Neural network functions for processing positioning measurement data in user equipment

Abstract

In one aspect, a network component transmits to the UE at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features at the UE, the at least one neural network function being dynamically generated based on machine learning associated with one or more historical measurement procedures. The UE may obtain positioning measurement data associated with the UE and process the positioning measurement data into a respective set of positioning measurement features based on the at least one neural network function. The UE may report the processed set of positioning measurement features to a network component, such as a BS or an LMF.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This patent application claims the benefit of U.S. Provisional Application No. 63 / 061,044, filed August 4, 2020, entitled "NEURAL NETWORK FUNCTIONS FOR POSITIONING MEASUREMENT DATA PROCESSING AT A USER EQUIPMENT," and U.S. Non-Provisional Application No. 17 / 391,373, filed August 2, 2021, entitled "NEURAL NETWORK FUNCTIONS FOR POSITIONING MEASUREMENT DATA PROCESSING AT A USER EQUIPMENT," both of which are assigned to the assignee of the present application and are expressly incorporated herein by reference in their entireties.

[0002] Aspects of the present disclosure relate generally to wireless communications, and more particularly to neural network functionality for positioning measurement data processing in user equipment (UE).

[0003] Wireless communication systems have evolved through various generations, including first-generation analog wireless telephone service (1G), second-generation (2G) digital wireless telephone service (including interim 2.5G networks), third-generation (3G) high-speed data, Internet-enabled wireless service, and fourth-generation (4G) service (e.g., LTE or WiMax). Currently, there are many different types of wireless communication systems in use, including cellular systems and personal communications services (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 Access (GSM) variants of TDMA, etc.

[0004] The fifth-generation (5G) wireless standard, known as New Radio (NR), will enable, among other improvements, higher data rates, a greater number of connections, and better coverage. According to the Next Generation Mobile Networks Alliance, the 5G standard is designed to provide data rates of tens of megabits per second to each of tens of thousands of users, delivering 1 gigabit per second to an office floor with dozens of employees. To support large-scale wireless sensor deployments, hundreds of thousands of simultaneous connections should be supported. Therefore, the spectral efficiency of 5G mobile communications must be significantly increased compared to the current 4G standard. Furthermore, signaling efficiency must be increased and latency significantly reduced compared to current standards. Summary of the Invention [Means for solving the problem]

[0005] The following presents a simplified summary related to one or more aspects disclosed herein. As such, the following summary is not intended to be an extensive overview related to all contemplated aspects, nor is it intended to identify key or critical elements related to all contemplated aspects or to delineate the scope related to any particular aspect. As such, the following summary has the sole purpose of presenting some concepts related to one or more aspects related to the mechanisms disclosed herein in a simplified form prior to the detailed description presented below.

[0006] In one aspect, a method of operating a user equipment (UE) includes obtaining at least one neural network function configured to facilitate positioning measurement data processing in the UE, wherein the at least one neural network function is dynamically generated based on machine learning associated with one or more historical measurement procedures; obtaining positioning measurement data associated with the UE; processing the positioning measurement data into a respective set of positioning measurement features based on the at least one neural network function; and reporting the processed set of positioning measurement features to a network component.

[0007] In some aspects, the obtaining step obtains the positioning measurement data by performing a set of positioning measurements on a reference signal for positioning.

[0008] In some aspects, the obtaining step obtains the positioning measurement data by capturing sensor data via one or more sensors communicatively coupled to the UE.

[0009] In some aspects, the at least one neural network function comprises a first neural network function configured to process the positioning measurement data into a first respective set of positioning measurement features, and the at least one neural network function comprises a second neural network function configured to process the positioning measurement data into a second respective set of the respective set of positioning measurement features.

[0010] In some aspects, the first respective set of positioning measurement features is associated with a lower positioning accuracy compared to the second respective set of positioning measurement features, and the reporting step reports the first respective set of positioning measurement features before the second respective set of positioning measurement features.

[0011] In some aspects, the positioning measurement data comprises an uncompressed representation of raw samples of a reference signal for the positioning, and the processed set of positioning measurement features comprises a compressed representation of the reference signal for the positioning.

[0012] In some aspects, the at least one neural network function comprises multiple neural network functions each configured to facilitate positioning measurement data processing in a UE for a single positioning measurement type or group of positioning measurement types, or the at least one neural network function comprises a single neural network function configured to facilitate positioning measurement data processing in a UE for multiple positioning measurement types.

[0013] In some aspects, the positioning measurement data comprises an estimate of a channel response associated with a reference signal.

[0014] In some aspects, the processing step processes the positioning measurement data into a probability distribution associated with the set of positioning measurement features based on at least one neural network function, and the reporting step reports the probability distribution or a metric based on the probability distribution.

[0015] In one aspect, a method of operating a base station (BS) includes transmitting to a user equipment (UE) at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features at the UE, wherein the at least one neural network function is dynamically generated based on machine learning associated with one or more historical measurement procedures; and receiving from the UE a set of positioning measurement features to be processed based on the at least one neural network function.

[0016] In some aspects, at least one neural network function is dynamically generated at the BS or another network component.

[0017] In some aspects, the positioning measurement data comprises a set of positioning measurements relative to a reference signal for positioning.

[0018] In some aspects, the positioning measurement data comprises sensor data captured via one or more sensors communicatively coupled to the UE.

[0019] In some aspects, the set of positioning measurement features comprises a condensed representation of an initial set of positioning measurements measured at the UE with respect to a reference signal for positioning.

[0020] In some aspects, the method includes recovering an initial set of positioning measurements measured at the UE by decompressing the processed set of positioning measurements into an uncompressed set of positioning measurements, and determining a positioning estimate for the UE based on the uncompressed set of positioning measurements.

[0021] In some aspects, the method includes determining a position estimate for the UE based on the received set of positioning measurements.

[0022] In some aspects, the receiving step comprises receiving from the UE a first respective set of the respective sets of positioning measurement features associated with a first neural network function, and receiving from the UE a second respective set of the respective sets of positioning measurement features based on the second neural network function after the first set of the respective sets of positioning measurement features is received.

[0023] In some aspects, the first respective set of positioning measurement features is associated with a lower positioning accuracy compared to the second respective set of positioning measurement features.

[0024] In some aspects, the positioning measurement data comprises an estimate of a channel response associated with a reference signal.

[0025] In some aspects, the at least one neural network function is configured to facilitate processing of the positioning measurement data into a probability distribution associated with the set of positioning measurement features based on the at least one neural network function, and the receiving step receives the probability distribution or a metric based on the probability distribution.

[0026] In one aspect, a user equipment (UE) includes a memory, at least one transceiver, and at least one processor communicatively coupled to the memory and the at least one transceiver, wherein the at least one processor is configured to: acquire at least one neural network function configured to facilitate positioning measurement data processing at the UE, wherein the at least one neural network function is dynamically generated based on machine learning associated with one or more historical measurement procedures; acquire positioning measurement data associated with the UE; process the positioning measurement data into a respective set of positioning measurement features based on the at least one neural network function; and report the processed set of positioning measurement features to a network component.

[0027] In some aspects, obtaining obtains the positioning measurement data by performing a set of positioning measurements on a reference signal for positioning.

[0028] In some aspects, the obtaining obtains the positioning measurement data by capturing sensor data via one or more sensors communicatively coupled to the UE.

[0029] In some aspects, the at least one neural network function comprises a first neural network function configured to process the positioning measurement data into a first respective set of positioning measurement features, and the at least one neural network function comprises a second neural network function configured to process the positioning measurement data into a second respective set of the respective set of positioning measurement features.

[0030] In some aspects, the first respective set of positioning measurement features is associated with a lower positioning accuracy compared to the second respective set of positioning measurement features, and the reporting includes reporting the first respective set of positioning measurement features before the second respective set of positioning measurement features.

[0031] In some aspects, the positioning measurement data comprises an uncompressed representation of raw samples of a reference signal for the positioning, and the processed set of positioning measurement features comprises a compressed representation of the reference signal for the positioning.

[0032] In some aspects, the at least one neural network function comprises multiple neural network functions each configured to facilitate positioning measurement data processing in a UE for a single positioning measurement type or group of positioning measurement types, or the at least one neural network function comprises a single neural network function configured to facilitate positioning measurement data processing in a UE for multiple positioning measurement types.

[0033] In some aspects, the positioning measurement data comprises an estimate of a channel response associated with a reference signal.

[0034] In some aspects, the processing processes the positioning measurement data into a probability distribution associated with the set of positioning measurement features based on at least one neural network function, and the reporting reports the probability distribution or a metric based on the probability distribution.

[0035] In one aspect, a base station (BS) includes a memory, at least one transceiver, and at least one processor communicatively coupled to the memory and the at least one transceiver, wherein the at least one processor is configured to: transmit, via the at least one transceiver, to a user equipment (UE), at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features at the UE, wherein the at least one neural network function is dynamically generated based on machine learning associated with one or more historical measurement procedures; and receive, via the at least one transceiver, from the UE, a set of positioning measurement features to be processed based on the at least one neural network function.

[0036] In some aspects, at least one neural network function is dynamically generated at the BS or another network component.

[0037] In some aspects, the positioning measurement data comprises a set of positioning measurements relative to a reference signal for positioning.

[0038] In some aspects, the positioning measurement data comprises sensor data captured via one or more sensors communicatively coupled to the UE.

[0039] In some aspects, the set of positioning measurement features comprises a condensed representation of an initial set of positioning measurements measured at the UE with respect to a reference signal for positioning.

[0040] In some aspects, the at least one processor is further configured to recover an initial set of positioning measurements measured at the UE by decompressing the processed set of positioning measurements into an uncompressed set of positioning measurements, and to determine a positioning estimate for the UE based on the uncompressed set of positioning measurements.

[0041] In some aspects, the at least one processor is further configured to determine a position estimate for the UE based on the received set of positioning measurements.

[0042] In some aspects, the receiving comprises receiving from the UE via the at least one transceiver a first respective set of positioning measurement features associated with the first neural network function, and receiving from the UE via the at least one transceiver a second respective set of positioning measurement features based on the second neural network function after the first respective set of positioning measurement features is received.

[0043] In some aspects, the first respective set of positioning measurement features is associated with a lower positioning accuracy compared to the second respective set of positioning measurement features.

[0044] In some aspects, the positioning measurement data comprises an estimate of a channel response associated with a reference signal.

[0045] In some aspects, the at least one neural network function is configured to facilitate processing of the positioning measurement data into a probability distribution associated with the set of positioning measurement features based on the at least one neural network function, and receiving receives the probability distribution or a metric based on the probability distribution.

[0046] In one aspect, a user equipment (UE) includes means for acquiring at least one neural network function configured to facilitate positioning measurement data processing at the UE, wherein the at least one neural network function is dynamically generated based on machine learning associated with one or more historical measurement procedures; means for acquiring positioning measurement data associated with the UE; means for processing the positioning measurement data into a respective set of positioning measurement features based on the at least one neural network function; and means for reporting the processed set of positioning measurement features to a network component.

[0047] In some aspects, obtaining obtains the positioning measurement data by performing a set of positioning measurements on a reference signal for positioning.

[0048] In some aspects, the obtaining obtains the positioning measurement data by capturing sensor data via one or more sensors communicatively coupled to the UE.

[0049] In some aspects, the at least one neural network function comprises a first neural network function configured to process the positioning measurement data into a first respective set of positioning measurement features, and the at least one neural network function comprises a second neural network function configured to process the positioning measurement data into a second respective set of the respective set of positioning measurement features.

[0050] In some aspects, the first respective set of positioning measurement features is associated with a lower positioning accuracy compared to the second respective set of positioning measurement features, and the reporting includes reporting the first respective set of positioning measurement features before the second respective set of positioning measurement features.

[0051] In some aspects, the positioning measurement data comprises an uncompressed representation of raw samples of a reference signal for the positioning, and the processed set of positioning measurement features comprises a compressed representation of the reference signal for the positioning.

[0052] In some aspects, the at least one neural network function comprises multiple neural network functions each configured to facilitate positioning measurement data processing in a UE for a single positioning measurement type or group of positioning measurement types, or the at least one neural network function comprises a single neural network function configured to facilitate positioning measurement data processing in a UE for multiple positioning measurement types.

[0053] In some aspects, the positioning measurement data comprises an estimate of a channel response associated with a reference signal.

[0054] In some aspects, the processing processes the positioning measurement data into a probability distribution associated with the set of positioning measurement features based on at least one neural network function, and the reporting reports the probability distribution or a metric based on the probability distribution.

[0055] In one aspect, a base station (BS) includes means for transmitting to a user equipment (UE) at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features in the UE, wherein the at least one neural network function is dynamically generated based on machine learning associated with one or more historical measurement procedures, and means for receiving from the UE a set of positioning measurement features to be processed based on the at least one neural network function.

[0056] In some aspects, at least one neural network function is dynamically generated at the BS or another network component.

[0057] In some aspects, the positioning measurement data comprises a set of positioning measurements relative to a reference signal for positioning.

[0058] In some aspects, the positioning measurement data comprises sensor data captured via one or more sensors communicatively coupled to the UE.

[0059] In some aspects, the set of positioning measurement features comprises a condensed representation of an initial set of positioning measurements measured at the UE with respect to a reference signal for positioning.

[0060] In some aspects, the method includes means for recovering an initial set of positioning measurements measured at the UE by decompressing the processed set of positioning measurements into an uncompressed set of positioning measurements, and means for determining a positioning estimate for the UE based on the uncompressed set of positioning measurements.

[0061] In some aspects, the method includes means for determining a position estimate for the UE based on the received set of positioning measurements.

[0062] In some aspects, the receiving comprises means for receiving from the UE a first respective set of the respective sets of positioning measurement features associated with the first neural network function, and means for receiving from the UE a second respective set of the respective sets of positioning measurement features based on the second neural network function after the first set of the respective sets of positioning measurement features is received.

[0063] In some aspects, the first respective set of positioning measurement features is associated with a lower positioning accuracy compared to the second respective set of positioning measurement features.

[0064] In some aspects, the positioning measurement data comprises an estimate of a channel response associated with a reference signal.

[0065] In some aspects, the at least one neural network function is configured to facilitate processing of the positioning measurement data into a probability distribution associated with the set of positioning measurement features based on the at least one neural network function, and receiving receives the probability distribution or a metric based on the probability distribution.

[0066] In one aspect, a non-transitory computer-readable medium having stored thereon computer-executable instructions that, when executed by a user equipment (UE), cause the UE to: obtain at least one neural network function configured to facilitate positioning measurement data processing at the UE, wherein the at least one neural network function is dynamically generated based on machine learning associated with one or more historical measurement procedures; obtain positioning measurement data associated with the UE; process the positioning measurement data based on the at least one neural network function into a respective set of positioning measurement features; and report the processed set of positioning measurement features to a network component.

[0067] In some aspects, obtaining obtains the positioning measurement data by performing a set of positioning measurements on a reference signal for positioning.

[0068] In some aspects, the obtaining obtains the positioning measurement data by capturing sensor data via one or more sensors communicatively coupled to the UE.

[0069] In some aspects, the at least one neural network function comprises a first neural network function configured to process the positioning measurement data into a first respective set of positioning measurement features, and the at least one neural network function comprises a second neural network function configured to process the positioning measurement data into a second respective set of the respective set of positioning measurement features.

[0070] In some aspects, the first respective set of positioning measurement features is associated with a lower positioning accuracy compared to the second respective set of positioning measurement features, and the reporting includes reporting the first respective set of positioning measurement features before the second respective set of positioning measurement features.

[0071] In some aspects, the positioning measurement data comprises an uncompressed representation of raw samples of a reference signal for the positioning, and the processed set of positioning measurement features comprises a compressed representation of the reference signal for the positioning.

[0072] In some aspects, the at least one neural network function comprises multiple neural network functions each configured to facilitate positioning measurement data processing in a UE for a single positioning measurement type or group of positioning measurement types, or the at least one neural network function comprises a single neural network function configured to facilitate positioning measurement data processing in a UE for multiple positioning measurement types.

[0073] In some aspects, the positioning measurement data comprises an estimate of a channel response associated with a reference signal.

[0074] In some aspects, the processing processes the positioning measurement data into a probability distribution associated with the set of positioning measurement features based on at least one neural network function, and the reporting reports the probability distribution or a metric based on the probability distribution.

[0075] In one aspect, a non-transitory computer-readable medium having stored thereon computer-executable instructions that, when executed by a base station (BS), cause the BS to transmit to a user equipment (UE) at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features at the UE, wherein the at least one neural network function is dynamically generated based on machine learning associated with one or more historical measurement procedures; and receive from the UE a set of positioning measurement features to be processed based on the at least one neural network function.

[0076] In some aspects, at least one neural network function is dynamically generated at the BS or another network component.

[0077] In some aspects, the positioning measurement data comprises a set of positioning measurements relative to a reference signal for positioning.

[0078] In some aspects, the positioning measurement data comprises sensor data captured via one or more sensors communicatively coupled to the UE.

[0079] In some aspects, the set of positioning measurement features comprises a condensed representation of an initial set of positioning measurements measured at the UE with respect to a reference signal for positioning.

[0080] In some aspects, the one or more instructions further cause the BS to recover an initial set of positioning measurements measured at the UE by decompressing the processed set of positioning measurements into an uncompressed set of positioning measurements, and to determine a positioning estimate for the UE based on the uncompressed set of positioning measurements.

[0081] In some aspects, the one or more instructions further cause the BS to determine a position estimate for the UE based on the received set of positioning measurements.

[0082] In some aspects, the receiving comprises receiving from the UE a first respective set of the respective sets of positioning measurement features associated with the first neural network function, and receiving from the UE a second respective set of the respective sets of positioning measurement features based on the second neural network function after the first set of the respective sets of positioning measurement features is received.

[0083] In some aspects, the first respective set of positioning measurement features is associated with a lower positioning accuracy compared to the second respective set of positioning measurement features.

[0084] In some aspects, the positioning measurement data comprises an estimate of a channel response associated with a reference signal.

[0085] In some aspects, the at least one neural network function is configured to facilitate processing of the positioning measurement data into a probability distribution associated with the set of positioning measurement features based on the at least one neural network function, and receiving receives the probability distribution or a metric based on the probability distribution.

[0086] Other objects and advantages associated with the embodiments disclosed herein will become apparent to those skilled in the art based on the accompanying drawings and detailed description.

[0087] The accompanying drawings are presented to aid in the explanation of various aspects of the present disclosure and are provided solely for the purpose of illustrating the aspects and not for the purpose of limiting the aspects. [Brief explanation of the drawings]

[0088] [Figure 1] FIG. 1 illustrates an exemplary wireless communication system in accordance with various aspects. [Figure 2A] FIG. 1 illustrates an exemplary wireless network structure in accordance with various aspects. [Figure 2B] FIG. 1 illustrates an exemplary wireless network structure in accordance with various aspects. [Figure 3A] 1 is a simplified block diagram of several sample aspects of components that may be employed in a wireless communication node and configured to support communication as taught herein; [Figure 3B] 1 is a simplified block diagram of several sample aspects of components that may be employed in a wireless communication node and configured to support communication as taught herein; [Figure 3C] 1 is a simplified block diagram of several sample aspects of components that may be employed in a wireless communication node and configured to support communication as taught herein; [Figure 4A] FIG. 1 illustrates an example frame structure according to an aspect of the present disclosure. [Figure 4B] FIG. 1 illustrates an example of channels within a frame structure according to aspects of the present disclosure. [Figure 5] FIG. 1 illustrates an example PRS configuration for a cell supported by a wireless node. [Figure 6] FIG. 1 illustrates an example wireless communication system according to various aspects of the present disclosure. [Figure 7] FIG. 1 illustrates an example wireless communication system according to various aspects of the present disclosure. [Figure 8A] 1 is a graph illustrating an RF channel response over time at a receiver according to an aspect of the present disclosure. [Figure 8B] FIG. 1 illustrates this separation of clusters in AoD. [Figure 9] FIG. 1 illustrates a process for wireless communication according to an aspect of the present disclosure. [Figure 10] FIG. 1 illustrates a process for wireless communication according to an aspect of the present disclosure. [Figure 11] 11A-11C illustrate exemplary implementations of the processes of FIGS. 9-10 according to aspects of the present disclosure. [Figure 12] 11A-11C illustrate exemplary implementations of the processes of FIGS. 9-10 according to aspects of the present disclosure. [Figure 13]FIG. 1 illustrates an exemplary neural network according to aspects of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0089] Aspects of the present disclosure are provided in the following description and related drawings, directed to various examples provided for illustrative purposes. Alternative aspects may be devised without departing from the scope of the present disclosure. Additionally, well-known elements of the present disclosure will not be described in detail or will be omitted so as not to obscure the relevant details of the present disclosure.

[0090] The words "exemplary" and / or "example" are used herein to mean "serving as an example, instance, or illustration." Any aspect described herein as "exemplary" and / or "example" is not necessarily to be construed as preferred or advantageous over other aspects. Likewise, the term "aspects of the present disclosure" does not require that all aspects of the present disclosure include the discussed feature, advantage or mode of operation.

[0091] Those skilled in the art will appreciate that the information and signals described below may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the following description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof, depending in part on the particular application, in part on the desired design, in part on the corresponding technology, etc.

[0092] Further, many aspects are described in terms of sequences of actions to be performed by, for example, elements of a computing device. It will be recognized that the various actions described herein may be performed by specific circuitry (e.g., an application-specific integrated circuit (ASIC)), by program instructions executed by one or more processors, or a combination of both. Additionally, the sequences of actions described herein may be considered to be embodied entirely in any form of non-transitory computer-readable storage medium storing a corresponding set of computer instructions that, when executed, cause or instruct the associated processor(s) of the device to perform the functionality described herein. Accordingly, various aspects of the present disclosure may be embodied in a number of different forms, all of which are contemplated to be within the scope of the claimed subject matter. Additionally, for each aspect described herein, the corresponding form of any such aspect may be described herein, for example, as “logic configured to” perform the described actions.

[0093] The terms “user equipment” (UE) and “base station,” as used herein, are not intended to be specific to or otherwise limited to any particular radio access technology (RAT) unless otherwise specified. In general, a UE may be any wireless communication device (e.g., a mobile phone, a router, a tablet computer, a laptop computer, a tracking device, a wearable (e.g., a smart watch, smart glasses, an augmented reality (AR) / virtual reality (VR) headset, etc.), a vehicle (e.g., an automobile, 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 some times) and may communicate with a radio access network (RAN). As used herein, the term “UE” may be referred to interchangeably as an “access terminal” or “AT,” “client device,” “wireless device,” “subscriber device,” “subscriber terminal,” “subscriber station,” “user terminal” or UT,” “mobile terminal,” “mobile station,” or variations thereof. Generally, a UE may communicate with a core network via a RAN, through which the UE may be connected to external networks such as the Internet and to other UEs. Of course, other mechanisms for connecting to the core network and / or the Internet are also possible for a UE, such as via a wired access network, a wireless local area network (WLAN) network (e.g., based on IEEE 802.11, etc.), etc.

[0094] A base station may operate according to one of several RATs with which the UE is communicating, depending on the network in which the UE is deployed, and may alternatively be referred to as an access point (AP), network node, Node B, evolved Node B (eNB), New Radio (NR) Node B (also referred to as gNB or gNode B), etc. Additionally, in some systems, a base station may provide purely edge node signaling functionality, while in other systems, a base station may provide additional control and / or network management functionality. In some systems, a base station may correspond to a customer premises equipment (CPE) or a roadside unit (RSU). In some designs, a base station may correspond to a high-power UE (e.g., a vehicular UE or VUE) that may provide some limited infrastructure functionality. The communication link through which a UE can send signals to a base station is called an uplink (UL) channel (e.g., a reverse traffic channel, a reverse control channel, an access channel, etc.). The communication links through which a base station may send signals to a UE are called downlink (DL) channels or forward link channels (e.g., paging channels, control channels, broadcast channels, forward traffic channels, etc.). As used herein, the term traffic channel (TCH) can refer to either a UL / reverse traffic channel or a DL / forward traffic channel.

[0095] The term "base station" can refer to a single physical transmit / receive point (TRP) or multiple physical TRPs, which may or may not be collocated. For example, when the term "base station" refers to a single physical TRP, the physical TRP may be an antenna of the base station corresponding to the base station's cell. When the term "base station" refers to multiple collocated physical TRPs, the physical TRPs may be an array of antennas of the base station (e.g., as in a multiple-input multiple-output (MIMO) system or when the base station employs beamforming). When the term "base station" refers to multiple non-collocated physical TRPs, the physical TRPs may be a distributed antenna system (DAS) (a network of spatially separated antennas connected to a common source via a transport medium) or a remote radio head (RRH) (a remote base station connected to a serving base station). Alternatively, a non-collocated physical TRP may be a serving base station that receives measurement reports from the UE and neighboring base stations whose reference RF signals the UE is measuring. A TRP is a point from which a base station transmits and receives wireless signals, and therefore, as used herein, references to transmission from or reception at a base station should be understood as references to the particular TRP of the base station.

[0096] An "RF signal" comprises electromagnetic waves of a given frequency that transport information through space between a transmitter and a receiver. As used herein, a transmitter may transmit a single "RF signal" or multiple "RF signals" to a receiver. However, due to the propagation characteristics of RF signals through a multipath channel, the receiver may receive multiple "RF signals" corresponding to each transmitted RF signal. The same transmitted RF signal over different paths between the transmitter and receiver is sometimes referred to as a "multipath" RF signal.

[0097] In accordance with various aspects, FIG. 1 illustrates an exemplary wireless communication system 100. The wireless communication system 100 (sometimes referred to as a wireless wide area network (WWAN)) may include various base stations 102 and various UEs 104. The base stations 102 may include macrocell base stations (high-power cellular base stations) and / or small cell base stations (low-power cellular base stations). In one aspect, the macrocell base stations may include eNBs, where the wireless communication system 100 corresponds to an LTE network, or gNBs, where the wireless communication system 100 corresponds to an NR network, or a combination of both, and the small cell base stations may include femtocells, picocells, microcells, etc.

[0098] The base stations 102 may collectively form a RAN and may interface with a core network 170 (e.g., Evolved Packet Core (EPC) or Next Generation Core (NGC)) through backhaul links 122, and through the core network 170 to one or more location servers 172. In addition to other functions, the base stations 102 may perform functions related to one or more of forwarding user data, radio channel encryption and decryption, integrity protection, header compression, mobility control functions (e.g., handover, dual connectivity), inter-cell interference coordination, connection setup and release, load balancing, distribution for non-access stratum (NAS) messages, NAS node selection, synchronization, RAN sharing, multimedia broadcast multicast services (MBMS), subscriber and equipment tracing, RAN information management (RIM), paging, positioning, and distribution of alert messages. The base stations 102 may communicate with each other directly or indirectly (e.g., through EPC / NGC) via backhaul links 134, which may be wired or wireless.

[0099] The base stations 102 may wirelessly communicate with the UEs 104. Each of the base stations 102 may provide communication coverage for a respective geographic coverage area 110. In one aspect, one or more cells may be supported by the base station 102 in each coverage area 110. A “cell” is a logical communication entity used for communication with a base station (e.g., over some frequency resources referred to as a carrier frequency, component carrier, carrier, band, etc.) and may be associated with an identifier (e.g., a physical cell identifier (PCI), a virtual cell identifier (VCI)) to distinguish between cells operating over the same or different carrier frequencies. In some cases, different cells may be configured according to different protocol types (e.g., machine type communication (MTC), narrowband IoT (NB-IoT), enhanced mobile broadband (eMBB), or others) that may provide access to different types of UEs. Because a cell is supported by a particular base station, the term “cell” can refer to either or both the logical communication entity and the base station that supports it, depending on the context. In some cases, the term "cell" may refer to the geographic coverage area (e.g., sector) of a base station, so long as the carrier frequency can be detected and used for communication within some portion of the geographic coverage area 110.

[0100] While adjacent to macrocell base stations 102, the geographic coverage areas 110 may partially overlap (e.g., within handover regions), and some of the geographic coverage areas 110 may be significantly overlapped by larger geographic coverage areas 110. For example, a small cell base station 102' may have a coverage area 110' that significantly overlaps with the coverage area 110 of one or more macrocell base stations 102. A network including both small cell base stations and macrocell base stations may be referred to as a heterogeneous network. A heterogeneous network may also include Home eNBs (HeNBs) that may serve restricted groups called closed subscriber groups (CSGs).

[0101] The communication link 120 between the base station 102 and the UE 104 may include UL (also called reverse link) transmissions from the UE 104 to the base station 102, and / or downlink (DL) (also called forward link) transmissions from the base station 102 to the UE 104. The communication link 120 may use MIMO antenna techniques, including spatial multiplexing, beamforming, and / or transmit diversity. The communication link 120 may be over one or more carrier frequencies. The allocation of carriers may be asymmetric for DL ​​and UL (e.g., more or fewer carriers may be allocated for DL ​​than for UL).

[0102] The wireless communication system 100 may further include a wireless local area network (WLAN) access point (AP) 150 communicating with a WLAN station (STA) 152 via a communication link 154 in an unlicensed frequency spectrum (e.g., 5 GHz). When communicating in the unlicensed frequency spectrum, the WLAN STA 152 and / or the WLAN AP 150 may perform a clear channel assessment (CCA) or listen-before-talk (LBT) procedure before communicating to determine whether a channel is available.

[0103] The small cell base station 102' may operate in a licensed and / or unlicensed frequency spectrum. When operating in an unlicensed frequency spectrum, the small cell base station 102' may employ LTE or NR technology and may use the same 5 GHz unlicensed frequency spectrum used by the WLAN AP 150. A small cell base station 102' employing LTE / 5G in an unlicensed frequency spectrum may extend coverage to and / or increase the capacity of an access network. NR in an unlicensed spectrum may be referred to as NR-U. LTE in an unlicensed spectrum may be referred to as LTE-U, licensed assisted access (LAA), or MultiFire.

[0104] The wireless communication system 100 may further include a millimeter-wave (mmW) base station 180 in communication with the UE 182 and capable of operating within mmW and / or quasi-mmW frequencies. Extremely high frequency (EHF) is the RF portion of the electromagnetic spectrum. EHF ranges from 30 GHz to 300 GHz and has wavelengths between 1 and 10 millimeters. Radio waves in this band are sometimes referred to as millimeter waves. Sub-mmW may extend down to frequencies of 3 GHz, with wavelengths of 100 millimeters. The very high frequency (SHF) band extends between 3 GHz and 30 GHz, also known as centimeter waves. Communications using the mmW / quasi-mmW radio frequency bands have high path loss and relatively short distances. The mmW base station 180 and the UE 182 may utilize beamforming (transmit and / or receive) over the mmW communication link 184 to compensate for the significant path loss and short distances. It will be appreciated that in alternative configurations, one or more base stations 102 may also transmit using mmW or quasi-mmW and beamforming. Therefore, it will be appreciated that the above illustrations are merely exemplary and should not be construed as limiting the various aspects disclosed herein.

[0105] Transmit beamforming is a technique for focusing an RF signal in a particular 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., UE) is located (relative to the transmitting network node) and projects a stronger downlink RF signal in that particular direction, thereby providing a faster and more powerful RF signal (in terms of data rate) to the receiving device. 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 one or more transmitters broadcasting the RF signal. For example, the network node may use an array of antennas (called a “phased array” or “antenna array”) that creates a beam of RF waves that can be “steered” to points in different directions without actually moving the antennas. Specifically, RF currents from the transmitter are fed to individual antennas with the appropriate phase relationship so that the radio waves from the separate antennas add together to enhance radiation in desired directions while suppressing or eliminating radiation in undesired directions.

[0106] A transmit beam may be quasi-collocated, meaning that the transmit beam appears to a receiver (e.g., a UE) to have the same parameters regardless of whether the network node's own transmit antennas are physically collocated. In NR, there are four types of quasi-collocation (QCL) relationships. In particular, a QCL relationship of a given type means that some parameters for a second reference RF signal on a second beam can be derived from information about a source reference RF signal on a source beam. Thus, if the source reference RF signal is QCL Type A, the receiver can use the source reference RF signal to estimate the Doppler shift, Doppler spread, mean delay, and delay spread of the second reference RF signal transmitted on the same channel. If the source reference RF signal is QCL Type B, the receiver can use the source reference RF signal to estimate the Doppler shift and Doppler spread of the second reference RF signal transmitted on the same channel. If the source reference RF signal is QCL Type C, the receiver can use the source reference RF signal to estimate the Doppler shift and average delay of a second reference RF signal transmitted on the same channel. If the source reference RF signal is QCL Type D, the receiver can use the source reference RF signal to estimate spatial reception parameters of a second reference RF signal transmitted on the same channel.

[0107] In receive beamforming, a receiver uses receive beams to amplify RF signals detected on a given channel. For example, the receiver can increase the gain setting and / or adjust the phase setting of an antenna array in a particular direction to amplify (e.g., increase the gain level of) RF signals received from that direction. Thus, when a receiver is said to beamform in a direction, it means that the beam gain in that direction is greater than the beam gains along other directions, or that the beam gain in that direction is greatest compared to the beam gains in that direction of all other receive beams available to the receiver. This results in a stronger received signal strength (e.g., reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference-plus-noise ratio (SINR), etc.) of RF signals received from that direction.

[0108] The receive beams may be spatially related. Spatial relationship means that parameters for a transmit beam for a second reference signal may be derived from information about the receive beam for the first reference signal. For example, a UE may use a particular receive beam to receive a reference downlink reference signal (e.g., a synchronization signal block (SSB)) from a base station. The UE can then form a transmit beam for sending an uplink reference signal (e.g., a sounding reference signal (SRS)) to that base station based on the parameters of the receive beam.

[0109] Note that a "downlink" beam may be either a transmit beam or a receive beam, depending on the entity that forms it. For example, if a base station forms a downlink beam to transmit a reference signal to a UE, the downlink beam is a transmit beam. However, if the UE forms a downlink beam, the downlink beam is a receive beam for receiving a downlink reference signal. Similarly, an "uplink" beam may be either a transmit beam or a receive beam, depending on the entity that forms it. For example, if a base station forms an uplink beam, the uplink beam is an uplink receive beam, and if the UE forms an uplink beam, the uplink beam is an uplink transmit beam.

[0110] In 5G, the frequency spectrum in which wireless nodes (e.g., base station 102 / 180, UE 104 / 182) operate is divided into multiple frequency ranges: FR1 (450 MHz to 6000 MHz), FR2 (24250 MHz to 52600 MHz), FR3 (above 52600 MHz), and FR4 (between FR1 and FR2). In a multi-carrier system such as 5G, one of the carrier frequencies is called the “primary carrier” or “anchor carrier” or “primary serving cell” or “PCell,” and the remaining carrier frequencies are called “secondary carriers” or “secondary serving cells” or “SCells.” In carrier aggregation, the anchor carrier is the carrier operating on the primary frequency (e.g., FR1) utilized by the UE 104 / 182 and the cell on which the UE 104 / 182 either 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 may be a carrier among licensed frequencies (although this is not always the case). The secondary carrier is a carrier operating on a second frequency (e.g., FR2) that may be configured once an RRC connection is established between the UE 104 and the anchor carrier and may be used to provide additional radio resources. In some cases, the secondary carrier may be a carrier among unlicensed frequencies. Because both the primary uplink carrier and the primary downlink carrier are typically UE-specific, the secondary carrier may contain only necessary signaling information and signals; for example, 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 can change the primary carrier of any UE 104 / 182 at any time. This is done, for example, to balance the load on different carriers.Since a "serving cell" (whether PCell or SCell) corresponds to a carrier frequency / component carrier over which several base stations are communicating, terms such as "cell," "serving cell," "component carrier," and "carrier frequency" may be used interchangeably.

[0111] For example, still referring to FIG. 1 , one of the frequencies utilized by the macrocell base station 102 may be an anchor carrier (i.e., “PCell”), and other frequencies utilized by the macrocell base station 102 and / or the mmW base station 180 may be secondary carriers (“SCells”). Simultaneous transmission and / or reception of multiple carriers allows the UE 104 / 182 to significantly increase its data transmission and / or data reception rates. For example, two aggregated 20 MHz carriers in a multi-carrier system would theoretically lead to a two-fold increase in data rate (i.e., 40 MHz) compared to that achieved with a single 20 MHz carrier.

[0112] The wireless communication system 100 may further include one or more UEs, such as a UE 190, that indirectly connect to one or more communication networks via one or more device-to-device (D2D) peer-to-peer (P2P) links. In the example of FIG. 1, the UE 190 has a D2D P2P link 192 with one of the UEs 104 connected to one of the base stations 102 (e.g., through which the UE 190 may indirectly obtain cellular connectivity) and a D2D P2P link 194 with a WLAN STA 152 connected to a WLAN AP 150 (through which the UE 190 may indirectly obtain WLAN-based Internet connectivity). In one example, the D2D P2P links 192 and 194 may be supported using any well-known D2D RAT, such as LTE Direct (LTE-D), WiFi Direct (WiFi-D), Bluetooth®, etc.

[0113] Wireless communications system 100 may further include a UE 164, which may communicate with macrocell base station 102 via communications link 120 and / or with mmW base station 180 via mmW communications link 184. For example, macrocell base station 102 may support a PCell and one or more SCells for UE 164, and mmW base station 180 may support one or more SCells for UE 164.

[0114] 2A illustrates an exemplary wireless network structure 200. For example, the NGC 210 (also referred to as a "5GC") may be viewed functionally as a control plane function 214 (e.g., UE registration, authentication, network access, gateway selection, etc.) and a user plane function 212 (e.g., UE gateway function, access to data network, IP routing, etc.), which operate cooperatively to form a core network. A user plane interface (NG-U) 213 and a control plane interface (NG-C) 215 connect the gNB 222 to the NGC 210, specifically to the control plane function 214 and the user plane function 212. In an additional configuration, the eNB 224 may also be connected to the NGC 210 via the NG-C 215 to the control plane function 214 and the NG-U 213 to the user plane function 212. Additionally, the eNB 224 may communicate directly with the gNB 222 via a backhaul connection 223. In some configurations, the New RAN 220 may have only one or more gNBs 222, while other configurations include one or more of both eNBs 224 and gNBs 222. Either the gNBs 222 or the eNBs 224 may be in communication with the UEs 204 (e.g., any of the UEs shown in FIG. 1). Another optional aspect may include a location server 230, which may be in communication with the NGC 210 to provide location assistance to the UEs 204. The location servers 230 may be implemented as multiple separate servers (e.g., physically separate servers, different software modules on a single server, different software modules spread across multiple physical servers, etc.), or alternatively, each may correspond to a single server. The location server 230 may be configured to support one or more location services for UEs 204 that can connect to the location server 230 via the core network NGC 210 and / or via the Internet (not shown). Furthermore, the location server 230 may be integrated into a component of the core network, or alternatively, may be external to the core network.

[0115] 2B illustrates another exemplary wireless network structure 250. For example, the NGC 260 (also referred to as a “5GC”) may be viewed functionally as a control plane function provided by an Access and Mobility Management Function (AMF) / User Plane Function (UPF) 264 and a user plane function provided by a Session Management Function (SMF) 262, operating cooperatively to form a core network (i.e., the NGC 260). A user plane interface 263 and a control plane interface 265 connect the eNB 224 to the NGC 260, specifically to the SMF 262 and the AMF / UPF 264, respectively. In an additional configuration, the gNB 222 may also be connected to the NGC 260 via the control plane interface 265 to the AMF / UPF 264 and the user plane interface 263 to the SMF 262. Additionally, eNB 224 may communicate directly with gNB 222 via backhaul connection 223, with or without gNB direct connectivity to NGC 260. In some configurations, New RAN 220 may have only one or more gNBs 222, while other configurations include one or more of both eNB 224 and gNB 222. Either gNB 222 or eNB 224 may communicate with UE 204 (e.g., any of the UEs shown in FIG. 1). Base stations of New RAN 220 communicate with the AMF side of AMF / UPF 264 via the N2 interface and with the UPF side of AMF / UPF 264 via the N3 interface.

[0116] The AMF functions include registration management, connection management, reachability management, mobility management, lawful interception, transport for session management (SM) messages between the UE 204 and the SMF 262, a transparent proxy service for routing SM messages, access authentication and access authorization, transport for short message service (SMS) messages between the UE 204 and a short message service function (SMSF) (not shown), and a security anchor functionality (SEAF). The AMF also interacts with an authentication server function (AUSF) (not shown) and the UE 204 to receive intermediate keys established as a result of the UE 204 authentication process. In the case of authentication based on a universal mobile telecommunications system (UMTS) subscriber identity module (USIM), the AMF retrieves security material from the AUSF. The AMF functions also include security context management (SCM). The SCM receives keys from the SEAF that the SCM uses to derive access network specific keys. The functionality of the AMF also includes location service management for regulated services, transport for location service messages between the UE 204 and the Location Management Function (LMF) 270 and between the New RAN 220 and the LMF 270, EPS bearer identifier allocation for interworking with the Evolved Packet System (EPS), and UE 204 mobility event notification. In addition, the AMF also supports functionality for non-3GPP access networks (3GPP is a registered trademark).

[0117] The functions of the UPF include acting as an anchor point for intra / inter-RAT mobility (when applicable), acting as an external protocol data unit (PDU) session point for interconnection to a data network (not shown), routing and forwarding of packets, packet inspection, user plane policy rule enforcement (e.g., gating, redirection, traffic steering), lawful interception (user plane collection), traffic usage reporting, Quality of Service (QoS) processing for the user plane (e.g., UL / DL rate enforcement, reflective QoS marking in DL), UL traffic validation (Service Data Flow (SDF) to QoS flow mapping), transport-level packet marking in UL and DL, DL packet buffering and DL data notification triggering, and sending and forwarding one or more "end markers" to the source RAN node.

[0118] The functions of the SMF 262 include session management, UE Internet Protocol (IP) address allocation and management, selection and control of user plane functions, configuration of traffic steering in the UPF to route traffic to the appropriate destination, control of policy enforcement and part of QoS, and downlink data notification. The interface through which the SMF 262 communicates with the AMF side of the AMF / UPF 264 is called the N11 interface.

[0119] Another optional aspect may include an LMF 270, which may be in communication with the NGC 260 to provide location assistance to the UE 204. The LMF 270 may be implemented as multiple separate servers (e.g., physically separate servers, different software modules on a single server, different software modules spread across multiple physical servers, etc.), or alternatively, each may correspond to a single server. The LMF 270 may be configured to support one or more location services for the UE 204 that can connect to the LMF 270 via the core network NGC 260 and / or via the Internet (not shown).

[0120] 3A, 3B, and 3C illustrate several example components (represented by corresponding blocks) that may be incorporated within 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 location server 230 and LMF 270) to support file transmission operations as taught herein. It will be appreciated that these components may be implemented in different types of devices in different implementations (e.g., in an ASIC, in a system-on-chip (SoC), etc.). The illustrated components may also be incorporated into other devices in a communication system. For example, other devices in the system may include components similar to the illustrated components to provide similar functionality. Also, a given device may include one or more of the components. For example, a device may include multiple transceiver components that enable the device to operate on multiple carriers and / or communicate via different technologies.

[0121] The UE 302 and the base station 304 each include a wireless wide area network (WWAN) transceiver 310 and 350, respectively, configured to communicate via one or more wireless communications networks (not shown), such as an NR network, an LTE network, a GSM network, etc. The WWAN transceivers 310 and 350 may 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.) over a targeted wireless communications medium (e.g., some set of time / frequency resources within a particular frequency spectrum). The WWAN transceivers 310 and 350 may be variously configured to transmit and encode signals 318 and 358, respectively (e.g., messages, indications, information, etc.), and conversely, to receive and decode signals 318 and 358, respectively (e.g., messages, indications, information, pilots, etc.), in accordance with the designated RAT. In particular, transceivers 310 and 350 include one or more transmitters 314 and 354, respectively, for transmitting and encoding signals 318 and 358, respectively, and one or more receivers 312 and 352, respectively, for receiving and decoding signals 318 and 358, respectively.

[0122] The UE 302 and base station 304 also, at least in some cases, include wireless local area network (WLAN) transceivers 320 and 360, respectively. The WLAN transceivers 320 and 360 may be connected to one or more antennas 326 and 366, respectively, for communicating with other network nodes, such as other UEs, access points, base stations, etc., via at least one designated RAT (e.g., WiFi, LTE-D, Bluetooth, etc.) over a target wireless communications medium. The WLAN transceivers 320 and 360 may be variously configured to transmit and encode signals 328 and 368, respectively (e.g., messages, indications, information, etc.), and conversely, to receive and decode signals 328 and 368, respectively (e.g., messages, indications, information, pilots, etc.), in accordance with the designated RAT. In particular, transceivers 320 and 360 include one or more transmitters 324 and 364, respectively, for transmitting and encoding signals 328 and 368, respectively, and one or more receivers 322 and 362, respectively, for receiving and decoding signals 328 and 368, respectively.

[0123] The transceiver circuitry including the transmitter and receiver may in some implementations comprise an integrated device (e.g., embodied as transmitter and receiver circuitry in a single communications device), in some implementations comprise separate transmitter and receiver devices, or in other implementations may be embodied in other ways. In one aspect, the transmitter may include or be coupled to multiple antennas (e.g., antennas 316, 336, and 376), such as an antenna array that enables each device to perform transmit “beamforming” as described herein. Similarly, the receiver may include or be coupled to multiple antennas (e.g., antennas 316, 336, and 376), such as an antenna array that enables each device to perform receive beamforming as described herein. In one aspect, the transmitter and receiver may share multiple identical antennas (e.g., antennas 316, 336, and 376), such that each device can only receive or transmit at a given time, but not both at the same time. The wireless communication devices of apparatus 302 and / or 304 (e.g., one or both of transceivers 310 and 320 and / or 350 and 360) may also include a network listen module (NLM) or the like for performing various measurements.

[0124] Devices 302 and 304 also, at least in some cases, include satellite positioning system (SPS) receivers 330 and 370. SPS receivers 330 and 370 may be connected to one or more antennas 336 and 376, respectively, for receiving SPS signals 338 and 378, respectively, such as Global Positioning System (GPS) signals, Global Navigation Satellite System (GLONASS) signals, Galileo signals, Beidou signals, Navigation Satellite System of India (NAVIC), Quasi-Zenith Satellite System (QZSS), etc. SPS receivers 330 and 370 may comprise any suitable hardware and / or software for receiving and processing SPS signals 338 and 378, respectively. SPS receivers 330 and 370 request information and actions from other systems as appropriate and perform the calculations necessary to determine the positions of devices 302 and 304 using acquired measurements via any suitable SPS algorithms.

[0125] The base station 304 and the network entity 306 each include at least one network interface 380 and 390 for communicating with other network entities. For example, the network interfaces 380 and 390 (e.g., one or more network access ports) may be configured to communicate with one or more network entities via a wire-based or wireless backhaul connection. In some aspects, the network interfaces 380 and 390 may be implemented as transceivers configured to support wire-based or wireless signal communication. This communication may involve, for example, sending and receiving messages, parameters, or other types of information.

[0126] The devices 302, 304, and 306 also include other components that may be used in conjunction with operations as disclosed herein. The UE 302 includes processor circuitry implementing a processing system 332, for example, for providing functionality related to false base station (FBS) detection as disclosed herein and for providing other processing functionality. The base station 304 includes a processing system 384, for example, for providing functionality related to FBS detection as disclosed herein and for providing other processing functionality. The network entity 306 includes a processing system 394, for example, for providing functionality related to FBS detection as disclosed herein and for providing other processing functionality. In an aspect, the processing systems 332, 384, and 394 may include, for example, one or more general-purpose processors, multi-core processors, ASICs, digital signal processors (DSPs), field programmable gate arrays (FPGAs), or other programmable logic devices or processing circuitry.

[0127] Apparatus 302, 304, and 306 include memory circuitry implementing memory components 340, 386, and 396, respectively (e.g., each including a memory device) for maintaining information (e.g., information indicative of reserved resources, thresholds, parameters, etc.). In some cases, apparatus 302, 304, and 306 may include measurement modules 342 and 388, respectively. Measurement modules 342 and 388 may be hardware circuits that are part of or coupled to processing systems 332, 384, and 394, respectively, that, when executed, cause apparatus 302, 304, and 306 to perform the functionality described herein. Alternatively, measurement modules 342 and 388 may be memory modules (as shown in Figures 3A-3C) stored in memory components 340, 386, and 396, respectively, that, when executed by processing systems 332, 384, and 394, cause devices 302, 304, and 306 to perform the functionality described herein.

[0128] The UE 302 may include one or more sensors 344 coupled to the processing system 332 to provide motion and / or orientation information that is independent of motion data derived from signals received by the WWAN transceiver 310, the WLAN transceiver 320, and / or the GPS receiver 330. By way of example, the sensors 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. Moreover, the sensors 344 may include multiple different types of devices and combine their outputs to provide motion information. For example, the sensors 344 may use a combination of a multi-axis accelerometer and an orientation sensor to provide the ability to calculate a position in a 2D and / or 3D coordinate system.

[0129] Additionally, the UE 302 includes a user interface 346 for providing indications (e.g., audio and / or visual indications) to a user and / or for receiving user input (e.g., upon user actuation of a sensing device such as a keypad, touch screen, microphone, etc.). Although not shown, the devices 304 and 306 may also include user interfaces.

[0130] Referring more particularly to the processing system 384, on the downlink, IP packets from the network entity 306 may be provided to the processing system 384. The processing system 384 may implement functionality for an RRC layer, a Packet Data Convergence Protocol (PDCP) layer, a Radio Link Control (RLC) layer, and a Medium Access Control (MAC) layer. The processing system 384 may provide RRC layer functionality related to 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 related to header compression / decompression, security (encryption, decryption, integrity protection, integrity verification), and handover support functions; RLC layer functionality related to transfer of upper layer packet data units (PDUs), error correction through ARQ, concatenation, segmentation, and reassembly of RLC service data units (SDUs), re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality related to mapping between logical channels and transport channels, scheduling information reporting, error correction, priority handling, and logical channel prioritization.

[0131] The transmitter 354 and receiver 352 may perform Layer 1 functionality related to various signal processing functions. Layer 1, including the physical (PHY) layer, may include error detection on transport channels, forward error correction (FEC) coding / decoding of transport channels, interleaving, rate matching, mapping onto physical channels, modulation / demodulation of physical channels, and MIMO antenna processing. The transmitter 354 handles mapping to signal constellations based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-phase shift keying (M-PSK), M-ary quadrature amplitude modulation (M-QAM)). The coded and modulated symbols may then be split into parallel streams. Each stream may then be mapped to an orthogonal frequency division multiplexing (OFDM) subcarrier, multiplexed with reference signals (e.g., pilots) in the time and / or frequency domains, and then combined together using an inverse fast Fourier transform (IFFT) to generate a physical channel carrying the time-domain OFDM symbol stream. The OFDM stream is spatially precoded to generate multiple spatial streams. Channel estimates from a channel estimator may be used to determine the coding and modulation scheme, as well as for spatial processing. The channel estimates may be derived from a reference signal and / or channel condition feedback transmitted by the UE 302. Each spatial stream may then be provided to one or more different antennas 356. The transmitter 354 may modulate an RF carrier with each spatial stream for transmission.

[0132] At the UE 302, the receiver 312 receives signals through its respective antenna 316. The receiver 312 recovers the information modulated onto the RF carriers and provides the information to the processing system 332. The transmitter 314 and receiver 312 perform Layer 1 functionality related to various signal processing functions. The receiver 312 may perform spatial processing on the information to recover any spatial streams destined for the UE 302. If multiple spatial streams are destined for the UE 302, they may be combined 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 comprises a separate OFDM symbol stream for each subcarrier of the OFDM signal. The symbols on each subcarrier, as well as the reference signal, are recovered and demodulated by determining the signal constellation point that was most likely transmitted by the base station 304. These soft decisions may be based on channel estimates calculated by a channel estimator. The soft decisions are then decoded and deinterleaved to recover the data and control signals originally transmitted on the physical channel by the base station 304. The data and control signals are then provided to a processing system 332 that performs Layer 3 and Layer 2 functionality.

[0133] In the UL, the processing system 332 performs demultiplexing between transport and logical channels, packet reassembly, decryption, header decompression, and control signal processing to recover IP packets from the core network. The processing system 332 is also responsible for error detection.

[0134] Similar to the functionality described with respect to DL transmission by the base station 304, the processing system 332 provides RRC layer functionality related to system information (e.g., MIB, SIB) acquisition, RRC connection, and measurement reporting; PDCP layer functionality related to header compression / decompression and security (encryption, decryption, integrity protection, integrity verification); RLC layer functionality related to transfer of upper layer PDUs, error correction via ARQ, concatenation, segmentation, and reassembly of RLC SDUs, resegmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality related to mapping between logical channels and transport channels, multiplexing of MAC SDUs onto transport blocks (TBs), demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction via HARQ, priority handling, and logical channel prioritization.

[0135] Channel estimates derived by the channel estimator from a reference signal or feedback transmitted by the base station 304 may be used by the transmitter 314 to select an appropriate coding and modulation scheme and to facilitate spatial processing. The spatial streams generated by the transmitter 314 may be provided to different antennas 316. The transmitter 314 may modulate an RF carrier with each spatial stream for transmission.

[0136] The UL transmission is processed at the base station 304 in a manner similar to that described with respect to the receiver function at the UE 302. The receiver 352 receives the signal through its respective antenna 356. The receiver 352 recovers the information modulated onto the RF carrier and provides the information to the processing system 384.

[0137] In the UL, the processing system 384 performs demultiplexing between transport and logical channels, packet reassembly, decryption, header decompression, and control signal processing to recover IP packets from the UE 302. The IP packets from the processing system 384 may be provided to the core network. The processing system 384 is also responsible for error detection.

[0138] For convenience, devices 302, 304, and / or 306 are illustrated in Figures 3A-3C as including various components that may be configured in accordance with various examples described herein. However, it will be appreciated that the illustrated blocks may have different functionality in different designs.

[0139] The various components of devices 302, 304, and 306 may communicate with each other via data buses 334, 382, ​​and 392, respectively. The components of FIGS. 3A-3C may be implemented in various ways. In some implementations, the components of FIGS. 3A-3C may be implemented in one or more circuits, such as, for example, one or more processors and / or one or more ASICs (which may include one or more processors), where each circuit may use and / or incorporate at least one memory component for storing information or executable code used by the circuit to provide its functionality. For example, some or all of the functionality represented by blocks 310-346 may be performed by the processor and memory components of UE 302 (e.g., by execution of appropriate code and / or by appropriate configuration of processor components). Similarly, some or all of the functionality represented by blocks 350-388 may be performed by the processor and memory components of base station 304 (e.g., by execution of appropriate code and / or by appropriate configuration of processor components). Additionally, some or all of the functionality represented by blocks 390-396 may be implemented by the processor and memory components of the network entity 306 (e.g., by execution of appropriate code and / or by appropriate configuration of the processor components). For simplicity, various operations, acts, and / or functions are described herein as being performed "by the UE," "by the base station," "by the positioning entity," etc. However, it will be appreciated that such operations, acts, and / or functions may actually be performed by particular components or combinations of components, such as the UE, base station, positioning entity, etc., such as the processing systems 332, 384, 394, the transceivers 310, 320, 350, and 360, the memory components 340, 386, and 396, the measurement modules 342 and 388, etc.

[0140] 4A is a diagram 400 illustrating an example of a DL frame structure according to an embodiment of the present disclosure. FIG. 4B is a diagram 430 illustrating an example of channels within a DL frame structure according to an embodiment of the present disclosure. Other wireless communication technologies may have different frame structures and / or different channels.

[0141] LTE, and possibly NR, utilizes OFDM on the downlink and single-carrier frequency division multiplexing (SC-FDM) on the uplink. However, unlike LTE, NR has the option to also use OFDM on the uplink. OFDM and SC-FDM partition the system bandwidth into multiple (K) orthogonal subcarriers, also commonly referred to as tones, bins, etc. Each subcarrier may be modulated with data. Generally, modulation symbols are sent in the frequency domain with OFDM and in the time domain with SC-FDM. The spacing between adjacent subcarriers may be fixed, and the total number of subcarriers (K) may depend on the system bandwidth. For example, the subcarrier spacing may be 15 kHz, and the minimum resource allocation (resource block) may be 12 subcarriers (i.e., 180 kHz). Thus, the nominal FFT size may be equal to 128, 256, 512, 1024, or 2048 for a system bandwidth of 1.25, 2.5, 5, 10, or 20 megahertz (MHz), respectively. The system bandwidth may also be partitioned into subbands. For example, a subband may cover 1.08 MHz (i.e., 6 resource blocks), and there may be 1, 2, 4, 8, or 16 subbands for a system bandwidth of 1.25, 2.5, 5, 10, or 20 MHz, respectively.

[0142] LTE supports a single numerology (subcarrier spacing, symbol length, etc.). In contrast, NR may support multiple numerologies; for example, subcarrier spacings of 15 kHz, 30 kHz, 60 kHz, 120 kHz, and 204 kHz or greater may be available. Table 1, provided below, lists some various parameters for different NR numerologies. [Table 1]

[0143] In the example of Figures 4A and 4B, a 15 kHz numerology is used. Thus, in the time domain, a frame (e.g., 10 ms) is divided into 10 equally sized subframes of 1 ms each, with each subframe containing one time slot. In Figures 4A and 4B, time is represented horizontally (e.g., on the X-axis) with time increasing from left to right, and frequency is represented vertically (e.g., on the Y-axis) with frequency increasing (or decreasing) from bottom to top.

[0144] A resource grid may be used to represent a time slot, and each time slot includes one or more time-parallel resource blocks (RBs) (also called physical RBs (PRBs)) in the frequency domain. The resource grid is further divided into multiple resource elements (REs). An RE may correspond to one symbol length in the time domain and one subcarrier in the frequency domain. In the numerology of FIGS. 4A and 4B, for a normal cyclic prefix, an RB may include 12 consecutive subcarriers in the frequency domain and 7 consecutive symbols in the time domain (OFDM symbols for DL ​​and SC-FDMA symbols for UL) to obtain a total of 84 REs. For an extended cyclic prefix, an RB may include 12 consecutive subcarriers in the frequency domain and 6 consecutive symbols in the time domain to obtain a total of 72 REs. The number of bits carried by each RE depends on the modulation scheme.

[0145] As shown in Figure 4A, some of the REs carry DL reference (pilot) signals (DL-RS) for channel estimation at the UE. The DL-RS may include demodulation reference signals (DMRS) and channel state information reference signals (CSI-RS), example locations of which are labeled "R" in Figure 4A.

[0146] 4B shows an example of various channels in a DL subframe of a frame. The physical downlink control channel (PDCCH) carries DL control information (DCI) in one or more control channel elements (CCEs), each containing nine RE groups (REGs), and each REG containing four consecutive REs in an OFDM symbol. The DCI carries information about UL resource allocation (persistent and non-persistent) and a description of DL data to be transmitted to the UE. Multiple (e.g., up to eight) DCIs may be configured in the PDCCH, and these DCIs may have one of several formats. For example, there are various DCI formats for UL scheduling, for non-MIMO DL scheduling, for MIMO DL scheduling, and for UL power control.

[0147] The primary synchronization signal (PSS) is used by the UE to determine subframe / symbol timing and physical layer identity. The secondary synchronization signal (SSS) is used by the UE to determine the physical layer cell identity group number and radio frame timing. Based on the physical layer identity and the physical layer cell identity group number, the UE can determine the PCI. Based on the PCI, the UE can determine the location of the DL-RS mentioned above. The physical broadcast channel (PBCH) carrying the MIB may be logically grouped with the PSS and SSS to form an SSB (also called SS / PBCH). The MIB provides the number of RBs in the DL system bandwidth and the system frame number (SFN). The physical downlink shared channel (PDSCH) carries user data, broadcast system information not transmitted over the PBCH, such as system information blocks (SIBs), and paging messages.

[0148] In some cases, the DL RS shown in Figure 4A may be a positioning reference signal (PRS). Figure 5 shows an example PRS configuration 500 for a cell supported by a wireless node (such as base station 102). Figure 5 shows a PRS configuration 500 for a cell supported by a wireless node (such as base station 102), including a system frame number (SFN), a cell-specific subframe offset (Δ PRS )552, and PRS periodicity (T PRS ) 520 shows how the PRS positioning occasion is determined. Typically, the cell-specific PRS subframe configuration is determined by a "PRS configuration index" I included in the Observed Time Difference of Arrival (OTDOA) assistance data. PRS The PRS periodicity (T PRS ) 520 and cell-specific subframe offset (Δ PRS ) is the PRS composition index I as shown in Table 2 below. PRS It is prescribed based on the following. [Table 2]

[0149] The PRS configuration is specified in terms of the SFN of the cell transmitting the PRS. PRS The PRS instance for the first subframe of the downlink subframes may satisfy the following:

number

[0150] As shown in Figure 5, the cell-specific subframe offset Δ PRS 552 may be defined in terms of the number of subframes transmitted, starting from system frame number 0 (slot "number 0" marked as slot 550) to the start of the first (subsequent) PRS positioning occasion. In the example in FIG. 5, the number of consecutive positioning subframes (N PRS ) is equal to 4, that is, each shaded block representing PRS positioning occasions 518a, 518b, and 518c represents four subframes.

[0151] In some aspects, in the OTDOA assistance data for a particular cell, the UE may include a PRS configuration index I PRS Upon receiving the PRS, the UE determines the PRS periodicity T PRS 520 and PRS subframe offset Δ PRSThe UE may then determine the radio frame, subframe, and slot in which the PRS is scheduled in the cell (e.g., using equation (1)). The OTDOA assistance data may be determined, for example, by a location server (e.g., location server 230, LMF 270) and includes assistance data for the reference cell and several neighboring cells supported by various base stations.

[0152] Typically, PRS occasions from all cells in a network that use the same frequency are aligned in time and may have a known, fixed time offset (e.g., cell-specific subframe offset 552) relative to other cells in the network that use different frequencies. In an SFN synchronous network, all wireless nodes (e.g., base stations 102) may be aligned on both frame boundaries and system frame numbers. Thus, in an SFN synchronous network, all cells supported by various wireless nodes may use the same PRS configuration index for any particular frequency of PRS transmission. On the other hand, in an SFN asynchronous network, various wireless nodes may be aligned on frame boundaries but may not be aligned on system frame numbers. Thus, in an SFN asynchronous network, the PRS configuration index for each cell may be configured separately by the network so that PRS occasions are aligned in time.

[0153] If the UE can acquire the cell timing (e.g., SFN) of at least one of the cells, e.g., the reference cell or the serving cell, the UE may determine the timing of the PRS occasions of the reference cell and neighbor cells for OTDOA positioning. The timing of other cells may then be derived by the UE, e.g., based on the assumption that PRS occasions from different cells overlap.

[0154] A set of resource elements used for transmitting a PRS is called a "PRS resource." The set of resource elements may span multiple PRBs in the frequency domain and N (e.g., one or more) consecutive symbols 460 within a slot 430 in the time domain. In a given OFDM symbol 460, the PRS resource occupies consecutive PRBs. A PRS resource is represented by at least the following parameters: a PRS resource identifier (ID), a sequence ID, a comb size N, a resource element offset in the frequency domain, a starting slot and symbol, the number of symbols per PRS resource (i.e., the duration of the PRS resource), and QCL information (e.g., QCL with other DL reference signals). In some designs, one antenna port is supported. The comb size indicates the number of subcarriers in each symbol carrying a PRS. For example, a comb size of comb4 means that every fourth subcarrier in a given symbol carries a PRS.

[0155] A "PRS resource set" is a set of RS resources used for transmitting PRS signals, and each PRS resource has a PRS resource ID. In addition, PRS resources within a PRS resource set are associated with the same transmission / reception point (TRP). A PRS resource ID within a PRS resource set is associated with a single beam transmitted from a single TRP (a TRP may transmit one or more beams). That is, each PRS resource in a PRS resource set may be transmitted on a different beam; therefore, a "PRS resource" may also be referred to as a "beam." Note that this does not affect whether the TRP and beam on which the PRS is transmitted are known to the UE. A "PRS occasion" is one instance of a periodically repeating time window (e.g., a group of one or more consecutive slots) in which a PRS is expected to be transmitted. A PRS occasion may also be referred to as a "PRS positioning occasion," a "positioning occasion," or simply an "occasion."

[0156] Note that the terms "positioning reference signal" and "PRS" may sometimes refer to specific reference signals used for positioning in LTE or NR systems. However, as used herein, unless otherwise indicated, the terms "positioning reference signal" and "PRS" refer to any type of reference signal that can be used for positioning, such as, but not limited to, a PRS signal in LTE or NR, a navigation reference signal (NRS) in 5G, a transmitter reference signal (TRS), a cell-specific reference signal (CRS), a channel state information reference signal (CSI-RS), a primary synchronization signal (PSS), a secondary synchronization signal (SSS), or an SSB.

[0157] SRS is an uplink-only signal transmitted by UEs to help the base station obtain channel state information (CSI) for each user. Channel state information describes how the RF signal propagates from the UE to the base station and accounts for the combined effects of scattering, fading, and power attenuation over distance. Systems use SRS for resource scheduling, link adaptation, massive MIMO, beam management, etc.

[0158] Several extensions beyond the previous definition of SRS have been proposed for SRS-P for positioning, such as a new staggered pattern in SRS resources, a new comb type for SRS, a new sequence for SRS, a larger number of SRS resource sets per component carrier, and a larger number of SRS resources per component carrier. Additionally, the parameters "SpatialRelationInfo" and "PathLossReference" will be configured based on DL RSs from neighboring TRPs. Furthermore, one SRS resource may be transmitted outside the active bandwidth portion (BWP), and one SRS resource may span multiple component carriers. Finally, a UE may transmit from multiple SRS resources through the same transmission beam for UL-AoA. All of these are additional features to the current SRS framework, configured through RRC higher layer signaling (and potentially triggered or activated through MAC Control Element (CE) or Downlink Control Information (DCI)).

[0159] As mentioned above, SRS in NR is a UE-specific configured reference signal transmitted by the UE used for the purpose of sounding the uplink radio channel. Similar to CSI-RS, such sounding provides knowledge of various levels of radio channel characteristics. At one extreme, SRS may be used in a gNB simply to obtain signal strength measurements, e.g., for UL beam management purposes. On the other hand, SRS may be used in a gNB to obtain detailed amplitude and phase estimates as a function of frequency, time, and space. In NR, channel sounding with SRS supports a more diverse set of use cases compared to LTE (e.g., downlink CSI acquisition for reciprocity-based gNB transmit beamforming (downlink MIMO), uplink CSI acquisition for link adaptation and codebook / non-codebook-based precoding for uplink MIMO, uplink beam management, etc.).

[0160] The SRS can be configured using various options. The time / frequency mapping of the SRS resource is defined by the following characteristics: Duration N symb SRS The time length of the SRS resource can be 1, 2, or 4 consecutive OFDM symbols within a slot, in contrast to LTE, which only allows a single OFDM symbol per slot. Starting symbol position l0 - The starting symbol of an SRS resource may be located anywhere within the last 6 OFDM symbols of a slot, as long as the resource does not straddle the last boundary of the slot. Repetition factor R—For SRS resources configured using frequency hopping, the repetition allows the same set of subcarriers to be sounded in R consecutive OFDM symbols before the next hop occurs (as used herein, “hop” specifically refers to a frequency hop). For example, values ​​of R are 1, 2, 4, and R≦N symb SRS is. Transmission comb spacing K TC and Com Offset k TC SRS resources may occupy resource elements (REs) of a frequency domain comb structure, with comb spacing either 2 REs or 4 REs as in LTE. Such a structure allows frequency domain multiplexing of different SRS resources of the same or different users on different combs, where the different combs are offset from each other by an integer number of REs. Comb offsets are defined with respect to PRB boundaries and range from 0, 1, ..., K. TC -1 RE. Therefore, the comb spacing K TC For = 2, there are two different combs available for multiplexing if needed, with comb spacing K TC At =4, there are four different combs available. Periodicity and slot offset in case of periodic / semi-persistent SRS. · Sounding bandwidth within the bandwidth portion.

[0161] For low-latency positioning, the gNB may trigger a UL SRS-P via DCI (e.g., the transmitted SRS-P may include repetition or beam sweeping to enable several gNBs to receive the SRS-P). Alternatively, the gNB may send information regarding aperiodic PRS transmissions to the UE (e.g., this configuration may include information regarding PRSs from multiple gNBs to enable the UE to perform timing calculations for positioning (UE-based) or reporting (UE-assisted)). While various aspects of the present disclosure relate to DL PRS-based positioning procedures, some or all of such aspects may also apply to UL SRS-P-based positioning procedures.

[0162] It should be noted that the terms "sounding reference signal," "SRS," and "SRS-P" sometimes refer to specific reference signals used for positioning in LTE or NR systems. However, as used herein, unless otherwise indicated, the terms "sounding reference signal," "SRS," and "SRS-P" refer to any type of reference signal that can be used for positioning, such as, but not limited to, an SRS signal in LTE or NR, a navigation reference signal (NRS) in 5G, a transmitter reference signal (TRS), a random access channel (RACH) signal for positioning (e.g., a RACH preamble such as Msg-1 in a four-step RACH procedure or Msg-A in a two-step RACH procedure).

[0163] 3GPP® Rel. 16 introduced various NR positioning aspects aimed at increasing the location accuracy of positioning schemes involving measurements related to one or more UL or DL ​​PRSs (e.g., larger bandwidth (BW), FR2 beam sweeping, angle-based measurements such as angle-of-arrival (AoA) and angle-of-departure (AoD) measurements, multi-cell round-trip time (RTT) measurements, etc.). When latency reduction is a priority, UE-based positioning techniques (e.g., DL-only techniques without UL location measurement reporting) are typically used. However, when latency is less of an issue, UE-assisted positioning techniques may be used, whereby UE-measured data is reported to a network entity (e.g., location server 230, LMF 270, etc.). The latency associated with UE-assisted positioning techniques can be reduced somewhat by implementing an LMF in the RAN.

[0164] Layer 3 (L3) signaling (e.g., RRC or Location Positioning Protocol (LPP)) is typically used to transport reports comprising location-based data related to UE-assisted positioning techniques. L3 signaling is associated with a relatively large latency (e.g., greater than 100 ms) compared to Layer 1 (L1, i.e., PHY layer) signaling or Layer 2 (L2, i.e., MAC layer) signaling. In some cases, a smaller latency (e.g., less than 100 ms, less than 10 ms, etc.) between the UE and the RAN for location-based reporting may be desired. In such cases, L3 signaling may not be able to reach these smaller latency levels. L3 signaling for positioning measurements may include any combination of the following: one or more TOA, TDOA, RSRP, or Rx-Tx measurements; One or more AoA / AoD (e.g. currently it is only agreed that gNB->LMF reports DL AoA and UL AoD) measurements, One or more multipath reporting measurements, e.g., ToA per path, RSRP, AoA / AoD (e.g., currently only ToA per path is allowed in LTE) One or more movement states (e.g., walking, driving, etc.) and trajectories (e.g., for the current UE), and / or One or more reporting quality indicators.

[0165] More recently, L1 and L2 signaling has been contemplated for use in conjunction with PRS-based reporting. For example, L1 and L2 signaling is currently used in some systems to transport CSI reports (e.g., reports of channel quality indication (CQI), precoding matrix indicator (PMI), layer indicator (Li), L1-RSRP, etc.). A CSI report may comprise a set of fields in a predefined order (e.g., defined by a relevant standard). A single UL transmission (e.g., on a PUSCH or PUCCH) may include multiple reports, referred to herein as “subordinate reports,” arranged according to a predefined priority (e.g., defined by a relevant standard). In some designs, the predefined order may be based on the associated subreport periodicity (e.g., aperiodic / semi-persistent / periodic (A / SP / P) on PUSCH / PUCCH), measurement type (e.g., whether L1-RSRP), serving cell index (e.g., in case of carrier aggregation (CA)), and reportconfigID. In two-part CSI reporting, Part 1 of all reports is grouped together, Part 2 is grouped separately, and each group is coded separately (e.g., Part 1 payload size is fixed based on configuration parameters, while Part 2 size is variable and depends on the configuration parameters and the associated Part 1 content). The number of coded bits / symbols output after coding and rate matching is calculated based on the number of input bits and beta coefficients per the associated standard. A link (e.g., a time offset) is defined between the measured RS instance and the corresponding report. In some designs, CSI-like reporting of PRS-based measurement data using L1 and L2 signaling may be implemented.

[0166] FIG. 6 illustrates an exemplary wireless communications system 600 according to various aspects of the present disclosure. In the example of FIG. 6, a UE 604, which may correspond to any of the UEs described above with respect to FIG. 1 (e.g., UE 104, UE 182, UE 190, etc.), is attempting to calculate an estimate of its location or assist another entity (e.g., a base station or core network component, another UE, a location server, a third-party application, etc.) to calculate an estimate of its location. The UE 604 may communicate wirelessly with multiple base stations 602a-d (collectively, base stations 602), which may correspond to any combination of base stations 102 or 180 and / or WLAN AP 150 in FIG. 1, using RF signals and standardized protocols for modulation of RF signals and exchange of information packets. By extracting different types of information from the exchanged RF signals and utilizing the layout of the wireless communications system 600 (i.e., base station locations, geometric arrangements, etc.), the UE 604 may determine, or assist in determining, its location in a predefined reference frame. In one aspect, the UE 604 may specify its location using a two-dimensional coordinate system, although the aspects disclosed herein are not so limited and may be applicable to determining location using a three-dimensional coordinate system if additional dimensions are desired. Additionally, while Figure 6 shows one UE 604 and four base stations 602, it will be appreciated that there may be more UEs 604 and more or fewer base stations 602.

[0167] To support position estimation, base stations 602 may be configured to broadcast reference RF signals (e.g., positioning reference signals (PRS), cell-specific reference signals (CRS), channel state information reference signals (CSI-RS), synchronization signals, etc.) to UEs 604 within their coverage areas to enable the UEs 604 to measure reference RF signal timing differences (e.g., OTDOA or RSTD) between pairs of network nodes and / or identify beams that best excite the LOS or shortest radio path between the UE 604 and the transmitting base station 602. Identifying LOS / shortest path beams is important not only because these beams can subsequently be used for OTDOA measurements between pairs of base stations 602, but also because identifying these beams can directly provide some positioning information based on the beam direction. Furthermore, these beams may then be used for other position estimation methods that require accurate ToA, such as methods based on round-trip time estimation.

[0168] As used herein, a "network node" may be a base station 602, a cell of a base station 602, a remote radio head, an antenna of a base station 602, the location of which is different from the location of the base station 602 itself, or any other network entity capable of transmitting a reference signal. Furthermore, as used herein, a "node" may refer to either a network node or a UE.

[0169] A location server (e.g., location server 230) may send assistance data to the UE 604, including identification of one or more neighboring cells of the base station 602 and configuration information for the reference RF signal transmitted by each neighboring cell. Alternatively, the assistance data may originate directly from the base station 602 itself (e.g., in periodically broadcast overhead messages, etc.). Alternatively, the UE 604 may detect neighboring cells of the base station 602 without using assistance data. The UE 604 may measure and (optionally) report (e.g., based in part on assistance data, if provided) the OTDOA from individual network nodes and / or the RSTD between reference RF signals received from pairs of network nodes. Using these measurements and the known location of the measured network node (i.e., the base station 602 or antenna that transmitted the reference RF signal measured by the UE 604), the UE 604 or location server can determine the distance between the UE 604 and the measured network node, thereby calculating the location of the UE 604.

[0170] The term “position estimate” is used herein to refer to an estimate of the location of a UE 604, which may be geographic (e.g., may comprise latitude, longitude, and possibly altitude) or urban (e.g., may comprise an address, a building designation, or a precise point or area in or near a building or address, e.g., a particular entrance to a building, a particular room or suite in a building, or a landmark such as a town square). A position estimate may also be referred to as a “location,” “position,” “fix,” “position fix,” “location fix,” “location estimate,” “fix estimate,” or some other term. Means of obtaining a location estimate may be generally referred to as “positioning,” “locating,” or “position fixing.” A particular solution for obtaining a position estimate may be referred to as a “position solution.” A particular method for obtaining a position estimate as part of a position solution may be referred to as a “position method” or a “positioning method.”

[0171] The term “base station” can refer to a single physical transmission point or multiple physical transmission points that may or may not be collocated. For example, when the term “base station” refers to a single physical transmission point, the physical transmission point may be a base station antenna corresponding to the cell of the base station (e.g., base station 602). When the term “base station” refers to multiple collocated physical transmission points, the physical transmission point may be an array of base station antennas (e.g., as in a MIMO system or when the base station employs beamforming). When the term “base station” refers to multiple non-collocated physical transmission points, the physical transmission point may be a distributed antenna system (DAS) (a network of spatially separated antennas connected to a common source via a transport medium) or a remote radio head (RRH) (a remote base station connected to a serving base station). Alternatively, a non-collocated physical transmission point may be a serving base station that receives measurement reports from the UE (e.g., UE 604) and neighbor base stations whose reference RF signals the UE is measuring. 6 illustrates an aspect in which base stations 602a and 602b form a DAS / RRH 620. For example, base station 602a may be a serving base station for UE 604, and base station 602b may be a neighboring base station for UE 604. Thus, base station 602b may be an RRH for base station 602a. Base stations 602a and 602b may communicate with each other via a wired or wireless link 622.

[0172] To accurately determine the location of a UE 604 using the OTDOA and / or RSTD between RF signals received from a pair of network nodes, the UE 604 needs to measure a reference RF signal received via the LOS path (or the shortest NLOS path where no LOS path is available) between the UE 604 and the network node (e.g., base station 602, antenna). However, the RF signal travels on several other paths, not just the LOS / shortest path between the transmitter and receiver, as the RF signal spreads from the transmitter and reflects off other objects, such as hills, buildings, and water, on its way to the receiver. Thus, FIG. 6 shows several LOS paths 610 and several NLOS paths 612 between the base station 602 and the UE 604. Specifically, FIG. 6 shows base station 602a transmitting via LOS path 610a and NLOS path 612a, base station 602b transmitting via LOS path 610b and two NLOS paths 612b, base station 602c transmitting via LOS path 610c and NLOS path 612c, and base station 602d transmitting via two NLOS paths 612d. As shown in FIG. 6, each NLOS path 612 reflects off several objects 630 (e.g., buildings). As will be appreciated, each LOS path 610 and NLOS path 612 transmitted by base station 602 may be transmitted by a different antenna of base station 602 (e.g., as in a MIMO system) or may be transmitted by the same antenna of base station 602 (thereby illustrating RF signal propagation). Furthermore, as used herein, the term “LOS path” refers to the shortest path between the transmitter and receiver, which may be the shortest NLOS path, rather than the actual LOS path.

[0173] In one aspect, one or more of the base stations 602 may be configured to use beamforming to transmit RF signals. In that case, some of the available beams may focus the transmitted RF signals along the LOS path 610 (e.g., the beams producing the highest antenna gain along the LOS path), while other available beams may focus the transmitted RF signals along the NLOS path 612. A beam that has high gain along one path and therefore focuses the RF signals along that path may still have some RF signals propagating along other paths, the strength of which, of course, depends on the beam gain along those other paths. An “RF signal” comprises electromagnetic waves that transport information through space between a transmitter and a receiver. As used herein, a transmitter may transmit a single “RF signal” or multiple “RF signals” to a receiver. However, as explained further below, a receiver may receive multiple “RF signals” corresponding to each transmitted RF signal due to the propagation characteristics of RF signals through a multipath channel.

[0174] When the base station 602 uses beamforming to transmit RF signals, the intended beam for data communication between the base station 602 and the UE 604 is the beam carrying the RF signal reaching the UE 604 with the highest signal strength (e.g., as indicated by received signal received power (RSRP) or SINR in the presence of directional interfering signals), while the intended beam for location estimation is the beam carrying the RF signal exciting the shortest path or LOS path (e.g., LOS path 610). For some frequency bands and typically used antenna systems, these are the same beam. However, in other frequency bands, such as mmW, multiple antenna elements may typically be used to create a narrow transmit beam, which may not be the same beam. As described below with reference to FIG. 7, in some cases, the signal strength of the RF signal on the LOS path 610 may be weaker (e.g., due to obstructions) than the signal strength of the RF signal on the NLOS path 612, through which the RF signal arrives later due to propagation delay.

[0175] 7 illustrates an exemplary wireless communication system 700 according to various aspects of the present disclosure. In the example of FIG. 7, a UE 704, which may correspond to the UE 604 of FIG. 6, is attempting to calculate an estimate of its location or to assist another entity (e.g., a base station or core network component, another UE, a location server, a third-party application, etc.) to calculate an estimate of its location. The UE 704 may communicate wirelessly with a base station 702, which may correspond to one of the base stations 602 in FIG. 6, using RF signals and standardized protocols for modulation of the RF signals and exchange of information packets.

[0176] As shown in Figure 7, a base station 702 utilizes beamforming to transmit multiple beams 711-715 of RF signals. Each beam 711-715 may be formed and transmitted by an array of antennas at the base station 702. While Figure 7 shows the base station 702 transmitting five beams 711-715, as can be appreciated, there may be more or fewer than five beams, beam shapes such as peak gain, width, and sidelobe gain may vary among the transmitted beams, and some of the beams may be transmitted by different base stations.

[0177] A beam index may be assigned to each of the multiple beams 711-715 to distinguish RF signals associated with one beam from RF signals associated with another beam. Furthermore, RF signals associated with a particular beam among the multiple beams 711-715 may carry a beam index indicator. The beam index may also be derived from the transmission time of the RF signal, e.g., frame, slot, and / or OFDM symbol number. The beam index indicator may be, for example, a 3-bit field for uniquely distinguishing up to eight beams. If two different RF signals are received with different beam indices, this indicates that the RF signals were transmitted using different beams. If two different RF signals share a common beam index, this indicates that the different RF signals are transmitted using the same beam. Another way to describe two RF signals being transmitted using the same beam is that the antenna port used for transmission of the first RF signal is quasi-colocated in space with the antenna port used for transmission of the second RF signal.

[0178] In the example of FIG. 7, UE 704 receives NLOS data stream 723 of RF signals transmitted on beam 713 and LOS data stream 724 of RF signals transmitted on beam 714. While FIG. 7 depicts NLOS data stream 723 and LOS data stream 724 as single lines (dashed and solid lines, respectively), it will be appreciated that NLOS data stream 723 and LOS data stream 724 may each comprise multiple rays (i.e., “clusters”) before reaching UE 704, for example, due to the propagation characteristics of RF signals through a multipath channel. For example, a cluster of RF signals is formed when electromagnetic waves reflect off multiple surfaces of an object, and the reflections reach a receiver (e.g., UE 704) from approximately the same angle, each traveling a few wavelengths (e.g., centimeters) more or less than the others. A “cluster” of received RF signals generally corresponds to a single transmitted RF signal.

[0179] In the example of FIG. 7, NLOS data stream 723 is not originally intended for UE 704, although as can be appreciated, like the RF signal on NLOS path 612 in FIG. 6, it could be. However, it is reflected from a reflector 740 (e.g., a building) and reaches UE 704 unobstructed, and therefore may still be a relatively strong RF signal. In contrast, LOS data stream 724 is intended for UE 704 but passes through obstacles 730 (e.g., vegetation, buildings, hills, disruptive environments such as clouds or smoke, etc.) that can significantly degrade the RF signal. As can be appreciated, although LOS data stream 724 is weaker than NLOS data stream 723, LOS data stream 724 arrives at UE 704 before NLOS data stream 723 because it follows a shorter path from base station 702 to UE 704.

[0180] As described above, the beam of interest for data communication between a base station (e.g., base station 702) and a UE (e.g., UE 704) is the beam that carries the RF signal that reaches the UE with the highest signal strength (e.g., highest RSRP or SINR), while the beam of interest for position estimation is the beam that excites the LOS path and carries the RF signal that has the highest gain along the LOS path among all other beams (e.g., beam 714). That is, even if beam 713 (an NLOS beam) weakly excites the LOS path (due to the propagation characteristics of RF signals despite not being focused along the LOS path), that weak signal, if any, on the LOS path of beam 713 may not be as reliably detectable (compared to that from beam 714) and thus result in a larger error in performing positioning measurements.

[0181] The target beam for data communications and the target beam for position estimation may be the same beam for some frequency bands, but may not be the same beam for other frequency bands, such as mmW. Thus, referring to Figure 7, if a UE 704 is engaged in a data communications session with a base station 702 (e.g., when the base station 702 is the serving base station for the UE 704) and attempts to simply measure a reference RF signal transmitted by the base station 702, the target beam for the data communications session may be beam 713 because it carries an unobstructed NLOS data stream 723. However, the target beam for position estimation is beam 714 because it carries the strongest LOS data stream 724, despite being obstructed.

[0182] FIG. 8A is a graph 800A illustrating an RF channel response over time at a receiver (e.g., UE 704) according to an embodiment of the present disclosure. Under the channel shown in FIG. 8A, the receiver receives a first cluster of two RF signals on channel taps at time T1, a second cluster of five RF signals on channel taps at time T2, a third cluster of five RF signals on channel taps at time T3, and a fourth cluster of four RF signals on channel taps at time T4. In the example of FIG. 8A, because the first cluster of RF signals at time T1 arrives first, it is presumed to be a line-of-sight (LOS) data stream (i.e., a data stream arriving via line-of-sight or shortest path) and may correspond to LOS data stream 724. The third cluster at time T3 is composed of the strongest RF signals and may correspond to NLOS data stream 723. From the transmitter's perspective, each cluster of received RF signals may comprise portions of RF signals transmitted at different angles; therefore, each cluster may be said to have a different angle of departure (AoD) from the transmitter. FIG. 8B is a diagram 800B illustrating this separation of clusters in the AoD. The RF signal transmitted in AoD range 802a may correspond to one cluster (e.g., "Cluster 1") in FIG. 8A, and the RF signal transmitted in AoD range 802b may correspond to a different cluster (e.g., "Cluster 3") in FIG. 8A. Note that while the AoD ranges of the two clusters shown in FIG. 8B are spatially separated, the clusters may be separated in time, or the AoD ranges of some clusters may partially overlap. For example, this may occur when two separate buildings at the same AoD from the transmitter reflect a signal toward the receiver. Note that while FIG. 8A illustrates clusters of 2 to 5 channel taps (or "peaks"), it will be appreciated that the clusters may have more or fewer channel taps than shown.

[0183] As explained above, for network-based positioning in cellular systems, the gNB typically transmits a reference signal (e.g., PRS), and the UE is configured to measure and report several pre-defined metrics, such as reference signal received power (RSRP), time of arrival (TOA), round trip time (RTT), reference signal time difference (RSTD), etc. The network (e.g., gNB, LMF, etc.) then combines information from the reported measurements to estimate the UE's position.

[0184] To reduce signaling overhead, the UE typically collects more measurement data than is actually reported to the gNB. However, the particular subset of measurement parameters that may facilitate more accurate positioning may vary between locations, based on gNB-specific or UE-specific configuration, etc. Such measurement parameters may include: Parameters that are neutral with respect to the UE or gNB, such as physics-based models (e.g., round trip times with circular contours). gNB-specific parameters such as gNB characteristics (e.g., location, downtilt, transmit power), gNB-side implementation issues (e.g., gNB time synchronization error, clock drift, antenna-baseband delay or hardware group delay), BSA errors (e.g., some eNB locations are wrong or inaccurate). UE specific parameters (e.g. clock drift, antenna-baseband delay or hardware group delay, device type such as vehicle or phone, or specific brand of vehicle or phone, chipset type, etc.).

[0185] Accordingly, one or more aspects of the present disclosure are directed to applying dynamically generated neural network functions based on machine learning (ML) based on historical measurement procedures to new positioning measurement data. In some designs, as described in more detail below, the neural network functions can be fine-tuned (or optimized) based on ML for various operating conditions. In some designs, such aspects may facilitate various technical advantages, such as more flexibility with respect to feature reporting (e.g., not having to use a set of pre-defined feature rules, so that the network can generate more sophisticated rules based on operating conditions, device information, or configuration, etc.) as well as more accurate UE position estimation while keeping signaling overhead at a manageable level (e.g., by using ML techniques to filter out some positioning measurement data in a manner that does not introduce inaccuracies into the UE positioning estimate).

[0186] Hereinafter, reference will be made to positioning measurement “features.” As used herein, a positioning measurement “feature” is a processed (e.g., compressed) representation of raw positioning measurement data. In some designs, processing (e.g., or refinement or compression) of the raw positioning measurement data into respective positioning measurement features may be performed for various reasons, such as reducing the amount of positioning measurement data to be transported over a physical channel between the UE and the gNB. Examples of positioning measurement features include time of arrival (e.g., TOA TDOA, OTDOA, etc.), reference signal time difference, angle of departure (AoD), angle of arrival (AoA), timing and magnitude of a predetermined number of peaks in the channel estimate, other channel estimation information such as a power delay profile (PDP), etc.

[0187] 9 illustrates an example process 900 for wireless communication according to an aspect of the present disclosure. In one aspect, the process 900 may be performed by a UE, such as the UE 302 of FIG.

[0188] At 910, the UE 302 (e.g., receiver 312, receiver 322, etc.) obtains at least one neural network function configured to facilitate positioning measurement data processing at the UE, where the at least one neural network function is dynamically generated based on machine learning associated with one or more historical measurement procedures. In some designs, the at least one neural network function may be received from a network entity (e.g., BS 304). In some designs, the at least one neural network function may be generated by a network entity (e.g., network entity 306, such as an LMF) or an external server and then relayed to the UE 302 via the serving BS. For example, the one or more historical measurement procedures may be filtered based on one or more criteria (e.g., location, gNB, carrier, etc.) and input as training data to a machine learning algorithm that outputs a set of offsets, algorithms, and / or processing rules, referred to herein as a “neural network function,” which can be used to filter or process the positioning measurement data into respective positioning measurement features. In some designs, one or more historical measurement procedures may be associated with different UEs (e.g., crowdsourcing), and the type of UE model or operating conditions may also be used to filter the training data fed to the machine learning algorithm that generates the neural network functions.

[0189] At 920, the UE 302 (e.g., receiver 312, receiver 322, receiver 330, sensor 344, measurement module 342, etc.) acquires positioning measurement data associated with the UE. For example, the positioning measurement data may comprise wireless wide area network (WWAN) positioning measurement data, WLAN positioning measurement data, global navigation satellite system (GNSS) positioning measurement data, sensor measurement data, etc. In some designs, the positioning measurement data may be obtained by performing a set of positioning measurements relative to a reference signal for positioning (e.g., a PRS, etc.). With respect to the sensor measurement data, in some designs, the positioning measurement data may comprise sensor data captured by one or more sensors, such as sensor 344 (e.g., visual data or image data captured by a camera of the UE 302, from which landmarks may be identified relative to a particular location, etc.). In one example, the positioning measurement data may comprise an estimate of a channel response associated with the reference signal. In some designs, the positioning measurement data may include channel estimation information such as PDP (e.g., measured on one antenna or beam or across multiple antennas or beams, where in the case of multiple antennas or beams, different PDPs can be used to jointly estimate time and angle measurements, such as AoA or AoD measurements).

[0190] At 930, the UE 302 (e.g., processing system 332, measurement module 342, etc.) processes the positioning measurement data into respective sets of positioning measurement features based on at least one neural network function. In some designs, each set of positioning measurement features may be acquired over a period of time. For example, an initial simplified set of positioning measurement features may be acquired initially to determine a coarse location estimate of the UE 302, and then a more complex set of positioning measurement features may be acquired to determine a more refined estimate of the UE 302. In this case, a different neural network function may be defined for each set of positioning measurement features.

[0191] At 940, the UE 302 (e.g., transmitter 314, transmitter 324, etc.) reports the processed set of positioning measurement features to a network component. In some designs, the processed set of positioning measurement features is reported to a serving BS (or gNB) of the UE 302 (e.g., if the LMF is integrated with the serving BS). In other designs, the processed set of positioning measurement features is transmitted to a network entity (e.g., an LMF) with the BS or gNB acting as a relay.

[0192] 10 illustrates an example process 1000 for wireless communication according to an aspect of the present disclosure. In one aspect, the process 1000 may be performed by a BS, such as the BS 304 of FIG.

[0193] At 1010, the BS 304 (e.g., transmitter 354, transmitter 364, etc.) transmits to the UE at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features at the UE, where the at least one neural network function is dynamically generated based on machine learning associated with one or more historical measurement procedures. In some designs, the at least one neural network function may be generated by a network entity (e.g., network entity 306, such as the LMF) or an external server. For example, the one or more historical measurement procedures may be filtered based on one or more criteria (e.g., location, gNB, carrier, etc.) and input as training data to a machine learning algorithm that outputs a set of offsets, algorithms, and / or processing rules, referred to herein as a “neural network function,” which can be used to filter or process the positioning measurement data into respective positioning measurement features. In some designs, the one or more historical measurement procedures may be associated with different UEs (e.g., crowdsourcing), and the type of UE model or operating conditions may also be used to filter the training data supplied to the machine learning algorithm that generates the neural network function. In one example, the positioning measurement data may comprise an estimate of a channel response associated with a reference signal (e.g., a PDP that may be measured on one antenna or beam or across multiple antennas or beams; in the case of multiple antennas or beams, different PDPs may be used to jointly estimate time and angle measurements such as AoA or AoD measurements).

[0194] At 1020, the BS 304 (e.g., receiver 352, receiver 362, etc.) receives from the UE a set of positioning measurement features that are processed based on at least one neural network function. In some designs, if the BS 304 supports an LMF, the BS 304 may determine a positioning estimate based on the received set of positioning measurement features. In other designs, the BS 304 may forward the received set of positioning measurement features to an LMF to perform the positioning estimate.

[0195] 9-10 , in some designs, the at least one neural network function may comprise a first neural network function configured to process the positioning measurement data into a first respective set of positioning measurement features, and the at least one neural network function may comprise a second neural network function configured to process the positioning measurement data into a second respective set of positioning measurement features. As described above, the first respective set of positioning measurement features may be associated with lower positioning accuracy compared to the second respective set of positioning measurement features, and the first respective set of positioning measurement features may be transmitted by the UE 302 to the network before the second respective set of positioning measurement features. In this case, the network (e.g., LMF) may quickly determine a coarse location estimate for the UE 302 and later refine the coarse location estimate to more accurately determine the UE location.

[0196] 9-10, in some designs, the positioning measurement data may comprise an uncompressed representation of raw samples of a reference signal for positioning, and the processed set of positioning measurement features may comprise a compressed representation of the reference signal for positioning.

[0197] 9-10 , in some designs, the at least one neural network function may comprise multiple neural network functions each configured to facilitate positioning measurement data processing in the UE for a single positioning measurement type or a group of positioning measurement types. In other designs, the at least one neural network function comprises a single neural network function configured to facilitate positioning measurement data processing in the UE for multiple positioning measurement types. Thus, the neural network functions may be implemented with different granularities with respect to location, gNB status (e.g., one neural network function for serving cells, another neural network function for non-serving cells, etc.). Thus, network operators can flexibly fine-tune the range of positioning feature reporting granularity to a desired level.

[0198] 9-10 , in some designs, the processed set of positioning measurement features comprises a compressed representation of an initial set of positioning measurements measured at the UE with respect to a reference signal for positioning. In some designs, the BS 304 may determine a positioning estimate for the UE based on the compressed set of positioning measurements (e.g., in a scenario where the BS 304 includes an integrated LMF). In this case, because there is no need to recreate the UE measurements, a loss function for training the neural network function may be directly related to the position estimation error. In other designs, the BS 304 may recover the initial set of positioning measurements measured at the UE by decompressing the processed set of positioning measurements into an uncompressed set of positioning measurements, and then determine a positioning estimate for the UE based on the uncompressed set of positioning measurements (e.g., in a scenario where the BS 304 includes an integrated LMF). In this case, the feature processing or extraction at the UE 302 may take the form of an encoder of an autoencoder neural network, and the loss function for training may be related to the discrepancy between the original UE measurements and the recreated or recovered UE measurements.

[0199] 9-10 , in some designs, the processing at 930 may comprise processing the positioning measurement data into a probability distribution associated with the set of positioning measurement features based on at least one neural network function, and the reporting at 940 (or 1020) may comprise the probability distribution or a metric based on the probability distribution. In a particular example, the at least one neural network function may be configured to process the estimated channel response as input and generate a probability distribution of the features (e.g., ToAs) as output. The UE may then report the probability distribution of the TOAs itself or a derived metric such as a variance or specified percentile based on the probability distribution of the TOAs.

[0200] 9-10, in some designs, the UE feature or BS feature processing neural network function may be specific to: A specific base station (BS) or group of BSs (e.g., based on cell ID), career, Location area, a positioning measurement type or a group of positioning measurement types, a beam or group of beams, or Any combination thereof.

[0201] FIG. 11 illustrates an example implementation 1100 of the processes 900-1000 of FIGS. 9-10 according to aspects of the present disclosure.

[0202] Referring to FIG. 11, the UE side measurements y1...y n are input to neural network function 1102 and neural network function 1104. Neural network functions 1102 and 1104 use the UE side measurements y1...y n9. In 1106-1108, the gNB extracts the processed set of positioning measurements from the UE side.

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[0203] Referring to FIG. 11, the recovered set of positioning measurements

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[0204] gNB side positioning measurement characteristics Z1…Z m are also input to gNB measurement processing modules 1114-1116. In some designs, the measurement processing modules may be mapped to neural network functions generated based on machine learning.

[0205] In some designs, the output of measurement processing modules 1110-1112 may include likelihoods of each UE positioning measurement feature across a candidate set of position estimates for the UE (or candidate region).

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[0206] Next, the likelihood

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[0207] 12 shows an example implementation 1200 of processes 900-1000 of FIGS. 9-10 according to another aspect of the present disclosure. Process 1200 of FIG. 12 is similar to process 1200, except that the autoencoder function is not used (e.g., the gNB processes the features itself in compressed form without first decompressing the features into the original measurements).

[0208] Referring to FIG. 12, the UE side measurements y1...y n are input to neural network functions 1202 and 1204. Neural network functions 1202 and 1204 use the UE side measurements y1...y n 9 to a set of UE-side positioning measurement features (e.g., values) suitable for transmission to the gNB. The resulting UE-side positioning measurement features may be transmitted by the UE to the gNB as part of a UE OTA report. The processing at 1102-1104 corresponds to an example implementation of 930 in FIG. 9.

[0209] 12, UE-side positioning measurement features are input to UE measurement processing modules 1206-1208. In some designs, the measurement processing modules may be mapped to neural network functions generated based on machine learning.

[0210] gNB side positioning measurement characteristics Z1…Z m are also input to the gNB measurement processing modules 1210-1212. In some designs, the measurement processing modules may be mapped to neural network functions generated based on machine learning.

[0211] In some designs, the output of measurement processing modules 1206-1208 may include likelihoods of each UE positioning measurement feature across a candidate set of position estimates for the UE (or candidate region).

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[0212] Next, the likelihood

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[0213] Next, we provide additional discussion of neural networks and machine learning in general.

[0214] Machine learning can be used to generate models that can be used to facilitate various aspects related to processing data. One particular application of machine learning relates to generating measurement models for processing reference signals for positioning (e.g., PRS), such as feature extraction, reporting of reference signal measurements (e.g., selecting which extracted features to report), etc.

[0215] Machine learning models are generally classified as either supervised or unsupervised. Supervised models may be further subcategorized as either regression models or classification models. Supervised learning involves learning a function that maps inputs to outputs based on example input-output pairs. For example, given a training dataset with two variables, age (input) and height (output), a supervised learning model can be generated to predict a person's height based on their age. In regression models, the output is continuous. An example of a regression model is linear regression, which simply attempts to find a line that best fits the data. Extensions of linear regression include multiple regression (e.g., finding a plane of best fit) and polynomial regression (e.g., finding a curve of best fit).

[0216] Another example of a machine learning model is a decision tree model. In a decision tree model, a tree structure is defined by multiple nodes. Decisions are used to move from a root node at the top of the decision tree to leaf nodes (i.e., nodes with no further child nodes) at the bottom of the decision tree. In general, a larger number of nodes in a decision tree model correlates with higher decision accuracy.

[0217] Another example of a machine learning model is a decision forest. A random forest is an ensemble learning technique that recreates decision trees. A random forest involves creating multiple decision trees using a bootstrapped dataset of the original data and randomly selecting a subset of variables at each step of the decision tree. The model then selects all modes of prediction for each decision tree. By relying on a "majority voting" model, the risk of error from individual trees is reduced.

[0218] Another example of a machine learning model is a neural network (NN). A neural network is essentially a network of mathematical equations. A neural network accepts one or more input variables and, by passing them through a network of equations, results in one or more output variables. In other words, a neural network takes in a vector of inputs and returns a vector of outputs.

[0219] 13 illustrates an exemplary neural network 1300 according to an embodiment of the present disclosure. The neural network 1300 includes an input layer "i" that receives "n" (one or more) inputs (denoted as "Input 1," "Input 2," and "Input n"), one or more hidden layers (denoted as hidden layers "h1," "h2," and "h3") for processing the inputs from the input layer, and an output layer "o" that provides "m" (one or more) outputs (labeled "Output 1" and "Output m"). The number of inputs "n," hidden layers "h," and outputs "m" may be the same or different. In some designs, hidden layer "h" may include linear functions and / or activation functions processed by the nodes (denoted as circles) of each successive hidden layer process from the nodes of the previous hidden layer.

[0220] In classification models, the output is discrete. One example of a classification model is logistic regression. Logistic regression is similar to linear regression, but is used to model the probability of a finite number of outcomes, typically two. Essentially, a logistic equation is created in such a way that the output value can only be between "0" and "1." Another example of a classification model is a support vector machine. For example, given two classes of data, a support vector machine finds a hyperplane or boundary between the two classes of data that maximizes the margin between the two classes. While there are many planes that can separate the two classes, only one plane can maximize the margin or distance between the classes. Another example of a classification model is naive Bayes, which is based on Bayes' theorem. Other examples of classification models include decision trees, random forests, and neural networks, which are similar to the examples described above, except that the output is discrete rather than continuous.

[0221] Unlike supervised learning, unsupervised learning is used to draw inferences and find patterns from input data without reference to labeled outcomes. Two examples of unsupervised learning models are clustering and dimensionality reduction.

[0222] Clustering is an unsupervised technique that involves grouping or clustering data points. Clustering is frequently used for customer segmentation, fraud detection, and document classification. Common clustering techniques include k-means clustering, hierarchical clustering, mean-shift clustering, and density-based clustering. Dimensionality reduction is the process of reducing the number of random variables under consideration by taking a set of principal variables. In simpler terms, dimensionality reduction is the process of reducing the dimensionality of a feature set (or, even simpler, reducing the number of features). Most dimensionality reduction techniques can be categorized as either feature removal or feature extraction. One example of dimensionality reduction is called principal component analysis (PCA). In its simplest sense, PCA involves projecting higher-dimensional data (e.g., three dimensions) into a smaller space (e.g., two dimensions). This results in a lower dimensionality of the data (e.g., two dimensions instead of three) while preserving all the original variables in the model.

[0223] Regardless of which machine learning model is used, at a high level, the machine learning module (e.g., implemented by a processing system such as processor 332, 384, or 394) may be configured to iteratively analyze training input data (e.g., measurements of reference signals to / from various target UEs) and associate this training input data with an output dataset (e.g., a set of possible or likely location candidates for various target UEs), thereby enabling subsequent determination of the same output dataset when similar input data is presented (e.g., from other target UEs at the same or similar locations).

[0224] In the above detailed description, it can be seen that various features are grouped together in each example. This manner of disclosure should not be understood as an intention that the example clauses have more features than are expressly recited in each clause. Rather, various aspects of the present disclosure may include fewer than all features of each disclosed example clause. Accordingly, the following clauses are hereby considered to be incorporated into this description, and each clause may stand alone as a separate example. Although each dependent clause may refer within that clause to a specific combination with one of the other clauses, the aspects of that dependent clause are not limited to that specific combination. It will be appreciated that other example clauses may also include combinations of aspects of the dependent clause with the subject matter of any other dependent clause or independent clause, or combinations of any features with other dependent clauses and independent clauses. Unless a specific combination is not intended (e.g., conflicting aspects, such as defining an element as both an insulator and a conductor), the various aspects disclosed herein expressly include these combinations. It is further contemplated that aspects of a clause may be included within any other independent clause, even if the clause is not directly dependent on the independent clause.

[0225] Example implementations are described in the numbered clauses below.

[0226] Clause 1. A method of operating a user equipment (UE), comprising: obtaining at least one neural network function configured to facilitate positioning measurement data processing in the UE, wherein the at least one neural network function is dynamically generated based on machine learning associated with one or more historical measurement procedures; obtaining positioning measurement data associated with the UE; processing the positioning measurement data based on the at least one neural network function into a respective set of positioning measurement features; and reporting the processed set of positioning measurement features to a network component.

[0227] Clause 2. The method of clause 1, wherein the obtaining step obtains positioning measurement data by performing a set of positioning measurements on a reference signal for positioning.

[0228] Clause 3. The method of any one of clauses 1 to 2, wherein the obtaining step obtains the positioning measurement data by capturing sensor data via one or more sensors communicatively coupled to the UE.

[0229] Clause 4. The method of any one of clauses 1 to 3, wherein the at least one neural network function comprises a first neural network function configured to process the positioning measurement data into a first respective set of positioning measurement features, and the at least one neural network function comprises a second neural network function configured to process the positioning measurement data into a second respective set of the respective set of positioning measurement features.

[0230] Clause 5. The method of clause 4, wherein a first respective set of positioning measurement features is associated with a lower positioning accuracy compared to a second respective set of positioning measurement features, and wherein the reporting step reports the first respective set of positioning measurement features before the second respective set of positioning measurement features.

[0231] Clause 6. The method of any one of clauses 1 to 5, wherein the positioning measurement data comprises an uncompressed representation of raw samples of a reference signal for positioning, and the processed set of positioning measurement features comprises a compressed representation of a reference signal for positioning.

[0232] Clause 7. The method of any one of clauses 1 to 6, wherein at least one neural network function comprises a plurality of neural network functions each configured to facilitate positioning measurement data processing in the UE for a single positioning measurement type or group of positioning measurement types, or wherein at least one neural network function comprises a single neural network function configured to facilitate positioning measurement data processing in the UE for a plurality of positioning measurement types.

[0233] Clause 8. The method of any one of clauses 1 to 7, wherein the positioning measurement data comprises an estimate of a channel response associated with a reference signal.

[0234] Clause 9. The method of any one of clauses 1 to 8, wherein the processing step processes the positioning measurement data into a probability distribution associated with the set of positioning measurement features based on at least one neural network function, and the reporting step reports the probability distribution or a metric based on the probability distribution.

[0235] Clause 10. A method of operating a base station (BS), comprising: transmitting to a user equipment (UE) at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features at the UE, the at least one neural network function being dynamically generated based on machine learning associated with one or more historical measurement procedures; and receiving from the UE a set of positioning measurement features to be processed based on the at least one neural network function.

[0236] Clause 11. The method of clause 10, wherein at least one neural network function is dynamically generated at the BS or another network component.

[0237] Clause 12. The method of any one of clauses 10 to 11, wherein the positioning measurement data comprises a set of positioning measurements relative to a reference signal for positioning.

[0238] Clause 13. The method of any one of clauses 10 to 12, wherein the positioning measurement data comprises sensor data captured via one or more sensors communicatively coupled to the UE.

[0239] Clause 14. The method of any one of clauses 10 to 13, wherein the set of positioning measurement features comprises a condensed representation of an initial set of positioning measurements measured at the UE with respect to a reference signal for positioning.

[0240] Clause 15. The method of clause 14, further comprising recovering an initial set of positioning measurements measured at the UE by decompressing the processed set of positioning measurements into an uncompressed set of positioning measurements, and determining a position estimate for the UE based on the uncompressed set of positioning measurements.

[0241] Clause 16. The method of any one of clauses 10 to 15, further comprising determining a position estimate for the UE based on the received set of positioning measurements.

[0242] Clause 17. The method of any one of clauses 10 to 16, wherein the receiving step comprises receiving from the UE a first respective set of positioning measurement features associated with a first neural network function, and receiving from the UE a second respective set of positioning measurement features based on a second neural network function after the first set of positioning measurement features has been received.

[0243] Clause 18. The method of clause 17, wherein a first respective set of positioning measurement features is associated with a lower positioning accuracy compared to a second respective set of positioning measurement features.

[0244] Clause 19. The method of any one of clauses 10 to 18, wherein the positioning measurement data comprises an estimate of a channel response associated with a reference signal.

[0245] Clause 20. The method of any one of clauses 10 to 19, wherein the at least one neural network function is configured to facilitate processing of the positioning measurement data into a probability distribution associated with the set of positioning measurement features based on the at least one neural network function, and wherein the receiving step receives a probability distribution or a metric based on the probability distribution.

[0246] Clause 21. A user equipment (UE), comprising: a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver, wherein the at least one processor is configured to: acquire at least one neural network function configured to facilitate positioning measurement data processing at the UE, wherein the at least one neural network function is dynamically generated based on machine learning associated with one or more historical measurement procedures; acquire positioning measurement data associated with the UE; process the positioning measurement data into a respective set of positioning measurement features based on the at least one neural network function; and report the processed set of positioning measurement features to a network component.

[0247] Clause 22. The UE of clause 21, wherein obtaining obtains the positioning measurement data by performing a set of positioning measurements on a reference signal for positioning.

[0248] Clause 23. The UE of any one of clauses 21 to 22, wherein obtaining obtains the positioning measurement data by capturing sensor data via one or more sensors communicatively coupled to the UE.

[0249] Clause 24. The UE of any one of clauses 21 to 23, wherein the at least one neural network function comprises a first neural network function configured to process the positioning measurement data into a first respective set of positioning measurement features, and the at least one neural network function comprises a second neural network function configured to process the positioning measurement data into a second respective set of the respective set of positioning measurement features.

[0250] Clause 25. The UE of clause 24, wherein a first respective set of positioning measurement features is associated with a lower positioning accuracy compared to a second respective set of positioning measurement features, and wherein reporting the first respective set of positioning measurement features before the second respective set of positioning measurement features.

[0251] Clause 26. The UE of any one of clauses 21 to 25, wherein the positioning measurement data comprises an uncompressed representation of raw samples of reference signals for positioning, and the processed set of positioning measurement features comprises a compressed representation of reference signals for positioning.

[0252] Clause 27. The UE of any one of clauses 21 to 26, wherein the at least one neural network function comprises a plurality of neural network functions each configured to facilitate positioning measurement data processing in the UE for a single positioning measurement type or group of positioning measurement types, or the at least one neural network function comprises a single neural network function configured to facilitate positioning measurement data processing in the UE for a plurality of positioning measurement types.

[0253] Clause 28. The UE of any one of clauses 21 to 27, wherein the positioning measurement data comprises an estimate of a channel response associated with a reference signal.

[0254] Clause 29. The UE of any one of clauses 21 to 28, wherein the processing processes the positioning measurement data based on at least one neural network function into a probability distribution associated with the set of positioning measurement features, and the reporting reports the probability distribution or a metric based on the probability distribution.

[0255] Clause 30. A base station (BS), comprising: a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver, wherein the at least one processor is configured to: transmit, via the at least one transceiver, to a user equipment (UE), at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features at the UE, wherein the at least one neural network function is dynamically generated based on machine learning associated with one or more historical measurement procedures; and receive, via the at least one transceiver, from the UE, a set of positioning measurement features to be processed based on the at least one neural network function.

[0256] Clause 31. The BS of clause 30, wherein at least one neural network function is dynamically generated at the BS or another network component.

[0257] Clause 32. The BS of any one of clauses 30 to 31, wherein the positioning measurement data comprises a set of positioning measurements relative to a reference signal for positioning.

[0258] Clause 33. The BS of any one of clauses 30 to 32, wherein the positioning measurement data comprises sensor data captured via one or more sensors communicatively coupled to the UE.

[0259] Clause 34. The BS of any one of clauses 30 to 33, wherein the set of positioning measurement features comprises a condensed representation of an initial set of positioning measurements measured at the UE with respect to a reference signal for positioning.

[0260] Clause 35. The BS of clause 34, wherein the at least one processor is further configured to recover an initial set of positioning measurements measured at the UE by decompressing the processed set of positioning measurements into an uncompressed set of positioning measurements, and determine a positioning estimate for the UE based on the uncompressed set of positioning measurements.

[0261] Clause 36. The BS of any one of clauses 30 to 35, wherein the at least one processor is further configured to determine a positioning estimate for the UE based on the received set of positioning measurements.

[0262] Clause 37. The BS of any one of clauses 30 to 36, wherein receiving comprises receiving from the UE via the at least one transceiver a first respective set of positioning measurement features associated with a first neural network function, and receiving from the UE via the at least one transceiver a second respective set of positioning measurement features based on a second neural network function after the first respective set of positioning measurement features is received.

[0263] Clause 38. The BS of clause 37, wherein a first respective set of positioning measurement features is associated with a lower positioning accuracy compared to a second respective set of positioning measurement features.

[0264] Clause 39. The BS of any one of clauses 30 to 38, wherein the positioning measurement data comprises an estimate of a channel response associated with a reference signal.

[0265] Clause 40. The BS of any one of clauses 30 to 39, wherein the at least one neural network function is configured to facilitate processing of the positioning measurement data into a probability distribution associated with the set of positioning measurement features based on the at least one neural network function, and wherein receiving receives the probability distribution or a metric based on the probability distribution.

[0266] Clause 41. A user equipment (UE), comprising: means for acquiring at least one neural network function configured to facilitate positioning measurement data processing in the UE, the at least one neural network function being dynamically generated based on machine learning associated with one or more historical measurement procedures; means for acquiring positioning measurement data associated with the UE; means for processing the positioning measurement data based on the at least one neural network function into a respective set of positioning measurement features; and means for reporting the processed set of positioning measurement features to a network component.

[0267] Clause 42. The UE of clause 41, wherein obtaining obtains the positioning measurement data by performing a set of positioning measurements on a reference signal for positioning.

[0268] Clause 43. The UE of any one of clauses 41 to 42, wherein obtaining obtains the positioning measurement data by capturing sensor data via one or more sensors communicatively coupled to the UE.

[0269] Clause 44. The UE of any one of clauses 41 to 43, wherein the at least one neural network function comprises a first neural network function configured to process the positioning measurement data into a first respective set of positioning measurement features, and the at least one neural network function comprises a second neural network function configured to process the positioning measurement data into a second respective set of the respective set of positioning measurement features.

[0270] Clause 45. The UE of clause 44, wherein a first respective set of positioning measurement features is associated with a lower positioning accuracy compared to a second respective set of positioning measurement features, and wherein reporting the first respective set of positioning measurement features before the second respective set of positioning measurement features.

[0271] Clause 46. The UE of any one of clauses 41 to 45, wherein the positioning measurement data comprises an uncompressed representation of raw samples of reference signals for positioning, and the processed set of positioning measurement features comprises a compressed representation of reference signals for positioning.

[0272] Clause 47. The UE of any one of clauses 41 to 46, wherein at least one neural network function comprises a plurality of neural network functions each configured to facilitate positioning measurement data processing in the UE for a single positioning measurement type or group of positioning measurement types, or wherein at least one neural network function comprises a single neural network function configured to facilitate positioning measurement data processing in the UE for a plurality of positioning measurement types.

[0273] Clause 48. The UE of any one of clauses 41 to 47, wherein the positioning measurement data comprises an estimate of a channel response associated with a reference signal.

[0274] Clause 49. The UE of any one of clauses 41 to 48, wherein the processing processes the positioning measurement data based on at least one neural network function into a probability distribution associated with the set of positioning measurement features, and the reporting reports the probability distribution or a metric based on the probability distribution.

[0275] Clause 50. A base station (BS) comprising: means for transmitting to a user equipment (UE) at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features in the UE, the at least one neural network function being dynamically generated based on machine learning associated with one or more historical measurement procedures; and means for receiving from the UE a set of positioning measurement features to be processed based on the at least one neural network function.

[0276] Clause 51. The BS of clause 50, wherein at least one neural network function is dynamically generated at the BS or another network component.

[0277] Clause 52. The BS of any one of clauses 50 to 51, wherein the positioning measurement data comprises a set of positioning measurements relative to a reference signal for positioning.

[0278] Clause 53. The BS of any one of clauses 50 to 52, wherein the positioning measurement data comprises sensor data captured via one or more sensors communicatively coupled to the UE.

[0279] Clause 54. The BS of any one of clauses 50 to 53, wherein the set of positioning measurement features comprises a condensed representation of an initial set of positioning measurements measured at the UE with respect to a reference signal for positioning.

[0280] Clause 55. The BS of clause 54, further comprising: means for recovering an initial set of positioning measurements measured at the UE by decompressing the processed set of positioning measurements into an uncompressed set of positioning measurements; and means for determining a positioning estimate for the UE based on the uncompressed set of positioning measurements.

[0281] Clause 56. The BS of any one of clauses 50 to 55, further comprising means for determining a position estimate for the UE based on the received set of positioning measurements.

[0282] Clause 57. The BS of any one of clauses 50 to 56, wherein receiving comprises means for receiving from the UE a first respective set of positioning measurement features associated with the first neural network function, and means for receiving from the UE a second respective set of positioning measurement features based on the second neural network function after the first set of positioning measurement features is received.

[0283] Clause 58. The BS of clause 57, wherein a first respective set of positioning measurement features is associated with a lower positioning accuracy compared to a second respective set of positioning measurement features.

[0284] Clause 59. The BS of any one of clauses 50 to 58, wherein the positioning measurement data comprises an estimate of a channel response associated with a reference signal.

[0285] Clause 60. The BS of any one of clauses 50 to 59, wherein the at least one neural network function is configured to facilitate processing of the positioning measurement data into a probability distribution associated with the set of positioning measurement features based on the at least one neural network function, and wherein receiving receives the probability distribution, or a metric based on the probability distribution.

[0286] Clause 61. A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a user equipment (UE), cause the UE to: obtain at least one neural network function configured to facilitate positioning measurement data processing at the UE, wherein the at least one neural network function is dynamically generated based on machine learning associated with one or more historical measurement procedures; obtain positioning measurement data associated with the UE; process the positioning measurement data based on the at least one neural network function into a respective set of positioning measurement features; and report the processed set of positioning measurement features to a network component.

[0287] Clause 62. The non-transitory computer-readable medium of Clause 61, wherein obtaining obtains the positioning measurement data by performing a set of positioning measurements on a reference signal for positioning.

[0288] Clause 63. The non-transitory computer-readable medium of any one of clauses 61 to 62, wherein the obtaining obtains the positioning measurement data by capturing sensor data via one or more sensors communicatively coupled to the UE.

[0289] Clause 64. The non-transitory computer-readable medium of any one of clauses 61 to 63, wherein the at least one neural network function comprises a first neural network function configured to process the positioning measurement data into a first respective set of positioning measurement features, and the at least one neural network function comprises a second neural network function configured to process the positioning measurement data into a second respective set of the respective set of positioning measurement features.

[0290] Clause 65. The non-transitory computer-readable medium of clause 64, wherein a first respective set of positioning measurement features is associated with a lower positioning accuracy compared to a second respective set of positioning measurement features, and reporting reports the first respective set of positioning measurement features before the second respective set of positioning measurement features.

[0291] Clause 66. The non-transitory computer-readable medium of any one of clauses 61 to 65, wherein the positioning measurement data comprises an uncompressed representation of raw samples of a reference signal for positioning, and the processed set of positioning measurement features comprises a compressed representation of a reference signal for positioning.

[0292] Clause 67. The non-transitory computer-readable medium of any one of clauses 61 to 66, wherein at least one neural network function comprises a plurality of neural network functions each configured to facilitate positioning measurement data processing in the UE for a single positioning measurement type or group of positioning measurement types, or wherein at least one neural network function comprises a single neural network function configured to facilitate positioning measurement data processing in the UE for a plurality of positioning measurement types.

[0293] Clause 68. The non-transitory computer-readable medium of any one of clauses 61 to 67, wherein the positioning measurement data comprises an estimate of a channel response associated with a reference signal.

[0294] Clause 69. The non-transitory computer-readable medium of any one of clauses 61 to 68, wherein the processing processes the positioning measurement data based on at least one neural network function into a probability distribution associated with the set of positioning measurement features, and the reporting reports the probability distribution or a metric based on the probability distribution.

[0295] Clause 70. A non-transitory computer-readable medium having stored thereon computer-executable instructions that, when executed by a base station (BS), cause the BS to: transmit to a user equipment (UE) at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features at the UE, wherein the at least one neural network function is dynamically generated based on machine learning associated with one or more historical measurement procedures; and receive from the UE a set of positioning measurement features to be processed based on the at least one neural network function.

[0296] Clause 71. The non-transitory computer-readable medium of clause 70, wherein the at least one neural network function is dynamically generated at a BS or another network component.

[0297] Clause 72. The non-transitory computer-readable medium of any one of clauses 70 to 71, wherein the positioning measurement data comprises a set of positioning measurements relative to a reference signal for positioning.

[0298] Clause 73. The non-transitory computer-readable medium of any one of clauses 70 to 72, wherein the positioning measurement data comprises sensor data captured via one or more sensors communicatively coupled to the UE.

[0299] Clause 74. The non-transitory computer-readable medium of any one of clauses 70 to 73, wherein the set of positioning measurement features comprises a condensed representation of an initial set of positioning measurements measured at the UE with respect to a reference signal for positioning.

[0300] Clause 75. The non-transitory computer-readable medium of clause 74, wherein the one or more instructions further cause the BS to recover an initial set of positioning measurements measured at the UE by decompressing the processed set of positioning measurements into an uncompressed set of positioning measurements, and determine a positioning estimate for the UE based on the uncompressed set of positioning measurements.

[0301] Clause 76. The non-transitory computer-readable medium of any one of clauses 70 to 75, wherein the one or more instructions further cause the BS to determine a positioning estimate for the UE based on the received set of positioning measurements.

[0302] Clause 77. The non-transitory computer-readable medium of any one of clauses 70 to 76, wherein receiving comprises receiving from the UE a first respective set of positioning measurement features associated with a first neural network function, and receiving from the UE a second respective set of positioning measurement features based on a second neural network function after the first set of positioning measurement features is received.

[0303] Clause 78. The non-transitory computer-readable medium of clause 77, wherein a first respective set of positioning measurement features is associated with a lower positioning accuracy compared to a second respective set of positioning measurement features.

[0304] Clause 79. The non-transitory computer-readable medium of any one of clauses 70 to 78, wherein the positioning measurement data comprises an estimate of a channel response associated with a reference signal.

[0305] Clause 80. The non-transitory computer-readable medium of any one of clauses 70 to 79, wherein the at least one neural network function is configured to facilitate processing of the positioning measurement data into a probability distribution associated with the set of positioning measurement features based on the at least one neural network function, and wherein receiving receives the probability distribution or a metric based on the probability distribution.

[0306] Those skilled in the art will appreciate that information and signals may be represented using any of a variety of different technologies and techniques. For example, the data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0307] Furthermore, those skilled in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the aspects disclosed herein may be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0308] The various example logic blocks, modules, and circuits described in connection with the aspects disclosed herein may be implemented or performed using a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0309] The methods, sequences, and / or algorithms described in connection with the aspects disclosed herein may be embodied directly in hardware, in software modules executed by a processor, or in a combination of the two. The software modules may reside in random access memory (RAM), flash memory, read-only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. Alternatively, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal (e.g., UE). Alternatively, the processor and the storage medium may reside as discrete components in the user terminal.

[0310] In one or more exemplary aspects, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media, including any medium that facilitates transfer of a computer program from one place to another. Storage media may be any available medium that can be accessed by a computer. By way of example, and not limitation, such computer-readable media may comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included within the definition of medium. Disk and disc, as used herein, include compact discs (CDs), laser discs, optical discs, digital versatile discs (DVDs), floppy disks, and Blu-ray discs, where disks typically reproduce data magnetically and discs reproduce data optically using lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0311] While the above disclosure illustrates exemplary aspects of the present disclosure, it should be noted that various changes and modifications can be made herein without departing from the scope of the present disclosure as defined by the appended claims. The functions, steps, and / or actions of the method claims in accordance with the aspects of the present disclosure described herein need not be performed in any particular order. Furthermore, although elements of the present disclosure may be described or claimed in the singular, the plural is contemplated unless limitation to the singular is explicitly stated. [Explanation of symbols]

[0312] 100 Wireless Communication System 102 Base station 104 User Equipment (UE) 110 Coverage Area 120 Communication Links 122, 134 backhaul links 150 Wireless Local Area Network (WLAN) Access Points (APs) 152 Wireless Local Area Network (WLAN) Station (STA) 154 communication links 164 User Equipment (UE) 170 Core Network 172 Location Server 180 mmW base station 182 User Equipment (UE) 184 Millimeter Wave (mmW) Communication Link 190 User Equipment (UE) 192, 194 Device-to-Device (D2D) Peer-to-Peer (P2P) Links 200 Wireless Network Structure 204 User Equipment (UE) 210 Next Generation Core (NGC) 212 User Plane Functions 213 User Plane Interface (NG-U) 214 Control Plane Functions 215 Control Plane Interface (NG-C) 220 New RAN 222 gNB 223 Backhaul Connection 224 eNB 230 Location Server 250 Wireless Network Structure 260 Next Generation Core (NGC) 262 Session Management Facility (SMF) 263 User Plane Interface 264 Access and Mobility Management Function (AMF) / User Plane Function (UPF) 265 Control Plane Interface 270 Location Management Function (LMF) 302 User Equipment (UE) 304 base station 306 Network Entity 310 Wireless Wide Area Network (WWAN) Transceiver 312 Receiver 314 Transmitter 316 Antenna 318 Signal 320 Wireless Local Area Network (WLAN) Transceiver 322 Receiver 324 Transmitter 326 Antenna 328 signal 330 Satellite Positioning System (SPS) Receiver 332 Processing System 334 Data Bus 336 Antenna 338 Satellite Positioning System (SPS) signals 340 Memory Components 342 Measurement Module 344 Sensors 346 User Interface 350 Wireless Wide Area Network (WWAN) Transceiver 352 receiver 354 Transmitter 356 Antenna 358 Signal 360 Wireless Local Area Network (WLAN) Transceiver 362 Receiver 364 Transmitter 366 Antenna 368 signal 370 Satellite Positioning System (SPS) Receiver 376 Antenna 378 Satellite Positioning System (SPS) Signals 380 Network Interface 382 Data Bus 384 Processing Systems 386 Memory Components 388 Measurement Module 390 Network Interface 392 Data Bus 394 Processing Systems 396 Memory Components 500 PRS configuration 518 PRS Positioning Occasion 520 PRS periodicity 552 Cell-specific subframe offset 600 Wireless Communication System 602 base station 604 UE 610 LOS Route 612 NLOS routes 620 DAS / RRH 622 wired or wireless link 630 Object 700 Wireless Communication System 702 base station 704 UE 711~715 Beam 723 NLOS data stream 724 LOS data stream 730 Obstacles 740 Reflector 900 processes 1000 processes 1102 Neural Network Function 1104 Neural Network Functions 1110 UE Measurement Processing Module 1112 UE Measurement Processing Module 1114 gNB measurement processing module 1116 gNB measurement processing module 1118 Feature Fusion Module 1200 processes 1202 Neural Network Function 1204 Neural Network Functions 1206 UE Measurement Processing Module 1208 UE Measurement Processing Module 1210 gNB measurement processing module 1212 gNB measurement processing module 1300 Neural Networks

Claims

1. 1. A method of operating a user equipment (UE), comprising: obtaining, from a base station (BS), at least one neural network function configured to facilitate positioning measurement data processing at the UE, wherein the at least one neural network function is dynamically generated based on machine learning associated with one or more historical measurement procedures; obtaining positioning measurement data associated with the UE; processing the positioning measurement data into a respective set of positioning measurement features based on the at least one neural network function, the positioning measurement features being refined and / or condensed representations of raw positioning measurement data; reporting the processed set of positioning measurement features to the BS; A method for providing

2. the obtaining step obtains the positioning measurement data by performing a set of positioning measurements on a reference signal for positioning; and / or The method of claim 1 , wherein the obtaining step obtains the positioning measurement data by capturing sensor data via one or more sensors communicatively coupled to the UE.

3. the at least one neural network function comprises a first neural network function configured to process the positioning measurement data into a first respective set of positioning measurement features; the at least one neural network function comprises a second neural network function configured to process the positioning measurement data into a second respective set of positioning measurement features; the first respective set of positioning measurement features is associated with a lower positioning accuracy compared to the second respective set of positioning measurement features; the reporting step reports the first respective set of positioning measurement features before the second respective set of positioning measurement features. The method of claim 1.

4. the positioning measurement data comprises an uncompressed representation of raw samples of a reference signal for positioning; the processed set of positioning measurement features comprises a compressed representation of the reference signal for positioning. The method of claim 1.

5. the at least one neural network function comprises a plurality of neural network functions each configured to facilitate positioning measurement data processing at the UE for a single positioning measurement type or a group of positioning measurement types; or the at least one neural network function comprises a single neural network function configured to facilitate positioning measurement data processing at the UE for a plurality of positioning measurement types. The method of claim 1.

6. The method of claim 1 , wherein the positioning measurement data comprises an estimate of a channel response associated with a reference signal.

7. the processing step processes the positioning measurement data into a probability distribution associated with the set of positioning measurement features based on the at least one neural network function; the reporting step reports the probability distribution or a metric based on the probability distribution. The method of claim 1.

8. 1. A method of operating a base station (BS), comprising: transmitting at least one neural network function to a user equipment (UE) configured to facilitate processing of positioning measurement data into one or more positioning measurement features at the UE, the positioning measurement features being refined and / or condensed representations of raw positioning measurement data, the at least one neural network function being dynamically generated based on machine learning associated with one or more historical measurement procedures; receiving from the UE a set of positioning measurement features to be processed based on the at least one neural network function; A method for providing

9. The method of claim 8 , wherein the at least one neural network function is dynamically generated at the BS or another network component.

10. the set of positioning measurement features comprises a condensed representation of an initial set of positioning measurements measured at the UE with respect to a reference signal for positioning; The method comprises: recovering the initial set of positioning measurements measured at the UE by decompressing the processed set of positioning measurements into an uncompressed set of positioning measurements; determining a position estimate for the UE based on the uncompressed set of positioning measurements; The method of claim 8 further comprising:

11. determining a position estimate for the UE based on the received set of positioning measurements. The method of claim 8 further comprising:

12. The step of receiving receiving from the UE a first respective set of positioning measurement features associated with a first neural network function; receiving a second respective set of positioning measurement features from the UE based on a second neural network function after the first respective set of positioning measurement features is received; Equipped with The method of claim 8 , wherein the first respective set of positioning measurement features is associated with a lower positioning accuracy compared to the second respective set of positioning measurement features.

13. the at least one neural network function is configured to facilitate processing of the positioning measurement data into probability distributions associated with the set of positioning measurement features based on the at least one neural network function; the receiving step receives the probability distribution or a metric based on the probability distribution. The method of claim 8.

14. A user equipment (UE), Memory and at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor configured to perform the method of any one of claims 1 to 7. User Equipment (UE).

15. A base station (BS), Memory and at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor configured to perform the method of any one of claims 8 to 13. Base station (BS).

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