel.

[0036] The term "base station" may refer to a single physical transmit receive point (TRP), or to multiple physical TRPs that may or may not be co-located. For example, where the term "base station" refers to a single physical TRP, the physical TRP may be an antenna of the base station corresponding to a cell (or several cell sectors) of the base station. Where the term "base station" refers to multiple co-located physical TRPs, the physical TRP may be an antenna array of the base station (e.g., as in a multiple-input multiple-output (MIMO) system or where the base station employs beamforming). Where the term "base station" refers to multiple non-co-located physical TRPs, the physical TRP may be a distributed antenna system (DAS) (a network of spatially separated antennas connected to a common resource via a transmission medium) or a remote radio head (RRH) (a remote base station connected to a serving base station). Alternatively, the non-co-located physical TRP may be a serving base station that receives measurement reports from the UE and a neighbor base station whose reference radio frequency (RF) signal the UE is measuring. Because as used herein, a TRP is the point from which a base station transmits and receives wireless signals, references to transmissions from a base station or receptions at a base station should be understood to refer to the specific TRP of the base station.

[0037] In some implementations of supporting positioning of a UE, a base station may not support wireless access for the UE (e.g., may not support data, voice, and / or signaling connections for the UE), but may instead send a reference signal to the UE for measurement by the UE, and / or may receive and measure a signal sent by the UE. Such a base station may be referred to as a positioning beacon (e.g., when sending a signal to the UE) and / or a position measurement unit (e.g., when receiving and measuring a signal from the UE).

[0038] "RF signals" include electromagnetic waves of a given frequency that transmit information through the space between a transmitter and a receiver. As used herein, a transmitter may send a single "RF signal" or multiple "RF signals" to a receiver. However, due to the propagation characteristics of RF signals through multipath channels, a receiver may receive multiple "RF signals" corresponding to each transmitted RF signal. The same RF signal transmitted on different paths between a transmitter and a receiver may be referred to as a "multipath" RF signal. As used herein, RF signals may also be referred to as "wireless signals" or simply "signals", where it is clear from the context that the term "signal" refers to either wireless signals or RF signals.

[0039] Figure 1An example wireless communication system 100 is shown in accordance with the present disclosure. The wireless communication system 100 (which may also be referred to as a wireless wide area network (WWAN)) may include various base stations 102 (labeled as "BS") and various UEs 104. The base stations 102 may include macrocell base stations (high-power cellular base stations) and / or small cell base stations (low-power cellular base stations). In an aspect, the macrocell base stations may include eNBs and / or ng-eNBs (where the wireless communication system 100 corresponds to an LTE network) or gNBs (where the wireless communication system 100 corresponds to an NR network) or a combination of both, and the small cell base stations may include femtocells, picocells, microcells, etc.

[0040] The base stations 102 may collectively form a RAN and interface with a core network 170 (e.g., an evolved packet core (EPC) or a 5G core (5GC)) via a backhaul link 122, and interface with one or more location servers 172 (e.g., a location management function (LMF) or a secure user plane location (SUPL) positioning platform (SLP)) via the core network 170. The location server 172 may be part of the core network 170, or may be external to the core network 170. The location server 172 may be integrated with the base station 102. The UE 104 may communicate with the location server 172 directly or indirectly. For example, the UE 104 may communicate with the location server 172 via the base station 102 currently serving the UE 104. The UE 104 may also communicate with the location server 172 via another path, such as via an application server (not shown), via another network (e.g., via a wireless local area network (WLAN) access point (AP) (e.g., the AP 150 described below)), and the like. For signaling purposes, communication between UE 104 and location server 172 may be represented as an indirect connection (e.g., through core network 170, etc.) or a direct connection (e.g., as shown via direct connection 128), with intermediate nodes (if any) omitted from the signaling diagram for clarity.

[0041] The base station 102 may also perform functions related to one or more of the following, among other functions: transmission of user data, radio channel encryption and decryption, integrity protection, header compression, mobility control functions (e.g., handover, dual connectivity), inter-cell interference coordination, connection establishment and release, load balancing, distribution of non-access stratum (NAS) messages, NAS node selection, synchronization, RAN sharing, multimedia broadcast multicast service (MBMS), subscriber and device tracking, RAN information management (RIM), paging, positioning, and transmission of warning messages. The base stations 102 may communicate with each other directly or indirectly (e.g., through EPC / 5GC) via a backhaul link 134 (which may be wired or wireless).

[0042] The base station 102 can communicate with the UE 104 wirelessly. Each of the base stations 102 can provide communication coverage for a corresponding geographic coverage area 110. In one aspect, the base station 102 can support one or more cells in each geographic coverage area 110. A "cell" is a logical communication entity for communicating with a base station (e.g., on a certain frequency resource (referred to as a carrier frequency, component carrier, carrier, frequency band, etc.) and can be associated with an identifier (e.g., a physical cell identifier (PCI), an enhanced cell identifier (FCI), a virtual cell identifier (VCI), a cell global identifier (CGI), etc.) for distinguishing cells operating via the same or different carrier frequencies. In some cases, different cells may be configured according to different protocol types (e.g., machine type communication (MTC), narrowband IoT (NB-IoT), enhanced mobile broadband (eMBB) or other protocol types) that can provide access to different types of UEs. Because a cell is supported by a specific base station, the term "cell" can refer to either or both of the logical communication entity and the base station supporting it, depending on the context. In addition, since the TRP is generally the physical transmission point of the cell, the terms "cell" and "TRP" can be used interchangeably. In some cases, the term "cell" can also refer to a geographic coverage area (e.g., a sector) of a base station, within which a carrier frequency can be detected and used for communications within a portion of the geographic coverage area 110.

[0043] Although the geographic coverage areas 110 of adjacent macrocell base stations 102 may partially overlap (e.g., in a handover region), some of the geographic coverage areas 110 may substantially overlap with a larger geographic coverage area 110. For example, a small cell base station 102' (labeled "SC" for "small cell") may have a geographic coverage area 110' that substantially overlaps with the geographic coverage areas 110 of one or more macrocell base stations 102. A network that includes both small cell base stations and macrocell base stations may be referred to as a heterogeneous network. A heterogeneous network may also include a home eNB (HeNB), which may provide services to a restricted group referred to as a closed subscriber group (CSG).

[0044] The communication link 120 between the base station 102 and the UE 104 may include an uplink (also referred to as a reverse link) transmission from the UE 104 to the base station 102 and / or a downlink (DL) (also referred to as a forward link) transmission from the base station 102 to the UE 104. The communication link 120 may use MIMO antenna technology, which includes spatial multiplexing, beamforming, and / or transmit diversity. The communication link 120 may be over one or more carrier frequencies. The allocation of carriers may be asymmetric with respect to the downlink and uplink (e.g., more or fewer carriers may be allocated for the downlink than for the uplink).

[0045] The wireless communication system 100 may also include a wireless local area network (WLAN) access point (AP) 150 that communicates with a WLAN station (STA) 152 in an unlicensed spectrum (e.g., 5 GHz) via a communication link 154. When communicating in the unlicensed spectrum, the WLAN STA 152 and / or the WLAN AP 150 may perform a clear channel assessment (CCA) or a listen before talk (LBT) procedure before communicating to determine whether a channel is available.

[0046] The small cell base station 102' can operate in a licensed and / or unlicensed spectrum. When operating in an unlicensed spectrum, the small cell base station 102' can adopt LTE or NR technology and use the same 5 GHz unlicensed spectrum as the 5 GHz unlicensed spectrum used by the WLAN AP 150. The small cell base station 102' adopting LTE / 5G in the unlicensed spectrum can improve the coverage of the access network and / or increase the capacity of the access network. NR in the unlicensed spectrum can be referred to as NR-U. LTE in the unlicensed spectrum can be referred to as LTE-U, License Assisted Access (LAA) or MulteFire.

[0047] The wireless communication system 100 may also include a millimeter wave (mmW) base station 180 for communicating with the UE 182, which may operate in mmW frequencies and / or near mmW frequencies. Extremely high frequency (EHF) is a portion of RF in the electromagnetic spectrum. EHF has a range of 30 GHz to 300 GHz and has a wavelength between 1 mm and 10 mm. Radio waves in this band may be referred to as millimeter waves. Near mmW can be extended down to a frequency of 3 GHz with a wavelength of 100 mm. The super high frequency (SHF) band extends between 3 GHz and 30 GHz and is also referred to as centimeter waves. Communications using mmW / near mmW radio frequency bands have high path loss and relatively short distances. The mmW base station 180 and the UE 182 may utilize beamforming (sending and / or receiving) on ​​the mmW communication link 184 to compensate for extremely high path loss and short distances. In addition, it will be appreciated that in alternative configurations, one or more base stations 102 may also use mmW or near mmW and beamforming for transmission. Accordingly, it will be appreciated that the foregoing description is merely exemplary and should not be construed as limiting the various aspects disclosed herein.

[0048] Transmit beamforming is a technique for focusing RF signals in a specific direction. Traditionally, when a network node (e.g., a base station) broadcasts an RF signal, it broadcasts the signal in all directions (omnidirectionally). With transmit beamforming, the network node determines where a given target device (e.g., a UE) is located (relative to the transmitting network node) and projects a stronger downlink RF signal in that specific direction, thereby providing a faster (in terms of data rate) and stronger RF signal for the receiving device. In order to change the direction of the RF signal when transmitting, the network node can control the phase and relative amplitude of the RF signal at each of the one or more transmitters that broadcast the RF signal. For example, the network node can use an array of antennas (referred to as a "phased array" or "antenna array") that create beams of RF waves that can be "guided" to points in different directions without actually moving the antennas. Specifically, the RF current from the transmitter is fed to individual antennas with the correct phase relationship so that the radio waves from the individual antennas are added together to increase radiation in the desired direction and destructively in the undesired direction to suppress radiation.

[0049] The transmit beams can be quasi-co-located, which means that they appear to have the same parameters to the receiver (e.g., UE), regardless of whether the transmit antennas of the network nodes themselves are physically co-located. In NR, there are four types of quasi-co-located (QCL) relationships. Specifically, a given type of QCL relationship means that certain parameters about the second reference RF signal on the second beam can be derived from information about the source reference RF signal on the source beam. Therefore, if the source reference RF signal is QCL type A, the receiver can use the source reference RF signal to estimate the Doppler shift, Doppler spread, average delay and delay spread of the second reference RF signal sent on the same channel. If the source reference RF signal is QCL type B, the receiver can use the source reference RF signal to estimate the Doppler shift and Doppler spread of the second reference RF signal sent on the same channel. If the source reference RF signal is QCL type C, the receiver can use the source reference RF signal to estimate the Doppler shift and average delay of the second reference RF signal sent on the same channel. If the source reference RF signal is QCL type D, the receiver may use the source reference RF signal to estimate spatial reception parameters of a second reference RF signal transmitted on the same channel.

[0050] In receive beamforming, a receiver uses a receive beam to amplify an RF signal detected on a given channel. For example, a receiver may increase a gain setting and / or adjust a phase setting of an antenna array in a particular direction to amplify an RF signal received from that direction (e.g., to increase the gain level of the RF signal). Thus, when a receiver is said to be beamforming in a certain direction, it means that the beam gain in that direction is high relative to the beam gain along other directions, or that the beam gain in that direction is the highest compared to the beam gain in that direction of all other receive beams available to the receiver. This results in a stronger received signal strength (e.g., reference signal received power (RSRP), reference signal received quality (RSRQ), signal to interference plus noise ratio (SINR), etc.) of the RF signal received from that direction.

[0051] The transmit beam and the receive beam may be spatially correlated. Spatial correlation means that the parameters of the second beam (e.g., transmit beam or receive beam) used for the second reference signal may be derived based on information about the first beam (e.g., receive beam or transmit beam) used for the first reference signal. For example, the UE may use a specific receive beam to receive a reference downlink reference signal (e.g., a synchronization signal block (SSB)) from a base station. The UE may then form a transmit beam for sending an uplink reference signal (e.g., a sounding reference signal (SRS), etc.) to the base station based on the parameters of the receive beam.

[0052] It is to be noted that a "downlink" beam can be a transmit beam or a receive beam, depending on the entity that forms it. For example, if the base station is forming a downlink beam for sending a reference signal to the UE, the downlink beam is a transmit beam. However, if the UE is forming a downlink beam, it is a receive beam for receiving a downlink reference signal. Similarly, an "uplink" beam can be a transmit beam or a receive beam, depending on the entity that forms it. For example, if the base station is forming an uplink beam, it is an uplink receive beam, and if the UE is forming an uplink beam, it is an uplink transmit beam.

[0053] The electromagnetic spectrum is typically subdivided into various categories, bands, channels, etc. based on frequency / wavelength. In 5G NR, two initial operating bands have been identified with the frequency range names FR1 (410 MHz-7.125 GHz) and FR2 (24.25 GHz-52.6 GHz). It should be understood that although a portion of FR1 is greater than 6 GHz, FR1 is often (interchangeably) referred to as the "sub-6 GHz" band in various documents and articles. A similar naming issue sometimes arises with respect to FR2, which is often (interchangeably) referred to as the "millimeter wave" band in documents and articles, although it is different from the extremely high frequency (EHF) band (30 GHz-300 GHz) which is identified as the "millimeter wave" band by the International Telecommunication Union (ITU).

[0054] The frequencies between FR1 and FR2 are generally referred to as intermediate frequencies. Recent 5G NR research has identified the operating bands of these intermediate frequencies as the frequency range name FR3 (7.125GHz-24.25GHz). The frequency bands falling within FR3 can inherit FR1 characteristics and / or FR2 characteristics, and therefore the characteristics of FR1 and / or FR2 can be effectively extended to the intermediate frequencies. In addition, higher frequency bands are currently being explored to extend 5G NR operations beyond 52.6GHz. For example, three higher operating bands have been identified as the frequency range names FR4a or FR4-1 (52.6GHz-71GHz), FR4 (52.6GHz-114.25GHz) and FR5 (114.25GHz-300GHz). Each of these higher frequency bands falls within the EHF band.

[0055] In view of the above aspects, unless otherwise specifically stated, it should be understood that if the term "sub-6 GHz" or the like is used herein, it can broadly represent frequencies that can be less than 6 GHz, can be within FR1, or can include mid-band frequencies. In addition, unless otherwise specifically stated, it should be understood that if the term "millimeter wave" or the like is used herein, it can broadly represent frequencies that can include mid-band frequencies, can be within FR2, FR4, FR4-a or FR4-1 and / or FR5, or can be within the EHF band.

[0056] In a multi-carrier system (such as 5G), one of the carrier frequencies is referred to as a "primary carrier" or "anchor carrier" or "primary serving cell" or "PCell", and the remaining carrier frequencies are referred to as "secondary carriers" or "secondary serving cells" or "SCells". In carrier aggregation, the anchor carrier is a carrier operating on the primary frequency (e.g., FR1) utilized by the UE 104 / 182 and the cell in which the UE 104 / 182 performs an initial radio resource control (RRC) connection establishment procedure or initiates an RRC connection re-establishment procedure. The primary carrier carries all common and UE-specific control channels and can be a carrier in a licensed frequency (however, this is not always the case). A secondary carrier is a carrier operating on a second frequency (e.g., FR2), which can be configured when an RRC connection is established between the UE 104 and the anchor carrier, and can be used to provide additional radio resources. In some cases, the secondary carrier can be a carrier in an unlicensed frequency. The secondary carrier may contain only necessary signaling information and signals, for example, there may be no UE-specific signaling information and signals in the secondary carrier, because both the primary uplink carrier and the primary downlink carrier are usually UE-specific. This means that different UEs 104 / 182 in a cell may have different downlink primary carriers. This is also true for the uplink primary carrier. The network is able to change the primary carrier of any UE 104 / 182 at any time. This is done to, for example, balance the load on different carriers. Since a "serving cell" (whether a PCell or an SCell) corresponds to a carrier frequency / component carrier on which a base station is communicating, the terms "cell", "serving cell", "component carrier", "carrier frequency", etc. can be used interchangeably.

[0057] For example, still referring to Figure 1, one of the frequencies utilized by macrocell base station 102 may be an anchor carrier (or "PCell"), and the other frequencies utilized by macrocell base station 102 and / or mmW base station 180 may be secondary carriers ("SCells"). Simultaneous transmission and / or reception of multiple carriers enables UE 104 / 182 to significantly increase its data transmission and / or reception rate. For example, two 20 MHz aggregated carriers in a multi-carrier system would theoretically result in a two-fold increase in data rate (i.e., 40 MHz) (compared to the data rate achieved with a single 20 MHz carrier).

[0058] The wireless communication system 100 may also include a UE 164, which may communicate with the macrocell base station 102 over the communication link 120 and / or communicate with the mmW base station 180 over the mmW communication link 184. For example, the macrocell base station 102 may support a PCell and one or more SCells for the UE 164, and the mmW base station 180 may support one or more SCells for the UE 164.

[0059] In some cases, UE 164 and UE 182 may be able to perform sidelink communications. A UE supporting a sidelink (SL-UE) may communicate with base station 102 over communication link 120 using a Uu interface (i.e., an air interface between a UE and a base station). SL-UEs (e.g., UE 164, UE 182) may also communicate directly with each other over a wireless sidelink 160 using a PC5 interface (i.e., an air interface between UEs supporting a sidelink). A wireless sidelink (or just "sidelink") is an adaptation to a core cellular (e.g., LTE, NR) standard that allows direct communication between two or more UEs without communicating through a base station. Sidelink communications may be unicast or multicast and may be used for device-to-device (D2D) media sharing, vehicle-to-vehicle (V2V) communications, vehicle-to-everything (V2X) communications (e.g., cellular V2X (cV2X) communications, enhanced V2X (eV2X) communications, etc.), emergency rescue applications, etc. One or more SL-UEs in a group of SL-UEs utilizing sidelink communications may be within the geographic coverage area 110 of the base station 102. Other SL-UEs in such a group may be outside the geographic coverage area 110 of the base station 102, or otherwise unable to receive transmissions from the base station 102. In some cases, a group of SL-UEs communicating via sidelink communications may utilize a one-to-many (1:M) system, in which each SL-UE transmits to each other SL-UE in the group. In some cases, the base station 102 facilitates the scheduling of resources for sidelink communications. In other cases, sidelink communications are performed between SL-UEs without the participation of the base station 102.

[0060] In one aspect, the sidelink 160 can operate on a wireless communication medium of interest, which can be shared with other wireless communications between other vehicles and / or infrastructure access points and other RATs. A "medium" can consist of one or more time, frequency, and / or spatial communication resources (e.g., including one or more channels across one or more carriers) associated with wireless communications between one or more transmitter / receiver pairs. In one aspect, the medium of interest can correspond to at least a portion of an unlicensed band shared between various RATs. Although different licensed bands have been reserved for certain communication systems (e.g., by government entities such as the Federal Communications Commission (FCC) of the United States), these systems (particularly those employing small cell access points) have recently expanded operations into unlicensed bands (such as the unlicensed national information infrastructure (U-NII) bands used by wireless local area network (WLAN) technologies, most notably IEEE 802.11x WLAN technologies, commonly referred to as "Wi-Fi"). Example systems of this type include different variants of CDMA systems, TDMA systems, FDMA systems, orthogonal FDMA (OFDMA) systems, single carrier FDMA (SC-FDMA) systems, and the like.

[0061] Note that although Figure 1 Only two of the UEs are shown as SL-UEs (i.e., UEs 164 and 182), but any of the shown UEs may be SL-UEs. In addition, although only UE 182 is described as being capable of beamforming, any of the shown UEs (including UE 164) may be capable of beamforming. In the case where the SL-UEs are capable of beamforming, they may beamform toward each other (i.e., toward other SLUEs), toward other UEs (e.g., UE 104), toward a base station (e.g., base station 102, 180, cell 102', access point 150), etc. Therefore, in some cases, UEs 164 and 182 may utilize beamforming on the sidelink 160.

[0062] exist Figure 1 In the example of Figure 1104) can receive a signal 124 from one or more earth orbiting spacecraft (SV) 112 (e.g., satellites). In one aspect, SV 112 can be part of a satellite positioning system that UE 104 can use as an independent source of location information. A satellite positioning system typically includes a system of transmitters (e.g., SV 112) positioned to enable a receiver (e.g., UE 104) to determine its position on or above the earth based at least in part on a positioning signal (e.g., signal 124) received from the transmitter. Such a transmitter typically sends a signal marked with a repeating pseudo-random noise (PN) code of a set number of chips. Although the transmitter is typically located in the SV 112, it can sometimes be located in a ground-based control station, base station 102, and / or other UE 104. UE 104 may include one or more dedicated receivers specifically designed to receive the signal 124 for deriving geographic location information from the SV 112.

[0063] In a satellite positioning system, the use of the signal 124 may be enhanced by various satellite-based augmentation systems (SBAS), which may be associated with or otherwise enabled for use with one or more global and / or regional navigation satellite systems. For example, the SBAS may include augmentation systems that provide integrity information, differential corrections, etc., such as the Wide Area Augmentation System (WAAS), the European Geostationary Navigation Overlay Service (EGNOS), the Multifunction Satellite Augmentation System (MSAS), the Global Positioning System (GPS) Assisted Geographic Augmentation Navigation, or the GPS and Geographic Augmentation Navigation System (GAGAN), etc. Thus, as used herein, a satellite positioning system may include any combination of one or more global and / or regional navigation satellites associated with such one or more satellite positioning systems.

[0064] In one aspect, SV 112 may additionally or alternatively be part of one or more non-terrestrial networks (NTNs). In an NTN, SV 112 is connected to an earth station (also referred to as a ground station, NTN gateway, or gateway), which in turn is connected to an element in a 5G network, such as a modified base station 102 (without a ground antenna) or a network node in a 5GC. This element in turn will provide access to other elements in the 5G network, and ultimately provide access to entities outside the 5G network (such as Internet web servers and other user devices). In this way, instead of or in addition to receiving communication signals from a ground base station 102, a UE 104 can receive communication signals (e.g., signal 124) from a SV 112.

[0065] The wireless communication system 100 may also include one or more UEs (such as UE 190) indirectly connected to one or more communication networks via one or more device-to-device (D2D) peer-to-peer (P2P) links (referred to as “sidelinks”). Figure 1 In the example of FIG. 1 , UE 190 has a D2D P2P link 192 with one of UEs 104 connected to one of base stations 102 (e.g., through D2D P2P link 192, UE 190 can indirectly obtain cellular connectivity) and a D2D P2P link 194 with WLAN STA 152 connected to WLAN AP 150 (through D2D P2P link 194, UE 190 can indirectly obtain WLAN-based Internet connectivity). In an example, any well-known D2D RAT (such as LTE Direct (LTE-D), WiFi Direct (WiFi-D), etc.) to support D2D P2P links 192 and 194.

[0066] Figure 2A An example wireless network architecture 200 is shown. For example, the 5GC 210 (also referred to as the Next Generation Core (NGC)) can be functionally viewed as a control plane (C-plane) function 214 (e.g., UE registration, authentication, network access, gateway selection, etc.) and a user plane (U-plane) function 212 (e.g., UE gateway function, access to data networks, IP routing, etc.), which operate in conjunction to form a core network. A user plane interface (NG-U) 213 and a control plane interface (NG-C) 215 connect the gNB 222 to the 5GC 210, and specifically, to the user plane function 212 and the control plane function 214, respectively. In another configuration, the ng-eNB 224 can also be connected to the 5GC 210 via the NG-C 215 to the control plane function 214 and the NG-U 213 to the user plane function 212. In addition, the ng-eNB 224 can communicate directly with the gNB 222 via the backhaul connection 223. In some configurations, the next generation RAN (NG-RAN) 220 can have one or more gNBs 222, while other configurations include one or more of the ng-eNB 224 and the gNB 222. Either the gNB 222 or the ng-eNB 224 (or both) can communicate with one or more UEs 204 (e.g., any UE described herein).

[0067] Another optional aspect may include a location server 230 that can communicate with the 5GC 210 to provide location assistance for the UE 204. The location server 230 can be implemented as multiple separate servers (e.g., physically separate servers, different software modules on a single server, different software modules spread across multiple physical servers, etc.), or alternatively, each can correspond to a single server. The location server 230 can be configured to support one or more location services for the UE 204 that can be connected to the location server 230 via the core network, the 5GC 210, and / or via the Internet (not shown). In addition, the location server 230 can be integrated into a component of the core network, or alternatively, can be external to the core network (e.g., a third-party server, such as an original equipment manufacturer (OEM) server or a service server).

[0068] Figure 2B Another example wireless network structure 240 is shown. 5GC 260 (which may correspond to Figure 2AThe 5GC 210 in the UE 200 may be functionally considered as a control plane function provided by an access and mobility management function (AMF) 264, and a user plane function provided by a user plane function (UPF) 262, which operate in conjunction to form a core network (i.e., 5GC 260). The functions of the AMF 264 include registration management, connection management, reachability management, mobility management, lawful interception, transmission of session management (SM) messages between one or more UEs 204 (e.g., any UE described herein) and a session management function (SMF) 266, a transparent proxy service for routing SM messages, access authentication and access authorization, transmission of short message service (SMS) messages between the UE 204 and a short message service function (SMSF) (not shown), and a security anchor function (SEAF). The AMF 264 also interacts with an authentication server function (AUSF) (not shown) and the UE 204, and receives an intermediate key established as a result of the UE 204 authentication process. In the case of authentication based on the UMTS (Universal Mobile Telecommunications System) Subscriber Identity Module (USIM), the AMF 264 obtains security materials from the AUSF. The functions of the AMF 264 also include security context management (SCM). The SCM receives keys from the SEAF, where the SCM uses the keys to derive keys specific to the access network. The functions of the AMF 264 also include location service management for management services, transmission of location service messages between the UE 204 and the Location Management Function (LMF) 270 (which acts as a location server 230), transmission of location service messages between the NG-RAN 220 and the LMF 270, allocation of EPS bearer identifiers for interworking with the Evolved Packet System (EPS), and notification of UE 204 mobility events. In addition, the AMF 264 also supports functions for non-3GPP (Third Generation Partnership Project) access networks.

[0069] The functions of the UPF 262 include: acting as an anchor point for intra-RAT / inter-RAT mobility (when applicable), acting as an external protocol data unit (PDU) session point for interconnection to a data network (not shown), providing packet routing and forwarding, packet inspection, user plane policy rule enforcement (e.g., gating, redirection, traffic steering), lawful interception (user plane collection), traffic utilization reporting, quality of service (QoS) processing for the user plane (e.g., uplink / downlink rate enforcement, reflective QoS marking in downlink), uplink traffic verification (service data flow (SDF) to QoS flow mapping), transmission level packet marking in uplink and downlink, downlink packet buffering and downlink data notification triggering, and sending and forwarding one or more "end flags" to the source RAN node. The UPF 262 may also support the transmission of location service messages on the user plane between the UE 204 and a location server (such as the SLP 272).

[0070] The functions of SMF 266 include session management, UE Internet Protocol (IP) address allocation and management, selection and control of user plane functions, configuring traffic steering at UPF 262 to route traffic to the correct destination, control of parts of policy enforcement and QoS, and downlink data notification. The interface over which SMF 266 communicates with AMF 264 is called the N11 interface.

[0071] Another optional aspect may include LMF 270, which can communicate with 5GC 260 to provide location assistance to UE 204. LMF 270 can be implemented as multiple separate servers (e.g., physically separate servers, different software modules on a single server, different software modules spread across multiple physical servers, etc.), or alternatively, each can correspond to a single server. LMF 270 can be configured to support one or more location services for UE 204, and UE 204 can connect to LMF 270 via the core network, 5GC 260 and / or via the Internet (not shown). SLP 272 can support similar functionality as LMF 270, but LMF 270 can communicate with AMF 264, NG-RAN 220, and UE 204 on the control plane (e.g., using interfaces and protocols intended to transmit signaling messages rather than voice or data), and SLP 272 can communicate with UE 204 and external clients (e.g., third-party servers 274) on the user plane (e.g., using protocols intended to carry voice and / or data, such as Transmission Control Protocol (TCP) and / or IP).

[0072] Yet another optional aspect may include a third-party server 274 that can communicate with the LMF 270, SLP 272, 5GC 260 (e.g., via AMF 264 and / or UPF 262), NG-RAN 220, and / or UE 204 to obtain location information (e.g., location estimate) of UE 204. Thus, in some cases, the third-party server 274 may be referred to as a location service (LCS) client or an external client. The third-party server 274 may be implemented as multiple separate servers (e.g., physically separate servers, different software modules on a single server, different software modules spread across multiple physical servers, etc.), or alternatively may each correspond to a single server.

[0073] The user plane interface 263 and the control plane interface 265 connect the 5GC 260 (specifically, the UPF 262 and the AMF 264) to one or more gNBs 222 and / or ng-eNBs 224 in the NG-RAN 220, respectively. The interface between the gNB 222 and / or ng-eNB 224 and the AMF 264 is referred to as the "N2" interface, and the interface between the gNB 222 and / or ng-eNB 224 and the UPF 262 is referred to as the "N3" interface. The gNBs 222 and / or ng-eNBs 224 of the NG-RAN 220 may communicate directly with each other via a backhaul connection 223 referred to as an "Xn-C" interface. One or more of the gNBs 222 and / or ng-eNBs 224 may communicate with one or more UEs 204 via a wireless interface referred to as a "Uu" interface.

[0074] The functions of the gNB 222 may be divided between a gNB Central Unit (gNB-CU) 226, one or more gNB Distributed Units (gNB-DU) 228, and one or more gNB Radio Units (gNB-RU) 229. The gNB-CU 226 is a logical node that includes base station functions for transmitting user data, mobility control, radio access network sharing, positioning, session management, etc., except for those functions that are specifically assigned to the gNB-DU 228. More specifically, the gNB-CU 226 typically hosts the Radio Resource Control (RRC), Service Data Adaptation Protocol (SDAP), and Packet Data Convergence Protocol (PDCP) protocols of the gNB 222. The gNB-DU 228 is a logical node that typically hosts the Radio Link Control (RLC) and Medium Access Control (MAC) layers of the gNB 222. Its operation is controlled by the gNB-CU 226. One gNB-DU 228 may support one or more cells, and one cell is supported by only one gNB-DU 228. The interface 232 between the gNB-CU 226 and one or more gNB-DUs 228 is referred to as the "F1" interface. The physical (PHY) layer functions of the gNB 222 are typically hosted by one or more independent gNB-RUs 229 that perform functions such as power amplification and signal transmission / reception. The interface between the gNB-DU 228 and the gNB-RU 229 is referred to as the "Fx" interface. Thus, the UE 204 communicates with the gNB-CU 226 via the RRC, SDAP, and PDCP layers, communicates with the gNB-DU 228 via the RLC and MAC layers, and communicates with the gNB-RU 229 via the PHY layer.

[0075] The deployment of a communication system (such as a 5G NR system) can be arranged in a variety of ways using various components or components. In a 5G NR system or network, a network node, a network entity, a mobile element of a network, a RAN node, a core network node, a network element or a network device (such as a base station or one or more units (or one or more components) performing base station functions) can be implemented in an aggregated or decomposed architecture. For example, a base station (such as a Node B (NB), an evolved NB (eNB), a NR base station, a 5G NB, an access point (AP), a transmit receive point (TRP) or a cell, etc.) can be implemented as an aggregated base station (also known as an independent base station or a monolithic base station) or a decomposed base station.

[0076] A converged base station can be configured to utilize a wireless protocol stack physically or logically integrated within a single RAN node. A decomposed base station can be configured to utilize a protocol stack physically or logically distributed between 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 across one or more other RAN nodes. A DU can be implemented to communicate with one or more RUs. Each of a CU, a DU, and a RU can also be implemented as a virtual unit (i.e., a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU)).

[0077] Base station type operations or network designs may take into account the aggregated nature of base station functionality. For example, a disaggregated base station may be utilized in an integrated access backhaul (IAB) network, an open radio access network (O-RAN (such as a network configuration sponsored by the O-RAN Alliance)), or a virtual radio access network (vRAN, also known as a cloud radio access network (C-RAN)). Disaggregation may include distributing functionality across two or more units at various physical locations, as well as virtually distributing functionality for at least one unit, which may enable flexibility in network design. Individual units of a disaggregated base station or disaggregated RAN architecture may be configured for wired or wireless communication with at least one other unit.

[0078] Figure 2C An example disaggregated base station architecture 250 is shown in accordance with aspects of the present disclosure. The disaggregated base station architecture 250 may include one or more central units (CUs) 280 (e.g., gNB-CUs 226) that may communicate directly with a core network 267 (e.g., 5GC 210, 5GC 260) via a backhaul link or indirectly with a core network 267 through one or more disaggregated base station units (e.g., near real-time (near-RT) RAN intelligent controller (RIC) 259 via an E2 link, or a non-real-time (non-RT) RIC 257 associated with a service management and orchestration (SMO) framework 255, or both). The CU 280 may communicate with one or more distributed units (DUs) 285 (e.g., gNB-DUs 228) via corresponding mid-haul links such as an F1 interface. The DUs 285 may communicate with one or more radio units (RUs) 287 (e.g., gNB-RUs 229) via corresponding fronthaul links. The RU 287 may communicate with a corresponding UE 204 via one or more radio frequency (RF) access links. In some implementations, a UE 204 may be served by multiple RUs 287 simultaneously.

[0079] Each of the units (i.e., CU 280, DU 285, RU 287, and near-RT RIC 259, non-RT RIC 257, and SMO framework 255) may include or be coupled to one or more interfaces configured to receive or send signals, data, or information (collectively referred to as signals) via a wired or wireless transmission medium. Each of the units or an associated processor or controller that provides instructions to the communication interface of the unit may be configured to communicate with one or more of the other units via a transmission medium. For example, a unit may include a wired interface configured to receive a signal on a wired transmission medium or to send a signal to one or more of the other units. In addition, a unit may include a wireless interface that may include a receiver, a transmitter, or a transceiver (such as a radio frequency (RF) transceiver) configured to receive a signal on a wireless transmission medium or to send a signal to one or more of the other units, or both.

[0080] In some aspects, CU 280 may host one or more higher layer control functions. Such control functions may include radio resource control (RRC), packet data convergence protocol (PDCP), service data adaptation protocol (SDAP), etc. Each control function may be implemented using an interface configured to transmit signals with other control functions hosted by CU 280. CU280 may be configured to process user plane functions (i.e., central unit-user plane (CU-UP)), control plane functions (i.e., central unit-control plane (CU-CP)), or a combination thereof. In some implementations, CU 280 may be logically split into one or more CU-UP units and one or more CU-CP units. The CU-UP unit may communicate bidirectionally with the CU-CP unit via an interface (e.g., via an E1 interface when implemented in an O-RAN configuration). If necessary, CU 280 may be implemented to communicate with DU 285 for network control and signaling.

[0081] DU 285 may correspond to a logical unit that includes one or more base station functions to control the operation of one or more RU 287. In some aspects, DU 285 may 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.) at least in part according to a functional split such as a functional split defined by the Third Generation Partnership Project (3GPP). In some aspects, DU 285 may also host one or more low PHY layers. Each layer (or module) may be implemented using an interface configured to transmit signals to other layers (and modules) hosted by DU 285 or control functions hosted by CU 280.

[0082] The lower layer functions may be implemented by one or more RUs 287. In some deployments, based at least in part on a functional split (such as a lower layer functional split), a RU 287 controlled by a DU 285 may correspond to a logical node that hosts RF processing functions or low PHY layer functions (e.g., performing fast Fourier transform (FFT), inverse FFT (iFFT), digital beamforming, physical random access channel (PRACH) extraction and filtering, etc.), or both. In such an architecture, a RU 287 may be implemented to handle over-the-air (OTA) communications with one or more UEs 204. In some implementations, real-time and non-real-time aspects of control and user plane communications with the RU 287 may be controlled by the corresponding DU 285. In some scenarios, this configuration may enable the DU 285 and CU 280 to be implemented in a cloud-based RAN architecture (such as a vRAN architecture).

[0083] The SMO framework 255 may be configured to support RAN deployment and provisioning of non-virtualized and virtualized network elements. For non-virtualized network elements, the SMO framework 255 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which may be managed via an operation and maintenance interface (such as an O1 interface). For virtualized network elements, the SMO framework 255 may be configured to interact with a cloud computing platform (such as an open cloud (O-cloud) 269) to perform network element lifecycle management (e.g., to instantiate virtualized network elements) via a cloud computing platform interface (such as an O2 interface). Such virtualized network elements may include, but are not limited to, CU 280, DU 285, RU 287, and near-RT RIC 289. In some implementations, the SMO framework 255 may communicate with hardware aspects of the 4G RAN (such as an open eNB (O-eNB) 261) via the O1 interface. In addition, in some implementations, the SMO framework 255 may communicate directly with one or more RUs 287 via the O1 interface. The SMO framework 255 may also include a non-RT RIC 257 configured to support the functionality of the SMO framework 255 .

[0084] The non-RT RIC 257 may be configured to include logic functions that implement non-real-time control and optimization of RAN elements and resources, artificial intelligence / machine learning (AI / ML) workflows (including model training and updating), or policy-based guidance of applications / features in the near-RT RIC 259. The non-RT RIC 257 may be coupled to or in communication with the near-RT RIC 259 (e.g., via an A1 interface). The near-RT RIC 259 may be configured to include logic functions that implement near-real-time control and optimization of RAN elements and resources via data collection and actions on an interface connecting one or more CUs 280, one or more DUs 285, or both, and O-eNBs with the near-RT RIC 259 (e.g., via an E2 interface).

[0085] In some implementations, in order to generate an AI / ML model to be deployed in the near-RT RIC 259, the non-RT RIC 257 may receive parameters or external enrichment information from an external server. Such information may be utilized by the near-RT RIC 259 and may be received from a non-network data source or network function at the SMO framework 255 or the non-RT RIC 257. In some examples, the non-RT RIC 257 or the near-RT RIC 259 may be configured to tune RAN behavior or performance. For example, the non-RT RIC 257 may monitor long-term trends and patterns for performance and employ AI / ML models to perform corrective actions through the SMO framework 255 (e.g., via reconfiguration of O1) or via the creation of a RAN management policy (such as an A1 policy).

[0086] Figure 3A , 3B 3C shows that the UE 302 (which may correspond to any UE described herein), the base station 304 (which may correspond to any base station described herein), and the network entity 306 (which may correspond to or embody any network function described herein, including the location server 230 and the LMF 270), or alternatively may be independent of the UE 302. Figure 2A and 2B 220 and / or 5GC 210 / 260 infrastructure depicted in the NG-RAN 220 and / or 5GC 210 / 260 infrastructure, such as a private network) to support operations as described herein. It will be understood that these components can be implemented in different types of devices in different implementations (e.g., in an ASIC, in a system on a chip (SoC), etc.). The components shown may also be incorporated into other devices in the communication system. For example, other devices in the system may include components similar to those described to provide similar functionality. In addition, a given device may include one or more of the components. For example, a device may include multiple transceiver components that enable the device to operate on multiple carriers and / or communicate via different technologies.

[0087] UE 302 and base station 304 each include one or more wireless wide area network (WWAN) transceivers 310 and 350, respectively, which provide means for communicating (e.g., means for transmitting, means for receiving, means for measuring, means for tuning, means for avoiding transmitting, etc.) via one or more wireless communication networks (not shown) (such as NR networks, LTE networks, GSM networks, etc.). WWAN transceivers 310 and 350 can each be connected to one or more antennas 316 and 356, respectively, to communicate with other network nodes (such as other UEs, access points, base stations (e.g., eNBs, gNBs), etc.) via at least one designated RAT (e.g., NR, LTE, GSM, etc.) on a wireless communication medium of interest (e.g., a certain set of time / frequency resources in a specific spectrum). The WWAN transceivers 310 and 350 may be configured differently to transmit and encode signals 318 and 358 (e.g., messages, indications, information, etc.) and conversely receive and decode signals 318 and 358 (e.g., messages, indications, information, pilots, etc.) according to a specified RAT. Specifically, the WWAN transceivers 310 and 350 include one or more transmitters 314 and 354, respectively, for transmitting and encoding signals 318 and 358, respectively, and one or more receivers 312 and 352, respectively, for receiving and decoding signals 318 and 358, respectively.

[0088] At least in some cases, the UE 302 and the base station 304 each further include one or more short-range wireless transceivers 320 and 360, respectively. The short-range wireless transceivers 320 and 360 can be connected to one or more antennas 326 and 366, respectively, and provide for communicating over the wireless communication medium of interest via at least one designated RAT (e.g., WiFi, LTE-D, The short-range transceivers 320 and 360 are units (e.g., units for sending, units for receiving, units for measuring, units for tuning, units for avoiding sending, etc.) for communicating with other network nodes (such as other UEs, access points, base stations, etc.) using a PC5, dedicated short-range communication (DSRC), wireless access in vehicular environments (WAVE), near field communication (NFC), ultra-wideband (UWB), etc.). The short-range transceivers 320 and 360 can be configured differently to respectively send and encode signals 328 and 368 (e.g., messages, indications, information, etc.) according to a specified RAT, and conversely, to respectively receive and decode signals 328 and 368 (e.g., messages, indications, information, pilots, etc.). Specifically, the short-range transceivers 320 and 360 include one or more transmitters 324 and 364, respectively, which are used to send and encode signals 328 and 368, respectively, and include one or more receivers 322 and 362, respectively, which are used to receive and decode signals 328 and 368, respectively. As a specific example, the short-range wireless transceivers 320 and 360 may be WiFi transceivers, Transceiver, and / or transceiver, NFC transceiver, UWB transceiver, or vehicle-to-vehicle (V2V) and / or vehicle-to-everything (V2X) transceiver.

[0089] At least in some cases, the UE 302 and the base station 304 also include satellite signal receivers 330 and 370. The satellite signal receivers 330 and 370 can be connected to one or more antennas 336 and 376, respectively, and can provide means for receiving and / or measuring satellite positioning / communication signals 338 and 378, respectively. In the case where the satellite signal receivers 330 and 370 are satellite positioning system receivers, the satellite positioning / communication signals 338 and 378 can be global positioning system (GPS) signals, global navigation satellite system (GLONASS) signals, Galileo signals, Beidou signals, Indian Regional Navigation Satellite System (NAVIC), Quasi-Zenith Satellite System (QZSS), etc. In the case where the satellite signal receivers 330 and 370 are non-terrestrial network (NTN) receivers, the satellite positioning / communication signals 338 and 378 can be communication signals (e.g., carrying control and / or user data) originating from a 5G network. Satellite signal receivers 330 and 370 may include any suitable hardware and / or software for receiving and processing satellite positioning / communication signals 338 and 378, respectively. Satellite signal receivers 330 and 370 may request information and operations from other systems as needed, and, at least in some cases, perform calculations using measurements obtained by any suitable satellite positioning system algorithm to determine the position of UE 302 and base station 304, respectively.

[0090] The base station 304 and the network entity 306 each include one or more network transceivers 380 and 390, respectively, which provide means (e.g., means for sending, means for receiving, etc.) for communicating with other network entities (e.g., other base stations 304, other network entities 306). For example, the base station 304 may employ one or more network transceivers 380 to communicate with other base stations 304 or network entities 306 over one or more wired or wireless backhaul links. As another example, the network entity 306 may employ one or more network transceivers 390 to communicate with one or more base stations 304 over one or more wired or wireless backhaul links, or communicate with other network entities 306 through one or more wired or wireless core network interfaces.

[0091] The transceiver can be configured to communicate over a wired or wireless link. The transceiver (whether a wired transceiver or a wireless transceiver) includes a transmitter circuit (e.g., transmitters 314, 324, 354, 364) and a receiver circuit (e.g., receivers 312, 322, 352, 362). The transceiver can be an integrated device in some implementations (e.g., embodying the transmitter circuit and the receiver circuit in a single device), can include separate transmitter circuits and separate receiver circuits in some implementations, or can be embodied in other ways in other implementations. The transmitter circuit and the receiver circuit of a wired transceiver (e.g., in some implementations, network transceivers 380 and 390) can be coupled to one or more wired network interface ports. The wireless transmitter circuitry (e.g., transmitters 314, 324, 354, 364) may include or be coupled to multiple antennas (e.g., antennas 316, 326, 356, 366) (such as antenna arrays), which allows the corresponding device (e.g., UE 302, base station 304) to perform transmit "beamforming", as described herein. Similarly, the wireless receiver circuitry (e.g., receivers 312, 322, 352, 362) may include or be coupled to multiple antennas (e.g., antennas 316, 326, 356, 366) (such as antenna arrays), which allows the corresponding device (e.g., UE 302, base station 304) to perform receive beamforming, as described herein. In an aspect, the transmitter circuitry and the receiver circuitry may share the same multiple antennas (e.g., antennas 316, 326, 356, 366) so that the corresponding device only receives or transmits at a given time, but not both at the same time. The wireless transceivers (eg, WWAN transceivers 310 and 350, short-range wireless transceivers 320 and 360) may also include a network listening module (NLM) or the like for performing various measurements.

[0092] As used herein, various wireless transceivers (e.g., in some implementations, transceivers 310, 320, 350, and 360 and network transceivers 380 and 390) and wired transceivers (e.g., in some implementations, network transceivers 380 and 390) may be generally characterized as a "transceiver," "at least one transceiver," or "one or more transceivers." Thus, whether a particular transceiver is a wired transceiver or a wireless transceiver may be inferred based on the type of communication being performed. For example, backhaul communications between network devices or servers typically involve signaling via a wired transceiver, while wireless communications between a UE (e.g., UE 302) and a base station (e.g., base station 304) typically involve signaling via a wireless transceiver.

[0093] UE 302, base station 304, and network entity 306 also include other components that can be used in conjunction with the operations disclosed herein. UE 302, base station 304, and network entity 306 include one or more processors 332, 384, and 394, respectively, which are used to provide functions related to wireless communication and to provide other processing functions. Therefore, processors 332, 384, and 394 can provide units for processing, such as units for determining, units for calculating, units for receiving, units for sending, units for indicating, etc. In an aspect, processors 332, 384, and 394 can include, for example, one or more general-purpose processors, multi-core processors, central processing units (CPUs), ASICs, digital signal processors (DSPs), field programmable gate arrays (FPGAs), other programmable logic devices or processing circuits, or various combinations thereof.

[0094] UE 302, base station 304, and network entity 306, respectively, include memory circuitry implementing memory 340, 386, and 396 (e.g., each including a memory device) for maintaining information (e.g., information indicating reserved resources, thresholds, parameters, etc.). Thus, memory 340, 386, and 396 may provide means for storing, means for retrieving, means for maintaining, etc. In some cases, UE 302, base station 304, and network entity 306 may include positioning components 342, 388, and 398, respectively. Positioning components 342, 388, and 398 may be hardware circuitry that is part of or coupled to processors 332, 384, and 394, respectively, which, when executed, causes UE 302, base station 304, and network entity 306 to perform the functions described herein. In other aspects, the positioning components 342, 388, and 398 can be external to the processors 332, 384, and 394 (e.g., can be part of a modem processing system, integrated with another processing system, etc.). Alternatively, the positioning components 342, 388, and 398 can be memory modules stored in the memories 340, 386, and 396, respectively, which when executed by the processors 332, 384, and 394 (or a modem processing system, another processing system, etc.), cause the UE 302, the base station 304, and the network entity 306 to perform the functions described herein. Figure 3A Possible locations are shown for a location component 342, which can be, for example, part of one or more WWAN transceivers 310, memory 340, one or more processors 332, or any combination thereof, or can be a standalone component. Figure 3B Possible locations are shown for a positioning component 388, which may be, for example, part of one or more WWAN transceivers 350, memory 386, one or more processors 384, or any combination thereof, or may be a stand-alone component. Figure 3C Possible locations are shown for a location component 398, which may be, for example, part of one or more network transceivers 390, memory 396, one or more processors 394, or any combination thereof, or may be a stand-alone component.

[0095] UE 302 may include one or more sensors 344 coupled to one or more processors 332 to provide a means for sensing or detecting motion and / or directional information independent of motion data derived from signals received by one or more WWAN transceivers 310, one or more short-range wireless transceivers 320, and / or satellite signal receivers 330. By way of example, sensor 344 may include an accelerometer (e.g., a micro-electromechanical system (MEMS) device), a gyroscope, a geomagnetic sensor (e.g., a compass), an altimeter (e.g., a barometric altimeter), and / or any other type of motion detection sensor. In addition, sensor 344 may include multiple different types of devices and combine their outputs to provide motion information. For example, sensor 344 may use a combination of a multi-axis accelerometer and an orientation sensor to provide the ability to calculate position in a two-dimensional (2D) and / or three-dimensional (3D) coordinate system.

[0096] In addition, the UE 302 includes a user interface 346, which provides a means for providing indications to the user (e.g., auditory and / or visual indications) and / or for receiving user input (e.g., when the user activates a sensing device such as a keyboard, touch screen, microphone, etc.). Although not shown, the base station 304 and the network entity 306 may also include a user interface.

[0097] Referring to the one or more processors 384 in more detail, in a downlink, IP packets from the network entity 306 may be provided to the processor 384. The one or more processors 384 may implement functions for an RRC layer, a packet data convergence protocol (PDCP) layer, a radio link control (RLC) layer, and a medium access control (MAC) layer. One or more processors 384 may provide: RRC layer functions associated with: broadcasting of system information (e.g., master information block (MIB), system information block (SIB)), RRC connection control (e.g., RRC connection paging, RRC connection establishment, RRC connection modification, and RRC connection release), inter-RAT mobility, and measurement configuration for UE measurement reporting; PDCP layer functions associated with: header compression / decompression, security (encryption, decryption, integrity protection, integrity verification), and switching support functions; RLC layer functions associated with: transmission of upper layer PDUs, error correction through automatic repeat request (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 functions associated with: mapping between logical channels and transport channels, scheduling information reporting, error correction, priority handling, and logical channel prioritization.

[0098] The transmitter 354 and the receiver 352 may implement layer 1 (L1) functions associated with various signal processing functions. Layer 1 (which includes the physical (PHY) layer) may include error detection on the transport channel, forward error correction (FEC) encoding / decoding of the transport channel, interleaving, rate matching, mapping onto the physical channel, modulation / demodulation of the physical channel, and MIMO antenna processing. The transmitter 354 processes the mapping to the signal constellation diagram 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 decoded and modulated symbols may then be split into parallel streams. Each stream may then be mapped to an orthogonal frequency division multiplexing (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 for carrying a time domain OFDM symbol stream. The OFDM symbol stream is spatially precoded to produce multiple spatial streams. The channel estimate from the channel estimator may be used to determine the coding and modulation scheme, as well as for spatial processing. The channel estimate may be derived based on a reference signal and / or channel condition feedback sent by the UE 302. Each spatial stream may then be provided to one or more different antennas 356. The transmitter 354 may modulate the RF carrier with the corresponding spatial stream for transmission.

[0099] At the UE 302, the receiver 312 receives the signal through its corresponding antenna 316. The receiver 312 recovers the information modulated onto the RF carrier and provides the information to one or more processors 332. The transmitter 314 and the receiver 312 implement layer 1 functions associated with various signal processing functions. The receiver 312 can perform spatial processing on the information to recover any spatial stream destined for the UE 302. If multiple spatial streams are destined for the UE 302, they can be combined into a single OFDM symbol stream by the receiver 312. The receiver 312 then transforms 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 and reference signals on each subcarrier are recovered and demodulated by determining the most likely signal constellation point sent by the base station 304. These soft decisions can be based on channel estimates calculated by a channel estimator. The soft decisions are then decoded and deinterleaved to recover the data and control signals originally sent on the physical channel by the base station 304. The data and control signals are then provided to one or more processors 332, which implement layer 3 (L3) and layer 2 (L2) functionality.

[0100] In the downlink, one or more processors 332 provide demultiplexing between transport and logical channels, packet reassembly, decryption, header decompression, and control signal processing to recover IP packets from the core network. One or more processors 332 are also responsible for error detection.

[0101] Similar to the functions described in conjunction with downlink transmissions performed by the base station 304, one or more processors 332 provide: RRC layer functions associated with the following: system information (e.g., MIB, SIB) capture, RRC connection, and measurement reporting; PDCP layer functions associated with the following: header compression / decompression, and security (encryption, decryption, integrity protection, integrity verification); RLC layer functions associated with the following: transmission 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 functions associated with the following: mapping between logical channels and transport channels, multiplexing of MAC SDUs onto transport blocks (TBs), demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction through hybrid automatic repeat request (HARQ), priority handling, and logical channel prioritization.

[0102] The transmitter 314 may select an appropriate coding and modulation scheme and facilitate spatial processing using channel estimates derived by a channel estimator based on a reference signal or feedback sent by the base station 304. The spatial streams generated by the transmitter 314 may be provided to different antennas 316. The transmitter 314 may modulate an RF carrier with a corresponding spatial stream for transmission.

[0103] At the base station 304, the uplink transmission is processed in a manner similar to that described in conjunction with the receiver functionality at the UE 302. The receiver 352 receives the signal through its respective antenna 356. The receiver 352 recovers the information modulated onto the RF carrier and provides the information to one or more processors 384.

[0104] In the uplink, one or more processors 384 provide demultiplexing between transport and logical channels, packet reassembly, decryption, header decompression, control signal processing to recover IP packets from UE 302. The IP packets from one or more processors 384 may be provided to the core network. One or more processors 384 are also responsible for error detection.

[0105] For convenience, in Figure 3A , 3B3C illustrate UE 302, base station 304, and / or network entity 306 as including various components that may be configured according to the various examples described herein. However, it will be appreciated that the components shown may have different functions in different designs. Specifically, Figures 3A to 3C The various components in are optional in alternative configurations, and the various aspects include configurations that may vary due to design choice, cost, use of the device, or other considerations. For example, in Figure 3A In the case of , a specific implementation of UE 302 may omit WWAN transceiver 310 (e.g., a wearable device or tablet or PC or laptop may have Wi-Fi and / or Bluetooth capabilities but not cellular capabilities), or may omit short-range wireless transceiver 320 (e.g., only cellular, etc.), or may omit satellite signal receiver 330, or may omit sensor 344, etc. In another example, in Figure 3B In the case of a wireless cellular network, a specific implementation of the base station 304 may omit the WWAN transceiver 350 (e.g., a Wi-Fi "hotspot" access point without cellular capabilities), or may omit the short-range wireless transceiver 360 (e.g., cellular only, etc.), or may omit the satellite signal receiver 370, etc. For the sake of brevity, a description of various alternative configurations is not provided herein, but will be readily understood by those skilled in the art.

[0106] The various components of the UE 302, base station 304, and network entity 306 may be communicatively coupled to one another via data buses 334, 382, ​​and 392, respectively. In an aspect, the data buses 334, 382, ​​and 392 may form a communication interface or part of a communication interface of the UE 302, base station 304, and network entity 306, respectively. For example, in the case where different logical entities are embodied in the same device (e.g., gNB and location server functionality are incorporated into the same base station 304), the data buses 334, 382, ​​and 392 may provide communication between them.

[0107] Figure 3A , 3B The components of and 3C can be implemented in various ways. In some implementations, Figure 3A , 3BThe components of 3C may be implemented in one or more circuits, such as, for example, one or more processors and / or one or more ASICs (which may include one or more processors). Here, each circuit may use and / or incorporate at least one memory component for storing information or executable code used by the circuit to provide such functionality. For example, some or all of the functions represented by blocks 310 to 346 may be implemented by the processor and memory components of UE 302 (e.g., by executing appropriate code and / or by appropriate configuration of the processor components). Similarly, some or all of the functions represented by blocks 350 to 388 may be implemented by the processor and memory components of base station 304 (e.g., by executing appropriate code and / or by appropriate configuration of the processor components). In addition, some or all of the functions represented by blocks 390 to 398 may be implemented by the processor and memory components of network entity 306 (e.g., by executing appropriate code and / or by appropriate configuration of the processor components). For simplicity, various operations, actions and / or functions are described herein as being performed by "UE", "by base station", "by network entity", etc. However, it will be appreciated that such operations, actions and / or functions may actually be performed by specific components or combinations of components of the UE 302, base station 304, network entity, etc., such as processors 332, 384, 394, transceivers 310, 320, 350 and 360, memories 340, 386 and 396, positioning components 342, 388 and 398, etc.

[0108] In some designs, the network entity 306 may be implemented as a core network component. In other designs, the network entity 306 may be different from the operation of the network operator or cellular network infrastructure (e.g., NG RAN 220 and / or 5GC 210 / 260). For example, the network entity 306 may be a component of a private network that may be configured to communicate with the UE 302 via the base station 304, or communicate with the UE 302 independently of the base station 304 (e.g., on a non-cellular communication link such as WiFi).

[0109] NR supports multiple cellular network-based positioning techniques, including downlink-based, uplink-based, and downlink and uplink-based positioning methods. Downlink-based positioning methods include Observed Time Difference of Arrival (OTDOA) in LTE, Downlink Time Difference of Arrival (DL-TDOA) in NR, and Downlink Angle of Detection (DL-AoD) in NR. Figure 4Examples of various positioning methods according to various aspects of the present disclosure are shown. In the OTDOA or DL-TDOA positioning process shown in scenario 410, the UE measures the difference between the arrival times (ToA) of reference signals (e.g., positioning reference signals (PRS)) received from each pair of base stations (referred to as reference signal time difference (RSTD) or arrival time difference (TDOA) measurement) and reports them to the positioning entity. More specifically, the UE receives identifiers (IDs) of a reference base station (e.g., a serving base station) and multiple non-reference base stations in auxiliary data. The UE then measures the RSTD between the reference base station and each non-reference base station. Based on the known positions of the base stations involved and the RSTD measurements, a positioning entity (e.g., a UE for UE-based positioning or a location server for UE-assisted positioning) can estimate the position of the UE.

[0110] For DL-AoD positioning shown in scenario 420, the positioning entity uses the measurement report from the UE regarding the received signal strength measurement of multiple downlink transmit beams to determine the angle between the UE and the transmitting base station. The positioning entity can then estimate the position of the UE based on the determined angle and the known position of the transmitting base station.

[0111] Uplink-based positioning methods include uplink time difference of arrival (UL-TDOA) and uplink angle of arrival (UL-AoA). UL-TDOA is similar to DL-TDOA, but is based on uplink reference signals (e.g., sounding reference signals (SRS)) sent by the UE to multiple base stations. Specifically, the UE sends one or more uplink reference signals measured by a reference base station and multiple non-reference base stations. Each base station then reports the reception time of the reference signal (referred to as the relative time of arrival (RTOA)) to a positioning entity (e.g., a location server) that knows the location and relative timing of the base stations involved. Based on the receive-to-receive (Rx-Rx) time difference between the reported RTOA of the reference base station and the reported RTOA of each non-reference base station, the known locations of the base stations, and their known timing offsets, the positioning entity can use TDOA to estimate the position of the UE.

[0112] For UL-AoA positioning, one or more base stations measure the received signal strength of one or more uplink reference signals (e.g., SRS) received from the UE on one or more uplink receive beams. The positioning entity uses the signal strength measurements and the angle of the receive beam to determine the angle between the UE and the base station. Based on the determined angle and the known location of the base station, the positioning entity can then estimate the location of the UE.

[0113] Downlink and uplink based positioning methods include enhanced cell ID (E-CID) positioning and multi-round trip time (RTT) positioning (also referred to as "multi-cell RTT" and "multi-RTT"). In the RTT process, a first entity (e.g., a base station or UE) sends a first RTT-related signal (e.g., a PRS or SRS) to a second entity (e.g., a UE or a base station), and the second entity sends a second RTT-related signal (e.g., an SRS or PRS) back to the first entity. Each entity measures the time difference between the arrival time (ToA) of the received RTT-related signal and the transmission time of the transmitted RTT-related signal. The time difference is called the received-to-transmit (Rx-Tx) time difference. The Rx-Tx time difference measurement can be made or can be adjusted to include only the time difference between the nearest time slot boundaries of the received and transmitted signals. The two entities may then send their Rx-Tx time difference measurements to a location server (e.g., LMF 270), which calculates the round-trip propagation time (i.e., RTT) between the two entities based on the two Rx-Tx time difference measurements (e.g., as the sum of the two Rx-Tx time difference measurements). Alternatively, one entity may send its Rx-Tx time difference measurement to another entity, which then calculates the RTT. The distance between the two entities may be determined based on the RTT and a known signal speed (e.g., the speed of light). For multi-RTT positioning shown in scenario 430, a first entity (e.g., a UE or a base station) performs an RTT positioning process with multiple second entities (e.g., multiple base stations or UEs) to enable the location of the first entity to be determined based on the distance to the second entity and the known location of the second entity (e.g., using multilateration). RTT and multi-RTT methods may be combined with other positioning techniques such as UL-AoA and DL-AoD to improve the location accuracy shown in scenario 440.

[0114] The E-CID positioning method is based on radio resource management (RRM) measurements. In E-CID, the UE reports the serving cell ID, timing advance (TA), and the identifiers of detected neighboring base stations, estimated timing, and signal strength. The UE's position is then estimated based on this information and the known locations of the base stations.

[0115] To assist the positioning operation, a location server (e.g., location server 230, LMF 270, SLP 272) may provide assistance data to the UE. For example, the assistance data may include an identifier of the base station (or cell / TRP of the base station) from which the reference signal is measured, reference signal configuration parameters (e.g., the number of consecutive time slots including PRS, the period of consecutive time slots including PRS, the muting sequence, the frequency hopping sequence, the reference signal identifier, the reference signal bandwidth, etc.), and / or other parameters applicable to a particular positioning method. Alternatively, the assistance data may originate directly from the base station itself (e.g., in a periodically broadcast overhead message, etc.). In some cases, the UE may be able to detect neighboring network nodes on its own without using assistance data.

[0116] In the case of OTDOA or DL-TDOA positioning process, the auxiliary data may also include the expected RSTD value and the associated uncertainty or search window around the expected RSTD. In some cases, the value range of the expected RSTD may be + / -500 microseconds (μs). In some cases, when any resource used for positioning measurement is in FR1, the value range of the uncertainty of the expected RSTD may be + / -32μs. In other cases, when all resources used for positioning measurement are in FR2, the value range of the uncertainty of the expected RSTD may be + / -8μs.

[0117] The position estimate may be referred to by other names, such as position estimate, location, position, position fix, fix, etc. The position estimate may be geodetic and include coordinates (e.g., latitude, longitude, and possibly altitude), or may be civil and include a street address, postal address, or some other verbal description of the location. The position estimate may also be defined relative to some other known location or in absolute terms (e.g., using latitude, longitude, and possibly altitude). The position estimate may include an expected error or uncertainty (e.g., by including an area or volume within which the location is expected to be included with some specified or default confidence level).

[0118] Figure 5500 is a diagram representing an example channel estimate for a multipath channel between a receiver device (e.g., any one of the UEs or base stations described herein) and a transmitter device (e.g., any other of the UEs or base stations described herein) in accordance with aspects of the present disclosure. The channel estimate represents the strength of a radio frequency (RF) signal (e.g., a PRS) received through a multipath channel as a function of time delay and may be referred to as a channel energy response (CER), a channel impulse response (CIR), or a power delay profile of the channel. Thus, the horizontal axis is in units of time (e.g., milliseconds) and the vertical axis is in units of signal strength (e.g., decibels). Note that a multipath channel is a channel between a transmitter and a receiver over which an RF signal follows multiple paths or multipaths due to transmission of the RF signal on multiple beams and / or propagation characteristics of the RF signal (e.g., reflection, refraction, etc.).

[0119] exist Figure 5 In the example of , the receiver detects / measures multiple (four) clusters of channel taps. Each channel tap represents a multipath followed by an RF signal between a transmitter and a receiver. That is, the channel taps represent the arrival of the RF signal on the multipath. Each cluster of channel taps indicates that the corresponding multipath follows substantially the same path. Different clusters may exist because the RF signals are sent on different transmit beams (and therefore at different angles), or because of the propagation characteristics of the RF signals (e.g., potentially following different paths due to reflections), or both.

[0120] The entire cluster of channel taps for a given RF signal represents the multipath channel (or simply, channel) between the transmitter and the receiver. Figure 5 In the illustrated channel, the receiver receives a first cluster of two RF signals on the channel taps at time T1, a second cluster of five RF signals on the channel taps at time T2, a third cluster of five RF signals on the channel taps at time T3, and a fourth cluster of four RF signals on the channel taps at time T4. Figure 5 In the example of , because the first cluster of RF signals at time T1 arrives first, it is assumed to correspond to RF signals sent on a transmit beam aligned with line of sight (LOS) or the shortest path. The third cluster at time T3 consists of the strongest RF signals and may correspond to, for example, RF signals sent on a transmit beam aligned with a non-line of sight (NLOS) path. Note that although Figure 5 Clusters having two to five channel taps are shown, but as will be understood, clusters may have more or fewer channel taps than the number of channel taps shown.

[0121] Machine learning can be used to generate models that can be used to facilitate various aspects associated with the processing of data. One specific application of machine learning involves the generation of measurement models for processing reference signals used for positioning (e.g., positioning reference signals (PRS)), such as feature extraction, reporting of reference signal measurements (e.g., selecting which extracted features to report), etc.

[0122] Machine learning models are often categorized as supervised or unsupervised. Supervised models can be further categorized as either regression models or classification models. Supervised learning involves learning a function that maps inputs to outputs based on example input-output pairs. For example, given a training data set with two variables, age (input) and height (output), a supervised learning model can be generated to predict a person's height based on their age. In a regression model, the output is continuous. An example of a regression model is linear regression, which simply tries to find the line that best fits the data. Extensions of linear regression include multiple linear regression (e.g., finding the best fit plane) and polynomial regression (e.g., finding the best fit curve).

[0123] Another example of a machine learning model is a decision tree model. In a decision tree model, a tree structure is defined using multiple nodes. Decisions are used to move from a root node at the top of the decision tree to a leaf node (i.e., a node with no further children) at the bottom of the decision tree. Typically, a larger number of nodes in a decision tree model is associated with a higher decision accuracy.

[0124] Another example of a machine learning model is a decision forest. Random forest is an ensemble learning technique built from decision trees. Random forest involves creating multiple decision trees using a bootstrap dataset of the original data, and randomly selecting a subset of variables at each step of the decision tree. The model then selects the mode of all predictions of each decision tree. By relying on a "majority wins" model, the risk of errors from a single tree can be reduced.

[0125] Another example of a machine learning model is a neural network (NN). A neural network is essentially a network of mathematical equations. A neural network accepts one or more input variables and produces one or more output variables by going through the network of equations. In other words, a neural network receives a vector of inputs and returns a vector of outputs.

[0126] Figure 6An example neural network 600 is shown in accordance with aspects of the present disclosure. The neural network 600 includes an input layer "i" that receives "n" input(s) (shown as "input 1", "input 2", and "input n"), one or more hidden layers (shown as hidden layers "h1", "h2", and "h3") for processing the inputs from the input layer, and an output layer "o" that provides "m" output(s) (labeled "output 1" and "output m"). The number of inputs "n", hidden layers "h", and outputs "m" can be the same or different. In some designs, the hidden layer "h" can include linear functions and / or activation functions that the nodes of each successive hidden layer (shown as circles) process from the nodes of the previous hidden layer.

[0127] In a classification model, the output is discrete. An example of a classification model is logistic regression. Logistic regression is similar to linear regression, but is used to model the probability of a limited number of results (usually two). In essence, the logistic equation is created in a way that the output value is only between "0" and "1". Another example of a classification model is a support vector machine. For example, for two classes of data, a support vector machine will look for a hyperplane or a boundary between the two classes of data that maximizes the margin between the two classes of data. There are many planes that can separate the two classes, but only one plane can maximize the margin or distance between the classes. Another example of a classification model is naive Bayes based on Bayes' theorem. Other examples of classification models include decision trees, random forests, and neural networks, which are similar to the above examples, except that the output is discrete rather than continuous.

[0128] Unlike supervised learning, unsupervised learning is used to make inferences and find patterns based on input data without reference to labeled results. Two examples of unsupervised learning models include clustering and dimensionality reduction.

[0129] Clustering is an unsupervised technique that involves grouping or clustering data points. Clustering is frequently used for customer segmentation, fraud detection, and document classification. Common clustering techniques include k-means clustering, hierarchical clustering, mean shift clustering, and density-based clustering. Dimensionality reduction is the process of reducing the number of random variables considered by obtaining a set of principal variables. Simply put, dimensionality reduction is the process of reducing the dimensionality of a feature set (more simply, reducing the number of features). Most dimensionality reduction techniques can be categorized as feature elimination or feature extraction. An example of dimensionality reduction is called principal component analysis (PCA). In the simplest sense, principal component analysis involves projecting higher dimensional data (e.g., three dimensions) into a smaller space (e.g., two dimensions). This results in lower dimensional data (e.g., two dimensions instead of three dimensions) while maintaining all the original variables in the model.

[0130] Regardless of which machine learning model is used, at a high level, a machine learning module (e.g., implemented by a processing system such as processor 332, 384, or 394) can be configured to iteratively analyze training input data (e.g., measurements of reference signals to / from various target UEs) and associate the training input data with an output data set (e.g., a set of possible or likely candidate locations for the various target UEs), thereby enabling the same output data set to be determined later when similar input data (e.g., other target UEs from the same or similar locations) is presented.

[0131] NR supports positioning based on radio frequency fingerprint (RFFP), which is a positioning and localization technology that uses RF fingerprints captured by mobile devices and / or base stations to determine the location of mobile devices. RFFP measurements can be histograms of received signal strength indicator (RSSI), CER, CIR, channel frequency response (CFR), RSRP, SINR, Doppler spread / statistics, or angle spread / statistics. RFFP measurements can represent a single channel received from a transmitter, all channels received from a specific transmitter, or all channels detectable at a receiver. In some aspects, the RFFP measurements measured by a mobile device (e.g., UE) and the location of the transmitter associated with the RFFP measurements (i.e., the transmitter that sends the RF signal measured by the mobile device to determine the RFFP) can be used to determine (e.g., triangulate) the location of the mobile device.

[0132] In machine learning RFFP-based positioning, RFFP measurements and their associated locations (i.e., the locations of transmitters associated with the measured RFFPs) are used as features and labels, respectively, to train a machine learning model (e.g., neural network 600) in a supervised manner. After being trained, the machine learning model can be used to estimate the location of the mobile device by processing RFFPs newly captured by the mobile device.

[0133] In addition, the location of the mobile device can be determined based on radar-like (e.g., based on a single-station sensing mode) positioning technology (or referred to as self-anchor positioning), where the mobile device can be positioned and localized based on reflections (e.g., backscatter reflections) caused by the interaction of signals sent by the mobile device with the surrounding environment. In some aspects, mobile devices that are full-duplex enabled or capable of operating in a pulsed radar manner may be able to capture reflections.

[0134] Fig. 7A A mobile device 710 is shown arranged in an ambient environment 720 in accordance with aspects of the present disclosure. The mobile device 710 includes a transmitter and a receiver. As a non-limiting example, the ambient environment 720 is depicted as an indoor environment. In some aspects, the ambient environment 720 of the mobile device 710 can be indoor, outdoor, or a mixture of indoor and outdoor.

[0135] The transmitter of the mobile device 710 can transmit one or more signals via one or more antennas. As a result of various types of interactions with the surrounding environment 720 (e.g., through reflection, refraction, scattering, attenuation, any combination thereof, etc.), the transmitted one or more signals can be reflected back to the mobile device 710. For example, Fig. 7A Example signal paths 732, 734, 736, and 737 are shown, which can be obtained through ray tracing simulations based on a co-located single transmit antenna and a single receive antenna. Signals sent by a transmitter of mobile device 710 can travel along signal paths 732, 734, 736, and 737 based on interactions with objects and obstacles in surrounding environment 720, and can travel back toward mobile device 710. In some aspects, received reflections caused by the interaction of signals sent by mobile device 710 with surrounding environment 720 can be considered a type of multipath signal where the transmitter and receiver are co-located. Received reflections can be presented as self-RFFP measurements.

[0136] Figure 7B It means that according to various aspects of the present disclosure, Fig. 7AA graph of a self-RFFP measurement 750 of a reflection is shown. In some aspects, the reflection can be presented in the form of a RFFP measurement, or also referred to in this disclosure as a self-anchor RFFP measurement or a self-RFFP measurement. Figure 7B A self-RFFP measurement in the form of a CIR of a backscatter channel is shown 750. In some aspects, examples of a UE's self-RFFP measurement may include CFR, CIR, or RSSI corresponding to reflections backscattered from the surrounding environment due to a reference signal (e.g., SRS or SL-PRS) transmitted by the UE.

[0137] In some aspects, for a signal transmitted by the mobile device 710, a backscattered signal (e.g., reflection) caused by the interaction of the transmitted signal with the surrounding environment 720 can arrive at the mobile device 710 at different arrival times with different signal strengths. Here, the horizontal axis is in units of time (e.g., milliseconds or nanoseconds), and the vertical axis is in units of signal strength (e.g., decibels). Figure 7B In the example of , each channel tap can represent a path that an RF signal travels within the surrounding environment 720 and then reflects back to the mobile device 710. For example, taps 762, 764, 766, and 768 can correspond to signal reflections along signal paths 732, 734, 736, and 738, respectively. Figure 7B The other taps in can correspond to Fig. 7A Other reflections not shown.

[0138] Thus, in some aspects, self-anchored positioning is a radar-like positioning technique in which a wireless device can be located and positioned based on signal reflections (i.e., backscatter reflections) of the wireless device from the surrounding environment. To capture single-station reflections, the wireless device may need to enable full-duplex or may operate in the manner of a pulsed radar. A machine learning (ML) model may be trained to map RFFP measurements corresponding to reflections and device positions. Self-anchored positioning may also be referred to as "self-anchored RFFP positioning" or "self-RFFP positioning" in this disclosure.

[0139] Fig. 8A An example of UE-based positioning operations based on self-RFFP measurements according to various aspects of the present disclosure is shown. Fig. 8A As shown, UE 810 and UE 820 (such as any UE described in the present disclosure) are placed in an ambient environment 830. The UE 810 and UE 820 (and / or Fig. 8ASidelink communications are established between UE 810 and other UEs or wireless devices (not shown in the figure). In addition, UE 810 is communicatively coupled to base station 840 (such as any base station described in the present disclosure). Base station 840 is communicatively coupled to network entity 850 (such as any network entity described in the present disclosure). One or more neighboring base stations (not shown) can be communicatively coupled to network entity 850, and signals from UE 810 can be detectable by one or more neighboring base stations. In some aspects, network entity 850 can be a location server, such as Figure 2A and 2B The location server 230, LMF 270 or SLP 272 in the network entity 850. In some aspects, the network entity 850 may be a third-party server providing location services, such as Figure 2B In some aspects, the network entity 850 may be a model repository server capable of training ML models for RFFP-based positioning operations.

[0140] UE 810 includes a transmitter 812, a receiver 814, a duplexer 816, and one or more antennas 818. The duplexer 816 can coordinate the operation of the transmitter 812, the receiver 814, and the one or more antennas 818 so that the UE 810 is full-duplex enabled or can operate in a pulse radar mode. The UE 810 can send one or more reference signals via the transmitter 812 and the one or more antennas 818, and receive signal reflections via the one or more antennas 818 and the receiver 814. The signal reflection is a reflection of the one or more reference signals generated by the interaction of the one or more reference signals with the surrounding environment 830. Therefore, the UE 810 can obtain one or more self-RFFP measurements 862 based on the reflections of the one or more reference signals sent by the UE 810.

[0141] In some aspects, UE 810 may obtain one or more downlink RFFP (DL-RFFP) measurements 864 based on downlink reference signals sent by base station 840 and / or neighboring base stations. In some aspects, UE 810 and / or UE 820 may obtain one or more downlink RFFP (DL-RFFP) measurements 864 based on downlink reference signals sent by base station 840 and / or neighboring base stations. Fig. 8A In one aspect, the one or more SL-RFFP measurements 866 are obtained by the UE 810 based on the sidelink reference signals sent by the UE 820 and / or other wireless devices. In one aspect, the one or more SL-RFFP measurements 866 are obtained by the UE 820 and / or other wireless devices based on the sidelink reference signals sent by the UE 810.

[0142] In operation, during a position inference phase in which the UE 810 may be configured as a target device according to the first scenario, the UE 810 may determine its own position based on applying the ML model to one or more self-RFFP measurements 862, or to one or more self-RFFP measurements 862 fused with one or more DL-RFFP measurements 864 and / or one or more SL-RFFP measurements 866. During a position inference phase in which the UE 810 may be configured as a target device according to the second scenario, the UE 820 may determine the position of the UE 810 based on applying the ML model to one or more self-RFFP measurements 862, or to one or more self-RFFP measurements 862 fused with one or more DL-RFFP measurements 864 and / or one or more SL-RFFP measurements 866.

[0143] In some aspects, for one or more self-RFFP measurements, one or more DL-RFFP measurements and / or one or more SL-RFFP measurements to be fused for position inference, the timing interval of the relevant reference signals does not exceed a predetermined timing threshold, so that the timing of the relevant reference signals is close enough to ensure the accuracy of the estimated position. In some aspects, the timing threshold can be set based on the speed estimated by the target UE (e.g., UE 810) and / or the SL anchor UE (e.g., UE 820). For example, at a speed at which a human can walk or run, the threshold can be set to 100 milliseconds. In some aspects, this can ensure that UE 810 or UE 820 has indeed moved too far so that the fused measurements cannot be mapped to the same ground truth position. The threshold can be set in different ways for different speeds. In some aspects, a higher speed can correspond to a smaller threshold.

[0144] In addition, during a model training phase in which UE 810 may be configured as an observer device, network entity 850, UE 810, or UE 820 may obtain one or more training self-RFFP measurements, one or more training DL-RFFP measurements, and / or one or more training SL-RFFP measurements for training the ML model from UE 810, UE 820, and / or one or more other observer devices. In some aspects, one or more observer devices may include a dedicated device for collecting a training data set for network entity 850.

[0145] In some aspects, one or more self-RFFP measurements, one or more DL-RFFP measurements, one or more SL-RFFP measurements, one or more trained self-RFFP measurements, one or more training DL-RFFP measurements and / or one or more training SL-RFFP measurements may correspond to a specific antenna arrangement, such as based on reflections received by a single antenna or multiple antennas of UE 810.

[0146] Figure 8B It is a diagram showing various aspects of the present disclosure. Fig. 8A The operations can be categorized into a location inference phase 870 and a model training phase 880.

[0147] During the position inference phase 870, the UE 810 (configured as a target device) may obtain one or more self-RFFP measurements based on reflections of one or more reference signals sent by the UE 810. In some aspects according to the first scenario, the UE 810 may determine an estimated position 874 of the UE 810 itself based on applying the ML model 872 to the one or more self-RFFP measurements. In some aspects according to the second scenario, the UE 820 (with which sidelink communication with the UE 810 may be established) may obtain one or more self-RFFP measurements from the UE 810, and determine an estimated position 874 of the UE 810 based on applying the ML model 872 to the one or more self-RFFP measurements. In some aspects, the ML model 872 may be trained based on one or more training self-RFFP measurements, each of which is obtained by a corresponding observer device based on reflections of a corresponding reference signal sent by the corresponding observer device.

[0148] In some aspects, during the position inference phase, the UE 810 may obtain one or more DL-RFFP measurements based on one or more downlink reference signals transmitted by the base station 840 and / or the neighboring base stations. In some aspects, the one or more DL-RFFP measurements may be fused with one or more self-RFFP measurements obtained by the UE 810 to determine an estimated position 874 of the UE 810. According to the first scenario, the UE 310 may determine the estimated position 874 of the UE 810 based on applying the ML model 872 to the one or more self-RFFP measurements and the one or more DL-RFFP measurements. According to the second scenario, the UE 320 may receive the one or more self-RFFP measurements and the one or more DL-RFFP measurements from the UE 810, and determine the estimated position 874 of the UE 810 based on applying the ML model 872 to the one or more self-RFFP measurements and the one or more DL-RFFP measurements. In some aspects, the ML model 872 may be further trained based on one or more training DL-RFFP measurements obtained by the UE 810 or one or more observer devices based on downlink reference signals transmitted by the base station 840 and / or the one or more neighboring base stations.

[0149] In some aspects, during the position inference phase, UE 810 and / or UE 820 may obtain one or more SL-RFFP measurements based on sidelink reference signals between UE 810 and UE 820 and / or other wireless devices. In some aspects, part or all of the one or more SL-RFFP measurements may be obtained by UE 810 based on sidelink signals sent by UE 820 and / or other wireless devices. In some aspects, part or all of the one or more SL-RFFP measurements may be obtained by UE 820 and / or other wireless devices based on sidelink signals sent by UE 810.

[0150] In some aspects, one or more SL-RFFP measurements may be fused with one or more self-RFFP measurements obtained by the UE 810 to determine an estimated location 874 of the UE 810. According to a first scenario, the UE 810 may be configured as a self-anchor UE and may determine an estimated location 874 of the UE 810 based on applying the ML model 872 to the one or more self-RFFP measurements and the one or more SL-RFFP measurements. According to a second scenario, the UE 820 may be configured as a sidelink anchor UE and may receive one or more self-RFFP measurements and determine an estimated location 874 of the UE 810 based on applying the ML model 872 to the one or more self-RFFP measurements and the one or more SL-RFFP measurements. In some aspects, the ML model 872 may be trained based on the one or more training self-RFFP measurements and the one or more training SL-RFFP measurements based on a sidelink reference signal between observer devices.

[0151] In some aspects, one or more self-RFFP measurements may be fused with one or more DL-RFFP measurements to determine an estimated location 874 of the UE 810. Thus, the UE 310 or UE 320 may determine an estimated location 874 of the UE 810 based on applying the ML model 872 to the one or more self-RFFP measurements and the one or more DL-RFFP measurements and the one or more SL-RFFP measurements. In some aspects, the ML model 872 may be trained based on one or more training SL-RFFP measurements, one or more training DL-RFFP measurements, and one or more training SL-RFFP measurements.

[0152] In some aspects, the one or more reference signals used to obtain self-RRFP measurements may include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SLCSI-RS), an uplink channel reference signal, an uplink channel signal carrying data (including control information or user data, such as PUCCH or PUSCH), a sidelink channel reference signal, or a sidelink channel signal carrying data (including control information or user data, such as PSCCH or PSSCH). In some aspects, the one or more downlink reference signals used to obtain DL-RRFP measurements may include a positioning reference signal (PRS), a downlink channel reference signal, or a downlink channel signal carrying data (control information or user data). In some aspects, the one or more sidelink reference signals used to obtain SL-RRFP measurements may include a SL-PRS, a SL-SSB, a SL CSI-RS, a sidelink channel reference signal, or a sidelink channel signal carrying data. In some aspects, one or more self-RFFP measurements and one or more SL-RFFP measurements may be based on the same one or more reference signals sent by the UE for location interference. In some aspects, one or more self-RFFP measurements and one or more SL-RFFP measurements may be based on two different reference signal sets sent by the UE for location interference, but the transmission timing of the two different reference signal sets is close enough to ensure the accuracy of the estimated location. In some aspects, the transmission timing of the reference signals may be configured back-to-back.

[0153] During the model training phase 880, a model training process 882 may be performed to train the ML model 872. In some aspects, the UE 810 may perform the model training process 882 to train the ML model 872 to be used by the UE 810. In some aspects, the UE 820 may perform the model training process 882 to train the ML model 872 to be used by the UE 820. In some aspects, the network entity 850 may perform the model training process 882 to train the ML model 872 to be used by the UE 810 or the UE 820.

[0154] In some aspects, the model training process 882 can train the ML model 872 based on training input data and reference output data. In some aspects, the training input data can include one or more training self-RFFP measurements stored in the self-RFFP database 884, one or more training DL-RFFP measurements stored in the DL-RFFP database 886, and / or one or more training SL-RFFP measurements stored in the SL-RFFP database 888, and the reference output data can include one or more training positions of the corresponding observer device associated with the one or more training self-RFFP measurements, one or more training DL-RFFP measurements, and / or one or more training SL-RFFP measurements. In some aspects, the ML model 872 can be a NN model, and the training process 882 can train the ML model 872 in a supervised manner.

[0155] In some aspects, the network entity 850 may be configured to perform a training process 882 (e.g., offline training or server-assisted training of an ML mode to be exchanged with the UE 810 or the UE 820). In some aspects, the network entity 850 may receive one or more trained self-RFFP measurements to be stored in a self-RFFP database 884, each of the trained self-RFFP measurements being obtained by a corresponding observer device based on reflections of a corresponding reference signal sent by the corresponding observer device. In at least one example, the UE 810 may be configured as one of the one or more observer devices.

[0156] In some aspects, the network entity 850 may obtain one of the one or more training positions of the corresponding observer device associated with one of the one or more training self-RFFP measurements. The training position may also be referred to as the ground truth position or ground truth label of the corresponding training data set. In some aspects, the training position represents the position where the observer device sends a reference signal, and the training self-RFFP measurement is based on the reference signal. For example, when UE 810 is used as an observer device for training ML model 872, the training self-RFFP measurement can be obtained based on the reference signal, and the position (X1, Y1, Z1) of UE 810 where UE 810 sends the reference signal can be used as the training position.

[0157] In some aspects, the network entity 850 may also receive one or more training DL-RFFP measurements from one or more observer devices to be stored in the DL-RFFP database 886, each of the training DL-RFFP measurements being based on a corresponding downlink reference signal sent by a base station and received by a corresponding observer device. In at least one example, the UE 810 may be configured as an observer device, and the training DL-RFFP measurements may be obtained based on a downlink reference signal received by the UE 810.

[0158] In some aspects, the network entity 850 may obtain one of the one or more training positions of the corresponding observer device associated with one of the one or more training DL-RFFP measurements. In some aspects, the training position represents the position where the observer device receives a downlink reference signal, and the training DL-RFFP measurement is based on the downlink reference signal. In some aspects, the training position used for training the self-RFFP measurement can be used as the training position associated with the DL-RFFP measurement, provided that the timing of the transmission of the reference signal for the self-RFFP measurement and the timing of the reception of the downlink reference signal for the DL-RFFP measurement are close enough for the accuracy of the training position. In some aspects, the network entity 850 may train the ML model 872 based on training input data and reference output data, the training input data including one or more training self-RFFP measurements and one or more training DL-RFFP measurements, and the reference output data including the corresponding training position of the corresponding observer device.

[0159] In some aspects, the network entity 850 may receive a training position of the observer device from the observer device, wherein the training position may be determined by the observer device based on: a downlink time difference of arrival (DL-TDoA), a downlink angle of arrival (DL-AoA), a round trip time (RTT) positioning, operating the observer device at a predetermined reference position, one or more sensors mounted on the observer device, or a global navigation satellite system (GNSS), or a combination thereof. For example, the observer device may be an automated guided vehicle (AGV) equipped with a Lidar or one or more high-precision sensors for obtaining a corresponding training position of the AGV.

[0160] In some aspects, the network entity 850 may determine the training position of the observer device based on one or more measurements provided by the observer device and / or the corresponding base station 840 or a neighboring base station based on uplink time difference of arrival (UL-TDoA), uplink angle of arrival (UL-AoA), or round trip time (RTT) positioning, or a combination thereof. In some aspects, the network entity 850 may receive the training position of the observer device from a location server, where the location server may determine the training position based on UL-TDoA, UL-AoA, or RTT positioning, or a combination thereof.

[0161] Thus, in some aspects, the network entity 850 may train the ML model 872 based on one or more self-RFFP measurements. In some aspects, the network entity 850 may train the ML model 872 based on one or more training self-RFFP measurements fused with one or more DL-RFFP measurements. In some aspects, the network entity 850 may train the ML model 872 based on one or more training self-RFFP measurements fused with one or more training SL-RFFP measurements. In some aspects, the network entity 850 may train the ML model 872 based on one or more training self-RFFP measurements fused with one or more training DL-RFFP measurements and one or more training SL-RFFP measurements.

[0162] In some aspects, the on-device ML model for UE-based self-RFFP positioning and the UE-based self-RFFP fused with DL-RFFP and / or SL-RFFP can be trained offline by a model repository server (e.g., UE vendor OTT server) and deployed to the UE or downloaded by the UE. In some aspects, the on-device ML model can be trained offline by a network entity (e.g., based on LMF / LMF assistance), and the ML model can be exchanged with the UE device.

[0163] In some aspects, the UE 810 may be configured to perform a training process 882 (e.g., for online training of a local ML model or on-device model training). Thus, the UE 810 may collect one or more training self-RFFP measurements and / or one or more training DL-RFFP measurements obtained by the UE itself. In some aspects, the UE 810 may collect one or more training SL-RFFP measurements obtained by itself or by the UE 820. In some aspects, the UE 810 may determine a corresponding training position based on DL-TDoA, DL-AoA, RTT positioning, operating the wireless device 810 at a predetermined reference position, one or more sensors installed on the wireless device 810, or a global navigation satellite system (GNSS), or a combination thereof. In some aspects, the UE 810 may receive a corresponding training position from the network entity 850, and the training position may be determined based on UL-TDoA, UL-AoA, or RTT positioning, or a combination thereof. In some aspects, UE 810 may receive corresponding training locations from UE 820, and the training locations may be determined by UE 820 based on sidelink TDoA (SL-TDoA), sidelink angle of arrival (SL-AoA), or sidelink-based RTT positioning, or a combination thereof.

[0164] Thus, in some aspects, the UE 810 may train the ML model 872 based on one or more trained self-RFFP measurements. In some aspects, the UE 810 may train the ML model 872 based on one or more trained self-RFFP measurements fused with one or more training DL-RFFP measurements. In some aspects, the UE 810 may train the ML model 872 based on one or more trained self-RFFP measurements fused with one or more training SL-RFFP measurements. In some aspects, the UE 810 may train the ML model 872 based on one or more trained self-RFFP measurements fused with one or more training DL-RFFP measurements and one or more training SL-RFFP measurements.

[0165] Thus, in some aspects, an on-device ML model for UE-based self-RFFP positioning and UE-based self-RFFP fused with DL-RFFP and / or SL-RFFP can be trained online at the UE side, where different positioning methods can be used by a network entity to generate a ground truth position, and / or RAT and non-RAT positioning methods are used at the UE to calculate the ground truth position.

[0166] In some aspects, the UE 820 may be configured to perform a training process 882 (e.g., online training or on-device model training for a local ML mode). Thus, the UE 820 may collect one or more training self-RFFP measurements and / or one or more training DL-RFFP measurements obtained by the UE 810 and / or one or more observer devices. The UE 820 may collect one or more training SL-RFFP measurements obtained by itself or by the UE 810. In some aspects, the UE 820 may determine the corresponding training positions of the UE 810 and / or one or more observer devices based on SL-TDoA, SL-AoA, or RTT positioning based on a sidelink, or a combination thereof. In some aspects, the UE 820 may receive the corresponding training positions of the UE 810 and one or more observer devices from the UE 810 and / or one or more observers, which may be determined by the UE 810 and / or the observer device or by the network entity 850.

[0167] Thus, in some aspects, the UE 820 may train the ML model 872 based on one or more trained self-RFFP measurements. In some aspects, the UE 820 may train the ML model 872 based on one or more trained self-RFFP measurements fused with one or more training DL-RFFP measurements. In some aspects, the UE 820 may train the ML model 872 based on one or more trained self-RFFP measurements fused with one or more training SL-RFFP measurements. In some aspects, the UE 820 may train the ML model 872 based on one or more trained self-RFFP measurements fused with one or more training DL-RFFP measurements and one or more training SL-RFFP measurements.

[0168] Thus, in some aspects, an on-device ML model for UE-based self-RFFP positioning and UE-based self-RFFP fused with DL-RFFP and / or SL-RFFP can be trained online at the UE side, where different positioning methods can be used by a network entity to generate a ground truth position, and / or RAT and non-RAT positioning methods are used at the UE to calculate the ground truth position.

[0169] In some aspects, resources for self-RFFP measurements and / or SL-RFFP measurements may be scheduled or pre-configured by the network entity 850. In some aspects, resources for self-RFFP measurements and / or SL-RFFP measurements may be pre-configured by the UE vendor using unlicensed spectrum.

[0170] Fig. 9 is a signaling and event diagram illustrating various operations during the position inference phase in accordance with aspects of the present disclosure. Fig. 91 shows an example interaction between UE 902 (a first UE configured as a target device), UE 903 (a second UE that can establish a sidelink connection with the first UE), base station 904, one or more neighboring base stations 906, location server 908a, and model repository server 908b. UE 902 can communicate with Fig. 8A 810 in the example, UE 903 may correspond to UE 820, and base station 904 and one or more neighboring base stations 906 may correspond to base station 840. In addition, location server 908a and model repository server 908b may correspond to network entity 850 individually or in combination.

[0171] The location server 908a may correspond to a server within or integrated into the core network of the wireless network, and may determine the location of the UE based on various measurements provided by the UE and / or base stations. The model repository server 908b may correspond to a server outside the core network, and may receive the location of the UE from the location server 908a. In one aspect, the model repository server 908b may be an over the top (OTT) server managed by the supplier of the UE.

[0172] Block 910 includes operations that are performed when DL-RFFP measurements are to be fused with self-RFFP measurements for RFFP-based positioning operations, and may be omitted if DL-RFFP measurements are not used. At 912, UE 902 may receive one or more downlink reference signals, such as PRS, downlink channel reference signals, or downlink channel signals carrying data, or a combination thereof. The one or more downlink reference signals may be from base station 904 or from neighboring base stations 906. At 914, UE 902 may obtain one or more DL-RFFP measurements based on the one or more downlink reference signals.

[0173] At 922, UE 902 may transmit one or more reference signals, such as an SRS, an uplink channel reference signal, or an uplink channel signal carrying data, or a combination thereof. At 924, the one or more reference signals may interact with the surrounding environment to cause reflections of the one or more reference signals. At 926, UE 902 may receive reflections of the one or more reference signals and obtain one or more self-RFFP measurements based on the received reflections.

[0174] In addition, at 931, UE 902 may send one or more reference signals, such as SL-PRS, SL-SSB, SL CSI-SB, sidelink channel reference signal, or sidelink channel signal carrying data, or a combination thereof to UE 903. At 933, the one or more reference signals may interact with the surrounding environment to cause reflections of the one or more reference signals. At 935, UE 902 may receive reflections of the one or more reference signals and obtain one or more self-RFFP measurements based on the received reflections. In addition, at 937, the one or more reference signals sent by UE 902 at 931 may travel to UE 903 via multipath.

[0175] In some aspects, only the self-RFFP measurements obtained according to 922-926 can be used for RFFP-based positioning operations. In some aspects, only the self-RFFP measurements according to 931-935 can be used for RFFP-based positioning operations. In some aspects, all of the self-RFFP measurements according to 922-926 and 931-935 can be used for RFFP-based positioning operations.

[0176] Block 940 includes operations that are performed when the SL-RFFP measurements are to be fused with the self-RFFP measurements for RFFP-based positioning operations, and may be omitted if the SL-RFFP measurements are not used. At 942, UE 903 may receive one or more reference signals sent by UE 902, and obtain one or more SL-RFFP measurements based on the one or more reference signals. Thus, the one or more reference signals used to obtain the one or more self-RFFP measurements at 935 may also be used as one or more sidelink reference signals used to obtain the one or more SL-RFFP measurements at 942.

[0177] At 943, UE 903 may send one or more sidelink reference signals, such as SL-PRS, SL-SSB, SL CSI-SB, sidelink channel reference signal, or a sidelink channel signal carrying data, or a combination thereof, to UE 902. At 945, UE 902 may receive the one or more sidelink reference signals sent by UE 903 and obtain one or more SL-RFFP measurements based on the one or more training sidelink reference signals.

[0178] In some aspects, only the SL-RFFP measurements obtained according to 931 and 942 can be used for RFFP-based positioning operations. In some aspects, only the SL-RFFP measurements according to 943 and 945 can be used for RFFP-based positioning operations. In some aspects, all of the SL-RFFP measurements according to 931 and 942 and 943 and 945 can be used for RFFP-based positioning operations. In addition, when the SL-RFFP measurements obtained according to 931 and 942 are used for RFFP-based positioning operations and a subsequent position inference phase is performed at UE 902, UE 903 can send one or more SL-RFFP measurements obtained at 942 to UE 902 at 948.

[0179] According to the first scenario in which the UE 902 is configured to perform a position inference phase of an RFFP-based positioning operation to determine the position of the UE 902 itself, the UE 902 may determine the position based on applying the ML model to one or more self-RFFP measurements at 952. In some aspects, the UE 902 may determine the position of the UE 902 based on applying the ML model to one or more self-RFFP measurements fused with one or more DL-RFFP measurements at 952. In some aspects, the UE 902 may determine the position of the UE 902 based on applying the ML model to one or more self-RFFP measurements fused with one or more SL-RFFP measurements at 952, wherein a portion or all of the one or more SL-RFFP measurements may be received at 948. In some aspects, the UE 902 may determine the position of the UE 902 based on applying the ML model to one or more self-RFFP measurements fused with one or more DL-RFFP measurements and one or more SL-RFFP measurements at 952.

[0180] In some aspects, the UE 902 can train the ML model itself. In some aspects, the UE 902 can receive at 953 an ML model trained by a network entity such as a location server 908a or a model repository server 908b.

[0181] According to a second scenario in which UE 903 is configured to perform a position inference phase of an RFFP-based positioning operation to determine the position of UE 902, UE 902 may send one or more self-RFFP measurements, one or more DL-RFFP measurements, and / or SL-RFFP measurements to UE 903 at 955. UE 903 may determine the position of UE 902 at 956 based on applying the ML model to the one or more self-RFFP measurements received at 955.

[0182] In some aspects, the UE 903 may determine the location of the UE 902 based on applying the ML model to one or more self-RFFP measurements fused with one or more DL-RFFP measurements received at 955 at 956. In some aspects, the UE 903 may determine the location of the UE 902 based on applying the ML model to one or more self-RFFP measurements fused with one or more SL-RFFP measurements, where a portion or all of the one or more SL-RFFP measurements may be received at 955 or a portion or all of the one or more SL-RFFP measurements may be obtained at 942. In some aspects, the UE 903 may determine the location of the UE 902 based on applying the ML model to one or more self-RFFP measurements fused with one or more DL-RFFP measurements and one or more SL-RFFP measurements.

[0183] In some aspects, the UE 903 can train the ML model itself. In some aspects, the UE 903 can receive at 953 an ML model trained by a network entity such as a location server 908a or a model repository server 908b.

[0184] Fig.10 is a signaling and event diagram illustrating various operations during a model training phase in accordance with aspects of the present disclosure. Fig.10 1002 (first UE, configured as a target device), UE 1003 (second UE, which can establish a sidelink connection with the first UE), base station 1004, one or more neighboring base stations 1006, location server 1008a and model repository server 1008b. UE 1002 can communicate with Fig. 8A 810 in FIG. 8 and may be configured as an observer device. Fig. 8A The base station 1004 and the one or more neighboring base stations 1006 may correspond to the base station 840. In addition, the location server 1008a and the model repository server 1008b may correspond to the network entity 850 individually or in combination.

[0185] The location server 1008a may correspond to a server within or integrated into the core network of the wireless network, and may determine the location of the UE based on various measurements provided by the UE and / or base stations. The model repository server 1008b may correspond to a server outside the core network, and may receive the location of the UE from the location server 1008a. In one aspect, the model repository server 1008b may be an over-the-top (OTT) server managed by the supplier of the UE.

[0186] Block 1010 includes operations that are performed when DL-RFFP measurements are to be fused with self-RFFP measurements for training an ML model for RFFP-based positioning, and may be omitted if DL-RFFP measurements are not used. At 1012, UE 1002 may receive one or more training downlink reference signals, such as PRS, downlink channel reference signals, or downlink channel signals carrying data, or a combination thereof. The one or more training downlink reference signals may be from base station 1004 or from neighboring base stations 1006. At 1014, UE 1002 may obtain one or more training DL-RFFP measurements based on the one or more training downlink reference signals.

[0187] At 1022, the UE 1002 may transmit one or more training reference signals, such as an SRS, an uplink channel reference signal, or an uplink channel signal carrying data (including control information or user data), or a combination thereof. At 1024, the one or more training reference signals may interact with the surrounding environment to cause reflections of the one or more reference signals. At 1026, the UE 1002 may receive reflections of the one or more training reference signals and obtain one or more training self-RFFP measurements based on the received reflections.

[0188] In addition, at 1031, UE 1002 may send one or more training reference signals, such as SL-PRS, SL-SSB, SL CSI-RS, sidelink channel reference signals, or sidelink channel signals carrying data, or a combination thereof, to UE 1003. At 1033, the one or more training reference signals may interact with the surrounding environment to cause reflections of the one or more training reference signals. At 1035, UE 1002 may receive reflections of the one or more training reference signals and obtain one or more training self-RFFP measurements based on the received reflections. In addition, at 1037, the one or more training reference signals sent by UE 1002 at 1031 may travel to UE 1003 via multipath.

[0189] In some aspects, only the self-RFFP measurements obtained according to 1022-1026 can be used to train the ML model for RFFP-based positioning operations. In some aspects, only the self-RFFP measurements according to 1031-1035 can be used to train the ML model for RFFP-based positioning operations. In some aspects, all of the self-RFFP measurements according to 1022-1026 and 1031-1035 can be used to train the ML model for RFFP-based positioning operations.

[0190] Block 1040 includes operations that are performed when SL-RFFP measurements are to be fused with self-RFFP measurements for training an ML model for RFFP-based positioning operations, and may be omitted if SL-RFFP measurements are not used. At 1042, UE 903 may receive one or more reference signals sent by UE 1002, and obtain one or more training SL-RFFP measurements based on the one or more reference signals. Thus, the one or more reference signals used to obtain one or more training self-RFFP measurements at 1035 may also be used as one or more training sidelink reference signals for obtaining one or more training SL-RFFP measurements at 1042.

[0191] At 1043, UE 1003 may send one or more training sidelink reference signals, such as SL-PRS, SL-SSB, SL CSI-RS, sidelink channel reference signal, or a sidelink channel signal carrying data, or a combination thereof, to UE 1002. At 1045, UE 1002 may receive the one or more training sidelink reference signals sent by sidelink device 1003 and obtain one or more training SL-RFFP measurements based on the one or more training sidelink reference signals.

[0192] In some aspects, only the training SL-RFFP measurements obtained according to 1031 and 1042 may be used to train the ML model. In some aspects, only the training SL-RFFP measurements according to 1043 and 1045 may be used to train the ML model. In some aspects, all of the training SL-RFFP measurements according to 1031 and 1042 and 1043 and 1045 may be used to train the ML model. In addition, when the training SL-RFFP measurements obtained according to 1031 and 1042 are used to train the ML model and the model inference phase is performed at UE 1002, UE 1003 may send one or more training SL-RFFP measurements obtained at 1042 to UE 1002 at 1048.

[0193] In some aspects, the UE 1002 may be configured to perform a model training phase of the RFFP-based positioning operation to train an ML model to be used in the position inference phase at 1051. In some aspects, the UE 1002 may train the ML model based on training input data and reference output data, wherein the training input data includes one or more training self-RFFP measurements or one or more training self-RFFP measurements fused with one or more training DL-RFFP measurements and / or one or more training SL-RFFP measurements. In addition, the reference output data includes one or more training positions associated with the one or more training self-RFFP measurements, the one or more training DL-RFFP measurements, and / or the one or more training SL-RFFP measurements.

[0194] Prior to 1051, at 1060, the UE 1002 may obtain ground truth labels or training positions associated with one or more training self-RFFP measurements, one or more training DL-RFFP measurements, and / or one or more training SL-RFFP measurements. In one aspect, the UE 1002 may determine the training positions at 1060 based on DL-TDoA, DL-AoA, RTT positioning, operating the UE 1002 at a predetermined reference position, one or more sensors installed on the UE 1002, or GNSS, or a combination thereof. In some aspects, a network entity (such as a location server 1008a) may receive uplink signals from the base station 1004 and / or neighboring base stations 1006 and / or measurements from the UE 1002 (e.g., path 1062), and determine the training positions of the UE 1002 based on UL-TDoA, UL-AoA, or RTT positioning, or a combination thereof. The UE 1002 may receive the one or more training positions from the location server 1008a at 1060. In some aspects, UE 1003 may receive a sidelink signal from UE 1002 and determine a training location of UE 1002 based on SL-TDoA, SL-AoA, or sidelink-based RTT positioning, or a combination thereof. UE 1002 may receive one or more training locations from UE 1003 at 1060.

[0195] In some aspects, the UE 1003 may be configured to perform a model training phase of the RFFP-based positioning operation to train an ML model to be used in the position inference phase at 1053. In some aspects, the UE 1003 may train the ML model based on training input data and reference output data, wherein the training input data includes one or more training self-RFFP measurements or one or more training self-RFFP measurements fused with one or more training DL-RFFP measurements and / or one or more training SL-RFFP measurements. In addition, the reference output data includes one or more training positions associated with the one or more training self-RFFP measurements, the one or more training DL-RFFP measurements, and / or the one or more training SL-RFFP measurements.

[0196] In addition, when training self-RFFP measurements and / or training DL-RFFP measurements are used to train the ML model, at 1072 and before 1053, UE 1002 may send one or more training self-RFFP measurements and / or one or more training DL-RFFP measurements to UE 1003.

[0197] Prior to 1053, at 1060, the UE 1003 may obtain ground truth labels or training positions associated with one or more training self-RFFP measurements, one or more training DL-RFFP measurements, and / or one or more training SL-RFFP measurements. In one aspect, the UE 1002 may determine the training positions based on DL-TDoA, DL-AoA, RTT positioning, operating the UE 1002 at a predetermined reference position, one or more sensors installed on the UE 1002, or GNSS, or a combination thereof. The UE 1003 may receive the one or more training positions from the UE 1002 at 1060. In some aspects, a network entity (such as a location server 1008a) may determine the training positions of the UE 1002 based on UL-TDoA, UL-AoA, or RTT positioning, or a combination thereof. The UE 1003 may receive the one or more training positions directly from the location server 1008a at 1060 or indirectly via the UE 1002. In some aspects, UE 1003 may receive a sidelink signal from UE 1002 and determine, at 1060, a training position of UE 1002 based on SL-TDoA, SL-AoA, or sidelink-based RTT positioning, or a combination thereof.

[0198] In some aspects, the network entity may be configured to perform a model training phase of the RFFP-based positioning operation to train an ML model to be used in the position inference phase. For example, the location server 1008a may train the ML model at 1055, or the model repository server 1008b may train the ML model at 1057. In some aspects, the location server 1008a or the model repository server 1008b may train the ML model based on training input data and reference output data, wherein the training input data includes one or more training self-RFFP measurements or one or more training self-RFFP measurements fused with one or more training DL-RFFP measurements and / or one or more training SL-RFFP measurements. In addition, the reference output data includes one or more training locations associated with one or more training self-RFFP measurements, one or more training DL-RFFP measurements, and / or one or more training SL-RFFP measurements.

[0199] Furthermore, at 1076 and before 1055 or 1057, the UE 1002 may send one or more training self-RFFP measurements, one or more DL-RFFP measurements, and / or one or more training SL-RFFP measurements to the location server 1008a and / or the model repository server 1008b for use in training the ML model.

[0200] Prior to 1055 or 1057, at 1060, the location server 1008a and / or the model repository server 1008b may obtain ground truth labels or training positions associated with one or more training self-RFFP measurements, one or more training DL-RFFP measurements, and / or one or more training SL-RFFP measurements. In one aspect, the UE 1002 may determine the training positions based on DL-TDoA, DL-AoA, RTT positioning, operating the UE 1002 at a predetermined reference position, one or more sensors installed on the UE 1002, or GNSS, or a combination thereof. The location server 1008a and / or the model repository server 1008b may receive the one or more training positions from the UE 1002 at 1060. In some aspects, the location server 1008a may determine the training positions of the UE 1002 based on UL-TDoA, UL-AoA, or RTT positioning, or a combination thereof. In one aspect, the model repository server 1008b may receive one or more training locations directly from the location server 1008a at 1060. In some aspects, the UE 1003 may determine the training locations of the UE 1002 based on SL-TDoA, SL-AoA, or sidelink-based RTT positioning, or a combination thereof. The location server 1008a and / or the model repository server 1008b may receive one or more training locations from the UE 1003 at 1060.

[0201] Fig.11 An example method 1100 of operating a UE during a position inference phase in accordance with aspects of the present disclosure is shown. In some aspects, the method 1100 may be performed by a UE (e.g., any UE described herein). In some aspects, the method 1100 may correspond to operations performed by UE 810 and / or 902. In one aspect, the method 1100 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340, and / or positioning component 342, any one or all of which may be considered as a means for performing one or more of the following operations of the method 1100.

[0202] At 1110, the UE may send one or more reference signals. In some aspects, the one or more reference signals may include an SRS, a SL-PRS, a SL-SSB, a SL CSI-RS, an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0203] At 1120, the UE may obtain one or more self-RFFP measurements based on reflections of one or more reference signals sent by the UE. In some aspects, the one or more self-RFFP measurements correspond to reflections received by a single antenna or multiple antennas of the UE.

[0204] At 1130, the UE may obtain one or more DL-RFFP measurements based on one or more downlink reference signals received by the UE, or may obtain one or more SL-RFFP measurements based on one or more sidelink reference signals between the UE and a second UE. In some aspects, the downlink reference signal may include a PRS, a downlink channel reference signal, or a downlink channel signal carrying data (control information or user data). In some aspects, the sidelink reference signal may include a SL-PRS, SL-SSB, SL CSI-RS, a sidelink channel reference signal, or a sidelink channel signal carrying data sent by the UE or by a second UE. In some aspects, one or more self-RFFP measurements and one or more SL-RFFP measurements are based on the same one or more reference signals.

[0205] In some aspects, when the DL-RFFP measurements are not fused with the self-RFFP measurements for RFFP-based positioning operations, the operation of obtaining one or more DL-RFFP measurements can be omitted. In some aspects, when the SL-RFFP measurements are not fused with the self-RFFP measurements for RFFP-based positioning operations, the operation of obtaining one or more SL-RFFP measurements can be omitted.

[0206] At 1140, the UE may determine the UE's own location based on applying the ML model to one or more self-RFFP measurements (or one or more self-RFFP measurements fused with one or more DL-RFFP measurements and / or one or more SL-RFFP measurements). In some aspects, the ML model is trained by the UE and stored in the UE. In some aspects, the ML model may be trained by a network entity (such as a location server or a model repository server), and the UE may obtain the ML model from the network entity.

[0207] As will be appreciated, a technical advantage of method 1100 is UE-based RFFP positioning, which can be helpful when the UE (i.e., the UE whose position is to be determined) is out of coverage and / or independent. In addition, UE-based RFFP positioning can help reduce network energy consumption and reduce PRS signal overhead (because PRS can be optional). In addition, self-RFFP measurements add diversity to RFFP-based positioning because self-RFFP measurements can also represent information about the co-location of the transmitter and receiver. In addition, self-RFFP measurements can be fused with other RFFP (e.g., DL-RFFP or SL-RFFP) measurements for RFFP-based positioning to enhance positioning accuracy.

[0208] Fig.12 An example method 1200 of operating a UE during a position inference phase according to various aspects of the present disclosure is shown. The UE may determine the location of a second UE with which it is exchanging sidelink signals. In some aspects, the method 1200 may be performed by a UE (e.g., any UE described herein). In some aspects, the method 1200 may correspond to operations performed by UE 820 and / or 903. In one aspect, the method 1200 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340, and / or positioning component 342, any one or all of which may be considered as a unit for performing one or more of the following operations of the method 1200.

[0209] At 1210, the UE may receive from the second UE one or more self-RFFP measurements obtained by the second UE based on reflections of one or more reference signals sent by the second UE. In some aspects, the one or more reference signals may include an SRS, a SL-PRS, a SL-SSB, a SL CSI-RS, an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0210] At 1220, the UE may obtain one or more SL-RFFP measurements based on one or more sidelink reference signals between the second UE and the UE. In some aspects, the sidelink reference signal may include a SL-PRS, SL-SSB, SL CSI-RS, a sidelink channel reference signal, or a sidelink channel signal carrying data sent by the UE or by the second UE. In some aspects, one or more self-RFFP measurements and one or more SL-RFFP measurements are based on the same one or more reference signals. In some aspects, when the SL-RFFP measurement is not fused with the self-RFFP measurement for RFFP-based positioning operations, the operation of obtaining one or more SL-RFFP measurements may be omitted.

[0211] At 1230, the UE may obtain one or more DL-RFFP measurements based on one or more downlink reference signals received by the second UE. In some aspects, the downlink reference signal may include a PRS, a downlink channel reference signal, or a downlink channel signal carrying data. In some aspects, when the DL-RFFP measurements are not fused with self-RFFP measurements for RFFP-based positioning operations, the operation of obtaining one or more DL-RFFP measurements may be omitted.

[0212] At 1240, the UE may determine the location of the second UE based on applying the ML model to one or more self-RFFP measurements (or one or more self-RFFP measurements fused with one or more DL-RFFP measurements and / or one or more SL-RFFP measurements). In some aspects, the ML model is trained by the UE and stored in the UE. In some aspects, the ML model may be trained by a network entity (such as a location server or a repository server), and the UE may obtain the ML model from the network entity.

[0213] As will be appreciated, a technical advantage of method 1200 is also UE-based RFFP positioning, which can be helpful when a second UE (i.e., the UE whose position is to be determined) is out of coverage and / or independent. In addition, UE-based RFFP positioning can help reduce network energy consumption and reduce PRS signal overhead (because PRS can be optional). In addition, self-RFFP measurements add diversity to RFFP-based positioning because self-RFFP measurements can also represent information about the co-location of the transmitter and receiver. In addition, self-RFFP measurements can be fused with other RFFP (e.g., DL-RFFP or SL-RFFP) measurements for RFFP-based positioning to enhance positioning accuracy.

[0214] Fig.13 Example methods of operating a network entity or UE during a model training phase in accordance with aspects of the present disclosure are shown.

[0215] In some aspects, the method 1300 may be performed by a UE (e.g., any UE described herein). In some aspects, the method 1300 may correspond to operations performed by UE 810, 820, 1002, and / or 1003. In one aspect, the method 1300 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340, and / or positioning component 342, any one or all of which may be considered as a means for performing one or more of the following operations of the method 1300.

[0216] In some aspects, the method 1300 may be performed by a network entity (e.g., any of the network entities, LMFs, SLPs, or servers described herein). In some aspects, the method 1300 may correspond to operations performed by the network entity 850, the location server 1008a, and / or the repository server 1008b. In one aspect, the method 1300 may be performed by one or more network transceivers 398, one or more processors 394, memory 398, and / or a positioning component 398, any or all of which may be considered as means for performing one or more of the following operations of the method 1300.

[0217] At 1310, a device performing ML model training may receive one or more training self-RFFP measurements obtained by an observer device (e.g., UE 810) based on reflections of one or more reference signals sent by the observer device. In some aspects, the reference signal may include an SRS, a SL-PRS, a SL-SSB, a SL CSI-RS, an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data. In some aspects, the one or more training self-RFFP measurements correspond to reflections received by a single antenna or multiple antennas of the observer device.

[0218] At 1320, the device performing ML model training may obtain one or more training locations of the observer device, wherein the one or more training locations are associated with one or more trained self-RFFP measurements. In some aspects, the observer device may determine the one or more training locations of the observer device based on DL-TDoA, DL-AoA, RTT positioning, operating the observer device at a predetermined reference position, one or more sensors installed on the observer device, or GNSS, or a combination thereof. In some aspects, the network entity may determine the one or more training locations of the observer device based on UL-TDoA, UL-AoA, or RTT positioning, or a combination thereof. In some aspects, the wireless device may determine the one or more training locations of the observer device based on SL-TDoA, SL-AoA, or RTT positioning based on a sidelink, or a combination thereof.

[0219] At 1330, the device performing ML model training may obtain one or more training DL-RFFP measurements based on one or more downlink reference signals received by the observer device, and / or obtain corresponding one or more training positions of the observer device. In some aspects, operation 1330 may be omitted if the ML model for RFFP-based positioning operations does not require any DL-RFFP measurements.

[0220] At 1340, the device performing ML model training may obtain one or more training SL-RFFP measurements based on one or more sidelink reference signals between the observer device and another wireless device, and / or obtain corresponding one or more training positions of the observer device. In some aspects, operation 1340 may be omitted if the ML model for RFFP-based positioning operations does not require any SL-RFFP measurements.

[0221] At 1350, the device performing ML model training may train the ML model based on the training input data and the reference output data. In some aspects, the training input data may include one or more training self-RFFP measurements, or may include one or more training self-RFFP measurements further fused with one or more training DL-RFFP measurements and / or one or more training SL-RFFP measurements. In some aspects, the reference output data may include the corresponding training position of the observer device.

[0222] As will be appreciated, the technical advantages of method 1300 are directed to the model training phase of UE-based RFFP positioning. According to method 1300, self-RFFP measurements can add diversity to RFFP-based positioning because self-RFFP measurements can also represent information about transmitter and receiver co-location. In addition, self-RFFP measurements can be fused with other RFFP (e.g., DL-RFFP or SL-RFFP) measurements for RFFP-based positioning to enhance positioning accuracy.

[0223] It can be seen in the detailed description above that different features are grouped together in the examples. This disclosure should not be understood as an intention that the example clauses have more features than those explicitly mentioned in each clause. Instead, various aspects of the present disclosure may include less than all the features of the disclosed individual example clauses. Therefore, the clauses below should be considered to be incorporated into the description accordingly, wherein each clause itself can be used as a separate example. Although each dependent clause can be cited in a specific combination with one of the other clauses in the clause, the various aspects of the dependent clause are not limited to a specific combination. It will be recognized that other example clauses may also include a combination of the subject matter of the dependent clause aspect with any other dependent clause or independent clause, or a combination of any feature with other dependent clauses and independent clauses. Various aspects disclosed herein explicitly include these combinations, unless it is explicitly stated or it can be easily inferred that a specific combination is not intended (e.g., contradictory aspects, such as defining an element as both an electrical insulator and an electrical conductor). In addition, it is also expected that various aspects of the clause are included in any other independent clause, even if the clause is not directly dependent on the independent clause.

[0224] Examples of implementations are described in the following numbered clauses:

[0225] Clause 1. A method of operating a user equipment (UE), comprising: sending one or more reference signals; obtaining one or more self-radio frequency fingerprint (self-RFFP) measurements based on reflections of the one or more reference signals sent by the UE; and determining a location of the UE based on applying a machine learning model to the one or more self-RFFP measurements.

[0226] Clause 2. The method according to Clause 1 further includes: obtaining one or more downlink RFFP (DL-RFFP) measurements based on one or more downlink reference signals received by the UE, or obtaining one or more sidelink RFFP (SL-RFFP) measurements based on one or more sidelink reference signals between the UE and a second UE, wherein the position of the UE is determined based on applying the machine learning model to: (i) the one or more self-RFFP measurements, and (ii) the one or more DL-RFFP measurements, or the one or more SL-RFFP measurements, or a combination thereof.

[0227] Clause 3. A method according to clause 2, wherein both the one or more self-RFFP measurements and the one or more SL-RFFP measurements are based on the one or more sidelink reference signals sent by the UE.

[0228] Clause 4. A method according to any one of clauses 2 to 3, wherein the one or more SL-RFFP measurements are based on the one or more sidelink reference signals sent by the UE or by the second UE.

[0229] Clause 5. A method according to clause 4, wherein the one or more sidelink reference signals include a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0230] Clause 6. A method according to any one of clauses 1 to 5, wherein the one or more reference signals include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0231] Clause 7. A method according to any one of clauses 1 to 6, wherein the machine learning model is trained based on one or more trained-from-RFFP measurements, each of the one or more trained-from-RFFP measurements being obtained by a corresponding observer device based on a reflection of a corresponding reference signal sent by the corresponding observer device.

[0232] Clause 8. A method according to clause 7, wherein the machine learning model is also trained based on: one or more training downlink RFFP measurements obtained based on one or more downlink training signals received by a specific observer device, or one or more training sidelink RFFP measurements obtained based on one or more sidelink training signals between the specific observer device and another UE.

[0233] Clause 9. The method according to any one of clauses 1 to 8 further includes: obtaining one or more trained self-RFFP measurements based on reflections of another one or more reference signals sent by the UE; obtaining one or more training positions of the UE, the one or more training positions being associated with the one or more trained self-RFFP measurements; and training the machine learning model based on training input data and reference output data, the training input data including the one or more trained self-RFFP measurements of the UE, and the reference output data including the one or more training positions of the UE.

[0234] Clause 10. The method according to Clause 9 further includes: determining one of the one or more training positions of the UE, wherein the one of the one or more training positions of the UE is determined based on: downlink time difference of arrival (DL-TDoA), downlink angle of arrival (DL-AoA), sidelink time difference of arrival (SL-TDoA), sidelink angle of arrival (SL-AoA), round-trip time (RTT) positioning, operating the UE at a predetermined reference position, one or more sensors installed on the UE, or a global navigation satellite system (GNSS), or a combination thereof.

[0235] Clause 11. The method according to any one of clauses 9 to 10 further includes: obtaining one or more training downlink RFFP (DL-RFFP) measurements based on one or more downlink training signals received by the UE, or obtaining one or more training sidelink RFFP (SL-RFFP) measurements based on one or more sidelink training signals between the UE and a second UE, wherein the training input data also includes the one or more training DL-RFFP measurements, or the one or more training SL-RFFP measurements, or both.

[0236] Clause 12. A method as described in any of clauses 9 to 11, further comprising: receiving a training position of the one or more training positions of the UE from a network entity or from a second UE.

[0237] Clause 13. The method according to any one of clauses 1 to 12 further includes: obtaining one or more trained self-RFFP measurements based on reflections of another one or more reference signals sent by the UE; sending the one or more trained self-RFFP measurements of the UE to a network entity for training the machine learning model; and receiving the machine learning model from the network entity after training the machine learning model based on at least the one or more trained self-RFFP measurements of the UE.

[0238] Clause 14. The method according to Clause 13 further includes: determining a training location of the UE, the training location being associated with one of the one or more trained self-RFFP measurements of the UE; and sending the training location of the UE to the network entity for training the machine learning model.

[0239] Clause 15. A method according to clause 14, wherein the training position of the UE is determined based on: downlink time difference of arrival (DL-TDoA), downlink angle of arrival (DL-AoA), sidelink time difference of arrival (SL-TDoA), sidelink angle of arrival (SL-AoA), round trip time (RTT) positioning, operating the UE at a predetermined reference position, one or more sensors installed on the UE, or a global navigation satellite system (GNSS), or a combination thereof.

[0240] Clause 16. The method according to any one of clauses 13 to 15 further includes: obtaining one or more training downlink RFFP (DL-RFFP) measurements based on one or more downlink training signals received by the UE, or obtaining one or more training sidelink RFFP (SL-RFFP) measurements based on one or more sidelink training signals between the UE and a second UE; and sending the one or more training DL-RFFP measurements, or the training SL-RFFP measurements, or both to the network entity for training the machine learning model.

[0241] Clause 17. The method of any one of clauses 13 to 16, wherein the network entity is a location server or a model repository server.

[0242] Clause 18. A method of operating a user equipment (UE), comprising: receiving from a second UE one or more self radio frequency fingerprint (self RFFP) measurements obtained by the second UE based on reflections of one or more reference signals sent by the second UE; obtaining one or more sidelink RFFP (SL-RFFP) measurements based on one or more sidelink reference signals between the UE and the second UE; and determining the position of the second UE based on applying a machine learning model to the one or more self RFFP measurements and the one or more SL-RFFP measurements.

[0243] Clause 19. A method according to clause 18, wherein the one or more self-RFFP measurements and the one or more SL-RFFP measurements are based on the one or more sidelink reference signals sent by the second UE.

[0244] Clause 20. A method as described in any of clauses 18 to 19, wherein the one or more SL-RFFP measurements are based on the one or more sidelink reference signals sent by the UE or by the second UE.

[0245] Clause 21. A method according to clause 20, wherein the one or more sidelink signals include a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SLCSI-RS), a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0246] Clause 22. A method according to any one of clauses 18 to 21, further comprising: receiving one or more trained self-RFFP measurements obtained by the second UE based on reflections of another one or more reference signals sent by the second UE; obtaining one or more training positions of the second UE, the one or more training positions being associated with the one or more trained self-RFFP measurements; and training the machine learning model based on training input data and reference output data, the training input data comprising the one or more trained self-RFFP measurements of the second UE, and the reference output data comprising the one or more training positions of the second UE.

[0247] Clause 23. The method according to clause 22 further includes: determining one of the one or more training positions of the second UE, wherein the one of the one or more training positions of the second UE is determined based on: sidelink time difference of arrival (SL-TDoA), sidelink angle of arrival (SL-AoA), or round-trip time (RTT) positioning, or a combination thereof.

[0248] Clause 24. A method as described in any of clauses 22 to 23, further comprising: receiving a training position of the one or more training positions of the second UE from the second UE.

[0249] Clause 25. A user equipment (UE) comprising: a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: send one or more reference signals via the at least one transceiver; obtain one or more self-radio frequency fingerprint (self-RFFP) measurements based on reflections of the one or more reference signals sent by the UE; and determine a location of the UE based on applying a machine learning model to the one or more self-RFFP measurements.

[0250] Clause 26. A UE according to clause 25, wherein the at least one processor is further configured to obtain one or more downlink RFFP (DL-RFFP) measurements based on one or more downlink reference signals received by the UE, or to obtain one or more sidelink RFFP (SL-RFFP) measurements based on one or more sidelink reference signals between the UE and a second UE, wherein the position of the UE is determined based on applying the machine learning model to: (i) the one or more self-RFFP measurements, and (ii) the one or more DL-RFFP measurements, or the one or more SL-RFFP measurements, or a combination thereof.

[0251] Clause 27. A UE as described in clause 26, wherein both the one or more self-RFFP measurements and the one or more SL-RFFP measurements are based on the one or more sidelink reference signals sent by the UE.

[0252] Clause 28. A UE as described in any of clauses 26 to 27, wherein the one or more SL-RFFP measurements are based on the one or more sidelink reference signals sent by the UE or by the second UE.

[0253] Clause 29. A UE according to clause 28, wherein the one or more sidelink reference signals include a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0254] Clause 30. A UE according to any one of clauses 25 to 29, wherein the one or more reference signals include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SLCSI-RS), an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0255] Clause 31. A UE according to any one of clauses 25 to 30, wherein the machine learning model is trained based on one or more trained-from-RFFP measurements, each of the one or more trained-from-RFFP measurements being obtained by a corresponding observer device based on a reflection of a corresponding reference signal sent by the corresponding observer device.

[0256] Clause 32. A UE according to clause 31, wherein the machine learning model is also trained based on: one or more training downlink RFFP measurements obtained based on one or more downlink training signals received by a specific observer device, or one or more training sidelink RFFP measurements obtained based on one or more sidelink training signals between the specific observer device and another UE.

[0257] Clause 33. A UE according to any one of clauses 25 to 32, wherein the at least one processor is further configured to: obtain one or more trained self-RFFP measurements based on reflections of another one or more reference signals sent by the UE; obtain one or more training positions of the UE, the one or more training positions being associated with the one or more trained self-RFFP measurements; and train the machine learning model based on training input data and reference output data, the training input data including the one or more trained self-RFFP measurements of the UE, and the reference output data including the one or more training positions of the UE.

[0258] Clause 34. A UE according to clause 33, wherein the at least one processor is further configured to: determine one of the one or more training positions of the UE, wherein the one of the one or more training positions of the UE is determined based on: downlink time difference of arrival (DL-TDoA), downlink angle of arrival (DL-AoA), sidelink time difference of arrival (SL-TDoA), sidelink angle of arrival (SL-AoA), round trip time (RTT) positioning, operating the UE at a predetermined reference position, one or more sensors installed on the UE, or a global navigation satellite system (GNSS), or a combination thereof.

[0259] Clause 35. A UE according to any one of clauses 33 to 34, wherein the at least one processor is further configured to obtain one or more training downlink RFFP (DL-RFFP) measurements based on one or more downlink training signals received by the UE, or to obtain one or more training sidelink RFFP (SL-RFFP) measurements based on one or more sidelink training signals between the UE and a second UE, wherein the training input data also includes the one or more training DL-RFFP measurements, or the one or more training SL-RFFP measurements, or both.

[0260] Clause 36. A UE according to any one of clauses 33 to 35, wherein the at least one processor is further configured to: receive, via the at least one transceiver, one of the one or more training locations of the UE from a network entity or from a second UE.

[0261] Clause 37. A UE according to any one of clauses 25 to 36, wherein the at least one processor is further configured to: obtain one or more trained self-RFFP measurements based on reflections of another one or more reference signals sent by the UE; send the one or more trained self-RFFP measurements of the UE to a network entity via the at least one transceiver for training the machine learning model; and after training the machine learning model based on at least the one or more trained self-RFFP measurements of the UE, receive the machine learning model from the network entity via the at least one transceiver.

[0262] Clause 38. A UE according to clause 37, wherein the at least one processor is further configured to: determine a training location of the UE, the training location being associated with one of the one or more trained self-RFFP measurements of the UE; and send the training location of the UE to the network entity via the at least one transceiver for training the machine learning model.

[0263] Clause 39. A UE according to clause 38, wherein the training position of the UE is determined based on: downlink time difference of arrival (DL-TDoA), downlink angle of arrival (DL-AoA), sidelink time difference of arrival (SL-TDoA), sidelink angle of arrival (SL-AoA), round trip time (RTT) positioning, operating the UE at a predetermined reference position, one or more sensors installed on the UE, or a global navigation satellite system (GNSS), or a combination thereof.

[0264] Clause 40. A UE according to any one of clauses 37 to 39, wherein the at least one processor is further configured to: obtain one or more training downlink RFFP (DL-RFFP) measurements based on one or more downlink training signals received by the UE, or obtain one or more training sidelink RFFP (SL-RFFP) measurements based on one or more sidelink training signals between the UE and a second UE; and send the one or more training DL-RFFP measurements, or the training SL-RFFP measurements, or both to the network entity via the at least one transceiver for training the machine learning model.

[0265] Clause 41. A UE as set forth in any of clauses 37 to 40, wherein the network entity is a location server or a model repository server.

[0266] Clause 42. A user equipment (UE) comprising: a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: receive from a second UE via the at least one transceiver one or more self-RFFP (self-RFFP) measurements obtained by the second UE based on reflections of one or more reference signals sent by the second UE; obtain one or more sidelink RFFP (SL-RFFP) measurements based on one or more sidelink reference signals between the UE and the second UE; and determine the position of the second UE based on applying a machine learning model to the one or more self-RFFP measurements and the one or more SL-RFFP measurements.

[0267] Clause 43. A UE according to clause 42, wherein the one or more self-RFFP measurements and the one or more SL-RFFP measurements are based on the one or more sidelink reference signals sent by the second UE.

[0268] Clause 44. A UE as set forth in any one of clauses 42 to 43, wherein the one or more SL-RFFP measurements are based on the one or more sidelink reference signals sent by the UE or by the second UE.

[0269] Clause 45. A UE according to clause 44, wherein the one or more sidelink signals include a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SLCSI-RS), a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0270] Clause 46. A UE according to any one of clauses 42 to 45, wherein the at least one processor is further configured to: receive, via the at least one transceiver, one or more trained self-RFFP measurements obtained by the second UE based on reflections of another one or more reference signals sent by the second UE; obtain one or more training positions of the second UE, the one or more training positions being associated with the one or more trained self-RFFP measurements; and train the machine learning model based on training input data and reference output data, the training input data including the one or more trained self-RFFP measurements of the second UE, and the reference output data including the one or more training positions of the second UE.

[0271] Clause 47. A UE according to clause 46, wherein the at least one processor is further configured to: determine one of the one or more training positions of the second UE, wherein the one of the one or more training positions of the second UE is determined based on: sidelink time difference of arrival (SL-TDoA), sidelink angle of arrival (SL-AoA), or round-trip time (RTT) positioning, or a combination thereof.

[0272] Clause 48. A UE as described in any of clauses 46 to 47, wherein the at least one processor is further configured to: receive one of the one or more training positions of the second UE from the second UE via the at least one transceiver.

[0273] Clause 49. A user equipment (UE), comprising: a unit for sending one or more reference signals; a unit for obtaining one or more self-radio frequency fingerprint (self-RFFP) measurements based on reflections of the one or more reference signals sent by the UE; and a unit for determining the location of the UE based on applying a machine learning model to the one or more self-RFFP measurements.

[0274] Clause 50. The UE according to clause 49 further includes: a unit for obtaining one or more downlink RFFP (DL-RFFP) measurements based on one or more downlink reference signals received by the UE, or obtaining one or more sidelink RFFP (SL-RFFP) measurements based on one or more sidelink reference signals between the UE and a second UE, wherein the position of the UE is determined based on applying the machine learning model to: (i) the one or more self-RFFP measurements, and (ii) the one or more DL-RFFP measurements, or the one or more SL-RFFP measurements, or a combination thereof.

[0275] Clause 51. A UE as described in clause 50, wherein both the one or more self-RFFP measurements and the one or more SL-RFFP measurements are based on the one or more sidelink reference signals sent by the UE.

[0276] Clause 52. A UE according to any one of clauses 50 to 51, wherein the one or more SL-RFFP measurements are based on the one or more sidelink reference signals sent by the UE or by the second UE.

[0277] Clause 53. A UE according to clause 52, wherein the one or more sidelink reference signals include a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0278] Clause 54. A UE according to any one of clauses 49 to 53, wherein the one or more reference signals include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SLCSI-RS), an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0279] Clause 55. A UE according to any one of clauses 49 to 54, wherein the machine learning model is trained based on one or more trained-from-RFFP measurements, each of the one or more trained-from-RFFP measurements being obtained by a corresponding observer device based on a reflection of a corresponding reference signal sent by the corresponding observer device.

[0280] Clause 56. A UE according to clause 55, wherein the machine learning model is also trained based on: one or more training downlink RFFP measurements obtained based on one or more downlink training signals received by a specific observer device, or one or more training sidelink RFFP measurements obtained based on one or more sidelink training signals between the specific observer device and another UE.

[0281] Clause 57. A UE according to any one of clauses 49 to 56, further comprising: a unit for obtaining one or more trained self-RFFP measurements based on reflections of another one or more reference signals sent by the UE; a unit for obtaining one or more training positions of the UE, the one or more training positions being associated with the one or more trained self-RFFP measurements; and a unit for training the machine learning model based on training input data and reference output data, the training input data comprising the one or more trained self-RFFP measurements of the UE, and the reference output data comprising the one or more training positions of the UE.

[0282] Clause 58. The UE according to Clause 57 further includes: a unit for determining one of the one or more training positions of the UE, wherein the one of the one or more training positions of the UE is determined based on: downlink time difference of arrival (DL-TDoA), downlink angle of arrival (DL-AoA), sidelink time difference of arrival (SL-TDoA), sidelink angle of arrival (SL-AoA), round-trip time (RTT) positioning, operating the UE at a predetermined reference position, one or more sensors installed on the UE, or a global navigation satellite system (GNSS), or a combination thereof.

[0283] Clause 59. A UE according to any one of clauses 57 to 58, further comprising: a unit for obtaining one or more training downlink RFFP (DL-RFFP) measurements based on one or more downlink training signals received by the UE, or for obtaining one or more training sidelink RFFP (SL-RFFP) measurements based on one or more sidelink training signals between the UE and a second UE, wherein the training input data also includes the one or more training DL-RFFP measurements, or the one or more training SL-RFFP measurements, or both.

[0284] Clause 60. A UE as defined in any of clauses 57 to 59, further comprising means for receiving, from a network entity or from a second UE, one of the one or more training positions of the UE.

[0285] Clause 61. A UE according to any one of clauses 49 to 60, further comprising: a unit for obtaining one or more trained self-RFFP measurements based on reflections of another one or more reference signals sent by the UE; a unit for sending the one or more trained self-RFFP measurements of the UE to a network entity for training the machine learning model; and a unit for receiving the machine learning model from the network entity after training the machine learning model based on at least the one or more trained self-RFFP measurements of the UE.

[0286] Clause 62. The UE according to Clause 61 further includes: a unit for determining a training location of the UE, the training location being associated with one of the one or more trained self-RFFP measurements of the UE; and a unit for sending the training location of the UE to the network entity for training the machine learning model.

[0287] Clause 63. A UE according to clause 62, wherein the training position of the UE is determined based on: downlink time difference of arrival (DL-TDoA), downlink angle of arrival (DL-AoA), sidelink time difference of arrival (SL-TDoA), sidelink angle of arrival (SL-AoA), round trip time (RTT) positioning, operating the UE at a predetermined reference position, one or more sensors installed on the UE, or a global navigation satellite system (GNSS), or a combination thereof.

[0288] Clause 64. A UE according to any one of clauses 61 to 63, further comprising: a unit for obtaining one or more training downlink RFFP (DL-RFFP) measurements based on one or more downlink training signals received by the UE, or obtaining one or more training sidelink RFFP (SL-RFFP) measurements based on one or more sidelink training signals between the UE and a second UE; and a unit for sending the one or more training DL-RFFP measurements, or the training SL-RFFP measurements, or both to the network entity for training the machine learning model.

[0289] Clause 65. A UE as set forth in any of clauses 61 to 64, wherein the network entity is a location server or a model repository server.

[0290] Clause 66. A user equipment (UE), comprising: a unit for receiving from a second UE one or more self radio frequency fingerprint (self RFFP) measurements obtained by the second UE based on reflections of one or more reference signals sent by the second UE; a unit for obtaining one or more sidelink RFFP (SL-RFFP) measurements based on one or more sidelink reference signals between the UE and the second UE; and a unit for determining the position of the second UE based on applying a machine learning model to the one or more self RFFP measurements and the one or more SL-RFFP measurements.

[0291] Clause 67. A UE according to clause 66, wherein the one or more self-RFFP measurements and the one or more SL-RFFP measurements are based on the one or more sidelink reference signals sent by the second UE.

[0292] Clause 68. A UE as described in any of clauses 66 to 67, wherein the one or more SL-RFFP measurements are based on the one or more sidelink reference signals sent by the UE or by the second UE.

[0293] Clause 69. A UE according to clause 68, wherein the one or more sidelink signals include a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0294] Clause 70. A UE according to any one of clauses 66 to 69, further comprising: a unit for receiving one or more trained self-RFFP measurements obtained by the second UE based on reflections of another one or more reference signals sent by the second UE; a unit for obtaining one or more training positions of the second UE, the one or more training positions being associated with the one or more trained self-RFFP measurements; and a unit for training the machine learning model based on training input data and reference output data, the training input data comprising the one or more trained self-RFFP measurements of the second UE, and the reference output data comprising the one or more training positions of the second UE.

[0295] Clause 71. The UE according to clause 70 further includes: a unit for determining one of the one or more training positions of the second UE, wherein the one of the one or more training positions of the second UE is determined based on: sidelink time difference of arrival (SL-TDoA), sidelink angle of arrival (SL-AoA), or round-trip time (RTT) positioning, or a combination thereof.

[0296] Clause 72. A UE as defined in any one of clauses 70 to 71, further comprising means for receiving, from the second UE, a training position of the one or more training positions of the second UE.

[0297] Clause 73. A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a user equipment (UE), cause the UE to: send one or more reference signals; obtain one or more self-radio frequency fingerprint (self-RFFP) measurements based on reflections of the one or more reference signals sent by the UE; and determine the location of the UE based on applying a machine learning model to the one or more self-RFFP measurements.

[0298] Clause 74. A non-transitory computer-readable medium according to clause 73, wherein the computer-executable instructions also include instructions that, when executed by the UE, cause the UE to perform the following operations: obtain one or more downlink RFFP (DL-RFFP) measurements based on one or more downlink reference signals received by the UE, or obtain one or more sidelink RFFP (SL-RFFP) measurements based on one or more sidelink reference signals between the UE and a second UE, wherein the position of the UE is determined based on applying the machine learning model to each of: (i) the one or more self-RFFP measurements, and (ii) the one or more DL-RFFP measurements, or the one or more SL-RFFP measurements, or a combination thereof.

[0299] Clause 75. A non-transitory computer-readable medium as described in clause 74, wherein both the one or more self-RFFP measurements and the one or more SL-RFFP measurements are based on the one or more sidelink reference signals sent by the UE.

[0300] Clause 76. A non-transitory computer-readable medium as described in any of clauses 74 to 75, wherein the one or more SL-RFFP measurements are based on the one or more sidelink reference signals sent by the UE or by the second UE.

[0301] Clause 77. A non-transitory computer-readable medium according to clause 76, wherein the one or more sidelink reference signals include a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0302] Clause 78. A non-transitory computer-readable medium according to any one of clauses 73 to 77, wherein the one or more reference signals include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SLCSI-RS), an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0303] Clause 79. A non-transitory computer-readable medium according to any one of clauses 73 to 78, wherein the machine learning model is trained based on one or more trained-from-RFFP measurements, each of the one or more trained-from-RFFP measurements being obtained by a corresponding observer device based on a reflection of a corresponding reference signal sent by the corresponding observer device.

[0304] Clause 80. A non-transitory computer-readable medium according to clause 79, wherein the machine learning model is also trained based on: one or more training downlink RFFP measurements obtained based on one or more downlink training signals received by a specific observer device, or one or more training sidelink RFFP measurements obtained based on one or more sidelink training signals between the specific observer device and another UE.

[0305] Clause 81. A non-transitory computer-readable medium according to any one of clauses 73 to 80, wherein the computer-executable instructions also include instructions that, when executed by the UE, cause the UE to: obtain one or more trained self-RFFP measurements based on reflections of another one or more reference signals sent by the UE; obtain one or more training positions of the UE, the one or more training positions being associated with the one or more trained self-RFFP measurements; and train the machine learning model based on training input data and reference output data, the training input data including the one or more trained self-RFFP measurements of the UE, and the reference output data including the one or more training positions of the UE.

[0306] Clause 82. A non-transitory computer-readable medium according to clause 81, wherein the computer-executable instructions further comprise instructions that, when executed by the UE, cause the UE to perform the following operations: determine one of the one or more training positions of the UE, wherein the one of the one or more training positions of the UE is determined based on: downlink time difference of arrival (DL-TDoA), downlink angle of arrival (DL-AoA), sidelink time difference of arrival (SL-TDoA), sidelink angle of arrival (SL-AoA), round trip time (RTT) positioning, operating the UE at a predetermined reference position, one or more sensors mounted on the UE, or a global navigation satellite system (GNSS), or a combination thereof.

[0307] Clause 83. A non-transitory computer-readable medium according to any one of clauses 81 to 82, wherein the computer-executable instructions also include instructions that, when executed by the UE, cause the UE to perform the following operations: obtain one or more training downlink RFFP (DL-RFFP) measurements based on one or more downlink training signals received by the UE, or obtain one or more training sidelink RFFP (SL-RFFP) measurements based on one or more sidelink training signals between the UE and a second UE, wherein the training input data also includes the one or more training DL-RFFP measurements, or the one or more training SL-RFFP measurements, or both.

[0308] Clause 84. A non-transitory computer-readable medium according to any one of clauses 81 to 83, wherein the computer-executable instructions also include instructions that, when executed by the UE, cause the UE to perform the following operations: receive one of the one or more training locations of the UE from a network entity or from a second UE.

[0309] Clause 85. A non-transitory computer-readable medium according to any one of clauses 73 to 84, wherein the computer-executable instructions also include instructions that, when executed by the UE, cause the UE to perform the following operations: obtain one or more trained self-RFFP measurements based on reflections of another one or more reference signals sent by the UE; send the one or more trained self-RFFP measurements of the UE to a network entity for training the machine learning model; and receive the machine learning model from the network entity after training the machine learning model based on at least the one or more trained self-RFFP measurements of the UE.

[0310] Clause 86. A non-transitory computer-readable medium according to clause 85, wherein the computer-executable instructions also include instructions that, when executed by the UE, cause the UE to perform the following operations: determine a training location of the UE, the training location being associated with one of the one or more trained self-RFFP measurements of the UE; and send the training location of the UE to the network entity for training the machine learning model.

[0311] Clause 87. A non-transitory computer-readable medium according to clause 86, wherein the training position of the UE is determined based on: downlink time difference of arrival (DL-TDoA), downlink angle of arrival (DL-AoA), sidelink time difference of arrival (SL-TDoA), sidelink angle of arrival (SL-AoA), round-trip time (RTT) positioning, operating the UE at a predetermined reference position, one or more sensors installed on the UE, or a global navigation satellite system (GNSS), or a combination thereof.

[0312] Clause 88. A non-transitory computer-readable medium according to any one of clauses 85 to 87, wherein the computer-executable instructions also include instructions that, when executed by the UE, cause the UE to perform the following operations: obtain one or more training downlink RFFP (DL-RFFP) measurements based on one or more downlink training signals received by the UE, or obtain one or more training sidelink RFFP (SL-RFFP) measurements based on one or more sidelink training signals between the UE and a second UE; and send the one or more training DL-RFFP measurements, or the training SL-RFFP measurements, or both to the network entity for training the machine learning model.

[0313] Clause 89. The non-transitory computer-readable medium of any one of clauses 85 to 88, wherein the network entity is a location server or a model repository server.

[0314] Item 90. A non-transitory computer-readable medium storing computer-executable instructions, which, when executed by a user equipment (UE), cause the UE to: receive from a second UE one or more self-RF fingerprint (self-RFFP) measurements obtained by the second UE based on reflections of one or more reference signals sent by the second UE; obtain one or more sidelink RFFP (SL-RFFP) measurements based on one or more sidelink reference signals between the UE and the second UE; and determine the position of the second UE based on applying a machine learning model to the one or more self-RFFP measurements and the one or more SL-RFFP measurements.

[0315] Clause 91. A non-transitory computer-readable medium as described in clause 90, wherein the one or more self-RFFP measurements and the one or more SL-RFFP measurements are based on the one or more sidelink reference signals sent by the second UE.

[0316] Clause 92. A non-transitory computer-readable medium as described in any of clauses 90 to 91, wherein the one or more SL-RFFP measurements are based on the one or more sidelink reference signals sent by the UE or by the second UE.

[0317] Clause 93. A non-transitory computer-readable medium according to clause 92, wherein the one or more sidelink signals include a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SLCSI-RS), a sidelink channel reference signal, or a sidelink channel signal carrying data.

[0318] Clause 94. A non-temporary computer-readable medium according to any one of clauses 90 to 93, wherein the computer-executable instructions also include instructions that, when executed by the UE, cause the UE to perform the following operations: receive one or more trained self-RFFP measurements obtained by the second UE based on reflections of another one or more reference signals sent by the second UE; obtain one or more training positions of the second UE, the one or more training positions being associated with the one or more trained self-RFFP measurements; and train the machine learning model based on training input data and reference output data, the training input data including the one or more trained self-RFFP measurements of the second UE, and the reference output data including the one or more training positions of the second UE.

[0319] Clause 95. A non-temporary computer-readable medium according to clause 94, wherein the computer-executable instructions also include instructions that, when executed by the UE, cause the UE to perform the following operations: determine one of the one or more training positions of the second UE, wherein the one of the one or more training positions of the second UE is determined based on: sidelink time difference of arrival (SL-TDoA), sidelink angle of arrival (SL-AoA), or round-trip time (RTT) positioning, or a combination thereof.

[0320] Clause 96. A non-transitory computer-readable medium according to any one of clauses 94 to 95, wherein the computer-executable instructions also include instructions that, when executed by the UE, cause the UE to perform the following operations: receive one of the one or more training positions of the second UE from the second UE.

[0321] It will be appreciated by those skilled in the art that information and signals may be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be mentioned throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0322] In addition, it will be appreciated by those skilled in the art that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the aspects disclosed herein can be implemented as electronic hardware, computer software, or a combination of the two. In order to clearly illustrate this interchangeability of hardware and software, the above has been generally described around the functions of various illustrative components, blocks, modules, circuits, and steps. Whether such functions are implemented as hardware or software depends on the specific application and the design constraints imposed on the entire system. A skilled person can implement the described functions in an alternative manner for each specific application, but such implementation decisions should not be interpreted as resulting in a departure from the scope of the present disclosure.

[0323] The various illustrative logic blocks, modules, and circuits described in conjunction with the various aspects disclosed herein may be implemented or performed using a general purpose processor, a digital signal processor (DSP), an ASIC, a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in an alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration).

[0324] The methods, sequences and / or algorithms described in conjunction with the various aspects disclosed herein may be directly embodied in hardware, in a software module executed by a processor, or in a combination of the two. The software module may be located in a random access memory (RAM), a flash memory, a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An example storage medium may be coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. In an alternative manner, the storage medium may be integrated into the processor. The processor and the storage medium may be located in an ASIC. The ASIC may be located in a user terminal (e.g., UE). In an alternative manner, the processor and the storage medium may exist as discrete components in a user device.

[0325] In one or more example aspects, the functions described can be implemented in hardware, software, firmware or any combination thereof. If implemented in software, the functions can be stored on a computer-readable medium as one or more instructions or codes or transmitted by one or more instructions or codes. Computer-readable media can include both computer storage media and communication media, and the communication media includes any medium that promotes the transfer of computer programs from one place to another. Storage media can be any available medium that can be accessed by a computer. By way of example and not limitation, such computer-readable media can include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage devices, or can be used to carry or store desired program codes and any other medium that can be accessed by a computer in the form of instructions or data structures. In addition, any connection is appropriately referred to as a computer-readable medium. For example, if software is sent from a website, server or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL) or wireless technology (such as infrared, radio and microwave), coaxial cable, fiber optic cable, twisted pair, DSL or wireless technology (such as infrared, radio and microwave) are included in the definition of medium. As used herein, disk and disc include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, wherein disk usually reproduces data magnetically, while disc utilizes laser to reproduce data optically. Combinations of the above should also be included in the scope of computer readable media.

[0326] Although the foregoing disclosure shows the illustrative aspects of the present disclosure, it should be noted that various changes and modifications may be made herein without departing from the scope of the present disclosure as defined by the appended claims. The steps and / or actions of the method claims according to the various aspects of the disclosure described herein do not need to be performed in any particular order. In addition, although the elements of the present disclosure may be described or claimed in singular form, the plural form is contemplated unless explicitly stated to be limited to the singular form.

Claims

1. A method of operating a user equipment (UE), comprising: sending one or more reference signals; obtaining one or more self radio frequency fingerprint (self RFFP) measurements based on reflections of the one or more reference signals sent by the UE; as well as The location of the UE is determined based on applying a machine learning model to the one or more self-RFFP measurements.

2. The method according to claim 1, further comprising: obtaining one or more downlink RFFP (DL-RFFP) measurements based on one or more downlink reference signals received by the UE, or obtaining one or more sidelink RFFP (SL-RFFP) measurements based on one or more sidelink reference signals between the UE and a second UE, The position of the UE is determined based on applying the machine learning model to: (i) the one or more self-RFFP measurements, and (ii) the one or more DL-RFFP measurements, or the one or more SL-RFFP measurements, or a combination thereof.

3. The method according to claim 2, wherein: Both the one or more self-RFFP measurements and the one or more SL-RFFP measurements are based on the one or more sidelink reference signals sent by the UE.

4. The method according to claim 2, wherein: The one or more SL-RFFP measurements are based on the one or more sidelink reference signals sent by the UE or by the second UE.

5. The method according to claim 4, wherein: The one or more sidelink reference signals include a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SLCSI-RS), a sidelink channel reference signal, or a sidelink channel signal carrying data.

6. The method according to claim 1, wherein: The one or more reference signals include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SLCSI-RS), an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data.

7. The method according to claim 1, wherein: The machine learning model is trained based on one or more trained-from-RFFP measurements, each of the one or more trained-from-RFFP measurements being obtained by a corresponding observer device based on reflections of a corresponding reference signal sent by the corresponding observer device.

8. The method according to claim 7, wherein: The machine learning model is also trained based on one or more training downlink RFFP measurements obtained based on one or more downlink training signals received by a specific observer device, or one or more training sidelink RFFP measurements obtained based on one or more sidelink training signals between the specific observer device and another UE.

9. The method according to claim 1, further comprising: obtaining one or more trained self-RFFP measurements based on reflections of another one or more reference signals sent by the UE; obtaining one or more training locations of the UE, the one or more training locations being associated with the one or more trained self-RFFP measurements; as well as The machine learning model is trained based on training input data and reference output data, wherein the training input data includes the one or more trained self-RFFP measurements of the UE, and the reference output data includes the one or more trained positions of the UE.

10. The method according to claim 9, further comprising: determining a training location of the one or more training locations of the UE, One of the one or more training positions of the UE is determined based on: downlink time difference of arrival (DL-TDoA), downlink angle of arrival (DL-AoA), sidelink time difference of arrival (SL-TDoA), sidelink angle of arrival (SL-AoA), round-trip time (RTT) positioning, operating the UE at a predetermined reference position, one or more sensors installed on the UE, or a global navigation satellite system (GNSS), or a combination thereof.

11. The method according to claim 9, further comprising: obtaining one or more training downlink RFFP (DL-RFFP) measurements based on one or more downlink training signals received by the UE, or obtaining one or more training sidelink RFFP (SL-RFFP) measurements based on one or more sidelink training signals between the UE and a second UE, The training input data further includes the one or more training DL-RFFP measurements, or the one or more training SL-RFFP measurements, or both.

12. The method according to claim 9, further comprising: A training location of the one or more training locations of the UE is received from a network entity or from a second UE.

13. The method according to claim 1, further comprising: obtaining one or more trained self-RFFP measurements based on reflections of another one or more reference signals sent by the UE; sending the one or more trained self-RFFP measurements of the UE to a network entity for use in training the machine learning model; as well as After training the machine learning model based on at least the one or more trained RFFP measurements of the UE, the machine learning model is received from the network entity.

14. The method according to claim 13, further comprising: determining a training location of the UE, the training location being associated with one of the one or more trained-from-RFFP measurements of the UE; as well as The training location of the UE is sent to the network entity for training the machine learning model.

15. The method according to claim 14, wherein: The training position of the UE is determined based on: downlink time difference of arrival (DL-TDoA), downlink angle of arrival (DL-AoA), sidelink time difference of arrival (SL-TDoA), sidelink angle of arrival (SL-AoA), round trip time (RTT) positioning, operating the UE at a predetermined reference position, one or more sensors installed on the UE, or a global navigation satellite system (GNSS), or a combination thereof.

16. The method according to claim 13, further comprising: Obtaining one or more training downlink RFFP (DL-RFFP) measurements based on one or more downlink training signals received by the UE, or obtaining one or more training sidelink RFFP (SL-RFFP) measurements based on one or more sidelink training signals between the UE and a second UE; and The one or more training DL-RFFP measurements, or the training SL-RFFP measurements, or both are sent to the network entity for use in training the machine learning model.

17. The method according to claim 13, wherein: The network entity is a location server or a model repository server.

18. A method of operating a user equipment (UE), comprising: receiving, from a second UE, one or more self radio frequency fingerprint (self RFFP) measurements obtained by the second UE based on reflections of one or more reference signals sent by the second UE; obtaining one or more sidelink RFFP (SL-RFFP) measurements based on one or more sidelink reference signals between the UE and the second UE; as well as The location of the second UE is determined based on applying a machine learning model to the one or more self-RFFP measurements and the one or more SL-RFFP measurements.

19. The method according to claim 18, wherein: The one or more self-RFFP measurements and the one or more SL-RFFP measurements are based on the one or more sidelink reference signals sent by the second UE.

20. The method according to claim 18, wherein: The one or more SL-RFFP measurements are based on the one or more sidelink reference signals sent by the UE or by the second UE.

21. The method according to claim 20, wherein: The one or more sidelink signals include a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SLCSI-RS), a sidelink channel reference signal, or a sidelink channel signal carrying data.

22. The method of claim 18, further comprising: receiving one or more trained self-RFFP measurements obtained by the second UE based on reflections of another one or more reference signals sent by the second UE; obtaining one or more training locations of the second UE, the one or more training locations being associated with the one or more training self-RFFP measurements; as well as The machine learning model is trained based on training input data and reference output data, wherein the training input data includes the one or more trained self-RFFP measurements of the second UE, and the reference output data includes the one or more trained positions of the second UE.

23. The method according to claim 22, further comprising: determining a training location of the one or more training locations of the second UE, Wherein, one of the one or more training positions of the second UE is determined based on: sidelink time difference of arrival (SL-TDoA), sidelink angle of arrival (SL-AoA), or round-trip time (RTT) positioning, or a combination thereof.

24. The method of claim 22, further comprising: A training location of the one or more training locations of the second UE is received from the second UE.

25. A user equipment (UE), comprising: Memory; at least one transceiver; as well as at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor configured to: transmitting, via the at least one transceiver, one or more reference signals; obtaining one or more self radio frequency fingerprint (self RFFP) measurements based on reflections of the one or more reference signals sent by the UE; as well as The location of the UE is determined based on applying a machine learning model to the one or more self-RFFP measurements.

26. The UE according to claim 25, wherein: The at least one processor is further configured to: obtaining one or more downlink RFFP (DL-RFFP) measurements based on one or more downlink reference signals received by the UE, or obtaining one or more sidelink RFFP (SL-RFFP) measurements based on one or more sidelink reference signals between the UE and a second UE, The position of the UE is determined based on applying the machine learning model to: (i) the one or more self-RFFP measurements, and (ii) the one or more DL-RFFP measurements, or the one or more SL-RFFP measurements, or a combination thereof.

27. The UE according to claim 25, wherein: The at least one processor is further configured to: obtaining one or more trained self-RFFP measurements based on reflections of another one or more reference signals sent by the UE; obtaining one or more training locations of the UE, the one or more training locations being associated with the one or more trained self-RFFP measurements; as well as The machine learning model is trained based on training input data and reference output data, wherein the training input data includes the one or more trained self-RFFP measurements of the UE, and the reference output data includes the one or more trained positions of the UE.

28. The UE according to claim 27, wherein: The at least one processor is further configured to: obtaining one or more training downlink RFFP (DL-RFFP) measurements based on one or more downlink training signals received by the UE, or obtaining one or more training sidelink RFFP (SL-RFFP) measurements based on one or more sidelink training signals between the UE and a second UE, The training input data further includes the one or more training DL-RFFP measurements, or the one or more training SL-RFFP measurements, or both.

29. A user equipment (UE), comprising: Memory; at least one transceiver; as well as at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor configured to: receiving, from a second UE via the at least one transceiver, one or more self radio frequency fingerprint (self RFFP) measurements obtained by the second UE based on reflections of one or more reference signals sent by the second UE; obtaining one or more sidelink RFFP (SL-RFFP) measurements based on one or more sidelink reference signals between the UE and the second UE; as well as The location of the second UE is determined based on applying a machine learning model to the one or more self-RFFP measurements and the one or more SL-RFFP measurements.

30. The UE according to claim 29, wherein: The at least one processor is further configured to: receiving, via the at least one transceiver, one or more trained self-RFFP measurements obtained by the second UE based on reflections of another one or more reference signals sent by the second UE; obtaining one or more training locations of the second UE, the one or more training locations being associated with the one or more training self-RFFP measurements; as well as The machine learning model is trained based on training input data and reference output data, wherein the training input data includes the one or more trained self-RFFP measurements of the second UE, and the reference output data includes the one or more trained positions of the second UE.

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