Bandwidth aggregation for radio frequency fingerprint positioning

Abstract

In one aspect, a method of wireless communication performed by a user equipment (UE) includes measuring a plurality of different bandwidth segments of a reference signal (RS) over a corresponding plurality of different RS occasions during a positioning session to obtain a plurality of radio frequency fingerprint positioning (RFFP) measurements corresponding to the plurality of different bandwidth segments; aggregating the plurality of RFFP measurements to provide at least one aggregated RFFP measurement; and applying a positioning model to the at least one aggregated RFFP measurement to obtain an estimate of one or more positioning parameters associated with the location of the UE.

Description

Technical Field

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

[0002] Wireless communication systems have evolved over many generations, including first generation analog wireless telephone service (1G), second generation (2G) digital wireless telephone service (including transitional 2.5G and 2.75G networks), third generation (3G) high speed data, wireless service with Internet capabilities, and fourth generation (4G) services (e.g., Long Term Evolution (LTE) or WiMax). There are many different types of wireless communication systems in use today, 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, known as New Radio (NR), enables higher data transfer speeds, a greater number of connections, and better coverage, among other improvements. According to the Next Generation Mobile Networks Alliance, the 5G standard is designed to provide higher data rates, more accurate positioning (e.g., based on reference signals for positioning (RS-P), such as downlink, uplink, or sidelink positioning reference signals (PRS)), and other technical enhancements compared to previous standards. These enhancements, along with the use of higher frequency bands, advances in PRS processes and technologies, and high-density deployments of 5G enable high-precision positioning based on 5G. Summary of the invention

[0004] The following presents a simplified summary of the invention related to one or more aspects disclosed herein. Therefore, the following summary of the invention should neither be considered as an exhaustive overview related to all conceived aspects, nor should it be considered to identify key or decisive elements related to all conceived aspects or to delineate the scope associated with any particular aspect. Therefore, the sole purpose of the following summary of the invention is to present certain concepts related to one or more aspects related to the mechanisms disclosed herein in a simplified form before the detailed embodiments presented below.

[0005] In one aspect, a method of wireless communication performed by a user equipment (UE) includes: measuring multiple different bandwidth segments of a reference signal (RS) at corresponding multiple different RS opportunities during a positioning session to obtain multiple radio frequency fingerprint positioning (RFFP) measurements corresponding to the multiple different bandwidth segments; aggregating the multiple RFFP measurements to provide at least one aggregated RFFP measurement; and applying a positioning model to the at least one aggregated RFFP measurement to obtain an estimate of one or more positioning parameters associated with the position of the UE.

[0006] On the one hand, a method of wireless communication performed by a user equipment (UE) includes: measuring multiple different bandwidth segments of a reference signal (RS) at corresponding multiple different RS opportunities during a positioning session to obtain multiple radio frequency fingerprint positioning (RFFP) measurements corresponding to the multiple different bandwidth segments; and applying a latent feature machine learning (ML) model to each of the multiple RFFP measurements to obtain multiple latent feature representations corresponding to the multiple RFFP measurements.

[0007] In one aspect, a method of wireless communication performed by a network server includes: receiving from a user equipment (UE) a plurality of potential feature representations corresponding to a plurality of radio frequency fingerprint positioning (RFFP) measurements obtained by the UE during a positioning session, wherein the plurality of potential feature representations are based on measurements of a plurality of different bandwidth segments of a reference signal (RS) at a corresponding plurality of different RS occasions by the UE during the positioning session; and applying a positioning model to the plurality of potential feature representations to obtain one or more positioning parameters associated with a position of the UE.

[0008] In one aspect, a user equipment (UE) includes: a memory; at least one transceiver; and at least one processor, the at least one processor being communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: measure a plurality of different bandwidth segments of a reference signal (RS) at corresponding plurality of different RS opportunities during a positioning session to obtain a plurality of radio frequency fingerprint positioning (RFFP) measurements corresponding to the plurality of different bandwidth segments; aggregate the plurality of RFFP measurements to provide at least one aggregated RFFP measurement; and apply a positioning model to the at least one aggregated RFFP measurement to obtain an estimate of one or more positioning parameters associated with the position of the UE.

[0009] In one aspect, a user equipment (UE) includes: a memory; at least one transceiver; and at least one processor, the at least one processor being communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: measure a plurality of different bandwidth segments of a reference signal (RS) at corresponding plurality of different RS opportunities during a positioning session to obtain a plurality of radio frequency fingerprint positioning (RFFP) measurements corresponding to the plurality of different bandwidth segments; and apply a latent feature machine learning (ML) model to each of the plurality of RFFP measurements to obtain a plurality of latent feature representations corresponding to the plurality of RFFP measurements.

[0010] In one aspect, a network server comprises: a memory; at least one transceiver; and at least one processor, the at least one processor being communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: receive from a user equipment (UE) via the at least one transceiver a plurality of potential feature representations corresponding to a plurality of radio frequency fingerprint positioning (RFFP) measurements obtained by the UE during a positioning session, wherein the plurality of potential feature representations are based on measurements of a plurality of different bandwidth segments of a reference signal (RS) at a corresponding plurality of different RS opportunities by the UE during the positioning session; and apply a positioning model to the plurality of potential feature representations to obtain one or more positioning parameters associated with the position of the UE.

[0011] In one aspect, a user equipment (UE) includes: a component for measuring multiple different bandwidth segments of a reference signal (RS) at corresponding multiple different RS opportunities during a positioning session to obtain multiple radio frequency fingerprint positioning (RFFP) measurements corresponding to the multiple different bandwidth segments; a component for aggregating the multiple RFFP measurements to provide at least one aggregated RFFP measurement; and a component for applying a positioning model to the at least one aggregated RFFP measurement to obtain an estimate of one or more positioning parameters associated with the position of the UE.

[0012] In one aspect, a user equipment (UE) includes: a component for measuring multiple different bandwidth segments of a reference signal (RS) at corresponding multiple different RS opportunities during a positioning session to obtain multiple radio frequency fingerprint positioning (RFFP) measurements corresponding to the multiple different bandwidth segments; and a component for applying a latent feature machine learning (ML) model to each of the multiple RFFP measurements to obtain multiple latent feature representations corresponding to the multiple RFFP measurements.

[0013] In one aspect, a network server includes: a component for receiving, from a user equipment (UE), a plurality of potential feature representations corresponding to a plurality of radio frequency fingerprint positioning (RFFP) measurements obtained by the UE during a positioning session, wherein the plurality of potential feature representations are based on measurements by the UE of a plurality of different bandwidth segments of a reference signal (RS) at a corresponding plurality of different RS occasions during the positioning session; and a component for applying a positioning model to the plurality of potential feature representations to obtain one or more positioning parameters associated with a position of the UE.

[0014] 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: measure multiple different bandwidth segments of a reference signal (RS) at corresponding multiple different RS occasions during a positioning session to obtain multiple radio frequency fingerprint positioning (RFFP) measurements corresponding to the multiple different bandwidth segments; aggregate the multiple RFFP measurements to provide at least one aggregated RFFP measurement; and apply a positioning model to the at least one aggregated RFFP measurement to obtain an estimate of one or more positioning parameters associated with the position of the UE.

[0015] 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: measure a plurality of different bandwidth segments of a reference signal (RS) at corresponding plurality of different RS occasions during a positioning session to obtain a plurality of radio frequency fingerprint positioning (RFFP) measurements corresponding to the plurality of different bandwidth segments; and apply a latent feature machine learning (ML) model to each of the plurality of RFFP measurements to obtain a plurality of latent feature representations corresponding to the plurality of RFFP measurements.

[0016] In one aspect, a non-transitory computer-readable medium stores computer-executable instructions that, when executed by a network server, cause the network server to: receive from a user equipment (UE) a plurality of potential feature representations corresponding to a plurality of radio frequency fingerprint positioning (RFFP) measurements obtained by the UE during a positioning session, wherein the plurality of potential feature representations are based on measurements by the UE of a plurality of different bandwidth segments of a reference signal (RS) at a corresponding plurality of different RS occasions during the positioning session; and apply a positioning model to the plurality of potential feature representations to obtain one or more positioning parameters associated with a position of the UE.

[0017] Other objects and advantages associated with the various aspects disclosed herein will be apparent to those skilled in the art based on the drawings and detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are presented to aid in describing various aspects of the present disclosure and are provided solely for illustration and not limitation of the various aspects.

[0019] Figure 1 An example wireless communication system in accordance with aspects of the present disclosure is illustrated.

[0020] Figure 2A , Figure 2B and Figure 2C Example wireless network structures according to aspects of the present disclosure are illustrated.

[0021] Figure 3A , Figure 3B and Figure 3C is a simplified block diagram of several sample aspects of components that may be employed in a user equipment (UE), a base station, and a network entity, respectively, and configured to support communications as taught herein.

[0022] Figure 4 Examples of various positioning methods supported in New Radio (NR) according to aspects of the present disclosure are illustrated.

[0023] Figure 5 is a diagram illustrating an example frame structure according to aspects of the present disclosure.

[0024] Figure 6 is a diagram representing radio frequency (RF) channel estimation in accordance with aspects of the present disclosure.

[0025] Figure 7 Example neural networks in accordance with aspects of the present disclosure are illustrated.

[0026] Figure 8 is a diagram illustrating the use of a machine learning model (ML) for RF fingerprinting (RFFP) based positioning according to aspects of the present disclosure.

[0027] Fig. 9 is a diagram illustrating an inference loop for UE-based downlink RFFP (DL-RFFP) positioning according to aspects of the present disclosure.

[0028] Fig.10 An example call flow for UE-based DL-RFFP positioning according to aspects of the present disclosure is illustrated.

[0029] Fig.11 is a diagram illustrating different bandwidth segments for measuring reference signals over multiple reference signal opportunities in accordance with aspects of the present disclosure.

[0030] Fig.12 is a diagram illustrating one manner of processing multiple RFFP measurements to obtain positioning parameters associated with the location of a UE in accordance with aspects of the present disclosure.

[0031] Fig.13 is a diagram illustrating another way of processing multiple RFFP measurements to obtain positioning parameters associated with the location of a UE according to aspects of the present disclosure.

[0032] Fig.14 Several different latent feature ML models with different latent feature representation dimensions are illustrated according to aspects of the present disclosure.

[0033] Fig.15Acceptable potential feature representations according to aspects of the present disclosure are depicted to provide a fusion / positioning model of positioning parameters associated with the location of a UE.

[0034] Fig.16 An example method of wireless communication performed by a UE according to aspects of the present disclosure is illustrated.

[0035] Fig.17 An example method of wireless communication performed by a UE according to aspects of the present disclosure is illustrated.

[0036] Fig.18 An example method of wireless communication performed by a network server according to aspects of the present disclosure is illustrated. DETAILED DESCRIPTION

[0037] Various aspects of the present disclosure are provided in the following description and related drawings for various examples provided for illustrative purposes. Alternative aspects may be designed 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 to avoid making the relevant details of the present disclosure difficult to understand.

[0038] The words "exemplary" and / or "example" are used herein to mean "serving as an example, instance, or illustration." Any aspect described herein as "exemplary" and / or "example" is not necessarily to be construed as preferred or advantageous over other aspects. Likewise, the term "aspects of the disclosure" does not require that all aspects of the disclosure include the discussed feature, advantage, or mode of operation.

[0039] It should be understood by those skilled in the art that any of a variety of different technologies and methods may be used to represent the information and signals described below. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be mentioned throughout the following description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or optical particles, or any combination thereof, which depends in part on the specific application, in part on the desired design, in part on the corresponding technology, and so on.

[0040] In addition, many aspects are described in terms of a sequence of actions to be performed by, for example, an element of a computing device. It will be appreciated that the various actions described herein may be performed by a specific circuit (e.g., an application specific integrated circuit (ASIC)), by program instructions executed by one or more processors, or by a combination of the two. Additionally, the sequence of actions described herein may be considered to be fully embodied in any form of non-transient computer-readable storage medium, in which a corresponding computer instruction set is stored, which, when executed, will cause or command the associated processor of the device to perform the functionality described herein. Therefore, various aspects of the present disclosure may be embodied in a variety of different forms, all of which have been contemplated to be within the scope of the claimed subject matter. In addition, for each of the various aspects described herein, the corresponding form of any such aspect may be described herein as, for example, "logic configured to perform the described actions".

[0041] As used herein, unless otherwise specified, the terms "user equipment" (UE) and "base station" are not intended to be specific or otherwise limited to any specific radio access technology (RAT). In general, a UE may be any wireless communication device (e.g., a mobile phone, a router, a tablet computer, a laptop computer, a consumer asset location device, a wearable device (e.g., a smart watch, glasses, an augmented reality (AR) / virtual reality (VR) head-mounted device, 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 certain times) and may communicate with a radio access network (RAN). As used herein, the term "UE" may be interchangeably referred to as an "access terminal" or "AT", "client device", "wireless device", "subscriber device", "subscriber terminal", "subscriber station", "user terminal" or "UT", "mobile device", "mobile terminal", "mobile station" or variations thereof. In general, a UE may communicate with a core network via a RAN, and through the core network, the UE may connect to external networks such as the Internet and to other UEs. Of course, other mechanisms for the UE to connect to the core network and / or the Internet are also possible, such as through a wired access network, a wireless local area network (WLAN) network (e.g., based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11 specification, etc.), etc.

[0042] A base station may operate according to one of several RATs to communicate with a UE, depending on the network in which the base station is deployed, and may alternatively be referred to as an access point (AP), a network node, a Node B, an evolved Node B (eNB), a next generation eNB (ng-eNB), a new radio (NR) Node B (also referred to as a gNB or gNodeB), etc. A base station may be primarily used to support wireless access for UEs, including supporting data, voice, and / or signaling connections for supported UEs. In some systems, a base station may provide only edge node signaling functions, while in other systems it may provide additional control and / or network management functions. The communication link by which a UE may transmit a signal to a base station is referred to as an uplink (UL) channel (e.g., a reverse traffic channel, a reverse control channel, an access channel, etc.). The communication link by which a base station may transmit a signal to a UE is referred to as a downlink (DL) or forward link channel (e.g., a paging channel, a control channel, a broadcast channel, a forward traffic channel, etc.). As used herein, the term "traffic channel (TCH)" may refer to an uplink / reverse traffic channel or a downlink / forward traffic channel.

[0043] The term "base station" may refer to a single physical transmit-receive point (TRP) or multiple physical TRPs that may or may not be co-located. For example, where the term "base station" refers to a single physical TRP, the physical TRP may be an antenna of the base station corresponding to a cell (or several cell sectors) of the base station. Where the term "base station" refers to multiple co-located physical TRPs, the physical TRP may be an antenna array of the base station (e.g., as in a multiple-input multiple-output (MIMO) system or where the base station employs beamforming). Where the term "base station" refers to multiple non-co-located physical TRPs, the physical TRP may be a distributed antenna system (DAS) (a network of spatially separated antennas connected to a common source via a transmission medium) or a remote radio head (RRH) (a remote base station connected to a serving base station). Alternatively, the non-co-located physical TRP may be a serving base station that receives measurement reports from a UE and an adjacent base station whose reference radio frequency (RF) signal the UE is measuring. Because, as used herein, a TRP is a point by which a base station transmits and receives wireless signals, references to transmitting from a base station or receiving at a base station should be understood to refer to a specific TRP of a base station.

[0044] In some specific implementations of supporting UE positioning, the base station may not support wireless access for the UE (e.g., may not support data, voice, and / or signaling connections for the UE), but may instead send a reference signal to be measured by the UE to the UE and / or may receive and measure a signal sent by the UE. Such a base station may be referred to as a positioning beacon (e.g., in the case of sending a signal to the UE) and / or as a position measurement unit (e.g., in the case of receiving and measuring a signal from the UE).

[0045] An "RF signal" includes an electromagnetic wave of a given frequency that transmits information through the 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, due to the propagation characteristics of RF signals through multipath channels, a receiver may receive multiple "RF signals" corresponding to each transmitted RF signal. The same transmitted RF signal on 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" where it is clear from the context that the term "signal" refers to a wireless signal or an RF signal.

[0046] Figure 1 An example wireless communication system 100 according to various aspects of the present disclosure is illustrated. The wireless communication system 100 (which may also be referred to as a wireless wide area network (WWAN)) may include various base stations 102 (labeled as "BS") and various UEs 104. The base stations 102 may include macro cell base stations (high power cellular base stations) and / or small cell base stations (low power cellular base stations). In one aspect, the macro cell 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 the two, and the small cell base stations may include femto cells, pico cells, micro cells, etc.

[0047] The base stations 102 may collectively form a RAN and interface with a core network 170 (e.g., an evolved packet core (EPC) or a 5G core (5GC)) through a backhaul link 122, and interface with one or more location servers 172 (e.g., a location management function (LMF) or a secure user plane location (SUPL) location platform (SLP)) through the core network 170. The location server 172 may be part of the core network 170 or may be external to the core network 170. The location server 172 may be integrated with the base station 102. The UE 104 may communicate with the location server 172 directly or indirectly. For example, the UE 104 may communicate with the location server 172 via the base station 102 currently serving the UE 104. The UE 104 may also communicate with the location server 172 via another path, such as via an application server (not shown), via another network, such as via a wireless local area network (WLAN) access point (AP) (e.g., AP 150 described below), etc. For signaling purposes, communications between UE 104 and location server 172 may be represented as an indirect connection (e.g., through core network 170, etc.) or a direct connection (e.g., as shown via direct connection 128), with intermediate nodes (if any) omitted from the signaling diagram for clarity.

[0048] Among other functions, the base station 102 may perform functions related to one or more of the following: delivering user data, radio channel encryption and decryption, integrity protection, header compression, mobility control functions (e.g., handover, dual connectivity), inter-cell interference coordination, connection establishment and release, load balancing, distribution of non-access stratum (NAS) messages, NAS node selection, synchronization, RAN sharing, multimedia broadcast multicast service (MBMS), subscriber and equipment tracking, RAN information management (RIM), paging, positioning, and delivery of warning messages. The base stations 102 may communicate with each other directly or indirectly (e.g., through EPC / 5GC) via a backhaul link 134, which may be wired or wireless.

[0049] Base station 102 may communicate wirelessly with UE 104. Each of base stations 102 may provide communication coverage for a corresponding geographic coverage area 110. In one aspect, one or more cells may be supported by base station 102 in each geographic coverage area 110. A "cell" is a logical communication entity used to communicate with a base station (e.g., on a certain frequency resource, which is referred to as a carrier frequency, component carrier, carrier, frequency 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.) used to distinguish cells operating via the same or different carrier frequencies. In some cases, different cells may be configured according to different protocol types (e.g., machine type communication (MTC), narrowband IoT (NB-IoT), enhanced mobile broadband (eMBB), or other protocol types) that may provide access to different types of UEs. Because a cell is supported by a specific base station, the term "cell" may refer to either or both of the logical communication entity and the base station supporting it, depending on the context. In addition, since the TRP is generally the physical transmission point of the cell, the terms "cell" and "TRP" may be used interchangeably. In some cases, the term "cell" may also refer to a geographic coverage area (e.g., a sector) of a base station, as long as a carrier frequency can be detected and used for communications within a portion of the geographic coverage area 110.

[0050] Although the geographic coverage areas 110 of neighboring macrocell base stations 102 may partially overlap (e.g., in a handover region), some of the geographic coverage areas 110 may substantially overlap with the 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 substantially overlaps with the geographic coverage areas 110 of one or more macrocell base stations 102. A network that includes both small cell base stations and macrocell base stations may be referred to as a heterogeneous network. A heterogeneous network may also include a home eNB (HeNB), which may provide services to a restricted group called a closed subscriber group (CSG).

[0051] The communication link 120 between the base station 102 and the UE 104 may include uplink (also referred to as a reverse link) transmissions from the UE 104 to the base station 102 and / or downlink (DL) (also referred to as a forward link) transmissions from the base station 102 to the UE 104. The communication link 120 may use MIMO antenna technology, 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 for the downlink and uplink (e.g., more or fewer carriers may be allocated to the downlink than to the uplink).

[0052] The wireless communication system 100 may also include a WLAN access point (AP) 150 that communicates with a wireless local area network (WLAN) station (STA) 152 in an unlicensed spectrum (e.g., 5 GHz) via a communication link 154. When communicating in the unlicensed spectrum, the WLAN STA 152 and / or the WLAN AP 150 may perform a clear channel assessment (CCA) or listen before talk (LBT) procedure prior to communication to determine whether a channel is available.

[0053] The small cell base station 102' can operate in licensed and / or unlicensed spectrum. When operating in the unlicensed spectrum, the small cell base station 102' can adopt LTE or NR technology and use the same 5GHz unlicensed spectrum used by the WLAN AP 150. The small cell base station 102' using LTE / 5G in the unlicensed spectrum can improve the coverage of the access network and / or increase the capacity of the access network. NR in the unlicensed spectrum can be referred to as NR-U. LTE in the unlicensed spectrum can be referred to as LTE-U, Licensed Assisted Access (LAA) or MulteFire.

[0054] The wireless communication system 100 may also include a millimeter wave (mmW) base station 180, which can operate at mmW frequencies and / or near mmW frequencies to communicate with UE 182. Extremely high frequency (EHF) is part of RF in the electromagnetic spectrum. EHF has a range of 30 GHz to 300 GHz, with a wavelength between 1 mm and 10 mm. The radio waves in this band may be referred to as millimeter waves. Near mmW can be extended downward to a frequency of 3 GHz, with a wavelength of 100 mm. The super high frequency (SHF) band extends between 3 GHz and 30 GHz, and it is also referred to as centimeter waves. Communications using mmW / near mmW radio frequency bands have high path loss and relatively short distances. The mmW base station 180 and UE 182 can utilize beamforming (transmitting and / or receiving) on ​​the mmW communication link 184 to compensate for extremely high path loss and short distances. In addition, it should be understood that in an alternative configuration, one or more base stations 102 may also use mmW or near mmW and beamforming for transmission. Therefore, it should be understood that the foregoing illustrations are merely examples and should not be construed as limiting the various aspects disclosed herein.

[0055] Transmit beamforming is a technique for focusing an RF signal in a specific direction. Traditionally, when a network node (e.g., a base station) broadcasts an RF signal, it broadcasts the signal in all directions (omnidirectionally). With transmit beamforming, the network node determines where a given target device (e.g., a UE) is located (relative to the transmitting network node) and projects a stronger downlink RF signal in that specific direction, thereby providing a faster (in terms of data rate) and stronger RF signal to the receiving device. In order 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 that broadcast the RF signal. For example, a network node can use an array of antennas (called a "phased array" or "antenna array") that creates RF beams that can be "steered" to point in different directions without actually moving the antennas. Specifically, the RF current from the transmitter is fed to each antenna in the correct phase relationship so that the radio waves from the separate antennas are added together to increase the radiation in the desired direction while canceling to suppress the radiation in the undesired direction.

[0056] The transmit beams can be quasi-co-located, which means that they appear to the receiver (e.g., UE) to have the same parameters, regardless of whether the network node's own transmit antenna is physically co-located. In NR, there are four types of quasi-co-location (QCL) relationships. Specifically, a given type of QCL relationship means that certain parameters about the second reference RF signal on the second beam can be derived based on information about the source reference RF signal on the source beam. Therefore, if the source reference RF signal is QCL type A, the receiver can use the source reference RF signal to estimate the Doppler shift, Doppler spread, average delay, and delay spread of the second reference RF signal sent on the same channel. If the source reference RF signal is QCL type B, the receiver can use the source reference RF signal to estimate the Doppler shift and Doppler spread of the second reference RF signal sent on the same channel. If the source reference RF signal is QCL type C, the receiver can use the source reference RF signal to estimate the Doppler shift and average delay of the second reference RF signal sent on the same channel. If the source reference RF signal is QCL type D, the receiver may use the source reference RF signal to estimate spatial reception parameters of a second reference RF signal transmitted on the same channel.

[0057] In receive beamforming, the receiver uses receive beams to amplify RF signals detected on a given channel. For example, the receiver may increase the gain setting of the antenna array in a particular direction and / or adjust the phase setting of the antenna array in a particular direction to amplify the RF signal received from that direction (e.g., increase its gain level). Therefore, when a receiver is said to perform beamforming in a certain direction, it means that the beam gain in that direction is high relative to the beam gain along other directions, or the beam gain in that direction is the highest compared to the beam gain in that direction of all other receive beams available to the receiver. This results in a stronger received signal strength (e.g., reference signal received power (RSRP), reference signal received quality (RSRQ), signal to interference plus noise ratio (SINR), etc.) of the RF signal received from that direction.

[0058] The transmit beam and the receive beam may be spatially correlated. The spatial relationship means that parameters of a second beam (e.g., a transmit beam or a receive beam) for a second reference signal may be derived based on information about a first beam (e.g., a receive beam or a transmit beam) for a first reference signal. For example, a UE may use a specific receive beam to receive a reference downlink reference signal (e.g., a synchronization signal block (SSB)) from a base station. The UE may then form a transmit beam for transmitting an uplink reference signal (e.g., a sounding reference signal (SRS)) to the base station based on the parameters of the receive beam.

[0059] Note that depending on the entity forming the "downlink" beam, the beam can be a transmit beam or a receive beam. For example, if the base station is forming a downlink beam to send a reference signal to the UE, the downlink beam is a transmit beam. However, if the UE is forming a downlink beam, the downlink beam is a receive beam that receives a downlink reference signal. Similarly, depending on the entity forming the "uplink" beam, the beam can be a transmit beam or a receive beam. For example, if the base station is forming an uplink beam, it is an uplink receive beam, while if the UE is forming an uplink beam, it is an uplink transmit beam.

[0060] The electromagnetic spectrum is typically subdivided into various categories, bands, channels, etc. based on frequency / wavelength. In 5GNR, 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 although a portion of FR1 is greater than 6 GHz, FR1 is often (interchangeably) referred to as the "below 6 GHz" band in various documents and articles. A similar naming problem sometimes occurs with respect to FR2, which is often (interchangeably) referred to as the "millimeter wave" band in documents and articles, although it is different from the extremely high frequency (EHF) band (30 GHz-300 GHz) identified as the "millimeter wave" band by the International Telecommunication Union (ITU).

[0061] Frequencies between FR1 and FR2 are generally referred to as mid-band frequencies. Recent 5G NR research has identified the operating bands for these mid-band frequencies as frequency range designation FR3 (7.125GHz-24.25GHz). The bands falling within FR3 can inherit FR1 characteristics and / or FR2 characteristics, so the features of FR1 and / or FR2 can be effectively extended to mid-band frequencies. In addition, higher frequency bands are currently being explored to extend 5G NR operations to more than 52.6GHz. For example, three higher operating bands have been identified as frequency range designations FR4a or FR4-1 (52.6GHz-71GHz), FR4 (52.6GHz-114.25GHz) and FR5 (114.25GHz-300GHz). Each of these higher frequency bands falls within the EHF band.

[0062] In view of the above aspects, unless otherwise specifically stated, it should be understood that if used herein, the term "below 6 GHz" or the like can broadly refer to frequencies that can be less than 6 GHz, can be within FR1, or can include mid-band frequencies. In addition, unless otherwise specifically stated, it should be understood that if the term "millimeter wave" or the like is used herein, it can broadly refer to frequencies that can include mid-band frequencies, can be within FR2, FR4, FR4-a or FR4-1 and / or FR5, or can be within the EHF band.

[0063] In a multi-carrier system (such as 5G), one of the carrier frequencies is referred to as the "primary carrier" or "anchor carrier" or "primary serving cell" or "PCell", and the remaining carrier frequencies are referred to as "secondary carriers" or "secondary serving cells" or "SCells". In carrier aggregation, the anchor carrier is a carrier operating on the primary frequency (e.g., FR1) utilized by the UE 104 / 182 and the cell in which the UE 104 / 182 performs an initial radio resource control (RRC) connection establishment procedure or initiates an RRC connection re-establishment procedure. The primary carrier carries all common and UE-specific control channels and can be a carrier in a licensed frequency (however, this is not always the case). A secondary carrier is a carrier operating on a second frequency (e.g., FR2) that can be configured and used to provide additional radio resources once an RRC connection is established between the UE 104 and the anchor carrier. In some cases, the secondary carrier can be a carrier in an unlicensed frequency. The secondary carrier may contain only necessary signaling information and signals, for example, since the primary uplink carrier and the primary downlink carrier are usually UE-specific, those UE-specific signaling information and signals may not be present in the secondary carrier. This means that different UEs 104 / 182 in a cell may have different downlink primary carriers. The same is true for the uplink primary carrier. The network is able to change the primary carrier of any UE 104 / 182 at any time. This is done, for example, to balance the load on different carriers. Because a "serving cell" (whether PCell or SCell) corresponds to a carrier frequency / component carrier on which a base station communicates, the terms "cell", "serving cell", "component carrier", "carrier frequency", etc. are used interchangeably.

[0064] For example, still referring to Figure 1, one of the frequencies utilized by the macrocell base station 102 may be an anchor carrier (or "PCell"), and the other frequencies used 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 enables the UE 104 / 182 to significantly increase its data transmission and / or reception rate. For example, two 20 MHz aggregated carriers in a multi-carrier system would theoretically result in a doubling of the data rate (i.e., 40 MHz) compared to the data rate obtained with a single 20 MHz carrier.

[0065] The wireless communication system 100 may also include a UE 164, which may communicate with the macrocell base station 102 via a communication link 120 and / or communicate with the mmW base station 180 via a mmW communication link 184. For example, the macrocell base station 102 may support a PCell and one or more SCells for the UE 164, and the mmW base station 180 may support one or more SCells for the UE 164.

[0066] In some cases, UE 164 and UE 182 are capable of sidelink communication. A UE with sidelink capability (SL-UE) can communicate with base station 102 via communication link 120 using a Uu interface (i.e., an air interface between UE and base station). SL-UEs (e.g., UE 164, UE 182) can also communicate directly with each other via a wireless sidelink 160 using a PC5 interface (i.e., an air interface between UEs with sidelink capability). A wireless sidelink (or just "sidelink") is an adaptation of a core cellular network (e.g., LTE, NR) standard that allows direct communication between two or more UEs without communicating through a base station. Sidelink communications can be unicast or multicast and can be used for device-to-device (D2D) media sharing, vehicle-to-vehicle (V2V) communications, vehicle-to-vehicle (V2X) communications (e.g., cellular V2X (cV2X) communications, enhanced V2X (eV2X) communications, etc.), emergency rescue applications, etc. One or more SL-UEs in a group of SL-UEs utilizing sidelink communications may be located 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 be unable to receive transmissions from the base station 102 for other reasons. In some cases, each group of SL-UEs communicating via sidelink communications may utilize a one-to-many (1:M) system, where each SL-UE transmits to each other SL-UE in the group. In some cases, the base station 102 facilitates the scheduling of resources for the sidelink communications. In other cases, the sidelink communications are performed between the SL-UEs without involving the base station 102.

[0067] In one aspect, the sidelink 160 may operate on a wireless communication medium of interest, which may be shared with other vehicles and / or infrastructure access points and other wireless communications between other RATs. A "medium" may include one or more time, frequency, and / or spatial communication resources associated with wireless communications between one or more transmitter / receiver pairs (e.g., covering one or more channels across one or more carriers). In one aspect, the medium of interest may correspond to at least a portion of an unlicensed band shared between various RATs. Although different licensed bands have been reserved for certain communication systems (e.g., by government entities such as the Federal Communications Commission (FCC) in the United States), these systems (particularly those employing small cell access points) have recently expanded operations into unlicensed bands such as the unlicensed National Information Infrastructure (U-NII) band used by wireless local area network (WLAN) technology (most notably the IEEE 802.11x WLAN technology commonly referred to as "Wi-Fi"). Example systems of this type include different variations of CDMA systems, TDMA systems, FDMA systems, orthogonal FDMA (OFDMA) systems, single carrier FDMA (SC-FDMA) systems, and the like.

[0068] Note that although Figure 1 Only two of these UEs are illustrated as SL-UEs (i.e., UEs 164 and 182), but any of the illustrated UEs may be SL-UEs. In addition, although only UE 182 is described as being capable of beamforming, any of the illustrated UEs (including UE 164) may be capable of beamforming. In the case where SL-UEs are capable of beamforming, 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 cells 102', access point 150), etc. Therefore, in some cases, UE 164 and UE 182 may utilize beamforming via side link 160.

[0069] exist Figure 1 In the example of FIG. 1 , the UE illustrated (for simplicity, in Figure 1Any of the UEs 104 (shown as a single UE 104 in FIG. 1 ) may receive a signal 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 UEs 104 may use as an independent source of location information. A satellite positioning system typically includes a transmitter system (e.g., SV 112) positioned to enable a receiver (e.g., UE 104) to determine its position on or above the earth based at least in part on a positioning signal (e.g., signal 124) received from the transmitter. Such a transmitter typically transmits a signal of a repeating pseudo-random noise (PN) code marked with a set number of chips. Although typically located in the SVs 112, the transmitters may sometimes be located on ground-based control stations, base stations 102, and / or other UEs 104. The UEs 104 may include one or more dedicated receivers that are specifically designed to receive the signal 124 in order to derive geographic location information from the SVs 112.

[0070] In a satellite positioning system, the use of signal 124 may be enhanced 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, SBAS may include augmentation systems that provide integrity information, differential corrections, etc., such as Wide Area Augmentation System (WAAS), European Geostationary Navigation Overlay Service (EGNOS), Multifunction Satellite Augmentation System (MSAS), Global Positioning System (GPS) Assisted Geographic Augmented Navigation, or GPS and Geographic Augmented Navigation System (GAGAN), etc. Thus, as used herein, a satellite positioning system may include any combination of one or more global and / or regional navigation satellites associated with such one or more satellite positioning systems.

[0071] In one aspect, SV 112 may additionally or alternatively be part of one or more non-terrestrial networks (NTNs). In an NTN, SV 112 is connected to an earth station (also referred to as a ground station, NTN gateway, or gateway), which in turn is connected to an element in a 5G network, such as an improved base station 102 (without a ground antenna) or a network node in a 5GC. This element in turn will provide access to other elements in the 5G network, and ultimately provide access to entities outside the 5G network (such as Internet web servers and other user equipment). In this way, instead of or in addition to communication signals from a ground base station 102, a UE 104 can receive communication signals (e.g., signal 124) from a SV 112.

[0072] The wireless communication system 100 may also include one or more UEs (such as UE 190) that are indirectly connected to one or more communication networks via one or more device-to-device (D2D) peer-to-peer (P2P) links (referred to as “side links”). Figure 1 In the example of FIG. 1 , UE 190 has a D2D P2P link 192 with one of UEs 104 connected to one of base stations 102 (e.g., UE 190 can indirectly obtain cellular connectivity through the D2D P2P link), and has a D2D P2P link 194 with WLAN STA 152 connected to WLAN AP 150 (UE 190 can indirectly obtain WLAN-based Internet connectivity through the D2D P2P link). In an example, D2D P2P links 192 and 194 can be supported by any well-known D2D RAT, such as LTE Direct (LTE-D), WiFi Direct (WiFi-D), etc.

[0073] Figure 2A An example wireless network structure 200 is illustrated. For example, the 5GC 210 (also referred to as the Next Generation Core (NGC)) can be functionally viewed as a control plane (C-plane) function 214 (e.g., UE registration, authentication, network access, gateway selection, etc.) and a user plane (U-plane) function 212 (e.g., UE gateway function, access to data networks, IP routing, etc.), which operate in conjunction to form a core network. The user plane interface (NG-U) 213 and the control plane interface (NG-C) 215 connect the gNB 222 to the 5GC 210, and specifically to the user plane function 212 and the control plane function 214, respectively. In an additional configuration, the ng-eNB 224 can also be connected to the 5GC 210 via the NG-C 215 to the control plane function 214 and the NG-U 213 to the user plane function 212. In addition, the ng-eNB 224 can communicate directly with the gNB 222 via the backhaul connection 223. In some configurations, the next generation RAN (NG-RAN) 220 may have one or more gNBs 222, while other configurations include one or more of ng-eNBs 224 and gNBs 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).

[0074] Another optional aspect may include a location server 230 that can communicate with the 5GC 210 to provide location assistance for the UE 204. The location server 230 may be implemented as multiple separate servers (e.g., physically separate servers, different software modules on a single server, different software modules distributed across multiple physical servers, etc.), or alternatively may each correspond to a single server. The location server 230 may be configured to support one or more location services for the UE 204 that may be connected to the location server 230 via the core network, the 5GC 210, and / or via the Internet (not illustrated). In addition, the location server 230 may be integrated into a component 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).

[0075] Figure 2B Another example wireless network structure 240 is illustrated. 5GC 260 (which may correspond to Figure 2AThe 5GC 210 in the 5GC 210 can be functionally considered as a control plane function provided by an access and mobility management function (AMF) 264, and a user plane function provided by a user plane function (UPF) 262, which operate in conjunction to form a core network (i.e., 5GC 260). The functions of the AMF 264 include: registration management, connection management, reachability management, mobility management, lawful interception, transmission of session management (SM) messages between one or more UEs 204 (e.g., any of the UEs described herein) and a session management function (SMF) 266, a transparent proxy service for routing SM messages, access authentication and access authorization, transmission of short message service (SMS) messages between the UE 204 and a short message service function (SMSF) (not shown), and security anchor functionality (SEAF). The AMF 264 also interacts with an authentication server function (AUSF) (not shown) and the UE 204, and receives an intermediate key established as a result of the UE 204 authentication process. In the case of authentication based on the UMTS (Universal Mobile Telecommunications System) Subscriber Identity Module (USIM), the AMF 264 retrieves security material from the AUSF. The functionality of the AMF 264 also includes security context management (SCM). The SCM receives keys from the SEAF, which it uses to derive access network specific keys. The functionality of the AMF 264 also includes location service management for regulatory services, for the transmission of location service messages between the UE 204 and the Location Management Function (LMF) 270 (which acts as a location server 230), for the transmission of location service messages between the NG-RAN 220 and the LMF 270, for the allocation of Evolved Packet System (EPS) bearer identifiers for interoperation with EPS, and UE 204 mobility event notifications. In addition, the AMF 264 also supports functionality for non-3GPP (Third Generation Partnership Project) access networks.

[0076] The functions of UPF 262 include: acting as an anchor point for intra-RAT / inter-RAT mobility (when applicable), acting as an external protocol data unit (PDU) session point for interconnection to a data network (not shown), providing packet routing and forwarding, packet inspection, user plane policy rule enforcement (e.g., gating, redirection, service steering), lawful interception (user plane collection), service usage reporting, user plane quality of service (QoS) handling (e.g., uplink / downlink rate enforcement, reflective QoS marking in downlink), uplink service verification (service data flow (SDF) to QoS flow mapping), transport level packet marking in uplink and downlink, downlink packet buffering and downlink data notification triggering, and transmitting and forwarding one or more "end markers" to the source RAN node. UPF 262 can also support the delivery of location service messages between UE 204 and a location server (such as SLP 272) on the user plane.

[0077] The functions of SMF 266 include session management, UE Internet Protocol (IP) address allocation and management, selection and control of user plane functions, traffic steering configuration for routing traffic to the correct destination at UPF 262, partial control of policy enforcement and QoS, and downlink data notification. The interface through which SMF 266 communicates with AMF 264 is called the N11 interface.

[0078] Another optional aspect may include an LMF 270 that can communicate with the 5GC 260 to provide location assistance for the UE 204. The LMF 270 can be implemented as multiple separate servers (e.g., physically separate servers, different software modules on a single server, different software modules distributed across multiple physical servers, etc.), or alternatively can each correspond to a single server. The LMF 270 can be configured to support one or more location services for the UE 204, which can be connected to the LMF 270 via the core network, the 5GC 260, and / or via the Internet (not illustrated). SLP 272 may support similar functionality as LMF 270, but LMF 270 may communicate with AMF 264, NG-RAN 220, and UE 204 on a control plane (e.g., using interfaces and protocols designed to carry signaling messages rather than voice or data), and SLP 272 may communicate with UE 204 and external clients (e.g., third-party servers 274) on a user plane (e.g., using protocols designed to carry voice and / or data, such as the Transmission Control Protocol (TCP) and / or IP).

[0079] Yet another optional aspect may include a third-party server 274 that can communicate with the LMF 270, SLP 272, 5GC 260 (e.g., via AMF 264 and / or UPF 262), NG-RAN 220, and / or UE 204 to obtain location information (e.g., location estimate) of UE 204. Thus, in some cases, the third-party server 274 may be referred to as a location service (LCS) client or an external client. The third-party server 274 may be implemented as a plurality of separate servers (e.g., physically separate servers, different software modules on a single server, different software modules distributed across multiple physical servers, etc.), or may alternatively each correspond to a single server.

[0080] The user plane interface 263 and the control plane interface 265 connect the 5GC 260, and specifically 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 222 and / or ng-eNB 224 and the AMF 264 is referred to as the "N2" interface, and the interface between the gNB 222 and / or ng-eNB 224 and the UPF 262 is referred to as the "N3" interface. The gNBs 222 and / or ng-eNBs 224 of the NG-RAN 220 may communicate directly with each other via a backhaul connection 223 referred to as an "Xn-C" interface. One or more of the gNBs 222 and / or ng-eNBs 224 may communicate with one or more UEs 204 via a wireless interface referred to as a "Uu" interface.

[0081] The functionality of the gNB 222 may be divided between a gNB central unit (gNB-CU) 226, one or more gNB distributed units (gNB-DU) 228, and one or more gNB radio units (gNB-RU) 229. The gNB-CU 226 is a logical node that includes base station functions other than those specifically assigned to the gNB-DU 228, including delivery of user data, mobility control, radio access network sharing, positioning, session management, and the like. More specifically, the gNB-CU 226 generally 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 generally hosts the radio link control (RLC) and medium access control (MAC) layers of the gNB 222. Its operation is controlled by the gNB-CU 226. One gNB-DU 228 may support one or more cells, and one cell is supported by only one gNB-DU 228. The interface 232 between the gNB-CU 226 and one or more gNB-DUs 228 is referred to as the "F1" interface. The physical (PHY) layer functionality of the gNB 222 is typically hosted by one or more independent 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 layer, the SDAP layer, and the PDCP layer, communicates with the gNB-DU 228 via the RLC layer and the MAC layer, and communicates with the gNB-RU 229 via the PHY layer.

[0082] The deployment of a communication system (such as a 5G NR system) can be arranged with various components or components in a variety of ways. In a 5G NR system or network, a network node, a network entity, a mobility element of a network, a RAN node, a core network node, a network element or a network equipment (such as a base station or one or more units (or one or more components) that perform base station functionality can be implemented in an aggregated or decomposed architecture. For example, a base station (such as a node B (NB), an evolved NB (eNB), an NR base station, a 5GNB, an access point (AP), a transmit receive point (TRP) or a cell, etc.) can be implemented as an aggregated base station (also referred to as an independent base station or a single-chip base station) or a decomposed base station.

[0083] A converged base station may be configured to utilize a radio protocol stack physically or logically integrated within a single RAN node. A decomposed base station may be configured to utilize a protocol stack physically or logically distributed between two or more units, such as one or more central or centralized units (CUs), one or more distributed units (DUs), or one or more radio units (RUs). In some aspects, a CU may be implemented within a RAN node, and one or more DUs may be co-located with the CU, or alternatively, may be geographically or virtually distributed in one or more other RAN nodes. A DU may be implemented to communicate with one or more RUs. Each of a CU, a DU, and a 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).

[0084] Base station type operations or network designs may take into account the aggregated nature of base station functionality. For example, a disaggregated base station may be used in an integrated access backhaul (IAB) network, an open radio access network (O-RAN (network configurations such as those initiated by the O-RAN Alliance)), or a virtualized radio access network (vRAN, also known as a cloud radio access network (C-RAN)). Decomposition may include distributing functions across two or more units at various physical locations, as well as virtually distributing the functions of at least one unit, which may enable flexibility in network design. Various units of a disaggregated base station or disaggregated RAN architecture may be configured for wired or wireless communication with at least one other unit.

[0085] Figure 2C An example disaggregated base station architecture 250 is illustrated in accordance with aspects of the present disclosure. The disaggregated base station architecture 250 may include one or more central units (CUs) 280 (e.g., gNB-CUs 226), which may communicate directly with a core network 267 (e.g., 5GC 210, 5GC 260) via a backhaul link, or indirectly with a core network 267 through one or more disaggregated base station units, such as a near real-time (near-RT) RAN intelligent controller (RIC) 259 via an E2 link or a non-real-time (non-RT) RIC 257 associated with a service management and orchestration (SMO) framework 255, or both. The CU 280 may communicate with one or more distributed units (DUs) 285 (e.g., gNB-DUs 228) via respective midhaul links (e.g., F1 interfaces). The DUs 285 may communicate with one or more radio units (RUs) 287 (e.g., gNB-RUs 229) via respective fronthaul links. The RU 287 can communicate with a corresponding UE 204 via one or more radio frequency (RF) access links. In some implementations, a UE 204 can be served by multiple RUs 287 simultaneously.

[0086] Each of these units (i.e., CU 280, DU 285, RU 287, and near-RT RIC 259, non-RT RIC 257, and SMO framework 255) may include or be coupled to one or more interfaces configured to receive or send signals, data, or information (collectively, signals) via a wired or wireless transmission medium. Each of the units or an associated processor or controller that provides instructions to the communication interface of these units may be configured to communicate with one or more of the other units via a transmission medium. For example, these units may include a wired interface configured to receive or send signals to one or more of the other units via a wired transmission medium. Additionally, the unit may include a wireless interface that may include a receiver, transmitter, or transceiver (such as a radio frequency (RF) transceiver) that is configured to receive or send signals, or both, to one or more of the other units via a wireless transmission medium.

[0087] In some aspects, CU 280 may host one or more higher layer control functions. Such control functions may include radio resource control (RRC), packet data convergence protocol (PDCP), service data adaptation protocol (SDAP), etc. Each control function may be implemented using an interface that is configured to communicate signals with other control functions hosted by CU 280. CU 280 may be configured to handle user plane functionality (i.e., central unit-user plane (CU-UP)), control plane functionality (i.e., central unit-control plane (CU-CP)), or a combination thereof. In some specific implementations, CU 280 may be logically split into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, the CU-UP unit may communicate bidirectionally with the CU-CP unit via an interface (such as an E1 interface). As needed, CU 280 may be implemented to communicate with DU 285 for network control and signaling.

[0088] DU 285 may correspond to a logical unit that includes one or more base station functions for controlling the operation of one or more RUs 287. In some aspects, DU 285 may host one or more of a radio link control (RLC) layer, a medium access control (MAC) layer, and one or more high physical (PHY) layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation and demodulation, etc.) depending at least in part on a functional split such as defined by the Third Generation Partnership Project (3GPP). In some aspects, DU 285 may also host one or more low PHY layers. Each layer (or module) may be implemented using an interface that is configured to communicate signals with other layers (and modules) hosted by DU 285 or with control functions hosted by CU 280.

[0089] The lower layer functionality may be implemented by one or more RUs 287. In some deployments, a RU 287 controlled by a DU 285 may correspond to a logical node that hosts RF processing functions or low PHY layer functions (such as performing Fast Fourier Transform (FFT), Inverse FFT (iFFT), digital beamforming, physical random access channel (PRACH) extraction and filtering, etc.), or both, based at least in part on a functional split (such as a lower layer functional split). In such an architecture, the RU 287 may be implemented to handle over-the-air (OTA) communications with one or more UEs 204. In some implementations, real-time and non-real-time aspects of control plane and user plane communications with the RU 287 may be controlled by the corresponding DU 285. In some scenarios, this configuration may enable the implementation of the DU 285 and the CU 280 in a cloud-based RAN architecture (such as a vRAN architecture).

[0090] The SMO framework 255 may be configured to support RAN deployment and provisioning of non-virtualized network elements and virtualized network elements. For non-virtualized network elements, the SMO framework 255 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which may be managed via an operation and maintenance interface (such as an O1 interface). For virtualized network elements, the SMO framework 255 may be configured to interact with a cloud computing platform (such as an open cloud (O-Cloud) 269) to perform network element lifecycle management (such as to instantiate virtualized network elements) via a cloud computing platform interface (such as an O2 interface). Such virtualized network elements may include, but are not limited to, CU 280, DU 285, RU 287, and near-RT RIC 259. In some specific implementations, the SMO framework 255 may communicate with hardware aspects of the 4G RAN (such as an open eNB (O-eNB) 261) via the O1 interface. Additionally, in some specific implementations, the SMO framework 255 may communicate directly with one or more RUs 287 via the O1 interface. The SMO framework 255 may also include a non-RT RIC 257 configured to support the functionality of the SMO framework 255 .

[0091] 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 updating, or policy-based guidance of applications / features in the near-RT RIC 259. The non-RT RIC 257 may be coupled to or communicate with the near-RT RIC 259 (such as via an A1 interface). The near-RT RIC 259 may be configured to include logic functions that enable near-real-time control and optimization of RAN elements and resources via data collection and actions through an interface (such as via an E2 interface) that connects one or more CUs 280, one or more DUs 285, or both, and the O-eNB with the near-RT RIC 259.

[0092] In some implementations, in order to generate an AI / ML model to be deployed in the near-RT RIC 259, the non-RT RIC 257 may receive parameters or external enrichment information from an external server. Such information may be utilized by the near-RT RIC 259 and may be received from a non-network data source or from a network function at the SMO framework 255 or the non-RT RIC 257. In some examples, the non-RT RIC 257 or the near-RT RIC 259 may be configured to tune RAN behavior or performance. For example, the non-RT RIC 257 may monitor long-term trends and patterns of performance and employ AI / ML models to perform corrective actions through the SMO framework 255 (such as via reconfiguration of O1) or via the creation of RAN management policies (such as A1 policies).

[0093] Figure 3A , Figure 3B and Figure 3C Several example components (represented by corresponding blocks) are illustrated, which may be incorporated into 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 the location server 230 and the LMF 270, or alternatively may be independent of Figure 2A and Figure 2B The NG-RAN 220 and / or 5GC 210 / 260 infrastructure depicted, such as a dedicated network, are implemented to support operations as described herein. It should be understood that these components may be implemented in different types of devices with different specific implementations (e.g., in an ASIC, in a system on a chip (SoC), etc.). The illustrated components may also be incorporated into other devices in the communication system. For example, other devices in the system may include components similar to those described as providing similar functionality. Moreover, a given device may include one or more of these components. For example, a device may include multiple transceiver components that enable the device to operate on multiple carriers and / or communicate via different technologies.

[0094] UE 302 and base station 304 each include one or more wireless wide area network (WWAN) transceivers 310 and 350, respectively, which provide means (e.g., means for transmitting, means for receiving, means for measuring, means for tuning, means for preventing transmitting, etc.) for communicating via one or more wireless communication networks (not shown), such as NR networks, LTE networks, GSM networks, etc. 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.) via at least one designated RAT (e.g., NR, LTE, GSM, etc.) on a wireless communication medium of interest (e.g., a set of time / frequency resources in a particular spectrum). The WWAN transceivers 310 and 350 can be configured in different ways to transmit and encode signals 318 and 358 (e.g., messages, indications, information, etc.) according to the specified RAT, and conversely, receive and decode signals 318 and 358 (e.g., messages, indications, information, pilots, etc.) respectively. Specifically, the WWAN transceivers 310 and 350 include: one or more transmitters 314 and 354 for transmitting and decoding the signals 318 and 358, respectively, and one or more receivers 312 and 352 for receiving and decoding the signals 318 and 358, respectively.

[0095] At least in some cases, the UE 302 and the base station 304 each further include one or more short-range wireless transceivers 320 and 360, respectively. The short-range wireless transceivers 320 and 360 can be connected to one or more antennas 326 and 366, respectively, and provide for communicating over the wireless communication medium of interest via at least one designated RAT (e.g., WiFi, LTE-D, The short-range wireless transceivers 320 and 360 are components (e.g., components for sending, components for receiving, components for measuring, components for tuning, components for preventing sending, etc.) for communicating with other network nodes (such as other UEs, access points, base stations, etc.) using a PC5, dedicated short-range communication (DSRC), wireless access for vehicle environments (WAVE), near field communication (NFC), ultra-wideband (UWB), etc.). The short-range wireless transceivers 320 and 360 can be configured in different ways to send and encode signals 328 and 368 (e.g., messages, indications, information, etc.) according to a specified RAT, and conversely, receive and decode signals 328 and 368 (e.g., messages, indications, information, pilots, etc.). Specifically, the short-range wireless transceivers 320 and 360 include: one or more transmitters 324 and 364 for sending and decoding signals 328 and 368, respectively, and one or more receivers 322 and 362 for receiving and decoding signals 328 and 368, respectively. As a specific example, the short-range wireless transceivers 320 and 360 may be WiFi transceivers, Transceiver, and / or transceiver, NFC transceiver, UWB transceiver or vehicle-to-vehicle (V2V) and / or vehicle-to-everything (V2X) transceiver.

[0096] At least in some cases, the UE 302 and the base station 304 also include satellite signal receivers 330 and 370. The satellite signal receivers 330 and 370 may be connected to one or more antennas 336 and 376, respectively, and may provide components for receiving and / or measuring satellite positioning / communication signals 338 and 378, respectively. In the case where the satellite signal receivers 330 and 370 are satellite positioning system receivers, the satellite positioning / communication signals 338 and 378 may be global positioning system (GPS) signals, global navigation satellite system (GLONASS) signals, Galileo signals, Beidou signals, Indian regional navigation satellite system (NAVIC), Quasi-Zenith Satellite System (QZSS), etc. In the case where the satellite signal receivers 330 and 370 are non-terrestrial network (NTN) receivers, the satellite positioning / communication signals 338 and 378 may be communication signals (e.g., carrying control and / or user data) originating from a 5G network. The satellite signal receivers 330 and 370 may include any suitable hardware and / or software for receiving and processing satellite positioning / communication signals 338 and 378, respectively. Satellite signal receivers 330 and 370 may request appropriate information and operations from other systems and, at least in some cases, perform calculations using measurements obtained by any suitable satellite positioning system algorithm to determine the positions of UE 302 and base station 304, respectively.

[0097] The base station 304 and the network entity 306 each include one or more network transceivers 380 and 390, respectively, which provide components (e.g., components for sending, components for receiving, etc.) for communicating with other network entities (e.g., other base stations 304, other network entities 306). For example, the base station 304 may employ one or more network transceivers 380 to communicate with other base stations 304 or network entities 306 through one or more wired or wireless backhaul links. For another example, the network entity 306 may employ one or more network transceivers 390 to communicate with one or more base stations 304 through one or more wired or wireless backhaul links, or communicate with other network entities 306 through one or more wired or wireless core network interfaces.

[0098] The transceiver may be configured to communicate over a wired or wireless link. A transceiver (whether a wired transceiver or a wireless transceiver) includes a transmitter circuit (e.g., transmitters 314, 324, 354, 364) and a receiver circuit (e.g., receivers 312, 322, 352, 362). In some implementations, the transceiver may be an integrated device (e.g., implementing the transmitter circuit and the receiver circuit in a single device), in some implementations may include separate transmitter circuits and separate receiver circuits, or in other implementations may be implemented in other ways. The transmitter circuit and the receiver circuit 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 antenna arrays, which allow the corresponding device (e.g., UE 302, base station 304) to perform transmit "beamforming", as described herein. Similarly, the wireless receiver circuitry (e.g., receivers 312, 322, 352, 362) may include or be coupled to multiple antennas (e.g., antennas 316, 326, 356, 366), such as antenna arrays, which allow the corresponding device (e.g., UE 302, base station 304) to perform receive beamforming, as described herein. In one aspect, the transmitter circuitry and the receiver circuitry may share the same multiple antennas (e.g., antennas 316, 326, 356, 366) so that the corresponding device can only receive or only transmit at a given time, rather than both receive and transmit at the same time. The wireless transceivers (eg, WWAN transceivers 310 and 350, short-range wireless transceivers 320 and 360) may also include a network listening module (NLM) or the like for performing various measurements.

[0099] As used herein, various wireless transceivers (e.g., transceivers 310, 320, 350, and 360 in some implementations, and network transceivers 380 and 390) 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 based on the type of communication being performed. For example, backhaul communications between network devices or servers typically involve signaling via a wired transceiver, while wireless communications between a UE (e.g., UE 302) and a base station (e.g., base station 304) will typically involve signaling via a wireless transceiver.

[0100] UE 302, base station 304, and network entity 306 also include other components that can be used in conjunction with the operations disclosed herein. UE 302, base station 304, and network entity 306 include one or more processors 332, 384, and 394, respectively, for providing functionality related to, for example, wireless communication, and for providing other processing functionality. Thus, processors 332, 384, and 394 may provide means for processing, such as means for determining, means for calculating, means for receiving, means for sending, means for indicating, etc. In one aspect, 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.

[0101] UE 302, base station 304, and network entity 306 include memory circuitry implementing memory 340, 386, and 396 (e.g., each including a memory device) for maintaining information (e.g., information indicating reserved resources, thresholds, parameters, etc.). Thus, memory 340, 386, and 396 may provide means for storing, means for retrieving, means for maintaining, etc. In some cases, UE 302, base station 304, and network entity 306 may include positioning components 342, 388, and 398, respectively. Positioning components 342, 388, and 398 may be hardware circuits that are part of or coupled to processors 332, 384, and 394, respectively, which when executed cause UE 302, base station 304, and network entity 306 to perform the functionality described herein. In other aspects, positioning components 342, 388, and 398 may be external to 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 can be memory modules stored in the memories 340, 386 and 396, respectively, which, when executed by the processors 332, 384 and 394 (or a modem processing system, another processing system, etc.), cause the UE 302, the base station 304 and the network entity 306 to perform the functionality described herein. Figure 3A Possible locations for a location component 342 are illustrated, which may be, for example, part of one or more WWAN transceivers 310, memory 340, one or more processors 332, or any combination thereof, or may be a standalone component. Figure 3B Possible locations for a location component 388 are illustrated, 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 standalone component. Figure 3C Possible locations for a location component 398 are illustrated, 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.

[0102] UE 302 may include one or more sensors 344 coupled to one or more processors 332 to provide a means for sensing or detecting movement and / or orientation information that is independent of motion data derived from signals received by one or more WWAN transceivers 310, one or more short-range wireless transceivers 320, and / or satellite signal receivers 330. By way of example, sensor 344 may include an accelerometer (e.g., a microelectromechanical system (MEMS) device), a gyroscope, a geomagnetic sensor (e.g., a compass), an altimeter (e.g., a barometric altimeter), and / or any other type of motion detection sensor. In addition, sensor 344 may include multiple different types of devices and combine their outputs to provide motion information. For example, sensor 344 may use a combination of a multi-axis accelerometer and an orientation sensor to provide the ability to calculate positioning in a two-dimensional (2D) and / or three-dimensional (3D) coordinate system.

[0103] In addition, the UE 302 includes a user interface 346, which provides means for providing indications to the user (e.g., audible and / or visual indications) and / or for receiving user input (e.g., when the user actuates 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 a user interface.

[0104] Referring to the one or more processors 384 in more detail, in a downlink, IP packets from the network entity 306 may be provided to the processor 384. The one or more processors 384 may implement 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. One or more processors 384 may provide: RRC layer functionality associated with broadcasting of system information (e.g., master information block (MIB), system information block (SIB)), RRC connection control (e.g., RRC connection paging, RRC connection establishment, RRC connection modification, and RRC connection release), inter-RAT mobility, and measurement configuration for UE measurement reporting; PDCP layer functionality associated with header compression / decompression, security (encryption, decryption, integrity protection, integrity verification), and handover support functions; RLC layer functionality associated with delivery of upper layer PDUs, error correction through automatic repeat request (ARQ), concatenation, segmentation and reassembly of RLC service data units (SDUs), re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, scheduling information reporting, error correction, priority handling, and logical channel prioritization.

[0105] The transmitter 354 and the receiver 352 may implement layer 1 (L1) functionality 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 to physical channels, modulation / demodulation of physical channels, and MIMO antenna processing. The transmitter 354 handles the mapping to the signal constellation 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 separated into parallel streams. Each stream may then be mapped to an orthogonal frequency division multiplexing (OFDM) subcarrier, multiplexed with a reference signal (e.g., a pilot) in the time and / or frequency domain, and then combined together using an inverse fast Fourier transform (IFFT) to produce a physical channel carrying a time domain OFDM symbol stream. The OFDM symbol stream is spatially pre-decoded to generate multiple spatial streams. Channel estimates from a channel estimator may be used to determine coding and modulation schemes and for spatial processing. Channel estimates may be derived from a reference signal and / or channel state feedback sent by the UE 302. Each spatial stream may then be provided to one or more different antennas 356. The transmitter 354 may modulate an RF carrier with a corresponding spatial stream for transmission.

[0106] At the UE 302, the receiver 312 receives the signal through its corresponding antenna 316. The receiver 312 recovers the information modulated onto the RF carrier and provides the information to one or more processors 332. The transmitter 314 and the receiver 312 implement layer 1 functionality associated with various signal processing functions. The receiver 312 can perform spatial processing on the information to recover any spatial streams destined for the UE 302. If the destination of multiple spatial streams is the UE 302, they can be combined into a single OFDM symbol stream by the receiver 312. 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 and the reference signal are recovered and demodulated by determining the most likely signal constellation point sent by the base station 304. These soft decisions can be based on channel estimates calculated by a channel estimator. The soft decisions are then decoded and deinterleaved to recover the data and control signals originally sent by the base station 304 on the physical channel. The data and control signals are then provided to one or more processors 332, which implement layer 3 (L3) and layer 2 (L2) functionality.

[0107] In the uplink, one or more processors 332 provide demultiplexing between transport and logical channels, packet reassembly, decryption, header decompression, and control signal processing to recover IP packets from the core network. One or more processors 332 are also responsible for error detection.

[0108] Similar to the functionality described in conjunction with downlink transmissions performed by the base station 304, the one or more processors 332 provide: RRC layer functionality associated with system information (e.g., MIB, SIB) acquisition, RRC connection, and measurement reporting; PDCP layer functionality associated with header compression / decompression and security (encryption, decryption, integrity protection, integrity verification); RLC layer functionality associated with delivery of upper layer PDUs, error correction through ARQ, concatenation, segmentation and reassembly of RLC SDUs, re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs onto transport blocks (TBs), demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction through hybrid automatic repeat request (HARQ), priority handling, and logical channel prioritization.

[0109] Channel estimates derived by the channel estimator from a reference signal or feedback sent by the base station 304 may be used by the transmitter 314 to select appropriate coding and modulation schemes and to facilitate spatial processing. The spatial streams generated by the transmitter 314 may be provided to different antennas 316. The transmitter 314 may modulate an RF carrier with a corresponding spatial stream for transmission.

[0110] The uplink transmissions are processed at the base station 304 in a manner similar to that described in conjunction with the receiver functionality at the UE 302. The receiver 352 receives the signal through its respective antenna 356. The receiver 352 recovers the information modulated onto the RF carrier and provides the information to one or more processors 384.

[0111] In the uplink, the one or more processors 384 provide demultiplexing between transport and logical channels, packet reassembly, decryption, header decompression, control signal processing to recover IP packets from the UE 302. The IP packets from the one or more processors 384 may be provided to the core network. The one or more processors 384 are also responsible for error detection.

[0112] For convenience, UE 302, base station 304 and / or network entity 306 may Figure 3A , Figure 3B and Figure 3C, is shown as including various components that can be configured according to the various examples described herein. However, it should be understood that the illustrated components may have different functionality in different designs. Specifically, FIG. 3A to FIG. 3C Various components in are optional in alternative configurations, and various aspects include configurations that may vary due to design choice, cost, use of the device, or other considerations. For example, in Figure 3A In the case of a wearable device or a tablet computer or a PC or a laptop computer, a specific implementation of the UE 302 may omit the WWAN transceiver 310 (e.g., a wearable device or a tablet computer or a PC or a laptop computer may have Wi-Fi and / or Bluetooth capabilities but no cellular capabilities), may omit the short-range wireless transceiver 320 (e.g., only cellular, etc.), may omit the satellite signal receiver 330, may omit the sensor 344, etc. In another example, in Figure 3B In the case of a wireless LAN, a specific implementation of the base station 304 may omit the WWAN transceiver 350 (e.g., a Wi-Fi "hotspot" access point without cellular capabilities), or may omit the short-range wireless transceiver 360 (e.g., cellular only, etc.), or may omit the satellite signal receiver 370, etc. For the sake of brevity, illustrations of various alternative configurations are not provided herein, but will be readily apparent to those skilled in the art.

[0113] 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 for the UE 302, base station 304, and network entity 306, respectively. For example, where different logical entities are embodied in the same device (e.g., gNB and location server functionality incorporated into the same base station 304), the data buses 334, 382, ​​and 392 may provide for communication between the different logical entities.

[0114] Figure 3A , Figure 3B and Figure 3C The components of can be implemented in various ways. In some specific implementations, Figure 3A , Figure 3B and Figure 3CThe components may be implemented in one or more circuits, such as, for example, one or more processors and / or one or more ASICs (which may include one or more processors). Here, each circuit may use and / or be combined with at least one memory component to store information or executable code used by the circuit to provide the functionality. For example, some or all of the functionality represented by blocks 310 to 346 may be implemented by the processor and memory components of UE 302 (e.g., by executing appropriate code and / or by appropriate configuration of the processor components). Similarly, some or all of the functionality represented by blocks 350 to 388 may be implemented by the processor and memory components of base station 304 (e.g., by executing appropriate code and / or by appropriate configuration of the processor components). Moreover, some or all of the functionality represented by blocks 390 to 398 may be implemented by the processor and memory components of network entity 306 (e.g., by executing appropriate code and / or by appropriate configuration of the processor components). For simplicity, various operations, actions and / or functions are described herein as being performed by "UE", "by base station", "by network entity", etc. However, as will be understood, such operations, actions and / or functions may actually be performed by specific components or combinations of components of the UE302, base station 304, network entity 306, etc. (such as processors 332, 384, 394, transceivers 310, 320, 350 and 360, memories 340, 386 and 396, positioning components 342, 388 and 398, etc.).

[0115] In some designs, the network entity 306 may be implemented as a core network component. In other designs, the network entity 306 may be operated separately from a network operator or a cellular network infrastructure (e.g., NG RAN 220 and / or 5GC 210 / 260). For example, the network entity 306 may be a component of a dedicated 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).

[0116] NR supports a variety of cellular network-based positioning technologies, 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. Figure 4Examples of various positioning methods according to various aspects of the present disclosure are illustrated. In the OTDOA or DL-TDOA positioning procedure illustrated in scenario 410, the UE measures the difference between the arrival times (ToA) of reference signals (e.g., positioning reference signals (PRS)) received from paired base stations (referred to as reference signal time difference (RSTD) or arrival time difference (TDOA) measurements), and reports these differences 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 auxiliary data. The UE then measures the RSTD between the reference base station and each non-reference base station. Based on the known positions of the base stations involved and the RSTD measurement results, a positioning entity (e.g., a UE for UE-based positioning or a location server for UE-assisted positioning) can estimate the position of the UE.

[0117] For DL-AoD positioning illustrated in scenario 420, the positioning entity uses measurement reports from the UE regarding received signal strength measurements of multiple downlink transmit beams to determine the angle between the UE and the transmitting base station. The positioning entity can then estimate the position of the UE based on the determined angle and the known position of the transmitting base station.

[0118] Uplink-based positioning methods include uplink time difference of arrival (UL-TDOA) and uplink angle of arrival (UL-AoA). UL-TDOA is similar to DL-TDOA, but is based on uplink reference signals (e.g., sounding reference signals (SRS)) sent by the UE to multiple base stations. Specifically, the UE sends one or more uplink reference signals, which are measured by a reference base station and multiple non-reference base stations. Each base station then reports the reception time of the reference signal (referred to as relative time of arrival (RTOA)) to a positioning entity (e.g., a location server) that knows the location and relative timing of the base stations involved. Based on the receive-to-receive (Rx-Rx) time difference between the reported RTOA of the reference base station and the reported RTOA of each non-reference base station, the known locations of the base stations, and their known timing offsets, the positioning entity can use TDOA to estimate the position of the UE.

[0119] 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 angles of the receive beams to determine the angle between the UE and the base station. Based on the determined angle and the known location of the base station, the positioning entity can then estimate the location of the UE.

[0120] Downlink and uplink based positioning methods include: Enhanced Cell ID (E-CID) positioning and multiple round trip time (RTT) positioning (also referred to as "multi-cell RTT" and "multi-RTT"). In the RTT procedure, a first entity (e.g., a base station or UE) sends a first RTT-related signal (e.g., a PRS or SRS) to a second entity (e.g., a UE or a base station), and the second entity sends a second RTT-related signal (e.g., an SRS or PRS) back to the first entity. Each entity measures the time difference between the arrival time (ToA) of the received RTT-related signal and the transmission time of the transmitted RTT-related signal. The time difference is called the received-to-transmit (Rx-Tx) time difference. The Rx-Tx time difference measurement can be made or adjusted to include only the time difference between the nearest time slot boundary of the received signal and the transmitted signal. The two entities may then transmit their Rx-Tx time difference measurements to a location server (e.g., LMF 270), which calculates the round trip propagation time (i.e., RTT) between the two entities based on the two Rx-Tx time difference measurements (e.g., calculated as the sum of the two Rx-Tx time difference measurements). Alternatively, one entity may transmit its Rx-Tx time difference measurements to the other entity, which then calculates the RTT. The distance between the two entities may be determined based on the RTT and a known signal speed (e.g., the speed of light). For multi-RTT positioning illustrated in scenario 430, a first entity (e.g., a UE or a base station) performs an RTT positioning procedure with a plurality of second entities (e.g., a plurality of base stations or UEs) to enable the position of the first entity to be determined (e.g., using multilateration) based on the distance to the second entities and the known positions of the second entities. RTT and multi-RTT methods may be combined with other positioning techniques (such as UL-AoA and DL-AoD) to improve position accuracy, as illustrated in scenario 440.

[0121] The E-CID positioning method is based on radio resource management (RRM) measurements. In E-CID, the UE reports the serving cell ID, timing advance (TA), and the identifiers of the detected neighboring base stations, the estimated timing, and the signal strength. The UE's position is then estimated based on this information and the known locations of the base stations.

[0122] To assist the positioning operation, a location server (e.g., location server 230, LMF 270, SLP 272) may provide assistance data to the UE. For example, the assistance data may include: an identifier of the base station (or cell / TRP of the base station) from which the reference signal is measured, reference signal configuration parameters (e.g., the number of consecutive time slots including PRS, the periodicity of consecutive time slots including PRS, a silent 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 originate directly from the base station itself (e.g., in a periodically broadcast overhead message, etc.). In some cases, the UE itself may be able to detect neighboring network nodes without using assistance data.

[0123] In the case of OTDOA or DL-TDOA positioning procedures, the assistance data may also include an expected RSTD value and an associated uncertainty or search window around the expected RSTD. In some cases, the value range of the expected RSTD may be + / -500 microseconds (μs). In some cases, when any of the resources used for positioning measurements are in FR1, the value range of the uncertainty of the expected RSTD may be + / -32μs. In other cases, when all resources used for positioning measurements are in FR2, the value range of the uncertainty of the expected RSTD may be + / -8μs.

[0124] The position estimate may be referred to by other names, such as a position estimate, a position, a fix, a position fix, a fix, etc. The position estimate may be geodetic and include coordinates (e.g., latitude, longitude, and possibly altitude), or may be municipal and include a street address, a postal address, or some other verbal description of the location. The position estimate may be further defined relative to some other known location or in absolute terms (e.g., using latitude, longitude, and possibly altitude). The position estimate may include an expected error or uncertainty (e.g., by including an area or volume within which the location is expected to be included with some specified or default confidence level).

[0125] Various frame structures may be used to support downlink and uplink transmissions between network nodes (eg, base stations and UEs). Figure 5 5 is a diagram illustrating an example frame structure according to aspects of the present 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.

[0126] LTE (and in some cases NR) utilizes 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 divide the system bandwidth into multiple (K) orthogonal subcarriers, which are also often called frequency tones, frequency bins, etc. Each subcarrier can be modulated with data. In general, modulation symbols are transmitted in the frequency domain using OFDM and in the time domain using SC-FDM. The spacing between adjacent subcarriers can be fixed, and the total number of subcarriers (K) can depend on the system bandwidth. For example, the spacing of the subcarriers can be 15 kilohertz (kHz), and the minimum resource allocation (resource block) can be 12 subcarriers (or 180kHz). Thus, for a system bandwidth of 1.25 megahertz (MHz), 2.5 MHz, 5 MHz, 10 MHz, or 20 MHz, the nominal fast Fourier transform (FFT) size may be equal to 128, 256, 512, 1024, or 2048, respectively. The system bandwidth may also be divided into multiple 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 MHz, 2.5 MHz, 5 MHz, 10 MHz, or 20 MHz, respectively.

[0127] LTE supports a single parameter set (subcarrier spacing (SCS), symbol length, etc.). In contrast, NR may support multiple parameter sets (μ), for example, 15kHz (μ=0), 30kHz (μ=1), 60kHz (μ=2), 120kHz (μ=3), and 240kHz (μ=4) or larger subcarrier spacings may be available. In each subcarrier spacing, there are 14 symbols per slot. For 15kHz SCS (μ=0), there is one slot per subframe, 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 a 4K FFT size is 50. For 30kHz SCS (μ=1), there are two slots per subframe, 20 slots per frame, the slot duration is 0.5ms, the symbol duration is 33.3μs, and the maximum nominal system bandwidth (in MHz) with a 4K FFT size is 100. For 60kHz SCS (μ=2), there are four slots per subframe, 40 slots per frame, the slot duration is 0.25ms, the symbol duration is 16.7μs, and the maximum nominal system bandwidth (in MHz) with a 4K FFT size is 200. For 120kHz SCS (μ=3), there are eight slots per subframe, 80 slots per frame, the slot duration is 0.125ms, the symbol duration is 8.33μs, and the maximum nominal system bandwidth (in MHz) with a 4K FFT size is 400. For 240kHz SCS (μ=4), there are 16 slots per subframe, 160 slots per frame, the slot duration is 0.0625ms, the symbol duration is 4.17μs, and the maximum nominal system bandwidth (in MHz) is 800 with 4K FFT size.

[0128] exist Figure 5 In the example of , a parameter set of 15 kHz is used. Therefore, in the time domain, a 10 ms frame is divided into 10 equally sized subframes, each subframe is 1 ms, and each subframe includes one time slot. Figure 5 , time is represented horizontally (on the X-axis), where time increases from left to right, while frequency is represented vertically (on the Y-axis), where frequency increases (or decreases) from bottom to top.

[0129] A resource grid may be used to represent a time slot, each of which includes one or more time-concurrent resource blocks (RBs) (also referred to as physical RBs (PRBs)) in the frequency domain. The resource grid is further divided into a plurality of resource elements (REs). An RE may correspond to one symbol length in the time domain and one subcarrier in the frequency domain. Figure 5In the parameter set of , for a normal cyclic prefix, an RB may contain 12 consecutive subcarriers in the frequency domain and seven consecutive symbols in the time domain, for a total of 84 REs. For an extended cyclic prefix, an RB may contain 12 consecutive subcarriers in the frequency domain and six consecutive symbols in the time domain, for a total of 72 REs. The number of bits carried by each RE depends on the modulation scheme.

[0130] Some REs may carry reference (pilot) signals (RS). These 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 (SSB), sounding reference signals (SRS), etc., depending on whether the illustrated frame structure is used for uplink communication or downlink communication. Figure 5 Example locations of REs carrying reference signals (labeled "R") are illustrated.

[0131] A set of resource elements (REs) used for transmission of PRS is referred to as a "PRS resource". A set of resource elements may span multiple PRBs in the frequency domain and "N" (such as 1 or more) consecutive symbols 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.

[0132] The transmission of PRS resources within a given PRB has a specific comb size (also referred to as "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", the PRS is transmitted in every Nth subcarrier of one symbol of the PRB. For example, for comb-4, for each symbol of the PRS resource configuration, the RE corresponding to every fourth subcarrier (such as subcarriers 0, 4, 8) is used to transmit the PRS of the PRS resource. Currently, for DL-PRS, comb sizes of comb-2, comb-4, comb-6, and comb-12 are supported. Figure 5 An example PRS resource configuration for comb-4 (which spans four symbols) is illustrated. That is, the positions of shaded REs (labeled "R") indicate a comb-4 PRS resource configuration.

[0133] Currently, DL-PRS resources can span 2, 4, 6, or 12 consecutive symbols in a slot using a full frequency domain staggered pattern. DL-PRS resources can be configured in any downlink or flexible (FL) symbol configured by higher layers in a slot. There may be a constant energy per resource element (EPRE) for all REs of a given DL-PRS resource. The following are the symbol-by-symbol frequency offsets for comb sizes 2, 4, 6, and 12 on 2, 4, 6, and 12 symbols. 2-symbol Comb-2: {0,1}; 4-symbol Comb-2: {0,1,0,1}; 6-symbol Comb-2: {0,1,0,1,0,1}; 12-symbol Comb-2: {0,1,0,1,0,1,0,1,0,1,0,1}; 4-symbol Comb-4: {0,2,1,3} (as in Figure 5 in the example); 12-symbol comb-4: {0,2,1,3,0,2,1,3,0,2,1,3}; 6-symbol comb-6: {0,3,1,4,2,5}; 12-symbol comb-6: {0,3,1,4,2,5,0,3,1,4,2,5}; and 12-symbol comb-12: {0,6,3,9,1,7,4,10,2,8,5,11}.

[0134] A "PRS resource set" is a set of PRS resources used to send 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 the TRP ID). In addition, the PRS resources in a PRS resource set have the same periodicity, a common muting pattern configuration, and the same repetition factor (such as "PRS-ResourceRepetitionFactor") across time slots. The periodicity is the time from the first setting 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 may have a length selected from: 2^μ*{4,5,8,10,16,20,32,40,64,80,160,320,640,1280,2560,5120,10240} time slots, where μ=0,1,2,3. The repetition factor may have a length selected from {1, 2, 4, 6, 8, 16, 32} slots.

[0135] A PRS resource ID in a PRS resource set is associated with a single beam (or beam ID) transmitted from a single TRP (where one TRP may transmit one or more beams). That is, each PRS resource in a PRS resource set may be transmitted on a different beam, and therefore, a "PRS resource" (or simply "resource") may also be referred to as a "beam". Note that this does not have any implication on whether the UE knows the TRP and beam on which the PRS is transmitted.

[0136] A "PRS instance" or "PRS opportunity" is an instance of a periodically repeated time window (such as a group of one or more consecutive time slots) in which a PRS is expected to be sent. A PRS opportunity may also be referred to as a "PRS positioning opportunity", "PRS positioning instance", "positioning opportunity", "positioning instance", "positioning repetition", or simply "opportunity", "instance", or "repetition".

[0137] A "positioning frequency layer" (also referred to simply as a "frequency layer") is a set of one or more PRS resource sets with the same values ​​for certain parameters across one or more TRPs. Specifically, the set of PRS resource sets has the same subcarrier spacing and cyclic prefix (CP) type (meaning that all parameter sets supported for the physical downlink shared channel (PDSCH) are also supported by 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") and is an identifier / code that specifies a pair of physical radio channels for transmission and reception. The downlink PRS bandwidth can have a granularity of four PRBs, and the minimum value is 24 PRBs and the maximum value is 272 PRBs. Currently, up to four frequency layers have been defined, and up to two PRS resource sets can be configured per TRP per frequency layer.

[0138] The concept of frequency layer is somewhat similar to the concept of component carrier and bandwidth part (BWP), but the difference is that component carrier and BWP are used by one base station (or macro cell base station and small cell base station) to send data channels, while frequency layer is used by several (usually three or more) base stations to send PRS. The UE can indicate the number of frequency layers that the UE can support when the UE transmits its positioning capabilities to the network (such as during an LTE Positioning Protocol (LPP) session). For example, the UE can indicate whether it can support one or four positioning frequency layers.

[0139] 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, as used herein, the terms "positioning reference signal" and "PRS" may also refer to any type of reference signal that can be used for positioning, such as but not limited to: PRS, TRS, PTRS, CRS, CSI-RS, DMRS, PSS, SSS, SSB, SRS, UL-PRS, etc. as defined in LTE and NR. In addition, the terms "positioning reference signal" and "PRS" may refer to a downlink positioning reference signal, an uplink positioning reference signal, or a sidelink positioning reference signal, unless otherwise indicated by the context. If further distinction is needed between the types of PRS, the downlink positioning reference signal may be referred to as a "DL-PRS", the uplink positioning reference signal (e.g., an SRS used for positioning, i.e., PTRS) may be referred to as a "UL-PRS", and the sidelink positioning reference signal may be referred to as a "SL-PRS". In addition, for signals (e.g., DMRS) that can be sent in the downlink, uplink, and / or sidelink, these signals may be preceded by "DL," "UL," or "SL" to distinguish the direction. For example, "UL-DMRS" may be different from "DL-DMRS."

[0140] Figure 6 600 is a diagram representing a channel estimate of a multipath channel between a receiving device (e.g., any of the UEs or base stations described herein) and a transmitter device (e.g., any other of the UEs or base stations described herein) according to aspects of the present disclosure. The channel estimate represents the strength of a radio frequency (RF) signal (e.g., a PRS) received through a multipath channel as a function of time delay and may be referred to as a channel energy response (CER), a channel impulse response (CIR), or a power delay profile (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). Note that a multipath channel is a channel between a transmitter and a receiver over which an RF signal follows multiple paths or multipaths due to the transmission of the RF signal on multiple beams and / or due to the propagation characteristics of the RF signal (e.g., reflection, refraction, etc.).

[0141] exist Figure 6 In the example of , the receiver detects / measures multiple (four) clusters of channel taps. Each channel tap represents the multipath that the RF signal follows between the transmitter and the receiver. That is, the channel taps represent the arrival of the RF signal on the multipath. Each cluster of channel taps indicates that the corresponding multipath follows substantially the same path. Different clusters may exist because the RF signal is transmitted on different transmit beams (and therefore at different angles), or because of the propagation characteristics of the RF signal (e.g., may follow different paths due to reflections), or both.

[0142] All clusters of channel taps for a given RF signal represent the multipath channel (or simply channel) between the transmitter and the receiver. Figure 6 Under the illustrated channel, the receiver receives a first cluster of two RF signals on the channel taps at time T1, a second cluster of five RF signals on the channel taps at time T2, a third cluster of five RF signals on the channel taps at time T3, and a fourth cluster of four RF signals on the channel taps at time T4. Figure 6 In the example of , since the first cluster of RF signals at time T1 arrives first, it is assumed to correspond to RF signals transmitted on a transmit beam aligned with line of sight (LOS) or the shortest path. The third cluster at time T3 includes the strongest RF signals and may correspond to, for example, RF signals transmitted on a transmit beam aligned with a non-line of sight (NLOS) path. Note that although Figure 6 Clusters of two to five channel taps are illustrated, but it should be understood that a cluster may have more or less channel taps than the illustrated number of channel taps.

[0143] Machine learning (ML) or other techniques may be used to generate models that may be used to facilitate various aspects associated with data processing. One particular application of ML involves generating a positioning model for processing a reference signal (e.g., a PRS) used for positioning, such as feature extraction, reporting of reference signal measurements (e.g., selecting which extracted features to report), etc.

[0144] ML models are generally categorized as supervised or unsupervised. Supervised models can be further subdivided into regression models or classification models. Supervised learning involves learning a function that maps inputs to outputs based on example input-output pairs. For example, given a training data set with two variables, age (input) and height (output), a supervised learning model can be generated that predicts a person's height based on their age. In a regression model, the output is continuous. An example of a regression model is linear regression, which simply tries to find the line that best fits the data. Extensions of linear regression include multiple linear regression (e.g., finding the plane of best fit) and polynomial regression (e.g., finding the curve of best fit).

[0145] Another example of an ML 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 a leaf node (i.e., a node that has no additional child nodes) at the bottom of the decision tree. In general, a higher number of nodes in a decision tree model is associated with a higher decision accuracy.

[0146] Another example of an ML model is a decision forest. Random forest is an ensemble learning technique built on top of 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 the mode of all predictions of each decision tree. By relying on a "majority decision" model, the risk of error from a single tree is reduced.

[0147] Another example of an ML model is a neural network (NN). A neural network is essentially a network of mathematical equations. A neural network accepts one or more input variables and produces one or more output variables by passing through the network of equations. In other words, a neural network receives a vector of inputs and returns a vector of outputs.

[0148] Figure 7 An example neural network 700 in accordance with aspects of the present disclosure is illustrated. The neural network 700 includes an input layer "i" that receives "n" (one or more) inputs (illustrated as "input 1", "input 2", and "input n"), one or more hidden layers (illustrated 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 as "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 a linear function and / or an activation function, with the nodes of each successive hidden layer (illustrated as circles) processing the linear function and / or activation function of the nodes from the previous hidden layer.

[0149] In a classification model, the output is discrete. An example of a classification model is logistic regression. Logistic regression is similar to linear regression, but is used to model the probability of a limited number of results (usually two). In essence, 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 categories of data, a support vector machine will find a hyperplane or boundary between the two categories of data that maximizes the margin between the two categories. There are many planes that can separate the two categories, but only one plane can maximize the margin or distance between these categories. Another example of a classification model is naive Bayes based on Bayes' theorem. Other examples of classification models include decision trees, random forests, and neural networks, which are similar to the above examples, except that the output is discrete rather than continuous.

[0150] Unlike supervised learning, unsupervised learning is used to draw inferences and find patterns based on input data without reference to labeled results. Two examples of unsupervised learning models include clustering and dimensionality reduction.

[0151] Clustering is an unsupervised technique that involves grouping or clustering of data points. Clustering is often 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 obtaining a set of principal variables. In simpler terms, dimensionality reduction is the process of reducing the dimensionality of a feature set (or, in simpler terms, reducing the number of features). Most dimensionality reduction techniques can be categorized as feature elimination or feature extraction. An example of dimensionality reduction is called principal component analysis (PCA). In the simplest sense, PCA involves projecting higher dimensional data (e.g., three dimensions) into a smaller space (e.g., two dimensions). This produces data of lower dimensionality (e.g., two dimensions instead of three dimensions) while retaining all the original variables in the model.

[0152] Regardless of which ML model is used, at a high level, the ML 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 the training input data with an output data set (e.g., a set of possible or probable candidate locations for the various target UEs) to enable the same output data set to be determined later when provided with similar input data (e.g., from other target UEs at the same or similar locations).

[0153] NR supports RF fingerprint (RFFP) based positioning, which is a positioning and location determination technology that uses RFFP captured by a mobile device to determine the location of a mobile device. RFFP can be a received signal strength indicator (RSSI), CER, CIR, PDP, or a histogram of channel frequency response (CFR). RFFP can represent a single channel (e.g., PRS) received from a transmitter, all channels received from a specific transmitter, or all channels that can be detected at a receiver. The RFFP measured by a mobile device (e.g., UE) and the position of the transmitter associated with the measured RFFP (i.e., the transmitter that sends the RF signal measured by the mobile device to determine the RFFP) can be used to determine (e.g., triangulate) the position of the mobile device.

[0154] Model-based positioning techniques have been shown to provide superior positioning performance compared to classical positioning schemes. In ML-RFFP-based positioning, the ML model (e.g., neural network 700) takes the RFFP of the downlink reference signal (e.g., PRS) as input and outputs positioning measurements (e.g., ToA, RSTD) or mobile device positions corresponding to the input RFFP. The ML model (e.g., neural network 700) is trained using "true value" (i.e., known) positioning measurements or mobile device positions as reference (i.e., expected) outputs of the training set of RFFPs.

[0155] For example, an ML model may be trained to determine the RSTD measurements of a pair of TRPs from the RFFP of the PRS sent by the TRPs. The reference output for training such a model would be the correct (i.e., true value) RSTD measurements of the location of the mobile device when the mobile device obtained the RFFP measurements of the PRS. The network (e.g., a location server) may 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 reported by the mobile device (e.g., GPS measurements).

[0156] Figure 8 is a diagram 800 illustrating the use of an ML model for RFFP-based positioning in accordance with aspects of the present disclosure. Figure 8 In an example, during the "offline" phase, RFFPs (e.g., CER / CIR / CFR) captured by the mobile device are stored in a database. The database may be located at the mobile device or a network entity (e.g., a location server), and each RFFP may include measurements of an RF signal (or channel or link) transmitted by one or more transmitters that are connected to the mobile device during the "offline" phase. Figure 8 , BS1 to BS N). For UE-based downlink RFFP (DL-RFFP) positioning, the network (e.g., location server) configures the base station to send a downlink reference signal (e.g., PRS) to the mobile device, and the RFFP is the CER / CIR / CFR of the configured downlink reference signal detected by the mobile device. Although the present disclosure describes applying the ML positioning model to the RFFP measurement, it will be appreciated that other types of positioning models may be used in addition to or as an alternative to the ML positioning model based on the teachings of the present disclosure.

[0157] Each measured RFFP is associated with a known location of the mobile device when the mobile device measured the RFFP, the known location being Figure 8The location of the mobile device can be determined by, for example, the above reference Figure 4 It is known from another positioning technology discussed. Note that although Figure 8 The RFFP information for a single mobile device is illustrated, but it should be understood that the RFFP information for multiple mobile devices may be collected and stored in a database.

[0158] Based on the information captured during the offline phase, the ML 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, the training set of RFFP measurements is used as input to the ML model, and the known location of the mobile device when the RFFP is captured is used as a label. After training, during the "online" phase, the trained ML model can be used to predict (infer) the location of the mobile device (illustrated as "Pos M") based on the RFFP currently measured by the mobile device. For UE-based RFFP positioning, the network (e.g., a location server) provides the trained ML model to the mobile device. For UE-assisted positioning, the mobile device can provide the RFFP measurement results to the network for processing.

[0159] Note that although Figure 8 The use of an RFFP-based ML model to estimate the position of the UE is illustrated, but the output (or extracted features) of the ML model may alternatively be positioning measurements based on the input RFFP, such as RSTD measurements, ToA measurements, DL-AoD measurements, etc.

[0160] Fig. 9 900 is a diagram illustrating an example inference loop for UE-based DL-RFFP positioning according to aspects of the present disclosure. Fig. 9 As shown, the location server (e.g., LMF 270) configures the DL-PRS resources to be sent by one or more TRPs during a positioning session with the UE. The TRP then sends the configured DL-PRS to the UE, and the UE measures the RFFP of the DL-PRS.

[0161] exist Fig. 9 In the example of , the location server previously trained an ML model (labeled as “RFFPML”) for RFFP positioning, as described above with reference to Figure 7 and Figure 8 As discussed. The location server provides the ML model to the UE to perform inference during the positioning session (e.g., determine positioning measurements based on the measured RFFP). Therefore, after measuring the RFFP of the DL-PRS, the UE inputs the measured RFFP to the received ML model to obtain the associated positioning measurements (e.g., ToA, RSTD).

[0162] Fig.10 An example call flow 1000 for UE-based DL-RFFP positioning in accordance with aspects of the present disclosure is illustrated. At stage 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. At stage 2, the LMF 270 provides assistance information, such as the PRS resource configuration of the DL-PRS to be sent to the UE 204, to the serving ng-eNB / gNB 222 / 224 of the UE 204 and any neighboring ng-eNB / gNBs 222 / 224. At stage 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 the configuration of the DL-PRS sent by the involved ng-eNB / gNBs 222 / 224 and the ML model to be used for reporting positioning measurements of the DL-PRS.

[0163] At stage 4, the LMF 270 optionally provides assistance information to the involved ng-eNB / gNBs 222 / 224 via a New Radio Positioning Protocol Type A (NRPPa) message. At 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). At stage 6, the LMF 270 and the UE 204 perform an LPP Request / Provide Location Information procedure, during which the UE 204 provides positioning measurements taken on the DL-PRS transmitted by the ng-eNB / gNBs 222 / 224. The positioning measurements may be derived by applying the ML model received in the assistance data to the RFFP of the measured DL-PRS. This will be discussed in more detail below. Fig.10 The various stages are illustrated.

[0164] The bandwidth of the reference signal measured to obtain the RFFP measurement used in RFFP positioning affects the accuracy of the positioning parameters output by the positioning model to which the RFFP measurement is applied. To this end, a larger bandwidth reference signal provides a finer and more detailed feature space for RFFP positioning. Therefore, when compared to the RFFP measurement obtained using a lower bandwidth reference signal, the RFFP measurement using a larger bandwidth reference signal can be used to achieve positioning parameters with higher accuracy.

[0165] However, the UE may not always be able to obtain RFFP measurements from large bandwidth reference signals. Limited bandwidth processing may be due to resource limitations, where only small continuous bandwidth segments are available for reference signals used in positioning. Additionally or alternatively, the UE may be a low complexity UE device that is only capable of processing small bandwidth reference signals and may not be able to measure large bandwidth reference signals (e.g., a redcap UE cannot tune to a large bandwidth at a given time).

[0166] Certain aspects of the present disclosure relate to measuring multiple different bandwidth segments of a reference signal at corresponding multiple different times during a positioning session to obtain multiple RFFP measurements corresponding to the multiple different bandwidth segments. According to certain aspects of the present disclosure, a positioning model is applied to the aggregation of RFFP measurements obtained from multiple different bandwidth signals. According to other aspects of the present disclosure, a positioning model is applied to a potential feature representation of the RFFP measurements obtained from multiple different bandwidth signals. In each scenario, although the RFFP measurements are obtained from a smaller bandwidth reference signal, the positioning model is able to provide positioning parameters with higher accuracy than the accuracy that would have been obtained from the RFFP measurements obtained using a single smaller bandwidth reference signal.

[0167] Fig.11 11 is a diagram 1100 showing the measurement of different bandwidth segments of a reference signal at multiple reference signal occasions in accordance with various aspects of the present disclosure. In this example, the reference signal is sent over a continuous 100 MHz bandwidth. However, the UE may not be able to measure a reference signal with such a large bandwidth. Therefore, in accordance with certain aspects of the present disclosure, the UE measures five smaller bandwidth segments labeled BWS1 to BWS 5 at five different reference signal occasions labeled t1 to t5. In this example, bandwidth segment BWS1 is measured by the UE at reference signal occasion t1 to obtain RFFP measurement x1. Bandwidth segment BWS 2 is measured by the UE at reference signal occasion t2 to obtain RFFP measurement x2. Bandwidth segment BWS 3 is measured by the UE at reference signal occasion t3 to obtain RFFP measurement x3. Bandwidth segment BWS 4 is measured by the UE at reference signal occasion t4 to obtain RFFP measurement x4. Bandwidth segment BWS 5 is measured by the UE at reference signal occasion t5 to obtain RFFP measurement x5. Thus, in this example, the UE has obtained multiple RFFP measurements (x1 to x5) of reference signals over smaller bandwidth segments (BWS1 to BWS 5) of a larger bandwidth (100 MHz) reference signal over multiple reference signal opportunities.

[0168] Multiple RFFP measurements x1 to x5 may be processed in different ways to obtain measurement values ​​corresponding to a larger bandwidth reference signal. Subsequently, a positioning model is applied to the processed measurement values ​​to obtain positioning parameters associated with the position of the UE. According to certain aspects of the present disclosure, the RFFP measurements may be aggregated before being applied to a positioning model trained using aggregated RFFP measurements. According to certain aspects of the present disclosure, the RFFP measurements may be processed to obtain a latent feature representation of the RFFP measurements before being applied to a positioning model trained using the latent feature representation.

[0169] Fig.12 1200 is a diagram illustrating one way of processing a plurality of RFFP measurements x1 to x5 to obtain positioning parameters associated with the position of a UE according to aspects of the present disclosure. In this example, each of the plurality of RFFP measurements x1 to x5 may be stored at the UE, for example, in a storage buffer 1202 and applied to an aggregation operation 1204. The aggregation operation 1204 may aggregate the plurality of RFFP measurements Aggr(x1...x5) to obtain an aggregated measurement corresponding to the RFFP x1 to x5 obtained at reference signal opportunities t1 to t5.

[0170] The localization model 1206 defined by the ML operation fθ1 is applied to the aggregated measurements To obtain one or more positioning parameters 1208 corresponding to the location of the UE. According to aspects of the present disclosure, the positioning parameters may include 1) the location of the UE, 2) one or more positioning coordinates associated with the location of the UE, 3) one or more positioning measurements associated with the location of the UE, or 4) any combination thereof. In one aspect, the positioning model 1206 may use the tagged corresponding known positioning parameters. Worth training.

[0171] UE and network server can follow the following Fig.12 The RFFP positioning is performed in the manner shown to exchange various types of information. In one aspect, the UE may send a capability message to the network server indicating the bandwidth aggregation capability of the UE. The capability message may include: 1) an indication that the UE is capable of aggregating RFFP measurements from different bandwidth segments, 2) an indication of the bandwidth of the maximum bandwidth segment of the reference signal that the UE can measure during a single reference signal opportunity, 3) an indication of the number of bandwidth segment measurements that can be buffered at the UE, 4) an indication of one or more model identifiers of positioning models stored at the UE that support UE positioning using aggregated RFFP measurements, or 5) any combination thereof.

[0172] The UE may receive an RFFP positioning configuration from the UE for use during a positioning session. According to certain aspects of the present disclosure, the UE may receive from a network server an indication of: 1) a bandwidth size corresponding to each of a plurality of different bandwidth segments to be measured by the UE, 2) a number of bandwidth segments to be measured by the UE, 3) a maximum time gap allowable between measurements of consecutive bandwidth segments of the plurality of different bandwidth segments, or 4) any combination thereof. In scenarios where the UE is capable of performing RFFP positioning using other means of combining RFFP measurements, the UE may receive from the network server a specific indication that the UE is to use an aggregation technique to combine the RFFP measurements. In certain aspects, the type of aggregation technique may be specified based on the transmission received from the network server.

[0173] Fig.13 1300 is a diagram illustrating another way of processing multiple RFFP measurements x1 to x5 to obtain positioning parameters associated with the location of the UE according to aspects of the present disclosure. Fig.12 In contrast to the example shown, the RFFP measurements x1 to x5 are not provided as input to the aggregation operation. Instead, each RFFP measurement x1 to x5 is reduced to a corresponding latent feature representation z1 to z5 by applying the latent feature ML model 1302 defined by the ML operation fθ2a. Each latent feature representation z1 to z5 has a reduced dimension (reduced data size) compared to its corresponding RFFP measurement x1 to x5. In this example, each of the latent feature representations z1 to z5 may be stored at the UE, for example, in a storage buffer 1304. Assuming that the latent feature representations z1 to z5 have reduced dimensions when compared to their corresponding RFFP measurements x1 to x5, the storage capacity of the storage buffer 1304 may be less than the storage capacity that would otherwise be required if all RFFP measurements x1 to x5 were stored at the UE. When the UE has limited storage capacity, the reduction of RFFP measurements to corresponding latent feature representations may be used.

[0174] Fig.14 Several different latent feature ML models with different latent feature representation dimensions according to various aspects of the present disclosure are illustrated. In one aspect, each latent feature ML model 1402, 1404, and 1406 can operate as a feature compressor that accepts xi corresponding to the i-th bandwidth segment (e.g., BWS i) in the RFFP measurement and provides a corresponding latent feature representation of reduced dimension at its output. Here, the latent feature ML model 1402 is defined by an ML operation fθ2a that accepts an RFFP measurement xi with dimension dim(xi) = 800 samples (e.g., CIR samples) and provides a latent feature representation zi corresponding to the RFFP measurement xi at its output dim(zi) = 30 samples.

[0175] Latent feature ML models 1404 and 1406 also accept RFFP measurements xi having a dimension dim(xi) = 800 samples, but provide latent feature representations of different dimensions at their outputs. In this example, latent feature ML model 1402 is defined by an ML operation fθ2a, which reduces the dimension of the RFFP measurement xi from dim(xi) = 800 samples to a latent feature representation output z1 having a dimension of dim(zi) = 30 samples. Similarly, latent feature ML model 1404 is defined by an ML operation fθ'2a, which reduces the dimension of the RFFP measurement xi from dim(xi) = 800 samples to a latent feature representation output z'1 having a dimension of dim(z'i) = 100 samples. In addition, latent feature ML model 1406 is defined by an ML operation fθ"2a, which reduces the dimension of the RFFP measurement xi from dim(xi) = 800 samples to a latent feature representation output z"i having a dimension of dim(z"i) = 400 samples.

[0176] In an aspect, the UE may store different latent feature ML models having different latent feature representation dimensions. According to certain aspects of the present disclosure, the UE may report to a network server the UE's ability to use different latent feature ML models and receive assistance data from the network server indicating an identifier (e.g., model ID) of a latent feature ML model from among a plurality of available latent feature ML models to be used by the UE during a positioning session.

[0177] Fig.15 A fusion / positioning model 1500 of acceptable latent feature representations z1 to z5 to provide positioning parameters associated with the location of a UE according to aspects of the present disclosure is depicted. According to aspects of the present disclosure, the positioning parameters may include 1) the location of the UE, 2) one or more positioning coordinates associated with the location of the UE, 3) one or more positioning measurements associated with the location of the UE, or 4) any combination thereof. In one aspect, the fusion / positioning model 1500 is defined by an ML operation fθ2b that fuses the latent feature representations z1 to z5 to provide positioning parameters at its output.

[0178] In one aspect, the fusion / positioning model 1500 may be trained using latent feature representation values ​​labeled by corresponding known positioning parameters. In one aspect, each fusion / positioning model may be trained and employed based on the dimensionality of the latent feature representation at its input. As an example, a first fusion / positioning model may be used when the latent feature representation output z1 has a dimension of dim(zi)=30 samples. A second fusion / positioning model may be used when the latent feature representation output z'1 has a dimension of dim(z'i)=100 samples. A third fusion / positioning model may be used when the latent feature representation output z"i has a dimension of dim(z"i)=400 samples.

[0179] According to certain aspects of the present disclosure, the fusion / positioning model 1500 may be located at a UE for UE-based positioning. In a UE-based positioning scenario, the fusion / positioning model 1500 may provide a positioning estimate indicating the location of the UE.

[0180] According to certain aspects of the present disclosure, the fusion / positioning model 1500 may be located at a network server for UE assisted positioning. In such a scenario, the UE may send a potential feature representation corresponding to the RFFP measurement to the network server. To this end, the UE may provide a report to the network server, the report including an indication of the following: 1) a potential feature model identifier corresponding to the potential feature ML model applied to each of the multiple RFFP measurements, 2) a data size associated with a separate potential feature representation provided by the potential feature ML model, 3) a dimension associated with a separate potential feature representation provided by the potential feature ML model, 4) a bandwidth size associated with a separate bandwidth segment for measuring multiple different bandwidth segments, 5) one or more timestamps corresponding to the measurement of multiple different bandwidth segments, 6) one or more timestamps corresponding to obtaining multiple potential feature representations, 7) multiple potential feature representations, 8) an indication of the maximum time gap between consecutive reference signals measured by the UE, or 9) any combination thereof.

[0181] According to certain aspects of the present disclosure, a UE may send a capabilities message to a network server (e.g., at the start of a positioning session) that includes: 1) an indication of the bandwidth of a maximum bandwidth segment of a reference signal that the UE may measure during a single reference signal opportunity, 2) an indication of the maximum dimension of a potential feature representation that may be processed at the UE, 3) an indication of the number of potential feature representations that may be stored at the UE for a given dimension of the potential feature representation, 4) an indication of a buffer size that may be used at the UE to store the potential feature representations, or 5) a combination thereof.

[0182] In one or both of UE-assisted and UE-based positioning scenarios, the UE may receive assistance data from a network server indicating a configuration (e.g., timing and frequency configuration) of resource opportunities for obtaining bandwidth segments by the UE for use in RFFP positioning with bandwidth segmentation. According to certain aspects of the present disclosure, the assistance data may include: 1) an indication of a latent feature model (e.g., among a plurality of latent feature ML models available at the UE) to be applied to a plurality of RFFP measurements during a positioning session, 2) an indication of a maximum time gap allowable between measurements of consecutive bandwidth segments of a plurality of different bandwidth segments, 3) an indication of reference signal opportunities during which a plurality of different bandwidth segments are sent reference signals during a positioning session, or 4) any combination thereof. In scenarios in which the UE is capable of performing positioning operations using both RFFP aggregation and RFFP latent feature representation, the assistance data may also include an indication of which positioning operations to use during the positioning session. For example, the assistance data may indicate a mode of RFFP positioning (eg, bandwidth aggregated RFFP, bandwidth segmented RFFP with fusion at the UE, bandwidth segmented RFFP with fusion at the network server).

[0183] To account for UE mobility and location changes between measurements of different bandwidth segments, reference signal resources should be configured with a minimum time gap between consecutive reference signal opportunities. That is, the time gap between measurements of consecutive reference signals should be as small as possible (e.g., when the UE is carried by a person, the maximum gap may be less than a few milliseconds).

[0184] In a scenario where the assistance data includes an indication of the maximum time gap allowable between measurements on consecutive bandwidth segments, if consecutive reference signals are measured by the UE within the maximum time gap, the UE may measure multiple different bandwidth segments during fewer than all indicated reference signal opportunities. For example, a network server may configure and transmit positioning resources with very small measurement gaps, but the UE cannot process every measurement opportunity due to its limited processing capabilities. To this end, assume that reference signals are transmitted at reference signal opportunities t1, t2, and t3. Assuming that the UE is only able to process reference signals received at reference signal opportunities t1 and t3, if the gap between consecutive measurements x1 and x3 performed by the UE occurs within the maximum time gap indicated in the assistance data, the UE may still use the corresponding RFFP measurements x1 and x3 for positioning operations. In this case, the UE can relax what reference signal opportunities to process as long as the processing of reference signal opportunities does not exceed the maximum timing.

[0185] The positioning model supporting bandwidth segmentation can be trained in different ways. In one aspect, the positioning model can be trained offline and / or online by the UE vendor's over-the-top (OTT) server and then deployed / downloaded to the UE. In one aspect, the positioning model can be trained offline and / or online on the network server side (e.g., based on LMF / LMF assistance) and exchanged with the UE. In one aspect, the positioning model can be trained online at the UE. In such instances, the ground truth position can be provided by the network server using different positioning methods and / or calculated at the UE using RAT and non-RAT positioning methods. In the UE training scenario, the training and data collection methods can be specified by the network server in the assistance data.

[0186] As described herein, the UE may use bandwidth aggregation to perform different inference operations. In one aspect, the UE may be configured with bandwidth segmented positioning resources, buffer RFFP measurements corresponding to the bandwidth segments, and obtain aggregated RFFP measurements, which are passed as input to a positioning model to obtain an estimated position of the UE at an output of the positioning model. In another aspect, the UE may be configured with bandwidth segmented positioning resources, obtain RFFP measurements corresponding to the bandwidth segments, extract latent feature representations corresponding to the RFFP measurements using a first ML model (e.g., a latent feature ML model), and fuse the latent feature representations using a second ML model (e.g., a positioning model) to obtain an estimated position of the UE as an output. In another aspect, the UE may be configured with segmented positioning resources, obtain RFFP measurements corresponding to the bandwidth segments, extract latent feature representations corresponding to the RFFP measurements using a first ML model (e.g., a latent feature ML model), and send the latent feature representations to a network server, which uses the latent feature representations by using a second ML model (e.g., a positioning model) to obtain an estimated position of the UE as an output.

[0187] Fig.16 An example method 1600 of wireless communication performed by a UE according to various aspects of the present disclosure is illustrated. At operation 1602, the UE measures a plurality of different bandwidth segments of a reference signal at a plurality of corresponding different reference signal opportunities during a positioning session to obtain a plurality of RFFP measurements corresponding to the plurality of different bandwidth segments. In one aspect, operation 1602 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 as means for performing the operation.

[0188] At operation 1604, the UE aggregates the plurality of RFFP measurements to provide at least one aggregated RFFP measurement. In an aspect, operation 1604 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 means for performing the operation.

[0189] At operation 1606, the UE applies the positioning model to the at least one aggregated RFFP measurement to obtain an estimate of one or more positioning parameters associated with the location of the UE. In an aspect, operation 1606 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 as means for performing the operation.

[0190] As should be appreciated, a technical advantage of method 1600 is that the method enables a UE to obtain positioning parameters based on multiple different bandwidth segments of a reference signal. By combining RFFP measurements corresponding to multiple different bandwidth segments of a reference signal, the method allows a UE with limited bandwidth processing resources to obtain more accurate positioning parameters than would otherwise be obtained if only a single bandwidth segment was measured. Certain advantages are also achieved because the method can be implemented in a reduced capability (RedCap) device that may have reduced bandwidth capabilities.

[0191] Fig.17 An example method 1700 of wireless communication performed by a UE according to various aspects of the present disclosure is illustrated. At operation 1702, the UE measures a plurality of different bandwidth segments of a reference signal at a plurality of corresponding different reference signal opportunities during a positioning session to obtain a plurality of RFFP measurements corresponding to the plurality of different bandwidth segments. In one aspect, operation 1702 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 as means for performing the operation.

[0192] At operation 1704, the UE applies the latent feature ML model to each of the plurality of RFFP measurements to obtain a plurality of latent feature representations corresponding to the plurality of RFFP measurements. In some aspects, the latent feature representations may be provided to a positioning model at the UE. In some aspects, the latent feature representations may be sent to a network server, which applies the positioning model to the received latent feature representations. In one aspect, operation 1704 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 as means for performing the operation.

[0193] As should be appreciated, a technical advantage of method 1700 is that the method enables a UE to obtain a potential feature representation of RFFP measurements corresponding to multiple bandwidth segments of a reference signal. In one aspect, the dimensionality of the potential feature representation is less than the dimensionality of the corresponding RFFP measurement, thereby reducing buffering requirements at the UE and / or reducing the transmission overhead associated with sending information corresponding to the RFFP measurement. By obtaining RFFP measurements corresponding to multiple different bandwidth segments of a reference signal, the method allows a UE with limited bandwidth processing resources to obtain more accurate positioning parameters than would otherwise be obtained if only a single bandwidth segment was measured.

[0194] Fig.18 An example method 1800 of wireless communication performed by a network server (e.g., LMF, model server, etc.) according to various aspects of the present disclosure is illustrated. At operation 1802, the network server receives from a UE a plurality of potential feature representations corresponding to a plurality of RFFP measurements obtained by the UE during a positioning session, wherein the plurality of potential feature representations are based on measurements of a plurality of different bandwidth segments of a reference signal by the UE at a corresponding plurality of different reference signal opportunities during the positioning session. In one aspect, operation 1802 may be performed by one or more network transceivers 390, one or more processors 394, memory 396, and / or positioning component 398, any or all of which may be considered as means for performing the operation.

[0195] At operation 1804, the network server applies the positioning model to the plurality of potential feature representations to obtain one or more positioning parameters associated with the location of the UE. In an aspect, operation 1804 may be performed by one or more network transceivers 390, one or more processors 394, memory 396, and / or positioning component 398, any or all of which may be considered means for performing the operation.

[0196] As should be appreciated, a technical advantage of method 1800 is that the method enables a network server to obtain from a UE a potential feature representation of RFFP measurements corresponding to multiple bandwidth segments of a reference signal. In one aspect, the dimensionality of the potential feature representation is less than the dimensionality of the corresponding RFFP measurement, thereby reducing the transmission overhead associated with transmitting information corresponding to the RFFP measurement. By obtaining RFFP measurements corresponding to multiple different bandwidth segments of a reference signal, the method allows the network server to obtain more accurate positioning parameters than would otherwise be obtained if only a single bandwidth segment was measured at the UE.

[0197] In the above specific embodiments, it can be seen that different features are grouped together in each example. This disclosure should not be understood as an intention that the example clauses have more features than the features explicitly mentioned in each clause. On the contrary, various aspects of the present disclosure may include less than all the features of the disclosed individual example clauses. Therefore, the following clauses should be considered to be incorporated into the description accordingly, wherein each clause itself can be used as a separate example. Although each subordinate clause may refer to a specific combination of one clause with other clauses in a clause, the aspects of the subordinate clause are not limited to a specific combination. It should be understood that other example clauses may also include a combination of the subject matter of the subordinate clause aspect with any other subordinate clause or independent clause or any feature with other subordinate clauses and independent clauses. Various aspects disclosed herein explicitly include these combinations, unless it is clearly expressed or can be easily inferred that a specific combination is not intended to be used (e.g., contradictory aspects, such as defining an element as both an electrical insulator and an electrical conductor). In addition, it is also expected that various aspects of the clause may be included in any other independent clause, even if the clause is not directly dependent on the independent clause.

[0198] Specific implementation examples are described in the following numbered clauses:

[0199] Clause 1. A method of wireless communication performed by a user equipment (UE), comprising: measuring multiple different bandwidth segments of a reference signal (RS) at corresponding multiple different RS opportunities during a positioning session to obtain multiple radio frequency fingerprint positioning (RFFP) measurements corresponding to the multiple different bandwidth segments; aggregating the multiple RFFP measurements to provide at least one aggregated RFFP measurement; and applying a positioning model to the at least one aggregated RFFP measurement to obtain an estimate of one or more positioning parameters associated with the position of the E.

[0200] Clause 2. The method of clause 1, further comprising: receiving an indication from a network server that the UE is to aggregate the plurality of RFFP measurements to provide the at least one aggregated RFFP measurement.

[0201] Clause 3. A method according to any one of clauses 1 to 2, wherein the one or more positioning parameters include: the location of the UE; one or more positioning coordinates associated with the location of the UE; one or more positioning measurements associated with the location of the UE; or any combination thereof.

[0202] Clause 4. According to the method described in any one of clauses 1 to 3, the method further includes: sending a capability message to a network server, the capability message including: an indication that the UE is capable of aggregating RFFP measurements from different bandwidth segments; an indication of the bandwidth of the maximum bandwidth segment of the RS that the UE is capable of measuring during a single RS opportunity; an indication of the number of bandwidth segment measurements that can be buffered at the UE; an indication of one or more model identifiers of a positioning model stored at the UE that supports UE positioning using aggregated RFFP measurements; or any combination thereof.

[0203] Clause 5. A method according to any one of clauses 1 to 4, the method further comprising: receiving from a network server an indication of: a bandwidth size corresponding to each of the plurality of different bandwidth segments; the number of bandwidth segments to be measured by the UE; a maximum time gap allowable between measurements of consecutive bandwidth segments of the plurality of different bandwidth segments by the UE; or any combination thereof.

[0204] Clause 6. A method of wireless communication performed by a user equipment (UE), comprising: measuring multiple different bandwidth segments of a reference signal (RS) at corresponding multiple different RS opportunities during a positioning session to obtain multiple radio frequency fingerprint positioning (RFFP) measurements corresponding to the multiple different bandwidth segments; and applying a latent feature machine learning (ML) model to each of the multiple RFFP measurements to obtain multiple latent feature representations corresponding to the multiple RFFP measurements.

[0205] Clause 7. The method of clause 6, wherein: the latent feature ML model compresses each RFFP measurement to provide a corresponding latent feature representation of reduced dimensionality.

[0206] Clause 8. A method according to any one of clauses 6 to 7, the method further comprising: applying a positioning model to the plurality of potential feature representations to obtain one or more positioning parameters associated with the position of the UE.

[0207] Clause 9. A method according to clause 8, wherein the one or more positioning parameters include: the location of the UE; one or more positioning coordinates associated with the location of the UE; one or more positioning measurements associated with the location of the UE; or any combination thereof.

[0208] Clause 10. A method according to any one of clauses 8 to 9, wherein: the positioning model fuses the multiple latent feature representations to obtain the one or more positioning parameters.

[0209] Clause 11. A method according to any one of clauses 6 to 10, the method further comprising: sending to a network server an indication of: a latent feature model identifier corresponding to the latent feature ML model applied to each of the multiple RFFP measurement values; a data size associated with a separate latent feature representation provided by the latent feature ML model; a dimension associated with a separate latent feature representation provided by the latent feature ML model; a bandwidth size associated with a separate bandwidth segment used to measure the multiple different bandwidth segments; one or more timestamps corresponding to the measurement of the multiple different bandwidth segments; one or more timestamps corresponding to obtaining the multiple latent feature representations; the multiple latent feature representations; or any combination thereof.

[0210] Clause 12. A method according to any one of clauses 6 to 11, wherein: the UE stores a plurality of potential feature ML models.

[0211] Clause 13. The method of clause 12, further comprising: receiving auxiliary data from a network server, the auxiliary data comprising an indication of a latent feature model from the plurality of latent feature ML models to be applied as the latent feature model to the plurality of RFFP measurements during the positioning session.

[0212] Clause 14. The method of any one of clauses 12 to 13, wherein: at least two or more of the latent feature ML models in the plurality of latent feature ML models provide latent feature representations having different dimensions.

[0213] Clause 15. The method of clause 14, further comprising: sending to a network server the capability of the UE to provide potential feature representations with different data dimensions.

[0214] Clause 16. A method according to any one of clauses 6 to 15, the method further comprising: receiving auxiliary data from a network server, the auxiliary data comprising: an indication of a maximum time gap allowable between measurements of consecutive bandwidth segments of the multiple different bandwidth segments; an indication of an RS opportunity during which the multiple different bandwidth segments of the RS are sent during the positioning session; an indication of a maximum time gap allowable between consecutive RSs measured by the UE; or any combination thereof.

[0215] Clause 17. A method according to clause 16, wherein the auxiliary data includes the indication of the maximum time gap allowable between measurements of consecutive bandwidth segments, and the method further includes: based on consecutive RS opportunities measured by the UE within the maximum time gap, measuring the multiple different bandwidth segments during less than all of the indicated RS opportunities.

[0216] Clause 18. A method according to any one of clauses 6 to 17, the method further comprising: sending a capability message to a network server, the capability message comprising: an indication of the bandwidth of a maximum bandwidth segment of the RS that the UE can measure during a single RS opportunity; an indication of the maximum dimension of a potential feature representation that can be processed at the UE; an indication of the number of potential feature representations that can be stored at the UE for a given dimension of the potential feature representation; an indication of a buffer size that can be used to store potential feature representations; or any combination thereof.

[0217] Clause 19. A method of wireless communication performed by a network server, the method comprising: receiving from a user equipment (UE) a plurality of potential feature representations corresponding to a plurality of radio frequency fingerprint positioning (RFFP) measurements obtained by the UE during a positioning session, wherein the plurality of potential feature representations are based on measurements of a plurality of different bandwidth segments of a reference signal (RS) at a corresponding plurality of different RS occasions by the UE during the positioning session; and applying a positioning model to the plurality of potential feature representations to obtain one or more positioning parameters associated with the position of the UE.

[0218] Clause 20. A method according to clause 19, wherein the one or more positioning parameters include: the location of the UE; one or more positioning coordinates associated with the location of the UE; one or more positioning measurements associated with the location of the UE; or any combination thereof.

[0219] Clause 21. The method of claim 19, wherein: the positioning model fuses the multiple latent feature representations to obtain the one or more positioning parameters.

[0220] Clause 22. A method according to any one of clauses 19 to 21, the method further comprising: receiving from the UE an indication of: a latent feature model identifier corresponding to the latent feature ML model applied to each of the multiple RFFP measurement values; a data size associated with a separate latent feature representation provided by the latent feature ML model; a dimension associated with a separate latent feature representation provided by the latent feature ML model; a bandwidth size associated with a separate bandwidth segment used to measure the multiple different bandwidth segments; one or more timestamps corresponding to the measurement of the multiple different bandwidth segments; one or more timestamps corresponding to obtaining the multiple latent feature representations; or any combination thereof.

[0221] Clause 23. A method according to any one of clauses 19 to 22, the method further comprising: sending auxiliary data to the UE, the auxiliary data comprising: an indication of a maximum time gap allowable between measurements of consecutive bandwidth segments of the multiple different bandwidth segments; an indication of an RS opportunity, during which the multiple different bandwidth segments of the RS are sent during the positioning session; an indication of a maximum time gap allowable between consecutive RSs measured by the UE; or any combination thereof.

[0222] Clause 24. A method according to any one of clauses 19 to 23, the method further comprising: receiving a capability message from the UE, the capability message comprising: an indication of the bandwidth of a maximum bandwidth segment of the RS that the UE can measure during a single RS opportunity; an indication of a maximum dimension of a potential feature representation that can be processed at the UE; an indication of the number of potential feature representations that can be stored at the UE for a given dimension of the potential feature representation; an indication of a buffer size that can be used to store potential feature representations; or any combination thereof.

[0223] Clause 25. A user equipment (UE), comprising: a memory; at least one transceiver; and at least one processor, the at least one processor being communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: measure a plurality of different bandwidth segments of a reference signal (RS) at corresponding plurality of different RS opportunities during a positioning session to obtain a plurality of radio frequency fingerprint positioning (RFFP) measurements corresponding to the plurality of different bandwidth segments; aggregate the plurality of RFFP measurements to provide at least one aggregated RFFP measurement; and apply a positioning model to the at least one aggregated RFFP measurement to obtain an estimate of one or more positioning parameters associated with the position of the UE.

[0224] Clause 26. The UE of clause 25, wherein the at least one processor is further configured to: receive, via the at least one transceiver, from a network server an indication that the UE is to aggregate the plurality of RFFP measurements to provide the at least one aggregated RFFP measurement.

[0225] Clause 27. A UE according to any one of clauses 25 to 26, wherein the one or more positioning parameters include: the location of the UE; one or more positioning coordinates associated with the location of the UE; one or more positioning measurements associated with the location of the UE; or any combination thereof.

[0226] Clause 28. A UE according to any one of clauses 25 to 27, wherein the at least one processor is further configured to: send a capability message to a network server, the capability message comprising: an indication that the UE is capable of aggregating RFFP measurements from different bandwidth segments; an indication of the bandwidth of the maximum bandwidth segment of the RS that the UE is capable of measuring during a single RS opportunity; an indication of the number of bandwidth segment measurements that can be buffered at the UE; an indication of one or more model identifiers of a positioning model stored at the UE that supports UE positioning using aggregated RFFP measurements; or any combination thereof.

[0227] Clause 29. A UE according to any one of clauses 25 to 28, wherein the at least one processor is further configured to: receive from a network server an indication of: a bandwidth size corresponding to each of the multiple different bandwidth segments; the number of bandwidth segments to be measured by the UE; the maximum time gap allowable between measurements of consecutive bandwidth segments of the multiple different bandwidth segments by the UE; or any combination thereof.

[0228] Clause 30. A user equipment (UE), the user equipment (UE) comprising: a memory; at least one transceiver; and at least one processor, the at least one processor being communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: measure a plurality of different bandwidth segments of a reference signal (RS) at corresponding plurality of different RS opportunities during a positioning session to obtain a plurality of radio frequency fingerprint positioning (RFFP) measurements corresponding to the plurality of different bandwidth segments; and apply a latent feature machine learning (ML) model to each of the plurality of RFFP measurements to obtain a plurality of latent feature representations corresponding to the plurality of RFFP measurements.

[0229] Clause 31. A UE as described in clause 30, wherein: the latent feature ML model compresses each RFFP measurement to provide a corresponding latent feature representation of reduced dimensionality.

[0230] Clause 32. A UE according to any one of clauses 30 to 31, wherein the at least one processor is further configured to: apply a positioning model to the plurality of potential feature representations to obtain one or more positioning parameters associated with the position of the UE.

[0231] Clause 33. A UE according to clause 32, wherein the one or more positioning parameters include: the location of the UE; one or more positioning coordinates associated with the location of the UE; one or more positioning measurements associated with the location of the UE; or any combination thereof.

[0232] Clause 34. A UE according to any one of clauses 32 to 33, wherein: the positioning model fuses the multiple potential feature representations to obtain the one or more positioning parameters.

[0233] Clause 35. A UE according to any one of clauses 30 to 34, wherein the at least one processor is further configured to: send to a network server an indication of: a potential feature model identifier corresponding to the potential feature ML model applied to each of the multiple RFFP measurement values; a data size associated with a separate potential feature representation provided by the potential feature ML model; a dimension associated with a separate potential feature representation provided by the potential feature ML model; a bandwidth size associated with a separate bandwidth segment used to measure the multiple different bandwidth segments; one or more timestamps corresponding to the measurement of the multiple different bandwidth segments; one or more timestamps corresponding to obtaining the multiple potential feature representations; the multiple potential feature representations; or any combination thereof.

[0234] Clause 36. A UE as described in any of clauses 30 to 35, wherein: the UE stores a plurality of latent feature ML models.

[0235] Clause 37. A UE according to clause 36, wherein the at least one processor is further configured to: receive auxiliary data from a network server via the at least one transceiver, the auxiliary data comprising an indication of a potential feature model from the multiple potential feature ML models to be applied as the potential feature model to the multiple RFFP measurements during the positioning session.

[0236] Clause 38. A UE as described in any of clauses 36 to 37, wherein: at least two or more of the latent feature ML models in the plurality of latent feature ML models provide latent feature representations having different dimensions.

[0237] Clause 39. The UE of clause 38, wherein the at least one processor is further configured to: send, via the at least one transceiver, to a network server the capability of the UE to provide potential feature representations having different data dimensions.

[0238] Clause 40. A UE according to any one of clauses 30 to 39, wherein the at least one processor is further configured to: receive auxiliary data from a network server, the auxiliary data comprising: an indication of a maximum time gap allowable between measurements of consecutive bandwidth segments of the multiple different bandwidth segments; an indication of an RS opportunity during which the multiple different bandwidth segments of the RS are sent during the positioning session; an indication of a maximum time gap allowable between consecutive RS measured by the UE; or any combination thereof.

[0239] Clause 41. A UE according to clause 40, wherein the auxiliary data includes the indication of the maximum time gap allowable between measurements of consecutive bandwidth segments, and the method further includes: based on consecutive RS opportunities measured by the UE within the maximum time gap, measuring the multiple different bandwidth segments during less than all of the indicated RS opportunities.

[0240] Clause 42. A UE according to any one of clauses 30 to 41, wherein the at least one processor is further configured to: send a capability message to a network server, the capability message comprising: an indication of the bandwidth of a maximum bandwidth segment of the RS that the UE can measure during a single RS opportunity; an indication of a maximum dimension of a potential feature representation that can be processed at the UE; an indication of the number of potential feature representations that can be stored at the UE for a given dimension of the potential feature representation; an indication of a buffer size that can be used to store potential feature representations; or any combination thereof.

[0241] Clause 43. A network server, comprising: a memory; at least one transceiver; and at least one processor, the at least one processor being communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: receive from a user equipment (UE) via the at least one transceiver a plurality of potential feature representations corresponding to a plurality of radio frequency fingerprint positioning (RFFP) measurements obtained by the UE during a positioning session, wherein the plurality of potential feature representations are based on measurements of a plurality of different bandwidth segments of a reference signal (RS) at corresponding plurality of different RS opportunities by the UE during the positioning session; and apply a positioning model to the plurality of potential feature representations to obtain one or more positioning parameters associated with the position of the UE.

[0242] Clause 44. A network server according to clause 43, wherein the one or more positioning parameters include: the location of the UE; one or more positioning coordinates associated with the location of the UE; one or more positioning measurements associated with the location of the UE; or any combination thereof.

[0243] Clause 45. A network server according to any one of clauses 43 to 44, a method according to claim 19, wherein: the positioning model fuses the multiple latent feature representations to obtain the one or more positioning parameters.

[0244] Clause 46. A network server according to any one of clauses 43 to 45, wherein the at least one processor is further configured to: receive from the UE an indication of: a latent feature model identifier corresponding to the latent feature ML model applied to each of the multiple RFFP measurement values; a data size associated with a separate latent feature representation provided by the latent feature ML model; a dimension associated with a separate latent feature representation provided by the latent feature ML model; a bandwidth size associated with a separate bandwidth segment used to measure the multiple different bandwidth segments; one or more timestamps corresponding to the measurement of the multiple different bandwidth segments; one or more timestamps corresponding to obtaining the multiple latent feature representations; or any combination thereof.

[0245] Clause 47. A network server according to any one of clauses 43 to 46, wherein the at least one processor is further configured to: send auxiliary data to the UE, the auxiliary data comprising: an indication of a maximum time gap allowable between measurements of consecutive bandwidth segments of the multiple different bandwidth segments; an indication of an RS opportunity during which the multiple different bandwidth segments of the RS are sent during the positioning session; an indication of a maximum time gap allowable between consecutive RS measured by the UE; or any combination thereof.

[0246] Clause 48. A network server according to any one of clauses 43 to 47, wherein the at least one processor is further configured to: receive a capability message from the UE, the capability message comprising: an indication of the bandwidth of a maximum bandwidth segment of the RS that the UE can measure during a single RS opportunity; an indication of the maximum dimension of a potential feature representation that can be processed at the UE; an indication of the number of potential feature representations that can be stored at the UE for a given dimension of the potential feature representation; an indication of a buffer size that can be used to store potential feature representations; or any combination thereof.

[0247] Clause 49. A user equipment (UE), comprising: a component for measuring multiple different bandwidth segments of a reference signal (RS) at corresponding multiple different RS opportunities during a positioning session to obtain multiple radio frequency fingerprint positioning (RFFP) measurements corresponding to the multiple different bandwidth segments; a component for aggregating the multiple RFFP measurements to provide at least one aggregated RFFP measurement; and a component for applying a positioning model to the at least one aggregated RFFP measurement to obtain an estimate of one or more positioning parameters associated with the position of the UE.

[0248] Clause 50. The UE of clause 49, the UE further comprising means for receiving an indication from a network server that the UE is to aggregate the plurality of RFFP measurements to provide the at least one aggregated RFFP measurement.

[0249] Clause 51. A UE according to any one of clauses 49 to 50, wherein the one or more positioning parameters include: the location of the UE; one or more positioning coordinates associated with the location of the UE; one or more positioning measurements associated with the location of the UE; or any combination thereof.

[0250] Clause 52. A UE according to any one of clauses 49 to 51, wherein the UE further comprises: a component for sending a capability message to a network server, the capability message comprising: an indication that the UE is capable of aggregating RFFP measurements from different bandwidth segments; an indication of the bandwidth of the maximum bandwidth segment of the RS that the UE is capable of measuring during a single RS opportunity; an indication of the number of bandwidth segment measurements that can be buffered at the UE; an indication of one or more model identifiers of a positioning model stored at the UE that supports UE positioning using aggregated RFFP measurements; or any combination thereof.

[0251] Clause 53. A UE according to any one of clauses 49 to 52, wherein the UE further comprises: a component for receiving, from a network server, indications of: a bandwidth size corresponding to each of the plurality of different bandwidth segments; a number of bandwidth segments to be measured by the UE; a maximum time gap allowable between measurements of consecutive bandwidth segments of the plurality of different bandwidth segments by the UE; or any combination thereof.

[0252] Clause 54. A user equipment (UE), comprising: a component for measuring multiple different bandwidth segments of a reference signal (RS) at corresponding multiple different RS opportunities during a positioning session to obtain multiple radio frequency fingerprint positioning (RFFP) measurements corresponding to the multiple different bandwidth segments; and a component for applying a latent feature machine learning (ML) model to each of the multiple RFFP measurements to obtain multiple latent feature representations corresponding to the multiple RFFP measurements.

[0253] Clause 55. A UE as described in clause 54, wherein: the latent feature ML model compresses each RFFP measurement to provide a corresponding latent feature representation of reduced dimensionality.

[0254] Clause 56. A UE as described in any of clauses 54 to 55, the UE further comprising: means for applying a positioning model to the plurality of potential feature representations to obtain one or more positioning parameters associated with the position of the UE.

[0255] Clause 57. A UE according to clause 56, wherein the one or more positioning parameters include: the location of the UE; one or more positioning coordinates associated with the location of the UE; one or more positioning measurements associated with the location of the UE; or any combination thereof.

[0256] Clause 58. A UE according to any one of clauses 56 to 57, wherein: the positioning model fuses the multiple potential feature representations to obtain the one or more positioning parameters.

[0257] Clause 59. A UE according to any one of clauses 54 to 58, wherein the UE further comprises: a component for sending to a network server an indication of: a potential feature model identifier corresponding to the potential feature ML model applied to each of the multiple RFFP measurement values; a data size associated with a separate potential feature representation provided by the potential feature ML model; a dimension associated with a separate potential feature representation provided by the potential feature ML model; a bandwidth size associated with a separate bandwidth segment used to measure the multiple different bandwidth segments; one or more timestamps corresponding to the measurement of the multiple different bandwidth segments; one or more timestamps corresponding to obtaining the multiple potential feature representations; the multiple potential feature representations; or any combination thereof.

[0258] Clause 60. A UE as described in any of clauses 54 to 59, the UE further comprising: means for storing a plurality of latent feature ML models.

[0259] Clause 61. A UE according to clause 60, the UE further comprising: a component for receiving auxiliary data from a network server, the auxiliary data comprising an indication of a latent feature model from the plurality of latent feature ML models to be applied as the latent feature model to the plurality of RFFP measurements during the positioning session.

[0260] Clause 62. A UE as described in any of clauses 60 to 61, wherein: at least two or more of the latent feature ML models in the plurality of latent feature ML models provide latent feature representations having different dimensions.

[0261] Clause 63. The UE of clause 62, further comprising means for sending to a network server the capability of the UE to provide potential feature representations having different data dimensions.

[0262] Clause 64. A UE according to any one of clauses 54 to 63, the UE further comprising: a component for receiving auxiliary data from a network server, the auxiliary data comprising: an indication of a maximum time gap allowable between measurements of consecutive bandwidth segments of the multiple different bandwidth segments; an indication of an RS opportunity during which the multiple different bandwidth segments of the RS are sent during the positioning session; an indication of a maximum time gap allowable between consecutive RSs measured by the UE; or any combination thereof.

[0263] Clause 65. A UE according to clause 64, wherein the auxiliary data includes the indication of the maximum time gap allowable between measurements of consecutive bandwidth segments, and the method further includes: a component for measuring the multiple different bandwidth segments during less than all of the indicated RS opportunities based on consecutive RS opportunities measured by the UE within the maximum time gap.

[0264] Clause 66. A UE according to any one of clauses 54 to 65, wherein the UE further comprises: a component for sending a capability message to a network server, the capability message comprising: an indication of the bandwidth of a maximum bandwidth segment of the RS that the UE can measure during a single RS opportunity; an indication of a maximum dimension of a potential feature representation that can be processed at the UE; an indication of the number of potential feature representations that can be stored at the UE for a given dimension of the potential feature representation; an indication of a buffer size that can be used to store potential feature representations; or any combination thereof.

[0265] Clause 67. A network server comprising: a component for receiving, from a user equipment (UE), a plurality of potential feature representations corresponding to a plurality of radio frequency fingerprint positioning (RFFP) measurements obtained by the UE during a positioning session, wherein the plurality of potential feature representations are based on measurements of a plurality of different bandwidth segments of a reference signal (RS) at a corresponding plurality of different RS occasions by the UE during the positioning session; and a component for applying a positioning model to the plurality of potential feature representations to obtain one or more positioning parameters associated with the position of the UE.

[0266] Clause 68. A network server according to Clause 67, wherein the one or more positioning parameters include: the location of the UE; one or more positioning coordinates associated with the location of the UE; one or more positioning measurements associated with the location of the UE; or any combination thereof.

[0267] Clause 69. A network server according to any one of clauses 67 to 68, wherein: the positioning model fuses the multiple potential feature representations to obtain the one or more positioning parameters.

[0268] Clause 70. A network server according to any one of clauses 67 to 69, wherein the network server further comprises: a component for receiving from the UE an indication of: a potential feature model identifier corresponding to the potential feature ML model applied to each of the plurality of RFFP measurement values; a data size associated with a separate potential feature representation provided by the potential feature ML model; a dimension associated with a separate potential feature representation provided by the potential feature ML model; a bandwidth size associated with a separate bandwidth segment used to measure the plurality of different bandwidth segments; one or more timestamps corresponding to the measurement of the plurality of different bandwidth segments; one or more timestamps corresponding to obtaining the plurality of potential feature representations; or any combination thereof.

[0269] Clause 71. A network server according to any one of clauses 67 to 70, wherein the network server further comprises: a component for sending auxiliary data to the UE, the auxiliary data comprising: an indication of a maximum time gap allowable between measurements of consecutive bandwidth segments of the multiple different bandwidth segments; an indication of an RS opportunity during which the multiple different bandwidth segments of the RS are sent during the positioning session; an indication of a maximum time gap allowable between consecutive RS measured by the UE; or any combination thereof.

[0270] Clause 72. A network server according to any one of clauses 67 to 71, wherein the network server further comprises: a component for receiving a capability message from the UE, the capability message comprising: an indication of the bandwidth of a maximum bandwidth segment of the RS that the UE can measure during a single RS opportunity; an indication of a maximum dimension of a potential feature representation that can be processed at the UE; an indication of the number of potential feature representations that can be stored at the UE for a given dimension of the potential feature representation; an indication of a buffer size that can be used to store potential feature representations; or any combination thereof.

[0271] Clause 73. A non-transitory computer-readable medium storing computer-executable instructions, which, when executed by a user equipment (UE), causes the UE to: measure multiple different bandwidth segments of a reference signal (RS) at corresponding multiple different RS occasions during a positioning session to obtain multiple radio frequency fingerprint positioning (RFFP) measurements corresponding to the multiple different bandwidth segments; aggregate the multiple RFFP measurements to provide at least one aggregated RFFP measurement; and apply a positioning model to the at least one aggregated RFFP measurement to obtain an estimate of one or more positioning parameters associated with the position of the UE.

[0272] Clause 74. A non-transitory computer-readable medium according to clause 73, wherein the non-transitory computer-readable medium further comprises computer-executable instructions which, when executed by the UE, cause the UE to: receive an indication from a network server that the UE will aggregate the multiple RFFP measurements to provide the at least one aggregated RFFP measurement.

[0273] Clause 75. A non-transitory computer-readable medium according to any one of clauses 73 to 74, wherein the one or more positioning parameters include: the location of the UE; one or more positioning coordinates associated with the location of the UE; one or more positioning measurements associated with the location of the UE; or any combination thereof.

[0274] Clause 76. A non-transitory computer-readable medium according to any one of clauses 73 to 75, wherein the non-transitory computer-readable medium also includes computer-executable instructions, which, when executed by the UE, cause the UE to: send a capability message to a network server, the capability message comprising: an indication that the UE is capable of aggregating RFFP measurements from different bandwidth segments; an indication of the bandwidth of the maximum bandwidth segment of the RS that the UE is capable of measuring during a single RS opportunity; an indication of the number of bandwidth segment measurements that can be buffered at the UE; an indication of one or more model identifiers of positioning models stored at the UE that support UE positioning using aggregated RFFP measurements; or any combination thereof.

[0275] Clause 77. A non-transitory computer-readable medium according to any one of clauses 73 to 76, wherein the non-transitory computer-readable medium also includes computer-executable instructions, which, when executed by the UE, cause the UE to: receive from a network server an indication of: a bandwidth size corresponding to each of the multiple different bandwidth segments; a number of bandwidth segments to be measured by the UE; a maximum time gap allowable between measurements of consecutive bandwidth segments of the multiple different bandwidth segments by the UE; or any combination thereof.

[0276] Clause 78. A non-transitory computer-readable medium storing computer-executable instructions, which, when executed by a user equipment (UE), causes the UE to: measure multiple different bandwidth segments of a reference signal (RS) at corresponding multiple different RS occasions during a positioning session to obtain multiple radio frequency fingerprint positioning (RFFP) measurements corresponding to the multiple different bandwidth segments; and apply a latent feature machine learning (ML) model to each of the multiple RFFP measurements to obtain multiple latent feature representations corresponding to the multiple RFFP measurements.

[0277] Clause 79. A non-transitory computer-readable medium as described in Clause 78, wherein: the latent feature ML model compresses each RFFP measurement to provide a corresponding latent feature representation of reduced dimensionality.

[0278] Clause 80. A non-transitory computer-readable medium according to any one of clauses 78 to 79, wherein the non-transitory computer-readable medium further comprises computer-executable instructions which, when executed by the UE, cause the UE to: apply a positioning model to the multiple potential feature representations to obtain one or more positioning parameters associated with the position of the UE.

[0279] Clause 81. A non-transitory computer-readable medium according to clause 80, wherein the one or more positioning parameters include: the location of the UE; one or more positioning coordinates associated with the location of the UE; one or more positioning measurements associated with the location of the UE; or any combination thereof.

[0280] Clause 82. The non-transitory computer-readable medium of any one of clauses 80 to 81, wherein: the positioning model fuses the plurality of latent feature representations to obtain the one or more positioning parameters.

[0281] Clause 83. A non-transitory computer-readable medium according to any one of clauses 78 to 82, wherein the non-transitory computer-readable medium also includes computer-executable instructions, which, when executed by the UE, cause the UE to: send to a network server an indication of: a latent feature model identifier corresponding to the latent feature ML model applied to each of the multiple RFFP measurement values; a data size associated with a separate latent feature representation provided by the latent feature ML model; a dimension associated with a separate latent feature representation provided by the latent feature ML model; a bandwidth size associated with a separate bandwidth segment for measuring the multiple different bandwidth segments; one or more timestamps corresponding to measurements of the multiple different bandwidth segments; one or more timestamps corresponding to obtaining the multiple latent feature representations; the multiple latent feature representations; or any combination thereof.

[0282] Clause 84. The non-transitory computer-readable medium of any one of clauses 78 to 83, wherein: the UE stores a plurality of latent feature ML models.

[0283] Clause 85. A non-transitory computer-readable medium according to clause 84, wherein the non-transitory computer-readable medium also includes computer-executable instructions, which when executed by the UE cause the UE to: receive auxiliary data from a network server, the auxiliary data including an indication of a potential feature model among the multiple potential feature ML models to be applied as the potential feature model to the multiple RFFP measurements during the positioning session.

[0284] Clause 86. The non-transitory computer-readable medium of any one of clauses 84 to 85, wherein: at least two or more of the latent feature ML models in the plurality of latent feature ML models provide latent feature representations having different dimensions.

[0285] Clause 87. A non-transitory computer-readable medium according to clause 86, wherein the non-transitory computer-readable medium further comprises computer-executable instructions which, when executed by the UE, cause the UE to: send to a network server the capability of the UE to provide potential feature representations having different data dimensions.

[0286] Clause 88. A non-transitory computer-readable medium according to any one of clauses 78 to 87, wherein the non-transitory computer-readable medium also includes computer-executable instructions, which, when executed by the UE, cause the UE to: receive auxiliary data from a network server, the auxiliary data comprising: an indication of a maximum time gap allowable between measurements of consecutive bandwidth segments of the multiple different bandwidth segments; an indication of an RS opportunity during which the multiple different bandwidth segments of the RS are sent during the positioning session; an indication of a maximum time gap allowable between consecutive RS measured by the UE; or any combination thereof.

[0287] Clause 89. A non-transitory computer-readable medium according to clause 88, wherein the auxiliary data includes the indication of the maximum time gap allowable between measurements of consecutive bandwidth segments, and the method further includes: based on consecutive RS opportunities measured by the UE within the maximum time gap, measuring the multiple different bandwidth segments during less than all of the indicated RS opportunities.

[0288] Clause 90. A non-transitory computer-readable medium according to any one of clauses 78 to 89, wherein the non-transitory computer-readable medium further comprises computer-executable instructions which, when executed by the UE, cause the UE to: send a capability message to a network server, the capability message comprising: an indication of the bandwidth of a maximum bandwidth segment of the RS that the UE can measure during a single RS opportunity; an indication of a maximum dimension of a potential feature representation that can be processed at the UE; an indication of the number of potential feature representations that can be stored at the UE for a given dimension of the potential feature representation; an indication of a buffer size that can be used to store potential feature representations; or any combination thereof.

[0289] Clause 91. A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a network server, cause the network server to: receive from a user equipment (UE) a plurality of potential feature representations corresponding to a plurality of radio frequency fingerprint positioning (RFFP) measurements obtained by the UE during a positioning session, wherein the plurality of potential feature representations are based on measurements of a plurality of different bandwidth segments of a reference signal (RS) at a corresponding plurality of different RS occasions by the UE during the positioning session; and apply a positioning model to the plurality of potential feature representations to obtain one or more positioning parameters associated with the position of the UE.

[0290] Clause 92. A non-transitory computer-readable medium according to clause 91, wherein the one or more positioning parameters include: the location of the UE; one or more positioning coordinates associated with the location of the UE; one or more positioning measurements associated with the location of the UE; or any combination thereof.

[0291] Clause 93. The non-transitory computer-readable medium of any one of clauses 91 to 92, wherein: the positioning model fuses the plurality of latent feature representations to obtain the one or more positioning parameters.

[0292] Clause 94. A non-transitory computer-readable medium according to any one of clauses 91 to 93, wherein the non-transitory computer-readable medium also includes computer-executable instructions, which, when executed by the network server, cause the network server to: receive from the UE an indication of: a latent feature model identifier corresponding to the latent feature ML model applied to each of the multiple RFFP measurement values; a data size associated with a separate latent feature representation provided by the latent feature ML model; a dimension associated with a separate latent feature representation provided by the latent feature ML model; a bandwidth size associated with a separate bandwidth segment for measuring the multiple different bandwidth segments; one or more timestamps corresponding to measurements of the multiple different bandwidth segments; one or more timestamps corresponding to obtaining the multiple latent feature representations; or any combination thereof.

[0293] Clause 95. A non-transitory computer-readable medium according to any one of clauses 91 to 94, wherein the non-transitory computer-readable medium also includes computer-executable instructions, which, when executed by the network server, cause the network server to: send auxiliary data to the UE, the auxiliary data comprising: an indication of a maximum time gap allowable between measurements of consecutive bandwidth segments of the multiple different bandwidth segments; an indication of an RS opportunity during which the multiple different bandwidth segments of the RS are sent during the positioning session; an indication of a maximum time gap allowable between consecutive RS measured by the UE; or any combination thereof.

[0294] Clause 96. A non-transitory computer-readable medium according to any one of clauses 91 to 95, wherein the non-transitory computer-readable medium also includes computer-executable instructions, which, when executed by the network server, cause the network server to: receive a capability message from the UE, the capability message comprising: an indication of the bandwidth of a maximum bandwidth segment of the RS that the UE can measure during a single RS opportunity; an indication of a maximum dimension of a potential feature representation that can be processed at the UE; an indication of the number of potential feature representations that can be stored at the UE for a given dimension of the potential feature representation; an indication of a buffer size that can be used to store potential feature representations; or any combination thereof.

[0295] It should be understood by those skilled in the art that information and signals may be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be mentioned throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or optical particles, or any combination thereof.

[0296] In addition, it will be appreciated by those skilled in the art that the various exemplary logic blocks, modules, circuits, and algorithmic steps described in conjunction with the various aspects disclosed herein may be implemented as electronic hardware, computer software, or a combination of the two. In order to clearly illustrate this interchangeability of hardware and software, various exemplary components, blocks, modules, circuits, and steps have been generally described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the entire system. Those skilled in the art may implement the described functionality in different ways for each specific application, but such specific implementation decisions should not be interpreted as resulting in a departure from the scope of the present disclosure.

[0297] The various illustrative logical blocks, modules, and circuits described in conjunction with the various aspects disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an ASIC, a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in an alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, a combination of one or more microprocessors and a DSP core, or any other such configuration.

[0298] The methods, sequences and / or algorithms described in conjunction with the various aspects disclosed herein may be directly embodied in hardware, in a software module executed by a processor, or in a combination of the two. The software module may 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 example storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. In an alternative, the storage medium may be integral with the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal (e.g., UE). In an alternative, the processor and the storage medium may reside in a user terminal as discrete components.

[0299] In one or more example aspects, the function can be implemented in hardware, software, firmware or any combination thereof. If implemented in software, the function can be stored on a computer-readable medium or sent by a computer-readable medium as one or more instructions or codes. Computer-readable media include both computer storage media and communication media, including any medium that facilitates the transfer of computer programs from one place to another. Storage media can be any available medium that can be accessed by a computer. By way of example and not limitation, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program codes in the form of instructions or data structures and can be accessed by a computer. Moreover, any connection is appropriately referred to as a computer-readable medium. For example, if the software is sent from a website, a server or other remote source using a coaxial cable, an optical fiber cable, a twisted pair, a digital subscriber line (DSL) or wireless technologies such as infrared, radio and microwaves, then coaxial cable, optical fiber cable, twisted pair, DSL or wireless technologies such as infrared, radio and microwaves are included in the definition of the medium. As used herein, disk and disc include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0300] Although the foregoing disclosure shows the exemplary aspects of the present disclosure, it should be noted that various changes and modifications may be made herein without departing from the scope of the present disclosure as defined by the appended claims. In addition, the functions, steps and / or actions of the method claims according to the various aspects of the present disclosure described herein do not need to be performed in any particular order. In addition, although the elements of the present disclosure may be described or claimed in the singular, the plural form may also be considered unless it is explicitly stated to be limited to the singular form.

Claims

1. A method of wireless communication performed by a user equipment (UE), the method comprising: measuring a plurality of different bandwidth segments of a reference signal (RS) at corresponding plurality of different RS opportunities during a positioning session to obtain a plurality of radio frequency fingerprint positioning (RFFP) measurements corresponding to the plurality of different bandwidth segments; aggregating the plurality of RFFP measurements to provide at least one aggregated RFFP measurement; as well as A positioning model is applied to the at least one aggregated RFFP measurement to obtain an estimate of one or more positioning parameters associated with the position of the UE.

2. The method according to claim 1, further comprising: An indication is received from a network server that the UE is to aggregate the plurality of RFFP measurements to provide the at least one aggregated RFFP measurement.

3. The method of claim 1, wherein the one or more positioning parameters include: the location of the UE; one or more positioning coordinates associated with the position of the UE; one or more positioning measurements associated with the position of the UE; or Any combination of them.

4. The method according to claim 1, further comprising: Send a capability message to the network server, the capability message including: an indication that the UE is capable of aggregating RFFP measurements from different bandwidth segments; an indication of a bandwidth of a maximum bandwidth segment of the RS that the UE is able to measure during a single RS opportunity; an indication of a number of bandwidth segment measurements that can be buffered at the UE; an indication of one or more model identifiers of positioning models stored at the UE that support UE positioning using aggregated RFFP measurements; or Any combination of them.

5. The method according to claim 1, further comprising: Receive instructions from the network server to: a bandwidth size corresponding to each of the plurality of different bandwidth segments; a number of bandwidth segments to be measured by the UE; a maximum time gap allowed between measurements of consecutive bandwidth segments of the plurality of different bandwidth segments by the UE; or Any combination of them.

6. A method of wireless communication performed by a user equipment (UE), the method comprising: Measuring a plurality of different bandwidth segments of a reference signal (RS) at a corresponding plurality of different RS opportunities during a positioning session to obtain a plurality of radio frequency fingerprint positioning (RFFP) measurements corresponding to the plurality of different bandwidth segments; and A latent feature machine learning (ML) model is applied to each of the plurality of RFFP measurements to obtain a plurality of latent feature representations corresponding to the plurality of RFFP measurements.

7. The method according to claim 6, wherein: The latent feature ML model compresses each RFFP measurement to provide a corresponding latent feature representation of reduced dimensionality.

8. The method according to claim 6, further comprising: A positioning model is applied to the plurality of potential feature representations to obtain one or more positioning parameters associated with the location of the UE.

9. The method of claim 8, wherein the one or more positioning parameters include: the location of the UE; one or more positioning coordinates associated with the position of the UE; one or more positioning measurements associated with the position of the UE; or Any combination of them.

10. The method according to claim 8, wherein: The localization model fuses the plurality of latent feature representations to obtain the one or more localization parameters.

11. The method according to claim 6, further comprising: Send instructions to the network server for: a latent feature model identifier corresponding to the latent feature ML model applied to each of the plurality of RFFP measurements; the data sizes associated with individual latent feature representations provided by the latent feature ML model; dimensions associated with individual latent feature representations provided by the latent feature ML model; a bandwidth size associated with an individual bandwidth segment for measuring the plurality of different bandwidth segments; one or more timestamps corresponding to measurements of the plurality of different bandwidth segments; one or more timestamps corresponding to obtaining the plurality of latent feature representations; The plurality of potential feature representations; or Any combination of them.

12. The method according to claim 6, wherein: The UE stores a plurality of potential feature ML models.

13. The method according to claim 12, further comprising: Assistance data is received from a network server, the assistance data comprising an indication of a latent feature model from the plurality of latent feature ML models to be applied as the latent feature model to the plurality of RFFP measurements during the positioning session.

14. The method of claim 12, wherein: At least two or more of the latent feature ML models of the plurality of latent feature ML models provide latent feature representations having different dimensions.

15. The method according to claim 14, further comprising: The UE's capability of providing potential feature representations with different data dimensions is sent to the network server.

16. The method according to claim 6, further comprising: Receive auxiliary data from a network server, the auxiliary data comprising: an indication of a maximum time gap that can be allowed between measurements of consecutive bandwidth segments of the plurality of different bandwidth segments; an indication of RS opportunities during which the plurality of different bandwidth segments of the RS are transmitted during the positioning session; an indication of a maximum time gap allowed between consecutive RSs measured by the UE; or any combination thereof.

17. The method of claim 16, wherein the assistance data comprises the indication of the maximum time gap that can be allowed between measurements of consecutive bandwidth segments, the method further comprising: The plurality of different bandwidth segments are measured during less than all of the indicated RS opportunities based on consecutive RS opportunities measured by the UE within the maximum time gap.

18. The method according to claim 6, further comprising: Send a capability message to the network server, the capability message including: an indication of a bandwidth of a maximum bandwidth segment of the RS that the UE is able to measure during a single RS opportunity; an indication of a maximum dimension of a potential feature representation capable of being processed at the UE; an indication of a number of potential feature representations that can be stored at the UE for a given dimension of the potential feature representations; an indication of a buffer size available for storing potential feature representations; or Any combination of them.

19. A method of wireless communication performed by a network server, the method comprising: receiving, from a user equipment (UE), a plurality of potential signature representations corresponding to a plurality of radio frequency fingerprint positioning (RFFP) measurements obtained by the UE during a positioning session, wherein the plurality of potential signature representations are based on measurements by the UE of a plurality of different bandwidth segments of a reference signal (RS) at a corresponding plurality of different RS occasions during the positioning session; as well as A positioning model is applied to the plurality of potential feature representations to obtain one or more positioning parameters associated with the location of the UE.

20. The method of claim 19, wherein the one or more positioning parameters include: the location of the UE; one or more positioning coordinates associated with the position of the UE; one or more positioning measurements associated with the position of the UE; or Any combination of them.

21. The method of claim 19, wherein: The localization model fuses the plurality of latent feature representations to obtain the one or more localization parameters.

22. The method according to claim 19, further comprising: Receiving from the UE an indication of: a latent feature model identifier corresponding to a latent feature ML model applied to each of the plurality of RFFP measurements; the data sizes associated with individual latent feature representations provided by the latent feature ML model; dimensions associated with individual latent feature representations provided by the latent feature ML model; a bandwidth size associated with an individual bandwidth segment for measuring the plurality of different bandwidth segments; one or more timestamps corresponding to measurements of the plurality of different bandwidth segments; one or more timestamps corresponding to obtaining the plurality of latent feature representations; or Any combination of them.

23. The method according to claim 19, further comprising: Sending auxiliary data to the UE, the auxiliary data comprising: an indication of a maximum time gap that can be allowed between measurements of consecutive bandwidth segments of the plurality of different bandwidth segments; an indication of RS opportunities during which the plurality of different bandwidth segments of the RS are transmitted during the positioning session; an indication of a maximum time gap allowed between consecutive RSs measured by the UE; or any combination thereof.

24. The method according to claim 19, further comprising: A capability message is received from the UE, where the capability message includes: an indication of a bandwidth of a maximum bandwidth segment of the RS that the UE is able to measure during a single RS opportunity; an indication of a maximum dimension of a potential feature representation capable of being processed at the UE; an indication of a number of potential feature representations that can be stored at the UE for a given dimension of the potential feature representations; an indication of a buffer size available for storing potential feature representations; or Any combination of them.

25. A user equipment (UE), the user equipment (UE) comprising: Memory; at least one transceiver; and at least one processor, the at least one processor being communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: Measuring a plurality of different bandwidth segments of a reference signal (RS) at a corresponding plurality of different RS opportunities during a positioning session to obtain a plurality of radio frequency fingerprint positioning (RFFP) measurements corresponding to the plurality of different bandwidth segments; and A latent feature machine learning (ML) model is applied to each of the plurality of RFFP measurements to obtain a plurality of latent feature representations corresponding to the plurality of RFFP measurements.

26. The UE according to claim 25, wherein: The latent feature ML model compresses each RFFP measurement to provide a corresponding latent feature representation of reduced dimensionality.

27. The UE of claim 25, wherein the at least one processor is further configured to: A positioning model is applied to the plurality of potential feature representations to obtain one or more positioning parameters associated with the location of the UE.

28. The UE of claim 27, wherein the one or more positioning parameters include: the location of the UE; one or more positioning coordinates associated with the position of the UE; one or more positioning measurements associated with the position of the UE; or Any combination of them.

29. The UE according to claim 27, wherein: The localization model fuses the plurality of latent feature representations to obtain the one or more localization parameters.

30. The UE of claim 25, wherein the at least one processor is further configured to: Send instructions to the network server for: a latent feature model identifier corresponding to the latent feature ML model applied to each of the plurality of RFFP measurements; the data sizes associated with individual latent feature representations provided by the latent feature ML model; dimensions associated with individual latent feature representations provided by the latent feature ML model; a bandwidth size associated with an individual bandwidth segment for measuring the plurality of different bandwidth segments; one or more timestamps corresponding to measurements of the plurality of different bandwidth segments; one or more timestamps corresponding to obtaining the plurality of latent feature representations; the plurality of latent feature representations; or Any combination of them.

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