User Equipment (UE)-Based Radio Frequency Fingerprint (RFFP) Positioning Using Downlink Positioning Reference Signals

Abstract

Techniques for wireless communications are disclosed. In one aspect, a user equipment (UE) receives from a first network entity one or more positioning assistance data messages for a downlink radio frequency fingerprinting (DL-RFFP) positioning procedure, the positioning assistance data messages including at least one parameter related to a machine learning model that the UE is configured to use for the DL-RFFP positioning procedure (1610), and transmits to a second network entity one or more location information messages including the one or more parameters related to the DL-RFFP positioning procedure (1620).

Description

[Technical field]

[0001] Aspects of the present disclosure relate generally to wireless communications.

[0002] 2. Description of Related Art Wireless communication systems have evolved through various generations, including first-generation analog wireless telephone service (1G), second-generation (2G) digital wireless telephone service (including intermediate 2.5G and 2.75G networks), third-generation (3G) high-speed data, Internet-enabled wireless service, and fourth-generation (4G) service (e.g., Long Term Evolution (LTE) or WiMax). Currently, many different types of wireless communication systems are in use, including cellular systems and personal communications service (PCS) systems. Examples of known cellular systems include the 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), and the like.

[0003] The fifth generation (5G) wireless standard, called New Radio (NR), will enable higher data rates, more connections, and better coverage, among other improvements. 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 technological advancements compared to previous standards, according to the Next Generation Mobile Network Alliance. These advancements, as well as the use of higher frequency bands, advancements in PRS processes and technologies, and dense deployment for 5G, will enable highly accurate 5G-based positioning. Summary of the Invention

[0004] The following presents a simplified summary of one or more aspects disclosed herein. As such, the following summary is not intended to be an extensive overview of all contemplated aspects, nor is it intended to identify key or critical elements of all contemplated aspects or to delineate the scope of any particular aspect. Thus, the sole purpose of the following summary is to present certain concepts of one or more aspects of the mechanisms disclosed herein in a simplified form prior to the detailed description presented below.

[0005] In one aspect, a method of wireless communication implemented by a user equipment (UE) includes receiving, from a first network entity, one or more positioning assistance data messages for a downlink radio frequency fingerprint (DL-RFFP) positioning procedure, the one or more positioning assistance data messages including at least one parameter related to a machine learning model configured for use by the UE for the DL-RFFP positioning procedure, and transmitting, to a second network entity, one or more location information messages including the one or more parameters related to the DL-RFFP positioning procedure.

[0006] In one aspect, a method of communication implemented by a base station includes receiving one or more assistance information control messages from a location server indicating one or more parameters related to a machine learning model configured for use by at least one user equipment (UE) for a Downlink Radio Frequency Fingerprint (DL-RFFP) positioning procedure, and transmitting one or more positioning system information blocks (posSIBs) specific to DL-RFFP positioning to the at least one UE indicating the at least one or more parameters.

[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, wherein the at least one processor is configured to receive, from a first network entity via the at least one transceiver, one or more positioning assistance data messages for a downlink radio frequency fingerprinting (DL-RFFP) positioning procedure, the one or more positioning assistance data messages including at least one parameter related to a machine learning model configured to be used by the UE for a DL-RFFP positioning procedure, and to transmit, to a second network entity via the at least one transceiver, one or more location information messages including the one or more parameters related to the DL-RFFP positioning procedure.

[0008] In one aspect, a base station includes a memory, at least one transceiver, and at least one processor communicatively coupled to the memory and the at least one transceiver, wherein the at least one processor is configured to: receive, via the at least one transceiver, one or more assistance information control messages from a location server indicating one or more parameters related to a machine learning model configured for at least one user equipment (UE) to use for a downlink radio frequency fingerprinting (DL-RFFP) positioning procedure; and transmit, via the at least one transceiver, one or more positioning system information blocks (posSIBs) specific to DL-RFFP positioning to the at least one UE, indicating the at least one parameter.

[0009] In one aspect, a user equipment (UE) includes means for receiving from a first network entity one or more positioning assistance data messages for a downlink radio frequency fingerprinting (DL-RFFP) positioning procedure, the positioning assistance data messages including at least one parameter related to a machine learning model configured for use by the UE for the DL-RFFP positioning procedure, and means for transmitting to a second network entity one or more location information messages including the one or more parameters related to the DL-RFFP positioning procedure.

[0010] In one aspect, the base station includes means for receiving one or more assistance information control messages from a location server indicating one or more parameters related to a machine learning model configured for use by at least one user equipment (UE) for a Downlink Radio Frequency Fingerprint (DL-RFFP) positioning procedure, and means for transmitting one or more positioning system information blocks (posSIBs) specific to DL-RFFP positioning to the at least one UE, indicating the at least one or more parameters.

[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 receive from a first network entity one or more positioning assistance data messages for a downlink radio frequency fingerprinting (DL-RFFP) positioning procedure, the positioning assistance data messages including at least one parameter related to a machine learning model configured to be used by the UE for the DL-RFFP positioning procedure, and to transmit to a second network entity one or more location information messages including the one or more parameters related to the DL-RFFP positioning procedure.

[0012] In one aspect, a non-transitory computer-readable medium stores computer-executable instructions that, when executed by a base station, cause the base station to receive one or more assistance information control messages from a location server indicating one or more parameters related to a machine learning model configured for use by at least one user equipment (UE) for a Downlink Radio Frequency Fingerprint (DL-RFFP) positioning procedure, and to transmit one or more positioning system information blocks (posSIBs) specific to DL-RFFP positioning to the at least one UE, indicating the at least one or more parameters.

[0013] Other objects and advantages associated with the embodiments disclosed herein will become apparent to those skilled in the art based on the accompanying drawings and detailed description.

[0014] The accompanying drawings are presented to aid in the explanation of various aspects of the present disclosure and are provided only to illustrate, not limit, the aspects. [Brief description of the drawings]

[0015] [Figure 1] FIG. 1 illustrates an example wireless communication system according to an aspect of the present disclosure. [Figure 2A] 1 illustrates an exemplary wireless network structure according to an aspect of the present disclosure. [Figure 2B] 1 illustrates an exemplary wireless network structure according to an aspect of the present disclosure. [Figure 2C] 1 illustrates an exemplary wireless network structure according to an aspect of the present disclosure. [Figure 3A] 1 is a simplified block diagram of several sample aspects of components that may be employed in a user equipment (UE) and configured to support communications as taught herein; [Figure 3B] 1 is a simplified block diagram of several sample aspects of components that may be employed in a base station and configured to support communications as taught herein. [Figure 3C]1 is a simplified block diagram of several sample aspects of components that may be employed in a network entity and configured to support communications as taught herein. [Figure 4] 1 illustrates examples of various positioning methods supported in New Radio (NR) according to an embodiment of the present disclosure. [Diagram 5] FIG. 2 illustrates an example frame structure according to an aspect of the present disclosure. [Figure 6] 1 is a graph illustrating radio frequency (RF) channel estimation according to an aspect of the present disclosure. [Figure 7] 1 illustrates an exemplary neural network according to an aspect of the present disclosure. [Figure 8] FIG. 1 illustrates the use of machine learning models for RF fingerprinting (RFFP) based positioning, according to an embodiment of the present disclosure. [Figure 9] FIG. 1 illustrates an inference cycle for UE-based downlink RFFP (DL-RFFP) positioning, according to an aspect of the disclosure. [Figure 10] 1 illustrates an example call flow for UE-based DL-RFFP positioning, according to an aspect of the disclosure. [Figure 11] There are two procedures for exchanging UE positioning capabilities with the network that are currently supported by the Long Term Evolution (LTE) positioning protocol (LPP). [Figure 12] There are two procedures for exchanging positioning assistance data currently supported by the LPP. [Figure 13] There are two procedures for exchanging location information currently supported by the LPP: [Figure 14] A diagram showing an assistance information control procedure between a location server and a Next Generation radio access network (NG-RAN) node. [Figure 15] 1 illustrates an example method of wireless communication according to an aspect of the present disclosure. [Figure 16] 1 illustrates an example method of wireless communication according to an aspect of the present disclosure. [Figure 17] 1 illustrates an example method of wireless communication according to an aspect of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] Aspects of the present disclosure are provided in the following description and associated drawings, directed to various examples provided for illustrative purposes. Alternative aspects may be devised without departing from the scope of the present disclosure. Additionally, well-known elements of the present disclosure will not be described in detail or will be omitted so as not to obscure the relevant details of the present disclosure.

[0017] 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 present disclosure" does not require that all aspects of the present disclosure include the discussed feature, advantage or mode of operation.

[0018] Those skilled in the art will appreciate that the information and signals described below may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the following description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof, depending in part on the particular application, desired design, corresponding technology, etc.

[0019] Further, many aspects are described in terms of sequences of actions to be performed by, for example, elements of a computing device. It will be appreciated that various actions described herein can be performed by specific circuitry (e.g., application specific integrated circuits (ASICs)), by program instructions executed by one or more processors, or by a combination of both. In addition, the sequence or sequences of actions described herein can be considered to be fully embodied in any form of non-transitory computer-readable storage medium storing a corresponding set of computer instructions that, when executed, cause or instruct an associated processor of a device to perform the functionality described herein. Thus, various aspects of the present disclosure may be embodied in a number of different forms, all of which are contemplated to be within the scope of the claimed subject matter. In addition, for each of the aspects described herein, the corresponding form of any such aspect may be described herein, for example, as "logic configured to" perform the described actions.

[0020] The terms "user equipment" (UE) and "base station" as used herein are not intended to be specific or limited to any particular radio access technology (RAT) unless otherwise specified. 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 location device, a wearable (e.g., a smart watch, glasses, augmented reality (AR) / virtual reality (VR) headset, 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 over a wireless communication network. A UE may be mobile or may be stationary (e.g., at a given time) and may communicate with a radio access network (RAN). As used herein, the term "UE" may be referred to interchangeably 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, through which the UE may be connected to external networks, such as the Internet, and to other UEs. Of course, other mechanisms for connecting to the core network and / or the Internet are also possible for a UE, such as via 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 specifications, etc.).

[0021] A base station may operate according to one of several RATs in communication with UEs depending on the network in which the base station is deployed and may alternatively be referred to as an access point (AP), network node, Node B, evolved Node B (eNB), next generation eNB (ng-eNB), New Radio (NR) Node B (also referred to as gNB or gNode B), etc. A base station may be used primarily to support wireless access by UEs, including supporting data, voice, and / or signaling connections for supported UEs. In some systems, a base station may provide only edge node signaling functionality, while in other systems, a base station may provide additional control and / or network management functionality. The communication link through which a UE can send signals to a base station is referred to as an uplink (UL) channel (e.g., reverse traffic channel, reverse control channel, access channel, etc.). The communication links through which a base station may transmit signals to a UE are referred to as downlink (DL) channels or forward link channels (e.g., paging channels, control channels, broadcast channels, forward traffic channels, etc.). As used herein, the term traffic channel (TCH) can refer to either an uplink / reverse traffic channel or a downlink / forward traffic channel.

[0022] The term "base station" may refer to a single physical transmission-reception point (TRP) or multiple physical TRPs that may or may not be collocated. For example, when the term "base station" refers to a single physical TRP, the physical TRP may be an antenna of the base station that corresponds to a cell (or several cell sectors) of the base station. When the term "base station" refers to multiple collocated physical TRPs, the physical TRP may be an array of antennas of the base station (e.g., as in a multiple-input multiple-output (MIMO) system or when the base station employs beamforming). When the term "base station" refers to multiple non-collocated physical TRPs, the physical TRP may be a distributed antenna system (DAS) (a network of spatially separated antennas connected to a common source via a transport medium) or a remote radio head (RRH) (a remote base station connected to a serving base station). Instead, non-co-located physical TRPs may be serving base stations that receive measurement reports from the UE and neighboring base stations whose reference radio frequency (RF) signals the UE is measuring. Since a TRP is a point from which a base station transmits and receives wireless signals, as used herein, references to transmission from or reception at a base station should be understood as referring to a particular TRP of the base station.

[0023] In some implementations that support positioning of UEs, a base station may not support wireless access by the UE (e.g., may not support data, voice, and / or signaling connections for the UE) but may instead transmit reference signals to the UE to be measured by the UE and / or receive and measure signals transmitted by the UE. Such a base station may be referred to as a positioning beacon (e.g., if it transmits signals to the UE) and / or a location measurement unit (e.g., if it receives and measures signals from the UE).

[0024] An "RF signal" includes electromagnetic waves of a given frequency that propagate information through space between a transmitter and a receiver. As used herein, a transmitter may transmit a single "RF signal" or multiple "RF signals" to a receiver. However, the receiver may receive multiple "RF signals" corresponding to each transmitted RF signal due to the propagation characteristics of RF signals through multipath channels. The same RF signal transmitted over different paths between a transmitter and a receiver may be referred to as a "multipath" RF signal. As used herein, an RF signal may also be referred to as a "wireless signal" or simply a "signal" when it is clear from the context that the term "signal" refers to a wireless signal or an RF signal.

[0025] 1 illustrates an example wireless communication system 100 according to an aspect of the disclosure. The wireless communication system 100 (sometimes referred to as a wireless wide area network (WWAN)) may include various base stations 102 (labeled "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.

[0026] The base stations 102 may collectively form a RAN and may interface with a core network 170 (e.g., evolved packet core (EPC) or 5G core (5GC)) through the backhaul links 122 and with one or more location servers 172 (e.g., location management function (LMF) or secure user plane location (SUPL) location platform (SLP)) through the core network 170. The location server(s) 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 through another path, such as through an application server (not shown), through another network, such as through a wireless local area network (WLAN) access point (AP) (e.g., AP 150 described below), etc. For purposes of signaling, communication between the UE 104 and the location server 172 may be represented as an indirect connection (e.g., through the core network 170), or a direct connection (e.g., as shown via direct connection 128), with intervening nodes (if any) omitted from the signaling diagrams for clarity.

[0027] In addition to other functions, the base stations 102 may perform functions related to one or more of the following: forwarding user data, radio channel encryption and decryption, integrity protection, header compression, mobility control functions (e.g., handover, dual connectivity), inter-cell interference coordination, connection setup and release, load balancing, non-access stratum (NAS) message delivery, NAS node selection, synchronization, RAN sharing, multimedia broadcast multicast service (MBMS), subscriber and equipment tracing, RAN information management (RIM), paging, positioning, and alert message delivery. The base stations 102 may communicate with each other directly or indirectly (e.g., through EPC / 5GC) via backhaul links 134, which may be wired or wireless.

[0028] The base stations 102 may wirelessly communicate with the UEs 104. Each of the base stations 102 may provide communication coverage for a respective geographic coverage area 110. In an aspect, one or more cells may be supported by the base stations 102 in each geographic coverage area 110. A "cell" is a logical communication entity used for communication with a base station (e.g., over some frequency resources, referred to as a carrier frequency, component carrier, carrier, band, etc.) and may be associated with an identifier (e.g., a physical cell identifier (PCI), an enhanced cell identifier (ECI), a virtual cell identifier (VCI), a cell global identifier (CGI), etc.) to distinguish cells operating over 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 others) that may provide access to different types of UEs. Since a cell is supported by a particular base station, the term "cell" may refer to one or both of the logical communication entity and the base station that supports it, depending on the context. In addition, since a TRP is typically a physical transmission point of a cell, the terms "cell" and "TRP" may be used interchangeably. In some cases, the term "cell" may also refer to the geographic coverage area (e.g., sector) of a base station, as long as the carrier frequency can be detected and used for communication within a portion of the geographic coverage area 110.

[0029] The geographic coverage areas 110 of neighboring macrocell base stations 102 may overlap partially (e.g., in handover regions) and some of the geographic coverage areas 110 may be significantly overlapped by larger geographic coverage areas 110. For example, a small cell base station 102' (labeled "SC" for "small cell") may have a geographic coverage area 110' that significantly overlaps with the geographic coverage area 110 of one or more macrocell base stations 102. A network including both small cell base stations and macrocell base stations may be known as a heterogeneous network. A heterogeneous network may also include home eNBs (HeNBs), which may serve closed groups known as closed subscriber groups (CSGs).

[0030] The communication link 120 between the base station 102 and the UE 104 may include uplink (also referred to as reverse link) transmissions from the UE 104 to the base station 102, and / or downlink (DL) (also referred to as forward link) transmissions from the base station 102 to the UE 104. The communication link 120 may use MIMO antenna techniques, including 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).

[0031] The wireless communication system 100 may further include a wireless local area network (WLAN) access point (AP) 150 in communication with WLAN stations (STAs) 152 over a communication link 154 in an unlicensed frequency spectrum (e.g., 5 GHz). When communicating in the unlicensed frequency spectrum, the WLAN STAs 152 and / or the WLAN AP 150 may perform a clear channel assessment (CCA) or listen before talk (LBT) procedure before communicating to determine if a channel is available.

[0032] The small cell base station 102' may operate in a licensed and / or unlicensed frequency spectrum. When operating in an unlicensed frequency spectrum, the small cell base station 102' may utilize LTE or NR technology and use the same 5 GHz unlicensed frequency spectrum used by the WLAN AP 150. A small cell base station 102' employing LTE / 5G in an unlicensed frequency spectrum may extend coverage to and / or increase capacity of an access network. NR in an unlicensed spectrum may be referred to as NR-U. LTE in an unlicensed spectrum may be referred to as LTE-U, licensed assisted access (LAA), or MulteFire.

[0033] The wireless communication system 100 may further include a mmW base station 180 that may operate at millimeter wave (mmW) frequencies and / or sub-mmW in communication with the UE 182. Extremely high frequency (EHF) is a portion of RF in the electromagnetic spectrum. EHF has a range of 30 GHz to 300 GHz and a wavelength of 1 millimeter to 10 millimeters. Radio waves in this band may be referred to as millimeter waves. Sub-mmW may go down to a frequency of 3 GHz with a wavelength of 100 millimeters. The super high frequency (SHF) band ranges from 3 GHz to 30 GHz and is also referred to as centimeter wave. Communications using the mmW / sub-mmW radio frequency bands have high path loss and relatively short range. The mmW base station 180 and the UE 182 may utilize beamforming (transmit and / or receive) over the mmW communication link 184 to compensate for the extremely high path loss and short range. It will be further understood that in alternative configurations, one or more base stations 102 may also transmit using mmW or quasi-mmW and beamforming. Accordingly, it will be understood that the above illustrations are merely examples and should not be construed as limiting various aspects disclosed herein.

[0034] Transmit beamforming is a technique for concentrating an RF signal in a particular direction. Traditionally, when a network node (e.g., a base station) broadcasts an RF signal, it broadcasts it in all directions (omnidirectional). With transmit beamforming, the network node determines where a given target device (e.g., UE) is located (relative to the transmitting network node) and launches a stronger downlink RF signal in that particular direction, thereby providing a faster and more powerful RF signal (in terms of data rate) to the receiving device(s). To change the directionality 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 broadcasting the RF signal. For example, the network node may use an array of antennas (also called a "phased array" or "antenna array") that creates beams of RF waves that can be "steered" to point in different directions without actually moving the antennas. Specifically, RF currents from the transmitters are fed to the individual antennas with the proper phase relationship so that the radio waves from the separate antennas are combined to cancel and suppress radiation in undesired directions while simultaneously enhancing radiation in desired directions.

[0035] A transmit beam may be quasi-co-located, meaning that the transmit beam appears to a receiver (e.g., UE) to have the same parameters regardless of whether the network node's own transmit antenna is physically co-located or not. In NR, there are four types of quasi-co-location (QCL) relationships. Specifically, a QCL relationship of a given type means that some parameters for a second reference RF signal on a second beam can be derived from information about a source reference RF signal on a source beam. Thus, 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 transmitted 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 transmitted 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 a second reference RF signal transmitted on the same channel. If the source reference RF signal is QCL Type D, the receiver can use the source reference RF signal to estimate spatial reception parameters of a second reference RF signal transmitted on the same channel.

[0036] In receive beamforming, a receiver uses receive beams to amplify RF signals detected on a given channel. For example, the receiver can increase the gain setting and / or adjust the phase setting of an array of antennas in a particular direction to amplify (e.g., increase its gain level) an RF signal received from that direction. Thus, when a receiver is said to beamform in a direction, it means that the beam gain in that direction is higher than the beam gains along other directions, or that the beam gain in that direction is the highest compared to the beam gains 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.

[0037] The transmit beam and the receive beam may be spatially related. The spatial relationship means that the parameters for the second beam (e.g., transmit beam or receive beam) for the second reference signal may be derived from information about the first beam (e.g., receive beam or transmit beam) for the first reference signal. For example, the UE may use a particular receive beam to receive a reference downlink reference signal (e.g., synchronization signal block (SSB)) from a base station. The UE may then form a transmit beam for sending an uplink reference signal (e.g., sounding reference signal (SRS)) to that base station based on the parameters of the receive beam.

[0038] Note that a "downlink" beam can be either a transmit beam or a receive beam, depending on the entity that forms it. For example, if the base station forms a downlink beam to transmit a reference signal to the UE, then the downlink beam is a transmit beam. However, if the UE forms a downlink beam, then it is a receive beam to receive a downlink reference signal. Similarly, an "uplink" beam can be either a transmit beam or a receive beam, depending on the entity that forms it. For example, if the base station forms an uplink beam, then it is an uplink receive beam, and if the UE forms an uplink beam, then it is an uplink transmit beam.

[0039] The electromagnetic spectrum is often subdivided into various classes, bands, channels, etc. based on frequency / wavelength. In 5G NR, two initial operating bands have been identified as frequency range designations FR1 (410 MHz-7.125 GHz) and FR2 (24.25 GHz-52.6 GHz). It should be understood that FR1 is often referred to (interchangeably) as the "sub-6 GHz" band in various documents and papers, although a portion of FR1 is higher than 6 GHz. A similar nomenclature issue may arise with respect to FR2, which is often referred to (interchangeably) as the "millimeter wave" band in documents and papers, even though it is different from the extremely high frequency (EHF) band (30 GHz-300 GHz) identified as the "millimeter wave" band by the International Telecommunications Union (ITU).

[0040] Frequencies between FR1 and FR2 are often referred to as mid-band frequencies. Recent 5G NR studies have identified operating bands for these mid-band frequencies as frequency range designation FR3 (7.125 GHz to 24.25 GHz). Frequency bands included within FR3 may inherit FR1 and / or FR2 characteristics, and thus may in effect extend the features of FR1 and / or FR2 to the mid-band frequencies. In addition, higher frequency bands are currently being explored to extend 5G NR operation beyond 52.6 GHz. For example, three higher operating bands have been identified as frequency range designations FR4a or FR4-1 (52.6 GHz to 71 GHz), FR4 (52.6 GHz to 114.25 GHz), and FR5 (114.25 GHz to 300 GHz). Each of these higher frequency bands is included within the EHF band.

[0041] With the above aspects in mind, it should be understood that unless specifically stated otherwise, terms such as "sub-6 GHz" as used herein may broadly refer to frequencies that may be below 6 GHz, may be within FR1, or may include mid-band frequencies. Furthermore, unless specifically stated otherwise, it should be understood that terms such as "mmWave" as used herein may broadly refer to frequencies that may include mid-band frequencies, may be within the ranges of FR2, FR4, FR4-a or FR4-1, and / or FR5, or may be within the EHF band.

[0042] In a multi-carrier system such as 5G, one of the carrier frequencies is called the "primary carrier" or "anchor carrier" or "primary serving cell" or "PCell", and the remaining carrier frequencies are called the "secondary carrier" or "secondary serving cell" or "SCell". In carrier aggregation, the anchor carrier is a carrier operating on a 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 control channels and UE-specific control channels and may (but is not always) be a carrier among licensed frequencies. The secondary carrier is a carrier operating on a second frequency (e.g., FR2) that may be configured once an RRC connection is established between the UE 104 and the anchor carrier and may be used to provide additional radio resources. In some cases, the secondary carrier may be a carrier among unlicensed frequencies. Since both the primary uplink carrier and the primary downlink carrier are typically UE specific, the secondary carrier shall contain only the necessary signaling information and signals, e.g., there should be no UE specific signaling information and signals in the secondary carrier. This means that different UEs 104 / 182 in a cell may have different downlink primary carriers. The same applies to the uplink primary carrier. The network may change the primary carrier of any UE 104 / 182 at any time. This is done, for example, to balance the load on different carriers. Since a "serving cell" (whether PCell or SCell) corresponds to a carrier frequency / component carrier over which several base stations are communicating, terms such as "cell", "serving cell", "component carrier", "carrier frequency" and the like may be used interchangeably.

[0043] For example, still referring to FIG. 1, one of the frequencies utilized by the macrocell base station 102 may be an anchor carrier (or "PCell"), and the other frequencies utilized by the macrocell base station 102 and / or the mmW base station 180 may be secondary carriers ("SCells"). Simultaneous transmission and / or reception of multiple carriers allows the UE 104 / 182 to significantly increase its data transmission and / or reception rates. For example, two 20 MHz carriers aggregated in a multi-carrier system would theoretically provide a two-fold increase in data rate (i.e., 40 MHz) compared to the data rate achieved by a single 20 MHz carrier.

[0044] The wireless communications system 100 may further include a UE 164, which may communicate with the macrocell base station 102 via communications link 120 and / or with the mmW base station 180 via an mmW communications 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.

[0045] In some cases, the UE 164 and the UE 182 may be capable of sidelink communication. Sidelink-capable UEs (SL-UEs) can communicate with the base station 102 over a communication link 120 that uses a Uu interface (i.e., the air interface between the UE and the base station). The SL-UEs (e.g., UE 164, UE 182) may also communicate directly with each other over a wireless sidelink 160 that uses a PC5 interface (i.e., the air interface between sidelink-capable UEs). Wireless sidelink (or simply "sidelink") is an adaptation of the core cellular (e.g., LTE, NR) standards that allows direct communication between two or more UEs without the communication having to go through a base station. Sidelink communications may be unicast or multicast and may be used for device-to-device (D2D) medium 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 of 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 may not be able to receive transmissions from the base station 102 in some cases. 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 all other SL-UEs 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 involvement of the base station 102.

[0046] In one aspect, the sidelink 160 may operate on a subject wireless communication medium, which may be shared with other vehicular and / or infrastructure access points, as well as other wireless communications between other RATs. The "medium" may consist of one or more time, frequency, and / or spatial communication resources (e.g., encompassing one or more channels across one or more carriers) associated with wireless communications between one or more transmitter / receiver pairs. In one aspect, the subject medium may correspond to at least a portion of an unlicensed frequency band shared among various RATs. Although different licensed frequency bands have been reserved for some communication systems (e.g., by government agencies such as the Federal Communications Commission (FCC) in the United States), these systems, particularly those employing small cell access points, have recently extended operation to unlicensed frequency bands, such as the Unlicensed National Information Infrastructure (U-NII) bands used by Wireless Local Area Network (WLAN) technologies, most notably the IEEE 802.11x WLAN technology commonly referred to as "Wi-Fi". Exemplary systems of this type include CDMA systems, TDMA systems, FDMA systems, orthogonal FDMA (OFDMA) systems, single-carrier FDMA (SC-FDMA) systems, and various variations thereof.

[0047] It should be noted that while FIG. 1 illustrates only two of the UEs as SL-UEs (i.e., UE 164 and 182), any of the illustrated UEs may be SL-UEs. Additionally, while only UE 182 has been described as being beamforming capable, any of the illustrated UEs may be beamforming capable, including UE 164. If SL-UEs are beamforming capable, they may beamform toward each other (i.e., toward other SL-UEs), toward other UEs (e.g., UE 104), toward base stations (e.g., base stations 102, 180, small cell 102′, access point 150), and so forth. Thus, in some cases, UE 164 and UE 182 may utilize beamforming over sidelink 160.

[0048] In the example of FIG. 1, any of the illustrated UEs (shown in FIG. 1 as a single UE 104 for simplicity) may receive signals 124 from one or more Earth-orbiting space vehicles (SVs) 112 (e.g., satellites). In one aspect, the SVs 112 may be part of a satellite positioning system that the UE 104 may use as an independent source of location information. A satellite positioning system typically includes a system of transmitters (e.g., SVs 112) positioned to enable receivers (e.g., UEs 104) to determine their location on or above the Earth based at least in part on positioning signals (e.g., signals 124) received from the transmitters. Such transmitters typically transmit signals marked with a repeating pseudo-random noise (PN) code of a set number of chips. Although typically located within the SVs 112, transmitters may sometimes be located on ground-based control stations, base stations 102, and / or other UEs 104. The UE 104 may include one or more dedicated receivers specifically designed to receive the signals 124 from the SV 112 to derive geolocation information.

[0049] In a satellite positioning system, the use of the signals 124 may be augmented by various satellite-based augmentation systems (SBAS) that 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 an augmentation system or systems that provide integrity information, error correction, and the like, such as the Wide Area Augmentation System (WAAS), the European Geostationary Navigation Overlay Service (EGNOS), the Multi-functional Satellite Augmentation System (MSAS), the Global Positioning System (GPS)-aided Geo-augmented navigation, or the GPS and Geo Augmented Navigation system (GAGAN). 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.

[0050] In one aspect, the SV 112 may additionally or alternatively be part of one or more non-terrestrial networks (NTNs). In an NTN, the SV 112 is connected to an earth station (also called 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 terrestrial antenna) or a network node in a 5G network. This element will then provide access to other elements in the 5G network and ultimately to entities outside the 5G network, such as Internet web servers and other user devices. In that way, the UE 104 may receive communication signals (e.g., signal 124) from the SV 112 instead of or in addition to communication signals from the terrestrial base station 102.

[0051] The wireless communication system 100 may further include one or more UEs, such as UE 190, that indirectly connect to one or more communication networks via one or more device-to-device (D2D) peer-to-peer (P2P) links (referred to as "sidelinks"). In the example of FIG. 1, the UE 190 has a D2D P2P link 192 (e.g., through which the UE 190 may indirectly obtain cellular connectivity) with one of the UEs 104 connected to one of the base stations 102, and a D2D P2P link 194 (through which the UE 190 may indirectly obtain WLAN-based Internet connectivity) with a WLAN STA 152 connected to a WLAN AP 150. In one example, the D2D P2P links 192 and 194 may be supported using any well-known D2D RAT, such as LTE Direct (LTE-D), WiFi Direct (WiFi-D), Bluetooth, etc.

[0052] 2A illustrates an exemplary wireless network structure 200. For example, a 5GC 210 (also referred to as Next Generation Core (NGC)) may be functionally considered as control plane (C-plane) functions 214 (e.g., UE registration, authentication, network access, gateway selection, etc.) and user plane (U-plane) functions 212 (e.g., UE gateway functions, access to data network, IP routing, etc.) that operate cooperatively 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, specifically to the user plane function 212 and the control plane function 214, respectively. In an additional configuration, the ng-eNB 224 may 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. Additionally, the ng-eNB 224 may communicate directly with the gNB 222 via a backhaul connection 223. In some configurations, the Next Generation RAN (NG-RAN) 220 may have one or more gNBs 222, while other configurations include one or more of both the ng-eNB 224 and the gNB 222. Either the gNB 222 or the ng-eNB 224 (or both) may communicate with one or more UEs 204 (e.g., any of the UEs described herein).

[0053] Another optional aspect may include a location server 230, which may be in communication with the 5GC 210 to provide location assistance to the UE(s) 204. The location servers 230 may be implemented as multiple separate servers (e.g., physically separate servers, different software modules on a single server, different software modules across multiple physical servers, etc.), or alternatively, each may correspond to a single server. The location servers 230 may be configured to support one or more location services for the UEs 204 that may connect to the location server 230 via the core network, the 5GC 210, and / or via the Internet (not shown). Furthermore, the location server 230 may be integrated into components of the core network, or alternatively, may be external to the core network (e.g., a third-party server, such as an original equipment manufacturer (OEM) server or a service server).

[0054] 2B illustrates another exemplary wireless network structure 240. A 5GC 260 (which may correspond to 5GC 210 in FIG. 2A) may be functionally considered as control plane functions provided by an access and mobility management function (AMF) 264 and user plane functions provided by a user plane function (UPF) 262, which operate cooperatively to form a core network (i.e., 5GC 260). The functions of the AMF 264 include registration management, attachment management, reachability management, mobility management, lawful interception, transport for session management (SM) messages between one or more UEs 204 (e.g., any of the UEs described herein) and a session management function (SMF) 266, transparent proxy services for routing SM messages, access authentication and access authorization, transport for short message service (SMS) messages between the UE 204 and a short message service function (SMSF) (not shown), and security anchor functionality (SEAF). The AMF 264 also interacts with an authentication server function (AUSF) (not shown) and the UE 204 to receive intermediate keys established as a result of the UE 204 authentication process. In case of UMTS (universal mobile telecommunications system) subscriber identity module (USIM) based authentication, the AMF 264 retrieves security material from the AUSF. The AMF 264 functions also include security context management (SCM). The SCM receives keys from the SEAF that it uses to derive access network specific keys.The functionality of the AMF 264 also includes location service management for regulated services, transport for location service messages between the UE 204 and the Location Management Function (LMF) 270 (acting as the location server 230), transport for location service messages between the NG-RAN 220 and the LMF 270, Evolved Packet System (EPS) bearer identifier allocation for interworking with EPS, and UE 204 mobility event notification. In addition, the AMF 264 also supports functions for non-3GPP (Third Generation Partnership Project) access networks.

[0055] The functions of the UPF 262 include acting as an anchor point for intra / inter-RAT mobility (when applicable), acting as an external protocol data unit (PDU) session point for interconnection to a data network (not shown), routing and forwarding of packets, packet inspection, user plane policy rule enforcement (e.g., gating, redirection, traffic steering), lawful interception (user plane collection), traffic usage reporting, quality of service (QoS) processing for the user plane (e.g., uplink / downlink rate enforcement, reflective QoS marking in the downlink), uplink traffic validation (service data flow (SDF) to QoS flow mapping), transport level packet marking in the uplink and downlink, downlink packet buffering and downlink data notification triggering, and sending and forwarding one or more "end markers" to the source RAN node. The UPF 262 may also support forwarding of location service messages on the user plane between the UE 204 and a location server such as the SLP 272.

[0056] The functions of the SMF 266 include session management, UE Internet protocol (IP) address allocation and management, selection and control of user plane functions, configuration of traffic steering in the UPF 262 to route traffic to the appropriate destination, some control of policy enforcement and QoS, and downlink data notification. The interface through which the SMF 266 communicates with the AMF 264 is called the N11 interface.

[0057] Another optional aspect may include an LMF 270, which may be in communication with the 5GC 260 to provide location assistance to the UE 204. The LMF 270 may be implemented as multiple separate servers (e.g., physically separate servers, different software modules on a single server, different software modules across multiple physical servers, etc.), or alternatively, each may represent a single server. The LMF 270 may be configured to support one or more location services for UEs 204 that may connect to the LMF 270 via a core network, the 5GC 260, and / or via the Internet (not shown). The SLP 272 may support similar functions as the LMF 270, while the LMF 270 may communicate with the AMF 264, the NG-RAN 220, and the UE 204 via the control plane (e.g., using interfaces and protocols intended to convey signaling messages rather than voice or data) and the SLP 272 may communicate with the UE 204 and external clients (e.g., third-party servers 274) via the user plane (e.g., using protocols intended to carry voice and / or data, such as Transmission Control Protocol (TCP) and / or IP).

[0058] Yet another optional aspect may include a third party server 274, which may be in communication with the LMF 270, the SLP 272, the 5GC 260 (e.g., via the AMF 264 and / or the UPF 262), the NG-RAN 220, and / or the UE 204 to obtain location information (e.g., a location estimate) for the UE 204. Thus, in some cases, the third party server 274 may be referred to as a location services (LCS) client or an external client. The third party servers 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, each may correspond to a single server.

[0059] The user plane interface 263 and the control plane interface 265 connect the 5GC 260, and in particular the UPF 262 and the AMF 264, respectively, to one or more gNBs 222 and / or ng-eNBs 224 in the NG-RAN 220. The interface between the gNB(s) 222 and / or ng-eNB(s) 224 and the AMF 264 is referred to as the “N2” interface, and the interface between the gNB(s) 222 and / or ng-eNB(s) B 224 and the UPF 262 is referred to as the “N3” interface. The gNB(s) 222 and / or ng-eNB(s) 224 of the NG-RAN 220 may communicate directly with each other via a backhaul connection 223 referred to as the “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.

[0060] The functionality of the gNB 222 may be divided between a gNB Central Unit (gNB-CU) 226, one or more gNB Distributed Units (gNB-DUs) 228, and one or more gNB Radio Units (gNB-RUs) 229. The gNB-CU 226 is a logical node that includes base station functions such as forwarding user data, mobility control, radio access network sharing, positioning, session management, etc., except for those functions exclusively allocated to the gNB-DU(s) 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), Medium Access Control (MAC) layers of the gNB 222. Its operation is controlled by the gNB-CU 226. One gNB-DU 228 can support one or multiple 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 functionality of the gNB 222 is generally hosted by one or more standalone gNB-RUs 229, which 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, with the gNB-DU 228 via the RLC and MAC layers, and with the gNB-RU 229 via the PHY layer.

[0061] The deployment of a communication system such as a 5G NR system can be configured in multiple ways with various components or components. In a 5G NR system or network, network equipment such as a network node, network entity, mobility element of the network, RAN node, core network node, network element, or base station, or one or more units (or one or more components) performing base station functionality, can be implemented in an aggregated or separated architecture. For example, a base station (such as a Node B (NB), evolved NB (eNB), NR base station, 5G NB, access point (AP), transmit receive point (TRP), or cell) can be implemented as an aggregated base station (also known as a standalone base station or monolithic base station) or a separated base station.

[0062] An aggregated base station may be configured to utilize a radio protocol stack that is physically or logically integrated within a single RAN node. A separated base station may be configured to utilize a protocol stack that is physically or logically distributed among two or more units (such as one or more centralized units (CUs), one or more distributed units (DUs), or one or more radio units (RUs)). In some aspects, a CU may be implemented within a RAN node, and one or more DUs may be co-located with the CU or alternatively distributed geographically or virtually across one or more other RAN nodes. A DU may be implemented to communicate with one or more RUs. Each of the CU, DU, and RU may 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).

[0063] The operation of a base station type or network design may take into account the aggregated nature of the base station functions. For example, a separated base station may be utilized in an integrated access backhaul (IAB) network, an open radio access network (O-RAN, such as a network configuration supported by the O-RAN alliance), or a virtualized radio access network (vRAN, also known as a cloud radio access network (C-RAN)). Disaggregation may include distributing functions across two or more units in different physical locations, as well as distributing functions virtually for at least one unit, which may allow flexibility in network design. Various units of a separated base station, or a separated RAN architecture, may be configured for wired or wireless communication with at least one other unit.

[0064] 2C illustrates an exemplary split base station architecture 250 according to an aspect of the disclosure. The split base station architecture 250 may include one or more central units (CUs) 280 (e.g., gNB-CU 226) that may communicate directly with a core network 267 (e.g., 5GC 210, 5GC 260) via a backhaul link or indirectly with the core network 267 through one or more split base station units (e.g., a Near-Real Time (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-DU 228) via respective midhaul links, such as an F1 interface. The DU 285 may communicate with one or more Radio Units (RUs) 287 (e.g., gNB-RU 229) via respective fronthaul links. The RUs 287 may communicate with respective UEs 204 via one or more Radio Frequency (RF) access links. In some implementations, a UE 204 may be served by multiple RUs 287 simultaneously.

[0065] Each of the units, i.e., CU 280, DU 285, RU 287, and quasi-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 transmit signals, data, or information (collectively, signals) over a wired or wireless transmission medium. Each of the units, or an associated processor or controller that provides instructions to the unit's communication interface, may be configured to communicate with one or more of the other units over a transmission medium. For example, the units may include a wired interface configured to receive or transmit signals to one or more of the other units over a wired transmission medium. Furthermore, the units may include a wireless interface, which may include a receiver, transmitter, or transceiver (such as a radio frequency (RF) transceiver) configured to receive or transmit or transmit signals over a wireless transmission medium to one or more of the other units.

[0066] In some aspects, the CU 280 can host one or more higher layer control functions. Such control functions can include radio resource control (RRC), packet data convergence protocol (PDCP), service data adaptation protocol (SDAP), etc. Each control function can be implemented with an interface configured to communicate signals with other control functions hosted by the CU 280. The CU 280 can be configured to handle user plane functions (i.e., central unit-user plane (CU-User Plane, CU-UP)), control plane functions (i.e., central unit-control plane (CU-CP)), or a combination thereof. In some implementations, the CU 280 can be logically divided into one or more CU-UP units and one or more CU-CP units. The CU-UP unit, when implemented in an O-RAN configuration, can communicate bidirectionally with the CU-CP unit via an interface, such as an E1 interface. The CU 280 may be implemented to communicate with the DU 285, as necessary, for network control and signaling.

[0067] The DU 285 may correspond to a logical unit including one or more base station functions for controlling the operation of one or more RUs 287. In some aspects, the DU 285 may host one or more of a radio link control (RLC) layer, a medium access control (MAC) layer, and one or more upper 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 division such as that defined by the 3rd Generation Partnership Project (3GPP). In some aspects, the DU 285 may further host one or more lower PHY layers. Each layer (or module) may be implemented with an interface configured to communicate signals with other layers (and modules) hosted by the DU 285 or with a control function hosted by the CU 280.

[0068] The lower layer functions may be implemented by one or more RUs 287. In some deployments, the RUs 287 controlled by the DUs 285 may correspond to logical nodes hosting RF processing functions, or lower PHY layer functions (such as performing fast Fourier transform (FFT), inverse FFT (iFFT), digital beamforming, physical random access channel (PRACH) extraction and filtering, etc.), or both, based at least in part on a functional division such as a lower layer functional division. In such an architecture, the RU(s) 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 plane and user plane communications with the RU(s) 287 may be controlled by the corresponding DUs 285. In some scenarios, this configuration may enable the DU(s) 285 and CU 280 to be implemented in a cloud-based RAN architecture, such as a vRAN architecture.

[0069] 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 deployment of dedicated physical resources for RAN coverage requirements that can 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., 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, the CU 280, the DU 285, the RU 287, and the quasi-RT RIC 259. In some implementations, the SMO framework 255 may communicate with hardware aspects of a 4G RAN, such as an open eNB (O-eNB) 261, via an O1 interface. Additionally, in some implementations, the SMO framework 255 can communicate directly with one or more RUs 287 via an O1 interface. The SMO framework 255 may also include a non-RT RIC 257 configured to support the functionality of the SMO framework 255.

[0070] The non-RT RIC 257 may be configured to include logic functions that enable non-real-time control and optimization of RAN elements and resources, Artificial Intelligence / Machine Learning (AI / ML) workflows including model training and updates, or policy-based guidance of applications / features in the quasi-RT RIC 259. The non-RT RIC 257 may be coupled to the quasi-RT RIC 259 or may communicate with the quasi-RT RIC 259 (e.g., via an A1 interface). The quasi-RT RIC 259 may be configured to include logic functions that enable near real-time control and optimization of RAN elements and resources by data collection and action over interfaces (e.g., via an E2 interface) that connect one or more CUs 280, one or more DUs 285, or both, and the O-eNB to the quasi-RT RIC 259.

[0071] In some implementations, the non-RT RIC 257 may receive parameters or external enrichment information from an external server to generate the AI / ML models deployed to the quasi-RT RIC 259. Such information may be utilized by the quasi-RT RIC 259 or may be received from a non-network data source or from a network function in the SMO framework 255 or the non-RT RIC 257. In some examples, the non-RT RIC 257 or the quasi-RT RIC 259 may be configured to adjust RAN behavior or performance. For example, the non-RT RIC 257 may employ the AI / ML models to monitor long-term trends and patterns in performance and implement corrective actions through the SMO framework 255 (e.g., reconfiguration via O1) or through the creation of RAN management policies (e.g., A1 policies).

[0072] 3A, 3B, and 3C illustrate several example components (represented by corresponding blocks) that may be incorporated in a UE 302 (which may correspond to any of the UEs described herein), a base station 304 (which may correspond to any of the base stations described herein), and a network entity 306 (which may correspond to or embody any of the network functions described herein, including a location server 230 and an LMF 270, or alternatively may be independent of the NG-RAN 220 and / or 5GC 210 / 260 infrastructure depicted in FIGS. 2A and 2B, such as a private network) to support the operations described herein. It will be understood that these components may be implemented in different types of devices in different implementations (e.g., in an ASIC, in a system-on-chip (SoC), etc.). The illustrated components may also be incorporated in other devices in a communication system. For example, other devices in the system may include components similar to the described components to provide similar functionality. Also, a given device may include one or more of the components. For example, a device may contain multiple transceiver components that enable the device to operate on multiple carriers and / or communicate via different technologies.

[0073] The UE 302 and base station 304 each include one or more wireless wide area network (WWAN) transceivers 310 and 350, respectively, providing means for communicating (e.g., means for transmitting, means for receiving, means for measuring, means for tuning, means for refraining from transmitting, etc.) over one or more wireless communications networks (not shown), such as an NR network, an LTE network, a GSM network, etc. The WWAN transceivers 310 and 350 may each be connected to one or more antennas 316 and 356, respectively, for communicating with other network nodes, such as other UEs, access points, base stations (e.g., eNBs, gNBs), etc., over at least one designated RAT (e.g., NR, LTE, GSM, etc.) over a wireless communications medium of interest (e.g., some set of time / frequency resources in a particular frequency spectrum). The WWAN transceivers 310 and 350 may be variously configured to transmit and encode signals 318 and 358 (e.g., messages, instructions, information, etc.), respectively, and conversely, to receive and decode signals 318 and 358 (e.g., messages, instructions, information, pilots, etc.), respectively, in accordance with a designated 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 include one or more receivers 312 and 352, respectively, for receiving and decoding signals 318 and 358, respectively.

[0074] The UE 302 and base station 304 also each, at least in some cases, include one or more short-range wireless transceivers 320 and 360, respectively. The short-range wireless transceivers 320 and 360 may be connected to one or more antennas 326 and 366, respectively, and may provide means for communicating (e.g., means for transmitting, means for receiving, means for measuring, means for tuning, means for refraining from transmitting, etc.) with other network nodes, such as other UEs, access points, base stations, etc., via at least one designated RAT (e.g., WiFi, LTE-D, Bluetooth, Zigbee, Z-Wave, PC5, dedicated short-range communications (DSRC), wireless access for vehicular environments (WAVE), near-field communication (NFC), ultra-wideband (UWB), etc.) over the wireless communication medium. The short-range wireless transceivers 320 and 360 may be variously configured to transmit and encode signals 328 and 368 (e.g., messages, instructions, information, etc.), respectively, and conversely, to receive and decode signals 328 and 368 (e.g., messages, instructions, information, pilots, etc.), respectively, in accordance with a specified RAT. Specifically, the short-range wireless transceivers 320 and 360 include one or more transmitters 324 and 364, respectively, to transmit and encode signals 328 and 368, respectively, and include one or more receivers 322 and 362, respectively, to receive and decode signals 328 and 368, respectively. As specific examples, the short-range wireless transceivers 320 and 360 may be WiFi transceivers, Bluetooth® transceivers, Zigbee® and / or Z-Wave® transceivers, NFC transceivers, UWB transceivers, or vehicle-to-vehicle (V2V) and / or vehicle-to-everything (V2X) transceivers.

[0075] The UE 302 and the base station 304 also, at least in some cases, include satellite signal receivers 330 and 370. The satellite signal receivers 330 and 370 may be connected to one or more antennas 336 and 376, respectively, and may provide means for receiving and / or measuring satellite positioning / communication signals 338 and 378, respectively. If the satellite signal receivers 330 and 370 are satellite positioning system receivers, the satellite positioning / communication signals 338 and 378 may be Global Positioning System (GPS) signals, Global Navigation Satellite System (GLONASS) signals, Galileo signals, Beidou signals, Navigation Satellite System of India (NAVIC), Quasi-Zenith Satellite System (QZSS), etc. If the satellite signal receivers 330 and 370 are non-terrestrial network (NTN) receivers, the satellite positioning / communication signals 338 and 378 may be communication signals (e.g., carrying control and / or user data) originating from a 5G network. Satellite signal receivers 330 and 370 may comprise any suitable hardware and / or software for receiving and processing satellite positioning / communications signals 338 and 378, respectively. Satellite signal receivers 330 and 370 may request information and action from other systems as appropriate and, at least in some cases, perform calculations using acquired measurements with any suitable satellite positioning system algorithms to determine the locations of UE 302 and base station 304, respectively.

[0076] The base station 304 and the network entity 306 each include one or more network transceivers 380 and 390, respectively, that provide a means for communicating (e.g., a means for transmitting, a means for receiving, etc.) 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 with other network entities 306 over one or more wired or wireless core network interfaces.

[0077] A transceiver may be configured to communicate over a wired link or a wireless link. A transceiver (whether a wired transceiver or a wireless transceiver) includes transmitter circuitry (e.g., transmitters 314, 324, 354, 364) and receiver circuitry (e.g., receivers 312, 322, 352, 362). A transceiver may be an integrated device (e.g., embodying transmitter and receiver circuitry in a single device) in some implementations, may comprise separate transmitter circuitry and separate receiver circuitry in some implementations, or may be embodied in other ways in other implementations. The transmitter and receiver circuitry of a wired transceiver (e.g., network transceivers 380 and 390 in some implementations) may 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 an antenna array that enables the respective 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 an antenna array that enables the respective device (e.g., UE 302, base station 304) to perform receive beamforming, as described herein. In one aspect, the transmitter circuitry and receiver circuitry may share multiple identical antennas (e.g., antennas 316, 326, 356, 366), such that the respective device can only receive or transmit 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 listen module (NLM) or the like for performing various measurements.

[0078] As used herein, various wireless transceivers (e.g., transceivers 310, 320, 350, and 360, and network transceivers 380 and 390, in some implementations) and wired transceivers (e.g., network transceivers 380 and 390, in some implementations) may be generally characterized as a "transceiver," "at least one transceiver," or "one or more transceivers." Thus, whether a particular transceiver is a wired or wireless transceiver may be inferred from the type of communication being performed. For example, backhaul communications between network devices or servers generally involve signaling via wired transceivers, while wireless communications between a UE (e.g., UE 302) and a base station (e.g., base station 304) generally involve signaling via wireless transceivers.

[0079] The UE 302, base station 304, and network entity 306 also include other components that may be used in conjunction with operations as disclosed herein. The UE 302, base station 304, and network entity 306 each include one or more processors 332, 384, and 394, for example, to provide functionality related to wireless communications and to provide other processing functionality. Thus, the processors 332, 384, and 394 may comprise processing means, such as means for determining, means for calculating, means for receiving, means for transmitting, means for directing, etc. In one aspect, the processors 332, 384, and 394 may 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.

[0080] The UE 302, the base station 304, and the network entity 306 include memory circuitry implementing memories 340, 386, and 396, respectively (e.g., each including a memory device) for maintaining information (e.g., information indicating reserved resources, thresholds, parameters, etc.). Thus, the memories 340, 386, and 396 may comprise storage means, retrieval means, maintaining means, etc. In some cases, the UE 302, the base station 304, and the network entity 306 may include positioning components 342, 388, and 398, respectively. The positioning components 342, 388, and 398 may be hardware circuits that are part of or coupled to the processors 332, 384, and 394, respectively, that, when executed, cause the UE 302, the base station 304, and the network entity 306 to perform the functions described herein. In other aspects, the positioning components 342, 388, and 398 may be external to the processors 332, 384, and 394 (e.g., part of a modem processing system, integrated with another processing system, etc.). Alternatively, the positioning components 342, 388, and 398 may be memory modules stored in the memories 340, 386, and 396, respectively, that 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 functions described herein. FIG. 3A illustrates possible locations of the positioning component 342, which may be part of, for example, one or more WWAN transceivers 310, the memory 340, the one or more processors 332, or any combination thereof, or may be a stand-alone component. FIG. 3B shows possible locations of 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.FIG. 3C illustrates possible locations of a positioning 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.

[0081] The UE 302 may include one or more sensors 344 coupled to the one or more processors 332 to provide a means for sensing or detecting movement and / or orientation information that is independent of movement data derived from signals received by the one or more WWAN transceivers 310, the one or more short-range wireless transceivers 320, and / or the satellite signal receiver 330. By way of example, the sensor(s) 344 may include an accelerometer (e.g., a micro-electrical mechanical systems (MEMS) device), a gyroscope, a geomagnetic sensor (e.g., a compass), an altimeter (e.g., a barometric altimeter), and / or any other type of movement detection sensor. Additionally, the sensor(s) 344 may include multiple different types of devices and combine their outputs to provide movement information. For example, the sensor(s) 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.

[0082] Additionally, the UE 302 includes a user interface 346 that provides a means for providing instructions to a user (e.g., audio and / or visual displays) and / or receiving user input (e.g., upon user actuation of a sensing device, such as a keypad, touch screen, microphone, etc.). Although not shown, the base station 304 and the network entity 306 may also include user interfaces.

[0083] Referring more particularly to the one or more processors 384, on the downlink, IP packets from the network entity 306 may be provided to the processor 384. The one or more processors 384 may implement functionality for an RRC layer, a Packet Data Convergence Protocol (PDCP) layer, a Radio Link Control (RLC) layer, and a Medium Access Control (MAC) layer. The one or more processors 384 may provide RRC layer functions associated with broadcasting 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 handover support functions; RLC layer functions associated with forwarding of upper layer PDUs, error correction with automatic repeat request (ARQ), concatenation, segmentation, and reassembly of RLC service data units (SDUs), resegmentation 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.

[0084] The transmitter 354 and receiver 352 may implement Layer-1 (L1) functions associated with various signal processing functions. Layer-1, including the physical (PHY) layer, may include error detection on the transport channel, forward error correction (FEC) coding / 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 handles mapping onto signal constellations based on various modulation schemes (e.g., binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), M-phase-shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The coded and modulated symbols may then be split into parallel streams. Each stream may then be mapped to orthogonal frequency division multiplexing (OFDM) subcarriers, 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 generate a physical channel carrying a time-domain OFDM symbol stream. The OFDM symbol streams are spatially precoded to generate multiple spatial streams. Channel estimates from a channel estimator may be used to determine the coding and modulation scheme, as well as for spatial processing. The channel estimates may be derived from a reference signal and / or channel condition feedback transmitted by the UE 302. Each spatial stream may then be provided to one or more different antennas 356. The transmitter 354 may modulate an RF carrier with the individual spatial streams for transmission.

[0085] At the UE 302, the receiver 312 receives signals through its respective antenna(s) 316. The receiver 312 recovers the information modulated onto the RF carriers and provides the information to one or more processors 332. The transmitter 314 and the receiver 312 perform layer 1 functions associated with various signal processing functions. The receiver 312 may perform spatial processing on the information to recover any spatial streams destined for the UE 302. If multiple spatial streams are destined for the UE 302, they may be combined by the receiver 312 into a single OFDM symbol stream. The receiver 312 then converts the OFDM symbol stream from the time domain to the frequency domain using a fast Fourier transform (FFT). The frequency domain signal includes a separate OFDM symbol stream for each subcarrier of the OFDM signal. The symbols on each subcarrier, as well as the reference signal, are recovered and demodulated by determining the most likely signal constellation point transmitted by the base station 304. These soft decisions may 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 transmitted on the physical channel by the base station 304. The data and control signals are then provided to one or more processors 332 that implement Layer 3 (L3) and Layer 2 (L2) functions.

[0086] In the uplink, one or more processors 332 provide demultiplexing between transport and logical channels, packet reassembly, decryption, header recovery, and control signal processing to recover IP packets from the core network. The one or more processors 332 are also responsible for error detection.

[0087] Similar to the functionality described in connection with downlink transmissions by the base station 304, the one or more processors 332 provide RRC layer functions related to system information (e.g., MIB, SIB) acquisition, RRC connection, and measurement reporting; PDCP layer functions associated with header compression / decompression and security (encryption, decryption, integrity protection, integrity verification); RLC layer functions associated with transfer of upper layer PDUs, error correction via ARQ, concatenation, segmentation, and reassembly of RLC SDUs, resegmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functions associated with 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 via hybrid automatic repeat request (HARQ), priority handling, and logical channel prioritization.

[0088] Channel estimates derived by the channel estimator from a reference signal or feedback transmitted by the base station 304 may be used by the transmitter 314 to select an appropriate coding and modulation scheme and to facilitate spatial processing. The spatial streams generated by the transmitter 314 may be provided to different antenna(s) 316. The transmitter 314 may modulate an RF carrier with the individual spatial streams for transmission.

[0089] Uplink transmissions are processed at the base station 304 in a manner similar to that described with respect to the receiver function at the UE 302. The receiver 352 receives signals via its respective antenna(s) 356. The receiver 352 recovers information modulated onto an RF carrier and provides the information to one or more processors 384.

[0090] In the uplink, the one or more processors 384 provide demultiplexing between transport and logical channels, packet reassembly, decryption, header recovery, and control signal processing to recover IP packets from the UE 302. The IP packets from the one or more processors 384 may be provided to a core network. The one or more processors 384 are also responsible for error detection.

[0091] For convenience, the UE 302, base station 304, and / or network entity 306 are illustrated in Figures 3A, 3B, and 3C as including various components that may be configured according to various examples described herein. However, it will be understood that the illustrated components may have different functions in different designs. In particular, various components in Figures 3A-3C are optional in alternative configurations, and various aspects include configurations that may vary due to design choice, cost, device use, or other considerations. For example, in the case of Figure 3A, a particular implementation of the UE 302 may omit the WWAN transceiver(s) 310 (e.g., a wearable device or tablet computer or PC or laptop may have Wi-Fi and / or Bluetooth capabilities without cellular capabilities), or may omit the short-range wireless transceiver(s) 320 (e.g., cellular only, etc.), or may omit the satellite signal receiver 330, or may omit the sensor(s) 344, etc. 3B, a particular implementation of base station 304 may omit WWAN transceiver(s) 350 (e.g., a Wi-Fi "hotspot" access point without cellular capability), or may omit short-range wireless transceiver(s) 360 (e.g., cellular only), or may omit satellite signal receiver 370, etc. For brevity, examples of various alternative configurations are not provided herein, but should be readily apparent to one of ordinary skill in the art.

[0092] 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 or be part of communication interfaces of the UE 302, base station 304, and network entity 306, respectively. For example, when different logical entities are embodied within the same device (e.g., gNB and location server functionality integrated within the same base station 304), the data buses 334, 382, ​​and 392 may provide communication between them.

[0093] The components of Figures 3A, 3B, and 3C may be implemented in a variety of ways. In some implementations, the components of Figures 3A, 3B, and 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), where each circuit may use and / or incorporate at least one memory component for storing information or executable code used by the circuit to provide this functionality. For example, some or all of the functionality represented by blocks 310-346 may be implemented by the processor and memory component(s) of the UE 302 (e.g., by execution of appropriate code and / or by appropriate configuration of the processor components). Similarly, some or all of the functionality represented by blocks 350-388 may be implemented by the processor and memory component(s) of the base station 304 (e.g., by execution of appropriate code and / or by appropriate configuration of the processor components). Also, some or all of the functionality represented by blocks 390-398 may be implemented by the processor and memory component(s) of the network entity 306 (e.g., by execution of 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 the UE," "by the base station," "by the network entity," etc. However, it will be understood that such operations, actions, and / or functions may actually be performed by a particular component or combination of components of the UE 302, base station 304, network entity 306, etc., such as the processors 332, 384, 394, transceivers 310, 320, 350, and 360, memories 340, 386, and 396, positioning components 342, 388, and 398, etc.

[0094] In some designs, the network entity 306 may be implemented as a core network component. In other designs, the network entity 306 may be separate from the network operator or operation of the cellular network infrastructure (e.g., the NG RAN 220 and / or the 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 independently of the base station 304 (e.g., via a non-cellular communication link such as WiFi).

[0095] NR supports several cellular network-based positioning techniques, including downlink-based positioning methods, uplink-based positioning methods, 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-departure (DL-AoD) in NR. FIG. 4 illustrates examples of various positioning methods according to aspects of the present disclosure. In an OTDOA or DL-TDOA positioning procedure illustrated by scenario 410, a UE measures the difference between times of arrival (ToAs) of reference signals (e.g., positioning reference signals (PRS)) received from pairs of base stations, called reference signal time difference (RSTD) measurements or time difference of arrival (TDOA) measurements, and reports them to a 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 the assistance data. The UE then measures the RSTD between the reference base station and each of the non-reference base stations. Based on the known locations of the involved base stations and the RSTD measurements, a positioning entity (e.g., the UE in case of UE-based positioning or a location server in case of UE-assisted positioning) can estimate the location of the UE.

[0096] For DL-AoD positioning, as illustrated by scenario 420, the positioning entity uses measurement reports from the UE of received signal strength measurements of multiple downlink transmit beams to determine the angle(s) between the UE and the transmitting base station(s). The positioning entity can then estimate the location of the UE based on the determined angle(s) and the known location(s) of the transmitting base station(s).

[0097] 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 an uplink reference signal (e.g., sounding reference signal (SRS)) transmitted by the UE to multiple base stations. In particular, the UE transmits one or more uplink reference signals that are measured by the reference base station and multiple non-reference base stations. Each base station then reports the reception time of the reference signal(s) (called relative time of arrival (RTOA)) to a positioning entity (e.g., a location server) that knows the locations and relative timing of the involved base stations. Based on the reception-to-reception (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 estimate the location of the UE using TDOA.

[0098] 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(s) of the receive beam(s) to determine the angle(s) between the UE and the base station(s). Based on the determined angle(s) and the known location(s) of the base station(s), the positioning entity can then estimate the location of the UE.

[0099] Downlink and uplink based positioning methods include Extended Cell ID (E-CID) positioning, and Multiple Round Trip Time (RTT) positioning (also called "Multi-cell RTT" and "Multi-RTT"). In an RTT procedure, a first entity (e.g., a base station or a UE) transmits a first RTT-related signal (e.g., a PRS or an SRS) to a second entity (e.g., a UE or a base station), and the second entity transmits a second RTT-related signal (e.g., an SRS or a PRS) back to the first entity. Each entity measures the time difference between the time of arrival (ToA) of the received RTT-related signal and the transmission time of the transmitted RTT-related signal. This time difference is called the reception-to-transmission (Rx-Tx) time difference. The Rx-Tx time difference measurement may be made or adjusted to include only the time difference between the nearest slot boundaries for the received and transmitted signals. Both 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 from 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 measurements to the other entity, which then calculates the RTT. The distance between the two entities may be determined from the RTT and a known signal speed (e.g., the speed of light). In the case of multi-RTT positioning illustrated by scenario 430, a first entity (e.g., a UE or a base station) performs an RTT positioning procedure with multiple second entities (e.g., multiple base stations or UEs) to allow 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). As illustrated by scenario 440, RTT and multi-RTT methods can be combined with other positioning techniques such as UL-AoA and DL-AoD to improve location accuracy.

[0100] 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 identities, estimated timing, and signal strength of detected neighboring base stations. The location of the UE is then estimated based on this information and the known location of the base station(s).

[0101] 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 a base station (or a cell / TRP of a base station) from which to measure a reference signal, reference signal configuration parameters (e.g., the number of consecutive slots containing a PRS, the periodicity of consecutive slots containing a PRS, a muting sequence, a frequency hopping sequence, a reference signal identifier, a reference signal bandwidth, etc.), and / or other parameters applicable to a particular positioning method. Alternatively, the assistance data may be obtained directly from the base station itself (e.g., in periodically broadcasted overhead messages, etc.). In some cases, the UE may be able to detect neighboring network nodes itself without using the assistance data.

[0102] In the case of OTDOA or DL-TDOA positioning procedures, the assistance data may further include an expected RSTD value and an associated uncertainty around the expected RSTD, i.e., a search window. In some cases, the value range for the expected RSTD may be + / - 500 microseconds (μs). In some cases, the value range for the expected RSTD uncertainty may be + / - 32 μs when any of the resources used for the positioning measurements are in FR1. In other cases, the value range for the expected RSTD uncertainty may be + / - 8 μs when all of the resources used for the positioning measurement(s) are in FR2.

[0103] A location estimate may be referred to by other names, such as a position estimate, location, position, position fix, fix, etc. A location estimate may be geodetic and comprise coordinates (e.g., latitude, longitude, and possibly altitude), or urban and comprise a street address, postal address, or some other linguistic description of the location. A location estimate may also be defined relative to some other known location, or defined absolutely (e.g., using latitude, longitude, and possibly altitude). A location estimate may include an expected error or uncertainty (e.g., by including an area or volume within which the location is expected to be contained with some specified or default level of confidence).

[0104] Various frame structures may be used to support downlink and uplink transmissions between network nodes (e.g., base stations and UEs). Figure 5 is a diagram 500 illustrating example frame structures according to aspects of the disclosure. The frame structure may be a downlink or uplink frame structure. Other wireless communication technologies may have different frame structures and / or different channels.

[0105] LTE, and possibly NR, employs orthogonal frequency division multiplexing (OFDM) on the downlink and single-carrier frequency division multiplexing (SC-FDM) on the uplink. However, unlike LTE, NR also has the option of using OFDM on the uplink. OFDM and SC-FDM partition the system bandwidth into multiple (K) orthogonal subcarriers, also commonly referred to as tones, bins, etc. Each subcarrier may be modulated with data. Generally, modulation symbols are sent in the frequency domain with OFDM and in the time domain with SC-FDM. The spacing between adjacent subcarriers may be fixed, or the total number of subcarriers (K) may be dependent on the system bandwidth. For example, the subcarrier spacing may be 15 kilohertz (kHz), and the minimum resource allocation (resource block) may be 12 subcarriers (i.e., 180 kHz). Thus, the nominal fast Fourier transform (FFT) size may be equal to 128, 256, 512, 1024, or 2048 for a system bandwidth of 1.25, 2.5, 5, 10, or 20 megahertz (MHz), respectively. The system bandwidth may also be partitioned into subbands. For example, a subband may cover 1.08 MHz (i.e., 6 resource blocks), and there may be 1, 2, 4, 8, or 16 subbands for a system bandwidth of 1.25, 2.5, 5, 10, or 20 MHz, respectively.

[0106] LTE supports a single numerology (subcarrier spacing (SCS), symbol length, etc.). In contrast, NR may support multiple numerologies (μ), e.g., subcarrier spacings of 15 kHz (μ=0), 30 kHz (μ=1), 60 kHz (μ=2), 120 kHz (μ=3), and 240 kHz (μ=4) or more may be available. At each subcarrier spacing, there are 14 symbols per slot. For a 15 kHz SCS (μ=0), there is one slot per subframe, i.e., 10 slots per frame, the slot duration is 1 millisecond (ms), the symbol duration is 66.7 microseconds (μs), and the maximum nominal system bandwidth (in MHz) with an FFT size of 4K is 50. For a 30 kHz SCS (μ=1), there are two slots per subframe, i.e., 20 slots per frame, the slot duration is 0.5 ms, the symbol duration is 33.3 μs, and the maximum nominal system bandwidth (in MHz) for a 4K FFT size is 100. For a 60 kHz SCS (μ=2), there are four slots per subframe, i.e., 40 slots per frame, the slot duration is 0.25 ms, the symbol duration is 16.7 μs, and the maximum nominal system bandwidth (in MHz) for a 4K FFT size is 200. For a 120 kHz SCS (μ=3), there are eight slots per subframe, i.e., 80 slots per frame, the slot duration is 0.125 ms, the symbol duration is 8.33 μs, and the maximum nominal system bandwidth (in MHz) for a 4K FFT size is 400. For a 240 kHz SCS (μ=4), there are 16 slots per subframe, i.e., 160 slots per frame, the slot duration is 0.0625 ms, the symbol duration is 4.17 μs, and the maximum nominal system bandwidth (in MHz) with an FFT size of 4K is 800.

[0107] In the example of Figure 5, a numerology of 15 kHz is used. Thus, in the time domain, a 10 ms frame is divided into 10 equally sized subframes of 1 ms each, with each subframe containing one time slot. In Figure 5, time is represented horizontally (on the X-axis), with time increasing from left to right, and frequency is represented vertically (on the Y-axis), with frequency increasing (or decreasing) from bottom to top.

[0108] A resource grid may be used to represent a time slot, with each time slot including one or more time-parallel resource blocks (RBs) (also called physical RBs (PRBs)) in the frequency domain. The resource grid is further divided into multiple resource elements (REs). An RE may correspond to one symbol length in the time domain and one subcarrier in the frequency domain. In the numerology of FIG. 5, for a normal cyclic prefix, an RB may include 12 consecutive subcarriers in the frequency domain and 7 consecutive symbols in the time domain to obtain a total of 84 REs. For an extended cyclic prefix, an RB may include 12 consecutive subcarriers in the frequency domain and 6 consecutive symbols in the time domain to obtain a total of 72 REs. The number of bits carried by each RE depends on the modulation scheme.

[0109] Some of the REs may carry reference (pilot) signals (RS). The reference signals may include positioning reference signals (PRS), tracking reference signals (TRS), phase tracking reference signals (PTRS), cell-specific reference signals (CRS), channel state information reference signals (CSI-RS), demodulation reference signals (DMRS), primary synchronization signals (PSS), secondary synchronization signals (SSS), synchronization signal blocks (SSBs), sounding reference signals (SRS), etc., depending on whether the illustrated frame structure is used for uplink or downlink communications. Figure 5 shows example locations of REs carrying reference signals (labeled "R").

[0110] A collection of resource elements (REs) used for transmission of a PRS is called a "PRS resource." A collection of resource elements can span multiple PRBs in the frequency domain and "N" consecutive symbol(s) (e.g., one or more) within a slot in the time domain. In a given OFDM symbol in the time domain, a PRS resource occupies consecutive PRBs in the frequency domain.

[0111] The transmission of PRS resources within a given PRB has a particular comb size (also called "comb density"). The comb size "N" represents the subcarrier spacing (or frequency / tone spacing) within each symbol of the PRS resource configuration. Specifically, for comb size "N", a PRS is transmitted in every Nth subcarrier of a symbol of the PRB. For example, for Com 4, for each symbol of the PRS resource configuration, an RE corresponding to every fourth subcarrier (such as subcarriers 0, 4, 8) is used to transmit the PRS of the PRS resource. Currently, the following comb sizes are supported for DL-PRS: Com 2, Com 4, Com 6, and Com 12. Figure 5 shows an example PRS resource configuration for Com 4 (spanning four symbols). That is, the location of the shaded RE (labeled "R") indicates the Com 4 PRS resource configuration.

[0112] Currently, DL-PRS resources may span 2, 4, 6, or 12 consecutive symbols in a slot with a staggered pattern across the frequency domain. DL-PRS resources may be configured in any higher layer configured downlink or flexible (FL) symbol of a slot. There may be a constant energy per resource element (EPRE) for all REs of a given DL-PRS resource. Below are the symbol-to-symbol frequency offsets for comb sizes 2, 4, 6, and 12 spanning 2, 4, 6, and 12 symbols. 2-symbol-comb2:{0,1}, 4-symbol-comb2:{0,1,0,1}, 6-symbol-comb2:{0,1,0,1,0,1}, 12-symbol-comb2:{0,1,0,1,0,1,0,1,0,1,0,1,0,1} (for the example in Figure 5), 4-symbol-comb4:{0,2,1,3}, 12-symbol-comb4:{0,2,1,3,0,2,1,3,0,2,1,3}, 6-symbol-comb6:{0,3,1,4,2,5}, 12-symbol-comb6:{0,3,1,4,2,5,0,3,1,4,2,5}, and 12-symbol-comb12:{0,6,3,9,1,7,4,10,2,8,5,11}.

[0113] A "PRS resource set" is a set of PRS resources used for transmission of a PRS signal, where each PRS resource has a PRS resource ID. In addition, the PRS resources in a PRS resource set are associated with the same TRP. A PRS resource set is identified by a PRS resource set ID and is associated with a specific TRP (identified by a TRP ID). In addition, the PRS resources in a PRS resource set have the same periodicity across slots, a common muting pattern configuration, and the same repetition factor (e.g., "PRS-ResourceRepetitionFactor"). The periodicity is the time from the first repetition of the first PRS resource of the first PRS instance to the same first repetition of the same first PRS resource of the next PRS instance. The periodicity is μ=0, 1, 2, 3, where μ is a function of 2^μ. *The repetition factor may have a length selected from {1, 2, 4, 6, 8, 16, 32} slots.

[0114] A PRS resource ID in a PRS resource set is associated with a single beam (or beam ID) transmitted from a single TRP (where a TRP may transmit one or multiple beams). That is, each PRS resource in a PRS resource set may be transmitted on a different beam, and thus may also be referred to as a "PRS resource" or simply a "resource", also sometimes as a "beam". Note that this does not have any implication as to whether the TRP and beam on which the PRS is transmitted are known to the UE.

[0115] A "PRS instance" or "PRS occasion" is one instance of a periodically repeating time window (e.g., a group of one or more contiguous slots) during which a PRS is expected to be transmitted. A PRS occasion may also be referred to as a "PRS positioning occasion", "PRS positioning instance", "positioning occasion", "positioning instance", "positioning repetition", or simply an "occasion", "instance", or "repetition".

[0116] A "positioning frequency layer" (also simply called "frequency layer") is a collection of one or more PRS resource sets across one or more TRPs with the same values ​​for some parameters. In particular, a collection of PRS resource sets has the same subcarrier spacing and cyclic prefix (CP) type (meaning that all numerologies supported for the physical downlink shared channel (PDSCH) are also supported for the PRS), the same Point A, the same value of the downlink PRS bandwidth, the same starting PRB (and center frequency), and the same comb size. The Point A parameter takes the value of the parameter "ARFCN-ValueNR" (where "ARFCN" stands for "absolute radio-frequency channel number"), which is an identifier / code that specifies a pair of physical radio channels used for transmission and reception. The downlink PRS bandwidth may have a granularity of 4 PRB, with a minimum of 24 PRB and a maximum of 272 PRB. Currently, up to four frequency layers are defined, and up to two PRS resource sets per TRP can be configured per frequency layer.

[0117] The concept of frequency layers is somewhat similar to that of component carriers and bandwidth portions (BWPs), but differs in that component carriers and BWPs are used by one base station (or macrocell base station and small cell base station) to transmit data channels, whereas frequency layers are used by several (usually three or more) base stations to transmit PRS. A UE may indicate the number of frequency layers it can support when it transmits its positioning capabilities to the network, such as during an LTE Positioning Protocol (LPP) session. For example, a UE may indicate whether it can support one positioning frequency layer or four positioning frequency layers.

[0118] It should be noted that the terms "positioning reference signal" and "PRS" generally refer to specific reference signals used for positioning in NR and LTE systems. However, the terms "positioning reference signal" and "PRS" as used herein may also refer to any type of reference signal that can be used for positioning, such as, but not limited to, PRS defined in LTE and NR, TRS, PTRS, CRS, CSI-RS, DMRS, PSS, SSS, SSB, SRS, UL-PRS, etc. Furthermore, the terms "positioning reference signal" and "PRS" may refer to downlink, uplink, or sidelink positioning reference signals, unless otherwise suggested by the context. If necessary to further distinguish between types of PRS, downlink positioning reference signals may be referred to as "DL-PRS", uplink positioning reference signals (e.g., SRS for positioning, PTRS) may be referred to as "UL-PRS", and sidelink positioning reference signals may be referred to as "SL-PRS". Additionally, for signals that may be transmitted in the downlink, uplink, and / or sidelink (e.g., DMRS), a "DL", "UL", or "SL" may be prepended to the signal to distinguish the direction. For example, "UL-DMRS" is different from "DL-DMRS".

[0119] FIG. 6 is a graph 600 illustrating a channel estimate of a multipath channel between a receiver device (e.g., any of the UEs or base stations described herein) and a transmitter device (e.g., any of the UEs or base stations described herein) according to an aspect of the disclosure. The channel estimate represents the strength of a radio frequency (RF) signal (e.g., PRS) received through the 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 (PDP) 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). It should be noted that a multipath channel is a channel between a transmitter and a receiver due to the transmission of the RF signal in multiple beams and / or due to the propagation characteristics of the RF signal (e.g., reflection, refraction, etc.) that cause the RF signal to follow multiple paths, or multipaths.

[0120] In the example of FIG. 6, the receiver detects / measures multiple (four) clusters of channel taps. Each channel tap represents a multipath that the RF signal has taken between the transmitter and the receiver. That is, the channel taps represent the arrival of the RF signal on the multipaths. Each cluster of channel taps indicates that the corresponding multipaths have essentially taken the same path. There may be different clusters due to the RF signal being transmitted on different transmit beams (and therefore at different angles) or due to the propagation characteristics of the RF signal (which may take different paths due to reflections, for example), or both.

[0121] Every cluster of channel taps for a given RF signal represents a multipath channel (or simply a channel) between the transmitter and the receiver. Under the channel shown in FIG. 6, 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. In the example of FIG. 6, the first cluster of RF signals at time T1 is assumed to correspond to RF signals transmitted on a transmit beam aligned with a line-of-sight (LOS), or shortest path, since it arrives first. The third cluster at time T3 consists of the strongest RF signals and may correspond, for example, to RF signals transmitted on a transmit beam aligned with a non-line-of-sight (NLOS) path. It should be noted that although FIG. 6 shows clusters of two to five channel taps, it will be appreciated that a cluster may have more or less than the number of channel taps shown.

[0122] Machine learning can be used to generate models that can be used to facilitate various aspects related to processing of data. One particular application of machine learning relates to generating measurement models for processing of reference signals for positioning (e.g., PRS), such as feature extraction, reporting of reference signal measurements (e.g., selecting which extracted features to report), etc.

[0123] Machine learning models are generally classified as either supervised or unsupervised. Supervised models may be further subcategorized as either regression models or classification models. Supervised learning involves learning a function that maps an input to an output 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 attempts to find a line that best fits the data. Extensions of linear regression include multiple regression (e.g., finding a plane of best fit) and polynomial regression (e.g., finding a curve of best fit).

[0124] Another example of a machine learning model is a decision tree model. In a decision tree model, a tree structure is defined with multiple nodes. Decisions are used to move from a root node at the top of the decision tree to leaf nodes (i.e., nodes that have no further child nodes) at the bottom of the decision tree. In general, a larger number of nodes in a decision tree model correlates with higher decision accuracy.

[0125] Another example of a machine learning model is decision forest. Random forest is an ensemble learning technique that recreates decision trees. Random forest involves creating multiple decision trees using a bootstrapped dataset of the original data and randomly selecting a subset of variables at each step of the decision tree. The model then selects all modes of prediction for each decision tree. By relying on a "majority voting" model, the risk of error from individual trees is reduced.

[0126] Another example of a machine learning model is a neural network (NN). A neural network is essentially a network of mathematical formulas. A neural network accepts one or more input variables and, by passing them through a network of equations, results in one or more output variables. In other words, a neural network takes in a vector of inputs and returns a vector of outputs.

[0127] 7 illustrates an exemplary neural network 700 according to an embodiment of the present disclosure. The neural network 700 includes an input layer "i" that receives "n" (one or more) inputs (depicted as "input 1", "input 2", and "input n"), one or more hidden layers (depicted as hidden layers "h1", "h2", and "h3") for processing the inputs from the input layer, and an output layer "o" that provides "m" (one or more) outputs (labeled "output 1" and "output m"). The number of inputs "n", hidden layers "h", and outputs "m" may be the same or different. In some designs, the hidden layer "h" may include linear function(s) and / or activation function(s) that each successive hidden layer process node (depicted as a circle) processes from the nodes of the previous hidden layer.

[0128] In classification models, the output is discrete. One example of a classification model is logistic regression. Logistic regression is similar to linear regression, but is used to model the probability of a finite number of outcomes, typically two. Essentially, a logistic equation is created in such a way that the output value can only be 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 finds a hyperplane or boundary between the two classes of data that maximizes the margin between the two classes. 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, which is based on Bayes' theorem. Other examples of classification models include decision trees, random forests, and neural networks, similar to the examples discussed above, except that the output is discrete rather than continuous.

[0129] Unlike supervised learning, unsupervised learning is used to draw inferences and find patterns from input data without reference to labeled outcomes. Two examples of unsupervised learning models are clustering and dimensionality reduction.

[0130] Clustering is an unsupervised technique that involves grouping or clustering of 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 under consideration by taking a set of main variables. In simpler terms, dimensionality reduction is the process of reducing the dimensionality of a feature set (or even simpler terms, reducing the number of features). Most dimensionality reduction techniques can be categorized as either feature removal or feature extraction. One example of dimensionality reduction is called principal component analysis (PCA). In the simplest sense, PCA involves projecting higher dimensional data (e.g., 3 dimensions) into a smaller space (e.g., 2 dimensions). This results in a lower dimensionality of the data (e.g., 2 dimensions instead of 3 dimensions) while maintaining all the original variables in the model.

[0131] 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) may be configured to iteratively analyze training input data (e.g., measurements of reference signals to / from various target UEs) and associate this training input data with an output dataset (e.g., a set of possible or probable location candidates for various target UEs), thereby enabling subsequent determination of the same output dataset when similar input data is presented (e.g., from other target UEs at the same or similar locations).

[0132] NR supports RF Fingerprint (RFFP)-based positioning, which is a type of positioning and localization technique that utilizes RFFPs captured by a mobile device to determine the location of the mobile device. The RFFP may be a histogram of a received signal strength indicator (RSSI), CER, CIR, PDP, or channel frequency response (CFR). The RFFP may represent a single channel received from a transmitter (e.g., PRS), all channels received from a particular transmitter, or all channels detectable at a receiver. The RFFP(s) measured by a mobile device (e.g., UE) and the location of the transmitter(s) associated with the measured RFFP(s) (i.e., the transmitters transmitting the RF signals measured by the mobile device to determine the RFFP(s)) can be used to determine (e.g., triangulate) the location of the mobile device.

[0133] Machine learning positioning techniques have been shown to provide superior positioning performance compared to classical positioning methods. In machine learning-RFFP-based positioning, a machine learning model (e.g., neural network 700) takes the RFFP of a downlink reference signal (e.g., PRS) as input and outputs positioning measurements (e.g., ToA, RSTD) or mobile device locations corresponding to the input RFFP. The machine learning model (e.g., neural network 700) is trained using "ground truth" (i.e., known) positioning measurements or mobile device locations as reference (i.e., expected) outputs for a training set of RFFPs.

[0134] For example, a machine learning model may be trained to determine an RSTD measurement for a pair of TRPs from the RFFP of the PRS transmitted by the TRP. A reference output for training such a model would be the correct (i.e., ground truth) RSTD measurement for the location of the mobile device when the mobile device obtained the RFFP measurement of the PRS. The network (e.g., a location server) can determine the RSTD that would be expected for the pair of TRPs based on the known location of the mobile device and the known locations of the involved (measured) TRPs. The known location of the mobile device may be determined from multiple reported RSTD measurements and / or any other measurements (e.g., GPS measurements) reported by the mobile device.

[0135] FIG. 8 is a diagram 800 illustrating the use of machine learning models for RFFP-based positioning, according to an aspect of the disclosure. In the example of FIG. 8, during the "offline" phase, the RFFP (e.g., CER / CIR / CFR) captured by the mobile device is stored in a database. The database may be located in the mobile device or a network entity (e.g., a location server), and each RFFP may include measurements of RF signals (or channels or links) transmitted by one or more transmitters, illustrated in FIG. 8 as base stations 1-N (i.e., "BS 1"-"BS N"). For UE-based downlink RFFP (DL-RFFP) positioning, the network (e.g., a location server) configures the base station to transmit a downlink reference signal (e.g., PRS) to the mobile device, and the RFFP is the CER(s) / CIR(s) / CFR(s) of the configured downlink reference signal detected by the mobile device.

[0136] Each measured RFFP is associated with the known location of the mobile device at the time the mobile device measured the RFFP, shown in FIG. 8 as Positions 1 through L (i.e., "Pos 1" through "Pos L"). The location of the mobile device may be known via another positioning technique, as discussed above with reference to FIG. 4. Note that while FIG. 8 shows RFFP information for a single mobile device, it will be understood that RFFP information for multiple mobile devices may be collected and stored in a database.

[0137] Based on the information captured during the offline phase, a machine learning model (e.g., neural network 700) is trained to predict the location of the mobile device based on the RFFP measured by the mobile device. More specifically, a training set of RFFP measurements is used as input to the machine learning model, and the known location of the mobile device when capturing the RFFP is used as a label. After training, during the "online" phase, the trained machine learning model can be used to predict (infer) the location of the mobile device (denoted as "Pos M") based on the RFFP(s) currently measured by the mobile device. In the case of UE-based RFFP positioning, the network (e.g., a location server) provides the trained machine learning model to the mobile device. In the case of UE-assisted positioning, the mobile device may provide the RFFP measurements to the network for processing.

[0138] It should be noted that while FIG. 9 illustrates using an RFFP-based machine learning model to estimate the UE's location, the output (or extracted features) of the machine learning model may instead be positioning measurements based on the input RFFP, such as RSTD measurements, ToA measurements, DL-AoD measurements, etc.

[0139] 9 is a diagram 900 illustrating an inference cycle for UE-based DL-RFFP positioning, according to an aspect of the disclosure. As shown in FIG. 9, a location server (e.g., LMF 270) configures DL-PRS resources to be transmitted by one or more TRPs during a positioning session with the UE. The TRP(s) then transmit the configured DL-PRS to the UE, and the UE measures the RFFP of the DL-PRS.

[0140] In the example of Figure 9, the location server has pre-trained a machine learning model for RFFP positioning (labeled "RFFP ML") as discussed above with reference to Figures 7 and 8. The location server provides the machine learning model to the UE to perform inferences (e.g., determining positioning measurements based on the measured RFFP) during a positioning session. Thus, after measuring the RFFP of the DL-PRS, the UE inputs the measured RFFP into the received machine learning model to obtain the associated positioning measurement(s) (e.g., ToA, RSTD).

[0141] FIG. 10 illustrates an example call flow 1000 for UE-based DL-RFFP positioning according to an aspect of the disclosure. In phase 1, the UE 204 and the LMF 270 perform an LPP positioning capability transfer procedure, during which the UE 204 provides its positioning capabilities to the LMF 270. In phase 2, the LMF 270 provides assistance information, such as PRS resource configurations for DL-PRS to be transmitted to the UE 204, to the UE's serving ng-eNB / gNB 222 / 224 and any neighboring ng-eNB / gNB 222 / 224. In phase 3, the UE 204 and the LMF 270 perform an LPP assistance data exchange. During the exchange, the LMF 270 provides the UE 204 with assistance data for the positioning session, such as configurations of DL-PRS transmitted by the involved ng-eNB / gNB 222 / 224 and machine learning models to use to report positioning measurements of the DL-PRS.

[0142] In stage 4, the LMF 270 optionally provides assistance information to the involved ng-eNB / gNB 222 / 224 via a New Radio Positioning Protocol type A (NRPPa) message. In stage 5, the serving ng-eNB / gNB 222 / 224 optionally broadcasts the assistance information received from the LMF 270 as assistance data in one or more positioning SIBs (posSIBs). In stage 6, the LMF 270 and the UE 204 perform an LPP request / location information provision procedure, during which the UE 204 provides positioning measurements taken from the DL-PRS transmitted by the ng-eNB / gNB 222 / 224. The positioning measurements may be derived by applying a machine learning model received in the assistance data to the RFFP of the measured DL-PRS. The various stages illustrated in FIG. 10 are discussed in more detail below.

[0143] Currently, LPP and NRPPa do not support RFFP positioning procedures. Therefore, this disclosure provides LPP and NRPPa signaling and procedures to enable UE-based DL-RFFP positioning.

[0144] Referring more specifically to the LPP positioning capability transfer in stage 1 of FIG. 10, FIG. 11 shows two procedures for exchanging UE positioning capabilities with the network currently supported by LPP. The first is the capability transfer procedure and the second is the capability indication procedure. FIG. 1100 shows the capability transfer procedure during which the location server (e.g., LMF 270) indicates the type of capabilities required from the UE 204 (e.g., in an LPP "RequestCapabilities" information element (IE)). FIG. 1150 shows the capability indication procedure during which the target (e.g., UE 204) provides unsolicited capabilities to the server (e.g., in an LPP "ProvideCapabilities" IE).

[0145] Currently, LPP does not support requesting or indicating UE-based DL-RFFP capabilities. Therefore, the present disclosure provides signaling to enable a location server to request, and a UE to provide, UE capabilities for UE-based DL-RFFP positioning. Specifically, the location server can request the target UE to provide capabilities related to UE-based DL-RFFP positioning as part of the LPP positioning capability transfer procedure in stage 1 of FIG. 10. For example, the request can be a "nr-DL-RFFP-RequestCapabilities" parameter (e.g., an IE) in the LPP "RequestCapabilities" IE, similar to the LPP "otdoa-RequestCapabilities" IE, the LPP "ecid-RequestCapabilities" IE, the LPP "nr-Multi-RTT-RequestCapabilities" IE, etc.

[0146] Such a request may first query the target UE for a general capability description of the target UE (e.g., whether the UE can support UE-based DL-RFFP positioning). The request may include a flag to inform the target UE whether it should provide a detailed capability response or wait for a specific request. A further specific request may solicit certain capabilities based on the UE's response.

[0147] The target UE may provide its UE-based DL-RFFP positioning capabilities to the location server as part of an LPP Capability Transfer procedure (illustrated by diagram 1100 of FIG. 11) or an LPP Capability Indication procedure (illustrated by diagram 1150 of FIG. 11). For the LPP Capability Indication procedure, the target UE may indicate an initial set of its UE-based DL-RFFP positioning capabilities and then wait for a capability request to be solicited from the location server. The UE may report its RFFP positioning capabilities in a "nr-DL-RFFP-ProvideCapabilities" parameter (e.g., IE) in the LPP "ProvideCapabilities" IE, similar to the "otdoa-ProvideCapabilities" IE, the LPP "nr-DL-AoD-ProvideCapabilities" IE, etc.

[0148] As another option, some of the UE-based DL-RFFP positioning capabilities can be exchanged as part of the LPP Capability Transfer or LPP Capability Indication procedure, and the remaining RFFP positioning capabilities can be provided through other artificial intelligence (AI) or machine learning network entities (e.g., 3GPP or non-3GPP). The location server would then need to coordinate with other network entities to retrieve these remaining capabilities.

[0149] The capability message from the target UE may include a flag indicating the capability of the target UE to perform DL-RFFP positioning using a network-provided machine learning model. For example, a value of "0" may indicate that UE-based DL-RFFP positioning is not supported, and a value of "1" may indicate that UE-based DL-RFFP positioning is supported.

[0150] If UE-based DL-RFFP positioning is supported, the capability message may include a flag indicating how the location server should retrieve the target UE's capabilities to handle the machine learning model for positioning. For example, a value of "0" may indicate that the machine learning model capability parameters will be sent as part of the LPP "ProvideCapabilities" message. A value of "1" may indicate that the machine learning model capability parameters will be sent via another AI or machine learning network entity. The location server will then need to coordinate with other network entities to retrieve these remaining capabilities. A flag may be associated with each capability parameter, or a group of capability parameters.

[0151] The capability message may include the maximum number of concurrent machine learning instances (i.e., concurrent instances of machine learning models that may be activated for concurrent positioning sessions) that the target UE can handle. The capability message may also include resource capabilities (e.g., LPP IE "NR-DL-PRS-ResourcesCapability") and resource processing capabilities (e.g., LPP IE "NR-DL-PRS-ProcessingCapability") related to UE-based DL-RFFP positioning. These capabilities may include, for example, the maximum number of frequency layers, frequency band indicators, bandwidth, PRS buffer capabilities, etc.

[0152] The capability message may further include a list of supported machine learning model formats (e.g., Open Neural Network Exchange (ONNX), etc.). The capability message may also include a maximum machine learning model size (e.g., maximum number of parameters). The capability message may also include parameters related to inference, such as a list of supported features (e.g., CFR, CIR, Doppler statistics, delay spread statistics, etc.), maximum IFFT size, maximum number of antenna pairs the target UE can handle (which may be buffering capabilities), maximum channel estimation (e.g., CIR, CFR) window (which may be buffering capabilities). The purpose of these parameters is that the machine learning models will be provided by the location server, and therefore the location server needs to know which machine learning model(s) the target UE can support.

[0153] Referring more specifically to the LPP Assistance Data exchange in stage 3 of FIG. 10, FIG. 12 shows two procedures for exchanging positioning assistance data currently supported by LPP. The first is the Assistance Data Forwarding procedure, and the second is the Assistance Data Delivery procedure. Diagram 1200 shows the Assistance Data Forwarding procedure, during which a location server (e.g., LMF 270) provides the necessary assistance data for positioning (e.g., in an LPP "ProvideAssistanceData" IE) in response to a request for assistance data from a target UE (e.g., an LPP "RequestAssistanceData" IE). The assistance data may be provided on-demand, periodically, or with periodic updates. Diagram 1250 shows the Assistance Data Delivery procedure, during which a location server provides the necessary unsolicited assistance data for positioning. The assistance data may be provided periodically or aperiodically.

[0154] Currently, LPP does not support UE-based DL-RFFP positioning assistance data request or delivery. Therefore, the present disclosure provides signaling to enable a target UE to request, and a location server to provide, positioning assistance data for UE-based DL-RFFP positioning. Specifically, the target UE can request the location server to provide assistance data related to UE-based DL-RFFP positioning as part of an LPP assistance data transfer procedure (illustrated by diagram 1200 of FIG. 12). For example, the request can be a "nr-DL-RFFP-RequestAssistanceData" parameter (e.g., IE) in the LPP "RequestAssistanceData" IE.

[0155] The location server may then provide assistance data related to the UE-based DL-RFFP positioning as part of an LPP Assistance Data Forwarding procedure (illustrated by diagram 1200 of FIG. 12) or an LPP Assistance Data Distribution procedure (illustrated by diagram 1250 of FIG. 12). For example, the assistance data may be provided in a "nr-DL-RFFP-ProvideAssistanceData" parameter (e.g., an IE) in the LPP "ProvideAssistanceData" IE.

[0156] The Assistance Data message may include a flag indicating whether the target UE should expect the machine learning model details as part of the LPP Assistance Data message or whether the target UE should retrieve the machine learning model through another AI or machine learning network entity (e.g., 3GPP or non-3GPP). For example, a value of "0" may indicate that the Assistance Data does not include a description of the machine learning model, and a value of "1" may indicate that the Assistance Data does include a description of the machine learning model.

[0157] The Assistance Data message may provide information related to the machine learning model description, such as the machine learning model identifier (ID), machine learning model details (format (e.g., ONNX), machine learning model structure and parameters (i.e., weights), etc.), input feature types (e.g., CFR, CIR, etc.). The Assistance Data message may also provide information related to mapping of measurements to machine learning model inputs (e.g., a bitmap of measurements to machine learning model inputs).

[0158] The Assistance Data message may further include a flag indicating whether the target UE should perform measurement pre-processing before positioning. For example, a value of "0" may indicate that measurement pre-processing is not required (meaning that any measurement pre-processing is part of the machine learning model design). A value of "1" may indicate that measurement pre-processing is required (meaning that a measurement pre-processing step is explicitly provided).

[0159] If the target UE is expected to perform pre-processing, the assistance data message may also provide information on how the target UE should pre-process the measurements before passing them to the machine learning model. Measurement pre-processing stages include (1) calibration, (2) IFFT, (3) cyclic sample shift, (4) windowing, and (5) scaling. The assistance data may indicate the sequence in which to perform these operations. Measurement pre-processing parameters may include calibration values, IFFT size, cyclic sample shift values, window parameters (weights, length, and center location), and / or scaling options (e.g., per antenna, per TRP, all).

[0160] The assistance data may also include information related to resources to be used for UE-based DL-RFFP positioning. For example, the assistance data may indicate physical cell IDs (PCIs), global cell IDs (GCIs), ARFCN, and PRS ID of candidate TRPs for measurement, DL-PRS configuration of candidate TRPs, SSB information of TRPs (e.g., time / frequency occupancy of SSBs), spatial direction information of DL-PRS resources of TRPs (e.g., azimuth, elevation, etc.), geographic coordinates of TRPs (including transmission reference location of each DL-PRS resource ID, reference location of transmitting antenna of reference TRP, relative location of transmitting antenna of other TRPs), PRS dedicated transmission point indication, etc.

[0161] Referring more specifically to the LPP location information exchange in stage 6 of FIG. 10, FIG. 13 shows two procedures for exchanging location information currently supported by LPP. The first is the Location Information Transfer procedure and the second is the Location Information Delivery procedure. FIG. 1300 shows the Location Information Transfer procedure and FIG. 1350 shows the Location Information Delivery procedure. The Location Information Transfer procedure (FIG. 1300) is used to support the transfer of positioning estimates based on the requested service. The location server may send an LPP "RequestLocationInformation" IE indicating the type of location information required and the associated QoS. The Location Information Delivery procedure (FIG. 1350) supports the delivery of positioning estimates based on unsolicited services. In both the Location Information Transfer and Location Information Delivery procedures, the target UE sends an LPP "ProvideLocationInformation" IE containing the requested or unsolicited information.

[0162] Currently, LPP does not support requesting or delivering location information related to UE-based DL-RFFP procedures. Therefore, the present disclosure provides signaling to enable a location server to request, and a target UE to provide, location information for UE-based DL-RFFP positioning. Specifically, the location server can request the target UE to provide location information related to UE-based DL-RFFP positioning as part of the LPP location information transfer procedure in step 6 of FIG. 10. For example, the request can be a "nr-DL-RFFP-RequestLocationInformation" parameter (e.g., IE) in the LPP "RequestLocationInformation" IE.

[0163] The location information request message may include a reporting configuration indicating the periodicity of reporting the estimated location of the target UE. The request message may also include the type of location requested (e.g., absolute, relative, TDOA estimate, ToA estimate, etc.). The request message may also include a coarse estimate of the target UE's location to enable the UE to utilize the coarse location estimate to improve positioning.

[0164] The location information request message may optionally include updates regarding downlink resources (e.g., DL-PRS resources) that can be used for positioning. The request message may also optionally include updates to measurement pre-processing steps (e.g., calibration, IFFT, windowing, etc.) to be applied by the target UE. The request message may also optionally include updates to machine learning models to be used for positioning (e.g., updated model IDs, a request for the target UE to download updated machine learning models from another network-side model repository). The request message may further optionally include a flag to trigger the target UE to report the downlink resources used for positioning.

[0165] In response to the location information request, the target UE performs UE-based DL-RFFP positioning as requested and provides the location information as part of a Provide Location Information message. For example, the UE may provide the requested location information in a "nr-DL-RFFP-ProvideLocationInformation" parameter (e.g., IE) as part of the location information transfer or delivery procedure shown in Figure 13. The "nr-DL-RFFP-ProvideLocationInformation" parameter / IE may be included in the LPP "ProvideLocationInformation" IE.

[0166] The provided location information message may include the estimated location of the target UE and / or the estimated ToA or RSTD if a machine learning model is used to estimate those measurements. The provided location information message may also include a metric to indicate the quality / reliability of the estimated target location. The provided location information message may also include the time needed to run the machine learning model and obtain an inference of the target UE's location. The provided location information message may also include timestamps of the reported measurements. The provided location information message may also include measurements of downlink resources (e.g., DL-PRS) used in RFFP positioning (e.g., RSRP). The provided location information may also include IDs of resources used in RFFP positioning.

[0167] The Provide Location Information message may further include customized additional measurement elements, which may be customized by the UE vendor, the network operator, the machine learning model vendor, etc.

[0168] Referring now to the optional broadcast of assistance information in steps 4 and 5 of FIG. 10, broadcast of positioning assistance data is currently supported via broadcast of posSIB. posSIB is carried in the RRC system information (SI) message. For NR RRC SI, a single "SIBpos" IE is defined, which is carried in the IE "PosSystemInformation". There is a mapping of positioning SIB types (in IE "posSibType") to assistance data elements carried in posSIB. For example, posSibType1-1 to posSibType1-8 provide common assistance data for global navigation satellite system (GNSS) positioning, posSibType3-1 provides assistance data for OTDOA positioning, and posSibType6-1 to posSibType6-3 provide assistance data for DL-TDOA / DL-AoD positioning.

[0169] The location server (e.g., LMF 270) may signal positioning assistance information to the NG-RAN node to broadcast the assistance data in the posSIB. FIG. 14 is a diagram 1400 illustrating an assistance information control procedure between a location server (e.g., LMF 270) and an NG-RAN node (e.g., gNB). The assistance information control procedure applies when the NG-RAN node is a gNB and may be implemented in stage 4 of FIG. 10. As described with reference to FIG. 10, the signaling between the location server and the NG-RAN node is via NRPPa. The purpose of the assistance information control procedure is to enable the location server to signal positioning assistance information to the NG-RAN node for assistance data broadcast. That is, the location server sends the content of the posSIB(s) to be broadcast via RRC. The location server may send the assistance information in response to a UE request for such assistance data, referred to as "on-demand assistance data" or simply cell-wide assistance data, to be broadcast by the NG-RAN node.

[0170] Currently, there is no support for broadcasting assistance information related to UE-based DL-RFFP positioning. Therefore, the present disclosure provides signaling to support broadcast of assistance data for UE-based DL-RFFP positioning. The location server may provide assistance information related to UE-based DL-RFFP positioning as part of the assistance information control procedure shown in Figure 14. The assistance information may then be broadcast in one or more RRC posSIBs.

[0171] Similar to the currently defined posSIB, there may be a mapping of positioning SIB types (in the IE "posSibType") to assistance data elements carried in the posSIB. Table 1 below shows an example mapping.

[0172] [Table 1]

[0173] The broadcasted posSIB may include a flag indicating whether the target UE should expect the machine learning model details as part of the LPP Assistance Data message (in stage 3 of FIG. 10 and as shown in FIG. 13) or whether the target UE should retrieve the model through another AI or machine learning model network entity. For example, a value of "0" may indicate that the broadcasted posSIB assistance data does not include a description of the machine learning model, and a value of "1" may indicate that the broadcasted posSIB assistance data does include a description of the machine learning model.

[0174] The posSIB message for DL-RFFP positioning may provide information related to the machine learning model description, such as the machine learning model ID, the machine learning model details (format (e.g., ONNX), the machine learning model structure and parameters (i.e., weights), etc.), the input feature type (e.g., CFR, CIR, etc.). The posSIB message may also provide information regarding the mapping of measurements to machine learning model inputs (e.g., a bitmap of measurements to machine learning model inputs).

[0175] The posSIB message for DL-RFFP positioning may include a flag indicating whether the target UE should perform measurement pre-processing before positioning. For example, a value of "0" may indicate that measurement pre-processing is not required (i.e., measurement pre-processing is part of the machine learning model design), and a value of "1" may indicate that measurement pre-processing is required (and a measurement pre-processing step is explicitly provided).

[0176] If the flag indicates that pre-processing is required, the posSIB message may provide assistance data on how the target UE should pre-process the measurements before passing them to the machine learning model. As described above, the measurement pre-processing stages include (1) calibration, (2) IFFT, (3) cyclic sample shift, (4) windowing, and (5) scaling. The assistance data may indicate the sequence in which to perform these operations. The measurement pre-processing parameters may include calibration values, IFFT size, cyclic sample shift values, window parameters (weights, length, and center location), and / or scaling options (e.g., per antenna, per TRP, all).

[0177] The posSIB message for DL-RFFP positioning may also provide assistance data related to resources (e.g., DL-PRS resources) to be used for positioning. For example, as shown in Table 1, posSIBType7-1 may provide assistance data for DL-PRS resources to measure.

[0178] 15 illustrates an example method 1500 of wireless communication according to an aspect of the disclosure. In one aspect, the method 1500 may be performed by a UE (e.g., any of the UEs described herein).

[0179] At 1510, the UE transmits one or more providing capabilities messages to the location server indicating at least a first set of the UE's capabilities for participating in the DL-RFFP positioning procedure. In one aspect, operation 1510 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340, and / or positioning component 342, any or all of which may be considered a means for performing this operation.

[0180] At 1520, the UE receives one or more positioning assistance data messages for a DL-RFFP positioning procedure from the location server based at least on the first set of capabilities. In one aspect, operation 1520 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340, and / or positioning component 342, any or all of which may be considered a means for performing this operation.

[0181] As will be appreciated, a technical advantage of the method 1500 is that it enables a UE to provide positioning capability and receive assistance data specific to DL-RFFP positioning.

[0182] 16 illustrates an example method 1600 of wireless communication according to an aspect of the disclosure. In one aspect, the method 1600 may be performed by a UE (e.g., any of the UEs described herein).

[0183] At 1610, the UE receives one or more positioning assistance data messages for a DL-RFFP positioning procedure from the first network entity, the positioning assistance data messages including at least one parameter related to a machine learning model that the UE is configured to use for the DL-RFFP positioning procedure. In one aspect, operation 1610 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340, and / or positioning component 342, any or all of which may be considered a means for performing this operation.

[0184] At 1620, the UE transmits one or more location information messages including one or more parameters related to the DL-RFFP positioning procedure to the second network entity. In one aspect, operation 1620 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340, and / or positioning component 342, any or all of which may be considered a means for performing this operation.

[0185] As will be appreciated, a technical advantage of the method 1600 is that it provides assistance data specific to DL-RFFP positioning to a UE, enabling the UE to provide location information specific to DL-RFFP positioning.

[0186] 17 illustrates an example method 1700 of wireless communication according to an aspect of the disclosure. In one aspect, the method 1700 may be performed by a UE (e.g., any of the UEs described herein).

[0187] At 1710, the base station receives one or more assistance information control messages from the location server indicating one or more parameters related to the machine learning model that the at least one UE is configured to use for the DL-RFFP positioning procedure. In one aspect, operation 1710 may be performed by one or more WWAN transceivers 350, one or more network transceivers 380, one or more processors 384, memories 386, and / or positioning components 388, any or all of which may be considered a means for performing this operation.

[0188] At 1720, the base station transmits one or more posSIBs specific to DL-RFFP positioning to the at least one UE indicating the at least one or more parameters. In one aspect, operation 1720 may be performed by one or more WWAN transceivers 350, one or more processors 384, memories 386, and / or positioning components 388, any or all of which may be considered a means for performing this operation.

[0189] As will be appreciated, a technical advantage of the method 1700 is that it provides assistance data to a UE that is specific to DL-RFFP positioning.

[0190] In the above detailed description, it can be seen that in each example, various features are grouped together. This manner of disclosure should not be understood as an intention that the exemplary clauses have more features than are expressly stated in each clause. Rather, various aspects of the disclosure may include fewer features than all features of each disclosed exemplary clause. Thus, the following clauses should be considered to be incorporated in the description, and each clause may stand alone as a separate example. Although each dependent clause may refer to a specific combination with one of the other clauses in the clause, the aspect(s) of the dependent clause are not limited to that specific combination. It will be understood that other exemplary clauses may also include combinations of the aspect(s) of the dependent clause with the subject matter of any other dependent clause or independent clause, or any combination of features with other dependent clauses and independent clauses. Various aspects disclosed herein expressly include these combinations, unless it is expressly expressed or can be easily inferred that a particular combination is not intended (e.g., inconsistent aspects, such as defining an element as both an electrical insulator and an electrical conductor). It is further contemplated that aspects of a clause may be included in any other independent clause, even if the clause is not directly dependent on the independent clause.

[0191] Example implementations are described in the following numbered clauses.

[0192] Clause 1. A method of wireless communications implemented by a user equipment (UE), comprising: receiving, from a first network entity, one or more positioning assistance data messages for a downlink radio frequency fingerprinting (DL-RFFP) positioning procedure, the one or more positioning assistance data messages including at least one parameter related to a machine learning model configured to be used by the UE for a DL-RFFP positioning procedure; and transmitting, to a second network entity, one or more location information messages including one or more parameters related to the DL-RFFP positioning procedure.

[0193] Clause 2. The method of clause 1, wherein the at least one parameter includes a flag indicating whether one or more positioning assistance data messages include a description of a machine learning model.

[0194] Clause 3. The method of clause 2, further comprising retrieving the machine learning model from a network entity other than the first network entity based on the flag indicating that the one or more positioning assistance data messages do not include a description of the machine learning model.

[0195] Clause 4. The method of any of clauses 1-3, wherein the at least one parameter includes an identifier of the machine learning model, a format of the machine learning model, an input feature type of the machine learning model, or any combination thereof.

[0196] Clause 5. The method of any of clauses 1 to 4, wherein at least one parameter indicates a mapping between RFFP measurements of downlink positioning reference signal (DL-PRS) resources and inputs of a machine learning model.

[0197] Clause 6. The method of any of clauses 1 to 5, wherein at least one parameter includes a flag for configuring the UE to perform measurement pre-processing before applying the machine learning model to RFFP measurements of DL-PRS resources to be measured by the UE for a DL-RFFP positioning procedure.

[0198] Clause 7. The method of clause 6, wherein the at least one parameter further comprises a series of measurement pre-processing steps, a calibration value for measurement pre-processing, an inverse fast Fourier transform (IFFT) size for measurement pre-processing, a cyclic shift sampling value for measurement pre-processing, a window size parameter for measurement pre-processing, a scaling option for measurement pre-processing, or any combination thereof.

[0199] Clause 8. The method of any of clauses 1 to 7, wherein the at least one parameter includes a parameter related to a DL-PRS resource to be measured by the UE for a DL-RFFP positioning procedure.

[0200] Clause 9. A method according to any of clauses 1 to 8, further comprising sending a request to a first network entity for positioning assistance data for a DL-RFFP positioning procedure, wherein one or more positioning assistance data messages are received in response to the request.

[0201] Clause 10. A method according to any one of clauses 1 to 9, wherein the first network entity is a location server, the second network entity is a location server, and the one or more positioning assistance data messages are one or more Long Term Evolution (LTE) Positioning Protocol (LPP) messages.

[0202] Clause 11. A method according to any of clauses 1 to 9, wherein the first network entity is a base station, the second network entity is a location server, and the one or more positioning assistance data messages are one or more positioning system information blocks (posSIBs) specific to DL-RFFP positioning.

[0203] Clause 12. A method according to any of clauses 1 to 11, further comprising receiving a request for location information from a second network entity, wherein one or more location information messages are sent in response to the request.

[0204] Clause 13. The method of clause 12, wherein the request includes a reporting configuration indicating a periodicity for reporting the location of the UE, a type of location of the UE, a coarse estimate of the location of the UE, updates to DL-PRS resources configured for a DL-RFFP positioning procedure, updates to a pre-processing step to be applied to measurements of DL-PRS resources configured for a DL-RFFP positioning procedure, updates to a machine learning model, a flag to configure the UE to report which of the DL-PRS resources have been used for a DL-RFFP positioning procedure, or any combination thereof.

[0205] Clause 14. The method of any of clauses 1 to 13, wherein the one or more location information messages include an estimated location of the UE determined based on a machine learning model, a metric indicative of the quality or reliability of the estimated location of the UE, an amount of time spent by the machine learning model to determine the estimated location of the UE, one or more positioning measurements of DL-PRS resources configured for the DL-RFFP positioning procedure determined based on the machine learning model, a timestamp of the one or more positioning measurements, an identifier of which DL-PRS resources configured for the DL-RFFP positioning procedure were used for the DL-RFFP positioning procedure, one or more signal strength measurements of DL-PRS resources configured for the DL-RFFP positioning procedure, one or more measurement elements customized by a network vendor, a UE vendor or a machine learning model vendor, or any combination thereof.

[0206] Clause 15. A method of communication implemented by a base station, comprising: receiving one or more assistance information control messages from a location server indicating one or more parameters related to a machine learning model configured to be used by at least one user equipment (UE) for a Downlink Radio Frequency Fingerprint (DL-RFFP) positioning procedure; and transmitting one or more positioning system information blocks (posSIBs) specific to DL-RFFP positioning to the at least one UE, indicating the at least one or more parameters.

[0207] Clause 16. The method of clause 15, wherein the one or more parameters include a flag indicating whether the one or more positioning assistance data messages include a description of a machine learning model.

[0208] Clause 17. The method of clause 15 or 16, wherein the one or more parameters include an identifier of the machine learning model, a format of the machine learning model, an input feature type of the machine learning model, or any combination thereof.

[0209] Clause 18. The method of any of clauses 15 to 17, wherein the one or more parameters indicate a mapping between RFFP measurements of downlink positioning reference signal (DL-PRS) resources and inputs of a machine learning model.

[0210] Clause 19. The method according to any of clauses 15 to 18, wherein the one or more parameters include a flag for configuring at least one UE to perform measurement pre-processing before applying a machine learning model to RFFP measurements of DL-PRS resources to be measured by at least one UE for a DL-RFFP positioning procedure.

[0211] Clause 20. The method of clause 19, wherein the one or more parameters further include a series of measurement pre-processing steps, a calibration value for measurement pre-processing, an inverse fast Fourier transform (IFFT) size for measurement pre-processing, a cyclic shift sampling value for measurement pre-processing, a window size parameter for measurement pre-processing, a scaling option for measurement pre-processing, or any combination thereof.

[0212] Clause 21. The method according to any of clauses 15 to 20, wherein the one or more parameters include a parameter related to a DL-PRS resource to be measured by at least one UE for a DL-RFFP positioning procedure.

[0213] Clause 22. 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, wherein the at least one processor is configured to: receive, from a first network entity, via the at least one transceiver, one or more positioning assistance data messages for a Downlink Radio Frequency Fingerprint (DL-RFFP) positioning procedure, the one or more positioning assistance data messages including at least one parameter related to a machine learning model configured to be used by the UE for a DL-RFFP positioning procedure; and transmit, via the at least one transceiver, one or more location information messages including the one or more parameters related to the DL-RFFP positioning procedure to a second network entity.

[0214] Clause 23. The UE of clause 22, wherein the at least one parameter includes a flag indicating whether the one or more positioning assistance data messages include a description of a machine learning model.

[0215] Clause 24. The UE of clause 23, wherein at least one processor is further configured to retrieve a machine learning model from a network entity other than the first network entity based on the flag indicating that one or more positioning assistance data messages do not include a description of a machine learning model.

[0216] Clause 25. The UE of any of clauses 22 to 24, wherein at least one parameter includes an identifier of the machine learning model, a format of the machine learning model, an input feature type of the machine learning model, or any combination thereof.

[0217] Clause 26. A UE as described in any of clauses 22 to 25, wherein at least one parameter indicates a mapping between RFFP measurements of downlink positioning reference signal (DL-PRS) resources and inputs of a machine learning model.

[0218] Clause 27. A UE as described in any of clauses 22 to 26, wherein at least one parameter includes a flag for configuring the UE to perform measurement pre-processing before applying a machine learning model to RFFP measurements of DL-PRS resources to be measured by the UE for a DL-RFFP positioning procedure.

[0219] Clause 28. The UE of clause 27, wherein the at least one parameter further comprises a series of measurement pre-processing steps, a calibration value for measurement pre-processing, an inverse fast Fourier transform (IFFT) size for measurement pre-processing, a cyclic shift sampling value for measurement pre-processing, a window size parameter for measurement pre-processing, a scaling option for measurement pre-processing, or any combination thereof.

[0220] Clause 29. A UE according to any of clauses 22 to 28, wherein the at least one parameter comprises a parameter relating to a DL-PRS resource to be measured by the UE for a DL-RFFP positioning procedure.

[0221] Clause 30. A UE as described in any of clauses 22 to 29, wherein at least one processor is further configured to send, via at least one transceiver, a request to a first network entity for positioning assistance data for a DL-RFFP positioning procedure, and one or more positioning assistance data messages are received in response to the request.

[0222] Clause 31. A UE as described in any of clauses 22 to 30, wherein the first network entity is a location server, the second network entity is a location server, and the one or more positioning assistance data messages are one or more Long Term Evolution (LTE) Positioning Protocol (LPP) messages.

[0223] Clause 32. A UE according to any of clauses 22 to 30, wherein the first network entity is a base station, the second network entity is a location server, and the one or more positioning assistance data messages are one or more positioning system information blocks (posSIBs) specific to DL-RFFP positioning.

[0224] Clause 33. A UE as described in any of clauses 22 to 32, wherein at least one processor is further configured to receive, via the at least one transceiver, a request for location information from a second network entity, and one or more location information messages are transmitted in response to the request.

[0225] Clause 34. The UE of clause 33, wherein the request includes a reporting configuration indicating a periodicity for reporting the location of the UE, a type of location of the UE, a coarse estimate of the location of the UE, updates to DL-PRS resources configured for a DL-RFFP positioning procedure, updates to a pre-processing step to be applied to measurements of DL-PRS resources configured for a DL-RFFP positioning procedure, updates to a machine learning model, a flag to configure the UE to report which of the DL-PRS resources were used for a DL-RFFP positioning procedure, or any combination thereof.

[0226] Clause 35. The UE of any of clauses 22 to 34, wherein the one or more location information messages include an estimated location of the UE determined based on a machine learning model, a metric indicative of a quality or reliability of the estimated location of the UE, an amount of time spent by the machine learning model to determine the estimated location of the UE, one or more positioning measurements of DL-PRS resources configured for the DL-RFFP positioning procedure determined based on the machine learning model, a timestamp of the one or more positioning measurements, an identifier of which DL-PRS resources configured for the DL-RFFP positioning procedure were used for the DL-RFFP positioning procedure, one or more signal strength measurements of DL-PRS resources configured for the DL-RFFP positioning procedure, one or more measurement elements customized by a network vendor, a UE vendor, or a machine learning model vendor, or any combination thereof.

[0227] Clause 36. A base station comprising: a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver, wherein the at least one processor is configured to: receive, via the at least one transceiver, one or more assistance information control messages from a location server indicating one or more parameters related to a machine learning model configured to be used by at least one user equipment (UE) for a downlink radio frequency fingerprinting (DL-RFFP) positioning procedure; and transmit, via the at least one transceiver, one or more positioning system information blocks (posSIBs) specific to DL-RFFP positioning to the at least one UE, the one or more parameters being indicative of the machine learning model.

[0228] Clause 37. A base station as described in clause 36, wherein the one or more parameters include a flag indicating whether the one or more positioning assistance data messages include a description of a machine learning model.

[0229] Clause 38. The base station of clause 36 or 37, wherein the one or more parameters include an identifier of the machine learning model, a format of the machine learning model, an input feature type of the machine learning model, or any combination thereof.

[0230] Clause 39. A base station according to any of clauses 36 to 38, wherein the one or more parameters indicate a mapping between RFFP measurements of downlink positioning reference signal (DL-PRS) resources and inputs of a machine learning model.

[0231] Clause 40. A base station according to any of clauses 36 to 39, wherein the one or more parameters include a flag for configuring at least one UE to perform measurement pre-processing before applying a machine learning model to RFFP measurements of DL-PRS resources to be measured by at least one UE for a DL-RFFP positioning procedure.

[0232] Clause 41. The base station of clause 40, wherein the one or more parameters further include a series of measurement pre-processing stages, a calibration value for measurement pre-processing, an inverse fast Fourier transform (IFFT) size for measurement pre-processing, a cyclic shift sampling value for measurement pre-processing, a window size parameter for measurement pre-processing, a scaling option for measurement pre-processing, or any combination thereof.

[0233] Clause 42. A base station according to any of clauses 36 to 41, wherein the one or more parameters include a parameter related to DL-PRS resources to be measured by at least one UE for a DL-RFFP positioning procedure.

[0234] Clause 43. A user equipment (UE), comprising: means for receiving from a first network entity one or more positioning assistance data messages for a downlink radio frequency fingerprinting (DL-RFFP) positioning procedure, the positioning assistance data messages including at least one parameter related to a machine learning model configured to be used by the UE for a DL-RFFP positioning procedure; and means for transmitting to a second network entity one or more location information messages including the one or more parameters related to the DL-RFFP positioning procedure.

[0235] Clause 44. The UE of clause 43, wherein the at least one parameter includes a flag indicating whether the one or more positioning assistance data messages include a description of a machine learning model.

[0236] Clause 45. The UE of clause 44, further comprising means for retrieving a machine learning model from a network entity other than the first network entity based on the flag indicating that one or more positioning assistance data messages do not include a description of a machine learning model.

[0237] Clause 46. The UE of any of clauses 43 to 45, wherein at least one parameter includes an identifier of the machine learning model, a format of the machine learning model, an input feature type of the machine learning model, or any combination thereof.

[0238] Clause 47. The UE of any of clauses 43 to 46, wherein at least one parameter indicates a mapping between RFFP measurements of downlink positioning reference signal (DL-PRS) resources and inputs of the machine learning model.

[0239] Clause 48. A UE as described in any of clauses 43 to 47, wherein at least one parameter includes a flag for configuring the UE to perform measurement pre-processing before applying the machine learning model to RFFP measurements of DL-PRS resources to be measured by the UE for a DL-RFFP positioning procedure.

[0240] Clause 49. The UE of clause 48, wherein the at least one parameter further comprises a series of measurement pre-processing steps, a calibration value for measurement pre-processing, an inverse fast Fourier transform (IFFT) size for measurement pre-processing, a cyclic shift sampling value for measurement pre-processing, a window size parameter for measurement pre-processing, a scaling option for measurement pre-processing, or any combination thereof.

[0241] Clause 50. The UE according to any of clauses 43 to 49, wherein the at least one parameter comprises a parameter related to a DL-PRS resource to be measured by the UE for a DL-RFFP positioning procedure.

[0242] Clause 51. A UE as described in any of clauses 43 to 50, further comprising means for sending a request to a first network entity for positioning assistance data for a DL-RFFP positioning procedure, wherein one or more positioning assistance data messages are received in response to the request.

[0243] Clause 52. A UE as described in any of clauses 43 to 51, wherein the first network entity is a location server, the second network entity is a location server, and the one or more positioning assistance data messages are one or more Long Term Evolution (LTE) Positioning Protocol (LPP) messages.

[0244] Clause 53. A UE according to any of clauses 43 to 51, wherein the first network entity is a base station, the second network entity is a location server, and the one or more positioning assistance data messages are one or more positioning system information blocks (posSIBs) specific to DL-RFFP positioning.

[0245] Clause 54. A UE according to any of clauses 43 to 53, further comprising means for receiving a request for location information from a second network entity, wherein one or more location information messages are sent in response to the request.

[0246] Clause 55. The UE of clause 54, wherein the request includes a reporting configuration indicating a periodicity for reporting the location of the UE, a type of location of the UE, a coarse estimate of the location of the UE, updates to DL-PRS resources configured for a DL-RFFP positioning procedure, updates to a pre-processing step to be applied to measurements of DL-PRS resources configured for a DL-RFFP positioning procedure, updates to a machine learning model, a flag to configure the UE to report which of the DL-PRS resources have been used for a DL-RFFP positioning procedure, or any combination thereof.

[0247] Clause 56. The UE of any of clauses 43 to 55, wherein the one or more location information messages include an estimated location of the UE determined based on a machine learning model, a metric indicative of a quality or reliability of the estimated location of the UE, an amount of time spent by the machine learning model to determine the estimated location of the UE, one or more positioning measurements of DL-PRS resources configured for the DL-RFFP positioning procedure determined based on the machine learning model, a timestamp of the one or more positioning measurements, an identifier of which DL-PRS resources configured for the DL-RFFP positioning procedure were used for the DL-RFFP positioning procedure, one or more signal strength measurements of DL-PRS resources configured for the DL-RFFP positioning procedure, one or more measurement elements customized by a network vendor, a UE vendor, or a machine learning model vendor, or any combination thereof.

[0248] Clause 57. A base station comprising: means for receiving one or more assistance information control messages from a location server, the control messages indicating one or more parameters related to a machine learning model configured to be used by at least one user equipment (UE) for a Downlink Radio Frequency Fingerprint (DL-RFFP) positioning procedure; and means for transmitting one or more positioning system information blocks (posSIBs) specific to DL-RFFP positioning to the at least one UE, the control messages indicating the at least one or more parameters.

[0249] Clause 58. The base station of clause 57, wherein the at least one parameter includes a flag indicating whether the one or more positioning assistance data messages include a description of a machine learning model.

[0250] Clause 59. The base station of clause 57 or 58, wherein the one or more parameters include an identifier of the machine learning model, a format of the machine learning model, an input feature type of the machine learning model, or any combination thereof.

[0251] Clause 60. A base station according to any of clauses 57 to 59, wherein the one or more parameters indicate a mapping between RFFP measurements of downlink positioning reference signal (DL-PRS) resources and inputs of a machine learning model.

[0252] Clause 61. A base station according to any of clauses 57 to 60, wherein the one or more parameters include a flag for configuring at least one UE to perform measurement pre-processing before applying a machine learning model to RFFP measurements of DL-PRS resources to be measured by at least one UE for a DL-RFFP positioning procedure.

[0253] Clause 62. The base station of clause 61, wherein the one or more parameters further include a series of measurement pre-processing stages, a calibration value for measurement pre-processing, an inverse fast Fourier transform (IFFT) size for measurement pre-processing, a cyclic shift sampling value for measurement pre-processing, a window size parameter for measurement pre-processing, a scaling option for measurement pre-processing, or any combination thereof.

[0254] Clause 63. A base station according to any of clauses 57 to 62, wherein the one or more parameters include a parameter related to DL-PRS resources to be measured by at least one UE for a DL-RFFP positioning procedure.

[0255] Clause 64. A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a user equipment (UE), cause the UE to receive from a first network entity one or more positioning assistance data messages for a downlink radio frequency fingerprinting (DL-RFFP) positioning procedure, the positioning assistance data messages including at least one parameter related to a machine learning model configured to be used by the UE for a DL-RFFP positioning procedure, and to transmit to a second network entity one or more location information messages including one or more parameters related to the DL-RFFP positioning procedure.

[0256] Clause 65. The non-transitory computer-readable medium of clause 64, wherein the at least one parameter includes a flag indicating whether the one or more positioning assistance data messages include a description of a machine learning model.

[0257] Clause 66. The non-transitory computer-readable medium of clause 65, further comprising computer-executable instructions that, when executed by the UE, cause the UE to retrieve a machine learning model from a network entity other than the first network entity based on the flag indicating that one or more positioning assistance data messages do not include a description of a machine learning model.

[0258] Clause 67. The non-transitory computer-readable medium of any of clauses 64 to 66, wherein at least one parameter includes an identifier of the machine learning model, a format of the machine learning model, an input feature type of the machine learning model, or any combination thereof.

[0259] Clause 68. The non-transitory computer-readable medium of any of clauses 64-67, wherein at least one parameter indicates a mapping between RFFP measurements of downlink positioning reference signal (DL-PRS) resources and inputs of a machine learning model.

[0260] Clause 69. The non-transitory computer-readable medium of any of clauses 64 to 68, wherein at least one parameter includes a flag that configures the UE to perform measurement pre-processing before applying a machine learning model to RFFP measurements of DL-PRS resources to be measured by the UE for a DL-RFFP positioning procedure.

[0261] Clause 70. The non-transitory computer-readable medium of clause 69, wherein the at least one parameter further comprises a series of measurement pre-processing stages, a calibration value for measurement pre-processing, an inverse fast Fourier transform (IFFT) size for measurement pre-processing, a cyclic shift sampling value for measurement pre-processing, a window size parameter for measurement pre-processing, a scaling option for measurement pre-processing, or any combination thereof.

[0262] Clause 71. The non-transitory computer-readable medium of any of clauses 64 to 70, wherein the at least one parameter includes a parameter related to a DL-PRS resource to be measured by the UE for a DL-RFFP positioning procedure.

[0263] Clause 72. A non-transitory computer-readable medium according to any of clauses 64 to 71, further comprising computer-executable instructions which, when executed by the UE, cause the UE to send a request to a first network entity for positioning assistance data for a DL-RFFP positioning procedure, and one or more positioning assistance data messages are received in response to the request.

[0264] Clause 73. A non-transitory computer-readable medium according to any of clauses 64 to 72, wherein the first network entity is a location server, the second network entity is a location server, and the one or more positioning assistance data messages are one or more Long Term Evolution (LTE) Positioning Protocol (LPP) messages.

[0265] Clause 74. A non-transitory computer-readable medium according to any of clauses 64 to 72, wherein the first network entity is a base station, the second network entity is a location server, and the one or more positioning assistance data messages are one or more positioning system information blocks (posSIBs) specific to DL-RFFP positioning.

[0266] Clause 75. A non-transitory computer-readable medium according to any of clauses 64 to 74, further comprising computer-executable instructions which, when executed by the UE, cause the UE to receive a request for location information from a second network entity, and one or more location information messages being transmitted in response to the request.

[0267] Clause 76. The non-transitory computer-readable medium of clause 75, wherein the request includes a reporting configuration indicating a periodicity for reporting the location of the UE, a type of location of the UE, a coarse estimate of the location of the UE, updates to DL-PRS resources configured for a DL-RFFP positioning procedure, updates to a pre-processing stage to be applied to measurements of DL-PRS resources configured for a DL-RFFP positioning procedure, updates to a machine learning model, a flag that configures the UE to report which of the DL-PRS resources were used for a DL-RFFP positioning procedure, or any combination thereof.

[0268] Clause 77. The non-transitory computer-readable medium of any of clauses 64 to 76, wherein the one or more location information messages include an estimated location of the UE determined based on a machine learning model, a metric indicative of a quality or reliability of the estimated location of the UE, an amount of time spent by the machine learning model to determine the estimated location of the UE, one or more positioning measurements of DL-PRS resources configured for the DL-RFFP positioning procedure determined based on the machine learning model, a timestamp of the one or more positioning measurements, an identifier of which DL-PRS resources configured for the DL-RFFP positioning procedure were used for the DL-RFFP positioning procedure, one or more signal strength measurements of DL-PRS resources configured for the DL-RFFP positioning procedure, one or more measurement elements customized by a network vendor, a UE vendor, or a machine learning model vendor, or any combination thereof.

[0269] Clause 78. A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a base station, cause the base station to receive one or more assistance information control messages from a location server indicating one or more parameters related to a machine learning model configured to be used by at least one user equipment (UE) for a Downlink Radio Frequency Fingerprint (DL-RFFP) positioning procedure, and to transmit one or more positioning system information blocks (posSIBs) specific to DL-RFFP positioning to the at least one UE, indicating the at least one or more parameters.

[0270] Clause 79. The non-transitory computer-readable medium of clause 78, wherein the one or more parameters include a flag indicating whether the one or more positioning assistance data messages include a description of a machine learning model.

[0271] Clause 80. The non-transitory computer-readable medium of clause 78 or 79, wherein the one or more parameters include an identifier of the machine learning model, a format of the machine learning model, an input feature type of the machine learning model, or any combination thereof.

[0272] Clause 81. The non-transitory computer-readable medium of any of clauses 78 to 80, wherein one or more parameters indicate a mapping between RFFP measurements of downlink positioning reference signal (DL-PRS) resources and inputs of a machine learning model.

[0273] Clause 82. A non-transitory computer-readable medium according to any of clauses 78 to 81, wherein the one or more parameters include a flag for configuring at least one UE to perform measurement pre-processing before applying a machine learning model to RFFP measurements of DL-PRS resources to be measured by at least one UE for a DL-RFFP positioning procedure.

[0274] Clause 83. The non-transitory computer-readable medium of clause 82, wherein the one or more parameters further include a series of measurement pre-processing steps, a calibration value for measurement pre-processing, an inverse fast Fourier transform (IFFT) size for measurement pre-processing, a cyclic shift sampling value for measurement pre-processing, a window size parameter for measurement pre-processing, a scaling option for measurement pre-processing, or any combination thereof.

[0275] Clause 84. The non-transitory computer-readable medium of any of clauses 78 to 83, wherein the one or more parameters include a parameter related to a DL-PRS resource to be measured by at least one UE for a DL-RFFP positioning procedure.

[0276] Those skilled in the art will appreciate that information and signals may be represented using any of a variety of different technologies and techniques. For example, the data, instructions, commands, information, signals, bits, symbols, and chips that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0277] Moreover, those skilled in the art will appreciate that the various exemplary logic blocks, modules, circuits, and algorithm steps described in connection with the aspects disclosed herein may be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability of hardware and software, various exemplary components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints imposed on the overall system. Those skilled in the art may realize the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0278] The various example logic blocks, modules, and circuits described in connection with the 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 alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0279] The methods, sequences, and / or algorithms described in connection with the aspects disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside 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 exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. Alternatively, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal (e.g., UE). Alternatively, the processor and the storage medium may reside as discrete components in a user terminal.

[0280] In one or more exemplary aspects, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted via a computer-readable medium as one or more instructions or code. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage medium may be any available medium that can be accessed by a computer. By way of example, and not limitation, such computer-readable media may comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included within the definition of media. As used herein, disk and disc include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray disc, where disks typically reproduce data magnetically and discs reproduce data optically using lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0281] Although the above disclosure illustrates exemplary aspects of the disclosure, it should be noted that various changes and modifications may be made herein without departing from the scope of the disclosure as defined by the appended claims. The functions, steps, and / or actions of the method claims according to the aspects of the disclosure described herein need not be performed in any particular order. Furthermore, although elements of the disclosure may be described or claimed in the singular, the plural is contemplated unless limitation to the singular is explicitly stated.

Claims

1. A method of wireless communication performed by user equipment (UE), Receiving one or more positioning assistance data messages for the DL-RFFP positioning procedure from a first network entity, which include at least one parameter relating to a machine learning model configured for use by the UE for the DL-RFFP positioning procedure, A method comprising transmitting one or more location information messages, each containing one or more parameters relating to the DL-RFFP positioning procedure, to a second network entity.

2. The at least one parameter includes a flag indicating whether the one or more positioning support data messages include a description of the machine learning model, The method according to claim 1, optionally further comprising retrieving the machine learning model from a network entity other than the first network entity based on the flag indicating that one or more positioning support data messages do not contain the description of the machine learning model.

3. The aforementioned at least one parameter is The identifier of the aforementioned machine learning model, The format of the aforementioned machine learning model, The input feature type of the aforementioned machine learning model, or The method according to claim 1, comprising any combination thereof.

4. The method according to claim 1, wherein the at least one parameter indicates a mapping between RFFP measurements of a downlink positioning reference signal (DL-PRS) resource and the input of the machine learning model.

5. The at least one parameter includes a flag that configures the UE to perform measurement preprocessing before applying the machine learning model to the RFFP measurements of DL-PRS resources to be measured by the UE for the DL-RFFP positioning procedure, Optionally, at least one of the above parameters is A series of pre-measurement processing steps, Calibration values ​​for the measurement pretreatment, The inverse fast Fourier transform (IFFT) size for the aforementioned measurement preprocessing, The cyclic shift sampling values ​​for the measurement preprocessing, The window size parameter for the measurement preprocessing, Scaling options for the measurement preprocessing, or The method according to claim 1, further comprising any combination thereof.

6. The method according to claim 1, wherein the at least one parameter includes a parameter relating to a DL-PRS resource to be measured by the UE for the DL-RFFP positioning procedure.

7. The method according to claim 1, further comprising transmitting a request to the first network entity for positioning assistance data for the DL-RFFP positioning procedure, wherein one or more positioning assistance data messages are received in response to the request.

8. The first network entity is a location server, The second network entity is the location server, The method according to claim 1, wherein the one or more positioning support data messages are one or more Long-Term Evolution (LTE) Positioning Protocol (LPP) messages.

9. The first network entity is a base station, The second network entity is a location server, The method according to claim 1, wherein the one or more positioning support data messages are one or more positioning system information blocks (posSIBs) specific to DL-RFFP positioning.

10. The process further includes receiving a request for location information from the second network entity, and one or more location information messages being transmitted in response to the request. Optionally, the above requirement is A reporting configuration that indicates the periodicity of reporting the location of the aforementioned UE, The type of the location of the aforementioned UE, A rough estimate of the location of the aforementioned UE, Updates to DL-PRS resources configured for the DL-RFFP positioning procedure, Updates to the preprocessing steps to be applied to the measurement values ​​of the DL-PRS resources configured for the DL-RFFP positioning procedure, Update to the aforementioned machine learning model, A flag that configures the UE to report which of the DL-PRS resources was used for the DL-RFFP positioning procedure, or The method according to claim 1, comprising any combination thereof.

11. The aforementioned one or more location information messages, The estimated location of the UE, determined based on the machine learning model, A metric indicating the quality or reliability of the estimated location of the UE, The amount of time required by the machine learning model to determine the estimated location of the aforementioned UE, One or more positioning measurements of DL-PRS resources configured for the DL-RFFP positioning procedure, determined based on the machine learning model, The timestamp of one or more positioning measurement values, An identifier indicating which DL-PRS resource configured for the DL-RFFP positioning procedure was used for the DL-RFFP positioning procedure, One or more signal intensity measurements of the DL-RFFP resource configured for the DL-RFFP positioning procedure, One or more measurement elements customized by a network vendor, UE vendor, or machine learning model vendor, or The method according to claim 1, comprising any combination thereof.

12. A method of communication carried out by a base station, At least one user device (UE) receives one or more support information control messages from a location server that indicate one or more parameters related to a machine learning model configured for use in a downlink radio frequency fingerprint (DL-RFFP) positioning procedure, A method comprising transmitting to the at least one UE one or more positioning system information blocks (posSIBs) specific to DL-RFFP positioning, each indicating at least one or more of the aforementioned parameters.

13. User equipment (UE), Memory and At least one transceiver, The system comprises the memory and at least one processor communicatively coupled to the at least one transceiver, wherein the at least one processor is The UE receives one or more positioning assistance data messages for the DL-RFFP positioning procedure from a first network entity via the at least one transceiver, each message including at least one parameter relating to a machine learning model configured for use by the UE for the DL-RFFP positioning procedure. A UE configured to transmit one or more location information messages, including one or more parameters related to the DL-RFFP positioning procedure, to a second network entity via the at least one transceiver.

14. The UE according to claim 13, further configured to perform the method described in any one of claims 2 to 11.

15. It is a base station, Memory and At least one transceiver, The system comprises the memory and at least one processor communicatively coupled to the at least one transceiver, wherein the at least one processor is At least one user device (UE) receives one or more support information control messages from the location server via the at least one transceiver, each indicating one or more parameters related to a machine learning model configured for use in a downlink radio frequency fingerprint (DL-RFFP) positioning procedure. A base station configured to transmit one or more positioning system information blocks (posSIBs) specific to DL-RFFP positioning, each indicating at least one of the parameters, to the at least one UE via the at least one transceiver.

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