WO2026007014A1

WO2026007014A1 - Neural network assisted ultra-wideband (uwb) position and heading determination

Info

Publication number: WO2026007014A1
Application number: PCT/CN2024/103240
Authority: WO
Inventors: Qianyun LIU; Bin Xue; Xunping ZHAO; Xiangyang Liu
Original assignee: Qualcomm Inc
Current assignee: Qualcomm Inc
Priority date: 2024-07-03
Filing date: 2024-07-03
Publication date: 2026-01-08
Anticipated expiration: 2027-01-03

Abstract

Disclosed are techniques for wireless positioning. In an aspect, a user equipment (UE) may receive one or more ultra-wideband (UWB) signals from one or more UWB network nodes in an environment. The UE may obtain one or more measurements of the one or more UWB signals. The UE may apply a long short-term memory (LSTM) neural network model to obtain an estimated position and an estimated heading based on the one or more measurements of the UWB signals.

Description

NEURAL NETWORK ASSISTED ULTRA-WIDEBAND (UWB) POSITION AND HEADING DETERMINATION

TECHNICAL FIELD

Aspects of the disclosure relate generally to wireless technologies.

BACKGROUND

Wireless communication systems have developed through various generations, including a first-generation analog wireless phone service (1G) , a second-generation (2G) digital wireless phone service (including interim 2.5G and 2.75G networks) , a third-generation (3G) high speed data, Internet-capable wireless service and a fourth-generation (4G) service (e.g., Long Term Evolution (LTE) or WiMax) . There are presently many different types of wireless communication systems in use, including cellular and personal communications service (PCS) systems. Examples of known cellular systems include the cellular analog advanced mobile phone system (AMPS) , and digital cellular systems based on code division multiple access (CDMA) , frequency division multiple access (FDMA) , time division multiple access (TDMA) , the Global System for Mobile communications (GSM) , etc.

A fifth generation (5G) wireless standard, referred to as New Radio (NR) , enables higher data transfer speeds, greater numbers of connections, and better coverage, among other improvements. The 5G standard, according to the Next Generation Mobile Networks Alliance, is designed to provide higher data rates as compared to previous standards, more accurate positioning (e.g., based on reference signals for positioning (RS-P) , such as downlink, uplink, or sidelink positioning reference signals (PRS) ) , radio frequency (RF) sensing, and other technical enhancements. These enhancements, as well as the use of higher frequency bands, advances in PRS processes and technology, and high-density deployments for 5G, enable highly accurate 5G-based sensing and positioning.

SUMMARY

The following presents a simplified summary relating to one or more aspects disclosed herein. Thus, the following summary should not be considered an extensive overview relating to all contemplated aspects, nor should the following summary be considered to identify key or critical elements relating to all contemplated aspects or to delineate the scope associated with any particular aspect. Accordingly, the following summary has the sole purpose to present certain concepts relating to one or more aspects relating to the mechanisms disclosed herein in a simplified form to precede the detailed description presented below.

In an aspect, a method of wireless positioning performed at a user equipment (UE) includes receiving one or more ultra-wideband (UWB) signals from one or more UWB network nodes in an environment; obtaining one or more measurements of the one or more UWB signals; and applying a long short-term memory (LSTM) neural network model to obtain an estimated position and an estimated heading based on the one or more measurements of the UWB signals.

In an aspect, a user equipment (UE) includes one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to: receive, via the one or more transceivers, one or more ultra-wideband (UWB) signals from one or more UWB network nodes in an environment; obtain one or more measurements of the one or more UWB signals; and apply a long short-term memory (LSTM) neural network model to obtain an estimated position and an estimated heading based on the one or more measurements of the UWB signals.

In an aspect, a user equipment (UE) includes means for receiving one or more ultra-wideband (UWB) signals from one or more UWB network nodes in an environment; means for obtaining one or more measurements of the one or more UWB signals; and means for applying a long short-term memory (LSTM) neural network model to obtain an estimated position and an estimated heading based on the one or more measurements of the UWB signals.

In an aspect, a non-transitory computer-readable medium stores computer-executable instructions that, when executed by a user equipment (UE) , cause the UE to: receive one or more ultra-wideband (UWB) signals from one or more UWB network nodes in an environment; obtain one or more measurements of the one or more UWB signals; and apply a long short-term memory (LSTM) neural network model to obtain an estimated position and an estimated heading based on the one or more measurements of the UWB signals.

Other objects and advantages associated with the aspects disclosed herein will be apparent to those skilled in the art based on the accompanying drawings and detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are presented to aid in the description of various aspects of the disclosure and are provided solely for illustration of the aspects and not limitation thereof.

FIG. 1 illustrates an example wireless communications system, according to aspects of the disclosure.

FIGS. 2A, 2B, and 2C illustrate example wireless network structures, according to aspects of the disclosure.

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

FIG. 4 illustrates an example neural network, according to aspects of the disclosure.

FIG. 5A is a diagram illustrating an example of direct artificial intelligence/machine learning (AIML) positioning and/or sensing, according to aspects of the disclosure.

FIG. 5B is a diagram illustrating an example of AIML assisted positioning and/or sensing, according to aspects of the disclosure.

FIG. 5C illustrates various AIML positioning and/or sensing scenarios, according to aspects of the disclosure.

FIG. 6 illustrates an example call flow for a New Radio (NR) -based sensing procedure in which the network configures the sensing parameters, according to aspects of the disclosure.

FIG. 7 illustrates an example of UWB-fused localization in indoor navigation, according to aspects of the disclosure.

FIGS. 8A and 8B illustrate examples of angles of arrival (AOAs) of a robot with different headings, according to aspects of the disclosure.

FIG. 9 illustrates an example of initialization and correction of the initial prose of a robot, according to aspects of the disclosure.

FIG. 10 illustrates an example of determining the initial pose of a robot, according to aspects of the disclosure.

FIG. 11 illustrates AOA estimation using a receiver antenna array, according to aspects of the disclosure.

FIG. 12 illustrates an example of estimating the position and heading of a robot with four anchor positions, according to aspects of the disclosure.

FIG. 13 illustrates an example of an LSTM model for estimating the position and heading with LSTM algorithm blocks for inputs X_t-1, X_t and X_t+1, according to aspects of the disclosure.

FIG. 14 illustrates an example of one of the LSTM algorithm blocks for input X_t, according to aspects of the disclosure.

FIG. 15 illustrates an example of an attention-LSTM neural network model with an attention mechanism and LSTM encoders and decoders, according to aspects of the disclosure.

FIG. 16 illustrates an example of position and heading estimation using a combination of attention-LSTM, EKF and AMCL techniques, according to aspects of the disclosure.

FIG. 17 illustrates an example method of wireless positioning, according to aspects of the disclosure.

DETAILED DESCRIPTION

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

Various aspects relate generally to wireless positioning. Some aspects more specifically relate to neural network assisted wireless positioning. In some examples, the position and heading of a mobile device, such as a robot, may be determined with a high level of accuracy by using ultra-wideband (UWB) sensing measurements and applying a neural network model.

Particular aspects of the subject matter described in this disclosure can be implemented to realize one or more of the following potential advantages. In some examples, by using UWB sensing measurements and applying a neural network model, the described techniques can be used to determine the position and heading of a mobile device automatically and motionless without human intervention, such as a robot, with a high degree of accuracy. In some examples, the described techniques can be used to provide an initial pose while the mobile device is motionless in both position and orientation, without a need for human observation or intervention. In some examples, the described techniques can be used to correct odometry drift of the position and direction of the mobile device during navigation with extended Kalman filter (EKF) fusion.

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

Those of skill 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 description below 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.

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 various actions described herein can be performed by specific circuits (e.g., application specific integrated circuits (ASICs) ) , by program instructions being executed by one or more processors, or by a combination of both. Additionally, the sequence (s) of actions described herein can be considered to be embodied entirely within any form of non-transitory computer-readable storage medium having stored therein a corresponding set of computer instructions that, upon execution, would cause or instruct an associated processor of a device to perform the functionality described herein. Thus, the various aspects of the disclosure may be embodied in a number of different forms, all of which have been contemplated to be within the scope of the claimed subject matter. In addition, for each of the aspects described herein, the corresponding form of any such aspects may be described herein as, for example, “logic configured to” perform the described action.

As used herein, the terms “user equipment” (UE) and “base station” are not intended to be specific or otherwise limited to any particular radio access technology (RAT) , unless otherwise noted. In general, a UE may be any wireless communication device (e.g., a mobile phone, router, tablet computer, laptop computer, consumer asset locating device, wearable (e.g., smartwatch, glasses, augmented reality (AR) /virtual reality (VR) headset, etc. ) , vehicle (e.g., automobile, motorcycle, bicycle, etc. ) , Internet of Things (IoT) device, etc. ) used by a user to communicate over a wireless communications network. A UE may be mobile or may (e.g., at certain times) be stationary, 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, ” a “client device, ” a “wireless device, ” a “subscriber device, ” a “subscriber terminal, ” a “subscriber station, ” a “user terminal” or “UT, ” a “mobile device, ” a “mobile terminal, ” a “mobile station, ” or variations thereof. Generally, UEs can communicate with a core network via a RAN, and through the core network the UEs can be connected with external networks such as the Internet and with other UEs. Of course, other mechanisms of connecting to the core network and/or the Internet are also possible for the UEs, such as over wired access networks, wireless local area network (WLAN) networks (e.g., based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11 specification, etc. ) and so on.

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

The term “base station” may refer to a single physical transmission-reception point (TRP) or to multiple physical TRPs that may or may not be co-located. For example, where the term “base station” refers to a single physical TRP, the physical TRP may be an antenna of the base station corresponding to a cell (or several cell sectors) of the base station. Where the term “base station” refers to multiple co-located physical TRPs, the physical TRPs may be an array of antennas (e.g., as in a multiple-input multiple-output (MIMO) system or where the base station employs beamforming) of the base station. Where the term “base station” refers to multiple non-co-located physical TRPs, the physical TRPs may be a distributed antenna system (DAS) (anetwork of spatially separated antennas connected to a common source via a transport medium) or a remote radio head (RRH) (aremote base station connected to a serving base station) . Alternatively, the non-co-located physical TRPs may be the serving base station receiving the measurement report from the UE and a neighbor base station whose reference radio frequency (RF) signals the UE is measuring. Because a TRP is the point from which a base station transmits and receives wireless signals, as used herein, references to transmission from or reception at a base station are to be understood as referring to a particular TRP of the base station.

In some implementations that support positioning of UEs, a base station may not support wireless access by UEs (e.g., may not support data, voice, and/or signaling connections for UEs) , but may instead transmit reference signals to UEs to be measured by the UEs, and/or may receive and measure signals transmitted by the UEs. Such a base station may be referred to as a positioning beacon (e.g., when transmitting signals to UEs) and/or as a location measurement unit (e.g., when receiving and measuring signals from UEs) .

An “RF signal” comprises an electromagnetic wave of a given frequency that transports information through the space between a transmitter and a receiver. As used herein, a transmitter may transmit a single “RF signal” or multiple “RF signals” to a receiver. However, the receiver may receive multiple “RF signals” corresponding to each transmitted RF signal due to the propagation characteristics of RF signals through multipath channels. The same transmitted RF signal on different paths between the transmitter and receiver may be referred to as a “multipath” RF signal. As used herein, an RF signal may also be referred to as a “wireless signal” or simply a “signal” where it is clear from the context that the term “signal” refers to a wireless signal or an RF signal.

FIG. 1 illustrates an example wireless communications system 100, according to aspects of the disclosure. The wireless communications system 100 (which may also be referred to as a wireless wide area network (WWAN) ) may include various base stations 102 (labeled “BS” ) and various UEs 104. The base stations 102 may include macro cell base stations (high power cellular base stations) and/or small cell base stations (low power cellular base stations) . In an aspect, the macro cell base stations may include eNBs and/or ng-eNBs where the wireless communications system 100 corresponds to an LTE network, or gNBs where the wireless communications system 100 corresponds to a NR network, or a combination of both, and the small cell base stations may include femtocells, picocells, microcells, etc.

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

In addition to other functions, the base stations 102 may perform functions that relate to one or more of transferring user data, radio channel ciphering and deciphering, 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 service (MBMS) , subscriber and equipment trace, RAN information management (RIM) , paging, positioning, and delivery of warning messages. The base stations 102 may communicate with each other directly or indirectly (e.g., through the EPC /5GC) over backhaul links 134, which may be wired or wireless.

The base stations 102 may wirelessly communicate with the UEs 104. Each of the base stations 102 may provide communication coverage for a respective geographic coverage area 110. In an aspect, one or more cells may be supported by a base station 102 in each geographic coverage area 110. A “cell” is a logical communication entity used for communication with a base station (e.g., over some frequency resource, referred to as a carrier frequency, component carrier, carrier, band, or the like) , and may be associated with an identifier (e.g., a physical cell identifier (PCI) , an enhanced cell identifier (ECI) , a virtual cell identifier (VCI) , a cell global identifier (CGI) , etc. ) for distinguishing cells operating via the same or a different carrier frequency. 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 for different types of UEs. Because a cell is supported by a specific base station, the term “cell” may refer to either or both of the logical communication entity and the base station that supports it, depending on the context. In addition, because a TRP is typically the physical transmission point of a cell, the terms “cell” and “TRP” may be used interchangeably. In some cases, the term “cell” may also refer to a geographic coverage area of a base station (e.g., a sector) , insofar as a carrier frequency can be detected and used for communication within some portion of geographic coverage areas 110.

While neighboring macro cell base station 102 geographic coverage areas 110 may partially overlap (e.g., in a handover region) , some of the geographic coverage areas 110 may be substantially overlapped by a larger geographic coverage area 110. For example, a small cell base station 102' (labeled “SC” for “small cell” ) may have a geographic coverage area 110' that substantially overlaps with the geographic coverage area 110 of one or more macro cell base stations 102. A network that includes both small cell and macro cell base stations may be known as a heterogeneous network. A heterogeneous network may also include home eNBs (HeNBs) , which may provide service to a restricted group known as a closed subscriber group (CSG) .

The communication links 120 between the base stations 102 and the UEs 104 may include uplink (also referred to as reverse link) transmissions from a UE 104 to a base station 102 and/or downlink (DL) (also referred to as forward link) transmissions from a base station 102 to a UE 104. The communication links 120 may use MIMO antenna technology, including spatial multiplexing, beamforming, and/or transmit diversity. The communication links 120 may be through one or more carrier frequencies. Allocation of carriers may be asymmetric with respect to downlink and uplink (e.g., more or less carriers may be allocated for downlink than for uplink) .

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

The small cell base station 102' may operate in a licensed and/or an unlicensed frequency spectrum. When operating in an unlicensed frequency spectrum, the small cell base station 102' may employ LTE or NR technology and use the same 5 GHz unlicensed frequency spectrum as used by the WLAN AP 150. The small cell base station 102' , employing LTE /5G in an unlicensed frequency spectrum, may boost coverage to and/or increase capacity of the access network. NR in 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

The wireless communications system 100 may further include a millimeter wave (mmW) base station 180 that may operate in mmW frequencies and/or near mmW frequencies in communication with a UE 182. Extremely high frequency (EHF) is part of the RF in the electromagnetic spectrum. EHF has a range of 30 GHz to 300 GHz and a wavelength between 1 millimeter and 10 millimeters. Radio waves in this band may be referred to as a millimeter wave. Near mmW may extend down to a frequency of 3 GHz with a wavelength of 100 millimeters. The super high frequency (SHF) band extends between 3 GHz and 30 GHz, also referred to as centimeter wave. Communications using the mmW/near mmW radio frequency band have high path loss and a relatively short range. The mmW base station 180 and the UE 182 may utilize beamforming (transmit and/or receive) over a mmW communication link 184 to compensate for the extremely high path loss and short range. Further, it will be appreciated that in alternative configurations, one or more base stations 102 may also transmit using mmW or near mmW and beamforming. Accordingly, it will be appreciated that the foregoing illustrations are merely examples and should not be construed to limit the various aspects disclosed herein.

Transmit beamforming is a technique for focusing an RF signal in a specific direction. Traditionally, when a network node (e.g., a base station) broadcasts an RF signal, it broadcasts the signal in all directions (omni-directionally) . With transmit beamforming, the network node determines where a given target device (e.g., a UE) is located (relative to the transmitting network node) and projects a stronger downlink RF signal in that specific direction, thereby providing a faster (in terms of data rate) and stronger RF signal for the receiving device (s) . To change the directionality of the RF signal when transmitting, a network node can control the phase and relative amplitude of the RF signal at each of the one or more transmitters that are broadcasting the RF signal. For example, a network node may use an array of antennas (referred to as a “phased array” or an “antenna array” ) that creates a beam of RF waves that can be “steered” to point in different directions, without actually moving the antennas. Specifically, the RF current from the transmitter is fed to the individual antennas with the correct phase relationship so that the radio waves from the separate antennas add together to increase the radiation in a desired direction, while cancelling to suppress radiation in undesired directions.

Transmit beams may be quasi-co-located, meaning that they appear to the receiver (e.g., a UE) as having the same parameters, regardless of whether or not the transmitting antennas of the network node themselves are physically co-located. In NR, there are four types of quasi-co-location (QCL) relations. Specifically, a QCL relation of a given type means that certain parameters about a second reference RF signal on a second beam can be derived from information about a source reference RF signal on a source beam. Thus, if the source reference RF signal is QCL Type A, the receiver can use the source reference RF signal to estimate the Doppler shift, Doppler spread, average delay, and delay spread of a 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 a 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 the spatial receive parameter of a second reference RF signal transmitted on the same channel.

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

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

Note that a “downlink” beam may be either a transmit beam or a receive beam, depending on the entity forming it. For example, if a base station is forming the downlink beam to transmit a reference signal to a UE, the downlink beam is a transmit beam. If the UE is forming the downlink beam, however, it is a receive beam to receive the downlink reference signal. Similarly, an “uplink” beam may be either a transmit beam or a receive beam, depending on the entity forming it. For example, if a base station is forming the uplink beam, it is an uplink receive beam, and if a UE is forming the uplink beam, it is an uplink transmit beam.

The electromagnetic spectrum is often subdivided, based on frequency/wavelength, into various classes, bands, channels, etc. In 5G NR two initial operating bands have been identified as frequency range designations FR1 (410 MHz –7.125 GHz) and FR2 (24.25 GHz –52.6 GHz) . It should be understood that although a portion of FR1 is greater than 6 GHz, FR1 is often referred to (interchangeably) as a “Sub-6 GHz” band in various documents and articles. A similar nomenclature issue sometimes occurs with regard to FR2, which is often referred to (interchangeably) as a “millimeter wave” band in documents and articles, despite being different from the extremely high frequency (EHF) band (30 GHz –300 GHz) which is identified by the INTERNATIONAL TELECOMMUNICATION as a “millimeter wave” band.

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

With the above aspects in mind, unless specifically stated otherwise, it should be understood that the term “sub-6 GHz” or the like if used herein may broadly represent frequencies that may be less than 6 GHz, may be within FR1, or may include mid-band frequencies. Further, unless specifically stated otherwise, it should be understood that the term “millimeter wave” or the like if used herein may broadly represent frequencies that may include mid-band frequencies, may be within FR2, FR4, FR4-a or FR4-1, and/or FR5, or may be within the EHF band.

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

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

The wireless communications system 100 may further include a UE 164 that may communicate with a macro cell base station 102 over a communication link 120 and/or the mmW base station 180 over a mmW communication link 184. For example, the macro cell base station 102 may support a PCell and one or more SCells for the UE 164 and the mmW base station 180 may support one or more SCells for the UE 164.

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

In an aspect, the sidelink 160 may operate over a wireless communication medium of interest, which may be shared with other wireless communications between other vehicles and/or infrastructure access points, as well as other RATs. A “medium” may be composed of one or more time, frequency, and/or space communication resources (e.g., encompassing one or more channels across one or more carriers) associated with wireless communication between one or more transmitter /receiver pairs. In an aspect, the medium of interest may correspond to at least a portion of an unlicensed frequency band shared among various RATs. Although different licensed frequency bands have been reserved for certain communication systems (e.g., by a government entity such as the Federal Communications Commission (FCC) in the United States) , these systems, in particular those employing small cell access points, have recently extended operation into unlicensed frequency bands such as the Unlicensed National Information Infrastructure (U-NII) band used by wireless local area network (WLAN) technologies, most notably IEEE 802.11x WLAN technologies generally referred to as “Wi-Fi. ” Example systems of this type include different variants of CDMA systems, TDMA systems, FDMA systems, orthogonal FDMA (OFDMA) systems, single-carrier FDMA (SC-FDMA) systems, and so on.

Note that although FIG. 1 only illustrates two of the UEs as SL-UEs (i.e., UEs 164 and 182) , any of the illustrated UEs may be SL-UEs. Further, although only UE 182 was described as being capable of beamforming, any of the illustrated UEs, including UE 164, may be capable of beamforming. Where SL-UEs are capable of beamforming, they may beamform towards each other (i.e., towards other SL-UEs) , towards other UEs (e.g., UEs 104) , towards base stations (e.g., base stations 102, 180, small cell 102’ , access point 150) , etc. Thus, in some cases, UEs 164 and 182 may utilize beamforming over sidelink 160.

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

In a satellite positioning system, the use of signals 124 can be augmented by various satellite-based augmentation systems (SBAS) that may be associated with or otherwise enabled for use with one or more global and/or regional navigation satellite systems. For example an SBAS may include an augmentation system (s) that provides integrity information, differential corrections, etc., such as the Wide Area Augmentation System (WAAS) , the European Geostationary Navigation Overlay Service (EGNOS) , the Multi- functional Satellite Augmentation System (MSAS) , the Global Positioning System (GPS) Aided Geo Augmented Navigation or GPS and Geo Augmented Navigation system (GAGAN) , and/or the like. Thus, as used herein, a satellite positioning system may include any combination of one or more global and/or regional navigation satellites associated with such one or more satellite positioning systems.

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

The wireless communications system 100 may further include one or more UEs, such as UE 190, that connects indirectly to one or more communication networks via one or more device-to-device (D2D) peer-to-peer (P2P) links (referred to as “sidelinks” ) . In the example of FIG. 1, 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 UE 190 may indirectly obtain cellular connectivity) and a D2D P2P link 194 with WLAN STA 152 connected to the WLAN AP 150 (through which UE 190 may indirectly obtain WLAN-based Internet connectivity) . In an example, the D2D P2P links 192 and 194 may be supported with any well-known D2D RAT, such as LTE Direct (LTE-D) , WI-FI and so on.

FIG. 2A illustrates an example wireless network structure 200. For example, a 5GC 210 (also referred to as a Next Generation Core (NGC) ) can be viewed functionally as control plane (C-plane) functions 214 (e.g., UE registration, authentication, network access, gateway selection, etc. ) and user plane (U-plane) functions 212, (e.g., UE gateway function, access to data networks, IP routing, etc. ) which operate cooperatively to form the core network. User plane interface (NG-U) 213 and control plane interface (NG-C) 215 connect the gNB 222 to the 5GC 210 and specifically to the user plane functions 212 and control plane functions 214, respectively. In an additional configuration, an ng-eNB 224 may also be connected to the 5GC 210 via NG-C 215 to the control plane functions 214 and NG-U 213 to user plane functions 212. Further, ng-eNB 224 may directly communicate with gNB 222 via a backhaul connection 223. In some configurations, a Next Generation RAN (NG-RAN) 220 may have one or more gNBs 222, while other configurations include one or more of both ng-eNBs 224 and gNBs 222. Either (or both) gNB 222 or ng-eNB 224 may communicate with one or more UEs 204 (e.g., any of the UEs described herein) .

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

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

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

The functions of the SMF 266 include session management, UE Internet protocol (IP) address allocation and management, selection and control of user plane functions, configuration of traffic steering at the UPF 262 to route traffic to the proper destination, control of part of policy enforcement and QoS, and downlink data notification. The interface over which the SMF 266 communicates with the AMF 264 is referred to as the N11 interface.

Another optional aspect may include an LMF 270, which may be in communication with the 5GC 260 to provide location assistance for UEs 204. The LMF 270 can be implemented as a plurality of separate servers (e.g., physically separate servers, different software modules on a single server, different software modules spread across multiple physical servers, etc. ) , or alternately may each correspond to a single server. The LMF 270 can be configured to support one or more location services for UEs 204 that can connect to the LMF 270 via the core network, 5GC 260, and/or via the Internet (not illustrated) . The SLP 272 may support similar functions to the LMF 270, but whereas the LMF 270 may communicate with the AMF 264, NG-RAN 220, and UEs 204 over a control plane (e.g., using interfaces and protocols intended to convey signaling messages and not voice or data) , the SLP 272 may communicate with UEs 204 and external clients (e.g., third-party server 274) over a user plane (e.g., using protocols intended to carry voice and/or data like the transmission control protocol (TCP) and/or IP) .

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

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

The functionality of a gNB 222 may be divided between a gNB central unit (gNB-CU) 226, one or more gNB distributed units (gNB-DUs) 228, and one or more gNB radio units (gNB-RUs) 229. A gNB-CU 226 is a logical node that includes the base station functions of transferring user data, mobility control, radio access network sharing, positioning, session management, and the like, except for those functions allocated exclusively to the gNB-DU (s) 228. More specifically, the gNB-CU 226 generally host the radio resource control (RRC) , service data adaptation protocol (SDAP) , and packet data convergence protocol (PDCP) protocols of the gNB 222. A gNB-DU 228 is a logical node that generally hosts the radio link control (RLC) and medium access control (MAC) layer of the gNB 222. Its operation is controlled by the gNB-CU 226. One gNB-DU 228 can support one or more cells, and one cell is supported by only one gNB-DU 228. The interface 232 between the gNB-CU 226 and the one or more gNB-DUs 228 is referred to as the “F1” interface. The physical (PHY) layer functionality of a gNB 222 is generally hosted by one or more standalone gNB-RUs 229 that perform functions such as power amplification and signal transmission/reception. The interface between a gNB-DU 228 and a gNB-RU 229 is referred to as the “Fx” interface. Thus, a UE 204 communicates with the gNB-CU 226 via the RRC, SDAP, and PDCP layers, with a gNB-DU 228 via the RLC and MAC layers, and with a gNB-RU 229 via the PHY layer.

Deployment of communication systems, such as 5G NR systems, may be arranged in multiple manners with various components or constituent parts. In a 5G NR system, or network, a network node, a network entity, a mobility element of a network, a RAN node, a core network node, a network element, or a network equipment, such as a base station, or one or more units (or one or more components) performing base station functionality, may be implemented in an aggregated or disaggregated architecture. For example, a base station (such as a Node B (NB) , evolved NB (eNB) , NR base station, 5G NB, AP, TRP, cell, etc. ) may be implemented as an aggregated base station (also known as a standalone base station or a monolithic base station) or a disaggregated base station.

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

Base station-type operation or network design may consider aggregation characteristics of base station functionality. For example, disaggregated base stations may be utilized in an integrated access backhaul (IAB) network, an open radio access network (O-RAN (such as the network configuration sponsored by the O-RAN ) ) , or a virtualized radio access network (vRAN, also known as a cloud radio access network (C-RAN) ) . Disaggregation may include distributing functionality across two or more units at various physical locations, as well as distributing functionality for at least one unit virtually, which can enable flexibility in network design. The various units of the disaggregated base station, or disaggregated RAN architecture, can be configured for wired or wireless communication with at least one other unit.

FIG. 2C illustrates an example disaggregated base station architecture 250, according to aspects of the disclosure. The disaggregated base station architecture 250 may include one or more central units (CUs) 280 (e.g., gNB-CU 226) that can communicate directly with a core network 267 (e.g., 5GC 210, 5GC 260) via a backhaul link, or indirectly with the core network 267 through one or more disaggregated base station units (such as a Near-Real Time (Near-RT) RAN Intelligent Controller (RIC) 259 via an E2 link, or a Non-Real Time (Non-RT) RIC 257 associated with a Service Management and Orchestration (SMO) Framework 255, or both) . A CU 280 may communicate with one or more DUs 285 (e.g., gNB-DUs 228) via respective midhaul links, such as an F1 interface. The DUs 285 may communicate with one or more radio units (RUs) 287 (e.g., gNB-RUs 229) via respective fronthaul links. The RUs 287 may communicate with respective UEs 204 via one or more radio frequency (RF) access links. In some implementations, the UE 204 may be simultaneously served by multiple RUs 287.

Each of the units, i.e., the CUs 280, the DUs 285, the RUs 287, as well as the Near-RT RICs 259, the Non-RT RICs 257 and the SMO Framework 255, may include one or more interfaces or be coupled to one or more interfaces configured to receive or transmit signals, data, or information (collectively, signals) via a wired or wireless transmission medium. Each of the units, or an associated processor or controller providing instructions to the communication interfaces of the units, can be configured to communicate with one or more of the other units via the transmission medium. For example, the units can include a wired interface configured to receive or transmit signals over a wired transmission medium to one or more of the other units. Additionally, the units can include a wireless interface, which may include a receiver, a transmitter or transceiver (such as a RF transceiver) , configured to receive or transmit signals, or both, over a wireless transmission medium to one or more of the other units.

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

The DU 285 may correspond to a logical unit that includes one or more base station functions to control the operation of one or more RUs 287. In some aspects, the DU 285 may host one or more of a RLC layer, a MAC layer, and one or more high PHY layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation and demodulation, or the like) depending, at least in part, on a functional split, such as those defined by the 3rd Generation Partnership Project In some aspects, the DU 285 may further host one or more low PHY layers. Each layer (or module) can be implemented with an interface configured to communicate signals with other layers (and modules) hosted by the DU 285, or with the control functions hosted by the CU 280.

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

The SMO Framework 255 may be configured to support RAN deployment and provisioning of non-virtualized and virtualized network elements. For non-virtualized network elements, the SMO Framework 255 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements which may be managed via an operations and maintenance interface (such as an O1 interface) . For virtualized network elements, the SMO Framework 255 may be configured to interact with a cloud computing platform (such as an open cloud (O-Cloud) 269) to perform network element life cycle management (such as to instantiate virtualized network elements) via a cloud computing platform interface (such as an O2 interface) . Such virtualized network elements can include, but are not limited to, CUs 280, DUs 285, RUs 287 and Near-RT RICs 259. In some implementations, the SMO Framework 255 can communicate with a hardware aspect of a 4G RAN, such as an open eNB (O-eNB) 261, via an O1 interface. Additionally, in some implementations, the SMO Framework 255 can communicate directly with one or more RUs 287 via an O1 interface. The SMO Framework 255 also may include a Non-RT RIC 257 configured to support functionality of the SMO Framework 255.

The Non-RT RIC 257 may be configured to include a logical function that enables non-real-time control and optimization of RAN elements and resources, artificial intelligence/machine learning (AI/ML) workflows including model training and updates, or policy-based guidance of applications/features in the Near-RT RIC 259. The Non-RT RIC 257 may be coupled to or communicate with (such as via an A1 interface) the Near-RT RIC 259. The Near-RT RIC 259 may be configured to include a logical function that enables near-real-time control and optimization of RAN elements and resources via data collection and actions over an interface (such as via an E2 interface) connecting one or more CUs 280, one or more DUs 285, or both, as well as an O-eNB, with the Near-RT RIC 259.

In some implementations, to generate AI/ML models to be deployed in the Near-RT RIC 259, the Non-RT RIC 257 may receive parameters or external enrichment information from external servers. Such information may be utilized by the Near-RT RIC 259 and may be received at the SMO Framework 255 or the Non-RT RIC 257 from non-network data sources or from network functions. In some examples, the Non-RT RIC 257 or the Near-RT RIC 259 may be configured to tune RAN behavior or performance. For example, the Non-RT RIC 257 may monitor long-term trends and patterns for performance and employ AI/ML models to perform corrective actions through the SMO Framework 255 (such as reconfiguration via O1) or via creation of RAN management policies (such as A1 policies) .

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

The UE 302 and the base station 304 each include one or more wireless wide area network (WWAN) transceivers 310 and 350, respectively, providing means for communicating (e.g., means for transmitting, means for receiving, means for measuring, means for tuning, means for refraining from transmitting, etc. ) via one or more wireless communication networks (not shown) , such as an NR network, an LTE network, a GSM network, and/or the like. The WWAN transceivers 310 and 350 may each be connected to one or more antennas 316 and 356, respectively, for communicating with other network nodes, such as other UEs, access points, base stations (e.g., eNBs, gNBs) , etc., via at least one designated RAT (e.g., NR, LTE, GSM, etc. ) over a wireless communication medium of interest (e.g., some set of time/frequency resources in a particular frequency spectrum) . The WWAN transceivers 310 and 350 may be variously configured for transmitting and encoding signals 318 and 358 (e.g., messages, indications, information, and so on) , respectively, and, conversely, for receiving and decoding signals 318 and 358 (e.g., messages, indications, information, pilots, and so on) , respectively, in accordance with the designated RAT. Specifically, the WWAN transceivers 310 and 350 include one or more transmitters 314 and 354, respectively, for transmitting and encoding signals 318 and 358, respectively, and one or more receivers 312 and 352, respectively, for receiving and decoding signals 318 and 358, respectively.

The UE 302 and the base station 304 each also include, at least in some cases, one or more short-range wireless transceivers 320 and 360, respectively. The short-range wireless transceivers 320 and 360 may be connected to one or more antennas 326 and 366, respectively, and provide means for communicating (e.g., means for transmitting, means for receiving, means for measuring, means for tuning, means for refraining from transmitting, etc. ) with other network nodes, such as other UEs, access points, base stations, etc., via at least one designated RAT (e.g., Wi-Fi, LTE Direct, PC5, dedicated short-range communications (DSRC) , wireless access for vehicular environments (WAVE) , near-field communication (NFC) , ultra-wideband (UWB) , etc. ) over a wireless communication medium of interest. The short-range wireless transceivers 320 and 360 may be variously configured for transmitting and encoding signals 328 and 368 (e.g., messages, indications, information, and so on) , respectively, and, conversely, for receiving and decoding signals 328 and 368 (e.g., messages, indications, information, pilots, and so on) , respectively, in accordance with the designated RAT. Specifically, the short-range wireless 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. As specific examples, the short-range wireless transceivers 320 and 360 may be Wi-Fi transceivers, transceivers, and/ortransceivers, NFC transceivers, UWB transceivers, or vehicle-to-vehicle (V2V) and/or vehicle-to-everything (V2X) transceivers.

The UE 302 and the base station 304 also include, at least in some cases, satellite signal interfaces 330 and 370, which each include one or more satellite signal receivers 332 and 372, respectively, and may optionally include one or more satellite signal transmitters 334 and 374, respectively. In some cases, the base station 304 may be a terrestrial base station that may communicate with space vehicles (e.g., space vehicles 112) via the satellite signal interface 370. In other cases, the base station 304 may be a space vehicle (or other non-terrestrial entity) that uses the satellite signal interface 370 to communicate with terrestrial networks and/or other space vehicles.

The satellite signal receivers 332 and 372 may be connected to one or more antennas 336 and 376, respectively, and may provide means for receiving and/or measuring satellite positioning/communication signals 338 and 378, respectively. Where the satellite signal receiver (s) 332 and 372 are satellite positioning system receivers, the satellite positioning/communication signals 338 and 378 may be global positioning system (GPS) signals, global navigation satellite system (GLONASS) signals, Galileo signals, Beidou signals, Indian Regional Navigation Satellite System (NAVIC) , Quasi-Zenith Satellite System (QZSS) signals, etc. Where the satellite signal receiver (s) 332 and 372 are non-terrestrial network (NTN) receivers, the satellite positioning/communication signals 338 and 378 may be communication signals (e.g., carrying control and/or user data) originating from a 5G network. The satellite signal receiver (s) 332 and 372 may comprise any suitable hardware and/or software for receiving and processing satellite positioning/communication signals 338 and 378, respectively. The satellite signal receiver (s) 332 and 372 may request information and operations as appropriate from the other systems, and, at least in some cases, perform calculations to determine locations of the UE 302 and the base station 304, respectively, using measurements obtained by any suitable satellite positioning system algorithm.

The optional satellite signal transmitter (s) 334 and 374, when present, may be connected to the one or more antennas 336 and 376, respectively, and may provide means for transmitting satellite positioning/communication signals 338 and 378, respectively. Where the satellite signal transmitter (s) 374 are satellite positioning system transmitters, the satellite positioning/communication signals 378 may be GPS signals, signals, Galileo signals, Beidou signals, NAVIC, QZSS signals, etc. Where the satellite signal transmitter (s) 334 and 374 are NTN transmitters, the satellite positioning/communication signals 338 and 378 may be communication signals (e.g., carrying control and/or user data) originating from a 5G network. The satellite signal transmitter (s) 334 and 374 may comprise any suitable hardware and/or software for transmitting satellite positioning/communication signals 338 and 378, respectively. The satellite signal transmitter (s) 334 and 374 may request information and operations as appropriate from the other systems.

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

A transceiver may be configured to communicate over a wired or wireless link. A transceiver (whether a wired transceiver or a wireless transceiver) includes transmitter circuitry (e.g., transmitters 314, 324, 354, 364) and receiver circuitry (e.g., receivers 312, 322, 352, 362) . A transceiver may be an integrated device (e.g., embodying transmitter circuitry and receiver circuitry in a single device) in some implementations, may comprise separate transmitter circuitry and separate receiver circuitry in some implementations, or may be embodied in other ways in other implementations. The transmitter circuitry and receiver circuitry of a wired transceiver (e.g., network transceivers 380 and 390 in some implementations) may be coupled to one or more wired network interface ports. Wireless transmitter circuitry (e.g., transmitters 314, 324, 354, 364) may include or be coupled to a plurality of antennas (e.g., antennas 316, 326, 356, 366) , such as an antenna array, that permits the respective apparatus (e.g., UE 302, base station 304) to perform transmit “beamforming, ” as described herein. Similarly, wireless receiver circuitry (e.g., receivers 312, 322, 352, 362) may include or be coupled to a plurality of antennas (e.g., antennas 316, 326, 356, 366) , such as an antenna array, that permits the respective apparatus (e.g., UE 302, base station 304) to perform receive beamforming, as described herein. In an aspect, the transmitter circuitry and receiver circuitry may share the same plurality of antennas (e.g., antennas 316, 326, 356, 366) , such that the respective apparatus can only receive or transmit at a given time, not both at the same time. A wireless transceiver (e.g., WWAN transceivers 310 and 350, short-range wireless transceivers 320 and 360) may also include a network listen module (NLM) or the like for performing various measurements.

As used herein, the various wireless transceivers (e.g., transceivers 310, 320, 350, and 360, and network transceivers 380 and 390 in some implementations) and wired transceivers (e.g., network transceivers 380 and 390 in some implementations) may generally be characterized as “a transceiver, ” “at least one transceiver, ” or “one or more transceivers. ” As such, whether a particular transceiver is a wired or wireless transceiver may be inferred from the type of communication performed. For example, backhaul communication between network devices or servers will generally relate to signaling via a wired transceiver, whereas wireless communication between a UE (e.g., UE 302) and a base station (e.g., base station 304) will generally relate to signaling via a wireless transceiver.

The UE 302, the base station 304, and the network entity 306 also include other components that may be used in conjunction with the operations as disclosed herein. The UE 302, the base station 304, and the network entity 306 include one or more processors 342, 384, and 394, respectively, for providing functionality relating to, for example, wireless communication, and for providing other processing functionality. The processors 342, 384, and 394 may therefore provide means for processing, such as means for determining, means for calculating, means for receiving, means for transmitting, means for indicating, etc. In an aspect, the processors 342, 384, and 394 may include, for example, one or more general purpose processors, multi-core processors, central processing units (CPUs) , ASICs, digital signal processors (DSPs) , field programmable gate arrays (FPGAs) , other programmable logic devices or processing circuitry, or various combinations thereof.

The UE 302, the base station 304, and the network entity 306 include memory circuitry implementing memories 340, 386, and 396 (e.g., each including a memory device) , respectively, for maintaining information (e.g., information indicative of reserved resources, thresholds, parameters, and so on) . The memories 340, 386, and 396 may therefore provide means for storing, means for retrieving, means for maintaining, etc. In some cases, the UE 302, the base station 304, and the network entity 306 may include positioning component 348, 388, and 398, respectively. The positioning component 348, 388, and 398 may be hardware circuits that are part of or coupled to the processors 342, 384, and 394, respectively, that, when executed, cause the UE 302, the base station 304, and the network entity 306 to perform the functionality described herein. In other aspects, the positioning component 348, 388, and 398 may be external to the processors 342, 384, and 394 (e.g., part of a modem processing system, integrated with another processing system, etc. ) . Alternatively, the positioning component 348, 388, and 398 may be memory modules stored in the memories 340, 386, and 396, respectively, that, when executed by the processors 342, 384, and 394 (or a modem processing system, another processing system, etc. ) , cause the UE 302, the base station 304, and the network entity 306 to perform the functionality described herein. FIG. 3A illustrates possible locations of the positioning component 348, which may be, for example, part of the one or more WWAN transceivers 310, the memory 340, the one or more processors 342, or any combination thereof, or may be a standalone component. FIG. 3B illustrates possible locations of the positioning component 388, which may be, for example, part of the one or more WWAN transceivers 350, the memory 386, the one or more processors 384, or any combination thereof, or may be a standalone component. FIG. 3C illustrates possible locations of the positioning component 398, which may be, for example, part of the one or more network transceivers 390, the memory 396, the one or more processors 394, or any combination thereof, or may be a standalone component.

The UE 302 may include one or more sensors 344 coupled to the one or more processors 342 to provide means for sensing or detecting movement and/or orientation information that is independent of motion data derived from signals received by the one or more WWAN transceivers 310, the one or more short-range wireless transceivers 320, and/or the satellite signal interface 330. By way of example, the sensor (s) 344 may include an accelerometer (e.g., a micro-electrical mechanical systems (MEMS) device) , a gyroscope, a geomagnetic sensor (e.g., a compass) , an altimeter (e.g., a barometric pressure altimeter) , and/or any other type of movement detection sensor. Moreover, the sensor (s) 344 may include a plurality of different types of devices and combine their outputs in order to provide motion information. For example, the sensor (s) 344 may use a combination of a multi-axis accelerometer and orientation sensors to provide the ability to compute positions in two-dimensional (2D) and/or three-dimensional (3D) coordinate systems.

In addition, the UE 302 includes a user interface 346 providing means for providing indications (e.g., audible and/or visual indications) to a user and/or for receiving user input (e.g., upon user actuation of a sensing device such a keypad, a touch screen, a microphone, and so on) . Although not shown, the base station 304 and the network entity 306 may also include user interfaces.

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

The transmitter 354 and the receiver 352 may implement Layer-1 (L1) functionality associated with various signal processing functions. Layer-1, which includes a physical (PHY) layer, may include error detection on the transport channels, forward error correction (FEC) coding/decoding of the 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-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 a reference signal (e.g., pilot) in the time and/or frequency domain, and then combined together using an inverse fast Fourier transform (IFFT) to produce a physical channel carrying a time domain OFDM symbol stream. The OFDM symbol stream is spatially precoded to produce 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 estimate 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 a respective spatial stream for transmission.

At the UE 302, the receiver 312 receives a signal through its respective antenna (s) 316. The receiver 312 recovers information modulated onto an RF carrier and provides the information to the one or more processors 342. The transmitter 314 and the receiver 312 implement Layer-1 functionality associated with various signal processing functions. The receiver 312 may perform spatial processing on the information to recover any spatial streams destined for the UE 302. If multiple spatial streams are destined for the UE 302, they may be combined by the receiver 312 into a single OFDM symbol stream. The receiver 312 then converts the OFDM symbol stream from the time-domain to the frequency domain using a fast Fourier transform (FFT) . The frequency domain signal comprises a separate OFDM symbol stream for each subcarrier of the OFDM signal. The symbols on each subcarrier, and the reference signal, are recovered and demodulated by determining the most likely signal constellation points transmitted by the base station 304. These soft decisions may be based on channel estimates computed by a channel estimator. The soft decisions are then decoded and de-interleaved to recover the data and control signals that were originally transmitted by the base station 304 on the physical channel. The data and control signals are then provided to the one or more processors 342, which implements Layer-3 (L3) and Layer-2 (L2) functionality.

In the downlink, the one or more processors 342 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, and control signal processing to recover IP packets from the core network. The one or more processors 342 are also responsible for error detection.

Similar to the functionality described in connection with the downlink transmission by the base station 304, the one or more processors 342 provides RRC layer functionality associated with system information (e.g., MIB, SIBs) acquisition, RRC connections, and measurement reporting; PDCP layer functionality associated with header compression/decompression, and security (ciphering, deciphering, integrity protection, integrity verification) ; RLC layer functionality associated with the transfer of upper layer PDUs, error correction through ARQ, concatenation, segmentation, and reassembly of RLC SDUs, re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs onto transport blocks (TBs) , demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction through hybrid automatic repeat request (HARQ) , priority handling, and logical channel prioritization.

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 the appropriate coding and modulation schemes, and to facilitate spatial processing. The spatial streams generated by the transmitter 314 may be provided to different antenna (s) 316. The transmitter 314 may modulate an RF carrier with a respective spatial stream for transmission.

The uplink transmission is processed at the base station 304 in a manner similar to that described in connection with the receiver function at the UE 302. The receiver 352 receives a signal through its respective antenna (s) 356. The receiver 352 recovers information modulated onto an RF carrier and provides the information to the one or more processors 384.

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

For convenience, the UE 302, the base station 304, and/or the network entity 306 are shown in FIGS. 3A, 3B, and 3C as including various components that may be configured according to the various examples described herein. It will be appreciated, however, that the illustrated components may have different functionality in different designs. In particular, various components in FIGS. 3A to 3C are optional in alternative configurations and the various aspects include configurations that may vary due to design choice, costs, use of the device, or other considerations. For example, in case of FIG. 3A, a particular implementation of UE 302 may omit the WWAN transceiver (s) 310 (e.g., a wearable device or tablet computer or personal computer (PC) or laptop may have Wi-Fi and/or capability without cellular capability) , or may omit the short-range wireless transceiver (s) 320 (e.g., cellular-only, etc. ) , or may omit the satellite signal interface 330, or may omit the sensor (s) 344, and so on. In another example, in case of FIG. 3B, a particular implementation of the base station 304 may omit the WWAN transceiver (s) 350 (e.g., a Wi-Fi “hotspot” access point without cellular capability) , or may omit the short-range wireless transceiver (s) 360 (e.g., cellular-only, etc. ) , or may omit the satellite signal interface 370, and so on. For brevity, illustration of the various alternative configurations is not provided herein, but would be readily understandable to one skilled in the art.

The various components of the UE 302, the base station 304, and the network entity 306 may be communicatively coupled to each other over data buses 308, 382, and 392, respectively. In an aspect, the data buses 308, 382, and 392 may form, or be part of, a communication interface of the UE 302, the base station 304, and the network entity 306, respectively. For example, where different logical entities are embodied in the same device (e.g., gNB and location server functionality incorporated into the same base station 304) , the data buses 308, 382, and 392 may provide communication between them.

The components of FIGS. 3A, 3B, and 3C may be implemented in various ways. In some implementations, the components of FIGS. 3A, 3B, and 3C may be implemented in one or more circuits such as, for example, one or more processors and/or one or more ASICs (which may include one or more processors) . Here, each circuit may use and/or incorporate at least one memory component for storing information or executable code used by the circuit to provide this functionality. For example, some or all of the functionality represented by blocks 310 to 346 may be implemented by processor and memory component (s) of the 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 to 388 may be implemented by processor and memory component (s) of the base station 304 (e.g., by execution of appropriate code and/or by appropriate configuration of processor components) . Also, some or all of the functionality represented by blocks 390 to 398 may be implemented by processor and memory component (s) of the network entity 306 (e.g., by execution of appropriate code and/or by appropriate configuration of processor components) . For simplicity, various operations, acts, and/or functions are described herein as being performed “by a UE, ” “by a base station, ” “by a network entity, ” etc. However, as will be appreciated, such operations, acts, and/or functions may actually be performed by specific components or combinations of components of the UE 302, base station 304, network entity 306, etc., such as the processors 342, 384, 394, the transceivers 310, 320, 350, and 360, the memories 340, 386, and 396, the positioning component 348, 388, and 398, etc.

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

Machine learning may be used to generate models that may be used to facilitate various aspects associated with processing of data. One specific application of machine learning relates to generation of measurement models for processing of reference signals for positioning (e.g., positioning reference signal (PRS) ) , such as feature extraction, reporting of reference signal measurements (e.g., selecting which extracted features to report) , and so on.

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

Another example of a machine learning model is a decision tree model. In a decision tree model, a tree structure is defined with a plurality of nodes. Decisions are used to move from a root node at the top of the decision tree to a leaf node at the bottom of the decision tree (i.e., a node with no further child nodes) . Generally, a higher number of nodes in the decision tree model is correlated with higher decision accuracy.

Another example of a machine learning model is a decision forest. Random forests are an ensemble learning technique that builds off of decision trees. Random forests involve creating multiple decision trees using bootstrapped datasets of the original data and randomly selecting a subset of variables at each step of the decision tree. The model then selects the mode of all of the predictions of each decision tree. By relying on a “majority wins” model, the risk of error from an individual tree is reduced.

Another example of a machine learning model is a neural network (NN) . A neural network is essentially a network of mathematical equations. Neural networks accept one or more input variables, and by going through a network of equations, result in one or more output variables. Put another way, a neural network takes in a vector of inputs and returns a vector of outputs.

FIG. 4 illustrates an example neural network 400, according to aspects of the disclosure. The neural network 400 includes an input layer ‘i’ that receives ‘n’ (one or more) inputs (illustrated as “Input 1, ” “Input 2, ” and “Input n” ) , one or more hidden layers (illustrated as hidden layers ‘h1, ’ ‘h2, ’ and ‘h3’ ) for processing the inputs from the input layer, and an output layer ‘o’ that provides ‘m’ (one or more) outputs (labeled “Output 1” and “Output m” ) . The number of inputs ‘n, ’ hidden layers ‘h, ’ and outputs ‘m’ may be the same or different. In some designs, the hidden layers ‘h’ may include linear function (s) and/or activation function (s) that the nodes (illustrated as circles) of each successive hidden layer process from the nodes of the previous hidden layer.

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. In essence, a logistic equation is created in such a way that the output values can only be between ‘0’ and ‘1. ’ Another example of a classification model is a support vector machine. For example, for two classes of data, a support vector machine will find a hyperplane or a boundary between the two classes of data that maximizes the margin between the two classes. There are many planes that can separate the two classes, but only one plane can maximize the margin or distance between the classes. Another example of a classification model is Bayes, which is based on Bayes Theorem. Other examples of classification models include decision tree, random forest, and neural network, similar to the examples described above except that the output is discrete rather than continuous.

Unlike supervised learning, unsupervised learning is used to draw inferences and find patterns from input data without references to labeled outcomes. Two examples of unsupervised learning models include clustering and dimensionality reduction.

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

Regardless of which machine learning model is used, at a high-level, a machine learning module (e.g., implemented by a processing system) may be configured to iteratively analyze training input data (e.g., measurements of reference signals to/from various target UEs) and to associate this training input data with an output data set (e.g., a set of possible or likely candidate locations of the various target UEs) , thereby enabling later determination of the same output data set when presented with similar input data (e.g., from other target UEs at the same or similar location) .

The artificial intelligence/machine learning (AIML) positioning and/or sensing provided by an AIML model may be “direct” AIML (denoted “D-AIML” ) positioning and/or sensing or AIML “assisted” (denoted “A-AIML” ) positioning and/or sensing. Note that, as used herein, an AIML model (whether an A-AIML model or a D-AIML model) may alternatively be referred to as an “ML model, ” an “AI model, ” an “ML-based model, ” an “AI-based model, ” and the like.

FIG. 5A is a diagram 510 illustrating an example of direct AIML positioning and/or sensing, according to aspects of the disclosure. As shown in FIG. 5A, direct AIML positioning and/or sensing is where the AIML model is trained to accept input features (e.g., downlink positioning reference signal (DL-PRS) measurements, sounding reference signal (SRS) measurements, sidelink positioning reference signal (SL-PRS) measurements, sensing signal measurements, beam measurements (e.g., synchronization signal block (SSB) measurements) , channel state information reference signal (CSI-RS) measurements, etc. ) and output a final result (referred to as a “direct label” ) , such as a target location (e.g., a UE location for positioning or a target object location for sensing) . The measurements of the reference signal (s) may include the channel energy response (CER) , channel impulse response (CIR) , channel frequency response (CFR) , received signal strength indicator (RSSI) , reference signal received power (RSRP) , path RSRP (RSRPP) , reference signal received quality (RSRQ) , time of arrival (ToA) , relative ToA (RTOA) , reference signal time difference (RSTD) , angle of departure (AoD) , angle of arrival (AoA) , and/or the like of the reference signal (s) .

FIG. 5B is a diagram 530 illustrating an example of AIML assisted positioning and/or sensing, according to aspects of the disclosure. As shown in FIG. 5B, AIML assisted positioning and/or sensing is where an AIML model is trained to accept input features (e.g., DL-PRS measurements, SRS measurements, SL-PRS measurements, sensing signal measurements, beam measurements, CSI-RS measurements, etc. ) and output one or more intermediate results (also referred to as “intermediate label (s) ” ) . In a positioning context, generating the intermediate result may be referred to as “positioning feature extraction, ” which may include determining timing/angle information, line of sight (LOS) identification, etc. The intermediate results may include the ToA, RTOA, RSTD, AoD, AoA, LOS indication, and/or the like. The intermediate result (s) may in turn be provided as an input to another AIML model or non-AIML model positioning and/or sensing technique (e.g., Chan’s algorithm, Kalman filtering, etc. ) to determine a target location (e.g., a UE location for positioning or a target object location for sensing) .

Note that as shown in FIG. 5B, the A-AIML model and the other model/technique may be implemented at the same entity (e.g., UE, base station, location server, sensing server, etc. ) or at different entities. For example, for network-assisted positioning, the UE may apply the A-AIML model to compress the measurement data and then report the compressed data to the location server, which may then apply the other position estimation model/technique. As another example, for UE-based positioning, a network component (e.g., a base station, location server, or another UE for sidelink positioning) may apply the A-AIML model to compress the measurement data and report the compressed data to the UE, which then applies the other position estimation model/technique.

FIG. 5C illustrates various AIML positioning and/or sensing scenarios, according to aspects of the disclosure. As shown in diagram 550, there are three AIML positioning and/or sensing deployment scenarios based on downlink reference signals (e.g., DL-PRS, CSI-RS, etc. ) . The first deployment scenario (labeled “Case 1” ) is a UE-based positioning and/or sensing case with a UE-side D-AIML positioning and/or sensing model (labeled “D-AIML” ) . In this case, the UE applies the D-AIML positioning and/or sensing model (or simply “D-AIML model” ) to the downlink reference signal measurements to determine a location of the UE or a target object and reports the target location to the network (e.g., LMF 270) .

The second deployment scenario (labeled “Case 2a” ) is UE-assisted/network-based positioning and/or sensing with a UE-side A-AIML positioning and/or sensing model that provides AIML-assisted positioning and/or sensing. That is, the UE inputs measurements of downlink reference signals (e.g., DL-PRS, CSI-RS) received from one or more TRPs into the A-AIML positioning and/or sensing model to obtain intermediate measurements (or quantities) of the downlink reference signals. The UE then reports the intermediate measurements to the network (e.g., LMF 270) . The network entity may then apply an AIML model or a non-AIML model technique to the intermediate measurements to determine a target location (e.g., of the UE for positioning scenarios or a target object for sensing scenarios) .

The third deployment scenario (labeled “Case 2b” ) is UE-assisted/network-based positioning and/or sensing scenario with a network-side D-AIML positioning and/or sensing model. That is, the UE reports the measurements of the downlink reference signals received from one or more TRPs to the network (e.g., LMF 270) . The network then applies the D-AIML positioning and/or sensing model to the measurements to determine the location of the UE or a target object.

As shown in diagram 570, there are two AIML positioning and/or sensing deployment scenarios based on uplink reference signals (e.g., SRS) . The first deployment scenario (labeled “Case 3a” ) is RAN node-assisted positioning and/or sensing with a RAN-side AIML model that provides AIML assisted positioning and/or sensing. In this case, the RAN node (e.g., a base station, TRP, or other base station component) applies an A-AIML positioning and/or sensing model to TRP measurements of one or more uplink reference signals (e.g., SRS) transmitted by a UE to obtain intermediate measurements of the received uplink reference signal (s) . The RAN node then reports the intermediate measurements to the core network (e.g., LMF 270) , which can use them to locate the UE (for positioning) or a target object (for sensing) .

The second deployment scenario (labeled “Case 3b” ) is RAN node-assisted positioning and/or sensing with a network-side AIML positioning and/or sensing model that provides direct AIML positioning and/or sensing. In this case, the RAN node reports measurements of one or more uplink reference signals received from a UE to the core network (e.g., LMF 270) . The core network then applies a D-AIML positioning and/or sensing model to the measurements of the uplink reference signal (s) to obtain a target location of the UE (for positioning) or a target object (for sensing) .

Note that there may be other deployment scenarios in which the UE, RAN, or the core network use an AIML positioning and/or sensing model to compute or report a positioning and/or sensing estimate (target location) , but these cases are implementation- specific and do not necessarily involve signaling between the UE, RAN, and/or the core network.

Further note that an AIML model may execute in a training mode or an inferencing mode. In the training mode, the AIML model is provided with pre-validated input data along with pre-validated output data to derive or modify weights of the AIML to increase the reliability of the AIML model to provide new (unvalidated) output data that is similar to the pre-validated output data in response to new (unvalidated) input data that is similar to the pre-validated input data. In the inferencing mode, the AIML model utilizes the weights determined during the training mode to process new (unvalidated) input data so as to generate new (unvalidated) output data (typically, without further adjusting the weights until/unless the AIML model returns to the training mode) . The (unvalidated) output data may be characterized as an “inference. ” Thus, the “final” positioning or sensing results described above with respect to FIGS. 5A to 5C may correspond to AIML model weights or inferences depending on whether the respective AIML model is executing in the training mode or the inferencing mode.

FIG. 6 illustrates an example call flow 600 for an NR-based sensing procedure (e.g., a bistatic sensing procedure) in which the network configures the sensing parameters, according to aspects of the disclosure. Although FIG. 6 illustrates a network-coordinated sensing procedure, the sensing procedure could be coordinated over sidelink channels.

At stage 605, a sensing server 670 (e.g., inside or outside the core network) sends a request for network (NW) information to a gNB 622 (e.g., the serving gNB of a UE 604) . The request may be for a list of the UE’s 604 serving cell and any neighboring cells. At stage 610, the gNB 622 sends the requested information to the sensing server 670. At stage 615, the sensing server 670 sends a request for sensing capabilities to the UE 604. At stage 620, the UE 604 provides its sensing capabilities to the sensing server 670.

At stage 625, the sensing server 670 sends a configuration to the UE 604 indicating one or more reference signal (RS) resources that will be transmitted for sensing. The reference signal resources may be transmitted by the serving and/or neighboring cells identified at stage 610. In some cases, the NR-based sensing procedure illustrated in FIG. 6 may be a sensing-only procedure or a joint communication and sensing (JCS) procedure. In the case of a sensing-only procedure, the reference signal resources may be reference signal resources specifically configured for sensing purposes. In the case of a JCS procedure, the reference signal resources may be reference signal resources for communication that can also be used for sensing purposes. Alternatively, the reference signal resources for sensing may be multiplexed (e.g., time-division multiplexed) with reference signal resources for communication. For example, the reference signal resources for communication may be an orthogonal frequency division multiplexing (OFDM) waveform, while the reference signal resources for sensing may be a frequency modulation continuous wave (FMCW) waveform.

At stage 630, the sensing server 670 sends a request for sensing information to the UE 604. The UE 604 then measures the transmitted reference signals and, at stage 635, sends the measurements, or any sensing results determined from the measurements, to the sensing server 670.

In an aspect, the communication between the UE 604 and the sensing server 670 may be via the LTE positioning protocol (LPP) . The communication between the sensing server 670 and the gNB may be via NR positioning protocol type A (NRPPa) .

Indoor positioning and navigation are widely used for various applications by mobile devices, such as robots, unmanned aerial vehicles (UAVs) , or other types of automated mobile devices. For example, robots may be used for delivering food in restaurants, transporting goods in warehouses, or performing household chores in residences. Indoor UAVs may perform various tasks in a restricted three-dimensional space. In these applications, mobile devices may need to have accurate and real-time position and heading estimations in a restricted environment (e.g., an indoor environment) for accurate path planning and navigation.

Robots may be equipped with one or more odom sensors associated with wheels, radars, lidars, cameras, and/or inertial measurement unit (IMU) sensors for positioning and navigation. In some scenarios, however, cumulative errors may be introduced by some of these sensors while the robot is moving. For example, wheel slip and IMU drift may cause cumulative errors which may lead to odometry drifts in the position and heading of the robot.

In some scenarios, the initial position and heading of the robot may be difficult to obtain based on measurements by robot-mounted sensors such as odom sensors, radars, lidars, cameras, and/or IMU sensors. Some types of sensors, such as magnetic sensors to measure the Earth’s magnetic field to obtain the heading of the robot, may be inaccurate for indoor environments because the presence of metals in such environments may affect the measurement results of magnetic sensors. In these scenarios, the initial position and heading may need to be manually set by an observer. The robot may move around in the environment in an attempt to recognize known objects or landmarks in order to find its position and heading on a map by using its radars or cameras, for example, but such an effort may take a long time.

According to aspects of the disclosure, a long short-term memory (LSTM) neural network model, which may consider the time influence of movements of the robot, may be applied to sensing measurements of UWB signals to obtain estimated position and heading. In some aspects, the LSTM neural network model is an attention-LSTM neural network model.

In some aspects, the attention-LSTM neural network model may be combined with other techniques, such as extended Kalman filter (EKF) and/or adaptive Monte Carlo localization (AMCL) algorithms, to obtain the current position and heading of the moving robot without manual intervention. In some aspects, the moving robot may perform self-calibration to avoid odom drift during navigation.

In some aspects, a UWB antenna array with multiple antennas may be provided on the robot to sense UWB signals from a UWB network node. For example, the robot may obtain position and heading information based on sensed angle of arrival (AOA) and time of flight (TOF) of the UWB signal. In some aspects, the accuracy of UWB positioning may be on the order of about 10cm, although the reception of UWB signals may be affected by surrounding obstacles in an indoor environment.

In some aspects, the positioning and heading of a moving robot may be time-sequenced. In order to improve the accuracy of positioning and heading estimations, an attention-LSTM neural network may be implemented to train and predict the position and heading of the robot. In some aspects, the results of the position and heading estimations by the attention-LSTM neural network may be combined with EKF and/or AMCL techniques in a data fusion to achieve a high degree of robustness in determining the position and heading in various indoor environments.

FIG. 7 illustrates an example of UWB-fused localization in indoor navigation, according to aspects of the disclosure. In the example shown in FIG. 7, TOF and AOA information 702 are obtained by measuring incoming UWB signals. An attention-LSTM neural network model 704 is applied to the TOF and AOA information. In some aspects, the output of the attention-LSTM neural network model 704 may be fed to an attention mechanism 706, which may also receive the results of an EKF algorithm 708. The EKF algorithm 708 may apply extended Kalman filtering to motion data of the vehicle, including, for example, IMU data 710 and wheel velocity 712.

In some aspects, the robot may know its movement goal 714 and the base 716 of its movement, that is, the starting point of the movement. Based on its knowledge of the movement goal and base, the robot may determine its desired velocity 718 and driver node 720, the information of which may be fed to the IMU data 710 and the wheel velocity 712.

In some aspects, the EKF algorithm 708 may be applied to the IMU data 710 and the wheel velocity 712 to generate odom data (x, y, z, θ) 722. In some aspects, the output of the attention mechanism 706 may also be applied to refine the odom data (x, y, z, θ) 722. In some aspects, an AMCL algorithm 724 may be applied to further refine the odom data (x, y, z, θ) .

In some aspects, the robot may feed its map information 726, lidar data 728, and initial pose 730 (i.e., position and heading) to the AMCL algorithm 724. In some aspects, the output of the attention-LSTM neural network model 704 may be applied to refine the initial pose 730.

FIGS. 8A and 8B illustrate examples of AOAs of a robot 802 with different headings, according to aspects of the disclosure. In the example illustrated in FIG. 8A, Cartesian coordinates with an x-axis and a y-axis is shown. (The z-axis is perpendicular to the surface of the figure. ) An anchor position 804, which is a known position in a given environment, is at the origin (0, 0, 0) of the Cartesian coordinates. In some implementations, the anchor position may be the known position of a UWB network node, for example.

In the example shown in FIG. 8A, the robot is located at (X_T, Y_T, 0) . The heading 806 of the robot 802 is at an angle θ_T with respect to the x-axis, and the angle between the x-axis and the line 808 from the anchor position 804 to the robot 802 is α_o. The angle between the lateral line 810 (i.e., a line perpendicular to the heading 806 of the robot 802) and the line 808 from the anchor position 804 to the robot 802 is φ_o. Thus, the relationship between α_o, φ_o, and θ_T is θ_T = α_o + (90° -φ_o) . In the example shown in FIG. 8A, the angle of the heading θ_T is greater than α_o.

In the example shown in FIG. 8B, the robot is also located at (X_T, Y_T, 0) , but in a different heading 822. In this example, the angle θ_T of the heading 822 with respect to the x-axis is less than the angle α_o between the x-axis and the line 808 from the anchor position 804 to the robot 802. The line coinciding with the direction of the heading 822 intersects the x-axis at a negative point on the x-axis, that is, to the left of the anchor position 804. In this example, the relationship between α_o, φ_o, and θ_T is θ_T = α_o - (φ_o -90°) , which is α_o + (90° -φ_o) , the same formula for calculating θ_T in the example shown in FIG. 8A.

In some aspects, the real-time pose of a robot may be obtained by TOF and AOA information based on sensing measurements of UWB signals. In some scenarios, for example, when a moving robot is navigating in an indoor environment, the real-time pose of the robot may need to be obtained and updated with a high degree of accuracy.

In some aspects, the position of the robot may be characterized as a position (x, y, z) on Cartesian coordinates referenced to (or superimposed on) an actual map. The heading of the robot may be characterized as an angle of the heading vector relative to the x-axis on the map. In some aspects, the heading may be represented by a Quaternion or Euler angle. For simplicity of illustration, the Euler angle θ_T is used to represent the actual heading in FIGS. 8A and 8B. In some scenarios, before the robot starts its navigation, the initial pose of the robot (X_T, Y_T, Z_T, θ_T) may need to be obtained.

FIG. 9 illustrates an example of initialization and correction of the initial prose of a robot, according to aspects of the disclosure. In the example shown in FIG. 9, the robot 902 may not know its actual initial position and heading on a map, and it is assumed, at initialization stage, that the robot is initially positioned at the origin (0, 0, 0) with an initial heading of 0° relative to the x-axis, even though the actual heading of the robot 902 may be at an angle θ_T relative to the x-axis, and the actual position of the robot 902 is at (X_T, Y_T, Z_T) .

In some aspects, the initial pose of the robot 902 may be updated or refined by making initial AOA and TOF measurements of UWB signals and applying an LSTM neural network model to the initial AOA and TOF measurements as well as the known position of a UWB anchor (e.g., the known position of a UWB network node) . In some aspects, the initial pose of the robot 902 may be progressively updated or refined in multiple iterations by applying the LSTM neural network model to multiple AOA and TOF measurements and multiple known UWB anchor positions. In the example illustrated in FIG. 9, the initial pose of the robot 902 may be progressively updated along a curved arrow 904 from the origin (0, 0, 0) and the initial heading of 0° to its actual initial pose (X_T, Y_T, Z_T, θ_T) .

FIG. 10 illustrates an example of determining the initial pose of a robot, according to aspects of the disclosure. In the example shown in FIG. 10, at initialization stage in block 1002, the robot, which may not know its own initial position and heading on the map, is initialized at the origin (0, 0, 0) and at a heading of 0° relative to the x-axis. In this example, multiple UWB anchor positions (e.g., anchor position (X_i, Y_i, Z_i) for i = 0, 1, …N-1^th anchor) are provided in block 1004 for the robot to conduct multiple TOF and AOA measurements in block 1006 to derive the distances and angles relative to the UWB anchor positions.

In some aspects, the distances and angles obtained by the TOF and AOA measurements of UWB signals may be used as input data for pre-processing in block 1008. The pre-processed input data may be fed to an LSTM encoder in block 1010, and the output from the LSTM encoder may be fed to an attention mechanism in block 1012. The output of the attention mechanism in block 1012 may be fed to an LSTM decoder in block 1014, and the output of the LSTM decoder in block 1014 may be used for estimating the actual pose (X_T, Y_T, Z_T, θ_T) in block 1016.

In some aspects, the robot may obtain its actual initial pose (X_T, Y_T, Z_T, θ_T) in block 1018 based on the LSTM output. Then the robot may use its actual initial pose to perform navigation in an indoor environment in block 1020.

FIG. 11 illustrates AOA estimation using a receiver antenna array, according to aspects of the disclosure. In the example illustrated in FIG. 11, a UWB receiver 1102 is equipped with multiple antennas 1104, 1106, 1108 and 1110 in an antenna array 1112. A UWB transmitter 1114 is equipped with a transmit antenna 1116 to transmit UWB signals for sensing measurements by the UWB receiver 1102. As shown in FIG. 11, UWB signals are transmitted along a path 1118 at an angle which is the angle of arrival (AOA) from the perspective of the UWB receiver 1102.

In some aspects, an LSTM model for UWB position and heading estimations using multiple anchor positions is provided. FIG. 12 illustrates an example of estimating the position and heading of a robot 1202 with four anchor positions A₀, A₁, A₂ and A₃, according to aspects of the disclosure. In the example illustrated in FIG. 12, the anchor positions A₀, A₁, A₂ and A₃ on three-dimensional Cartesian coordinates are P_0,t(x_0, t, y_0, t, z_0, t) , P_1, t (x_1, t, y_1, t, z_1, t) , P_2, t (x_2, t, y_2, t, z_2, t) and P3,_t (x_3, t, y_3, t, z_3, t) , respectively. The distances between the robot 1202 and the anchor positions A₀, A₁, A₂ and A₃ are D_0, t, D_1, t, D_2, t and D_3, t, respectively. The angles between the robot 1202 and the anchor positions A₀, A₁, A₂ and A₃ areand respectively. (There are two angles representing the relative angular position between the robot 1202 and each of the anchor positions in two different planes on three-dimensional Cartesian coordinates. )

In some aspects, the LSTM model for UWB position and heading estimations may be applied to any number of anchor positions. For example, given N number of UWB anchors A_i (i = 0.. N-1) with known positions P_i (i = 0.. N-1) , the position and heading of the robot may be calculated as follows:

Input:

1. Anchor positions in map coordinate : P_i, t (x_i, t, y_i, t, z_i, t) (i = 0... N-1)

2. Distance between target T and each anchor : D_i, t (i = 0.. N-1)

3. Angle between target T and each anchor :

4. Line and angle velocity : V_x, t, V_a, t

Output:

1. Robot’s position : P_T, t (X_T, t, Y_T, t, Z_T, t)

2. Robot’s heading (Euler yaw angle) : θ_T, t

where:

Y_t= [x_T, t, y_T, t, z_T, t, θ_T, t]

FIG. 13 illustrates an example of an LSTM model 1302 for estimating the position and heading with LSTM algorithm blocks for inputs X_t-1, X_t and X_t+1, according to aspects of the disclosure. FIG. 14 illustrates an example of one of the LSTM algorithm blocks for input X_t, according to aspects of the disclosure.

In the example illustrated in FIG. 13, the LSTM model 1302 includes a first LSTM algorithm block 1304 for an input X at time t-1 (X_t-1) , a second LSTM algorithm block 1306 for an input X at time t (X_t) , and a third LSTM algorithm block 1308 for an input X at time t+1 (X_t+1) .

In some aspects, the LSTM neural network model may be a more effective time-sequence model than other neural network models such as convolutional neural network (CNN) or recurrent neural network (RNN) models. In some aspects, the location and heading of a mobile device, such as a robot, are time-related parameters in practical applications. In a given environment, such as an indoor environment, there may be various factors that affect the accuracy of positioning and heading measurements, including, for example, measurement errors, signal interference, and/or environmental influences. In some aspects, the LSTM neural network model may help improve the accuracy of position and heading estimations by applying a time-related LSTM algorithm to UWB signal measurements.

FIG. 14 illustrates further details of an example of the LSTM algorithm block 1306 for the input X_t as shown in FIG. 13, according to aspects of the disclosure. In the example shown in FIG. 14, the LSTM algorithm block 1306 includes a forget gate 1402, an input gate 1404, and an output gate 1406.

In some aspects, the function f_t of the forget gate 1402 is given as follows:

f_t=σ (W_f· [h_t-1, x_t] +b_f)

In some aspects, the functions i_t, and C_t of the input gate 1404 are given as follows:

i_t=σ(W_i· [h_t-1, x_t] +b_i)

In some aspects, the functions ot_, and h_t of the output gate 1406 are given as follows:

o_t=σ (W_o· [h_t-1, x_t] +b_o)

h_t=o_t*tanh (C_t)

In the example shown in FIG. 14, C_t-1 is a memory cell internal state and h_t-1 is a hidden state of the previous LSTM algorithm block 1304 for X_t-1 (shown in FIG. 13) , whereas C_t is the memory cell internal state and h_t is the hidden state of the current LSTM algorithm block 1306 for X_t. LSTM algorithms may be implemented in various manners according to aspects of the disclosure.

In some aspects, an attention-LSTM algorithm may be used to calculate different weights of hidden layers of LSTM at different times to further improve the accuracy of positioning and heading estimations.

FIG. 15 illustrates an example of an attention-LSTM neural network model with an attention mechanism and LSTM encoders and decoders, according to aspects of the disclosure. In the example illustrated in FIG. 15, the attention-LSTM neural network model includes an attention mechanism 1502, multiple encoders 1504, 1506, 1508 and 1510 for receiving inputs X₁, X₂, X₃ and X₄, respectively, and multiple encoders 1512, 1514, 1516 and 1518 for generating outputs Y₁, Y₂, Y₃ and Y₄, respectively.

In some aspects, multiple LSTM encoders (e.g., encoders 1504, 1506, 1508 and 1510) may perform LSTM encoding of inputs [x₁, x₂, x₃, …, x_n] to generate outputs [h₁, h₂, h₃,…, h_n] .

In some aspects, attention weights [c₁, c₂, c₃, …, c_n] may be computed as follows: The output of LSTM is transformed nonlinearly to obtain e_tj, which is then used to calculate attention weight values α_tj, α_tj to represent the importance of each middle state h_j.

In some aspects, a context vector may be created by calculating a weighted sum for the LSTM output and weight to obtain a context vector c_t.

In some aspects, multiple LSTM decoders (e.g., decoders 1512, 1514, 1516 and 1518) may perform LSTM decoding and translation to obtain outputs [y₁, y₂, y₃, …, y_n] as follows:

In some aspects, the loss function of the attention-LSTM algorithm may be a mean square error (MSE) :

where is the predicted output, y is the actual location, and M is the number of samples in a batch.

In some aspects, the attention-LSTM neural network algorithm may be combined with non-AI/ML techniques, for example, EKF and/or AMCL, in a data fusion to derive the pose of a robot in real time.

In some aspects, non-AI/ML techniques for position location in an indoor environment may include the use of EKF and/or AMCL to fuse motion data, including, for example, wheel velocity, IMU data, or other types of motion data. In some aspects, the EKF output may be combined with initial pose and lidar data to calculate the real-time location.

In some scenarios, an attention-LSTM neural network model may be applied to TOF and AOA data based on measurements of UWB signals to calculate the real-time location. In some scenarios, however, the robot may not be in a line of sight (LOS) with the UWB network node. In such scenarios, UWB measurement results alone may not provide the correct position and heading, or may provide position and heading at a relatively low confidence level. In some aspects, the attention-LSTM neural network model may be combined with one or more non-AI/ML techniques, such as EKF and/or AMCL, to provide estimated real-time position and heading with a greater degree of accuracy and robustness.

FIG. 16 illustrates an example of position and heading estimation using a combination of attention-LSTM, EKF and AMCL techniques, according to aspects of the disclosure. In the example illustrated in FIG. 16, motion data such as wheel velocity 1602 and IMU data 1604 may be fed to an EKF 1606, which applies extended Kalman filtering to the input motion data. The output of the EKF 1606 may be fed to an AMCL algorithm 1608.

In some aspects, the AMCL algorithm 1608 may also receive lidar data 1610 and initial post (X₀, Y₀, Z₀, θ₀) 1612 as inputs. The initial pose (X₀, Y₀, Z₀, θ₀) 1612 at time zero (0) may be generated by applying an attention-LSTM algorithm 1614 to measured UWB signal TOF and ToA 1616, for example. In some aspects, the attention-LSTM algorithm 1614 may generate an updated pose P_AL, _t (X_AL, t, Y_AL, t, Z_AL, t, θ_AL, t) 1618 at time t, where “AL” in the subscript stands for attention-LSTM. In some aspects, the AMCL algorithm 1608 may generate an updated pose PK_F,_t (X_KF, t, Y_KF, t, Z_KF, t, θ_KF, t) 1620 at time t, where “KF” in the subscript stands for Kalman filter.

In some aspects, an attention mechanism 1622 may perform a data fusion of the pose P_AL,t(X_AL, t, Y_AL, t, Z_AL, t, θ_AL, t) 1618 and the pose P_KF, t (X_KF, t, Y_KF, t, Z_KF, t, θ_KF, t) 1620 to generate a fused final pose P_t (X_t, Y_t, Z_t, θ_t) 1624 at time t. In some aspects, the attention mechanism 1622 may apply a weight vector α_KF, α_AL for EKF and LSTM, respectively, to obtain the fused final pose P_t, where P_t=α_KFP_KF, t+ α_ALP_AL, t. In some aspects, the fused final pose P_t may have a higher degree of accuracy and more robustness for various types of indoor environments compared to a pose obtained by EKF/AMCL alone or by attention-LSTM alone.

In some aspects, the initial pose (X₀, Y₀, Z₀, θ₀) may be obtained when the robot is initially motionless at a fixed location and orientation. In some aspects, no human observation or intervention is needed when the robot obtains its initial pose based on applying the attention-LSTM algorithm to the measured UWB signal TOF and AOA. In some aspects, the fused final pose P_t (X_t, Y_t, Z_t, θ_t) may be used to correct any odometry drift of the position and heading of the robot during navigation, for example.

FIG. 17 illustrates an example method 1700 of wireless positioning, according to aspects of the disclosure. In some aspects, method 1700 may be performed by a UE (e.g., UE 302 described herein) .

At 1710, the UE may receive one or more ultra-wideband (UWB) signals from one or more UWB network nodes in an environment.

Means for performing the operation of block 1710 may include the processor (s) , memory, or transceiver (s) of any of the UE 302 described herein. For example, the operation of block 1710 may be performed by the one or more short-range wireless transceivers 320, the one or more processors 342, memory 340, and/or positioning component 348, any or all of which may be considered means for performing this operation.

At 1720, the UE may obtain one or more measurements of the one or more UWB signals.

Means for performing the operation of block 1720 may include the processor (s) , memory, or transceiver (s) of any of the UE 302 described herein. For example, the operation of block 1720 may be performed by the one or more short-range wireless transceivers 320, the one or more processors 342, memory 340, and/or positioning component 348, any or all of which may be considered means for performing this operation.

At 1730, the UE may apply a long short-term memory (LSTM) neural network model to obtain an estimated position and an estimated heading based on the one or more measurements of the UWB signals.

Means for performing the operation of block 1730 may include the processor (s) , memory, or transceiver (s) of any of the UE 302 described herein. For example, the operation of block 1730 may be performed by the one or more short-range wireless transceivers 320, the one or more processors 342, memory 340, and/or positioning component 348, any or all of which may be considered means for performing this operation.

Method 1700 may include additional implementations, such as any single implementation or any combination of implementations described below and/or in connection with one or more other processes described elsewhere herein.

In some aspects, the one or more measurements of the one or more UWB signals include one or more angles of arrival (AOAs) of the one or more UWB signals.

In some aspects, the UE includes a UWB antenna array to detect the one or more AOAs of the one or more UWB signals.

In some aspects, the one or more measurements of the one or more UWB signals include one or more times of flight (TOFs) of the one or more UWB signals.

In some aspects, method 1700 includes training the LSTM neural network model with one or more additional measurements of one or more additional UWB signals.

In some aspects, the estimated position and the estimated heading are obtained by applying the LSTM neural network model and an extended Kalman filter (EKF) .

In some aspects, the estimated position and the estimated heading obtained by LSTM are applied as inputs to an adaptive Monte Carlo localization (AMCL) filter.

In some aspects, the estimated position and the estimated heading are obtained by applying the the EKF and UE motion data including inertial measurement unit (IMU) data, wheel velocity, odom data, lidar data, or any combination thereof.

In some aspects, the UE motion data is applied as one or more inputs to the EKF.

In some aspects, a first weight vector is applied to the EKF and a second weight vector is applied to the LSTM neural network model.

In some aspects, the estimated position and the estimated heading are obtained based at least in part on one or more anchor positions in the environment.

In some aspects, the one or more anchor positions are one or more known positions of the one or more UWB network nodes.

In some aspects, the LSTM neural network model includes a forget gate, an input gate, and an output gate.

In some aspects, the LSTM neural network model is an attention-LSTM neural network model.

In some aspects, the environment is an indoor environment.

In some aspects, the UE is a robot.

Although FIG. 17 shows example blocks of method 1700, in some implementations, method 1700 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in FIG. 17. Additionally, or alternatively, two or more of the blocks of method 1700 may be performed in parallel, or performed in a sequence different from the sequence listed in FIG. 17.

As will be appreciated, a technical advantage of the method 1700 is that, by using UWB sensing measurements and applying a neural network model, the described techniques can be used to determine the position and heading of a mobile device, such as a robot, with a high degree of accuracy, which may be required for various applications, for example, for automated movements by the robot in an indoor environment. The described techniques can determine the position and heading automatically and motionless without human intervention starting from the initialization, which is a high efficiency and robust real-time indoor localization system. In some aspects, the described techniques can be used to provide an initial pose while the mobile device is motionless in both position and orientation, without a need for human observation or intervention. In some aspects, the described techniques can be used to correct odometry drift of the position and direction of the mobile device during navigation with extended Kalman filter (EKF) fusion.

In the detailed description above it can be seen that different features are grouped together in examples. This manner of disclosure should not be understood as an intention that the example clauses have more features than are explicitly mentioned in each clause. Rather, the various aspects of the disclosure may include fewer than all features of an individual example clause disclosed. Therefore, the following clauses should hereby be deemed to be incorporated in the description, wherein each clause by itself can stand as a separate example. Although each dependent clause can refer in the clauses to a specific combination with one of the other clauses, the aspect (s) of that dependent clause are not limited to the specific combination. It will be appreciated that other example clauses can also include a combination of the dependent clause aspect (s) with the subject matter of any other dependent clause or independent clause or a combination of any feature with other dependent and independent clauses. The various aspects disclosed herein expressly include these combinations, unless it is explicitly expressed or can be readily inferred that a specific combination is not intended (e.g., contradictory aspects, such as defining an element as both an electrical insulator and an electrical conductor) . Furthermore, it is also intended that aspects of a clause can be included in any other independent clause, even if the clause is not directly dependent on the independent clause.

Implementation examples are described in the following numbered clauses:

Clause 1. A method of wireless positioning performed at a user equipment (UE) , comprising: receiving one or more ultra-wideband (UWB) signals from one or more UWB network nodes in an environment; obtaining one or more measurements of the one or more UWB signals; and applying a long short-term memory (LSTM) neural network model to obtain an estimated position and an estimated heading based on the one or more measurements of the UWB signals.

Clause 2. The method of clause 1, wherein the one or more measurements of the one or more UWB signals include one or more angles of arrival (AOAs) of the one or more UWB signals.

Clause 3. The method of clause 2, wherein the UE includes a UWB antenna array to detect the one or more AOAs of the one or more UWB signals.

Clause 4. The method of any of clauses 1 to 3, wherein the one or more measurements of the one or more UWB signals include one or more times of flight (TOFs) of the one or more UWB signals.

Clause 5. The method of any of clauses 1 to 4, further comprising: training the LSTM neural network model with one or more additional measurements of one or more additional UWB signals.

Clause 6. The method of any of clauses 1 to 5, wherein the estimated position and the estimated heading are obtained by applying the LSTM neural network model and an extended Kalman filter (EKF) .

Clause 7. The method of clause 6, wherein the estimated position and the estimated heading obtained by LSTM are applied as inputs to an adaptive Monte Carlo localization (AMCL) filter.

Clause 8. The method of clause 7, wherein the estimated position and the estimated heading are obtained by applying the EKF and UE motion data including: inertial measurement unit (IMU) data; wheel velocity; odom data; lidar data; or any combination thereof.

Clause 9. The method of clause 8, wherein the UE motion data is applied as one or more inputs to the EKF.

Clause 10. The method of any of clauses 6 to 9, wherein a first weight vector is applied to the EKF and a second weight vector is applied to the LSTM neural network model.

Clause 11. The method of any of clauses 1 to 10, wherein the estimated position and the estimated heading are obtained based at least in part on one or more anchor positions in the environment.

Clause 12. The method of clause 11, wherein the one or more anchor positions are one or more known positions of the one or more UWB network nodes.

Clause 13. The method of any of clauses 1 to 12, wherein the LSTM neural network model includes a forget gate, an input gate, and an output gate.

Clause 14. The method of any of clauses 1 to 13, wherein the LSTM neural network model is an attention-LSTM neural network model.

Clause 15. The method of any of clauses 1 to 14, wherein the environment is an indoor environment.

Clause 16. The method of any of clauses 1 to 15, wherein the UE is a robot.

Clause 17. A user equipment (UE) , comprising: one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to: receive, via the one or more transceivers, one or more ultra-wideband (UWB) signals from one or more UWB network nodes in an environment; obtain one or more measurements of the one or more UWB signals; and apply a long short-term memory (LSTM) neural network model to obtain an estimated position and an estimated heading based on the one or more measurements of the UWB signals.

Clause 18. The UE of clause 17, wherein the one or more measurements of the one or more UWB signals include one or more angles of arrival (AOAs) of the one or more UWB signals.

Clause 19. The UE of clause 18, wherein the UE includes a UWB antenna array to detect the one or more AOAs of the one or more UWB signals.

Clause 20. The UE of any of clauses 17 to 19, wherein the one or more measurements of the one or more UWB signals include one or more times of flight (TOFs) of the one or more UWB signals.

Clause 21. The UE of any of clauses 17 to 20, wherein the one or more processors, either alone or in combination, are further configured to: train the LSTM neural network model with one or more additional measurements of one or more additional UWB signals.

Clause 22. The UE of any of clauses 17 to 21, wherein the estimated position and the estimated heading are obtained by applying the LSTM neural network model and an extended Kalman filter (EKF) .

Clause 23. The UE of clause 22, wherein the estimated position and the estimated heading obtained by LSTM are applied as inputs to an adaptive Monte Carlo localization (AMCL) filter.

Clause 24. The UE of clause 23, wherein the estimated position and the estimated heading are obtained by applying the EKF and UE motion data including: inertial measurement unit (IMU) data; wheel velocity; odom data; lidar data; or any combination thereof.

Clause 25. The UE of clause 24, wherein the UE motion data is applied as one or more inputs to the EKF.

Clause 26. The UE of any of clauses 22 to 25, wherein a first weight vector is applied to the EKF and a second weight vector is applied to the LSTM neural network model.

Clause 27. The UE of any of clauses 17 to 26, wherein the estimated position and the estimated heading are obtained based at least in part on one or more anchor positions in the environment.

Clause 28. The UE of clause 27, wherein the one or more anchor positions are one or more known positions of the one or more UWB network nodes.

Clause 29. The UE of any of clauses 17 to 28, wherein the LSTM neural network model includes a forget gate, an input gate, and an output gate.

Clause 30. The UE of any of clauses 17 to 29, wherein the LSTM neural network model is an attention-LSTM neural network model.

Clause 31. The UE of any of clauses 17 to 30, wherein the environment is an indoor environment.

Clause 32. The UE of any of clauses 17 to 31, wherein the UE is a robot.

Clause 33. A user equipment (UE) , comprising: means for receiving one or more ultra-wideband (UWB) signals from one or more UWB network nodes in an environment; means for obtaining one or more measurements of the one or more UWB signals; and means for applying a long short-term memory (LSTM) neural network model to obtain an estimated position and an estimated heading based on the one or more measurements of the UWB signals.

Clause 34. The UE of clause 33, wherein the one or more measurements of the one or more UWB signals include one or more angles of arrival (AOAs) of the one or more UWB signals.

Clause 35. The UE of clause 34, wherein the UE includes a UWB antenna array to detect the one or more AOAs of the one or more UWB signals.

Clause 36. The UE of any of clauses 33 to 35, wherein the one or more measurements of the one or more UWB signals include one or more times of flight (TOFs) of the one or more UWB signals.

Clause 37. The UE of any of clauses 33 to 36, further comprising: means for training the LSTM neural network model with one or more additional measurements of one or more additional UWB signals.

Clause 38. The UE of any of clauses 33 to 37, wherein the estimated position and the estimated heading are obtained by applying the LSTM neural network model and an extended Kalman filter (EKF) .

Clause 39. The UE of clause 38, wherein the estimated position and the estimated heading obtained by LSTM are applied as inputs to an adaptive Monte Carlo localization (AMCL) filter.

Clause 40. The UE of clause 39, wherein the estimated position and the estimated heading are obtained by applying the EKF and UE motion data including: inertial measurement unit (IMU) data; wheel velocity; odom data; lidar data; or any combination thereof.

Clause 41. The UE of clause 40, wherein the UE motion data is applied as one or more inputs to the EKF.

Clause 42. The UE of any of clauses 38 to 41, wherein a first weight vector is applied to the EKF and a second weight vector is applied to the LSTM neural network model.

Clause 43. The UE of any of clauses 33 to 42, wherein the estimated position and the estimated heading are obtained based at least in part on one or more anchor positions in the environment.

Clause 44. The UE of clause 43, wherein the one or more anchor positions are one or more known positions of the one or more UWB network nodes.

Clause 45. The UE of any of clauses 33 to 44, wherein the LSTM neural network model includes a forget gate, an input gate, and an output gate.

Clause 46. The UE of any of clauses 33 to 45, wherein the LSTM neural network model is an attention-LSTM neural network model.

Clause 47. The UE of any of clauses 33 to 46, wherein the environment is an indoor environment.

Clause 48. The UE of any of clauses 33 to 47, wherein the UE is a robot.

Clause 49. A non-transitory computer-readable medium stores computer-executable instructions that, when executed by a user equipment (UE) , cause the UE to: receive one or more ultra-wideband (UWB) signals from one or more UWB network nodes in an environment; obtain one or more measurements of the one or more UWB signals; and apply a long short-term memory (LSTM) neural network model to obtain an estimated position and an estimated heading based on the one or more measurements of the UWB signals.

Clause 50. The non-transitory computer-readable medium of clause 49, wherein the one or more measurements of the one or more UWB signals include one or more angles of arrival (AOAs) of the one or more UWB signals.

Clause 51. The non-transitory computer-readable medium of clause 50, wherein the UE includes a UWB antenna array to detect the one or more AOAs of the one or more UWB signals.

Clause 52. The non-transitory computer-readable medium of any of clauses 49 to 51, wherein the one or more measurements of the one or more UWB signals include one or more times of flight (TOFs) of the one or more UWB signals.

Clause 53. The non-transitory computer-readable medium of any of clauses 49 to 52, further comprising computer-executable instructions that, when executed by the UE, cause the UE to: train the LSTM neural network model with one or more additional measurements of one or more additional UWB signals.

Clause 54. The non-transitory computer-readable medium of any of clauses 49 to 53, wherein the estimated position and the estimated heading are obtained by applying the LSTM neural network model and an extended Kalman filter (EKF) .

Clause 55. The non-transitory computer-readable medium of clause 54, wherein the estimated position and the estimated heading obtained by LSTM are applied as inputs to an adaptive Monte Carlo localization (AMCL) filter.

Clause 56. The non-transitory computer-readable medium of clause 55, wherein the estimated position and the estimated heading are obtained by applying the EKF and UE motion data including: inertial measurement unit (IMU) data; wheel velocity; odom data; lidar data; or any combination thereof.

Clause 57. The non-transitory computer-readable medium of clause 56, wherein the UE motion data is applied as one or more inputs to the EKF.

Clause 58. The non-transitory computer-readable medium of any of clauses 54 to 57, wherein a first weight vector is applied to the EKF and a second weight vector is applied to the LSTM neural network model.

Clause 59. The non-transitory computer-readable medium of any of clauses 49 to 58, wherein the estimated position and the estimated heading are obtained based at least in part on one or more anchor positions in the environment.

Clause 60. The non-transitory computer-readable medium of clause 59, wherein the one or more anchor positions are one or more known positions of the one or more UWB network nodes.

Clause 61. The non-transitory computer-readable medium of any of clauses 49 to 60, wherein the LSTM neural network model includes a forget gate, an input gate, and an output gate.

Clause 62. The non-transitory computer-readable medium of any of clauses 49 to 61, wherein the LSTM neural network model is an attention-LSTM neural network model.

Clause 63. The non-transitory computer-readable medium of any of clauses 49 to 62, wherein the environment is an indoor environment.

Clause 64. The non-transitory computer-readable medium of any of clauses 49 to 63, wherein the UE is a robot.

.

Those of skill in the art will appreciate that information and signals 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 above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

Further, those of skill 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 combinations 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 upon the particular application and design constraints imposed on the overall system. Skilled artisans 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.

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

The methods, sequences and/or algorithms described in connection with the aspects disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module 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, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An example storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, 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) . In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

In one or more example 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. A storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL) , or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, includes compact disc (CD) , laser disc, optical disc, digital versatile disc (DVD) , floppy disk and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

While the foregoing disclosure shows illustrative aspects of the disclosure, it should be noted that various changes and modifications could be made herein without departing from the scope of the disclosure as defined by the appended claims. For example, the functions, steps and/or actions of the method claims in accordance with the aspects of the disclosure described herein need not be performed in any particular order. Further, no component, function, action, or instruction described or claimed herein should be construed as critical or essential unless explicitly described as such. Furthermore, as used herein, the terms “set, ” “group, ” and the like are intended to include one or more of the stated elements. Also, as used herein, the terms “has, ” “have, ” “having, ” “comprises, ” “comprising, ” “includes, ” “including, ” and the like does not preclude the presence of one or more additional elements (e.g., an element “having” A may also have B) . Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise. Also, as used herein, the term “or” is intended to be inclusive when used in a series and may be used interchangeably with “and/or, ” unless explicitly stated otherwise (e.g., if used in combination with “either” or “only one of” ) or the alternatives are mutually exclusive (e.g., “one or more” should not be interpreted as “one and more” ) . Furthermore, although components, functions, actions, and instructions may be described or claimed in the singular, the plural is contemplated unless limitation to the singular is explicitly stated. Accordingly, as used herein, the articles “a, ” “an, ” “the, ” and “said” are intended to include one or more of the stated elements. Additionally, as used herein, the terms “at least one” and “one or more” encompass “one” component, function, action, or instruction performing or capable of performing a described or claimed functionality and also “two or more” components, functions, actions, or instructions performing or capable of performing a described or claimed functionality in combination.

Claims

A user equipment (UE) , comprising:

one or more memories;

one or more transceivers; and

one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to:

receive, via the one or more transceivers, one or more ultra-wideband (UWB) signals from one or more UWB network nodes in an environment;

obtain one or more measurements of the one or more UWB signals; and

apply a long short-term memory (LSTM) neural network model to obtain an estimated position and an estimated heading based on the one or more measurements of the UWB signals.
The UE of claim 1, wherein the one or more measurements of the one or more UWB signals include one or more angles of arrival (AOAs) of the one or more UWB signals.
The UE of claim 2, wherein the UE includes a UWB antenna array to detect the one or more AOAs of the one or more UWB signals.
The UE of claim 1, wherein the one or more measurements of the one or more UWB signals include one or more times of flight (TOFs) of the one or more UWB signals.
The UE of claim 1, wherein the one or more processors, either alone or in combination, are further configured to:

train the LSTM neural network model with one or more additional measurements of one or more additional UWB signals.
The UE of claim 1, wherein the estimated position and the estimated heading are obtained by applying the LSTM neural network model and an extended Kalman filter (EKF) .
The UE of claim 6, wherein the estimated position and the estimated heading obtained by LSTM are applied as inputs to an adaptive Monte Carlo localization (AMCL) filter.
The UE of claim 7, wherein the estimated position and the estimated heading are obtained by applying the EKF and UE motion data including:

inertial measurement unit (IMU) data;

wheel velocity;

odom data;

lidar data;

or any combination thereof.
The UE of claim 8, wherein the UE motion data is applied as one or more inputs to the EKF.
The UE of claim 6, wherein a first weight vector is applied to the EKF and a second weight vector is applied to the LSTM neural network model.
The UE of claim 1, wherein the estimated position and the estimated heading are obtained based at least in part on one or more anchor positions in the environment.
The UE of claim 11, wherein the one or more anchor positions are one or more known positions of the one or more UWB network nodes.
The UE of claim 1, wherein the LSTM neural network model includes a forget gate, an input gate, and an output gate.
The UE of claim 1, wherein the LSTM neural network model is an attention-LSTM neural network model.
The UE of claim 1, wherein the environment is an indoor environment.
The UE of claim 1, wherein the UE is a robot.
A method of wireless positioning performed at a user equipment (UE) , comprising:

receiving one or more ultra-wideband (UWB) signals from one or more UWB network nodes in an environment;

obtaining one or more measurements of the one or more UWB signals; and

applying a long short-term memory (LSTM) neural network model to obtain an estimated position and an estimated heading based on the one or more measurements of the UWB signals.
The method of claim 17, wherein the estimated position and the estimated heading are obtained by applying the LSTM neural network model and an extended Kalman filter (EKF) .
The method of claim 18, wherein the estimated position and the estimated heading obtained by LSTM are applied as inputs to an adaptive Monte Carlo localization (AMCL) filter.
A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a user equipment (UE) , cause the UE to:

receive one or more ultra-wideband (UWB) signals from one or more UWB network nodes in an environment;

obtain one or more measurements of the one or more UWB signals; and

apply a long short-term memory (LSTM) neural network model to obtain an estimated position and an estimated heading based on the one or more measurements of the UWB signals.