Machine learning model positioning performance monitoring and reporting

Abstract

This discloses a technique for wireless communication. In one scenario, a network entity receives a location information message from a user equipment (UE), the location information message including one or more location estimates derived by the UE during one or more location inference times of a machine learning model, wherein the machine learning model is applied to one or more measurements of a wireless channel between the UE and a network node during each of the one or more location inference times; and sends a performance report indicating the performance of the machine learning model in deriving the one or more location estimates at least during the one or more location inference times.

Description

[Technical Field]

[0001] This patent application claims the benefit of U.S. Provisional Application No. 63 / 363,929, filed on April 29, 2022, entitled “RADIO FREQUENCY FINGERPRINT (RFFP) POSITIONING PERFORMANCE MONITORING AND REPORTING”, which has been assigned to the assignee of this application and whose entire contents are expressly incorporated herein by reference.

[0002] The nature of this case is largely related to wireless communication. [Previous Technology]

[0003] Wireless communication systems have evolved through multiple generations, including first-generation analog wireless telephony (1G), second-generation (2G) digital wireless telephony (including transitional 2.5G and 2.75G networks), third-generation (3G) high-speed data, wireless services supporting the Internet, and fourth-generation (4G) services (e.g., Long Term Evolution (LTE) or WiMax). Currently, many different types of wireless communication systems are in use, including cellular and Personal Communication Services (PCS) systems. Known examples of cellular systems include the Cellular Analog Advanced System (AMPS) and digital cellular systems based on Code Division Multiple Access (CDMA), Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), and Global System for Mobile Communications (GSM).

[0004] The fifth-generation (5G) wireless standard, known as New Radio (NR), achieves higher data transmission speeds, more connections, better coverage, and other improvements. According to the Next Generation Mobile Networks Alliance, the 5G standard aims to provide higher data rates, more accurate positioning (e.g., based on Reference Signals for Positioning (RS-P), such as downlink, uplink, or sidelink Positioning Reference Signals (PRS)), and other technological enhancements than previous standards. These enhancements, along with the use of higher frequency bands, advancements in PRS procedures and technologies, and the high-density deployment of 5G, enable highly accurate 5G-based positioning. [Summary of the Invention]

[0005] The following is a brief overview relating to one or more states disclosed herein. Therefore, this overview should not be considered a broad summary relating to all expected states, nor should it be considered a determination of the categories associated with any particular state, determining the key or important elements or diagrams relating to all expected states. Thus, the sole purpose of the following overview is to present, in a simplified form, certain concepts relating to one or more states involving the mechanisms disclosed herein, prior to the detailed descriptions presented below.

[0006] In one instance, a communication method performed by a network entity includes: receiving a location information provision message from a user equipment (UE), the location information provision message including one or more location estimates derived by the UE during one or more location inference times of a machine learning model, wherein the machine learning model is applied to one or more measurements of a radio channel between the UE and a network node during each of the one or more location inference times; and sending a performance report indicating the performance of the machine learning model in deriving one or more location estimates during at least one or more location inference times.

[0007] In one instance, a method of wireless communication performed by a user equipment (UE) includes: sending a capability provision message to a location server, the capability provision message instructing the UE to report a confidence metric associated with a location estimate, the location estimate being derived by the UE based on a machine learning model applied to one or more measurements of a wireless channel between the UE and a network node; and sending a location information message to the location server, the location information message including the location estimate and the confidence metric.

[0008] In one embodiment, a method of wireless communication performed by a user equipment (UE) includes: sending a request for assistance data message to a location server, the request for assistance data message requesting the location server to configure the UE to report a confidence metric associated with a location estimate, the location estimate being derived by the UE based on a machine learning model applied to one or more measurements of a wireless channel between the UE and a network node; receiving a provide assistance data message from the location server, the provide assistance data message configuring the UE to report at least the confidence metric; and sending a location information message to the location server, the location information message including the location estimate and the confidence metric.

[0009] In one configuration, the network entity includes: a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: receive a location information provision message from a user equipment (UE) via the at least one transceiver, the location information provision message including one or more location estimates derived by the UE during one or more location inference times of a machine learning model, wherein the machine learning model is applied to one or more measurements of a radio channel between the UE and a network node during each of the one or more location inference times; and transmit a performance report via the at least one transceiver indicating the performance of the machine learning model in deriving one or more location estimates during at least one or more location inference times.

[0010] In one embodiment, a user equipment (UE) includes: a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: send a capability provisioning message to a location server via the at least one transceiver, the capability provisioning message instructing the UE to report a confidence metric associated with a location estimate, the location estimate being derived by the UE from a machine learning model based on one or more measurements applied to a wireless channel between the UE and a network node; and send a location information message to a location server via the at least one transceiver, the location information message including the location estimate and the confidence metric.

[0011] In one embodiment, a user equipment (UE) includes: a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: send a request for assistance data message to a location server via the at least one transceiver, the request for assistance data message requesting the location server to configure the UE to report a confidence metric associated with a location estimate, the location estimate being derived by the UE based on a machine learning model applied to one or more measurements of a wireless channel between the UE and a network node; receive a provision for assistance data message from the location server via the at least one transceiver, the provision for assistance data message configuring the UE to at least report a confidence metric; and send a location information message to the location server via the at least one transceiver, the location information message including the location estimate and the confidence metric.

[0012] In one configuration, the network entity includes: a component for receiving a location information message from a user equipment (UE), the location information message including one or more location estimates derived by the UE during one or more location inference times of a machine learning model, wherein the machine learning model is applied to one or more measurements of a radio channel between the UE and a network node during each of the one or more location inference times; and a component for transmitting a performance report indicating the performance of the machine learning model in deriving one or more location estimates during at least one or more location inference times.

[0013] In one embodiment, a user equipment (UE) includes: a component for sending a capability provision message to a location server, the capability provision message instructing the UE to report a confidence metric associated with a location estimate, the location estimate being derived by the UE based on a machine learning model of one or more measurements applied to a wireless channel between the UE and a network node; and a component for sending a location information message to the location server, the location information message including the location estimate and the confidence metric.

[0014] In one embodiment, a user equipment (UE) includes: a component for sending a request for assistance information message to a location server, the request for assistance information message requesting the location server to configure the UE to report a confidence metric associated with a location estimate, the location estimate being derived by the UE based on a machine learning model of one or more measurements applied to a wireless channel between the UE and a network node; a component for receiving a provision for assistance information message from the location server, the provision for assistance information message configuring the UE to report at least the confidence metric; and a component for sending a location information message to the location server, the location information message including the location estimate and the confidence metric.

[0015] In one configuration, a non-transitory computer-readable medium stores computer-executable instructions that, when executed by a network entity, cause the network entity to: receive a location information message from a user equipment (UE), the location information message including one or more location estimates derived by the UE during one or more location inference times of a machine learning model, wherein the machine learning model is applied to one or more measurements of a wireless channel between the UE and a network node during each of the one or more location inference times; and send a performance report indicative of the performance of the machine learning model in deriving one or more location estimates during at least one or more location inference times.

[0016] In one instance, a non-transitory computer-readable medium stores computer-executable instructions that, when executed by a user equipment (UE), cause the UE to: send a capability provision message to a location server, the capability provision message instructing the UE to report a confidence metric associated with a location estimate, the location estimate being derived by the UE based on a machine learning model of one or more measurements applied to a wireless channel between the UE and a network node; and send a location information message to the location server, the location information message including the location estimate and the confidence metric.

[0017] In one instance, a non-transitory computer-readable medium stores computer-executable instructions that, when executed by a user equipment (UE), cause the UE to: send a request for assistance information message to a location server, the request for assistance information message requesting the location server to configure the UE to report a confidence metric associated with a location estimate, the location estimate being derived by the UE based on a machine learning model of one or more measurements applied to a wireless channel between the UE and a network node; receive a provision for assistance information message from the location server, the provision for assistance information message configuring the UE to report at least the confidence metric; and send a location information message to the location server, the location information message including the location estimate and the confidence metric.

[0018] Other objects and advantages associated with the morphology disclosed herein will be apparent to those skilled in the art to which this invention pertains, based on the accompanying drawings and detailed description.

Implementation Method

[0035] The forms of this application are provided in the following description and related drawings, which are for illustrative purposes and represent various examples. Alternative forms may be designed without departing from the scope of this application. Additionally, well-known elements of this application will not be described in detail or will be omitted in order not to obscure the relevant details.

[0036] The terms “exemplary” and / or “example” are used herein to mean “serving as an example, illustration or explanation.” Any manner described herein as “exemplary” and / or “example” is not necessarily to be construed as preferred or superior to other manners. Similarly, the term “manner of this case” does not require that all manner of this case include the features, advantages or modes of operation discussed.

[0037] Those skilled in the art will understand that the information and signals described below can be represented using any of a variety of different technologies and processes. For example, depending in part on the specific application, in part on the desired design, in part on the corresponding technology, etc., data, instructions, commands, information, signals, bits, symbols and chips referenced throughout the following description can be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or optical particles or any combination thereof.

[0038] Furthermore, many states are described based on sequences of actions to be performed by, for example, components of a computing device. It will be appreciated that the various actions described herein can be performed by specific circuitry (e.g., an application-specific integrated circuit (ASIC)), program instructions executed by one or more processors, or a combination of both. Additionally, the sequences of actions described herein can be considered entirely embodied in any form of non-transitory computer-readable storage medium storing a corresponding set of computer instructions that, when executed, will cause or instruct the associated processor of the device to perform the functions described herein. Therefore, the various states of this application can be embodied in a variety of different forms, all of which are considered within the scope of the claimed subject matter. Furthermore, for each state described herein, any corresponding form of such state can be described herein as, for example, "logic" "configured" to perform the described actions.

[0039] As used herein, unless otherwise stated, the terms “User Equipment” (UE) and “Base Station” are not intended to be specific to or otherwise limited to any particular Radio Access Technology (RAT). Generally, a UE can be any wireless communication device used by a user to communicate via a wireless communication network (e.g., mobile phone, router, tablet, laptop, consumer asset positioning device, wearable device (e.g., smartwatch, glasses, augmented reality (AR) / virtual reality (VR) headset, etc.), vehicle (e.g., car, motorcycle, bicycle, etc.), Internet of Things (IoT) device, etc.). A UE can be mobile or can (e.g., at certain times) be stationary and can communicate with a Radio Access Network (RAN). As used herein, the term “UE” may be interchangeably referred to as “Access Terminal” or “AT”, “Client Equipment”, “Wireless Equipment”, “User Equipment”, “User Terminal”, “User Station”, “User Terminal” or “UT”, “Mobile Equipment”, “Mobile Terminal”, “Mobile Station”, or variations thereof. Typically, a UE can communicate with the core network via the RAN, and via the core network, the UE can connect to external networks (such as the Internet) and other UEs. Of course, other mechanisms for the UE to connect to the core network and / or the Internet are also possible, such as via wired access networks, wireless local area network (WLAN) networks (e.g., based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard), etc.

[0040] A base station can operate according to one of several RATs communicating with the UE, depending on the network in which it is deployed, and may be alternatively referred to as an Access Point (AP), Network Node, NodeB, Evolved NodeB (eNB), Next Generation eNB (ng-eNB), New Radio (NR) NodeB (also referred to as gNB or gNodeB), etc. The base station may primarily be used to support the UE's radio access, including supporting the data, voice, and / or signal transmission connections of the supported UE. In some systems, the base station may provide purely edge node signal transmission functions, while in others, it may provide additional control and / or network management functions. The communication link through which the UE can send signals to the base station is called an uplink (UL) channel (e.g., reverse transport channel, reverse control channel, access channel, etc.). The communication link through which the base station can send signals to the UE is called a downlink (DL) or forward link channel (e.g., paging channel, control channel, broadcast channel, forward transport channel, etc.). As used in this article, the term Transport Channel (TCH) can refer to either the uplink / reverse or the downlink / forward transport channel.

[0041] The term "base station" can refer to a single physical transmit / receive point (TRP) or multiple physical TRPs, which may be co-located or non-co-located. For example, when the term "base station" refers to a single physical TRP, the physical TRP may be the antenna of the base station corresponding to a cell (or several cell sectors) of the base station. When the term "base station" refers to multiple co-located physical TRPs, the physical TRP may be the antenna array of the base station (e.g., in a multiple-input multiple-output (MIMO) system or when beamforming is used at the base station). When the term "base station" refers to multiple non-co-located physical TRPs, the physical TRP may be a distributed antenna system (DAS) (a spatially separated antenna network connected to a common source via a transmission medium) or a remote radio head (RRH) (a remote base station connected to a serving base station). Alternatively, a non-co-located physical TRP may be the serving base station receiving measurement reports from the UE and a neighboring base station where the UE is measuring its reference radio frequency (RF) signal. As used in this article, since the TRP is the point from which a base station transmits and receives wireless signals, references to transmissions from or receptions at a base station will be understood to refer to the specific TRP of the base station.

[0042] In some implementations that support UE positioning, the base station may not support the UE's radio access (e.g., may not support the UE's data, voice, and / or signal transmission connections), but may instead send reference signals to the UE for measurement by the UE, and / or receive and measure signals sent by the UE. Such a base station may be referred to as a positioning beacon (e.g., when sending signals to the UE) and / or a location measurement unit (e.g., when receiving and measuring signals from the UE).

[0043] An "RF signal" includes electromagnetic waves of a given frequency that transmit information spatially between a transmitter and a receiver. As used herein, a transmitter may send a single "RF signal" or multiple "RF signals" to a receiver. However, due to the multipath propagation characteristics of RF signals, a receiver may receive multiple "RF signals" corresponding to each transmitted RF signal. 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.

[0044] Figure 1 illustrates an example of a wireless communication system 100 according to the present invention. The wireless communication system 100 (also referred to as a wireless wide area network (WWAN)) may include various base stations 102 (labeled "BS") and various UEs 104. Base stations 102 may include macrocell base stations (high-power cellular base stations) and / or small cell base stations (low-power cellular base stations). In one embodiment, macrocell base stations may include eNBs and / or ng-eNBs corresponding to the wireless communication system 100 for an LTE network, gNBs corresponding to the wireless communication system 100 for an NR network, or a combination of both, and small cell base stations may include femtocells, picocells, microcells, etc.

[0045] Base stations 102 can collectively form a RAN and interface with a core network 170 (e.g., an Evolved Packet Core (EPC) or a 5G Core (5GC)) via a backhaul link 122, and with one or more location servers 172 (e.g., a Location Management Function (LMF) or a Secure User Plane Location (SUPL) Location Platform (SLP)) via the core network 170. Location servers 172 can be part of the core network 170 or located outside the core network 170. Location servers 172 can be integrated with base stations 102. UE 104 can communicate directly or indirectly with location servers 172. For example, UE 104 can communicate with location servers 172 via the base station 102 currently serving UE 104. UE 104 may also communicate with location server 172 via another path, such as via an application server (not shown), via another network (such as via a wireless local area network (WLAN) access point (AP) (e.g., AP 150 described below)), etc. For signal transmission purposes, communication between UE 104 and location server 172 can be represented as an indirect connection (e.g., via core network 170, etc.) or a direct connection (e.g., as shown via direct connection 128). For clarity, intermediate nodes (if any) are omitted from the signal transmission diagram.

[0046] Among other functions, base station 102 may perform one or more related functions, including transmitting user data, radio channel encryption and decryption, integrity protection, header compression, mobility control functions (e.g., handover, dual connectivity), inter-cell interference coordination, connection establishment and release, load balancing, distribution of non-access stratum (NAS) messages, NAS node selection, synchronization, RAN sharing, multimedia broadcast multicast service (MBMS), user and device tracking, RAN information management (RIM), paging, location, and warning message delivery. Base stations 102 may communicate directly or indirectly with each other via backhaul link 134 (e.g., via EPC / 5GC), which may be wired or wireless.

[0047] Base station 102 can wirelessly communicate with UE 104. Each base station 102 can provide communication coverage for a corresponding geographic coverage area 110. In one instance, one or more cells can be supported by base station 102 in each geographic coverage area 110. A "cell" is a logical communication entity used to communicate with a base station (e.g., via some frequency resources, referred to as carrier frequency, component carrier, carrier, frequency band, etc.) and can be associated with an identifier used to distinguish cells operating via the same or different carrier frequencies (e.g., Physical Cell Identifier (PCI), Enhanced Cell Identifier (ECI), Virtual Cell Identifier (VCI), Cell Global Identifier (CGI), etc.). In some cases, different cells can be configured according to different protocol types that can provide access for different types of UEs (e.g., Machine Type Communication (MTC), Narrowband IoT (NB-IoT), Enhanced Mobile Broadband (eMBB), or others). Because a cell is supported by a specific base station, depending on the context, the term "cell" can refer to one or both of the logical communication entity and the base station that supports it. Furthermore, since TRP is typically the physical transmission point of a cell, the terms "cell" and "TRP" can be used interchangeably. In some cases, the term "cell" can also refer to the geographic coverage area of ​​a base station (e.g., a sector), provided that the carrier frequency can be detected and used for communication within a portion of the geographic coverage area 110.

[0048] Although the geographic coverage areas 110 of adjacent macrocell base stations 102 may partially overlap (e.g., in handover areas), some geographic coverage areas 110 may substantially overlap with larger geographic coverage areas 110. For example, a small cell base station 102' (labeled "SC" for "small cell") may have a coverage area 110' that substantially overlaps with the geographic coverage areas 110 of one or more macrocell base stations 102. A network including small cell base stations and macrocell base stations may be referred to as a heterogeneous network. A heterogeneous network may also include a home eNB (HeNB) that can provide services to a restricted group referred to as a Closed Subscriber Group (CSG).

[0049] The communication link 120 between base station 102 and UE 104 may include uplink (also known as reverse link) transmission from UE 104 to base station 102 and / or downlink (DL) (also known as forward link) transmission from base station 102 to UE 104. The communication link 120 may use MIMO antenna technology, including spatial multiplexing, beamforming, and / or transmit diversity. The communication link 120 may be transmitted via one or more carrier frequencies. Carrier allocation may be asymmetrical relative to the downlink and uplink (e.g., the downlink may be allocated more or fewer carriers than the uplink).

[0050] The wireless communication system 100 may also include a wireless local area network (WLAN) access point (AP) 150, which communicates with a WLAN station (STA) 152 via a communication link 154 in unlicensed spectrum (e.g., 5 GHz). When communicating in unlicensed spectrum, the WLAN STA 152 and / or WLAN AP 150 may perform an idle channel assessment (CCA) or listen before talk procedure before communication to determine whether the channel is available.

[0051] The small cell base station 102' can operate in licensed and / or unlicensed spectrum. When operating in unlicensed spectrum, the small cell base station 102' can employ LTE or NR technology and use the same 5 GHz unlicensed spectrum as the WLAN AP 150. Employing LTE / 5G in unlicensed spectrum can improve the coverage and / or increase the capacity of the access network. NR in unlicensed spectrum can be referred to as NR-U. LTE in unlicensed spectrum can be referred to as LTE-U, Licensed Assisted Access (LAA), or Unlicensed Assisted Access (MulteFire).

[0052] The wireless communication system 100 may also include a millimeter-wave (mmW) base station 180, which can operate at mmW and / or near-mmW frequencies to communicate with the UE 182. Extremely high frequency (EHF) is a portion of the electromagnetic spectrum of radio frequency (RF). The frequency range of EHF is from 30 GHz to 300 GHz, with wavelengths between 1 mm and 10 mm. Radio waves in this band can be referred to as millimeter waves. Near-mmW can extend to frequencies up to 3 GHz with wavelengths of 100 mm. The ultra-high frequency (SHF) band extends between 3 GHz and 30 GHz and is also referred to as centimeter waves. Communication using mmW / near-mmW radio frequency bands has high path loss and relatively short range. The mmW base station 180 and the UE 182 can use beamforming (transmit and / or receive) on the mmW communication link 184 to compensate for the extremely high path loss and short range. Furthermore, it will be understood that in alternative configurations, one or more base stations 102 may also use mmW or near-mmW and beamforming for transmission. Therefore, it should be understood that the above description is merely an example and should not be construed as limiting the various states disclosed herein.

[0053] Transmit beamforming is a technique for focusing RF signals in a specific direction. Traditionally, when a network node (e.g., a base station) broadcasts an RF signal, it broadcasts the signal in all directions (omnidirectional). Using transmit beamforming, the network node determines the location of a given target device (e.g., a UE) (relative to the transmitting network node) and projects a stronger downlink RF signal in that specific direction, thereby providing the receiving device with a faster (in terms of data rate) and stronger RF signal. To change the directivity of the RF signal during transmission, the network node can control the phase and relative amplitude of the RF signal at each of one or more transmitters broadcasting the RF signal. For example, the network node can use an antenna array (referred to as a "phased array" or "antenna array") that generates an RF beam that can be "manipulated" to point in different directions without actually moving the antennas. Specifically, RF currents from the transmitters are fed to individual antennas with the correct phase relationship, such that radio waves from different antennas are added together to increase radiation in the desired direction while canceling out radiation in undesired directions.

[0054] Transmit beams can be quasi-co-located, meaning they have the same parameters for the receiver (e.g., UE), regardless of whether the transmit antennas of the network nodes are physically co-located. In NR, there are four types of quasi-co-located (QCL) relationships. Specifically, a given type of QCL relationship means that certain parameters about the second reference RF signal on the second beam can be derived from information about the source reference RF signal on the source beam. Therefore, if the source reference RF signal is QCL type A, the receiver can use the source reference RF signal to estimate the Doppler shift, Doppler spread, average delay, and delay spread of the second reference RF signal transmitted on the same channel. If the source reference RF signal is QCL type B, the receiver can use the source reference RF signal to estimate the Doppler shift and Doppler spread of the second reference RF signal transmitted on the same channel. If the source reference RF signal is QCL type C, the receiver can use the source reference RF signal to estimate the Doppler shift and average delay of the 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 reception parameters of a second reference RF signal transmitted on the same channel.

[0055] In receive beamforming, a receiver uses a receive beam to amplify an RF signal detected on a given channel. For example, the receiver may increase the gain setting and / or adjust the phase setting of the antenna array in a particular direction to amplify the RF signal received from that direction (e.g., increase its gain level). Therefore, when a receiver is said to be beamforming in a certain direction, it means that the beam gain in that direction is higher than the beam gain along other directions, or that the beam gain in that direction is the highest compared to the beam gain of all other receive beams available to the receiver in that direction. This results in a stronger received signal strength (e.g., reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to-interference-plus-noise ratio (SINR), etc.) of the RF signal received from that direction.

[0056] The transmit beam and receive beam can be spatially correlated. Spatial correlation means that the parameters of the second beam (e.g., transmit or receive beam) of the second reference signal can be derived from information about the first beam (e.g., receive or transmit beam) of the first reference signal. For example, the UE can use a specific receive beam to receive a reference downlink reference signal (e.g., a synchronization signal block (SSB)) from a base station. Subsequently, the UE can form a transmit beam based on the parameters of the receive beam for transmitting an uplink reference signal (e.g., a sounding reference signal (SRS)) to that base station.

[0057] It should be noted that a "downlink" beam can be either a transmit beam or a receive beam, depending on the entity forming the beam. For example, if a base station is forming a downlink beam to transmit a reference signal to a UE, then the downlink beam is a transmit beam. However, if a UE is forming a downlink beam, then the beam is a receive beam for receiving downlink reference signals. Similarly, an "uplink" beam can be either a transmit beam or a receive beam, depending on the entity forming the beam. For example, if a base station is forming an uplink beam, then the beam is an uplink receive beam, and if a UE is forming an uplink beam, then the beam is an uplink transmit beam.

[0058] The electromagnetic spectrum is typically subdivided into various categories, bands, channels, etc., based on frequency / wavelength. In 5G NR, the two initial operating frequency bands have been identified as the frequency range names FR1 (410 MHz–7.125 GHz) and FR2 (24.25 GHz–52.6 GHz). It should be understood that although a portion of FR1 is greater than 6 GHz, FR1 is generally (interchangeably) referred to as the "Sub-6 GHz" band in various documents and articles. Similar naming issues sometimes arise with FR2; although FR2 differs from the Extremely High Frequency (EHF) band (30 GHz–300 GHz) designated as the "millimeter wave" band by the International Telecommunication Union (ITU), FR2 is generally (interchangeably) referred to as the "millimeter wave" band in documents and articles.

[0059] The frequencies between FR1 and FR2 are generally referred to as mid-band frequencies. Recent 5G NR studies have designated the operating frequency bands of these mid-band frequencies as the frequency range name FR3 (7.125 GHz – 24.25 GHz). Frequency bands falling within FR3 can inherit FR1 and / or FR2 characteristics, thus effectively extending the features of FR1 and / or FR2 to mid-band frequencies. Furthermore, higher frequency bands are currently being explored to extend 5G NR operation above 52.6 GHz. For example, three higher operating frequency bands are designated as the frequency range names 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.

[0060] In view of the above, unless otherwise stated, it should be understood that the term "sub-6 GHz" and the like, when used herein, can broadly refer to frequencies below 6 GHz, within FR1, or may include mid-band frequencies. Furthermore, unless otherwise stated, it should be understood that the term "millimeter wave" and the like, when used herein, can broadly refer to frequencies that may include mid-band frequencies, within FR2, FR4, FR4-a or FR4-1 and / or FR5, or within the EHF band.

[0061] In multi-carrier systems, such as 5G, one of the carrier frequencies is referred to as the "primary carrier," "anchor carrier," "primary serving cell," or "PCell," while the remaining carrier frequencies are referred to as "secondary carriers," "secondary serving cells," or "SCell." In carrier aggregation, the anchor carrier is a carrier operating on a primary frequency (e.g., FR1) used by UE 104 / 182 and the cell in which UE 104 / 182 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 can be a carrier in a licensed frequency (however, this is not always the case). The secondary carrier is a carrier operating on a second frequency (e.g., FR2), which can be configured once an RRC connection is established between UE 104 and the anchor carrier, and this second frequency can be used to provide additional radio resources. In some cases, the secondary carrier can be a carrier in an unlicensed frequency. The secondary carrier may contain only the necessary signal transmission information and signals. For example, UE-specific information and signals may not be present in the secondary carrier because the primary uplink and downlink carriers are typically UE-specific. This means that different UEs 104 / 182 within a cell can have different downlink primary carriers. The same applies to the uplink primary carrier. The network can change the primary carrier of any UE 104 / 182 at any time. For example, this is done to balance the load on different carriers. Because a "serving cell" (whether PCell or SCell) corresponds to the carrier frequency / component carrier that a base station is communicating on, the terms "cell," "serving cell," "component carrier," and "carrier frequency" can be used interchangeably.

[0062] For example, still referring to Figure 1, one of the frequencies used by macrocell base station 102 may be an anchor carrier (or "PCell"), and the other frequencies used by macrocell base station 102 and / or mmW base station 180 may be subcarriers ("SCell"). Simultaneous transmission and / or reception on multiple carriers enables UE 104 / 182 to significantly increase its data transmission and / or reception rates. For example, compared to a single 20 MHz carrier, two aggregated 20 MHz carriers in a multicarrier system would theoretically result in a doubling of the data rate (i.e., 40 MHz).

[0063] The wireless communication system 100 may also include a UE 164, which can communicate with the macrocell base station 102 via communication link 120 and / or with the mmW base station 180 via mmW communication link 184. For example, the macrocell base station 102 may support one PCell and one or more SCells of the UE 164, and the mmW base station 180 may support one or more SCells of the UE 164.

[0064] In some cases, UE 164 and UE 182 can communicate via sidelink. UEs supporting sidelinks (SL-UEs) can communicate with base station 102 via communication link 120 using the Uu interface (i.e., the air interface between the UE and the base station). SL-UEs (e.g., UE 164, UE 182) can also communicate directly with each other via radio sidelink 160 using the PC5 interface (i.e., the air interface between UEs supporting sidelinks). Radio sidelink (or simply "sidelink") is an improvement to core cellular (e.g., LTE, NR) standards that allows direct communication between two or more UEs without requiring communication through a base station. Sidelink communication can be unicast or multicast and can 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, emergency rescue applications, etc. One or more of a group of SL-UEs utilizing sidelink communication may be within the geographic coverage area 110 of base station 102. Other SL-UEs in such a group may be outside the geographic coverage area 110 of base station 102 or may not be able to receive transmissions from base station 102. In some cases, a group of SL-UEs communicating via sidelink communication may utilize a one-to-many (1:M) system, where each SL-UE transmits to each of the other SL-UEs in the group. In some cases, base station 102 facilitates resource scheduling for sidelink communication. In other cases, sidelink communication is performed between SL-UEs without involving base station 102.

[0065] In one embodiment, the side link 160 may operate via a wireless communication medium of interest, which may be shared with other wireless communications between other vehicles and / or infrastructure access points and other RATs. The "medium" may consist of one or more time, frequency, and / or spatial communication resources (e.g., comprising one or more channels spanning one or more carriers) associated with wireless communications between one or more transmitter / receiver pairs. In one embodiment, the medium of interest may correspond to at least a portion of unlicensed frequency bands shared among various RATs. Although different licensed frequency bands have been reserved for certain communication systems (e.g., by government entities such as the U.S. Federal Communications Commission (FCC), these systems, specifically those employing small cell access points, have recently extended their operation to unlicensed frequency bands such as those used by Wireless Local Area Network (WLAN) technologies (most notably the IEEE 802.11x WLAN technology commonly referred to as "Wi-Fi"). This type of example system includes different variants of CDMA systems, TDMA systems, FDMA systems, orthogonal FDMA (OFDMA) systems, single-carrier FDMA (SC-FDMA) systems, etc.

[0066] It should be noted that although Figure 1 only illustrates two of the UEs as SL-UEs (i.e., UE 164 and UE 182), any UE shown can be an SL-UE. Furthermore, although only UE 182 is described as capable of beamforming, any UE shown, including UE 164, can be beamformed. When SL-UEs are capable of beamforming, they can beamform towards each other (i.e., towards other SL-UEs), towards other UEs (e.g., UE 104), towards base stations (e.g., base stations 102, 180, small cells 102', access points 150), etc. Therefore, in some cases, UE 164 and UE 182 can utilize beamforming on sidelink 160.

[0067] In the example of Figure 1, any UE shown (represented as a single UE 104 in Figure 1 for simplicity) can receive signal 124 from one or more Earth-orbiting spacecraft (SV) 112 (e.g., satellites). In one instance, SV 112 may be part of a satellite positioning system, which UE 104 may use as a separate source of location information. A satellite positioning system typically includes a transmitter system (e.g., SV 112) positioned such that receivers (e.g., UE 104) can determine their location on or above the Earth based at least in part on positioning signals (e.g., signal 124) received from the transmitter. Such transmitters typically transmit signals marked with a set number of repeating pseudo-random noise (PN) codes. Although typically located in SV 112, transmitters may sometimes be located at ground-based control stations, base stations 102, and / or other UEs 104. UE 104 may include one or more dedicated receivers specifically designed to receive signal 124 used to derive geographic location information from SV 112.

[0068] In a satellite positioning system, the use of signal 124 can be enhanced via various satellite-based augmentation systems (SBAS), which may be associated with or otherwise enabled to be used with one or more global and / or regional navigation satellite systems. For example, SBAS may include augmentation systems that provide integrity information, differential correction, etc., such as Wide Area Augmentation System (WAAS), European Geosynchronous Navigation Coverage Service (EGNOS), Multifunctional Satellite Augmentation System (MSAS), Global Positioning System (GPS) Assisted Geographic Augmentation Navigation, or GPS and Geographic Augmentation Navigation System (GAGAN). Therefore, 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.

[0069] In one instance, supplementally or alternatively, SV 112 may be part of one or more non-terrestrial networks (NTNs). In the NTN, SV 112 is connected to an earth station (also referred to as a ground station, NTN gateway, or gateway), which in turn is connected to elements in the 5G network, such as the modified base station 102 (without a terrestrial antenna) or network nodes in the 5GC. This element will in turn provide access to other elements in the 5G network and ultimately provide access to entities outside the 5G network, such as internet web servers and other user equipment. Thus, instead of receiving communication signals from the terrestrial base station 102, or in addition to receiving communication signals from the terrestrial base station 102, UE 104 may receive communication signals (e.g., signal 124) from SV 112.

[0070] The wireless communication system 100 may also include one or more UEs, such as UE 190, which are indirectly connected to one or more communication networks via one or more device-to-device (D2D) peer-to-peer (P2P) links (referred to as sidelinks). In the example of Figure 1, UE 190 has a D2D P2P link 192 with one of UEs 104 connected to one of the base stations 102 (e.g., via D2D P2P link 192, UE 190 can indirectly obtain cellular connectivity) and a D2D P2P link 194 with WLAN STA 152 connected to WLAN AP 150 (via D2D P2P link 194, UE 190 can indirectly obtain WLAN-based Internet connectivity). In one example, D2D P2P links 192 and 194 may be supported by any well-known D2D RAT (such as LTE Direct (LTE-D), WiFi Direct (WiFi-D), Bluetooth®, etc.).

[0071] Figure 2A illustrates an example wireless network architecture 200. For example, the 5GC 210 (also known as the next-generation core "NGC") can be functionally considered as control plane (C plane) functions 214 (e.g., UE registration, authentication, network access, gateway selection, etc.) and user plane (U plane) functions 212 (e.g., UE gateway functions, access to the data network, IP routing, etc.), which work together to form the core network. The user plane interface (NG-U) 213 and the control plane interface (NG-C) 215 connect the gNB 222 to the 5GC 210, and specifically connect to the user plane function 212 and the control plane function 214, respectively. In an additional configuration, the ng-eNB 224 can also connect to the 5GC 210 via the NG-C 215 to the control plane function 214 and the NG-U 213 to the user plane function 212. Furthermore, the ng-eNB 224 can communicate directly with the gNB 222 via the backhaul connection 223. In some configurations, the next-generation RAN (NG-RAN) 220 may have one or more gNBs 222, while other configurations include one or more of both the ng-eNB 224 and the gNB 222. Either the gNB 222 or the ng-eNB 224 (or both) can communicate with one or more UEs 204 (e.g., any UE described herein).

[0072] Another alternative configuration may include a location server 230, which can communicate with the 5GC 210 to provide location assistance to the UE 204. The location server 230 may be implemented as a plurality of independent servers (e.g., physically independent servers, different software modules on a single server, different software modules distributed across multiple physical servers, etc.), or alternatively, each location server 230 may correspond to a single server. The location server 230 may be configured to support one or more location services for the UE 204, which may connect to the location server 230 via the core network, the 5GC 210, and / or via the Internet (not shown). Furthermore, the location server 230 may be integrated into a component of the core network, or alternatively, may be located outside the core network (e.g., a vendor server, such as an original equipment manufacturer (OEM) server or a traffic server).

[0073] Figure 2B illustrates another example of a wireless network architecture 240. 5GC 260 (which may correspond to 5GC 210 in Figure 2A) can be functionally considered as control plane functions provided by Access and Mobility Management Function (AMF) 264 and user plane functions provided by User Plane Function (UPF) 262, which work together to form the core network (i.e., 5GC 260). The functions of AMF 264 include registration management, connection management, reachability management, mobility management, lawful interception, transmission of Period Management (SM) messages between one or more UEs 204 (any of the UEs described herein) and Period Management Function (SMF) 266, transparent proxy service for routing SM messages, access authentication and access authorization, transmission of SMS service messages between UE 204 and SMS Service Function (SMSF) (not shown), and Security Anchor Function (SEAF). AMF 264 also interacts with the Authentication Server Function (AUSF) (not shown) and UE 204, and receives an intermediate key established as a result of the UE 204 authentication process. In the case of UMTS (Universal Mobile Telecommunications System) User Identity Module (USIM)-based authentication, AMF 264 retrieves security material from the AAUSF. AMF 264's functionality also includes Security Context Management (SCM). The SCM receives a key from the SEAF and uses this key to derive a network-specific key for access. AMF 264's functionality also includes location service management for regulatory services, transmission of location service messages between UE 204 and Location Management Function (LMF) 270 (which can be used as a location server 230), transmission of location service messages between NG-RAN 220 and LMF 270, EPS bearer identifier allocation for interaction with the Evolved Packet System (EPS), and UE 204 mobility event notification. In addition, AMF 264 also supports non-3GPP (3rd Generation Partnership Project) network access capabilities.

[0074] The functions of UPF 262 include serving as an anchor point for intra-RAT / inter-RAT mobility (where applicable), serving as an external Protocol Data Unit (PDU) communication point for interconnection to a data network (not shown), providing packet routing and forwarding, packet inspection, user plane policy rule enforcement (e.g., gating, redirection, traffic manipulation), lawful interception (user plane collection), traffic usage reporting, user plane quality of service (QoS) processing (e.g., uplink / downlink rate enforcement, reflected QoS marking in the downlink), uplink traffic verification (mapping of Service Data Stream (SDF) to QoS stream), transport-level packet marking in the uplink and downlink, downlink packet buffering and downlink data notification triggering, and sending and forwarding one or more "end markers" to the source RAN node. UPF 262 can also support the transmission of user plane location service messages between UE 204 and location servers (such as SLP 272).

[0075] The functions of SMF 266 include communication period management, UE Internet Protocol (IP) address allocation and management, selection and control of user plane functions, configuration of transport manipulation in UPF 262 to route transports to the correct destination, control of policy enforcement and QoS, and downlink data notification. The interface through which SMF 266 communicates with AMF 264 is called the N11 interface.

[0076] Another optional configuration may include an LMF 270, which can communicate with the 5GC 260 to provide location assistance to the UE 204. The LMF 270 may be implemented as a plurality of independent servers (e.g., physically independent servers, different software modules on a single server, different software modules distributed across multiple physical servers, etc.), or alternatively, each LMF 270 may correspond to a single server. The LMF 270 may be configured to support one or more location services for the UE 204, which may connect to the LMF 270 via the core network, the 5GC 260, and / or via the Internet (not shown). SLP 272 can support similar functions to LMF 270, but LMF 270 can communicate with AMF 264, NG-RAN 220 and UE 204 via the control plane (e.g., using interfaces and protocols intended to transmit signals to transmit messages instead of voice or data), while SLP 272 can communicate with UE 204 and external clients (company server 274) via the user plane (e.g., using protocols intended to carry voice or data, such as Transmission Control Protocol (TCP) and / or IP).

[0077] Another optional configuration may include a cooperating server 274, which can communicate with LMF 270, SLP 272, 5GC 260 (e.g., via AMF 264 and / or UPF 262), NG-RAN 220 and / or UE 204 to obtain location information (e.g., location estimation) of UE 204. Thus, in some cases, the cooperating server 274 may be referred to as a Location Service (LCS) client or an external client. The cooperating server 274 may be implemented as a plurality of independent servers (e.g., physically independent servers, different software modules on a single server, different software modules distributed across multiple physical servers, etc.), or alternatively, each location server 274 may correspond to a single server.

[0078] The user plane interface 263 and the control plane interface 265 connect 5GC 260, specifically UPF 262 and AMF 264, to one or more gNB 222 and / or ng-eNB 224 in NG-RAN 220, respectively. The interface between gNB 222 and / or ng-eNB 224 and AMF 264 is referred to as the "N2" interface, and the interface between gNB 222 and / or ng-eNB 224 and UPF 262 is referred to as the "N3" interface. The gNB 222 and / or ng-eNB 224 of NG-RAN 220 can communicate directly with each other via backhaul connection 223 (referred to as the "Xn-C" interface). One or more of gNB 222 and / or ng-eNB 224 can communicate with one or more UEs 204 via a radio interface referred to as the "Uu" interface.

[0079] The functions of gNB 222 can be divided among gNB Central Unit (gNB-CU) 226, one or more gNB Distributed Units (gNB-DU) 228, and one or more gNB Radio Units (gNB-RU) 229. Apart from the functions specifically allocated to gNB-DU 228, gNB-CU 226 is a logical node that includes base station functions such as user data transmission, mobility control, radio access network sharing, location, and communication period management. More specifically, gNB-CU 226 typically hosts the Radio Resource Control (RRC), Service Data Adaptation Protocol (SDAP), and Packet Data Convergence Protocol (PDCP) protocols of gNB 222. gNB-DU 228 is a logical node that typically hosts the Radio Link Control (RLC) and Media Access Control (MAC) layers of gNB 222. Its operation is controlled by gNB-CU 226. One gNB-DU 228 can support one or more cells, and a cell is supported by only one gNB-DU 228. The interface 232 between the gNB-CU 226 and one or more gNB-DU 228 is called the "F1" interface. The physical (PHY) layer functions of the gNB 222 are typically managed by one or more independent gNB-RU 229s, which perform functions such as power amplification and signal transmission / reception. The interface between the gNB-DU 228 and the gNB-RU 229 is called the "Fx" interface. Therefore, the UE 204 communicates with the gNB-CU 226 via the RRC, SDAP, and PDCP layers, with the gNB-DU 228 via the RLC and MAC layers, and with the gNB-RU 229 via the PHY layer.

[0080] The deployment of communication systems such as 5G NR systems can be arranged in a variety of ways using various components or parts. In a 5G NR system or network, network nodes, network entities, network mobile components, RAN nodes, core network nodes, network components, or network devices (such as base stations or one or more units (or one or more components) performing base station functions) can be implemented in aggregated or decomposed architectures. For example, base stations (such as Node B (NB), evolved NB (eNB), NR base stations, 5G NBs, access points (APs), transmit / receive points (TRPs), or cells, etc.) can be implemented as aggregated base stations (also known as stand-alone base stations or monolithic base stations) or decomposed base stations.

[0081] Aggregated base stations can be configured to utilize radio protocol stacks that are physically or logically integrated within a single RAN node. Decomposed base stations can be configured to utilize protocol stacks that are physically or logically distributed between two or more units (such as one or more central or centralized units (CUs), one or more distributed units (DUs), or one or more radio units (RUs)). In some cases, a CU can be implemented within a RAN node, and one or more DUs can co-located with a CU, or alternatively, can be geographically or virtually distributed in one or more other RAN nodes. A DU can be implemented to communicate with one or more RUs. Each of the CU, DU, and RU can also be implemented as a virtual unit, i.e., a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU).

[0082] The operation or network design of base station types can consider the aggregation characteristics of base station functions. For example, decomposed base stations can be used in integrated access backload (IAB) networks, open radio access networks (O-RAN (such as network configurations sponsored by the O-RAN Alliance)), or virtualized radio access networks (vRAN, also known as cloud radio access networks (C-RAN)). Decomposition can include the allocation of functions between two or more units in different physical locations, as well as the virtual allocation of functions of at least one unit, which enables flexibility in network design. Various units of a decomposed base station or decomposed RAN architecture can be configured to communicate with at least one other unit via wired or wireless means.

[0083] Figure 2C illustrates an example of a decomposed base station architecture 250 according to the present invention. The decomposed base station architecture 250 may include one or more central units (CUs) 280 (e.g., gNB-CU 226), which may communicate directly with the core network 267 (e.g., 5GC 210, 5GC 260) via a backhaul link, or indirectly with the core network 267 via one or more decomposed base station units (e.g., near-real-time (near-RT) RAN intelligent controllers (RICs) 259 via E2 links, or non-real-time (non-RT) RICs 257 associated with the Service Management and Coordination (SMO) framework 255, or both). CUs 280 may communicate with one or more distributed units (DUs) 285 (e.g., gNB-DU 228) via appropriate midrange links (e.g., F1 interfaces). DU 285 can communicate with one or more radio units (RU) 287 (e.g., gNB-RU 229) via a corresponding fronthaul link. RU 287 can communicate with a corresponding UE 204 via one or more radio frequency (RF) access links. In some embodiments, UE 204 can be served by multiple RU 287 simultaneously.

[0084] Each unit (i.e., CU 280, DU 285, RU 287, and near-RT RIC 259, non-RT RIC 257, and SMO frame 255) may include or be coupled to one or more interfaces configured to receive or transmit signals, data, or information (collectively, signals) via wired or wireless transmission media. Each unit or an associated processor or controller providing instructions to the unit's communication interface may be configured to communicate with one or more other units via transmission media. For example, these units may include wired interfaces configured to receive or transmit signals to one or more other units via wired transmission media. Additionally, these units may include wireless interfaces configured to receive signals from or transmit signals to one or more other units, or simultaneously receive and transmit signals, via wireless transmission media. These wireless interfaces may include receivers, transmitters, or transceivers (such as radio frequency (RF) transceivers).

[0085] In some configurations, CU 280 may host one or more higher-level control functions. Such control functions may include Radio Resource Control (RRC), Packet Data Convergence Protocol (PDCP), Service Data Adaptation Protocol (SDAP), etc. Each control function may be implemented using an interface configured to transmit signals with other control functions hosted by CU 280. CU 280 may be configured to handle user plane functions (i.e., Central Unit-User Plane (CU-UP)), control plane functions (i.e., Central Unit-Control Plane (CU-CP)), or combinations thereof. In some implementations, CU 280 may be logically divided into one or more CU-UP units and one or more CU-CP units. CU-UP units may communicate bidirectionally with CU-CP units via an interface (such as an E1 interface implemented in an O-RAN configuration). CU 280 may be implemented to communicate with DU 285 for network control and signal transmission as needed.

[0086] DU 285 may correspond to a logic unit that includes one or more base station functions to control the operation of one or more RU 287s. In some configurations, DU 285 may at least partially host one or more of the Radio Link Control (RLC) layer, Media Access Control (MAC) layer, and one or more high-physical (PHY) layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation and demodulation, etc.) according to functional partitioning (such as functional partitioning defined by the 3rd Generation Partnership Project (3GPP)). In some configurations, DU 285 may also host one or more low-PHY layers. Each layer (or module) may be implemented using an interface configured to transmit signals with other layers (and modules) hosted by DU 285 or with control functions hosted by CU 280.

[0087] Lower-layer functions can be implemented by one or more RU 287s. 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 both, based at least partially on functional segmentation (such as lower-layer functional segmentation). In such architectures, the RU 287 can be implemented to handle over-the-air (OTA) communications with one or more UEs 204. In some implementations, the real-time and non-real-time modes of control and user plane communications with the RU 287 can be controlled by the corresponding DU 285. In some cases, this configuration allows the DU 285 and CU 280 to be implemented in a cloud-based RAN architecture (such as a vRAN architecture).

[0088] The SMO framework 255 can be configured to support RAN deployment and provisioning of both non-virtualized and virtualized network elements. For non-virtualized network elements, the SMO framework 255 can be configured to support the deployment of dedicated physical resources for RAN coverage needs, which can be managed via an operation and maintenance interface (such as the O1 interface). For virtualized network elements, the SMO framework 255 can be configured to interact with a cloud computing platform (such as Open Cloud (O-Cloud) 269) to perform network element lifecycle management (such as generating physical virtualized network elements) via a cloud computing platform interface (such as the O2 interface). Such virtualized network elements may include, but are not limited to, CU 280, DU 285, RU 287, and near-RT RIC 259. In some implementations, the SMO framework 255 can communicate with 4G RAN hardware models (such as Open eNB (O-eNB) 261) via the O1 interface. Additionally, in some implementations, the SMO framework 255 can communicate directly with one or more RUs 287 via an O1 interface. The SMO framework 255 may also include a non-RT RIC 257 configured to support the functionality of the SMO framework 255.

[0089] The non-RT RIC 257 can be configured to include logical functions that enable non-real-time control and optimization of RAN elements and resources, including artificial intelligence / machine learning (AI / ML) workflows for model training and updates, or policy-based guidance for applications / features in the near-RT RIC 259. The non-RT RIC 257 can be coupled to or communicate with the near-RT RIC 259 (e.g., via an A1 interface). The near-RT RIC 259 can be configured to include logical functions that enable near-real-time control and optimization of RAN elements and resources via data collection and actions on an interface (e.g., via an E2 interface) that connects one or more CU 280s, one or more DU 285s, or both, and O-eNBs to the near-RT RIC 259.

[0090] In some implementations, in order to generate an AI / ML model to be deployed in the near-RT RIC 259, the non-RT RIC 257 may receive parameters or external enrichment information from an external server. This information may be used by the near-RT RIC 259 and may be received from non-network data sources or network functions at the SMO framework 255 or the non-RT RIC 257. In some instances, the non-RT RIC 257 or the near-RT RIC 259 may be configured to adjust RAN behavior or performance. For example, the non-RT RIC 257 may monitor long-term trends and patterns of performance and perform corrective actions using the AI / ML model via the SMO framework 255 (such as reconfiguration via O1) or via the establishment of RAN management policies (such as A1 policies).

[0091] Figures 3A, 3B, and 3C illustrate several example components (represented by corresponding blocks) that can be incorporated into UE 302 (which can correspond to any UE described herein), base station 304 (which can correspond to any base station described herein), and network entity 306 (which can correspond to or embody any network function described herein, including location server 230 and LMF 270, or alternatively, can be independent of the NG-RAN 220 and / or 5GC 210 / 260 infrastructure shown in Figures 2A and 2B, such as a private network) to support the operations described herein. It will be understood that these components can be implemented in different types of devices in different implementations (e.g., in an ASIC, in a system-on-a-chip (SoC), etc.). The components shown can also be incorporated into other devices in a communication system. For example, other devices in the system may include components similar to those described to provide similar functionality. Similarly, a given device may contain one or more of these components. For example, the device may include multiple transceiver components that enable the device to operate on multiple carriers and / or communicate via different technologies.

[0092] UE 302 and base station 304 each include one or more wireless wide area network (WWAN) transceivers 310 and 350, providing components (e.g., components for transmitting, components for receiving, components for measurement, components for tuning, components for avoiding transmission, etc.) for communication via one or more wireless communication networks (not shown) (such as NR networks, LTE networks, GSM networks, etc.). WWAN transceivers 310 and 350 may each be connected to one or more antennas 316 and 356 for communication with other network nodes (such as other UEs, access points, base stations (e.g., eNB, gNB, etc.) via at least one designated RAT (e.g., NR, LTE, GSM, etc.) through a wireless communication medium of interest (e.g., a set of time / frequency resources in a specific spectrum). According to the specified RAT, WWAN transceivers 310 and 350 can be configured differently to transmit and encode signals 318 and 358 (e.g., messages, indications, information, etc.) respectively, and conversely configured to receive and decode signals 318 and 358 (e.g., messages, indications, information, boot signals, etc.) respectively. Specifically, WWAN transceivers 310 and 350 each include one or more transmitters 314 and 354 for transmitting and encoding signals 318 and 358 respectively, and one or more receivers 312 and 352 for receiving and decoding signals 318 and 358 respectively.

[0093] In at least some cases, UE 302 and base station 304 also include one or more short-range radio transceivers 320 and 360, respectively. The short-range radio transceivers 320 and 360 may be connected to one or more antennas 326 and 366, respectively, and provide components (e.g., components for transmitting, components for receiving, components for measurement, components for tuning, components for avoiding transmission, etc.) for communicating with other network nodes (such as other UEs, access points, base stations, etc.) via at least one designated RAT (e.g., WiFi, LTE-D, Bluetooth®, Zigbee®, Z-Wave®, PC5, Dedicated Short Range Communication (DSRC), Wireless Access for Vehicle Environments (WAVE), Near Field Communication (NFC), Ultra Wideband (UWB), etc.) via a wireless communication medium of interest. According to the specified RAT, short-range wireless transceivers 320 and 360 can be configured differently for transmitting and encoding signals 328 and 368 (e.g., messages, indications, information, etc.) respectively, and conversely configured for receiving and decoding signals 328 and 368 (e.g., messages, indications, information, boot signals, etc.) respectively. Specifically, short-range wireless transceivers 320 and 360 each include one or more transmitters 324 and 364 for transmitting and encoding signals 328 and 368 respectively, and one or more receivers 322 and 362 for receiving and decoding signals 328 and 368 respectively. As specific examples, short-range wireless transceivers 320 and 360 can be WiFi transceivers, Bluetooth® transceivers, Zigbee® and / or Z-Wave® transceivers, NFC transceivers, UWB transceivers, or vehicle-to-vehicle (V2V) and / or vehicle-to-everything (V2X) transceivers.

[0094] In at least some cases, UE 302 and base station 304 also include satellite signal receivers 330 and 370. Satellite signal receivers 330 and 370 may be connected to one or more antennas 336 and 376, respectively, and may each provide components for receiving and / or measuring satellite positioning / communication signals 338 and 378. When satellite signal receivers 330 and 370 are satellite positioning system receivers, satellite positioning / communication signals 338 and 378 may be Global Positioning System (GPS) signals, Global Navigation Satellite System (GLONASS) signals, Galileo signals, BeiDou signals, Indian Regional Navigation Satellite System (NAVIC), Quasi-Zenith Satellite System (QZSS), etc. When satellite signal receivers 330 and 370 are non-terrestrial network (NTN) receivers, satellite positioning / communication signals 338 and 378 may be communication signals originating from a 5G network (e.g., carrying control and / or user data). Satellite signal receivers 330 and 370 may include any suitable hardware and / or software for receiving and processing satellite positioning / communication signals 338 and 378, respectively. Satellite signal receivers 330 and 370 may request information and operations from other systems as needed, and in at least some cases, use measurements obtained via any suitable satellite positioning system algorithm to perform calculations for determining the positions of UE 302 and base station 304, respectively.

[0095] Base station 304 and network entity 306 each include one or more network transceivers 380 and 390, thereby providing components (e.g., components for transmitting, components for receiving, etc.) for communicating with other network entities (e.g., other base stations 304, other network entities 306). For example, base station 304 may employ one or more network transceivers 380 to communicate with other base stations 304 or network entities 306 via one or more wired or wireless backhaul links. As another example, network entity 306 may employ one or more network transceivers 390 to communicate with one or more base stations 304 via one or more wired or wireless backhaul links, or to communicate with other network entities 306 via one or more wired or wireless core network interfaces.

[0096] The transceiver can be configured to communicate via a wired or wireless link. The transceiver (whether wired or wireless) includes transmitter circuitry (e.g., transmitters 314, 324, 354, 364) and receiver circuitry (e.g., receivers 312, 322, 352, 362). In some embodiments, the transceiver may be an integrated device (e.g., embodying transmitter and receiver circuitry in a single device), in some embodiments it may include separate transmitter and receiver circuitry, or in other embodiments it may be embodied in other ways. The transmitter and receiver circuitry of a wired transceiver (e.g., network transceivers 380 and 390 in some embodiments) 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 antenna arrays, that allow corresponding devices (e.g., UE 302, base station 304) to perform 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 antenna arrays, that allow corresponding devices (e.g., UE 302, base station 304) to perform beamforming as described herein. In one configuration, transmitter and receiver circuitry may share the same plurality of antennas (e.g., antennas 316, 326, 356, 366) such that the corresponding devices can only receive or transmit at a given time, and not simultaneously. Wireless transceivers (e.g., WWAN transceivers 310 and 350, short-range wireless transceivers 320 and 360) may also include network listening modules (NLMs) for performing various measurements.

[0097] As used herein, various wireless transceivers (e.g., transceivers 310, 320, 350, and 360 in some embodiments, and network transceivers 380 and 390) and wired transceivers (e.g., network transceivers 380 and 390 in some embodiments) can generally be characterized as "transceiver," "at least one transceiver," or "one or more transceivers." Thus, whether a particular transceiver is wired or wireless can be inferred from the type of communication performed. For example, backhaul communication between network devices or servers typically involves signal transmission via a wired transceiver, while wireless communication between a UE (e.g., UE 302) and a base station (e.g., base station 304) typically involves signal transmission via a wireless transceiver.

[0098] UE 302, base station 304, and network entity 306 also include other components that can be used in conjunction with the operations disclosed herein. UE 302, base station 304, and network entity 306 each include one or more processors 332, 384, and 394 for providing functions related to, for example, wireless communication and for providing other processing functions. Thus, processors 332, 384, and 394 can provide components for processing, such as components for decision-making, components for calculation, components for receiving, components for transmitting, components for indicating, etc. In one embodiment, processors 332, 384, and 394 may include, for example, one or more general-purpose processors, multi-core processors, central processing units (CPUs), ASICs, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), other programmable logic devices or processing circuits, or various combinations thereof.

[0099] UE 302, base station 304, and network entity 306 respectively include memory circuitry implementing memories 340, 386, and 396 (e.g., each including a memory device) for maintaining information (e.g., information indicating reserved resources, thresholds, parameters, etc.). Therefore, memories 340, 386, and 396 can provide components for storage, retrieval, and retention. In some cases, UE 302, base station 304, and network entity 306 may each include positioning components 342, 388, and 398. Positioning components 342, 388, and 398 may be part of or coupled to processors 332, 384, and 394 respectively, and when executed, these positioning components enable UE 302, base station 304, and network entity 306 to perform the functions described herein. In other configurations, positioning components 342, 388, and 398 may be external to processors 332, 384, and 394 (e.g., part of a modem processing system, integrated with another processing system, etc.). Alternatively, positioning components 342, 388, and 398 may be memory modules stored respectively in memories 340, 386, and 396, which, when executed by processors 332, 384, and 394 (or a modem processing system, another processing system, etc.), enable UE 302, base station 304, and network entity 306 to perform the functions described herein. Figure 3A illustrates possible locations of positioning component 342, which may be part of, for example, one or more WWAN transceivers 310, memory 340, one or more processors 332, or any combination thereof, or may be a separate component. Figure 3B illustrates the possible locations of the positioning component 388, which may be part of, for example, one or more WWAN transceivers 350, memory 386, one or more processors 384, or any combination thereof, or may be a separate component. Figure 3C illustrates the possible locations of the positioning component 398, which may be part of, for example, one or more network transceivers 390, memory 396, one or more processors 394, or may be a separate component.

[0100] UE 302 may include one or more sensors 344 coupled to one or more processors 332 to provide components for sensing or detecting motion and / or orientation information independent of motion data derived from signals received by one or more WWAN transceivers 310, one or more short-range wireless transceivers 320, and / or satellite signal receivers 330. For example, sensor 344 may include an accelerometer (e.g., a microelectromechanical system (MEMS) device), a gyroscope, a geomagnetic sensor (e.g., a compass), an altimeter (e.g., a barometric altimeter), and / or any other type of motion detection sensor. Furthermore, sensor 344 may include a plurality of different types of devices and combine their outputs to provide motion information. For example, sensor 344 may use a combination of a multi-axis accelerometer and an orientation sensor to provide the ability to calculate position in a two-dimensional (2D) and / or three-dimensional (3D) coordinate system.

[0101] In addition, UE 302 includes a user interface 346 that provides components for providing instructions to the user (e.g., auditory and / or visual instructions) and / or for receiving user input (e.g., when the user actuates sensing devices such as a keypad, touch screen, microphone, etc.). Although not shown, base station 304 and network entity 306 may also include user interfaces.

[0102] Referring more specifically to one or more processors 384, in the downlink, IP packets from network entity 306 can be provided to processor 384. One or more processors 384 can implement the functions of the RRC layer, Packet Data Convergence Protocol (PDCP) layer, Radio Link Control (RLC) layer, and Media Access Control (MAC) layer. One or more processors 384 may provide RRC layer functions associated with broadcasting system information (e.g., main block (MIB), system block (SIB)), RRC connection control (e.g., RRC connection paging, RRC connection establishment, RRC connection modification, and RRC connection release), RAT mobility, and measurement configuration for UE measurement reporting; PDCP layer functions associated with header compression / decompression, security (encryption, decryption, integrity protection, integrity verification), and handover support functions; RLC layer functions associated with transmission of upper-layer PDUs, error correction via automatic repeat request (ARQ), concatenation, segmentation, and reassembly of RLC service data units (SDUs), resegmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functions associated with mapping between logical channels and transport channels, scheduling information reporting, error correction, priority processing, and logical channel priority ordering.

[0103] Transmitter 354 and receiver 352 can implement Layer-1 (L1) functions associated with various signal processing functions. Layer-1, including the physical (PHY) layer, can include error detection on the transmission channel, forward error correction (FEC) decoding / decoding of the transmission channel, interleaving, rate matching, mapping to the physical channel, modulation / demodulation of the physical channel, and MIMO antenna processing. Transmitter 354 processes the mapping to the signal cluster based on various modulation schemes (e.g., binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), M-phase keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). Subsequently, the decoded and modulated symbols can be split into parallel streams. Each stream can then be mapped to an Orthogonal Frequency Division Multiplexing (OFDM) subcarrier, multiplexed with a reference signal (e.g., a pilot signal) in the time and / or frequency domains, and subsequently combined using an Inverse Fast Fourier Transform (IFFT) to generate a physical channel carrying a time-domain OFDM symbol stream. The OFDM symbol stream is spatially pre-decoded to generate multiple spatial streams. Channel estimates from the channel estimator can be used to determine the decoding and modulation scheme, as well as for spatial processing. The channel estimates can be derived from the reference signal and / or channel condition feedback transmitted by UE 302. Each spatial stream can then be provided to one or more different antennas 356. Transmitter 354 can use the corresponding spatial stream to modulate an RF carrier for transmission.

[0104] At UE 302, receiver 312 receives signals via its corresponding antenna 316. Receiver 312 recovers the information modulated onto the RF carrier and provides this information to one or more processors 332. Transmitter 314 and receiver 312 implement Layer-1 functions associated with various signal processing functions. Receiver 312 can perform spatial processing on the information to recover any spatial streams destined for UE 302. If multiple spatial streams are destined for UE 302, they can be combined by receiver 312 into a single OFDM symbol stream. Subsequently, receiver 312 uses Fast Fourier Transform (FFT) to transform the OFDM symbol stream from the time domain to the frequency domain. The frequency domain signal includes a separate OFDM symbol stream for each subcarrier of the OFDM signal. Symbols and reference signals on each subcarrier are recovered and demodulated by determining the most likely signal clustering point transmitted by base station 304. These soft decisions can be based on channel estimates calculated by a channel estimator. Subsequently, the soft decision is decoded and deinterleaved to recover the data and control signals originally transmitted by base station 304 on the physical channel. The data and control signals are then provided to one or more processors 332, which implement Layer 3 (L3) and Layer 2 (L2) functions.

[0105] In the uplink, one or more processors 332 provide demultiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transmission channel and the logical channel to recover IP packets from the core network. One or more processors 332 are also responsible for error detection.

[0106] Similar to the functions described in the downlink transmission description of base station 304, one or more processors 332 provide RRC layer functions associated with system information (e.g., MIB, SIB) acquisition, RRC connection and measurement reporting; PDCP layer functions associated with header compression / decompression and security (encryption, decryption, integrity protection, integrity verification); RLC layer functions associated with transmission of upper-layer PDUs, error correction via ARQ, concatenation, segmentation and reassembly of RLC SDUs, resegmentation of RLC data PDUs and reordering of RLC data PDUs; and MAC layer functions associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs to transport blocks (TBs), demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction via Hybrid Automatic Repeat Request (HARQ), priority processing and logical channel priority ordering.

[0107] The channel estimate derived by the channel estimator from the reference signal or feedback transmitted by the base station 304 can be used by the transmitter 314 to select an appropriate decoding and modulation scheme and facilitate spatial processing. The spatial stream generated by the transmitter 314 can be provided to different antennas 316. The transmitter 314 can use the corresponding spatial stream to modulate an RF carrier for transmission.

[0108] Uplink transmissions are processed at base station 304 in a manner similar to that described in conjunction with the receiver function at UE 302. Receiver 352 receives signals via its respective antenna 356. Receiver 352 recovers the information modulated onto the RF carrier and provides that information to one or more processors 384.

[0109] In the uplink, one or more processors 384 provide demultiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transmission channel and the logical channel to recover IP packets from the UE 302. IP packets from one or more processors 384 can be provided to the core network. One or more processors 384 are also responsible for error detection.

[0110] For convenience, UE 302, base station 304, and / or network entity 306 are shown in Figures 3A, 3B, and 3C as including various components that can be configured according to the various instances described herein. However, it will be understood that the components shown may have different functions in different designs. Specifically, the various components in Figures 3A through 3C are optional in alternative configurations, and each configuration includes configurations that may vary due to device design choices, cost, use, or other considerations. For example, in the case of Figure 3A, a particular implementation of UE 302 may omit WWAN transceiver 310 (e.g., a wearable device, tablet, PC, or laptop may have Wi-Fi and / or Bluetooth capabilities but no cellular capabilities), or may omit short-range wireless transceiver 320 (e.g., cellular only, etc.), or may omit satellite signal receiver 330, or may omit sensor 344, etc. In another example, in the case of Figure 3B, a particular implementation of base station 304 may omit WWAN transceiver 350 (e.g., a Wi-Fi "hotspot" access point without cellular capability), or may omit short-range wireless transceiver 360 (e.g., cellular only), or may omit satellite signal receiver 370, and so on. For the sake of brevity, illustrations of various alternative configurations are not provided herein, but they will be readily understood by those skilled in the art to which this invention pertains.

[0111] The various components of UE 302, base station 304, and network entity 306 can be communicatively coupled to each other via data buses 334, 382, ​​and 392, respectively. In one configuration, data buses 334, 382, ​​and 392 can form or be part of the communication interface for UE 302, base station 304, and network entity 306, respectively. For example, when different logical entities are implemented in the same device (e.g., gNB and location server functions are combined into the same base station 304), data buses 334, 382, ​​and 392 can provide communication between them.

[0112] The components of Figures 3A, 3B, and 3C can be implemented in various ways. In some embodiments, the components of Figures 3A, 3B, and 3C can be implemented in one or more circuits, such as 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 the function. For example, some or all of the functions represented by blocks 310 to 346 can be implemented by the processor and memory component of UE 302 (e.g., via executing appropriate code and / or via appropriate configuration of the processor component). Similarly, some or all of the functions represented by blocks 350 to 388 can be implemented by the processor and memory component of base station 304 (e.g., via executing appropriate code and / or via appropriate configuration of the processor component). Furthermore, some or all of the functions represented by blocks 390 to 398 can be implemented by the processor and memory components of network entity 306 (e.g., by executing appropriate code and / or by appropriate configuration of the processor components). For simplicity, this document describes various operations, actions, and / or functions as being performed "by the UE," "by the base station," "by the network entity," etc. However, it will be understood that such operations, actions, and / or functions can actually be performed by specific components or combinations of components of UE 302, base station 304, network entity 306, etc., such as processors 332, 384, 394, transceivers 310, 320, 350, and 360, memory 340, 386, and 396, positioning components 342, 388, 398, etc.

[0113] In some designs, network entity 306 may be implemented as a core network component. In other designs, network entity 306 may be a network service provider or enterprise different from the cellular network infrastructure (e.g., NG RAN 220 and / or 5GC 210 / 260). For example, network entity 306 may be a component of a private network that can be configured to communicate with UE 302 via base station 304 or independently of base station 304 (e.g., via a non-cellular communication link such as WiFi).

[0114] Application services are pools of services needed to optimize the deployment, execution, and improvement of applications, such as load balancing, application performance monitoring, application acceleration, autoscaling, micro-segmentation, service proxying, and service discovery. Both services and applications are software programs, but they typically have different characteristics. Broadly speaking, services usually aim to provide smaller and more isolated functionalities than applications, while applications typically expose and invoke services, including services within other applications.

[0115] A Web service is an application service that can be accessed via a web address for direct application-to-application interaction. Web services can be local, distributed, or web-based. Web services are built on open standards such as TCP / IP, Hypertext Transfer Protocol (HTTP), Java, Hypertext Markup Language (HTML), and Extensible Markup Language (XML), and therefore, web services are independent of any single operating system or programming language. Thus, software applications written in various programming languages ​​and running on various platforms can use web services to exchange data via computer networks such as the Internet, in a manner similar to inter-program communication on a single computer. For example, a client can invoke a web service by sending an XML message to the web service and waiting for a corresponding XML response.

[0116] An Application Programming Interface (API) is an interface that facilitates interaction between different systems (e.g., hardware, firmware, and / or software entities or levels). More specifically, an API is a defined set of rules, commands, permissions, and / or agreements that allow one system to interact with and access data from another system. For example, an API can provide an interface for higher-level software (e.g., applications, web services, application services, etc.) to access lower-level software (e.g., microservices, operating systems, BIOS, firmware, device drivers, etc.) or hardware components (e.g., Universal Serial Bus (USB) controllers, memory controllers, transceivers, etc.). Because web services expose the data and functionality of applications, every web service is actually an API, but not every API is a web service.

[0117] One type of API used to build microservice applications is the Representational State Transfer API, also known as a "REST API" or "RESTful API." A REST API is a set of web API architectural principles, meaning that to be a REST API, an interface must adhere to certain architectural constraints. REST APIs typically use HTTP commands and Secure Sockets Layer (SSL) encryption. It is language-agnostic because it can be used to connect applications and microservices written in different programming languages. Commonly used REST API commands include HTTP PUT, HTTP POST, HTTP DELETE, HTTP GET, and HTTP PATCH. Developers can use these REST API commands to perform operations on different "resources" (such as data in a database) within an application or service. REST APIs can use Uniform Resource Locators (URLs) to locate and indicate the resources on which operations should be performed.

[0118] Microservices are small, autonomous, and independent services and / or functions that together form a larger microservices-based application. Within an application, each microservice performs a defined function, such as authenticating users or retrieving specific types of data. Microservices are typically language-independent, with the goal of enabling them to adapt to any type of application and communicate or collaborate with each other to achieve the overall purpose of the larger microservices-based application. When connecting microservices to build a microservices-based application, the API defines the rules that prevent and allow actions and interactions between individual microservices. For example, a REST API can be used as the rules, commands, permissions, and / or agreements for integrating individual microservices to act as a single application.

[0119] Webhooks support interaction between web-based applications using custom rollbacks. The use of webhooks allows web-based applications to automatically communicate with other web-based applications. Unlike traditional systems where one system (the "subject" system) constantly polls another system (the "observer" system) for certain data, webhooks allow the observer system to automatically push data to the subject system whenever an event occurs. This significantly reduces the load on both systems because calls between them only occur when a specified event occurs.

[0120] Webhooks communicate via HTTP and rely on the existence of static URLs pointing to APIs in the subject system, which should be notified when events occur on the observer system. Therefore, the subject system needs to specify one or more URLs that will receive event notifications from the observer system.

[0121] Figure 4 is a schematic diagram 400 illustrating instance interactions between application 410, application service 420, operating system (OS) 430, and hardware 440 using various APIs according to the present invention. In one embodiment, application 410, application service 420, operating system 430, and hardware 440 may be integrated into the same device (e.g., UE, base station, etc.).

[0122] As shown in Figure 4, application service 420 (which may be a web service) includes two microservices 422a and 422b (collectively referred to as microservice 422). However, it will be understood that application service 420 may include more or fewer than two microservices 422. In some cases, application 410 may directly access individual microservice 422 via the corresponding APIs 424a and 424b (collectively referred to as API 424). This is illustrated in Figure 4 by application 410 calling microservice 422b via API 424b. Alternatively, application 410 may call application service 420 via API 424c. Subsequently, application service 420 may call the appropriate microservice 422 via the corresponding API 424. This is illustrated in Figure 4 by application service 420 representing application 410 calling microservice 422a via API 424a.

[0123] If called by application 410, microservice 422 can respond to application 410 via a rollback 412. Alternatively, if called by application service 420, microservice 422 can respond to application service 420 via a rollback 426c. In either case, the client (application 410 or application service 420) can invoke microservice 422 by sending, for example, an XML message to microservice 422 via the corresponding API 424, and microservice 422 can respond to the client by sending a corresponding XML response to rollback 412.

[0124] Microservice 422 can access various subsystems within operating system 430 via the corresponding APIs of the subsystems. In the example of FIG4, operating system 430 includes a positioning subsystem 432a and a communication subsystem 432b (collectively referred to as subsystem 432). Positioning subsystem 432a may include software and / or firmware for determining the location of a mobile device (e.g., UE). The mobile device being located may be a device including operating system 430 (e.g., a UE calculating its own location in the case of UE-based positioning) or another device not including operating system 430 (e.g., in the case of a location server estimating the location of the UE). Communication subsystem 432b may similarly include software and / or firmware for implementing wireless communication via a device including operating system 430. For example, communication subsystem 432b may implement low-level communication functions (e.g., MAC layer functions, RRC layer functions, etc.).

[0125] Subsystem 432 each exposes its corresponding APIs 434a and 434b (collectively referred to as API 434) to a higher architectural level. Microservice 422 can call subsystem 432 via its corresponding API 434, and subsystem 432 can respond to microservice 422 via rollbacks 426a and 426b (collectively referred to as rollback 426). In the example of Figure 4, microservice 422a calls location subsystem 432a, and microservice 422b calls communication subsystem 432b within operating system 430. Thus, microservice 422a can be a location-related microservice, and microservice 422b can be a communication-related microservice. However, it will be understood that any microservice 422 can call any subsystem 432 via its corresponding API 434.

[0126] In the example of FIG4, hardware 440 includes a satellite signal receiver 442a, one or more WWAN transceivers 442b, and one or more short-range wireless transceivers 442c (collectively referred to as hardware component 442). The satellite signal receiver 442a may correspond to, for example, the satellite signal receiver 330 or 370 in FIG3A and FIG3B. The one or more WWAN transceivers 442b may correspond to, for example, the one or more WWAN transceivers 310 or 350 in FIG3A and FIG3B. The one or more short-range wireless transceivers 442c may correspond to, for example, the one or more short-range wireless transceivers 320 or 360 in FIG3A and FIG3B.

[0127] In the example of Figure 4, the positioning subsystem 432a can send commands (e.g., requests to measure reference signals, requests to send reference signals, etc.) to the satellite signal receiver 442a, one or more WWAN transceivers 442b, and / or one or more short-range wireless transceivers 442c via APIs 444a, API 444b, and API 444c, respectively. The satellite signal receiver 442a, one or more WWAN transceivers 442b, and / or one or more short-range wireless transceivers 442c can send responses to the commands (e.g., measurement of reference signals, acknowledgment, etc.) to the positioning subsystem 432a via callback 436a. Similarly, the communication subsystem 432b can send information to be wirelessly transmitted (e.g., user data, measurement reports, etc.) to one or more WWAN transceivers 442b and / or one or more short-range wireless transceivers 442c via APIs 444b and 444c, respectively. One or more WWAN transceivers 442b and / or one or more short-range wireless transceivers 442c can send information to be wirelessly received (e.g., user data, location requests, location assistance data, etc.) to the communication subsystem 432b via callback 436b.

[0128] As a specific location instance in the context of FIG4, the device incorporating the illustrated architecture can be a mobile device, and application 410 can be an application that uses the location of the mobile device (e.g., UE), such as (e.g., a navigation application executed locally on the mobile device). Therefore, application 410 (via API 424c) invokes application service 420, and application service 420 (via API 424a) invokes microservice 422a or directly (via API 424a) microservice 422a. Commands from application 410 indicate that application 410 is requesting the location of the mobile device and may include (or additional commands may include) other information related to the determination of the requested location, such as the requested quality of service (QoS) (e.g., accuracy and latency).

[0129] Based on the QoS of the location request, the known capabilities of the mobile device (e.g., available positioning technologies, such as satellite-based, NR-based, Wi-Fi-based, etc.), and available reference signal configuration (e.g., from nearby base stations), microservice 422a (via API 434a) calls the positioning subsystem 432a. It should be noted that microservice 422a can coordinate with other microservices, other application services, other applications, etc., to obtain the information required to locate the mobile device. For example, microservice 422a may need to access another microservice associated with one or more base stations that the mobile device is expected to measure in order to execute an NR-based positioning procedure.

[0130] Microservice 422a can select a positioning technology to obtain the location of the mobile device based on the known capabilities of the mobile device and the requested QoS. For example, using a satellite signal receiver 442a can provide high accuracy and low latency, but it can be turned off. As another example, using one or more WWAN transceivers 442b can provide low latency, but the accuracy will be poor if the mobile device is indoors. Based on the selected positioning technology, microservice 422a sends one or more commands to positioning subsystem 432a, requesting positioning subsystem 432a to invoke satellite signal receiver 442a, one or more WWAN transceivers 442b, or one or more short-range wireless transceivers 442c. Also depending on the type of positioning technology selected, microservice 422a can provide commands regarding which reference signals to measure, which reference signals to transmit, etc. In addition, microservice 422a can indicate the required accuracy and latency for positioning measurements.

[0131] Based on commands from microservice 422a, the positioning subsystem 432a (via one or more of APIs 444) invokes appropriate hardware components. For example, if the positioning technology is NR-based, the positioning subsystem 432a can send commands to one or more WWAN transceivers 442b to measure and / or transmit specific reference signals at specific times and frequencies. Furthermore, based on the requested accuracy and latency, the positioning subsystem 432a can increase or decrease the amount of power and / or processing resources allocated to one or more WWAN transceivers 442b. For example, for higher accuracy requirements, the positioning subsystem 432a can dedicate more power and / or processing resources to one or more WWAN transceivers 442b.

[0132] The positioning subsystem 432a (via archive 436a) receives positioning measurements (e.g., reception time, transmission time, signal strength, etc.) from one or more WWAN transceivers 442b and passes them (via archive 426a) to the microservice 422a. The microservice 422a can then calculate the location of the mobile device based on the measurements and any other available information (e.g., the location of the base station transmitting the measured reference signal). The microservice 422a provides the calculated location of the mobile device to the application 410 via archive 412 or via application service 420 (depending on which entity invoked microservice 422a).

[0133] In some cases, application 410 may provide credentials or other authorization to microservice 422a, indicating that application 410 is permitted to access the location of the mobile device. Alternatively, upon receiving a request from application 410, microservice 422a may determine whether application 410 is authorized. This check may be performed, for example, via another microservice or by invoking operating system 430, to determine whether application 410 has permission to access the location of the mobile device. Similarly, microservice 422a may need to provide credentials or other authorization to operating system 430 to indicate that microservice 422a is permitted to access the location of the mobile device. Alternatively, upon receiving a request from microservice 422a, operating system 430 may determine whether microservice 422a is authorized.

[0134] In some cases, application 410 can use a webhook to obtain the location of the mobile device. Thus, application 410 will be notified whenever the mobile device moves from one location to another. In this case, the observer system will be microservice 422a, and the subject system will be application 410. Instead of application 410 periodically calling microservice 422a to check if the location of the mobile device has changed, the webhook established in application 410 will allow microservice 422a to automatically push any changes in the location of the mobile device to application 410 via a registered URL. Microservice 422a can periodically perform location operations to determine the location of the mobile device in order to report the change to application 410.

[0135] Similarly, microservice 422a can use a webhook to obtain changes in the location of the mobile device. However, in this case, because microservice 422a coordinates location decisions for certain types of positioning technologies (e.g., NR-based, Wi-Fi-based), the webhook may only be applicable to certain other types of positioning technologies (e.g., satellite-based, sensor-based). For example, if positioning subsystem 432a coordinates satellite-based positioning via satellite signal receiver 442a, it can report any detected location changes to microservice 422a via a webhook.

[0136] In some cases, application 410, application service 420, operating system 430, and hardware 440 may be distributed across multiple devices (e.g., UE, web server, location server, etc.). For example, application 410 may run on a location server (e.g., LMF 270), application service 420 may run on a web server, and operating system 430 and hardware 440 may be integrated into a UE (e.g., UE 204).

[0137] NR supports many cellular network-based positioning technologies, including downlink-based, uplink-based, and downlink-and-uplink-based positioning methods. Downlink-based positioning methods include Observed Time Difference of Arrival (OTDOA) in LTE, Downlink Time Difference of Arrival (DL-TDOA) in NR, and Downlink Angle of Departure (DL-AoD) in NR. Figure 5 illustrates examples of various positioning methods according to this case. In the OTDOA or DL-TDOA positioning procedure shown in scenario 510, the UE measures the difference between the time of arrival (ToA) of a received reference signal (e.g., a positioning reference signal (PRS)) from the base station, referred to as the Reference Signal Time Difference (RSTD) or Time Difference of Arrival (TDOA) measurement, and reports them to the positioning entity. More specifically, the UE receives identifiers (IDs) of a reference base station (e.g., a serving base station) and multiple non-reference base stations in auxiliary data. Subsequently, the UE measures the RSTD between the reference base station and each non-reference base station. Based on the known locations of the base stations involved and RSTD measurements, a positioning entity (e.g., a UE for UE-based positioning or a location server for UE-assisted positioning) can estimate the location of the UE.

[0138] For DL-AoD positioning as shown in scenario 520, the positioning entity uses measurement reports of received signal strength measurements from multiple downlink transmit beams of the UE to determine the angle between the UE and the transmitting base station. Subsequently, the positioning entity can estimate the UE's position based on the determined angle and the known location of the transmitting base station.

[0139] Uplink-based positioning methods include uplink time difference of arrival (UL-TDOA) and uplink angle of arrival (UL-AoA). UL-TDOA is similar to DL-TDOA, but is based on uplink reference signals (e.g., sounding reference signals (SRS)) transmitted by the UE to multiple base stations. Specifically, the UE transmits one or more uplink reference signals measured by a reference base station and a plurality of non-reference base stations. Subsequently, each base station reports the reception time of the reference signal (called relative time of arrival (RTOA)) to a positioning entity (e.g., a location server) that knows the location and relative timing of the base stations involved. Based on the received-receive (Rx-Rx) time difference between the reported RTOA of the reference base station and the reported RTOA of each non-reference base station, the known location of the base stations, and their known timing offsets, the positioning entity can use TDOA to estimate the UE's location.

[0140] For UL-AoA positioning, one or more base stations measure the received signal strength of one or more uplink reference signals (e.g., SRS) received from the UE on one or more uplink receive beams. The positioning entity uses the signal strength measurement and the angle of the receive beam to determine the angle between the UE and the base stations. Subsequently, based on the determined angle and the known location of the base stations, the positioning entity can estimate the location of the UE.

[0141] Downlink and uplink-based positioning methods include Enhanced Cell Identifier (E-CID) positioning and Multiple Round Trip Time (RTT) positioning (also known as "Multi-Cell RTT" and "Multi-RTT"). In an RTT procedure, a first entity (e.g., a base station or a UE) sends a first RTT-related signal (e.g., PRS or SRS) to a second entity (e.g., a UE or a base station), and the second entity sends a second RTT-related signal (e.g., SRS or PRS) back to the first entity. Each entity measures the time difference between the arrival time (ToA) of the received RTT-related signal and the transmission time of the transmitted RTT-related signal. This time difference is called the receive-to-transmit (Rx-Tx) time difference. The Rx-Tx time difference measurement can be performed or adjusted to include only the time difference between the nearest time slot boundary of the received signal and the nearest time slot boundary of the transmitted signal. Subsequently, the two entities can send their Rx-Tx time difference measurements to a location server (e.g., LMF 270), which calculates the round-trip time (RTT) between the two entities based on the two Rx-Tx time difference measurements (e.g., the sum of the two Rx-Tx time difference measurements). Alternatively, one entity can send its Rx-Tx time difference measurement to another entity, which then calculates the RTT. The distance between the two entities can be determined based on the RTT and a known signal speed (e.g., the speed of light). For multi-RTT positioning as shown in scenario 530, a first entity (e.g., a UE or base station) performs an RTT positioning procedure with multiple second entities (e.g., multiple base stations or UEs) such that the position of the first entity can be determined based on the distance to the second entities and the known positions of the second entities (e.g., using multipoint positioning). As shown in scenario 540, RTT and multi-RTT methods can be combined with other positioning technologies such as UL AoA and DL-AoD to improve positioning accuracy.

[0142] The E-CID positioning method is based on Radio Resource Management (RRM) measurements. In E-CID, the UE reports the serving cell ID, timing advance (TA), and the identifiers, estimated timings, and signal strengths of detected neighboring base stations. Subsequently, the UE's location is estimated based on this information and the known locations of the base stations.

[0143] To assist in positioning operations, a location server (e.g., location server 230, LMF 270, SLP 272) may provide auxiliary data to the UE. For example, the auxiliary data may include the identifier of the base station (or the cell / TRP of the base station) measuring the reference signal, reference signal configuration parameters (e.g., including the number of consecutive time slots of the PRS, the periodicity of the consecutive time slots of the PRS, silence sequences, frequency hopping sequences, reference signal identifiers, reference signal bandwidth, etc.), and / or other parameters applicable to a specific positioning method. Alternatively, the auxiliary data may be derived directly from the base station itself (e.g., in periodically broadcast management burden messages, etc.). In some cases, the UE may be able to detect neighboring network nodes itself without using auxiliary data.

[0144] In the case of OTDOA or DL-TDOA positioning procedures, auxiliary data may also include expected RSTD values ​​around the expected RSTD and associated indeterminacy or search window. In some cases, the expected RSTD value range may be + / - 500 microseconds (µs). In some cases, when any resources used for positioning measurements are in FR1, the indeterminacy value range of the expected RSTD may be + / - 32 µs. In other cases, when all resources used for positioning measurements are in FR2, the indeterminacy value range of the expected RSTD may be + / - 8 µs.

[0145] Location estimates can be represented by other names, such as position estimate, location, position, position fix, fix, etc. A location estimate can be geodetic and includes coordinates (e.g., latitude, longitude, and possible altitude), or it can be urban and include street addresses, postal addresses, or some other verbal description of the location. A location estimate can also be defined relative to another known location, or in absolute terms (e.g., using latitude, longitude, and possible altitude). A location estimate can include anticipated errors or indeterminacy (e.g., by including an area or volume within which the location is expected to be included with some specified or predetermined confidence level).

[0146] Figure 6A illustrates two procedures currently supported by the LTE Positioning Protocol (LPP) for exchanging UE positioning capabilities with the network. The first procedure is a capability transmission procedure 600, and the second procedure is a capability indication procedure 610. In capability transmission procedure 600, the server (e.g., LMF 270) indicates the type of positioning capability required by the target (e.g., UE 204) in an LPP "RequestCapabilities" message, and the target provides the requested positioning capability to the server in an LPP "ProvideCapabilities" message. In capability indication procedure 610, the target provides the proactively offered positioning capability to the server in an LPP "ProvideCapabilities" message.

[0147] Figure 6B illustrates two procedures currently supported by LPP for exchanging location assistance data. The first procedure is assistance data transmission procedure 630, and the second procedure is assistance data delivery procedure 640. In assistance data transmission procedure 630, the target (e.g., UE 204) sends a request for assistance data to the server (e.g., LMF 270) in an LPP "RequestAssistanceData" message, and the server provides the assistance data required for location to the target in an LPP "ProvideAssistanceData" message. Assistance data can be provided on demand, periodically, or periodically updated. In assistance data delivery procedure 640, the server provides proactively provided assistance data required for location to the target in an LPP "ProvideAssistanceData" message. Assistance data can be provided periodically or non-periodically.

[0148] Figure 6C illustrates two procedures currently supported by LPP for exchanging location information. The first procedure is location information transmission procedure 650, and the second procedure is location information delivery procedure 660. Location information transmission procedure 650 is used to support the transmission of location estimates based on requested services. A server (e.g., LMF 270) sends an LPP "RequestLocationInformation" message to a target (e.g., UE 204), indicating the type of location information required and the associated QoS, and the target provides the requested location information to the server in an LPP "ProvideLocationInformation" message. Location information delivery procedure 660 supports the transmission of location estimates based on proactively provided services. In location information delivery procedure 660, the target sends an LPP "ProvideLocationInformation" message including proactively provided location information.

[0149] Various frame structures can be used to support downlink and uplink transmissions between network nodes (e.g., base stations and UEs). Figure 7 is a schematic diagram 700 illustrating an example frame structure according to this invention. The frame structure can be a downlink or uplink frame structure. Other wireless communication technologies can have different frame structures and / or different channels.

[0150] LTE, and in some cases NR, uses Orthogonal Frequency Division Multiplexing (OFDM) on the downlink and Single Carrier Frequency Division Multiplexing (SC-FDM) on the uplink. However, unlike LTE, NR can also choose to use OFDM on the uplink. OFDM and SC-FDM divide the system bandwidth into multiple (K) orthogonal subcarriers, which are often referred to as tones, frequency bands, etc. Each subcarrier can be modulated with data. Typically, modulation symbols are transmitted in the frequency domain using OFDM and in the time domain using SC-FDM. The spacing between adjacent subcarriers can be fixed, and the total number of subcarriers (K) can depend on the system bandwidth. For example, the spacing between subcarriers can be 15 kHz, and the minimum resource configuration (resource block) can be 12 subcarriers (or 180 kHz). Therefore, for system bandwidths of 1.25, 2.5, 5, 10, or 20 MHz, the nominal Fast Fourier Transform (FFT) size can be equal to 128, 256, 512, 1024, or 2048, respectively. The system bandwidth can also be divided into subbands. For example, a subband can cover 1.08 MHz (i.e., 6 resource blocks), and for system bandwidths of 1.25, 2.5, 5, 10, or 20 MHz, there can be 1, 2, 4, 8, or 16 subbands, respectively.

[0151] LTE supports a single system parameter (subcarrier spacing (SCS), symbol length, etc.). In contrast, NR can support multiple system parameters (µ), for example, subcarrier spacings of 15 kHz (µ=0), 30 kHz (µ=1), 60 kHz (µ=2), 120 kHz (µ=3), and 240 kHz (µ=4) or greater may be available. Within each subcarrier spacing, there are 14 symbols per time slot. For a 15 kHz SCS (µ=0), there is one time slot per subframe, 10 time slots per frame, a time slot duration of 1 millisecond (ms), a symbol duration of 66.7 microseconds (µs), and a maximum nominal system bandwidth (in MHz) of 4K FFT size of 50. For a 30 kHz SCS (µ=1), each sub-frame has two time slots, each frame has 20 time slots, the time slot duration is 0.5 ms, the symbol duration is 33.3 µs, and the maximum nominal system bandwidth (in MHz) is 100, which is the size of a 4K FFT. For a 60 kHz SCS (µ=2), each sub-frame has four time slots, each frame has 40 time slots, the time slot duration is 0.25 ms, the symbol duration is 16.7 µs, and the maximum nominal system bandwidth (in MHz) is 200, which is the size of a 4K FFT. For a 120 kHz SCS (µ=3), each sub-frame has eight time slots, each frame has 80 time slots, the time slot duration is 0.125 ms, the symbol duration is 8.33 µs, and the maximum nominal system bandwidth (in MHz) is 400, which is the size of a 4K FFT. For a 240 kHz SCS (µ=4), there are 16 time slots per subframe, 160 time slots per frame, a time slot duration of 0.0625 ms, a symbol duration of 4.17 µs, and a maximum nominal system bandwidth (in MHz) of 800 with a 4K FFT size.

[0152] In the example of Figure 7, a system parameter of 15 kHz was used. Therefore, in the time domain, the 10 ms frame is divided into 10 equal-sized sub-frames, each 1 ms long, and each sub-frame includes a time slot. In Figure 7, the horizontal axis (on the X-axis) represents time, which increases from left to right, while the vertical axis (on the Y-axis) represents frequency, which increases (or decreases) from bottom to top.

[0153] The resource grid can be used to represent time slots, each of which includes one or more concurrent time resource blocks (RBs) (also known as physical RBs (PRBs)) in the frequency domain. The resource grid is also divided into multiple resource elements (REs). An RE can correspond to a symbol length in the time domain and a subcarrier in the frequency domain. In the system parameters of Figure 7, for a normal cyclic prefix, an RB can contain 12 consecutive subcarriers in the frequency domain and 7 consecutive symbols in the time domain, for a total of 84 REs. For an extended cyclic prefix, an RB can contain 12 consecutive subcarriers in the frequency domain and 6 consecutive symbols in the time domain, for a total of 72 REs. The number of bits carried by each RE depends on the modulation scheme.

[0154] Some REs can carry reference (guide frequency) signals (RS). Reference signals may include positioning reference signals (PRS), tracking reference signals (TRS), phase tracking reference signals (PTRS), cell-specific reference signals (CRS), channel state information reference signals (CSI-RS), demodulation reference signals (DMRS), primary synchronization signals (PSS), secondary synchronization signals (SSS), synchronization signal blocks (SSB), probe reference signals (SRS), etc., depending on whether the frame structure shown is for uplink or downlink communication. Figure 7 illustrates the location of an instance of an RE carrying a reference signal (labeled "R").

[0155] The set of resource elements (REs) used for PRS transmission is called a "PRS resource". The set of resource elements can span multiple PRBs in the frequency domain and "N" (such as one or more) consecutive symbols in a time slot in the time domain. In a given OFDM symbol in the time domain, the PRS resource occupies a consecutive PRB in the frequency domain.

[0156] The transmission of PRS resources within a given PRB has a specific comb size (also known as "comb density"). The comb size "N" represents the subcarrier spacing (or frequency / tone spacing) within each symbol of the PRS resource configuration. Specifically, for a comb size "N", the PRS is transmitted in every Nth subcarrier of a symbol in the PRB. For example, for comb-4, for each symbol of the PRS resource configuration, the RE corresponding to every fourth subcarrier (such as subcarriers 0, 4, 8) is used to transmit the PRS resource. Currently, DL-PRS supports comb sizes for comb-2, comb-4, comb-6, and comb-12. Figure 7 illustrates an example PRS resource configuration for comb-4 (which spans four symbols). That is, the location of the shaded RE (marked as "R") indicates the comb-4 PRS resource configuration.

[0157] Currently, DL-PRS resources can span 2, 4, 6, or 12 consecutive symbols within time slots with a fully frequency-domain interleaved pattern. DL-PRS resources can be configured in downlink or flexible (FL) symbols in any higher-layer configuration of the time slot. For all REs of a given DL-PRS resource, there exists a constant energy per resource element (EPRE). The following are the symbol-to-symbol frequency offsets with comb sizes of 2, 4, 6, and 12 over 2, 4, 6, and 12 symbols. 2-symbol comb-2: {0, 1}; 4-symbol comb-2: {0, 1, 0, 1}; 6-symbol comb-2: {0, 1, 0, 1, 0, 1}; 12-symbol comb-2: {0, 1, 0, 1, 0, 1, 0, 1, 0, 1}; 4-symbol comb-4: {0, 2, 1, 3} (as shown in the example in Figure 7); 12-symbol comb-4: {0, 2, 1, 3, 0, 2, 1, 3, 0, 2, 1, 3}; 6-symbol comb-6: {0, 3, 1, 4, 2, 5}; 12-symbol comb-6: {0, 3, 1, 4, 2, 5, 0, 3, 1, 4, 2, 5}; and 12-symbol comb-12: {0, 6, 3, 9, 1, 7, 4, 10, 2, 8, 5, 11}.

[0158] A "PRS resource set" is a group of PRS resources used for the transmission of PRS signals, where each PRS resource has a PRS resource ID. Furthermore, the PRS resources in the PRS resource set are associated with the same TRP. The PRS resource set is identified by the PRS resource set ID and associated with a specific TRP (identified by the TRP ID). Additionally, the PRS resources in the PRS resource set have the same period, a common silence mode configuration, and the same repetition factor (such as "PRS-ResourceRepetitionFactor") across time slots. The period is the time from the first repetition of the first PRS resource in the first PRS instance to the same first repetition of the same first PRS resource in the next PRS instance. The period can have a length selected from 2^µ*{4, 5, 8, 10, 16, 20, 32, 40, 64, 80, 160, 320, 640, 1280, 2560, 5124, 10240} time slots, where µ = 0, 1, 2, 3. The repetition factor can have a length selected from {1, 2, 4, 6, 8, 16, 32} time slots.

[0159] The PRS resource ID in the PRS resource set is associated with a single beam (or beam ID) transmitted from a single TRP (where the TRP can transmit one or more beams). That is, each PRS resource in the PRS resource set can be transmitted on a different beam, so a "PRS resource," or simply a "resource," can also be referred to as a "beam." It should be noted that this has no impact on whether the UE knows the TRP and the beam transmitting the PRS.

[0160] A “PRS instance” or “PRS timing” is an instance of a periodically repeating time window (such as a group of one or more consecutive time slots) in which a PRS is expected to be sent. A PRS timing may also be referred to as a “PRS positioning timing”, “PRS positioning instance”, “positioning timing”, “positioning instance”, “positioning repetition”, or simply “timing”, “instance” or “repetition”.

[0161] A "Frequency Layer" (also simply "Frequency Layer") is a collection of one or more PRS resource sets spanning one or more TRPs, where these TRPs have the same values ​​for certain parameters. Specifically, the collection of PRS resource sets has the same subcarrier spacing and cyclic prefix (CP) type (meaning the PRS also supports all system parameters supported by the Physical Downlink Shared Channel (PDSCH)), the same point A, the same downlink PRS bandwidth value, the same starting PRB (and center frequency), and the same comb size. The point A parameter takes the value of the parameter "ARFCN-ValueNR" (where "ARFCN" represents "Absolute Radio Channel Number") and is an identifier / code specifying a pair of physical radio channels used for transmission and reception. The downlink PRS bandwidth can have a granularity of 4 PRBs, a minimum of 24 PRBs, and a maximum of 272 PRBs. Currently, up to four frequency layers have been defined, and each TRP of each frequency layer can be configured with up to two PRS resource sets.

[0162] The concept of a frequency layer is somewhat similar to that of component carriers and bandwidth portions (BWP), but the difference is that component carriers and BWPs are used by a single base station (or macrocell base station and smallcell base station) to transmit data channels, while a frequency layer is used by several (usually three or more) base stations to transmit PRS. When a UE transmits its positioning capabilities to the network, such as during an LTE Positioning Protocol (LPP) communication, the UE can indicate the number of frequency layers it can support. For example, the UE can indicate whether it can support one or four positioning frequency layers.

[0163] It should be noted that the terms "location reference signal" and "PRS" generally refer to a specific reference signal used for positioning in NR or LTE systems. However, as used herein, the terms "location reference signal" and "PRS" can also refer to any type of reference signal that can be used for positioning, such as, but not limited to, PRS, TRS, PTRS, CRS, CSI-RS, DMRS, PSS, SSS, SSB, SRS, UL-PRS, etc., as defined in LTE and NR. Furthermore, the terms "location reference signal" and "PRS" can refer to downlink, uplink, or sidelink positioning reference signals, unless the context otherwise indicates. If further differentiation of the type of PRS is required, a downlink positioning reference signal can be referred to as "DL-PRS," an uplink positioning reference signal (e.g., SRS, PTRS used for positioning) can be referred to as "UL-PRS," and a sidelink positioning reference signal can be referred to as "SL-PRS." Furthermore, for signals that can be transmitted in the downlink, uplink, and / or sidelink (e.g., DMRS), "DL", "UL", or "SL" can be added before the signal to distinguish the direction. For example, "UL-DMRS" is different from "DL-DMRS".

[0164] Figure 8 is a diagram 800 illustrating channel estimation of a multipath channel between a receiver device (e.g., any UE or base station described herein) and a transmitter device (e.g., any other UE or base station described herein) according to the present invention. The channel estimation expresses the strength of a radio frequency (RF) signal (e.g., PRS) received via the multipath channel as a function of time delay and may be referred to as the channel energy response (CER), channel impulse response (CIR), or power delay distribution (PDP) of the channel. Therefore, the horizontal axis is in units of time (e.g., milliseconds), and the vertical axis is in units of signal strength (e.g., decibels). It should be noted that a multipath channel is a channel between a transmitter and a receiver where the RF signal follows multiple paths or multipaths due to the transmission of the RF signal across multiple beams and / or the propagation characteristics of the RF signal (e.g., reflection, refraction, etc.).

[0165] In the example of Figure 8, the receiver detects / measures multiple (four) channel tap clusters. Each channel tap represents the multipath followed by the RF signal between the transmitter and the receiver. That is, the channel tap represents the multipath of the RF signal arrival. Each channel tap cluster represents the corresponding multipath that essentially follows the same path. Different clusters may exist because the RF signal is transmitted on different transmit beams (and therefore at different angles), or because of the propagation characteristics of the RF signal (e.g., it may follow different paths due to reflection), or both.

[0166] A cluster of all channel taps for a given RF signal represents a multipath channel (or simply a channel) between the transmitter and receiver. In the channel shown in Figure 8, the receiver receives a first cluster of two RF signals at the channel tap at time T1, a second cluster of five RF signals at the channel tap at time T2, a third cluster of five RF signals at the channel tap at time T3, and a fourth cluster of four RF signals at the channel tap at time T4. In the example of Figure 8, because the first cluster of RF signals arrives first at time T1, it is assumed to correspond to an RF signal transmitted on a transmit beam aligned with the line-of-sight (LOS) or shortest path. The third cluster at time T3 consists of the strongest RF signal and may correspond, for example, to an RF signal transmitted on a transmit beam aligned with a non-line-of-sight (NLOS) path. It should be noted that although Figure 8 illustrates clusters of two to five channel taps, it will be understood that a cluster may have more or fewer channel taps than shown.

[0167] Machine learning can be used to generate models that can facilitate various aspects associated with data processing. A specific application of machine learning involves the generation of measurement models for processing reference signals used for localization (e.g., PRS) (such as feature extraction, reporting of reference signal measurements (e.g., selecting which extracted features to report)).

[0168] Machine learning models are generally divided into supervised and unsupervised models. Supervised models can be further subdivided into regression or classification models. Supervised learning involves learning a function that maps inputs to outputs based on instance input-output pairs. For example, given a training dataset with two variables, age (input) and height (output), a supervised learning model can be generated to predict a person's height based on their age. In regression models, the output is continuous. An example of a regression model is linear regression, which only attempts to find a straight line that best fits the data. Extensions of linear regression include multiple linear regression (e.g., finding a best-fitting plane) and multinomial regression (e.g., finding a best-fitting curve).

[0169] Another example of a machine learning model is the decision tree model. In a decision tree model, the tree structure is defined by multiple nodes. Decisions are used to move from the 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 larger number of nodes in a decision tree model is associated with higher decision accuracy.

[0170] Another example of a machine learning model is the decision forest. Random forest is an ensemble learning technique based on decision tree construction. Random forest involves building multiple decision trees using a bootstrap dataset of the original data, and randomly selecting a subset of variables at each step of the decision trees. The model then selects the pattern of all predictions from each decision tree. By relying on a "majority wins" model, the risk of errors from a single tree is reduced.

[0171] Another example of a machine learning model is a neural network (NN). A neural network is essentially a network of mathematical equations. A neural network takes one or more input variables and, through the network of equations, produces one or more output variables. In other words, a neural network receives an input vector and returns an output vector.

[0172] Figure 9 illustrates an example neural network 900 according to the present invention. The neural network 900 includes an input layer "i" that receives "n" (one or more) inputs (denoted as "input 1", "input 2", and "input n"), one or more hidden layers (denoted as hidden layers "h1", "h2", and "h3") for processing the inputs from the input layers, and an output layer "o" that provides "m" (one or more) outputs (labeled as "output 1" and "output m"). The number of inputs "n", hidden layers "h", and outputs "m" can be the same or different. In some designs, hidden layers "h" may include linear functions and / or activation functions, with each node (denoted as a circle) of a consecutive hidden layer processing the linear function and / or activation function based on the nodes of the previous hidden layer.

[0173] In classification models, the output is discrete. An example of a classification model is logistic regression. Logistic regression is similar to linear regression, but it is used to analogize the probabilities of a finite number (usually two) of outcomes. Essentially, the logistic equation is constructed in such a way that the output value can only be "0" and "1". Another example of a classification model is the support vector machine (SVM). For example, for two classes of data, an SVM will find a hyperplane or boundary between the two classes that maximizes the margin between the two classes. There are many hyperplanes that can separate the two classes, but only one hyperplane can maximize the margin or distance between the classes. Another example of a classification model is the simple Bayesian hyperplane, which is based on Bayes' theorem. Other examples of classification models include decision trees, random forests, and neural networks, which are similar to the examples described above, but the output is discrete rather than continuous.

[0174] Unlike supervised learning, unsupervised learning is used to infer and discover patterns from input data without referring to labeled results. Two examples of unsupervised learning models include clustering and dimensionality reduction.

[0175] Clustering is an unsupervised technique involving the encapsulation 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 a procedure that reduces the number of random variables under consideration by obtaining a set of principal variables. More simply, dimensionality reduction is a procedure that reduces the dimension of a feature set (or, more simply, reduces the number of features). Most dimensionality reduction techniques can be categorized as feature elimination or feature extraction. An example of dimensionality reduction is called Principal Component Analysis (PCA). In its simplest sense, PCA involves projecting higher-dimensional data (e.g., three-dimensional) into a smaller space (e.g., two-dimensional). This results in a lower data dimensionality (e.g., two-dimensional instead of three-dimensional) while preserving all the original variables in the model.

[0176] Regardless of the machine learning model used, at a high level, (e.g., implemented by a processing system such as processor 332, 384 or 394) the machine learning module can be configured to iteratively analyze training input data (e.g., measurements of reference signals to / from various target UEs) and correlate the training input data with an output data set (e.g., a set of possible candidate locations for various target UEs), so that the same output data set can be determined later when similar input data (e.g., from other target UEs at the same or similar locations) is presented.

[0177] NR supports Radio Frequency Fingerprint (RFFP)-based positioning, a positioning technique that uses an RFFP captured by a mobile device to determine the location of the mobile device. The RFFP can be a bar graph of Received Signal Strength Indicator (RSSI), CER, CIR, PDP, or Channel Frequency Response (CFR). The RFFP can represent a single channel received from a transmitter (e.g., PRS), all channels received from a specific transmitter, or all channels detectable at a receiver. The location of the RFFP measured by the mobile device (e.g., UE) and the transmitter associated with the measured RFFP (i.e., the transmitter that sends the RF signal measured by the mobile device to determine the RFFP) can be used to determine (e.g., triangulation) the location of the mobile device.

[0178] Machine learning-based positioning technology has been shown to offer superior positioning performance compared to traditional positioning methods. In machine learning-RFFP-based positioning, a machine learning model (e.g., Neural Network 900) takes the RFFP of a downlink reference signal (e.g., PRS) as input and outputs a positioning measurement (e.g., ToA, RSTD) or the mobile device location corresponding to the input RFFP. The machine learning model (e.g., Neural Network 900) is trained using "real-world" (i.e., known) positioning measurements or mobile device locations as references (i.e., expected) outputs to the RFFP training set.

[0179] For example, a machine learning model can be trained to determine the RSTD measurement of a pair of TRPs from the RFFP of the PRS sent by the TRP. The reference output used to train such a model would be the correct (i.e., real-world) RSTD measurement of the location of the mobile device when it obtains the RFFP measurement of the PRS. The network (e.g., a location server) can determine the expected RSTD of this pair of TRPs based on the known location of the mobile device and the known location of the (measured) TRPs involved. The known location of the mobile device can be determined based on multiple reported RSTD measurements and / or any other measurements (e.g., GPS measurements) reported by the mobile device.

[0180] Figure 10 is a schematic diagram 1000 illustrating the use of a machine learning model for RFFP-based positioning according to the present invention. In the example of Figure 10, during the "offline" phase, RFFPs (e.g., CER / CIR / CFRs) captured by the mobile device are stored in a database. This database may be located at the mobile device or a network entity (e.g., a location server), and each RFFP may include measurements of RF signals (or channels or links) transmitted by one or more transmitters (shown in Figure 10 as base stations 1 to N (i.e., "BS 1" to "BS N")). For UE-based downlink RFFP (DL-RFFP) positioning, the network (e.g., the location server) configures the base stations to transmit downlink reference signals (e.g., PRS) to the mobile device, and the RFFP is the CER / CIR / CFR of the configured downlink reference signal detected by the mobile device.

[0181] Each measured RFFP is associated with a known location of the mobile device when the RFFP is measured (shown as location 1 to location L in Figure 10, i.e., "Pos 1" to "Pos L")). The location of the mobile device can be obtained via another positioning technique (such as discussed above with reference to Figure 5). It should be noted that although Figure 10 illustrates the RFFP information of a single mobile device, it will be understood that the RFFP information of multiple mobile devices can be collected and stored in a database.

[0182] Based on information acquired during the offline phase, a machine learning model (e.g., neural network 900) is trained to predict the location of the mobile device based on the RFFP measured by the mobile device. More specifically, the training set of RFFP measurements is used as input to the machine learning model, and the known location of the mobile device at the time of RFFP acquisition is used as a reference output. After training, during the "online" phase, the trained machine learning model can be used to predict (infer) the location of the mobile device (denoted as "Pos M") based on the RFFP currently measured by the mobile device. For UE-based RFFP positioning, the network (e.g., a location server) provides the trained machine learning model to the mobile device. For UE-assisted positioning, the mobile device can provide RFFP measurements to the network for processing.

[0183] It should be noted that although Figure 10 illustrates the use of an RFFP-based machine learning model to estimate the UE's location, the output (or extracted features) of the machine learning model can be replaced with localization measurements based on the input RFFP, such as RSTD measurements, ToA measurements, DL-AoD measurements, etc.

[0184] For machine learning models generated by the UE, it would be beneficial for the network to monitor the performance of the machine learning model and report it back to the machine learning model maintenance engine of the UE or the UE vendor. This would enable the UE or UE vendor to update the machine learning model or apply online training / fine-tuning. This is possible because the network can check the performance of some deployed reference UEs with known locations. Therefore, this application provides technology for network monitoring of the location performance of a UE-based RFFP machine learning model and reporting that performance back to the UE, the UE vendor, or another entity of interest.

[0185] Figure 11 is a schematic diagram 1100 illustrating a network-based report on the performance monitoring of the DL-RFFP machine learning model according to the present invention. As shown in Figure 11, the UE provides its estimated location (determined using the RFFP machine learning (ML) model (labeled "RFFP ML") in its local storage area) to the location server and / or the UE's serving TRP. For example, the UE may provide its estimated location to the location server via the serving TRP in the LPP location information message (as shown in Figure 6C). It should be noted that the estimated location shown in Figure 11 may be the estimated geographic location of the UE based on the input RFFP, or based on one or more location measurements of the input RFFP, such as RSTD measurement, ToA measurement, DL-AoD measurement, etc.

[0186] The location server and / or TRP monitor the performance of the UE-based RFFP machine learning model (the "RFFP ML" module in Figure 11) and report the performance monitoring results to the UE, UE vendor, location server (where the TRP performs the monitoring), and / or machine learning model maintenance engine in the form of an RFFP machine learning model performance report (labeled "Performance Report" in Figure 11). The RFFP machine learning model performance report can be triggered by the TRP and / or location server based on the occurrence of certain events, or the report can be triggered periodically. The RFFP machine learning model performance report can be delivered immediately, or multiple reports can be accumulated over a period of time for batch delivery.

[0187] In one instance, the RFFP machine learning model performance report may include summary statistics across multiple location inference moments (i.e., multiple executions or applications of the machine learning model to RFFP measurements) or during a specific time frame (time window). For example, the report may indicate the average location error across multiple location inference moments or during a specific time frame. This can also be understood as rewarding training data assistance in the form of signal delivery, which allows the UE (or UE provider) to update the machine learning model through reinforcement training.

[0188] In one instance, the RFFP machine learning model performance report may include a per-location inference timing report. For example, each location inference timing may be associated with the RFFP location inference error compared to (noisy) real-world conditions and the confidence level of the error value. This can be understood as training data assistance in the form of (noisy) real-world signal transmission, which allows the UE (or UE vendor) to update the machine learning model via supervised training.

[0189] In one case, the RFFP machine learning model performance report may include a report for each inference timing, but may only include certain timings where the performance is worse than a threshold.

[0190] For network-generated machine learning models, it would be beneficial for the UE to report a measure of their determinism (or confidence) in location estimations (e.g., the UE's estimated geographic location or estimated location measurement) based on the DL-RFFP machine learning model. This would enable the network (e.g., a location server) to update the network-generated machine learning model, switch between different location methods, or fuse DL-RFFP-based location estimations with location estimations based on other location methods (e.g., DL-TDOA, multiple RTT, etc.).

[0191] Therefore, this application provides a technique for a UE to calculate a confidence value of a location estimate generated by a DL-RFFP machine learning model and report the confidence value (and the location estimate) to the network. For a machine learning model generated by the UE, the UE can provide the network with the confidence value of the output of its calculated model. The UE can provide this capability, for example (e.g., as shown in FIG. 6A), in an LPP provision capability message. For a machine learning model generated by the network, the network can notify the UE (e.g., in an LPP provision auxiliary information message and / or an LPP provision location information message) that the confidence report is part of the downloaded machine learning model design. In either case, the UE performs DL-RFFP-based positioning (using the RFFP machine learning model) and reports the confidence value and the location estimate configured in the auxiliary information (e.g., as shown in the LPP provision auxiliary information message in FIG. 6B).

[0192] Figure 12 is a schematic diagram 1200 illustrating a UE reporting a confidence measure of a location estimate based on a DL-RFFP machine learning model according to the present invention. As shown in Figure 12, the UE reports the estimated location and a confidence measure representing the confidence of the estimated location to its serving TRP and / or location server. For example, the UE may provide the estimated location and confidence measure to the location server via the serving TRP in a location information message provided by the LPP (e.g., as shown in Figure 6C). It should be noted that the estimated location shown in Figure 12 may be the estimated geographic location of the UE based on the RFFP machine learning model (labeled "RFFP ML"), or based on one or more location measurements based on the RFFP, such as RSTD measurement, ToA measurement, DL-AoD measurement, etc. As shown in Figure 12, the estimated location is represented as and the confidence measure is the associated covariance:

[0193] This case considers the following two scenarios for UE-based confidence calculation: (1) a machine learning model generated by the UE, and (2) a machine learning model generated by the network. For the machine learning model generated by the UE, the machine learning model may be developed by the UE vendor, and therefore the network may not be aware of the UE's ability to calculate confidence metrics. The UE should notify the network of its ability to provide confidence metrics with DL-RFFP positioning estimation (e.g., in the LPP capability provision message).

[0194] For the machine learning model generated by the network, the machine learning model can be developed by the network provider and downloaded by the UE. In this case, the network will need to notify the UE that the machine learning model generates a confidence metric and how the UE should report the confidence metric to the network. The network can provide this information, for example (as shown in Figure 6B) in an LPP providing auxiliary information message or (as shown in Figure 6C) in an LPP requesting location information message.

[0195] In one scenario, for a machine learning model generated by the UE, the location server may request the target UE to provide its ability to calculate a confidence metric for the DL-RFFP positioning estimate using, for example, the LPP capability transmission procedure 600 in Figure 6A. The target UE may provide its ability to calculate the confidence metric for the DL-RFFP positioning estimate using, for example, the LPP capability transmission procedure 600 in Figure 6A or the LPP capability indication procedure 610 in Figure 6A.

[0196] In one instance, for a machine learning model generated by the UE, the capability message indicating whether the UE can compute a confidence metric may include a description of the type and format of the confidence metric. For example, the confidence metric may be a confidence interval of the average value of the DL-RFFP positioning estimates, including the interval and the range of resolution. Assume the UE obtains a sequence of redundant DL-RFFP positioning estimates at a given UE location and computes their statistics and confidence intervals. As another example, the confidence metric may be the inverse of the covariance of the DL-RFFP positioning estimates, including the interval and the range of resolution.

[0197] For a machine learning model generated by the network, the target UE requests the location server to provide auxiliary information related to how the UE should use the provided machine learning model to calculate a confidence metric and report the confidence metric back to the location server. The UE may send such a request in an LPP request for auxiliary information message (e.g., as in LPP auxiliary information transmission procedure 630 in Figure 6B). The location server may use, for example, LPP auxiliary information transmission procedure 630 or LPP auxiliary information transmission procedure 640 to provide the UE with auxiliary information related to the confidence metric calculation.

[0198] In one instance, for a machine learning model generated by a network, auxiliary information can be provided using machine learning techniques to offer information related to the calculation of a confidence metric. For example, this information could be a description of the machine learning model output corresponding to the confidence metric and / or the resolution and range of the confidence metric.

[0199] Alternatively or supplementarily, auxiliary information may use non-machine learning techniques to provide information related to the calculation of confidence metrics. For example, a confidence metric may be the confidence interval of the average DL-RFFP positioning estimate, including the interval and the expected range of resolution. Assume that the UE obtains a sequence of redundant DL-RFFP positioning estimates for a given UE positioning, and calculate their statistics and confidence intervals.

[0200] In one scenario, the auxiliary information message can provide information related to the confidence measurement report, such as reporting flags, reporting frequency and conditions, and the number of reports. Reporting flags can be included in the auxiliary information message to trigger the target UE to report the confidence measurement of the DL-RFFP location estimate. Reporting frequency and conditions can indicate whether the UE is expected to provide a confidence value periodically (periodic triggering) or in response to an event (event triggering). For example, the UE may only be expected to provide a confidence value when the confidence level drops below a certain threshold.

[0201] Regarding the number of reports, the auxiliary data message can configure the target UE to (1) report a single confidence metric for each RFFP location instance, (2) use a report to report a large number (batch) of confidence metrics for a group of RFFP location instances, (3) use a report to report statistics of confidence metrics for a group of RFFP location instances (e.g., average confidence values ​​over a time window of location measurements), and / or (4) report only the confidence metrics of RFFP location instances with values ​​below a threshold in a single report.

[0202] Figure 13 illustrates an example method 1300 of communication according to the present case. In one case, method 1300 may be performed by a network entity (e.g., a location server or a TRP).

[0203] At 1310, the network entity receives a location information message from the UE, the location information message including one or more location estimates derived by the UE during one or more location inference times of a machine learning model, wherein the machine learning model is applied to one or more measurements of the wireless channel between the UE and the network node during each of the one or more inference times. In one configuration, when the network entity is a TRP, operation 1310 may be performed by one or more WWAN transceivers 350, one or more processors 384, memory 386, and / or positioning components 388, any one or all of which can be considered as components for performing the operation. In another configuration, when the network entity is a location server, operation 1310 may be performed by one or more network transceivers 390, one or more processors 394, memory 396, and / or positioning components 398, any one or all of which can be considered as components for performing the operation.

[0204] At 1320, the network entity (e.g., to the UE, UE vendor, machine learning model maintenance engine, etc.) sends a performance report instructing the machine learning model to derive one or more location estimation states at least during one or more location inference times. In one state, if the network entity is a TRP, operation 1320 may be performed by one or more WWAN transceivers 350, one or more processors 384, memory 386, and / or positioning components 388, any one or all of which can be considered as components for performing the operation. In one state, if the network entity is a location server, operation 1320 may be performed by one or more network transceivers 390, one or more processors 394, memory 396, and / or positioning components 398, any one or all of which can be considered as components for performing the operation.

[0205] It will be understood that the technical advantage of method 1300 is to provide the UE with performance feedback of the machine learning model, thereby enabling the UE to improve the positioning performance of the machine learning model.

[0206] Figure 14 illustrates an example method 1400 of wireless communication according to the present invention. In one embodiment, method 1400 can be performed by a UE (e.g., any UE described herein).

[0207] At 1410, the UE sends a capability provision message to the location server, which instructs the UE to report a confidence metric associated with a location estimate, which is derived by the UE based on a machine learning model applied to one or more measurements of the wireless channel between the UE and the network node. In one instance, operation 1410 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340, and / or positioning unit 342, any one or all of which can be considered as a component for performing the operation.

[0208] At 1420, the UE sends a location information message to the location server, which includes a location estimate and a confidence measure. In one state, operation 1420 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340 and / or positioning unit 342, any one or all of which can be considered as a component for performing the operation.

[0209] Figure 15 illustrates an example method 1500 of wireless communication according to the present invention. In one embodiment, method 1500 can be performed by a UE (e.g., any UE described herein).

[0210] At 1510, the UE sends a Request for Assistance message to the location server, requesting the location server to configure the UE to report a confidence metric associated with the location estimate, which is derived by the UE based on a machine learning model of one or more measurements applied to the wireless channel between the UE and the network node. In one state, operation 1510 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340, and / or positioning unit 342, any one or all of which can be considered as the component for performing the operation.

[0211] At 1520, the UE receives a Provide Assistance Information message from the location server, which configures the UE to report at least a confidence metric. In one state, operation 1520 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340 and / or positioning unit 342, any one or all of which may be considered as a component for performing the operation.

[0212] At 1530, the UE sends a location information message to the location server, which includes a location estimate and a confidence measure. In one state, operation 1530 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340 and / or positioning unit 342, any one or all of which can be considered as a component for performing the operation.

[0213] It will be understood that the technical advantage of methods 1400 and 1500 is that they enable the UE to report confidence metrics associated with the location estimate based on machine learning models.

[0214] As can be seen in the detailed description above, different features are grouped together in the examples. This manner of disclosure should not be construed as an intention for the example clauses to have more features than explicitly mentioned in each clause. Rather, the various forms of this document may include fewer features than those of the individual example clauses disclosed. Therefore, the following clauses should be considered as included in the specification, whereby each clause may be considered a separate example. Although each subordinate clause may refer in the clause to a specific combination of one of the other clauses, the form of that subordinate clause is not limited to that specific combination. It should be understood that other example clauses may also include combinations of subordinate clause forms with the subject matter of any other subordinate or independent clause, or combinations of any feature with other subordinate and independent clauses. The various forms disclosed herein explicitly include these combinations unless explicitly stated or readily inferred that a particular combination is not intended (e.g., contradictory forms, such as defining an element as both an electrical insulator and an electrical conductor). Furthermore, it is also intended that the form of a clause may be included in any other independent clause, even if that clause is not directly subordinate to that independent clause.

[0215] The following numbered clauses describe examples of implementation methods:

[0216] Clause 1. A communication method performed by a network entity, comprising: receiving a location information provision message from a user equipment (UE), the location information provision message including one or more location estimates derived by the UE during one or more location inference times of a machine learning model, wherein the machine learning model is applied to one or more measurements of a radio channel between the UE and a network node during each of the one or more location inference times; and sending a performance report indicating the performance of the machine learning model in deriving the one or more location estimates at least during the one or more location inference times.

[0217] Clause 2. The method of Clause 1, wherein: the performance report is sent periodically or in response to an event trigger, or in response to the receipt of the location information message.

[0218] Clause 3. The method according to any one of Clauses 1 to 2, wherein: the performance report is sent in response to the number of a plurality of location information messages received exceeding a threshold, the performance report instructing the performance of the machine learning model in deriving a plurality of location estimation patterns received in the plurality of location information messages, and the plurality of location estimations are derived by the UE during a plurality of location inference times of the machine learning model.

[0219] Clause 4. The method according to Clause 3, wherein: the performance report indicates a summary of the plurality of location inference opportunities, or the performance report indicates a summary of the location inference opportunities within a time window among the plurality of location inference opportunities.

[0220] Clause 5. The method according to any one of Clauses 3 to 4, wherein the performance report indicates: the location inference error of each of the plurality of location inference timings, and the confidence level of the location inference error.

[0221] Clause 6. The method according to any one of Clauses 3 to 5, wherein the performance report indicates the positioning inference error of each positioning inference timing having an error above a threshold among the plurality of positioning inference timings.

[0222] Article 7. The method of any one of Articles 1 to 6, wherein the performance report is sent to: the UE, the UE vendor, the machine learning model maintenance engine or any combination thereof.

[0223] Article 8. The method according to any one of Articles 1 to 7, wherein the network node is: a Transmitting Receiver Point (TRP) or a second UE.

[0224] Article 9. The method of any one of Articles 1 to 8, wherein the network entity is: a location server or a TRP serving the UE.

[0225] Clause 10. The method according to any one of Clauses 1 to 9, wherein: the one or more measurements include one or more location measurements, one or more radio frequency fingerprint (RFFP) measurements, or both, of the radio channel between the UE and the network node, and the one or more location estimates include one or more geolocation estimates of the UE.

[0226] Clause 11. The method according to any one of Clauses 1 to 9, wherein: the one or more measurements include one or more radio frequency fingerprint (RFFP) measurements of the radio channel between the UE and the network node, and the one or more location estimates include one or more location measurements of the radio channel between the UE and the network node.

[0227] Clause 12. A wireless communication method performed by a user equipment (UE), comprising: sending a capability provision message to a location server, the capability provision message instructing the UE to report a confidence metric associated with a location estimate, the location estimate being derived by the UE based on a machine learning model of one or more measurements applied to a wireless channel between the UE and a network node; and sending a location information message to the location server, the location information message including the location estimate and the confidence metric.

[0228] Clause 13. The method according to Clause 12 also includes: receiving a request capability message from the location server, the request capability message requesting the UE to report whether the UE is able to report a confidence metric associated with the location estimation.

[0229] Clause 14. The method according to any one of Clauses 12 to 13, wherein the providing capability message indicates the type of the confidence measure and the format of the confidence measure.

[0230] Clause 15. The method according to Clause 14, wherein the type of the confidence measure includes: a confidence interval comprising the average of a plurality of location estimates, or the inverse covariance of the location estimates.

[0231] Clause 16. The method of any one of Clauses 12 to 15, wherein the machine learning model is generated by the UE, the UE vendor, the network entity, or the network entity vendor.

[0232] Article 17. The method according to any one of Articles 12 to 16, wherein the network node is: a Transmitting Receiver Point (TRP) or a second UE.

[0233] Clause 18. The method according to any one of Clauses 12 to 17, wherein: the one or more measurements include one or more location measurements, one or more radio frequency fingerprint (RFFP) measurements, or both, of the radio channel between the UE and the network node, and the one or more location estimates include one or more geolocation estimates of the UE.

[0234] Clause 19. The method according to any one of Clauses 12 to 17, wherein: the one or more measurements include one or more radio frequency fingerprint (RFFP) measurements of the radio channel between the UE and the network node, and the one or more location estimates include one or more location measurements of the radio channel between the UE and the network node.

[0235] Clause 20. A method of wireless communication performed by a user equipment (UE), comprising: sending a request for assistance data message to a location server, the request for assistance data message requesting the location server to configure the UE to report a confidence metric associated with a location estimate, the location estimate being derived by the UE based on a machine learning model applied to one or more measurements of a wireless channel between the UE and a network node; receiving a provide assistance data message from the location server, the provide assistance data message configuring the UE to report at least the confidence metric; and sending a location information message to the location server, the location information message including the location estimate and the confidence metric.

[0236] Clause 21. The method according to Clause 20, wherein the information providing supplementary data indicates the type of the confidence measure and the format of the confidence measure.

[0237] Clause 22. The method according to Clause 21, wherein the type of the confidence measure includes: a confidence interval comprising the average of a plurality of location estimates, or the inverse covariance of the location estimates.

[0238] Clause 23. The method according to any one of Clauses 20 to 22, wherein the information provided includes: a reporting flag, reporting conditions and reporting quantity that triggers the UE to report the confidence metric.

[0239] Clause 24. The method of Clause 23, wherein the reporting conditions are periodic or event-triggered.

[0240] Clause 25. The method according to any one of Clauses 23 to 24, wherein the number of reports includes: a single confidence measure, a batch of confidence measures, a statistical analysis of a plurality of confidence measures, or a confidence measure of a location estimate associated only with an error greater than a threshold.

[0241] Clause 26. The method of any one of Clauses 20 to 25, wherein the machine learning model is generated by a network entity.

[0242] Article 27. The method according to any one of Articles 20 to 26, wherein the network node is: a Transmitting Receiver Point (TRP) or a second UE.

[0243] Clause 28. The method according to any one of Clauses 20 to 27, wherein: the one or more measurements include one or more location measurements, one or more radio frequency fingerprint (RFFP) measurements, or both, of the radio channel between the UE and the network node, and the one or more location estimates include one or more geolocation estimates of the UE.

[0244] Clause 29. The method according to any one of Clauses 20 to 27, wherein: the one or more measurements include one or more radio frequency fingerprint (RFFP) measurements of the radio channel between the UE and the network node, and the one or more location estimates include one or more location measurements of the radio channel between the UE and the network node.

[0245] Clause 30. A network entity comprising: a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: receive, via the at least one transceiver, a location information provision message from a user equipment (UE), the location information provision message including one or more location estimates derived by the UE during one or more location inference times of a machine learning model, wherein the machine learning model is applied to one or more measurements of a radio channel between the UE and a network node during each of the one or more location inference times; and transmit via the at least one transceiver a performance report indicating the performance of the machine learning model in deriving the one or more location estimates at least during the one or more location inference times.

[0246] Article 31. A network entity pursuant to Article 30, wherein: the performance report is sent periodically or in response to an event trigger, or in response to the receipt of the location information message.

[0247] Clause 32. A network entity pursuant to any one of Clauses 30 to 31, wherein: the performance report is sent in response to the number of a plurality of location information messages received exceeding a threshold, the performance report instructing the performance of the machine learning model in deriving a plurality of location estimation patterns received in the plurality of location information messages, and the plurality of location estimations are derived by the UE during a plurality of location inference times of the machine learning model.

[0248] Clause 33. A network entity pursuant to Clause 32, wherein: the performance report indicates a summary of the plurality of location inference opportunities, or the performance report indicates a summary of the plurality of location inference opportunities within a time window.

[0249] Clause 34. A network entity pursuant to any of Clauses 32 to 33, wherein the performance report indicates: the location inference error of each of the plurality of location inference times, and the confidence level of the location inference error.

[0250] Clause 35. A network entity pursuant to any of Clauses 32 to 34, wherein the performance report indicates the location inference error for each location inference moment having an error above a threshold among the plurality of location inference moments.

[0251] Clause 36. A network entity pursuant to any of Clauses 30 to 35, wherein the performance report is sent to: the UE, the UE vendor, the machine learning model maintenance engine, or any combination thereof.

[0252] Article 37. A network entity pursuant to any one of Articles 30 to 36, wherein the network node is: a Transmitting Receiver Point (TRP) or a second UE.

[0253] Article 38. A network entity pursuant to any one of Articles 30 to 37, wherein the network entity is: a location server or a TRP serving the UE.

[0254] Clause 39. A network entity pursuant to any one of Clauses 30 to 38, wherein: the one or more measurements include one or more location measurements, one or more radio frequency fingerprint (RFFP) measurements, or both, of the radio channel between the UE and the network node, and the one or more location estimates include one or more geolocation estimates of the UE.

[0255] Clause 40. A network entity pursuant to any one of Clauses 30 to 38, wherein: the one or more measurements include one or more radio frequency fingerprint (RFFP) measurements of the radio channel between the UE and the network node, and the one or more location estimates include one or more location measurements of the radio channel between the UE and the network node.

[0256] Clause 41. A user equipment (UE) comprising: a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: transmit a capability provision message to a location server via the at least one transceiver, the capability provision message instructing the UE to report a confidence metric associated with a location estimate, the location estimate being derived by the UE based on a machine learning model of one or more measurements applied to a wireless channel between the UE and a network node; and transmit a location information message to the location server via the at least one transceiver, the location information message including the location estimate and the confidence metric.

[0257] Clause 42. The UE according to Clause 41, wherein the at least one processor is also configured to receive a request capability message from the location server via the at least one transceiver, the request capability message requesting the UE to report whether the UE is able to report a confidence metric associated with the location estimation.

[0258] Clause 43. A UE pursuant to any of Clauses 41 to 42, wherein the providing capability message indicates the type of the confidence metric and the format of the confidence metric.

[0259] Clause 44. The UE pursuant to Clause 43, wherein the type of the confidence measure includes: a confidence interval comprising the average of a plurality of location estimates, or the inverse covariance of the location estimate.

[0260] Clause 45. A UE pursuant to any of Clauses 41 to 44, wherein the machine learning model is generated by the UE, the UE vendor, the network entity, or the network entity vendor.

[0261] Article 46. A UE pursuant to any one of Articles 41 to 45, wherein the network node is: a Transmitting Receiver Point (TRP) or a second UE.

[0262] Clause 47. A UE pursuant to any one of Clauses 41 to 46, wherein: the one or more measurements include one or more location measurements, one or more radio frequency fingerprint (RFFP) measurements, or both, of the radio channel between the UE and the network node, and the one or more location estimates include one or more geolocation estimates of the UE.

[0263] Clause 48. A UE pursuant to any one of Clauses 41 to 46, wherein: the one or more measurements include one or more radio frequency fingerprint (RFFP) measurements of the radio channel between the UE and the network node, and the one or more location estimates include one or more location measurements of the radio channel between the UE and the network node.

[0264] Clause 49. A user equipment (UE) comprising: a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: transmit a request for assistance information message to a location server via the at least one transceiver, the request for assistance information message requesting the location server to configure the UE to report a confidence metric associated with a location estimate, the location estimate being derived by the UE based on a machine learning model of one or more measurements applied to a wireless channel between the UE and a network node; receive a provision for assistance information message from the location server via the at least one transceiver, the provision for assistance information message configuring the UE to at least report the confidence metric; and transmit a location information message to the location server via the at least one transceiver, the location information message including the location estimate and the confidence metric.

[0265] Clause 50. The UE pursuant to Clause 49, wherein the information provided indicates the type of confidence measure and the format of the confidence measure.

[0266] Clause 51. The UE pursuant to Clause 50, wherein the type of the confidence measure includes: a confidence interval comprising the average of a plurality of location estimates, or the inverse covariance of the location estimates.

[0267] Clause 52. A UE pursuant to any of Clauses 49 to 51, wherein the information provided includes: a reporting flag that triggers the UE to report the confidence metric, reporting conditions, and the number of reports.

[0268] Clause 53. UE pursuant to Clause 52, wherein the reporting condition is periodic or event-triggered.

[0269] Clause 54. A UE pursuant to any of Clauses 52 to 53, wherein the number of reports includes: a single confidence metric, a batch of confidence metrics, a statistic of multiple confidence metrics, or a confidence metric of a location estimate associated only with an error greater than a threshold.

[0270] Clause 55. A UE pursuant to any of Clauses 49 to 54, wherein the machine learning model is generated by a network entity.

[0271] Article 56. A UE pursuant to any of Articles 49 to 55, wherein the network node is: a Transmitting Receiver Point (TRP) or a second UE.

[0272] Clause 57. A UE pursuant to any one of Clauses 49 to 56, wherein: the one or more measurements include one or more location measurements, one or more radio frequency fingerprint (RFFP) measurements, or both, of the radio channel between the UE and the network node, and the one or more location estimates include one or more geolocation estimates of the UE.

[0273] Clause 58. A UE pursuant to any one of Clauses 49 to 56, wherein: the one or more measurements include one or more radio frequency fingerprint (RFFP) measurements of the radio channel between the UE and the network node, and the one or more location estimates include one or more location measurements of the radio channel between the UE and the network node.

[0274] Clause 59. A network entity comprising: means for receiving a location information provision message from a user equipment (UE), the location information provision message including one or more location estimates derived by the UE during one or more location inference times of a machine learning model, wherein the machine learning model is applied to one or more measurements of a radio channel between the UE and a network node during each of the one or more location inference times; and means for transmitting a performance report indicating the performance of the machine learning model in deriving the one or more location estimates at least during the one or more location inference times.

[0275] Article 60. A network entity pursuant to Article 59, wherein: the performance report is sent periodically or in response to an event trigger, or in response to receiving a location information message.

[0276] Clause 61. A network entity pursuant to any one of Clauses 59 to 60, wherein: the performance report is sent in response to the number of a plurality of location information messages received exceeding a threshold, the performance report instructing the performance of the machine learning model in deriving a plurality of location estimation patterns received in the plurality of location information messages, and the plurality of location estimations are derived by the UE during a plurality of location inference times of the machine learning model.

[0277] Article 62. A network entity pursuant to Article 61, wherein: the performance report indicates a summary of the plurality of location inference opportunities, or the performance report indicates a summary of location inference opportunities within a time window among the plurality of location inference opportunities.

[0278] Clause 63. A network entity pursuant to any one of Clauses 61 to 62, wherein the performance report indicates: the location inference error of each of the plurality of location inference times, and the confidence level of the location inference error.

[0279] Clause 64. A network entity pursuant to any of Clauses 61 to 63, wherein the performance report indicates the location inference error for each location inference moment having an error above a threshold among the plurality of location inference moments.

[0280] Clause 65. A network entity pursuant to any of Clauses 59 to 64, wherein the performance report is sent to: the UE, the UE vendor, the machine learning model maintenance engine, or any combination thereof.

[0281] Article 66. A network entity pursuant to any one of Articles 59 to 65, wherein the network node is: a Transmitting Receiver Point (TRP) or a second UE.

[0282] Article 67. A network entity pursuant to any one of Articles 59 to 66, wherein the network entity is: a location server or a TRP serving the UE.

[0283] Clause 68. A network entity pursuant to any one of Clauses 59 to 67, wherein: the one or more measurements include one or more location measurements, one or more radio frequency fingerprint (RFFP) measurements, or both, of the radio channel between the UE and the network node, and the one or more location estimates include one or more geolocation estimates of the UE.

[0284] Clause 69. A network entity pursuant to any one of Clauses 59 to 67, wherein: the one or more measurements include one or more radio frequency fingerprint (RFFP) measurements of the radio channel between the UE and the network node, and the one or more location estimates include one or more location measurements of the radio channel between the UE and the network node.

[0285] Clause 70. A user equipment (UE) comprising: means for sending a capability provision message to a location server, the capability provision message instructing the UE to report a confidence metric associated with a location estimate, the location estimate being derived by the UE based on a machine learning model of one or more measurements applied to a wireless channel between the UE and a network node; and means for sending a location information message to the location server, the location information message including the location estimate and the confidence metric.

[0286] Clause 71. The UE pursuant to Clause 70 also includes: a component for receiving a request capability message from the location server, the request capability message requesting the UE to report whether the UE is able to report a confidence metric associated with the location estimation.

[0287] Clause 72. A UE pursuant to any of Clauses 70 to 71, wherein the providing capability message indicates the type of the confidence metric and the format of the confidence metric.

[0288] Clause 73. The UE pursuant to Clause 72, wherein the type of the confidence measure includes: a confidence interval comprising the average of a plurality of location estimates, or the inverse covariance of the location estimate.

[0289] Clause 74. A UE pursuant to any of Clauses 70 to 73, wherein the machine learning model is generated by the UE, the UE vendor, the network entity, or the network entity vendor.

[0290] Article 75. A UE pursuant to any of Articles 70 to 74, wherein the network node is: a Transmitting Receiver Point (TRP) or a second UE.

[0291] Clause 76. A UE pursuant to any one of Clauses 70 to 75, wherein: the one or more measurements include one or more location measurements, one or more radio frequency fingerprint (RFFP) measurements, or both, of the radio channel between the UE and the network node, and the one or more location estimates include one or more geolocation estimates of the UE.

[0292] Clause 77. A UE pursuant to any one of Clauses 70 to 75, wherein: the one or more measurements include one or more radio frequency fingerprint (RFFP) measurements of the radio channel between the UE and the network node, and the one or more location estimates include one or more location measurements of the radio channel between the UE and the network node.

[0293] Clause 78. A user equipment (UE) comprising: means for sending a request for assistance information message to a location server, the request for assistance information message requesting the location server to configure the UE to report a confidence metric associated with a location estimate, the location estimate being derived by the UE based on a machine learning model of one or more measurements applied to a wireless channel between the UE and a network node; means for receiving a provision for assistance information message from the location server, the provision for assistance information message configuring the UE to report at least the confidence metric; and means for sending a location information message to the location server, the location information message including the location estimate and the confidence metric.

[0294] Clause 79. The UE pursuant to Clause 78, wherein the information provided indicates the type of confidence measure and the format of the confidence measure.

[0295] Clause 80. The UE pursuant to Clause 79, wherein the type of the confidence measure includes: a confidence interval comprising the average of a plurality of location estimates, or the inverse covariance of the location estimate.

[0296] Clause 81. A UE pursuant to any of Clauses 78 to 80, wherein the information provided includes: a reporting flag that triggers the UE to report the confidence metric, reporting conditions, and the number of reports.

[0297] Article 82. UE pursuant to Article 81, wherein the reporting condition is periodic or event-triggered.

[0298] Clause 83. A UE pursuant to any of Clauses 81 to 82, wherein the number of reports includes: a single confidence metric, a batch of confidence metrics, a statistic of multiple confidence metrics, or a confidence metric of a location estimate associated only with an error greater than a threshold.

[0299] Clause 84. A UE pursuant to any of Clauses 78 to 83, wherein the machine learning model is generated by a network entity.

[0300] Article 85. A UE pursuant to any one of Articles 78 to 84, wherein the network node is: a Transmitting Receiver Point (TRP) or a second UE.

[0301] Clause 86. A UE pursuant to any one of Clauses 78 to 85, wherein: the one or more measurements include one or more location measurements, one or more radio frequency fingerprint (RFFP) measurements, or both, of the radio channel between the UE and the network node, and the one or more location estimates include one or more geolocation estimates of the UE.

[0302] Clause 87. A UE pursuant to any one of Clauses 78 to 85, wherein: the one or more measurements include one or more radio frequency fingerprint (RFFP) measurements of the radio channel between the UE and the network node, and the one or more location estimates include one or more location measurements of the radio channel between the UE and the network node.

[0303] Clause 88. A non-transitory computer-readable medium storing computer-executable instructions, which, when executed by a network entity, cause the network entity to: receive a location information message from a user equipment (UE), the location information message including one or more location estimates derived by the UE during one or more location inference times of a machine learning model, wherein the machine learning model is applied to one or more measurements of a radio channel between the UE and a network node during each of the one or more location inference times; and send a performance report instructing the machine learning model on the performance of deriving the one or more location estimates at least during the one or more location inference times.

[0304] Clause 89. Non-transitory computer-readable media pursuant to Clause 88, wherein: the performance report is sent periodically or in response to an event trigger, or in response to the receipt of a location information message.

[0305] Clause 90. A non-transitory computer-readable medium pursuant to any one of Clauses 88 to 89, wherein: the performance report is sent in response to the number of a plurality of location-providing messages received exceeding a threshold, the performance report instructing the performance of the machine learning model in deriving a plurality of location estimation patterns received in the plurality of location-providing messages, and the plurality of location estimations are derived by the UE during a plurality of location inference times of the machine learning model.

[0306] Clause 91. A non-transitory computer-readable medium pursuant to Clause 90, wherein: the performance report indicates a summary of the plurality of location inference opportunities, or the performance report indicates a summary of location inference opportunities within a time window among the plurality of location inference opportunities.

[0307] Clause 92. A non-transitory computer-readable medium pursuant to any one of Clauses 90 to 91, wherein the performance report indicates: the location inference error of each of the plurality of location inference times, and the confidence level of the location inference error.

[0308] Clause 93. A non-transitory computer-readable medium pursuant to any one of Clauses 90 to 92, wherein the performance report indicates the positioning inference error for each positioning inference moment having an error above a threshold among the plurality of positioning inference moments.

[0309] Clause 94. Non-transitory computer-readable media pursuant to any of Clauses 88 to 93, wherein the performance report is sent to: the UE, the UE vendor, the machine learning model maintenance engine, or any combination thereof.

[0310] Article 95. Non-transitory computer-readable media pursuant to any of Articles 88 to 94, wherein the network node is: a Transmitting Receiver Point (TRP) or a second UE.

[0311] Article 96. Non-transitory computer-readable media pursuant to any of Articles 88 to 95, wherein the network entity is: a location server or a TRP serving the UE.

[0312] Clause 97. A non-transitory computer-readable medium pursuant to any one of Clauses 88 to 96, wherein: the one or more measurements include one or more location measurements, one or more radio frequency fingerprint (RFFP) measurements, or both, of the radio channel between the UE and the network node, and the one or more location estimates include one or more geolocation estimates of the UE.

[0313] Clause 98. A non-transitory computer-readable medium pursuant to any one of Clauses 88 to 96, wherein: the one or more measurements include one or more radio frequency fingerprint (RFFP) measurements of the radio channel between the UE and the network node, and the one or more location estimates include one or more location measurements of the radio channel between the UE and the network node.

[0314] Clause 99. A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a user equipment (UE), cause the UE to: send a capability provision message to a location server, the capability provision message instructing the UE to report a confidence metric associated with a location estimate, the location estimate being derived by the UE based on a machine learning model applied to one or more measurements of a wireless channel between the UE and a network node; and send a location information message to the location server, the location information message including the location estimate and the confidence metric.

[0315] Article 100. Non-transitory computer-readable media pursuant to Article 99 also include computer-executable instructions that, when executed by the UE, cause the UE to: receive a request for capability message from the location server, the request for capability message requesting the UE to report whether the UE is able to report a confidence metric associated with the location estimate.

[0316] Clause 101. A non-transitory computer-readable medium pursuant to any of Clauses 99 to 100, wherein the provision capability message indicates the type of the confidence measure and the format of the confidence measure.

[0317] Clause 102. Non-transitory computer-readable media pursuant to Clause 101, wherein the type of confidence measure includes: a confidence interval comprising the average of a plurality of location estimates, or the inverse covariance of the location estimate.

[0318] Clause 103. Non-transitory computer-readable media pursuant to any of Clauses 99 to 102, wherein the machine learning model is generated by the UE, the UE vendor, the network entity, or the network entity vendor.

[0319] Clause 104. Non-transitory computer-readable media pursuant to any of Clauses 99 to 103, wherein the network node is: a Transmitting Receiver Point (TRP) or a second UE.

[0320] Clause 105. Non-transitory computer-readable media pursuant to any one of Clauses 99 to 104, wherein: the one or more measurements include one or more location measurements, one or more radio frequency fingerprint (RFFP) measurements, or both, of the radio channel between the UE and the network node, and the one or more location estimates include one or more geolocation estimates of the UE.

[0321] Clause 106. A non-transitory computer-readable medium pursuant to any one of Clauses 99 to 104, wherein: the one or more measurements include one or more radio frequency fingerprint (RFFP) measurements of the radio channel between the UE and the network node, and the one or more location estimates include one or more location measurements of the radio channel between the UE and the network node.

[0322] Clause 107. A non-transitory computer-readable medium storing computer-executable instructions, which, when executed by a user equipment (UE), cause the UE to: send a request for assistance information message to a location server, the request for assistance information message requesting the location server to configure the UE to report a confidence metric associated with a location estimate, the location estimate being derived by the UE based on a machine learning model of one or more measurements applied to a wireless channel between the UE and a network node; receive a provision for assistance information message from the location server, the provision for assistance information message configuring the UE to at least report the confidence metric; and send a location information message to the location server, the location information message including the location estimate and the confidence metric.

[0323] Clause 108. Non-transitory computer-readable media pursuant to Clause 107, wherein the information providing supplementary information indicates the type and format of the confidence measure.

[0324] Clause 109. Non-transitory computer-readable media pursuant to Clause 108, wherein the type of confidence measure includes: a confidence interval comprising the average of a plurality of location estimates, or the inverse covariance of the location estimate.

[0325] Clause 110. Non-transitory computer-readable media pursuant to any of Clauses 107 to 109, wherein the information providing supplementary data includes: a reporting flag that triggers the UE to report the confidence metric, reporting conditions, and the number of reports.

[0326] Clause 111. Non-transitory computer-readable media pursuant to Clause 110, wherein the reporting condition is periodic or event-triggered.

[0327] Clause 112. A non-transitory computer-readable medium pursuant to any of Clauses 110 to 111, wherein the number of reports includes: a single confidence measure, a batch of confidence measures, a statistic of multiple confidence measures, or a confidence measure of a location estimate associated only with an error greater than a threshold.

[0328] Clause 113. Non-transitory computer-readable media pursuant to any of Clauses 107 to 112, wherein the machine learning model is generated by a network entity.

[0329] Clause 114. Non-transitory computer-readable media pursuant to any of Clauses 107 to 113, wherein the network node is: a Transmitting Receiver Point (TRP) or a second UE.

[0330] Clause 115. A non-transitory computer-readable medium pursuant to any one of Clauses 107 to 114, wherein: the one or more measurements include one or more location measurements, one or more radio frequency fingerprint (RFFP) measurements, or both, of the radio channel between the UE and the network node, and the one or more location estimates include one or more geolocation estimates of the UE.

[0331] Clause 116. A non-transitory computer-readable medium pursuant to any one of Clauses 107 to 114, wherein: the one or more measurements include one or more radio frequency fingerprint (RFFP) measurements of the radio channel between the UE and the network node, and the one or more location estimates include one or more location measurements of the radio channel between the UE and the network node.

[0332] Those skilled in the art will understand that information and signals can be represented using any of a variety of different techniques and processes. For example, data, instructions, commands, information, signals, bits, symbols, and chips referenced throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or optical particles, or any combination thereof.

[0333] Furthermore, those skilled in the art will understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described herein in conjunction with the forms disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, various illustrative components, blocks, modules, circuits, and steps have been generally described above in accordance with their functions. Whether such functions are implemented as hardware or software depends on the specific application and design constraints on the overall system. Those skilled in the art can implement the described functions in different ways for each specific application; however, such implementation decisions should not be construed as leading away from the scope of this invention.

[0334] The various exemplary logic blocks, modules, and circuits described herein can be implemented or executed using a general-purpose processor, digital signal processor (DSP), ASIC, field-programmable gate array (FPGA) or other programmable logic device, individual gate or transistor logic, individual hardware component, or any combination thereof, designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, a combination of one or more microprocessors with a DSP core, or any other such configuration.

[0335] The methods, sequences, and / or algorithms described herein can be embodied directly in hardware, in a software module executed by a processor, or a combination of both. The software module can reside in random access memory (RAM), flash memory, read-only memory (ROM), erasable programmable ROM (EPROM), electronically erasable programmable ROM (EEPROM), registers, hard disks, removable disks, CD-ROMs, or any other form of storage media known in the art. An instance of the storage media is coupled to a processor, allowing the processor to read information from and write information to the storage media. Alternatively, the storage media can be integrated into the processor. The processor and storage media can reside in an ASIC. The ASIC can reside in a user terminal (e.g., a UE). Alternatively, the processor and storage media can reside as separate components in the user terminal.

[0336] In one or more instances, the described functionality may be implemented using hardware, software, firmware, or any combination thereof. If implemented via software, such functionality may be stored or transmitted as one or more instructions or codes in a computer-readable medium. Computer-readable media includes computer storage media and communication media, with communication media including any media that facilitates the transfer of computer programs from one place to another. Storage media may be any available media accessible to a computer. By way of example and not limitation, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk memory, magnetic disk memory or other magnetic storage devices, or any other media that may be used to carry or store desired program code in the form of instructions or data structures and is accessible to a computer. Furthermore, any connection is properly referred to as computer-readable media. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technology (such as infrared, radio, and microwave), then coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technology (such as infrared, radio, and microwave) are all included in the definition of media. As used herein, magnetic disks and optical disks include CDs, laser discs, optical discs, digital versatile discs (DVDs), floppy disks, and Blu-ray discs, where magnetic disks typically reproduce data magnetically, while optical discs reproduce data optically using lasers. Combinations of these should also be included within the scope of computer-readable media.

[0337] Although the foregoing disclosure illustrates the illustrative form of this case, it should be noted that various changes and modifications may be made to this document without departing from the scope of this case as defined by the appended claims. The functions, steps, and / or actions of the method claims according to the form of the disclosure described herein do not need to be performed in any particular order. Furthermore, although elements of this case may be described or declared in the singular, plural forms are also contemplated unless expressly stated to be limited to the singular. [Simplified Explanation of the Diagram]

[0019] The accompanying drawings are presented to help describe the various aspects of this case, and are provided merely for illustrative purposes and not for limiting them.

[0020] Figure 1 illustrates an example of a wireless communication system based on the state of this case.

[0021] Figures 2A, 2B and 2C illustrate an example wireless network structure according to the present case.

[0022] Figures 3A, 3B and 3C are simplified block diagrams of several instance types of components that can be used in user equipment (UE), base station and network entity respectively and configured to support the communications taught herein.

[0023] Figure 4 is a schematic diagram illustrating the interaction between instances of applications, application services, operating systems (OS) and hardware using various application programming interfaces (APIs) according to the present case.

[0024] Figure 5 illustrates examples of various positioning methods supported in the new radio (NR) according to the present case.

[0025] Figure 6A illustrates two procedures currently supported by the Long Term Evolution (LTE) Positioning Protocol (LPP) for exchanging UE positioning capabilities with the network.

[0026] Figure 6B illustrates two programs currently supported by LPP for exchanging location assistance data.

[0027] Figure 6C illustrates the two programs currently supported by LPP for exchanging location information.

[0028] Figure 7 is a schematic diagram showing an example message frame structure according to the case.

[0029] Figure 8 is a diagram showing the estimation of the radio frequency (RF) channel according to the state of this case.

[0030] Figure 9 illustrates an example neural network based on the state pattern of this case.

[0031] Figure 10 is a schematic diagram illustrating the use of a machine learning model for RF fingerprint-based localization according to the case.

[0032] Figure 11 is a schematic diagram showing a network-based report on the performance monitoring of the downlink RFFP (DL-RFFP) machine learning model according to the present case.

[0033] Figure 12 is a schematic diagram showing the confidence measure of the location estimation based on the DL-RFFP machine learning model in the UE report of the case.

[0034] Figures 13 to 15 illustrate example methods of wireless communication according to the present invention. [Biomaterial Storage]

[0339] Domestic storage information (please note in order of storage institution, date, and number): None. International storage information (please note in order of storage country, institution, date, and number): None.

Claims

1. A method for communication performed by a network entity, comprising the following steps: Receive a location information message from a user equipment (UE), the location information message including one or more location estimates derived by the UE during one or more location inference times of a machine learning model, wherein the machine learning model is applied to one or more measurements of a radio channel between the UE and a network node during each of the one or more location inference times; and send a performance report indicating the performance of the machine learning model in deriving the one or more location estimates at least during the one or more location inference times.

2. According to the method of request item 1, wherein: The performance report is sent periodically or in response to an event trigger, or in response to the receipt of a location information message.

3. According to the method of request item 1, wherein: The performance report is sent in response to a number of received location information messages exceeding a threshold. The performance report indicates the performance of the machine learning model in deriving a plurality of location estimation patterns received in the plurality of location information messages, and the plurality of location estimations are derived by the UE during the plurality of location inference events of the machine learning model.

4. According to the method of request item 3, wherein: The performance report indicates the summary statistics of the plurality of location inference opportunities, or the performance report indicates the summary statistics of the location inference opportunities within a time window of the plurality of location inference opportunities.

5. According to the method of request item 3, wherein the performance report indicates: a positioning inference error for each of the plurality of positioning inference times, and a confidence level of the positioning inference error.

6. The method according to claim 3, wherein the performance report indicates a positioning inference error for each positioning inference time having an error higher than a threshold among the plurality of positioning inference times.

7. According to the method of request item 1, wherein the performance report is sent to: the UE, a UE vendor, a machine learning model maintenance engine, or any combination thereof.

8. According to the method of request item 1, wherein the network node is: a Transmitting and Receiving Point (TRP) or a second UE.

9. According to the method of request item 1, wherein the network entity is: a location server, or a TRP serving the UE.

10. According to the method of request item 1, wherein: The one or more measurements include one or more location measurements, one or more radio frequency fingerprint (RFFP) measurements, or both, of the radio channel between the UE and the network node, and the one or more location estimates include one or more geolocation estimates of the UE.

11. According to the method of request item 1, wherein: The one or more measurements include one or more radio frequency fingerprint (RFFP) measurements of the radio channel between the UE and the network node, and the one or more location estimates include one or more location measurements of the radio channel between the UE and the network node.

12. A method for wireless communication performed by a user equipment (UE), comprising the following steps: Send a capability provision message to a location server, the capability provision message instructing the UE to report a confidence metric associated with a location estimate, the location estimate being derived by the UE based on a machine learning model of one or more measurements applied to a wireless channel between the UE and a network node; and send a location information message to the location server, the location information message including the location estimate and the confidence metric.

13. The method according to request item 12 also includes the following steps: The UE receives a capability request message from the location server, which requests the UE to report whether it can report a confidence metric associated with the location estimate.

14. The method according to request item 12, wherein the providing capability message indicates a type of confidence measure and a format of the confidence measure.

15. The method of claim 14, wherein the type of the confidence measure includes the following steps: including a confidence interval comprising the average of a plurality of location estimates, or an inverse covariance of the location estimates.

16. The method according to request item 12, wherein the machine learning model is generated by: the UE, a UE vendor, a network entity, or a network entity vendor.

17. According to the method of request item 12, wherein the network node is: a Transmitting and Receiving Point (TRP) or a second UE.

18. According to the method of request item 12, wherein: The one or more measurements include one or more location measurements, one or more radio frequency fingerprint (RFFP) measurements, or both, of the radio channel between the UE and the network node, and the one or more location estimates include one or more geolocation estimates of the UE.

19. According to the method of request item 12, wherein: The one or more measurements include one or more radio frequency fingerprint (RFFP) measurements of the radio channel between the UE and the network node, and the one or more location estimates include one or more location measurements of the radio channel between the UE and the network node.

20. A method of wireless communication performed by a user equipment (UE), comprising the steps of: sending a request for assistance information message to a location server, the request for assistance information message requesting the location server to configure the UE to report a confidence metric associated with a location estimate, the location estimate being derived by the UE based on a machine learning model of one or more measurements applied to a wireless channel between the UE and a network node; receiving a provide assistance information message from the location server, the provide assistance information message configuring the UE to report at least the confidence metric; and sending a location information message to the location server, the location information message including the location estimate and the confidence metric.

21. The method according to request item 20, wherein the information providing auxiliary data indicates a type of confidence measure and a format of the confidence measure.

22. The method of claim 21, wherein the type of the confidence measure includes the following steps: including a confidence interval of an average of a plurality of location estimates, or an inverse covariance of the location estimates.

23. The method according to request item 20, wherein the provision of auxiliary information message includes the following steps: triggering the UE to report a reporting flag for the confidence metric, a reporting condition, and a reporting quantity.

24. According to the method of request item 23, wherein the reporting condition is periodic or event-triggered.

25. According to the method of request item 23, wherein the number of reports includes: A single confidence measure, a batch of confidence measures, a statistical analysis of multiple confidence measures, or a confidence measure of a location estimate that is associated only with an error greater than a threshold.

26. The method of request item 20, wherein the machine learning model is generated by a network entity.

27. According to the method of request item 20, wherein the network node is: a Transmitting and Receiving Point (TRP) or a second UE.

28. According to the method of request item 20, wherein: The one or more measurements include one or more location measurements, one or more radio frequency fingerprint (RFFP) measurements, or both, of the radio channel between the UE and the network node, and the one or more location estimates include one or more geolocation estimates of the UE.

29. According to the method of request item 20, wherein: The one or more measurements include one or more radio frequency fingerprint (RFFP) measurements of the radio channel between the UE and the network node, and the one or more location estimates include one or more location measurements of the radio channel between the UE and the network node.

30. A network entity, comprising: One memory; At least one transceiver; The system includes at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: receive, via the at least one transceiver, a location information message from a user equipment (UE), the location information message including one or more location estimates derived by the UE during one or more location inference times of a machine learning model, wherein the machine learning model is applied to one or more measurements of a wireless channel between the UE and a network node during each of the one or more location inference times; and transmit via the at least one transceiver a performance report indicating the performance of the machine learning model in deriving the one or more location estimates at least during the one or more location inference times.

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