Machine learning model for localization based on respective combinations of anchor devices

Abstract

Techniques for wireless communication are disclosed. In an aspect, a user equipment may send observable anchor information to a network entity, the observable anchor information indicating a set of anchor devices observable by the user equipment. A user equipment may obtain, from a network entity, assistance information indicating a machine learning model corresponding to a set of anchor devices. The user device may participate in a machine learning model-based positioning process using at least a subset of the set of anchor devices to determine an estimated position of the user device.

Description

BACKGROUND 1. TECHNICAL FIELD

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

[0002] 2. Related Art

[0003] Wireless communication systems have developed through various generations, including first-generation analog wireless telephones, second-generation (2G) digital wireless telephones, and third-generation (3G) high speed data modems, with Internet capability. Today's wireless communication systems are currently in a transitional stage, where different technologies coexist. For example, 2G systems such as 2.5G and 2.75G, 3G systems, and pre-5G systems are being used concurrently. The next generation wireless communication systems, referred to as 5G, are now being developed. It is expected that 5G systems will deliver much higher data rates, greater numbers of connected devices, and lower latency relative to current 3G and 4G wireless communications systems.

[0004] The 5th Generation (5G) wireless standard, referred to as New Radio (NR), enables higher data transfer speeds, greater numbers of connected devices, and lower latency relative to previous standards. According to the Next Generation Mobile Networks Alliance, 5G technology should provide bitrates on the order of 100 megabits per second (Mbps) to 1 gigabit per second (Gbps) and peak data rates of 10 to 20 Gbps. 5G should also provide system capacity of approximately 50,000 users per square kilometer in a 20 megahertz (MHz) bandwidth, 100 times the capacity of existing 4G systems. Additionally, 5G should improve spectral efficiency and energy efficiency, and provide reduced latency relative to 4G. SUMMARY

[0005] The following presents a simplified summary relating to one or more aspects disclosed herein. Thus, the following summary should not be considered an extensive overview relating to all contemplated aspects, nor should the following summary be considered to identify key or critical elements relating to all contemplated aspects or to delineate the scope associated with any particular aspect. Accordingly, the following summary is merely a precursor to the detailed description presented below and described herein.

[0006] In an aspect, a method of wireless communication performed by a user equipment includes transmitting, to a network entity, observable anchor point information indicating a set of anchor devices observable by the user equipment; obtaining, from the network entity, assistance information indicating a machine learning model corresponding to the set of anchor devices; and participating, with at least a subset of the set of anchor devices, in a positioning procedure based on the machine learning model to determine an estimated position of the user equipment.

[0007] In an aspect, a method of wireless communication performed by a network entity includes receiving, from a user equipment, observable anchor point information indicating a set of anchor devices observable by the user equipment; and transmitting, to the user equipment, assistance information based on a machine learning model corresponding to the set of anchor devices being available, wherein the machine learning model can be used to determine an estimated position of the user equipment.

[0008] In an aspect, a method of wireless communication performed by a user equipment includes obtaining, from a network entity, assistance information indicating one or more candidate machine learning models corresponding to respective one or more candidate sets of anchor devices; selecting, from the one or more candidate machine learning models, a machine learning model based on a set of anchor devices observable by the user equipment; and participating, with at least a subset of a corresponding set of anchor devices of the one or more candidate sets of anchor devices, in a positioning procedure based on the selected machine learning model to determine an estimated position of the user equipment.

[0009] In an aspect, a method of wireless communication performed by a network entity includes obtaining device information of a user equipment; and transmitting, to the user equipment, assistance information based on the device information, the assistance information indicating one or more candidate machine learning models corresponding to respective one or more candidate sets of anchor devices, wherein at least one machine learning model of the one or more candidate machine learning models can be selected for determining an estimated position of the user equipment.

[0010] In an aspect, a user equipment 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 configured to transmit, to a network entity via the at least one transceiver, observable anchor point information indicating a set of anchor devices observable by the user equipment; obtain, from the network entity, assistance information indicating a machine learning model corresponding to the set of anchor devices; and participate, with at least a subset of the set of anchor devices, in a positioning procedure based on the machine learning model to determine an estimated position of the user equipment.

[0011] In one aspect, a 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 observable anchor information from a user equipment via the at least one transceiver, the observable anchor information indicating a set of anchor devices that the user equipment can observe; and transmit auxiliary information to the user equipment via the at least one transceiver based on the availability of a machine learning model corresponding to the set of anchor devices, wherein the machine learning model can be used to determine the estimated location of the user equipment.

[0012] In one aspect, a user equipment 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: obtain auxiliary information from a network entity, the auxiliary information indicating one or more candidate machine learning models corresponding to a corresponding set of one or more candidate anchor devices; select a machine learning model from the one or more candidate machine learning models based on a set of anchor devices observable by the user equipment; and utilize at least a subset of the corresponding set of anchor devices in the one or more candidate anchor devices to participate in a localization process based on the selected machine learning model to determine the estimated location of the user equipment.

[0013] In one aspect, a 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: obtain device information of a user equipment; and based on the device information, transmit auxiliary information to the user equipment via the at least one transceiver, the auxiliary information indicating one or more candidate machine learning models corresponding to a corresponding set of one or more candidate anchor devices, wherein at least one of the one or more candidate machine learning models can be selected to determine the estimated location of the user equipment.

[0014] In one aspect, a user equipment includes: components for sending observable anchor information to a network entity, the observable anchor information indicating a set of anchor devices that the user equipment can observe; components for obtaining auxiliary information from the network entity, the auxiliary information indicating a machine learning model corresponding to the set of anchor devices; and components for utilizing at least a subset of the set of anchor devices to participate in a localization process based on the machine learning model to determine an estimated location of the user equipment.

[0015] In one aspect, a network entity includes: a component for receiving observable anchor information from a user equipment, the observable anchor information indicating a set of anchor devices that the user equipment can observe; and a component for sending auxiliary information to the user equipment based on the availability of a machine learning model corresponding to the set of anchor devices, wherein the machine learning model can be used to determine the estimated location of the user equipment.

[0016] In one aspect, a user equipment includes: components for obtaining auxiliary information from a network entity, the auxiliary information indicating one or more candidate machine learning models corresponding to a set of one or more candidate anchor devices; components for selecting a machine learning model from the one or more candidate machine learning models based on a set of anchor devices that the user equipment can observe; and components for using at least a subset of the corresponding anchor devices in the one or more candidate anchor devices to participate in a localization process based on the selected machine learning model to determine an estimated location of the user equipment.

[0017] In one aspect, a network entity includes: a component for obtaining device information of a user equipment; and a component for sending auxiliary information to the user equipment based on the device information, the auxiliary information indicating one or more candidate machine learning models corresponding to a set of one or more candidate anchor devices, wherein at least one of the one or more candidate machine learning models can be selected to determine the estimated location of the user equipment.

[0018] In one aspect, a non-transitory computer-readable medium stores computer-executable instructions that, when executed by a user equipment, cause the user equipment to: send observable anchor information to a network entity, the observable anchor information indicating a set of anchor devices that the user equipment can observe; obtain auxiliary information from the network entity, the auxiliary information indicating a machine learning model corresponding to the set of anchor devices; and utilize at least a subset of the set of anchor devices to participate in a localization process based on the machine learning model to determine the estimated location of the user equipment.

[0019] In one aspect, a non-transitory computer-readable medium stores computer-executable instructions that, when executed by a network entity, cause the network entity to: receive observable anchor information from a user equipment, the observable anchor information indicating a set of anchor devices that the user equipment can observe; and send auxiliary information to the user equipment based on the availability of a machine learning model corresponding to the set of anchor devices, wherein the machine learning model can be used to determine the estimated location of the user equipment.

[0020] In one aspect, a non-transitory computer-readable medium stores computer-executable instructions that, when executed by a user equipment, cause the user equipment to: obtain auxiliary information from a network entity, the auxiliary information indicating one or more candidate machine learning models corresponding to a set of one or more candidate anchor devices; select a machine learning model from the one or more candidate machine learning models based on a set of anchor devices that the user equipment can observe; and utilize at least a subset of the corresponding anchor devices in the one or more candidate anchor devices to participate in a localization process based on the selected machine learning model to determine the estimated location of the user equipment.

[0021] In one aspect, a non-transitory computer-readable medium stores computer-executable instructions that, when executed by a network entity, cause the network entity to: obtain device information of a user equipment; and send auxiliary information to the user equipment based on the device information, the auxiliary information indicating one or more candidate machine learning models corresponding to a corresponding set of one or more candidate anchor devices, wherein at least one of the one or more candidate machine learning models can be selected to determine the estimated location of the user equipment.

[0022] Based on the accompanying drawings and detailed description, other objects and advantages associated with the aspects disclosed herein will be apparent to those skilled in the art. Attached Figure Description

[0023] The accompanying drawings are provided to help describe various aspects of this disclosure, and are provided for illustrative purposes only and not to limit the various aspects.

[0024] Figure 1 Example wireless communication systems according to various aspects of this disclosure are illustrated.

[0025] Figure 2A , Figure 2B and Figure 2C Example wireless network architectures based on various aspects of this disclosure are illustrated.

[0026] Figure 3A , Figure 3B and Figure 3C It is a simplified block diagram of several example aspects of components that can be used in user equipment (UE), base stations, and network entities and configured to support communications as taught herein.

[0027] Figure 4 Examples of various positioning methods supported in new radios (NR) according to various aspects of this disclosure are illustrated.

[0028] Figure 5It is a graph representing the time-varying impulse response of a radio frequency (RF) channel according to various aspects of this disclosure.

[0029] Figure 6 Example neural networks according to various aspects of this disclosure are illustrated.

[0030] Figure 7 This is an illustration of the use of a machine learning model for localization based on RF fingerprinting (RFFP) according to various aspects of this disclosure.

[0031] Figure 8 This is a diagram illustrating an inference loop for UE-based downlink RFFP (DL-RFFP) positioning according to various aspects of this disclosure.

[0032] Figure 9 An example process flow for UE-based downlink-based RFFP positioning is illustrated according to various aspects of this disclosure.

[0033] Figure 10 This is an illustration of using a machine learning model to determine the estimated time of arrival (ToA) for positioning according to various aspects of this disclosure.

[0034] Figure 11A This is a diagram illustrating an example setup for determining the estimated location of a target device and the channel response measured by the target device, according to various aspects of this disclosure.

[0035] Figure 11B This is an example of various aspects of the present disclosure. Figure 11A A diagram illustrating the probability distribution of the channel response converted to ToA.

[0036] Figure 11C This is an example based on various aspects of this disclosure. Figure 11B The probability distribution of ToA in the data is used to determine... Figure 11A A diagram showing the estimated location of the target device.

[0037] Figure 12 This is an illustration of an indoor environment including a Transmitting and Receiving Point (TRP) and multiple Access Points (APs) according to various aspects of this disclosure.

[0038] Figure 13 An example process flow is illustrated according to various aspects of this disclosure for implementing a machine learning (ML) model corresponding to a set of TRPs that a UE can observe.

[0039] Figure 14 An example process flow is illustrated according to various aspects of this disclosure for implementing the use of an ML model corresponding to a set of anchor devices that can be observed by a user device.

[0040] Figure 15 An example process flow is illustrated according to various aspects of this disclosure for implementing the use of one or more candidate ML models corresponding to one or more candidate TRP sets.

[0041] Figure 16 An example process flow is illustrated according to various aspects of this disclosure for implementing the use of one or more candidate ML models from one or more candidate anchor device sets.

[0042] Figure 17 Example methods for operating a user equipment according to various aspects of this disclosure are illustrated.

[0043] Figure 18 Example methods for operating network entities according to various aspects of this disclosure are illustrated.

[0044] Figure 19 Example methods for operating a user equipment according to various aspects of this disclosure are illustrated.

[0045] Figure 20 Example methods for operating network entities according to various aspects of this disclosure are illustrated. Detailed Implementation

[0046] Various aspects of this disclosure are provided below in the description of various examples provided for illustrative purposes and in the accompanying drawings. Alternative aspects may be devised without departing from the scope of this disclosure. Additionally, well-known elements of this disclosure will not be described in detail or will be omitted so as not to obscure the relevant details of this disclosure.

[0047] Various aspects typically involve localization processes based on machine learning models. Some aspects involve more specifically the use of machine learning models corresponding to a particular set of anchor devices. In some examples, a user device or network entity may select or identify a machine learning model suitable for a localization process performed based on the set of anchor devices that the user device can observe.

[0048] Specific aspects of the subject matter described in this disclosure can be implemented to achieve one or more of the following potential advantages. In some examples, by identifying or selecting a machine learning model specific to a set of anchor devices that a user device can observe, the described techniques can be used to perform a machine learning model-based localization process that exhibits improved performance due to a balance between the accuracy of the estimated location and the computational complexity of the applied machine learning model.

[0049] The terms “exemplary” and / or “example” are used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” and / or “example” is not necessarily to be construed as superior to or better than other aspects. Similarly, the term “aspects of this disclosure” does not require that all aspects of this disclosure include the features, advantages, or modes of operation discussed.

[0050] Those skilled in the art will understand that any of a variety of different techniques and methods can be used to represent the information and signals described below. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be mentioned throughout the following description can be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or optical particles, or any combination thereof, depending in part on the specific application, in part on the desired design, in part on the corresponding technology, and so on.

[0051] Furthermore, many aspects are described according to a sequence of actions to be performed by elements of, for example, a computing device. It will be appreciated that the various actions described herein can be performed by a particular circuit (e.g., an application-specific integrated circuit (ASIC)), by program instructions executed by one or more processors, or by a combination of both. Additionally, the sequence of actions described herein can be considered to be 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 command the associated processor of the device to perform the functionality described herein. Therefore, various aspects of this disclosure can be embodied in a variety of different forms, all of which are contemplated within the scope of the claimed subject matter. Furthermore, for each aspect described herein, the corresponding form of any such aspect can be described herein as, for example, "logic configured to perform the described actions."

[0052] As used herein, unless otherwise stated, the terms “User Equipment” (UE) and “Base Station” are not intended to be specific 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 over a wireless communication network (e.g., mobile phone, router, tablet computer, laptop computer, 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” can be interchangeably referred to as “Access Terminal” or “AT”, “Client Equipment”, “Wireless Equipment”, “Subscriber Equipment”, “Subscriber Terminal”, “Subscriber Station”, “User Terminal” or “UT”, “Mobile Equipment”, “Mobile Terminal”, “Mobile Station”, or variations thereof. Generally, a UE can communicate with a core network via the RAN, and through the core network, a UE can connect to external networks such as the Internet and to other UEs. Of course, other mechanisms for connecting to the core network and / or the Internet are also possible for the UE, such as through 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.).

[0053] A base station may operate according to one of several RATs to communicate with the UE, depending on the network in which it is deployed, and may alternatively be referred to as an Access Point (AP), Network Node, Node B, Evolved Node B (eNB), Next Generation eNB (ng-eNB), New Radio (NR) Node B (also referred to as gNB or gNodeB), etc. The base station may primarily be used to support the UE's radio access, including supporting data, voice, and / or signaling connections for the supported UE. In some systems, the base station may only provide edge node signaling functions, while in others, it may provide additional control and / or network management functions. The communication link through which the UE can transmit signals to the base station is called an uplink (UL) channel (e.g., reverse traffic channel, reverse control channel, access channel, etc.). The communication link through which the base station can transmit signals to the UE is called a downlink (DL) or forward link channel (e.g., paging channel, control channel, broadcast channel, forward traffic channel, etc.). As used herein, the term "traffic channel (TCH)" may refer to an uplink / reverse traffic channel or a downlink / forward traffic channel.

[0054] The term "base station" can refer to a single physical transmit / receive point (TRP) or multiple physical TRPs that may be co-located or non-co-located. For example, when the term "base station" refers to a single physical TRP, the physical TRP can be the antenna of a 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 can be the antenna array of the base station (e.g., as in a multiple-input multiple-output (MIMO) system or where the base station employs beamforming). When the term "base station" refers to multiple non-co-located physical TRPs, the physical TRP can be a distributed antenna system (DAS) (a network of spatially separated antennas connected via a transmission medium to a common source) or a remote radio headend (RRH) (a remote base station connected to a serving base station). Alternatively, a non-co-located physical TRP can be the serving base station from which the UE receives measurement reports and a neighboring base station where the UE is measuring its reference radio frequency (RF) signal. Because, as used herein, a TRP is the point by which a base station transmits and receives radio signals, references to transmitting from or receiving at a base station should be understood to refer to a specific TRP of the base station.

[0055] In some specific implementations supporting UE positioning, the base station may not support the UE's radio access (e.g., it may not support data, voice, and / or signaling connections for the UE), but may instead transmit reference signals to the UE for measurement and / or receive and measure signals transmitted by the UE. Such a base station may be referred to as a positioning beacon (e.g., in the case of transmitting signals to the UE) and / or as a location measurement unit (e.g., in the case of receiving and measuring signals from the UE).

[0056] An “RF signal” refers to an electromagnetic wave of a given frequency that transmits information across the space 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 propagation characteristics of RF signals through multipath channels, a receiver may receive multiple “RF signals” corresponding to each transmitted RF signal. The same transmitted RF signal on different paths between the transmitter and receiver may be referred to as a “multipath” RF signal. As used herein, where the context clearly indicates that the term “signal” refers to a wireless signal or RF signal, an RF signal may also be referred to as a “wireless signal” or simply a “signal.”

[0057] Figure 1An example wireless communication system 100 according to various aspects of this disclosure is illustrated. The wireless communication system 100 (which may also be referred to as a wireless wide area network (WWAN)) may include various base stations 102 (labeled "BS") and various UEs 104. Base station 102 may include macro cell base stations (high-power cellular base stations) and / or small cell base stations (low-power cellular base stations). In one aspect, the macro cell base station may include an eNB and / or an ng-eNB (where the wireless communication system 100 corresponds to an LTE network), or a gNB (where the wireless communication system 100 corresponds to an NR network), or a combination of both, and the small cell base station may include femtocells, picocells, microcells, etc.

[0058] Base station 102 can collectively form a RAN and interface with core network 170 (e.g., evolved packet core (EPC) or 5G core (5GC)) via backhaul link 122, and interface with one or more location servers 172 (e.g., location management function (LMF) or secure user plane location (SUPL) location platform (SLP)) via core network 170. Location server 172 can be part of core network 170 or can be external to core network 170. Location server 172 can be integrated with base station 102. UE 104 can communicate with location server 172 directly or indirectly. For example, UE 104 can communicate with location server 172 via base station 102 currently serving UE 104. UE 104 can also communicate with location server 172 via another path, such as via application server (not shown), via another network, such as via wireless local area network (WLAN) access point (AP) (e.g., AP 150 described below), etc. For signaling purposes, communication between UE 104 and location server 172 may 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), wherein intermediate nodes (if present) are omitted from the signaling diagram for clarity.

[0059] In addition to other functions, base station 102 may perform functions associated with one or more of the following: 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), subscriber and equipment tracking, RAN information management (RIM), paging, location, and delivery of warning messages. Base stations 102 may communicate with each other directly or indirectly (e.g., via EPC / 5GC) on backhaul link 134, which may be wired or wireless.

[0060] Base station 102 can wirelessly communicate with UE 104. Each base station in base station 102 can provide communication coverage for a corresponding geographic coverage area 110. In one aspect, 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 a frequency resource, which is referred to as a carrier frequency, component carrier, carrier, or frequency band, etc.) and can be associated with an identifier (e.g., Physical Cell Identifier (PCI), Enhanced Cell Identifier (ECI), Virtual Cell Identifier (VCI), Cell Global Identifier (CGI), etc.) used to distinguish cells operating via the same or different carrier frequencies. 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 other protocol types). Because a cell is supported by a specific base station, the term “cell” can refer to either or both of the logical communication entity and the base station supporting the logical communication entity, depending on the context. Furthermore, since the 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" may also refer to the geographical coverage area of ​​a base station (e.g., a sector), provided that a carrier frequency can be detected and used for communication within a portion of the geographical coverage area 110.

[0061] While the geographic coverage areas 110 of adjacent macro cell base stations 102 may partially overlap (e.g., in handover areas), some areas within 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 geographic coverage area 110' that substantially overlaps with the geographic coverage areas 110 of one or more macro cell base stations 102. A network that includes both small cell base stations and macro cell base stations may be referred to as a heterogeneous network. A heterogeneous network may also include home eNBs (HeNBs) that provide service to restricted groups referred to as closed subscriber groups (CSGs).

[0062] 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 techniques, including spatial multiplexing, beamforming, and / or transmit diversity. The communication link 120 may use one or more carrier frequencies. Carrier allocation may be asymmetric for the downlink and uplink (e.g., more or fewer carriers may be allocated to the downlink compared to the uplink).

[0063] The wireless communication system 100 may also include a WLAN access point (AP) 150 that communicates with a wireless local area network (WLAN) station (STA) 152 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 a free channel assessment (CCA) or listen-before-talk (LBT) process before communication to determine whether the channel is available.

[0064] Small cell base station 102' can operate in licensed and / or unlicensed spectrum. When operating in unlicensed spectrum, small cell base station 102' can employ LTE or NR technology and use the same 5 GHz unlicensed spectrum as WLAN AP 150. Small cell base station 102' employing LTE / 5G in unlicensed spectrum can improve access network coverage and / or increase access network capacity. NR in unlicensed spectrum may be referred to as NR-U. LTE in unlicensed spectrum may be referred to as LTE-U, Licensed Assisted Access (LAA), or MulteFire.

[0065] 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 that contains radio frequency (RF). EHF has a range of 30 GHz to 300 GHz, with wavelengths between 1 mm and 10 mm. Radio waves in this band are referred to as millimeter waves. Near-mmW extends down to frequencies of 3 GHz with wavelengths of 100 mm. Ultra-high frequency (SHF) bands extend between 3 GHz and 30 GHz, and are also referred to as centimeter waves. Communication using mmW / near-mmW radio bands has high path loss and relatively short range. The mmW base station 180 and the UE 182 can utilize beamforming (transmit and / or receive) on the mmW communication link 184 to compensate for the extremely high path loss and short range. Additionally, it should 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 foregoing examples are merely illustrative and should not be construed as limiting the various aspects disclosed herein.

[0066] Transmit beamforming is a technique used to focus 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 (omnidirectionally). Using transmit beamforming, the network node determines where a given target device (e.g., a UE) is located (relative to the transmitting network node) and projects a stronger downlink RF signal in that specific direction, thus providing the receiving device with a faster and stronger RF signal (in terms of data rate). 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 array of antennas (called a "phased array" or "antenna array") that forms an RF beam that can be "manipulated" to be pointed 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 the individual antennas add up in the desired direction to increase radiation, while canceling out in the undesired direction to suppress radiation.

[0067] Transmit beams can be quasi-co-located, meaning they appear to the receiver (e.g., UE) as having the same parameters regardless of whether the network node's own transmit antennas 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 of a second reference RF signal on a second beam can be derived based on information about the source reference RF signal on the source beam. Therefore, if the source reference RF signal is QCL type A, the receiver can use the source reference RF signal to estimate the Doppler shift, Doppler spread, average delay, and delay spread of the second reference RF signal 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 of type QCL 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.

[0068] 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 of an antenna array in a particular direction and / or adjust the phase setting of the antenna array in a particular direction to amplify the RF signal received from that direction (e.g., increase its gain level). Therefore, when a receiver is described as performing beamforming in a certain direction, it means that the beam gain in that direction is high relative to the beam gain along other directions, or 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.

[0069] The transmit and receive beams can be spatially correlated. Spatial correlation means that parameters for a second beam (e.g., transmit or receive beam) for a second reference signal can be derived based on information about a first beam (e.g., receive or transmit beam) for a first reference signal. For example, a 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. The UE can then form a transmit beam for transmitting an uplink reference signal (e.g., a sounding reference signal (SRS)) to that base station based on the parameters of the receive beam.

[0070] It is important to note that, depending on the entity forming the "downlink" beam, the beam can be either a transmit beam or a receive beam. For example, if the base station is forming a downlink beam to transmit a reference signal to the UE, the downlink beam is a transmit beam. However, if the UE is forming a downlink beam, the downlink beam is a receive beam for receiving the downlink reference signal. Similarly, depending on the entity forming the "uplink" beam, the beam can be either a transmit beam or a receive beam. For example, if the base station is forming an uplink beam, the uplink beam is an uplink receive beam, while if the UE is forming an uplink beam, the uplink beam is an uplink transmit beam.

[0071] The electromagnetic spectrum is typically subdivided into various categories, bands, channels, etc., based on frequency / wavelength. In 5G NR, two initial operating bands have been designated as frequency ranges FR1 (410MHz-7.125GHz) and FR2 (24.25GHz-52.6GHz). It should be understood that although a portion of FR1 is greater than 6GHz, in various documents and articles, FR1 is often (interchangeably) referred to as the "sub-6GHz" band. Similar naming issues sometimes occur with FR2, which is often (interchangeably) referred to as the "millimeter wave" band in documents and articles, although this is different from the Extremely High Frequency (EHF) band (30GHz–300GHz) designated as "millimeter wave" by the International Telecommunication Union (ITU).

[0072] The frequencies between FR1 and FR2 are generally referred to as intermediate frequency (IF) bands. Recent 5G NR studies have identified the operating bands used for these IF bands as the frequency range designation FR3 (7.125 GHz – 24.25 GHz). Bands falling within FR3 can inherit FR1 and / or FR2 characteristics, thus effectively extending the features of FR1 and / or FR2 to IF band frequencies. Furthermore, higher frequency bands are currently being explored to extend 5G NR operation beyond 52.6 GHz. For example, three higher operating frequency bands have been identified as the frequency range designations FR4a or FR4-1 (52.6 GHz – 71 GHz), FR4 (52.6 GHz – 114.25 GHz), and FR5 (114.25 GHz – 300 GHz). Each of these higher frequency bands falls within the EHF band.

[0073] In light of the foregoing, unless otherwise specifically stated, it should be understood that, as used herein, the term "below 6 GHz" and the like can broadly refer to frequencies less than 6 GHz, within FR1, or including intermediate frequency band frequencies. Furthermore, unless otherwise specifically stated, it should be understood that, as used herein, the term "millimeter wave" and the like can broadly refer to frequencies that can include intermediate frequency band frequencies, within FR2, FR4, FR4-a or FR4-1 and / or FR5, or within the EHF band.

[0074] In multi-carrier systems such as 5G, one carrier frequency is referred to as the "primary carrier," "anchor carrier," "primary serving cell," or "PCell," and the remaining carrier frequencies are referred to as "secondary carriers," "secondary serving cells," or "SCell." In carrier aggregation, the anchor carrier is the carrier operating on the 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 on a licensed frequency (however, this is not always the case). The secondary carrier is a carrier operating on a second frequency (e.g., FR2) that can be configured and used to provide additional radio resources once an RRC connection is established between UE 104 and the anchor carrier. In some cases, the secondary carrier can be a carrier on an unlicensed frequency. Secondary carriers may contain only the necessary signaling information and signals. For example, since both the primary uplink and primary downlink carriers are typically UE-specific, the UE-specific signaling information and signals may not be present in the secondary carrier. 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. This is done, for example, to balance the load on different carriers. Since a "serving cell" (whether PCell or SCell) corresponds to the carrier frequency / component carrier through which a base station communicates, the terms "cell," "serving cell," "component carrier," and "carrier frequency" can be used interchangeably.

[0075] For example, still refer to Figure 1 One of the frequencies used by macro cell base station 102 may be an anchor carrier (or "PCell"), and the other frequencies used by macro cell base station 102 and / or mmW base station 180 may be secondary carriers ("SCell"). Simultaneous transmission and / or reception on multiple carriers allows UE 104 / 182 to significantly increase its data transmission and / or reception rates. For example, compared to the data rate obtained by a single 20MHz carrier, two aggregated 20MHz carriers in a multi-carrier system would theoretically result in a doubling of the data rate (i.e., 40MHz).

[0076] The wireless communication system 100 may also include a UE 164, which can communicate with the macro cell base station 102 via communication link 120 and / or with the mmW base station 180 via mmW communication link 184. For example, the macro cell base station 102 may support PCells and one or more SCells for the UE 164, and the mmW base station 180 may support one or more SCells for the UE 164.

[0077] In some cases, UE 164 and UE 182 may be able to communicate via sidelink. A sidelink-capable UE (SL-UE) 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 sidelink-capable UEs). Radio sidelink (or simply "sidelink") is an adaptation of core cellular network (e.g., LTE, NR) standards that allows direct communication between two or more UEs without the need for 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 SL-UEs in a group of SL-UEs utilizing sidelink communication may be located within the geographical coverage area 110 of base station 102. Other SL-UEs in this group may be outside the geographical coverage area 110 of base station 102, or may be unable to receive transmissions from base station 102 for other reasons. In some cases, the groups of SL-UEs communicating via sidelink communication may utilize a one-to-many (1:M) system, where each SL-UE transmits to every other SL-UE in the group. In some cases, base station 102 facilitates the scheduling of resources for sidelink communication. In other cases, sidelink communication is performed between the individual SL-UEs without involving base station 102.

[0078] On one hand, the sidelink 160 can operate via a wireless communication medium of interest that can be shared with other vehicles and / or infrastructure access points and other RATs for wireless communication. "Medium" can include one or more time, frequency, and / or space communication resources (e.g., covering one or more channels across one or more carriers) associated with wireless communication between one or more transmitter / receiver pairs. On another hand, the medium of interest can correspond to at least a portion of unlicensed frequency bands shared among various RATs. While 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 (particularly those employing small cell access points) have recently expanded their operation to unlicensed frequency bands such as those used by unlicensed National Information Infrastructure (U-NII) bands used by Wireless Local Area Network (WLAN) technologies (most notably the IEEE 802.11x WLAN technology commonly referred to as "Wi-Fi"). Example systems of this type include various variants of CDMA, TDMA, FDMA, Orthogonal FDMA (OFDMA), Single-Carrier FDMA (SC-FDMA), and so on.

[0079] It should be noted that, although Figure 1 Only two of these UEs are exemplified as SL-UEs (i.e., UE 164 and UE 182), but any UE exemplified can be an SL-UE. Furthermore, although only UE 182 is described as capable of beamforming, any UE exemplified (including UE 164) can be capable of beamforming. 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 station 102, base station 180, small cell 102', access point 150), etc. Therefore, in some cases, UE 164 and UE 182 can utilize beamforming via sidelink 160.

[0080] exist Figure 1 In the example, the UE shown (for simplicity, in) Figure 1Any UE (shown as a single UE 104) may receive signal 124 from one or more Earth-orbiting spacecraft (SV) 112 (e.g., satellites). In one aspect, SV 112 may be part of a satellite positioning system that allows UE 104 to use as an independent source of location information. Satellite positioning systems typically include a system of transmitters (e.g., SV 112) positioned such that a receiver (e.g., UE 104) can determine its location on or above the Earth based at least in part on positioning signals (e.g., signal 124) received from the transmitters. Such transmitters typically transmit signals marked with a set number of repeating pseudo-random noise (PN) codes. While 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 in order to derive geographic location information from SV 112.

[0081] In a satellite positioning system, the use of signal 124 can be enhanced by various satellite-based augmentation systems (SBAS), which may be associated with or otherwise made available to 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 Geostationary Navigation Overlay Service (EGNOS), Multifunctional Satellite Augmentation System (MSAS), 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.

[0082] On one hand, SV 112 may additionally or alternatively be part of one or more non-terrestrial networks (NTNs). In an NTN, SV 112 connects to an earth station (also referred to as a ground station, NTN gateway, or gateway), which in turn connects to elements in the 5G network, such as a modified base station 102 (without a ground antenna) or a network node in a 5GC. This element, in turn, provides access to other elements in the 5G network and ultimately to entities outside the 5G network, such as internet web servers and other user equipment. Thus, as a replacement or supplement to communication signals from ground base station 102, UE 104 may receive communication signals (e.g., signal 124) from SV 112.

[0083] The wireless communication system 100 may also include one or more UEs (such as UE 190) that are indirectly connected to one or more communication networks via one or more device-to-device (D2D) peer-to-peer (P2P) links (referred to as “side links”). Figure 1 In the example, UE 190 has a D2D P2P link 192 with one of UEs 104 connected to one of the base stations 102 (e.g., UE 190 can indirectly obtain cellular connectivity through this D2D P2P link), and has a D2D P2P link 194 with a WLAN STA 152 connected to WLAN AP 150 (UE 190 can indirectly obtain WLAN-based Internet connectivity through this D2D P2P link). In one example, D2D P2P links 192 and 194 can be supported by any well-known D2D RAT, such as LTE Direct (LTE-D) or WiFi Direct (WiFi-D). wait.

[0084] Figure 2A An example wireless network architecture 200 is illustrated. For instance, the 5GC 210 (also referred to as the Next Generation Core (NGC)) can be functionally viewed as control plane (C-plane) functions 214 (e.g., UE registration, authentication, network access, gateway selection, etc.) and user plane (U-plane) functions 212 (e.g., UE gateway functions, access to data networks, 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, specifically to user plane functions 212 and control plane functions 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 a backhaul connection 223. In some configurations, the next-generation RAN (NG-RAN) 220 may have one or more gNBs 222, while other configurations include one or more of both ng-eNBs 224 and gNBs 222. Either or both of the gNBs 222 or ng-eNBs 224 may communicate with one or more UEs 204 (e.g., any of the UEs described herein).

[0085] Another optional aspect may include a location server 230 that can communicate with the 5GC 210 to provide location assistance to the UE 204. The location server 230 may be implemented as multiple separate servers (e.g., physically separate servers, different software modules on a single server, different software modules distributed across multiple physical servers, etc.), or alternatively, each may correspond to a single server. The location server 230 may be configured to support one or more location services for the UE 204 that can be connected to the location server 230 via the core network, the 5GC 210, and / or via the Internet (not illustrated). 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 third-party server, such as an original equipment manufacturer (OEM) server or a service server).

[0086] Figure 2B Another example wireless network architecture 240.5GC 260 is illustrated (which can correspond to...). Figure 2A5GC 210 can be functionally considered as a control plane function provided by Access and Mobility Management Function (AMF) 264 and a user plane function 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 session management (SM) messages between one or more UEs 204 (e.g., any of the UEs described herein) and Session Management Function (SMF) 266, transparent proxy service for routing SM messages, access authentication and access authorization, transmission of short message service (SMS) messages between UE 204 and Short Message Service Function (SMSF) (not shown), and Security Anchor Functionality (SEAF). AMF 264 also interacts with 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) Subscriber Identity Module (USIM) authentication, the AMF 264 retrieves security material from the AUSF. The AMF 264 also includes Security Context Management (SCM). The SCM receives a key from the SEAF and uses this key to derive access network-specific keys. The AMF 264's functionality also includes location service management for regulated services, transmission of location service messages between the UE 204 and the Location Management Function (LMF) 270 (which acts as a location server 230), transmission of location service messages between the NG-RAN 220 and the LMF 270, Evolved Packet System (EPS) bearer identifier allocation for interoperability with EPS, and UE 204 mobility event notification. Furthermore, the AMF 264 also supports functionality for non-3GPP (3rd Generation Partnership Project) access networks.

[0087] The functions of UPF 262 include acting as an anchor point for intra / inter-RAT mobility (where applicable), acting as an external Protocol Data Unit (PDU) session point for interconnection to a data network (not shown), providing packet routing and forwarding, packet inspection, user plane policy rule enforcement (e.g., gating, redirection, traffic orientation), lawful interception (user plane collection), traffic usage reporting, user plane Quality of Service (QoS) handling (e.g., uplink / downlink rate enforcement, reflected QoS marking in downlink), uplink traffic verification (Service Data Flow (SDF) to QoS flow mapping), transport-level packet marking in uplink and downlink, downlink packet buffering and downlink data notification triggering, and delivering and forwarding one or more "end markers" to the source RAN node. UPF 262 may also support the delivery of location service messages between UE 204 and a location server (such as SLP272) on the user plane.

[0088] The functions of SMF 266 include session management, UE Internet Protocol (IP) address allocation and management, selection and control of user plane functions, service orientation configuration at UPF 262 for routing services to the correct destination, partial control of policy enforcement and QoS, and downlink data notification. The interface through which SMF 266 communicates with AMF 264 is called the N11 interface.

[0089] Another optional aspect 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 multiple separate servers (e.g., physically separate servers, different software modules on a single server, different software modules distributed across multiple physical servers, etc.), or alternatively, each 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 illustrated). SLP 272 can support similar functions to LMF 270, but while LMF 270 can communicate with AMF264, NG-RAN 220 and UE 204 on the control plane (e.g., using interfaces and protocols designed to transmit signaling messages rather than voice or data), SLP 272 can communicate with UE 204 and external clients (e.g., third-party server 274) on the user plane (e.g., using protocols designed to carry voice and / or data, such as Transmit Control Protocol (TCP) and / or IP).

[0090] Another optional aspect may include a third-party server 274 that 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. Therefore, in some cases, the third-party server 274 may be referred to as a Location Services (LCS) client or an external client. The third-party server 274 may be implemented as multiple separate servers (e.g., physically separate servers, different software modules on a single server, different software modules distributed across multiple physical servers, etc.), or alternatively, each may correspond to a single server.

[0091] User plane interface 263 and control plane interface 265 connect 5GC 260, and specifically connect UPF 262 and AMF 264 to one or more gNB 222 and / or ng-eNB 224 in NG-RAN 220. The interface between gNB 222 and / or ng-eNB 224 and AMF 264 is referred to as the "N2" interface, while 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.

[0092] The functionality of the gNB 222 can be divided among the gNB Central Unit (gNB-CU) 226, one or more gNB Distributed Units (gNB-DU) 228, and one or more gNB Radio Units (gNB-RU) 229. The gNB-CU 226 is a logical node that includes base station functions other than those specifically allocated to the gNB-DU 228, including user data delivery, mobility control, radio access network sharing, location, session management, etc. More specifically, the gNB-CU 226 typically hosts the Radio Resource Control (RRC), Serving Data Adaptation Protocol (SDAP), and Packet Data Convergence Protocol (PDCP) protocols of the gNB 222. The gNB-DU 228 is a logical node that typically hosts the Radio Link Control (RLC) and Media Access Control (MAC) layers of the gNB 222. Its operation is controlled by the gNB-CU 226. One gNB-DU 228 can support one or more cells, and a cell is supported by only one gNB-DU 228. The interface 232 between gNB-CU 226 and one or more gNB-DU 228 is referred to as the "F1" interface. The physical (PHY) layer functionality of gNB 222 is typically managed by one or more independent gNB-RU 229s, which perform functions such as power amplification and signal transmission / reception. The interface between gNB-DU 228 and gNB-RU 229 is referred to as the "Fx" interface. Therefore, UE 204 communicates with gNB-CU 226 via the RRC, SDAP, and PDCP layers, with gNB-DU 228 via the RLC and MAC layers, and with gNB-RU 229 via the PHY layer.

[0093] Communication systems (such as 5G NR systems) can be deployed in various ways with a variety of components or parts. In a 5G NR system or network, network nodes, network entities, network mobility elements, RAN nodes, core network nodes, network elements, or network equipment (such as base stations or one or more units (or components) that perform 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, 5GNB, access points (APs), transmit / receive points (TRPs), or cells, etc.) can be implemented as aggregated base stations (also known as self-contained base stations or monolithic base stations) or decomposed base stations.

[0094] Aggregated base stations can be configured to utilize a radio protocol stack that is physically or logically integrated within a single RAN node. Decentralized base stations can be configured to utilize a protocol stack that is physically or logically distributed across two or more units, such as one or more central or centralized units (CUs), one or more distributed units (DUs), or one or more radio units (RUs). In some aspects, the CU may be implemented within a RAN node, and one or more DUs may co-located with the CU, or alternatively, may be geographically or virtually distributed across one or more other RAN nodes. DUs may be implemented to communicate with one or more RUs. Each of the CUs, DUs, and RUs may also be implemented as a virtual unit, namely a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU).

[0095] Base station type operation or network design can take into account the aggregation characteristics of base station functionality. For example, decomposed base stations can be utilized in Integrated Access Backhaul (IAB) networks, Open Radio Access Networks (O-RAN (such as network configurations initiated by the O-RAN Alliance)), or Virtualized Radio Access Networks (vRAN, also known as Cloud Radio Access Networks (C-RAN)). Decomposition can include distributing functionality across two or more units in various physical locations, as well as virtually distributing the functionality of at least one unit, which enables flexibility in network design. The various units in a decomposed base station or decomposed RAN architecture can be configured for wired or wireless communication with at least one other unit.

[0096] Figure 2C An example disaggregated base station architecture 250 according to various aspects of this disclosure is illustrated. The disaggregated base station architecture 250 may include one or more central units (CUs) 280 (e.g., gNB-CU 226) that can communicate directly with 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 disaggregated base station units (such as a near real-time (near-RT) RAN intelligent controller (RIC) 259 via an E2 link or a non-real-time (non-RT) RIC 257 associated with a Service Management and Orchestration (SMO) framework 255, or both). CUs 280 may communicate with one or more distributed units (DUs) 285 (e.g., gNB-DU 228) via a corresponding midhaul link (e.g., an F1 interface). DUs 285 may communicate with one or more radio units (RUs) 287 (e.g., gNB-RU 229) via a corresponding fronthaul link. RU 287 can communicate with the corresponding UE 204 via one or more radio frequency (RF) access links. In some implementations, UE 204 can be served by multiple RU 287s simultaneously.

[0097] Each of these units (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 of the units, or an associated processor or controller providing instructions to the communication interfaces of these units, 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 that may include receivers, transmitters, or transceivers (such as radio frequency (RF) transceivers) configured to receive signals or transmit signals to one or more other units via wireless transmission media, or both.

[0098] In some aspects, the CU 280 can host one or more higher-level control functions. Such control functions may include Radio Resource Control (RRC), Packet Data Convergence Protocol (PDCP), Serving Data Adaptation Protocol (SDAP), etc. Each control function can be implemented using an interface configured to signal to other control functions hosted by the CU 280. The CU 280 can be configured to handle user plane functionality (i.e., Central Unit-User Plane (CU-UP)), control plane functionality (i.e., Central Unit-Control Plane (CU-CP)), or a combination thereof. In some implementations, the CU 280 can be logically divided into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, the CU-UP units can communicate bidirectionally with the CU-CP units via an interface (such as an E1 interface). The CU 280 can be implemented to communicate with the DU 285 for network control and signaling as needed.

[0099] DU 285 may correspond to a logical unit comprising one or more base station functions for controlling the operation of one or more RU 287s. In some aspects, DU 285 may at least partially host one or more of the Radio Link Control (RLC) layer, Medium Access Control (MAC) layer, and one or more high physical (PHY) layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, or modulation and demodulation) according to functional splits (such as those defined by the 3rd Generation Partnership Project (3GPP)). In some aspects, DU 285 may further host one or more low PHY layers. Each layer (or module) may be implemented using an interface configured to communicate signals with other layers (and modules) hosted by DU 285 or with control functions hosted by CU 280.

[0100] Lower-layer functionality can be implemented by one or more RU 287s. In some deployments, the RU287 controlled by the 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 in part on functional decomposition (such as lower-layer functional decomposition). In such architectures, the RU 287 may be implemented to handle over-the-air (OTA) communications with one or more UE 204s. In some specific implementations, the real-time and non-real-time aspects of control plane and user plane communications with the RU 287 may be controlled by the corresponding DU 285. In some scenarios, this configuration enables the DU 285 and CU280 to be implemented in cloud-based RAN architectures (such as vRAN architectures).

[0101] 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, SMO framework 255 can be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which can be managed via operation and maintenance interfaces such as the O1 interface. For virtualized network elements, 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 instantiating 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, SMO framework 255 can communicate with the hardware aspects of the 4G RAN (such as Open eNB (O-eNB) 261) via the O1 interface. Additionally, in some implementations, SMO framework 255 can communicate directly with one or more RU 287s via the O1 interface. SMO framework 255 may also include a non-RT RIC 257 configured to support the functionality of SMO framework 255.

[0102] 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 an interface (e.g., via an E2 interface) through data collection and action, connecting one or more CU 280s, one or more DU 285s, or both, and O-eNBs to the near-RT RIC 259.

[0103] In some implementations, to generate AI / ML models to be deployed in the near-RT RIC 259, the non-RT RIC 257 may receive parameters or external enrichment information from an external server. This information can be utilized 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 examples, the non-RT RIC 257 or the near-RT RIC 259 may be configured to tune RAN behavior or performance. For example, the non-RT RIC 257 may monitor long-term trends and patterns in performance and employ AI / ML models to perform corrective actions via the SMO framework 255 (such as reconfiguration via O1) or by creating RAN management policies (such as A1 policies).

[0104] Figure 3A , Figure 3B and Figure 3C Examples are shown that can be incorporated into UE 302 (which may correspond to any UE described herein), base station 304 (which may correspond to any base station described herein), and network entity 306 (which may correspond to or embody any network function described herein, including location server 230 and LMF 270, or alternatively may be independent of...). Figure 2A and Figure 2B Several example components (represented by corresponding boxes) in the NG-RAN 220 and / or 5GC 210 / 260 infrastructure (such as private networks) depicted herein support the operation as described herein. It should be understood that these components may be implemented in different specific implementations in different types of devices (e.g., in ASICs, in System-on-Chip (SoCs), etc.). The illustrated components may also be incorporated into other devices in a communication system. For example, other devices in the system may include components similar to those described as providing similar functionality. Furthermore, a given device may contain one or more of these components. For example, a device may include multiple transceiver components that enable the device to operate on multiple carriers and / or communicate via different technologies.

[0105] UE 302 and base station 304 each include one or more Wireless Wide Area Network (WWAN) transceivers 310 and 350, which provide components (e.g., components for transmitting, components for receiving, components for measuring, components for tuning, and / or components for blocking transmission, etc.) for communicating via one or more wireless communication networks (not shown), such as NR networks, LTE networks, GSM networks, etc. WWAN transceivers 310 and 350 may each be connected to one or more antennas 316 and 356 for communicating 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 time / frequency resource set in a specific spectrum). WWAN transceivers 310 and 350 can be configured in different ways to transmit and encode signals 318 and 358 (e.g., messages, indications, information, etc.) according to a specified RAT, and conversely, to receive and decode signals 318 and 358 (e.g., messages, indications, information, pilots, etc.). 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.

[0106] In at least some cases, UE 302 and base station 304 each further include one or more short-range wireless transceivers 320 and 360, respectively. The short-range wireless transceivers 320 and 360 may be connected to one or more antennas 326 and 366, respectively, and provide the capability to communicate via a wireless communication medium of interest through at least one designated RAT (e.g., WiFi, LTE-D, etc.). Components (e.g., components for transmitting, components for receiving, components for measuring, components for tuning, components for blocking transmission, etc.) for communication between PC5, Dedicated Short-Range Communication (DSRC), Wireless Access for Vehicle Environments (WAVE), Near Field Communication (NFC), Ultra-Wideband (UWB), etc.) and other network nodes (such as other UEs, access points, base stations, etc.). Short-range transceivers 320 and 360 can be configured in different ways to transmit and encode signals 328 and 368 (e.g., messages, indications, information, etc.) according to a specified RAT, and conversely, to receive and decode signals 328 and 368 (e.g., messages, indications, information, pilots, etc.). Specifically, short-range 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, the short-range wireless transceivers 320 and 360 can be WiFi transceivers, transceiver and / or Transceivers, NFC transceivers, UWB transceivers, or vehicle-to-vehicle (V2V) and / or vehicle-to-everything (V2X) transceivers.

[0107] 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 can be connected to one or more antennas 336 and 376 respectively, and can provide components for receiving and / or measuring satellite positioning / communication signals 338 and 378 respectively. When satellite signal receivers 330 and 370 are satellite positioning system receivers, satellite positioning / communication signals 338 and 378 can 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 can 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, perform calculations using measurements obtained by any suitable satellite positioning system algorithm to determine the locations of UE 302 and base station 304, respectively.

[0108] Base station 304 and network entity 306 each include one or more network transceivers 380 and 390, which provide components (e.g., transmitting components, receiving components, etc.) for communicating with other network entities (e.g., other base station 304, other network entity 306). For example, base station 304 may use one or more network transceivers 380 to communicate with other base station 304 or network entity 306 via one or more wired or wireless backhaul links. As another example, network entity 306 may use 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.

[0109] The transceiver can be configured to communicate via a wired or wireless link. The transceiver (whether wired or wireless) includes transmitter circuitry (e.g., transmitter 314, transmitter 324, transmitter 354, transmitter 364) and receiver circuitry (e.g., receiver 312, receiver 322, receiver 352, receiver 362). In some embodiments, the transceiver may be an integrated device (e.g., implementing transmitter and receiver circuitry in a single device); in some embodiments, the transceiver may include separate transmitter and receiver circuitry; or in other embodiments, the transceiver may be implemented in other ways. The transmitter and receiver circuitry of a wired transceiver (e.g., in some embodiments, network transceivers 380 and 390) 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 multiple antennas (e.g., antennas 316, 326, 356, 366), such as antenna arrays, which permit corresponding devices (e.g., UE 302, base station 304) to perform transmit "beamforming," as described herein. Similarly, wireless receiver circuitry (e.g., receivers 312, 322, 352, 362) may include or be coupled to multiple antennas (e.g., antennas 316, 326, 356, 366), such as antenna arrays, which permit corresponding devices (e.g., UE 302, base station 304) to perform receive beamforming, as described herein. In one aspect, transmitter and receiver circuitry may share the same multiple antennas (e.g., antennas 316, 326, 356, 366), such that corresponding devices may perform only receive or only transmit at a given time, rather than both receive and transmit 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.

[0110] 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) may generally be described as "transceiver," "at least one transceiver," or "one or more transceivers." Therefore, whether a particular transceiver is a wired or wireless transceiver can be inferred from the type of communication performed. For example, backhaul communication between network devices or servers typically involves signaling via a wired transceiver, while wireless communication between a UE (e.g., UE 302) and a base station (e.g., base station 304) will typically involve signaling via a wireless transceiver.

[0111] 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 functionality related to, for example, wireless communication, and for providing other processing functionality. Thus, processors 332, 384, and 394 may provide components for processing, such as components for determining, components for calculating, components for receiving, components for transmitting, components for indicating, etc. In one aspect, processors 332, 384, and 394 may include, for example, one or more general-purpose processors, multi-core processors, central processing units (CPUs), ASICs, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), other programmable logic devices or processing circuits, or various combinations thereof.

[0112] UE 302, base station 304, and network entity 306 each 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, maintenance, etc. 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 hardware circuitry that is part of or coupled to processors 332, 384, and 394, respectively, which, when executed, cause UE 302, base station 304, and network entity 306 to perform the functionality described herein. In other aspects, positioning components 342, 388, and 398 may be external to processors 332, 384, and 394 (e.g., part of a modem processing system, integrated with another processing system, etc.). Alternatively, positioning components 342, 388, and 398 may be memory modules stored in memories 340, 386, and 396, respectively, which, when executed by processors 332, 384, and 394 (or modem processing system, another processing system, etc.), enable UE 302, base station 304, and network entity 306 to perform the functionality described herein. Figure 3A Possible locations for the positioning component 342 are illustrated. The positioning component 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 standalone component. Figure 3B Possible locations for the positioning component 388 are illustrated. The positioning component 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 standalone component. Figure 3C Possible locations for the positioning component 398 are illustrated. The positioning component may be part of, for example, one or more network transceivers 390, memory 396, one or more processors 394, or any combination thereof, or may be a standalone component.

[0113] 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. By way of example, sensor 344 may include accelerometers (e.g., microelectromechanical systems (MEMS) devices), gyroscopes, geomagnetic sensors (e.g., compasses), altimeters (e.g., barometric altimeters), and / or any other type of motion detection sensor. Furthermore, sensor 344 may include multiple different types of devices and combine their outputs to provide motion information. For example, sensor 344 may use a combination of multi-axis accelerometers and orientation sensors to provide the ability to calculate positioning in two-dimensional (2D) and / or three-dimensional (3D) coordinate systems.

[0114] In addition, UE 302 includes a user interface 346 that provides components for providing instructions to a user (e.g., audible and / or visual instructions) and / or for receiving user input (e.g., when the user actuates a sensing device (such as a keypad, touchscreen, microphone, etc.)). Although not shown, base station 304 and network entity 306 may also include user interfaces.

[0115] 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 functionality for 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 functionality associated with broadcasting system information (e.g., Master Information Block (MIB), System Information Block (SIB)), RRC connection control (e.g., RRC connection paging, RRC connection establishment, RRC connection modification, and RRC connection release), inter-RAT mobility, and measurement configuration for UE measurement reporting; PDCP layer functionality associated with header compression / decompression, security (encryption, decryption, integrity protection, integrity verification), and handover support functions; RLC layer functionality associated with the delivery 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 functionality associated with mapping between logical channels and transport channels, scheduling information reporting, error correction, priority handling, and logical channel priority ordering.

[0116] Transmitter 354 and receiver 352 implement Layer 1 (L1) functionality associated with various signal processing functions. Layer 1, including the physical (PHY) layer, may include error detection on the transport channel, forward error correction (FEC) decoding / decoding of the transport 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 constellation based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-phase shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The decoded and modulated symbols can then be divided into parallel streams. Each stream can then be mapped to orthogonal frequency division multiplexing (OFDM) subcarriers, multiplexed with a reference signal (e.g., pilot) in the time and / or frequency domains, and then combined using inverse fast Fourier transform (IFFT) to produce a physical channel carrying a time-domain OFDM symbol stream. The OFDM symbol stream is spatially pre-decoded to generate multiple spatial streams. Channel estimates from the channel estimator are used to determine the decoding and modulation schemes, as well as for spatial processing. The channel estimates can be derived from reference signals and / or channel condition feedback transmitted by UE 302. Each spatial stream can then be provided to one or more different antennas 356. The transmitter 354 can use the corresponding spatial stream to modulate an RF carrier for transmission.

[0117] 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 functionality associated with various signal processing functions. Receiver 312 can perform spatial processing on the information to recover any spatial stream 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. Receiver 312 then uses a 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 probable signal constellation points transmitted by base station 304. These soft decisions can be based on channel estimates calculated by a channel estimator. The soft decisions are then decoded and deinterleaved to recover the data and control signals originally transmitted by base station 304 on the physical channel. Then, data and control signals are provided to one or more processors 332, which implement layer 3 (L3) and layer 2 (L2) functionality.

[0118] In the downlink, one or more processors 332 provide demultiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transport and logical channels to recover IP packets from the core network. One or more processors 332 are also responsible for error detection.

[0119] Similar to the functionality described in conjunction with downlink transmissions performed by base station 304, one or more processors 332 provide: RRC layer functionality associated with system information (e.g., MIB, SIB) acquisition, RRC connectivity, and measurement reporting; PDCP layer functionality associated with header compression / decompression and security (encryption, decryption, integrity protection, integrity verification); RLC layer functionality associated with upper-layer PDU delivery, error correction via ARQ, concatenation, segmentation, and reassembly of RLC SDUs, resegmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs onto transport blocks (TBs), demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction via Hybrid Automatic Repeat Request (HARQ), priority handling, and logical channel priority ordering.

[0120] 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 facilitates 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 the RF carrier for transmission.

[0121] 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 corresponding antenna 356. Receiver 352 recovers the information modulated onto the RF carrier and provides this information to one or more processors 384.

[0122] In the uplink, one or more processors 384 provide demultiplexing, packet reassembly, decryption, header decompression, and control signal processing between the transport channel and the logical channel to recover IP packets from 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.

[0123] For convenience, UE 302, base station 304 and / or network entity 306 are in Figure 3A , Figure 3B and Figure 3CThe example shown herein includes various components that can be configured according to the various examples described herein. However, it should be understood that the illustrated components may have different functionalities in different designs. In particular, Figure 3A through Figure 3C Various components are optional in alternative configurations, and various aspects include configurations that can vary due to design choices, cost, equipment usage, or other considerations. For example, in Figure 3A In certain cases, specific implementations of UE 302 may omit WWAN transceiver 310 (e.g., wearable devices, tablets, PCs, or laptops may have Wi-Fi and / or Bluetooth capabilities but no cellular capabilities), or short-range wireless transceiver 320 (e.g., cellular only), or satellite signal receiver 330, or sensor 344, etc. In another example, in Figure 3B In certain cases, specific implementations of base station 304 may omit WWAN transceiver 350 (e.g., a Wi-Fi "hotspot" access point without cellular capabilities), or short-range wireless transceiver 360 (e.g., cellular only), or satellite signal receiver 370, etc. For the sake of brevity, examples of various alternative configurations are not provided herein, but will be readily understood by those skilled in the art.

[0124] 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 aspect, data buses 334, 382, ​​and 392 can form or be part of the communication interfaces of UE 302, base station 304, and network entity 306, respectively. For example, in cases where different logical entities are embodied in the same device (e.g., gNB and location server functionality integrated into the same base station 304), data buses 334, 382, ​​and 392 can provide communication between the different logical entities.

[0125] Figure 3A , Figure 3B and Figure 3C The components can be implemented in various ways. In some specific implementations, Figure 3A , Figure 3B and Figure 3CThe components may be implemented in one or more circuits, such as, for example, one or more processors and / or one or more ASICs (which may include one or more processors). Here, each circuit may use and / or incorporate at least one memory component for storing information or executable code used by the circuit to provide that functionality. For example, some or all of the functionalities represented by boxes 310 to 346 may be implemented by the processor and memory components of UE 302 (e.g., by executing appropriate code and / or by appropriately configuring the processor components). Similarly, some or all of the functionalities represented by boxes 350 to 388 may be implemented by the processor and memory components of base station 304 (e.g., by executing appropriate code and / or by appropriately configuring the processor components). Furthermore, some or all of the functionalities represented by boxes 390 to 398 may be implemented by the processor and memory components of network entity 306 (e.g., by executing appropriate code and / or by appropriately configuring the processor components). For simplicity, various operations, actions, and / or functions are described herein as being performed "by the UE," "by the base station," "by the network entity," etc. However, as will be understood, such operations, actions and / or functions may actually be performed by specific components or combinations of components of the UE 302, base station 304, network entity 306, etc. (such as processors 332, 384, 394, transceivers 310, 320, 350 and 360, memories 340, 386 and 396, positioning components 342, 388 and 398, etc.).

[0126] In some designs, network entity 306 may be implemented as a core network component. In other designs, network entity 306 may operate differently from the network operator or 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 is 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).

[0127] NR supports various cellular network-based positioning technologies, including downlink-based positioning methods, uplink-based positioning methods, and positioning methods based on both downlink and uplink. Downlink-based positioning methods include Observed Time Difference of Arrival (OTDOA) in LTE, Downlink Time Difference of Arrival (DL-TDOA) in NR, and Downlink Angle of Departure (DL-AoD) in NR. Figure 4Examples of various positioning methods according to aspects of this disclosure are illustrated. In the OTDOA or DL-TDOA positioning process illustrated in scenario 410, the UE measures the difference between the times of arrival (ToA) of reference signals (e.g., positioning reference signals (PRS)) received from paired base stations (referred to as reference signal time difference (RSTD) or time difference of arrival (TDOA) measurement) and reports these differences 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. The UE then 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 the RSTD measurement, the positioning entity (e.g., a UE for UE-based positioning or a location server for UE-assisted positioning) can estimate the UE's location.

[0128] For the DL-AoD positioning illustrated in scenario 420, the positioning entity uses measurement reports from the UE regarding the received signal strength of multiple downlink transmit beams to determine the angle between the UE and the transmitting base station. The positioning entity can then estimate the UE's position based on the determined angle and the known location of the transmitting base station.

[0129] 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, which are measured by a reference base station and multiple non-reference base stations. Each base station then reports the reception time of the reference signal (referred to as relative time of arrival (RTOA)) to a positioning entity (e.g., a location server) that knows the location and relative timing of the base stations involved. Based on the 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 the TDOA to estimate the UE's location.

[0130] For UL-AoA positioning, one or more base stations measure the received signal strength of one or more uplink reference signals (e.g., SRS) received from the UE on one or more uplink receive beams. The positioning entity uses the signal strength measurements and the angle of the receive beams to determine the angle between the UE and the base stations. Based on the determined angle and the known location of the base stations, the positioning entity can then estimate the location of the UE.

[0131] Downlink and uplink-based positioning methods include Enhanced Cell ID (E-CID) positioning and Multiple Round-Trip Time (RTT) positioning (also known as "Multi-Cell RTT" and "Multi-RTT"). During RTT, 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), which then sends a second RTT-related signal (e.g., SRS or PRS) back to the first entity. Each entity measures the time difference between the time of arrival (ToA) of the received RTT-related signal and the time of transmission 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 and transmitted signals. The two entities can then transmit 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 these two Rx-Tx time difference measurements (e.g., calculated as the sum of the two Rx-Tx time difference measurements). Alternatively, one entity can transmit 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 the multi-RTT positioning illustrated in scenario 430, a first entity (e.g., a UE or base station) performs an RTT positioning process with multiple second entities (e.g., multiple base stations or UEs) to enable the location of the first entity to be determined based on the distance to the second entities and the known location of the second entities (e.g., using polygonal measurements). RTT and multi-RTT methods can be combined with other positioning technologies (such as UL-AoA and DL-AoD) to improve location accuracy, as illustrated in scenario 440.

[0132] 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), identifiers of detected neighboring base stations, estimated timing, and signal strength. The UE's location is then estimated based on this information and the known locations of the base stations.

[0133] 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, auxiliary data may include the identifier of the base station (or the cell / TRP of the base station) from which the reference signal is measured, reference signal configuration parameters (e.g., including the number of consecutive time slots of the PRS, the periodicity of consecutive time slots of the PRS, silence sequences, frequency hopping sequences, reference signal identifier, reference signal bandwidth, etc.), and / or other parameters applicable to a particular positioning method. Alternatively, auxiliary data may be derived directly from the base station itself (e.g., in periodically broadcast overhead messages, etc.). In some cases, the UE may be able to detect neighboring network nodes without using auxiliary data.

[0134] In the case of OTDOA or DL-TDOA positioning procedures, auxiliary data may also include the expected RSTD value and the associated uncertainty or search window around the expected RSTD. In some cases, the expected RSTD value may range from + / - 500 microseconds (μs). In some cases, when any of the resources used for positioning measurements is in FR1, the uncertainty of the expected RSTD may range from + / - 32 μs. In other cases, when all resources used for positioning measurements are in FR2, the uncertainty of the expected RSTD may range from + / - 8 μs.

[0135] Location estimates can be referred to by other names, such as location estimation, location, positioning, fixed location, or fixed location. A location estimate can be geodesic and include coordinates (e.g., latitude, longitude, and possible elevation), or it can be municipal and include street addresses, postal addresses, or some other verbal description of the location. A location estimate can be further defined relative to another known location or in absolute terms (e.g., using latitude, longitude, and possible elevation). A location estimate can include expected errors or uncertainties (e.g., by including the area or volume that the location is expected to include with a specified or default confidence level).

[0136] Figure 5This is a graph 500 illustrating an example channel estimation of a multipath channel between a receiver device (e.g., any of the UEs or base stations described herein) and a transmitter device (e.g., any other of the UEs or base stations described herein) according to various aspects of this disclosure. The channel estimation expresses the strength of a radio frequency (RF) signal (e.g., a positioning reference signal (PRS)) received through 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 represents time (e.g., milliseconds), and the vertical axis represents 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 transmission on multiple beams and / or due to the propagation characteristics of the RF signal (e.g., reflection, refraction, etc.).

[0137] exist Figure 5 In the example, the receiver detects / measures multiple (four) channel taps of the RF signal. Each channel tap is a cluster of one or more rays and corresponds to the multipath followed by the RF signal between the transmitter and receiver. Therefore, the channel tap represents the arrival time and signal strength of the RF signal on the multipath. Multiple channel taps may exist because the RF signal is transmitted on different transmit beams (and therefore at different angles), or due to the propagation characteristics of the RF signal (e.g., it may follow different paths due to reflection), or both. Note that although... Figure 5 Channel taps with two to five rays are illustrated, but it should be understood that a channel tap may have more or fewer rays than the illustrated number.

[0138] exist Figure 5 In the example, the channel tap detected at time T3 consists of stronger rays compared to the channel tap detected at time T1. This could be due to obstacles on the LOS path between the transmitter and receiver. Alternatively or additionally, there may be strong reflectors along the NLOS path corresponding to the channel tap detected at time T3.

[0139] Machine learning can be used to generate models that can facilitate various aspects associated with data processing. A specific application of machine learning involves generating measurement models for processing reference signals used for localization (e.g., localization reference signals (PRS)) (such as feature extraction, reporting of reference signal measurements (e.g., selecting which extracted features to report)).

[0140] Machine learning models are generally categorized as supervised or unsupervised. Supervised models can be further subdivided into regression models or classification models. Supervised learning involves learning a function that maps inputs to outputs based on example input-output pairs. For example, given a training dataset with two variables, age (input) and height (output), a supervised learning model can be generated to predict a person's height based on their age. In regression models, the output is continuous. An example of a regression model is linear regression, which simply attempts to find a line that best fits the data. Extensions of linear regression include multiple linear regression (e.g., finding a best-fitting plane) and multinomial regression (e.g., finding a best-fitting curve).

[0141] Another example of a machine learning model is the decision tree model. In a decision tree model, the tree structure is defined as having multiple nodes. Decisions are made to move from the root node at the top of the decision tree to a leaf node at the bottom (i.e., a node that has no other children). Generally, a higher number of nodes in a decision tree model is associated with higher decision accuracy.

[0142] Another example of a machine learning model is the decision forest. Random forests are an ensemble learning technique built on top of decision trees. Random forests involve creating 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 decision" model, the risk of errors from individual trees is reduced.

[0143] Another example of a machine learning model is a neural network (NN). A neural network is essentially a network of mathematical equations. It takes one or more input variables and produces one or more output variables by passing them through the network of equations. In other words, a neural network takes a vector of inputs and returns a vector of outputs.

[0144] Figure 6 An example neural network 600 according to various aspects of this disclosure is illustrated. The neural network 600 includes an input layer "i" that receives "n" (or more) inputs (illustrated as "input 1", "input 2", and "input n"), one or more hidden layers (illustrated as hidden layers "h1", "h2", and "h3") for processing the inputs from the input layer, and an output layer "o" that provides "m" (or more) outputs (labeled as "output 1" and "output m"). The number of inputs "n", hidden layers "h", and outputs "m" may be the same or different. In some designs, hidden layers "h" may include linear functions and / or activation functions, with each node of a successive hidden layer (illustrated as a circle) processing the linear function and / or activation function from the node of the previous hidden layer.

[0145] 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's used to model the probabilities of a finite number of outcomes (usually two). Essentially, it's a logistic equation created in a way that ensures the output values ​​can only be between "0" and "1". Another example of a classification model is a support vector machine (SVM). For example, given data from two classes, an SVM will find a hyperplane, or boundary, that maximizes the margin between the two classes. Many hyperplanes can separate the two classes, but only one hyperplane maximizes the margin or distance between them. Another example of a classification model is Naive Bayes, based on Bayes' theorem. Other examples of classification models include decision trees, random forests, and neural networks, which are similar to the examples described above, except that the output is discrete rather than continuous.

[0146] Unlike supervised learning, unsupervised learning is used to derive inferences and find patterns from input data without referring to labeled results. Two examples of unsupervised learning models include clustering and dimensionality reduction.

[0147] Clustering is an unsupervised technique involving the grouping or clustering of data points. Clustering is commonly used for customer segmentation, fraud detection, and document classification. Common clustering techniques include k-means clustering, hierarchical clustering, mean-shift clustering, and density-based clustering. Dimensionality reduction is the process of reducing the number of random variables under consideration by obtaining a set of main variables. More simply, dimensionality reduction is the process of reducing the dimension of a feature set (or, even more simply, reducing the number of features). Most dimensionality reduction techniques can be categorized as feature elimination or feature extraction. An example of dimensionality reduction is called Principal Component Analysis (PCA). In its simplest sense, PCA involves projecting higher-dimensional data (e.g., three-dimensional) onto a smaller space (e.g., two-dimensional). This produces lower-dimensional (e.g., two-dimensional instead of three-dimensional) data while preserving all the original variables in the model.

[0148] Regardless of the machine learning model used, at a high level, the machine learning module (e.g., implemented by a processing system such as processor 332, 384, or 394) can be configured to iteratively analyze training input data (e.g., measurements of reference signals to / from various target UEs) and correlate that training input data with an output dataset (e.g., a set of possible or highly probable candidate locations for various target UEs), so that the same output dataset can be determined later when similar input data (e.g., from other target UEs at the same or similar locations) is provided.

[0149] NR supports RF fingerprint-based positioning (RFFP), a positioning and localization technique that uses the RFFP captured by the mobile device to determine the location of the mobile device. The RFFP can be a histogram 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 the 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.

[0150] Machine learning-based localization techniques have proven to offer superior localization performance compared to classical localization schemes. In machine learning-RFFP-based localization, the machine learning model (e.g., neural network 600) takes the RFP of the downlink reference signal (e.g., PRS) as input and outputs a localization measurement (e.g., ToA, RSTD) or the mobile device location corresponding to the input RFP. The machine learning model (e.g., neural network 700) is trained using the "true value" (i.e., known) localization measurement or mobile device location as a reference (i.e., expected) output of the RFP training set.

[0151] For example, a machine learning model can be trained to determine the RSTD measurement for 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., true) RSTD measurement of the mobile device's location at the time the RFFP measurement of the PRS is obtained. A network (e.g., a location server) can determine the expected RSTD for this pair of TRPs based on the known location of the mobile device and the known location of the involved (measured) TRPs. The known location of the mobile device can be determined from multiple reported RSTD measurements and / or any other measurements reported by the mobile device (e.g., GPS measurements).

[0152] Figure 7 Figure 700 illustrates the use of a machine learning model for RFFP-based localization according to various aspects of this disclosure. Figure 7 In the example, during the "offline" phase, RFFPs (e.g., CER / CIR / CFRs) captured by the mobile device are stored in a database. The database may reside on 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. Figure 7The base stations are exemplified as base stations 1 to N (i.e., "BS1" to "BS N"). For UE-based downlink RFFP (DL-RFFP) positioning, the network (e.g., a location server) configures the base stations to send 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.

[0153] Each measured RFFP is associated with a known location of the mobile device at the time the RFFP was measured, and this known location is in Figure 8 The locations are illustrated as positions 1 to L (i.e., "Pos 1" to "Pos L"). The location of the mobile device can be determined via references such as those above. Figure 4 This is known from another positioning technology discussed. It should be noted that, although... Figure 7 The example illustrates RFFP information for a single mobile device, but it should be understood that RFFP information for multiple mobile devices can be collected and stored in a database.

[0154] Based on information captured during the offline phase, a machine learning model (e.g., neural network 600) is trained to estimate 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 capture is used as a label. After training, during the "online" phase, the trained machine learning model can be used to estimate (infer) the location of the mobile device (exemplified 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 may provide RFFP measurements to the network for processing.

[0155] It should be noted that, although Figure 7 An example is shown using an RFFP-based machine learning model to estimate the UE's location, but the output of the machine learning model (or the extracted features) can instead be a localization measurement based on the input RFFP, such as RSTD measurement, ToA measurement, DL-AoD measurement, etc.

[0156] Figure 8 This is a diagram 800 illustrating an inference loop for UE-based DL-RFFP positioning according to various aspects of this disclosure. Figure 8 As shown, the location server (e.g., LMF 270) configures DL-PRS resources to be sent by one or more TRPs during a location session with the UE. The TRP then sends the configured DL-PRS to the UE, and the UE measures the RFFP of the DL-PRS.

[0157] exist Figure 8 In the example, the location server previously trained a machine learning model (labeled "RFFP ML") for RFFP localization, as referenced above. Figure 6 and Figure 7 The location server provides a machine learning model to the UE to perform inferences during the location session (e.g., determining location measurements based on the measured RFFP). Therefore, after measuring the RFFP of the DL-PRS, the UE inputs the measured RFFP into the received machine learning model to obtain the associated location measurements (e.g., ToA, RSTD).

[0158] Figure 9 An example process flow 900 for UE-based downlink-based RFFP positioning according to various aspects of this disclosure is illustrated. In Phase 1, UE 204 and LMF 270 perform an LPP positioning capability transfer procedure, during which UE 204 provides its positioning capabilities to LMF 270. In Phase 2, LMF 270 provides auxiliary information to UE 204's serving ng-eNB / gNB 222 / 224 and any neighboring ng-eNB / gNBs 222 / 224, such as the PRS resource configuration for DL-PRS to be sent to UE 204. In Phase 3, UE 204 and LMF 270 perform LPP auxiliary data exchange. During the exchange, LMF 270 provides UE 204 with auxiliary data for the positioning session, such as the configuration of the DL-PRS sent by the involved ng-eNB / gNBs 222 / 224 and a machine learning model to be used for reporting positioning measurements of the DL-PRS.

[0159] At phase 4, the LMF 270 optionally provides auxiliary information to the involved ng-eNB / gNBs 222 / 224 via a New Radio Positioning Protocol Type A (NRPPa) message. At phase 5, the serving ng-eNB / gNB 222 / 224 optionally broadcasts the auxiliary information received from the LMF 270 as auxiliary data in one or more positioning SIBs (posSIBs). At phase 6, the LMF 270 and UE 204 perform an LPP request / provide location information procedure, during which the UE 204 provides positioning measurements obtained from the DL-PRS transmitted by the ng-eNB / gNBs 222 / 224. The positioning measurements can be derived by applying a machine learning model received in the auxiliary data to the RFFP of the measured DL-PRS.

[0160] Currently, several representative use cases are being used as guidelines to study machine learning tools, their impact on over-the-air interfaces, and their lifecycle management. One such use case, as mentioned above, is localization. The identified areas of investigation include the lifecycle management of AI / ML models, such as model training, deployment, inference, monitoring, and updates. The areas of investigation also include datasets used for training, validation, testing, and inference.

[0161] Although machine learning (ML) techniques can be used as reference Figure 7 through Figure 9 The RFFP localization process is described, but ML techniques can also be used for other parts of the localization process. In some examples, ML techniques can be used to determine or refine intermediate measurements used in the localization process (e.g., ToA, RTT, AoA, TDoA, AoD, or any combination thereof).

[0162] Figure 10 This is an illustration 1000 illustrating the use of a machine learning model to determine an estimated ToA for positioning, according to various aspects of this disclosure. In this non-limiting example, a target device (e.g., a UE) may participate in a positioning process having N TRPs (labeled TRP0, TRP1, ..., TRP(N-1)). In this example, each of the N TRPs may obtain a measurement (e.g., time-domain CIR) of a signal from the target device and may each determine a corresponding estimated ToA by applying a corresponding ML model to the measurement. The N TRPs may send their respective determined ToAs to a location server (e.g., an LMF). The location server may obtain an estimated location of the target device based on the estimated ToA from the ML model.

[0163] Furthermore, in some examples, ML techniques can be used to map intermediate measurements (e.g., ToA, RTT, AoA, TDoA, AoD, or any combination thereof) to a probability distribution that represents a probabilistic view of where the target device might be located (e.g., in two-dimensional or three-dimensional space).

[0164] Figure 11AThis is a diagram illustrating an example setup 1100 for determining the estimated location of a target device and the channel response 1110 measured by the target device according to various aspects of this disclosure. In example setup 1100, the target device is located at actual location 1122. Example setup 1100 also includes three anchor devices: a first TRP 1130, a second TRP 1140, and a third TRP 1150. Channel response 1110 represents the channel response of the signal from the first TRP 1130 measured by the target device. In channel response 1110, tap 1112 represents the signal along the line-of-sight (LOS) path from the first TRP 1130 to the target device, and tap 1114 represents the signal along the non-line-of-sight (NLOS) path from the first TRP 1130 to the target device. In some scenarios, the channel conditions of the LOS and NLOS paths may result in NLOS tap 1114 having a stronger signal strength than LOS tap 1112.

[0165] Based on channel response 1110, the estimated ToA of LOS tap 1112 and NLOS tap 1114 can be described as estimated ranges 1132 and 1134, respectively. Furthermore, based on the channel responses from signals from the second TRP 1140 and the third TRP 1150, the corresponding ToA can be described as estimated ranges 1142 and 1152, respectively. In some aspects, the localization process may be unaware of the LOS or NLOS status of the received signal, and the estimated range 1134 (e.g., whose ToA is overestimated) can be used to determine the estimated location of the target device. Therefore, in this example, the estimated location of the target device based on estimated range 1134 could be at estimated location 1124, with a significant error compared to the true location 1122.

[0166] Figure 11B This is an example of various aspects of the present disclosure. Figure 11A A diagram illustrating the probability distribution of the channel response 1110 converted to ToA 1160. To address, as in the reference... Figure 11A The problem shown is that ToA is overestimated. Figure 11A The channel response 1110 in the equation can be transformed into a probability distribution of ToA 1160, which can be determined based on machine learning (ML) techniques. However, in some other examples, the transformation can be performed based on probability mapping without using ML techniques.

[0167] In this example, LOS tap 1112 and NLOS tap 1114 can be converted into LOS ToA probability distribution 1162 and NLOS ToA probability distribution 1164. In some respects, when the conversion is performed based on ML technology, since the ML model used for the conversion may have been trained based on training data and ground real-world data, the probability distribution of ToA 1160 can better reflect the likelihood of the target device's location, and thus reduce the influence of the NLOS signal.

[0168] Figure 11C This is an example based on various aspects of this disclosure. Figure 11B The probability distribution of ToA in the data is used to determine... Figure 11A A diagram showing the estimated location of the target device. (Compared to...) Figure 11A Components that are identical or similar to those in the drawings are given the same reference numerals, and therefore their detailed descriptions may be omitted. For example... Figure 11C The method shown can also be called likelihood fusion ("ML model-based likelihood fusion", which has a probability distribution of ToA obtained based on ML techniques, or "standard likelihood fusion", which has a probability distribution of ToA obtained without using ML techniques).

[0169] In this non-limiting example (which is a likelihood fusion based on an ML model), based on the probability distribution of ToA 1160, the LOS ToA probability distribution 1162 and the NLOS ToA probability distribution 1164 can be depicted as probability distributions for estimated ranges 1136 and 1138, respectively. Furthermore, the ToA probability distributions for signals from the second TRP 1140 and the third TRP 1150 can be depicted as probability distributions for estimated ranges 1146 and 1156, respectively. In this non-limiting example, a localization process can be performed to determine the estimated location of the target device based on combining likelihood estimates (e.g., probability distributions for estimated ranges 1136, 1138, 1146, and 1150) across anchor point devices (e.g., TRPs 1130, 1140, and 1150) in a soft fusion manner. Since the influence of the NLOS signal from the first TRP 1130 can be reduced by converting the channel response 1110 into a probability distribution of ToA 1160, the probability distribution of the estimated range 1136 will be considered, and the estimated position of the target device is comparable to... Figure 11A The example is closer to the actual location 1122.

[0170] Although reference Figure 7 through Figure 11CThe example shown is based on using a TRP as an anchor device to determine the estimated location of a target device (e.g., a UE), but a similar positioning process can be implemented using any anchor device based on different communication technologies to enhance positioning estimation performance. In some environments, the target device may be able to perform measurements using several anchor devices with different RATs (including different communication technologies and / or different versions of those technologies). In some aspects, the anchor device can be one or more TRPs, one or more UEs, one or more roadside units (RSUs), one or more access points (APs), or any combination thereof.

[0171] Figure 12 This is an illustration of an indoor environment 1200 comprising a TRP 1210 based on a first RAT (e.g., LTE or 5G) and multiple APs based on a second RAT (e.g., Wi-Fi or Bluetooth), according to various aspects of this disclosure. The TRP 1210 provides service in cell 1220 (the unshaded octagonal area) based on the first RAT. Furthermore, multiple target devices may be present in the indoor environment 1200. In some aspects, some of the target devices may be able to communicate with both the TRP and the APs, while others may be able to communicate only with the TRP or only with the APs. Furthermore, the indoor environment 1200 is used as a non-limiting example. In some aspects, the challenges and solutions illustrated based on the indoor environment 1200 may be applicable to outdoor environments or combined indoor-outdoor environments.

[0172] In such Figure 12 In the non-limiting example shown, the target device 1230 in indoor environment 1200 may participate in the RFFP-based positioning process using a set of anchor devices, including TRP 1210, one or more APs in indoor environment 1200, one or more other TRPs (not shown) outside indoor environment 1200, and / or one or more APs (not shown) outside indoor environment 1200. However, due to the physical setup in indoor environment 1200 (e.g., based on walls, windows, furniture, fixtures, etc.) and channel characteristics (e.g., based on materials, temperature, humidity, interference, etc.), not every AP placed in cell 1220 may be observable by target device 1230 or may actually improve the accuracy of the positioning process. Furthermore, the more APs considered in the positioning process, the more complex and computationally demanding the positioning process and machine learning techniques (including training and inference phases) may become.

[0173] Therefore, in such Figure 12In the non-limiting example shown, the APs deployed in cell 1220 can be arranged into three groups, including APs in areas 1242, 1244, and 1246, respectively. Each AP group, together with TRP 1210, can better serve the positioning of the target device in the corresponding area. Therefore, a specific combination of anchor devices can be mapped to the use of a corresponding (even custom or unique) ML model configured to operate on that specific combination of anchor devices. In some aspects, the target device or a location server involved in the positioning process to determine the estimated location of the target device can identify the set of observable anchor devices and / or a suitable ML model for the observable set of anchor devices used in the positioning process, as further illustrated below.

[0174] Figure 13 An example process flow 1300 is illustrated according to various aspects of this disclosure for implementing an ML model corresponding to a set of TRPs that a UE can observe. In this non-limiting example, UE 1302 (e.g., any UE described herein) may first provide LMF 1306 (e.g., LMF 270 or any location server described herein) with a list of TRPs 1304 (e.g., any base station or TRP described herein) that the UE can observe (e.g., TRPs from which the UE may appropriately perform measurements of signals, or TRPs corresponding to measurements exceeding a certain value). LMF 1306 may then instruct UE 1302 to use an ML model corresponding to the set of TRPs 1304, or fall back to a positioning process not based on an ML model (e.g., referring to...). Figure 4 Any positioning process shown in the positioning process or the "standard likelihood fusion" in Figure 11).

[0175] At stage 1310, UE 1302 may receive signals from TRP 1304. In some aspects, the signals may include reference signals (e.g., DL-PRS or Channel State Information Reference Signal (CSI-RS)) or control signals or data signals (e.g., physical channel signals carrying RRC configuration information). The UE may compile a set of TRPs for the positioning process that it considers observable by UE 1302.

[0176] At stage 1320, the UE may transmit, and the LMF 1306 may therefore receive, observable TRP information indicating the set of TRPs that the UE 1302 can observe. In some aspects, the observable TRP information may indicate a list of observable TRP sets, a cell identifier corresponding to an observable TRP set, or a group identifier corresponding to an observable TRP set.

[0177] At stage 1330, LMF 1306 may search for applicable ML models corresponding to the set of observable TRPs indicated in the observable TRP information. In some aspects, LMF 1306 may maintain records of one or more candidate ML models corresponding to one or more candidate TRP sets respectively. LMF 1306 may check whether one of the candidate ML models is applicable to the set of observable TRPs provided by UE 1302. In some aspects, if the corresponding candidate TRP set matches the set of observable TRPs, the candidate ML model may be considered applicable to the set of observable TRPs. In some aspects, if the corresponding candidate TRP set is a superset of the set of observable TRPs, the candidate ML model may be considered applicable to the set of observable TRPs.

[0178] At stage 1340, LMF 1306 may transmit and UE 1302 may receive auxiliary information for the positioning procedure. In some aspects, if LMF 1306 successfully identifies an applicable ML model corresponding to the observable set of TRPs, the auxiliary information may indicate the identified ML model corresponding to the observable set of TRPs. In some aspects, the auxiliary information may provide the ML model, a model identifier for the ML model, or both. In some aspects, if the positioning procedure is a UE-assisted positioning procedure, LMF 1306 may include a request to UE 1302 in the auxiliary information, requesting UE 1302 to provide measurements for the positioning procedure (and optionally may not indicate the identified ML model).

[0179] However, in some respects, if LMF 1306 cannot identify the applicable ML model corresponding to the observable TRP set, the auxiliary information can indicate the unavailability of a suitable ML model, guide the UE to participate in a localization process that does not require an ML model, or a combination thereof (e.g., not using ML techniques or references). Figure 4 Other methods illustrated in the “standard likelihood fusion”. In some aspects, if the localization process is a UE-assisted localization process, LMF 1306 may include a request to UE 1302 in the assist information, which requests UE 1302 to provide measurements for the localization process.

[0180] At stage 1350 (including 1352 and 1354, or 1356 and 1358), UE 1302 may participate in an ML model-based localization process using at least a subset of the observable TRP set to determine the estimated location of UE 1302. In some aspects, stages 1352 and 1354 correspond to a UE-assisted localization process. In some aspects, stages 1356 and 1358 correspond to a UE-based localization process.

[0181] In the case of performing a UE-assisted positioning procedure, at stage 1352, the UE may obtain measurements of signals (e.g., CIR, ToA, AoA, etc.) between UE 1302 and at least a subset of the observable TRP set. At stage 1352, UE 1302 may transmit and LMF 1306 may receive this measurement. In some aspects, the measurement may be provided in response to a request included in the assistance information at stage 1340.

[0182] At stage 1354, LMF 1306 may determine the estimated location of UE 1302 based on applying the identified ML model to the received measurements. In some aspects, LMF 1306 may also use some prior information specific to a region corresponding to the observable set of TPRs (which can be derived from other UEs previously located in that region) as input to the ML model. In some aspects, LMF 1306 may also provide UE 1302 with the estimated location of UE 1302.

[0183] In the case of performing a UE-based positioning procedure, at stage 1356, LMF 1306 may provide an ML model. In some aspects, UE 1302 may send a request to LMF 1306 at stage 1356, and LMF 1306 may provide an ML model in response to the request. In some aspects, LMF 1306 may have already provided the ML model at stage 1340, or UE 1302 may have already downloaded the ML model prior to stage 1352, and UE 1302 may simply load the stored ML model at stage 1352.

[0184] In some aspects, LMF 1306 may provide UE 1302 with prior information specific to a region corresponding to the observable set of TPRs, to be used as input to the ML model. In some aspects, LMF 1306 may access ML models stored locally within LMF 1306 or remotely in a database outside of LMF 1306. In some aspects, LMF 1306 may indicate a model identifier for the ML model, and UE 1302 may request and obtain the ML model from a server different from LMF 1306 based on the model identifier.

[0185] At stage 1358, UE 1302 may obtain measurements of signals (e.g., CIR, ToA, AoA, etc.) between UE 1302 and at least a subset of the observable TRP set, and determine the estimated location of UE 1302 based on applying an ML model to the obtained measurements. In some aspects, UE 1302 may also use prior information provided by LMF 1306 as input to the ML model. In some aspects, UE 1302 may also provide the estimated location of UE 1302 to LMF 1306.

[0186] Figure 14 Example process flow 1400 is illustrated according to various aspects of this disclosure for implementing an ML model corresponding to a set of anchor devices observable by a user device. Process flow 1400 can be considered as follows: Figure 13 The process flow 1300 shown is an extension or variation thereof. Figure 13 Compared to the example shown, UE 1302 can be replaced by user equipment 1402; TRP 1304 can be replaced by anchor device 1404; and LMF 1306 can be replaced by network entity 1406.

[0187] In some aspects, user equipment 1402 may be a UE that supports communication with a TRP. In some aspects, user equipment 1402 may be any communication device capable of communicating with one or more anchor devices based on one or more communication standards (such as any wireless communication technology described in this disclosure). In some aspects, anchor device 1404 may include one or more TRPs, one or more UEs, one or more RSUs, one or more APs, or any combination thereof.

[0188] In some respects, network entity 1406 may be an LMF (Low-Level Model). In some respects, network entity 1406 may be any server capable of supporting ML-based positioning, such as a dedicated server or a connected intelligent edge (CIE) server. In some respects, network entity 1406 may store ML models or have access to a database storing ML models.

[0189] In some respects, process flow 1400 can be considered as... Figure 13 The process flow 1300 shown is an extension or variation thereof, and the operations of stages 1410, 1420, 1430, 1440, and 1450 (including stages 1452 and 1454 or 1456 and 1458) can be the same as or similar to the operations of stages 1310, 1320, 1330, 1340, and 1350 (including stages 1352 and 1354 or 1356 and 1358), respectively. Therefore, some steps can be omitted. Figure 14 Detailed descriptions of each stage in the process. Additional details regarding process flow 1400 are illustrated below.

[0190] In some aspects, at stage 1410, user equipment 1402 may receive signals from anchor point equipment 1404. In some aspects, the signals may include reference signals (e.g., DL-PRS, CSI-RS, pilot sequences, beacons, etc.), control signals, or data signals.

[0191] In some aspects, at stage 1420, user equipment 1402 may send observable anchor information to network entity 1406, wherein the observable anchor information may indicate a set of anchor devices (e.g., anchor device 1404) that user equipment 1402 can observe. In some aspects, the set of anchor devices may be indicated based on information identifying the anchor devices (such as cell identifier, MAC identifier, communication technology type, application layer data, or any combination thereof).

[0192] Figure 15 An example process flow 1500 is illustrated according to aspects of this disclosure for implementing the use of one or more candidate ML models corresponding to one or more candidate TRP sets. In this non-limiting example, LMF 1506 (e.g., LMF 270 or any location server described herein) may maintain a table of candidate ML models corresponding to the candidate TRP sets used for the positioning process. UE 1502 (e.g., any UE described herein) may provide LMF 1506 with the UE's device information (e.g., a coarse location, such as the UE's actual location being no further than the tolerance, or the cell identifier of the cell serving the UE). Based on the device information, LMF may send auxiliary information indicating one or more candidate ML models corresponding to one or more candidate TRP sets. UE 1502 (e.g., any UE described herein) may then select an ML model from the one or more candidate ML models for positioning based on a TRP 1504 (e.g., a base station or any of the TRPs described herein) that the UE can observe.

[0193] At stage 1510, LMF 1506 may transmit, and UE 1502 may therefore receive, auxiliary information for positioning. The auxiliary information indicates one or more candidate ML models corresponding to a corresponding set of one or more candidate TRPs. In some aspects, the auxiliary information may come from LMF 1506 via broadcast, multicast, or unicast. In some aspects, the auxiliary information may indicate a model identifier for one or more candidate ML models.

[0194] At stage 1520, UE 1502 may receive signals from TRP 1504. In some aspects, the signals may include reference signals (e.g., DL-PRS or CSI-RS) or control signals or data signals (e.g., physical channel signals carrying RRC configuration information). UE 1502 may compile a set of TRPs that UE 1502 considers observable based on the signal coverage, signal strength, and / or signal quality of the signals from TRP 1504.

[0195] At stage 1530, UE 1502 may select an ML model from one or more candidate ML models based on the set of observable TRPs 1504. In some aspects, a candidate ML model may be selected if the corresponding set of candidate TRPs matches the set of observable TRPs. In other aspects, a candidate ML model may be selected if the corresponding set of candidate TRPs is a superset of the set of observable TRPs.

[0196] At stage 1540, UE 1502 may send, and LMF 1506 may receive, an ML model indication indicating the selected ML model (or, if no suitable candidate ML model is available, the selected ML model is missing). In some aspects, if UE 1502 selects a suitable ML model corresponding to the observable TRP set, the ML model indication may provide a model identifier for the selected ML model. In some aspects, if the localization process is based on the UE's localization process and the UE has already obtained the selected ML model, stage 1540 may be omitted. However, in some aspects, if UE 1502 does not select any suitable ML model, the ML model indication may indicate the unavailability of a suitable ML model, in which case UE 1502 may subsequently participate in a localization process that does not require an ML model (e.g., without using machine learning techniques or references). Figure 4 (Standard likelihood fusion of other methods shown).

[0197] At stage 1550 (including 1552 and 1554, or 1556 and 1558), UE 1502 may participate in a localization process based on a selected ML model to determine the estimated location of UE 1502 using at least a subset of the observable set of TRP 1504. In some aspects, stages 1552 and 1554 correspond to a UE-assisted localization process. In some aspects, stages 1556 and 1558 correspond to a UE-based localization process. The operation of stages 1552 and 1554 may be similar to... Figure 13 The operations of stages 1352 and 1354 are omitted here, and therefore their detailed descriptions are omitted. Furthermore, the operations of stages 1556 and 1558 can be similar to... Figure 13 The operations in stages 1356 and 1358 are omitted here, and therefore their detailed descriptions are omitted.

[0198] Figure 16 An example process flow is illustrated according to various aspects of this disclosure for implementing the use of one or more candidate ML models corresponding to one or more sets of candidate anchor devices. Process flow 1600 can be considered as follows: Figure 15 The process flow shown is an extension or variation of 1500. (And...) Figure 15Compared to the example shown, UE 1502 can be replaced by user equipment 1602; TRP 1504 can be replaced by anchor device 1604; and LMF 1506 can be replaced by network entity 1606.

[0199] In some aspects, user equipment 1602 may be a UE that supports communication with a TRP. In some aspects, user equipment 1602 may be any communication device capable of communicating with one or more anchor devices based on one or more communication standards (such as any wireless communication technology described in this disclosure). In some aspects, anchor device 1604 may include one or more TRPs, one or more UEs, one or more RSUs, one or more APs, or any combination thereof.

[0200] In some respects, network entity 1606 may be an LMF (Learning Model Provider). In some respects, network entity 1606 may be a server capable of supporting ML-based positioning, such as a dedicated server or a connected intelligent edge (CIE) server. In some respects, network entity 1606 may store one or more candidate ML models, or have access to a database storing one or more candidate ML models.

[0201] In some respects, process flow 1600 can be considered as... Figure 15 The process flow 1500 shown is an extension or variation thereof, so the operations of stages 1610, 1620, 1630, 1640 and 1650 (including stages 1652 and 1654 or 1656 and 1658) can be the same as or similar to the operations of stages 1510, 1520, 1530, 1540 and 1550 / 1350 (including stages 1552 / 1352 and 1554 / 1354 or 1556 / 1356 and 1558 / 1358), respectively.

[0202] Figure 17 An example method 1700 for operating a user equipment according to various aspects of this disclosure is illustrated. In some aspects, method 1700 may be performed by a UE (e.g., any UE described herein). In some aspects, method 1700 may correspond to an operation performed by... Figure 13 UE 1302 or Figure 14 The operation performed by user equipment 1402. In one aspect, method 1700 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340 and / or positioning components 342, any or all of which may be considered as components for performing one or more of the following operations of method 1700.

[0203] At operation 1710, the user equipment may send observable anchor information to a network entity. The observable anchor information may indicate a set of anchor devices that the user equipment can observe. In some aspects, the observable anchor information may indicate a list of anchor device sets, a cell identifier corresponding to an anchor device set, or a group identifier corresponding to an anchor device set. In some aspects, the anchor device set may include one or more TRPs, one or more UEs, one or more RSUs, one or more APs, or any combination thereof. In some aspects, operation 1710 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340, and / or positioning components 342, any one or all of which may be considered components for performing operation 1710.

[0204] At operation 1720, the user equipment may obtain auxiliary information from network entities. The auxiliary information may indicate an ML model corresponding to the set of anchor devices. In some aspects, the auxiliary information may provide an ML model, a model identifier for the ML model, or both. In some aspects, operation 1720 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340, and / or positioning components 342, any or all of which may be considered components for performing operation 1720.

[0205] At operation 1730, the user equipment may utilize at least a subset of the set of anchor devices to participate in a positioning process based on an ML model to determine the estimated location of the user equipment. In some aspects, operation 1730 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340, and / or positioning components 342, any or all of which may be considered as components for performing operation 1730.

[0206] In some aspects, when the positioning process is based on a user equipment (UE) positioning process, the UE may obtain measurements of signals between the UE and at least a subset of the set of anchor point devices, and apply an ML model to the measurements to obtain an estimated location for the UE. In some aspects, the UE may receive the ML model from a network entity or a server device other than a network entity.

[0207] In some aspects, when the positioning process is a user equipment-assisted positioning process, the user equipment can obtain measurements of signals between the user equipment and a subset of the set of anchor point devices, and transmit the measurements to the network entity. The network entity can then apply an ML model to the measurements to obtain an estimated location for the user equipment.

[0208] As will be understood, the technical advantage of method 1700 involves obtaining an ML model specific to the set of anchor devices that can be observed from network entities. Each set of anchor devices can be associated with a specific ML model, which is optimized to operate on the corresponding set of anchor devices. As a result of optimization, not all anchor devices present in the environment need to be considered by the ML model specific to the set of anchor devices that can be observed. Based on the obtained ML model, an ML-based localization process can be performed with improved performance by balancing the factors of the accuracy of the estimated location and the computational complexity of the ML model. Furthermore, the user equipment can obtain an ML model optimized for the set of anchor devices that the user equipment can observe.

[0209] Figure 18 Example method 1800 for operating a network entity according to various aspects of this disclosure is illustrated. In some aspects, method 1800 may be performed by a server device (e.g., a location server, LMF, SLP, proprietary server, CIE server, or any of the servers described herein). In some aspects, method 1800 may correspond to a method performed by... Figure 13 LMF 1306 or Figure 14 The operation performed by network entity 1406. In one aspect, method 1800 may be performed by one or more network transceivers 398, one or more processors 394, memory 398 and / or positioning components 398, any or all of which may be considered as components for performing one or more of the following operations of method 1800.

[0210] At operation 1810, the network entity may receive observable anchor information from the user equipment. The observable anchor information may indicate a set of anchor devices that the user equipment can observe. In some aspects, the observable anchor information may indicate a list of anchor device sets, a cell identifier corresponding to an anchor device set, or a group identifier corresponding to an anchor device set. In some aspects, the anchor device set may include one or more TRPs, one or more UEs, one or more RSUs, one or more APs, or any combination thereof. In some aspects, operation 1810 may be performed by one or more network transceivers 398, one or more processors 394, memory 398, and / or positioning components 398, any one or all of which may be considered components for performing operation 1810.

[0211] At operation 1820, the network entity may send auxiliary information to the user equipment based on the availability of an ML model corresponding to the set of anchor devices. In some aspects, the ML model can be used to determine the estimated location of the user equipment. In some aspects, the auxiliary information may provide the ML model, a model identifier of the ML model, or both. In some aspects, operation 1820 may be performed by one or more network transceivers 398, one or more processors 394, memory 398, and / or positioning components 398, any one or all of which may be considered as components for performing operation 1820.

[0212] In some respects, after Operation 1820, network entities may participate in a user equipment-assisted positioning process, which may include receiving measurements of signals between the user equipment and at least a subset of the set of anchor devices, and applying an ML model to the measurements to obtain an estimated location of the user equipment.

[0213] As will be understood, the technical advantage of method 1800 involves providing the user equipment with an ML model specific to the set of anchor devices that can be observed. Each set of anchor devices can be associated with a specific ML model, which is optimized to operate on the corresponding set of anchor devices. As a result of optimization, not all anchor devices present in the environment need to be considered by the ML model specific to the set of anchor devices that can be observed. Based on the provided ML model, the ML-based localization process can be performed with improved performance by balancing the factors of the accuracy of the estimated location and the computational complexity of the ML model. Furthermore, the network entity can provide the user equipment with at least one ML model optimized for the set of anchor devices that the user equipment can observe.

[0214] Figure 19 An example method 1900 for operating a user equipment according to various aspects of this disclosure is illustrated. In some aspects, method 1900 may be performed by a UE (e.g., any UE described herein). In some aspects, method 1900 may correspond to an operation performed by... Figure 15 UE 1502 or Figure 16 The operation performed by user equipment 1602. In one aspect, method 1900 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340 and / or positioning components 342, any or all of which may be considered as components for performing one or more of the following operations of method 1900.

[0215] At operation 1910, the user equipment may obtain auxiliary information from a network entity. The auxiliary information may indicate one or more candidate ML models corresponding to a corresponding set of one or more candidate anchor devices. In some aspects, the auxiliary information may be received from the network entity via broadcast, multicast, or unicast. In some aspects, the auxiliary information may indicate a model identifier for one or more candidate ML models. In some aspects, the set of one or more candidate anchor devices may include one or more TRPs, one or more UEs, one or more RSUs, one or more APs, or any combination thereof. In some aspects, operation 1910 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340, and / or positioning components 342, any or all of which may be considered components for performing operation 1910.

[0216] At operation 1920, the user equipment may select an ML model from one or more candidate ML models based on one or more anchor devices that the user equipment can observe. In some aspects, operation 1920 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340 and / or positioning components 342, any or all of which may be considered as components for performing operation 1920.

[0217] At operation 1930, the user equipment may utilize at least a subset of the corresponding anchor device set from one or more candidate anchor device sets to participate in a localization process based on a selected ML model to determine the estimated location of the user equipment. In some aspects, operation 1930 may be performed by one or more WWAN transceivers 310, one or more processors 332, memory 340, and / or localization components 342, any or all of which may be considered as components for performing operation 1930.

[0218] In some aspects, when the positioning process is based on a user equipment (UE) positioning process, the UE may obtain measurements of signals between the UE and at least a subset of the corresponding set of anchor point devices, and apply a selected ML model to the measurements to obtain an estimated location for the UE. In some aspects, the UE may receive the selected ML model from a network entity or a server device other than a network entity.

[0219] In some aspects, when the positioning process is a user equipment-assisted positioning process, the user equipment can obtain measurements of signals between the user equipment and at least a subset of the corresponding set of anchor point devices, and transmit the measurements to the network entity. The network entity can then apply a selected ML model to the measurements to obtain an estimated location for the user equipment.

[0220] As will be understood, the technical advantage of Method 1900 involves obtaining one or more candidate ML models from network entities and selecting a suitable ML model specific to the set of anchor devices that can be observed from the candidate ML models. Therefore, not all anchor devices present in the environment need to be considered by the selected ML model. User equipment can have the flexibility to select an ML model based on the set of anchor devices that the user equipment can observe. Based on the selected ML model, the ML-based localization process can be performed with improved performance by balancing the factors of the accuracy of the estimated location and the computational complexity of the ML model. Furthermore, the user equipment can actively perform measurements using only the set of anchor devices corresponding to the selected ML model. This reduces time / energy costs because the user equipment can omit performing measurements using other anchor devices (anchor devices not associated with the ML model).

[0221] Figure 20 Example method 2000 for operating a network entity according to various aspects of this disclosure is illustrated. In some aspects, method 2000 may be performed by a server device (e.g., a location server, LMF, SLP, proprietary server, CIE server, or any of the servers described herein). In some aspects, method 2000 may correspond to a method performed by... Figure 15 LMF 1506 or Figure 16 The operation performed by network entity 1606. In one aspect, method 2000 may be performed by one or more network transceivers 398, one or more processors 394, memory 398 and / or positioning components 398, any or all of which may be considered as components for performing one or more of the following operations of method 2000.

[0222] At operation 2010, the network entity can obtain device information for the user equipment. In some aspects, the device information may indicate a coarse location of the user equipment (e.g., a location based on the user equipment's actual location not being far from the tolerance, or a cell / AP identifier serving the user equipment). In some aspects, operation 2010 may be performed by one or more network transceivers 398, one or more processors 394, memory 398, and / or positioning components 398, any one or all of which may be considered as components for performing operation 2010.

[0223] At operation 2020, the network entity may send auxiliary information to the user equipment based on device information. In some aspects, the auxiliary information may indicate one or more candidate ML models corresponding to a set of one or more candidate anchor devices. In some aspects, at least one ML model among the one or more candidate ML models may be selected for determining the estimated location of the user equipment. In some aspects, operation 2020 may be performed by one or more network transceivers 398, one or more processors 394, memory 398, and / or positioning components 398, any or all of which may be considered as components for performing operation 2020.

[0224] In some respects, ancillary information can be sent by network entities via broadcast, multicast, or unicast. In other respects, ancillary information can indicate model identifiers for one or more candidate ML models.

[0225] In some aspects, following Operation 2020, network entities may obtain an instruction from user equipment, which may indicate a selected ML model from one or more candidate ML models for determining the estimated location of the user equipment. In some aspects, network entities may, in response to the instruction, send one or more candidate ML models to the user equipment.

[0226] In some aspects, following Operation 2020, network entities may participate in a user equipment-assisted positioning process, which may include receiving measurements of signals between the user equipment and at least a subset of corresponding anchor device sets from one or more candidate anchor device sets that correspond to an ML model; and applying the ML model to the measurements to obtain an estimated location of the user equipment.

[0227] As will be understood, the technical advantage of Method 2000 involves providing a user equipment with one or more candidate ML models, enabling the user equipment to select a suitable ML model specific to the set of anchor devices that can be observed. Therefore, not all anchor devices present in the environment need to be considered by the selected ML model. Based on the selected ML model, the ML-based localization process can be performed with improved performance by balancing the accuracy of the estimated location with the computational complexity of the ML model.

[0228] 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 to include more features in the example clauses than are expressly mentioned in each clause. Rather, the various aspects of this disclosure may include fewer features than those in the individual example clauses disclosed. Therefore, the following clauses should be regarded accordingly as incorporated into the description, where each clause may serve as a separate example. Although each dependent clause may refer in the clause to a specific combination with one of the other clauses, the aspect of that dependent clause is not limited to that specific combination. It should be understood that other example clauses may also include combinations of aspects of a dependent clause with the subject matter of any other dependent or independent clause, or combinations of any feature with other dependent and independent clauses. The various aspects disclosed herein expressly include these combinations unless expressly stated or readily inferred that a particular combination is not intended for use (e.g., contradictory aspects, such as defining an element as both an electrical insulator and an electrical conductor). Furthermore, it is contemplated that aspects of a clause may be included in any other independent clause, even if that clause does not directly depend on the independent clause.

[0229] Specific implementation examples are described in the following numbered clauses:

[0230] Clause 1. A method for wireless communication performed by a user equipment, the method comprising: sending observable anchor information to a network entity, the observable anchor information indicating a set of anchor devices that the user equipment can observe; obtaining auxiliary information from the network entity, the auxiliary information indicating a machine learning model corresponding to the set of anchor devices; and utilizing at least a subset of the set of anchor devices to participate in a localization process based on the machine learning model to determine an estimated location of the user equipment.

[0231] Clause 2. The method according to Clause 1, wherein the auxiliary information provides the machine learning model, the model identifier of the machine learning model, or both.

[0232] Clause 3. The method according to any one of Clauses 1 to 2, wherein the observable anchor information indicates a list of anchor device sets, a cell identifier corresponding to the anchor device set, or a group identifier corresponding to the anchor device set.

[0233] Clause 4. The method according to any one of Clauses 1 to 3, wherein participating in the positioning process includes: obtaining a measurement of a signal between the user equipment and at least a subset of the set of anchor devices; and applying the machine learning model to the measurement to obtain the estimated position of the user equipment.

[0234] Clause 5. The method according to any one of Clauses 1 to 4, the method further comprising: receiving the machine learning model from the network entity or a server device different from the network entity.

[0235] Clause 6. The method according to any one of Clauses 1 to 3, wherein participating in the positioning process includes: obtaining a measurement of a signal between the user equipment and the subset of the set of anchor devices; and sending the measurement to the network entity.

[0236] Clause 7. The method according to any one of Clauses 1 to 6, wherein the set of anchor point devices includes: one or more Transmit / Receive Points (TRPs), one or more User Equipment (UEs), one or more Roadside Units (RSUs), one or more Access Points (APs), or any combination thereof.

[0237] Clause 8. A method of wireless communication performed by a network entity, the method comprising: receiving observable anchor information from a user equipment, the observable anchor information indicating a set of anchor devices that the user equipment can observe; and sending auxiliary information to the user equipment based on the availability of a machine learning model corresponding to the set of anchor devices, wherein the machine learning model is capable of determining an estimated location of the user equipment.

[0238] Clause 9. The method according to Clause 8, wherein the observable anchor information indicates a list of anchor device sets, a cell identifier corresponding to the anchor device set, or a group identifier corresponding to the anchor device set.

[0239] Clause 10. The method according to any one of Clauses 8 to 9, wherein the auxiliary information provides the machine learning model, the model identifier of the machine learning model, or both.

[0240] Clause 11. The method according to any one of Clauses 8 to 10, the method further comprising: receiving from the user equipment a measurement of a signal between the user equipment and at least a subset of the set of anchor devices; and applying the machine learning model to the measurement to obtain the estimated position of the user equipment.

[0241] Clause 12. The method according to any one of Clauses 8 to 11, wherein the set of anchor point devices includes: one or more Transmit / Receive Points (TRPs), one or more User Equipment (UEs), one or more Roadside Units (RSUs), one or more Access Points (APs), or any combination thereof.

[0242] Clause 13. A method for wireless communication performed by a user equipment, the method comprising: obtaining auxiliary information from a network entity, the auxiliary information indicating one or more candidate machine learning models corresponding to a set of one or more candidate anchor devices; selecting a machine learning model from the one or more candidate machine learning models based on a set of anchor devices observable by the user equipment; and utilizing at least a subset of the corresponding set of anchor devices in the one or more candidate anchor devices to participate in a localization process based on the selected machine learning model to determine an estimated location of the user equipment.

[0243] Clause 14. The method according to Clause 13, wherein the auxiliary information is received from the network entity via broadcast, multicast or unicast.

[0244] Clause 15. The method according to any one of Clauses 13 to 14, wherein the auxiliary information indicates a model identifier of the one or more candidate machine learning models.

[0245] Clause 16. The method according to any one of Clauses 13 to 15, wherein participating in the positioning process includes: obtaining a measurement of a signal between the user equipment and at least a subset of the corresponding set of anchor devices; and applying a selected machine learning model to the measurement to obtain the estimated position of the user equipment.

[0246] Clause 17. The method according to any one of Clauses 13 to 16, the method further comprising: receiving a selected machine learning model from the network entity or a server device other than the network entity.

[0247] Clause 18. The method according to any one of Clauses 13 to 15, wherein participating in the positioning process includes: obtaining a measurement of a signal between the user equipment and at least a subset of the corresponding set of anchor devices; and sending the measurement to the network entity.

[0248] Clause 19. The method according to any one of Clauses 13 to 18, wherein the one or more candidate anchor point devices comprise: one or more Transmit / Receive Points (TRPs), one or more User Equipment (UEs), one or more Roadside Units (RSUs), one or more Access Points (APs), or any combination thereof.

[0249] Clause 20. A method for wireless communication performed by a network entity, the method comprising: obtaining device information of a user equipment; and sending auxiliary information to the user equipment based on the device information, the auxiliary information indicating one or more candidate machine learning models corresponding to a corresponding set of one or more candidate anchor devices, wherein at least one of the one or more candidate machine learning models can be selected to determine the estimated location of the user equipment.

[0250] Clause 21. The method according to Clause 20, wherein the device information indicates the approximate location of the user equipment.

[0251] Clause 22. The method according to any one of Clauses 20 to 21, wherein the auxiliary information is transmitted by the network entity via broadcast, multicast or unicast.

[0252] Clause 23. The method according to any one of Clauses 20 to 22, wherein the auxiliary information indicates a model identifier of the one or more candidate machine learning models.

[0253] Clause 24. The method according to any one of Clauses 20 to 23, the method further comprising: obtaining an indication from the user equipment, the indication indicating the machine learning model among the one or more candidate machine learning models for determining the estimated location of the user equipment.

[0254] Clause 25. The method according to Clause 24, the method further comprising: sending the machine learning model among the one or more candidate machine learning models to the user equipment in response to the instruction.

[0255] Clause 26. The method according to any one of Clauses 24 to 25, the method further comprising: receiving from the user equipment a measurement of a signal between the user equipment and at least a subset of the corresponding anchor device sets corresponding to the machine learning model from the one or more candidate anchor device sets; and applying the machine learning model to the measurement to obtain the estimated position of the user equipment.

[0256] Clause 27. The method according to any one of Clauses 20 to 26, wherein the one or more candidate anchor point devices comprise: one or more Transmit / Receive Points (TRPs), one or more User Equipment (UEs), one or more Roadside Units (RSUs), one or more Access Points (APs), or any combination thereof.

[0257] Clause 28. A user equipment 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 observable anchor information to a network entity via the at least one transceiver, the observable anchor information indicating a set of anchor devices observable by the user equipment; obtain auxiliary information from the network entity, the auxiliary information indicating a machine learning model corresponding to the set of anchor devices; and utilize at least a subset of the set of anchor devices to participate in a localization process based on the machine learning model to determine an estimated location of the user equipment.

[0258] Clause 29. The user equipment as described in Clause 28, wherein the auxiliary information provides the machine learning model, the model identifier of the machine learning model, or both.

[0259] Clause 30. The user equipment according to any one of Clauses 28 to 29, wherein the observable anchor information indicates a list of anchor device sets, a cell identifier corresponding to the anchor device set, or a group identifier corresponding to the anchor device set.

[0260] Clause 31. The user equipment according to any one of Clauses 28 to 30, wherein the at least one processor configured to participate in the positioning process is further configured to: obtain a measurement of a signal between the user equipment and at least a subset of the set of anchor devices; and apply the machine learning model to the measurement to obtain the estimated position of the user equipment.

[0261] Clause 32. The user equipment according to any one of Clauses 28 to 31, wherein the at least one processor is further configured to receive the machine learning model from the network entity or a server device other than the network entity via the at least one transceiver.

[0262] Clause 33. The user equipment according to any one of Clauses 28 to 30, wherein the at least one processor configured to participate in the positioning process is further configured to: obtain a measurement of a signal between the user equipment and the subset of the set of anchor devices; and transmit the measurement to the network entity via the at least one transceiver.

[0263] Clause 34. The user equipment pursuant to any one of Clauses 28 to 33, wherein the set of anchor point equipment comprises: one or more Transmit / Receive Points (TRPs), one or more User Equipment (UEs), one or more Roadside Units (RSUs), one or more Access Points (APs), or any combination thereof.

[0264] Clause 35. 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 observable anchor information from a user equipment via the at least one transceiver, the observable anchor information indicating a set of anchor devices observable by the user equipment; and transmit auxiliary information to the user equipment via the at least one transceiver based on the availability of a machine learning model corresponding to the set of anchor devices, wherein the machine learning model is capable of determining an estimated location of the user equipment.

[0265] Clause 36. A network entity as described in Clause 35, wherein the observable anchor information indicates a list of anchor device sets, a cell identifier corresponding to the anchor device set, or a group identifier corresponding to the anchor device set.

[0266] Clause 37. A network entity pursuant to any one of Clauses 35 to 36, wherein the auxiliary information provides the machine learning model, a model identifier of the machine learning model, or both.

[0267] Clause 38. A network entity according to any one of Clauses 35 to 37, wherein the at least one processor is further configured to: receive, via the at least one transceiver, a measurement of a signal between the user equipment and at least a subset of the set of anchor devices; and apply the machine learning model to the measurement to obtain the estimated location of the user equipment.

[0268] Clause 39. A network entity pursuant to any one of Clauses 35 to 38, wherein the set of anchor devices comprises: one or more Transmit / Receive Points (TRPs), one or more User Equipment (UEs), one or more Roadside Units (RSUs), one or more Access Points (APs), or any combination thereof.

[0269] Clause 40. A user equipment 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: obtain auxiliary information from a network entity, the auxiliary information indicating one or more candidate machine learning models corresponding to a set of one or more candidate anchor devices; select a machine learning model from the one or more candidate machine learning models based on a set of anchor devices observable by the user equipment; and utilize at least a subset of the corresponding anchor devices in the one or more candidate anchor devices to participate in a localization process based on the selected machine learning model to determine an estimated location of the user equipment.

[0270] Clause 41. The user equipment as described in Clause 40, wherein the auxiliary information is received from the network entity via broadcast, multicast or unicast.

[0271] Clause 42. The user equipment pursuant to any one of Clauses 40 to 41, wherein the auxiliary information indicates a model identifier for the one or more candidate machine learning models.

[0272] Clause 43. The user equipment according to any one of Clauses 40 to 42, wherein the at least one processor configured to participate in the positioning process is further configured to: obtain a measurement of a signal between the user equipment and at least a subset of the corresponding set of anchor point devices; and apply a selected machine learning model to the measurement to obtain the estimated position of the user equipment.

[0273] Clause 44. The user equipment according to any one of Clauses 40 to 43, wherein the at least one processor is further configured to receive a selected machine learning model from the network entity or a server device other than the network entity via the at least one transceiver.

[0274] Clause 45. The user equipment according to any one of Clauses 40 to 42, wherein the at least one processor configured to participate in the positioning process is further configured to: obtain a measurement of a signal between the user equipment and at least a subset of the corresponding set of anchor devices; and transmit the measurement to the network entity via the at least one transceiver.

[0275] Clause 46. The user equipment pursuant to any one of Clauses 40 to 45, wherein the set of one or more candidate anchor point devices includes: one or more Transmit / Receive Points (TRPs), one or more User Equipment (UEs), one or more Roadside Units (RSUs), one or more Access Points (APs), or any combination thereof.

[0276] Clause 47. A network entity comprising: a memory; at least one transceiver; and at least one processor, the at least one processor being communicatively coupled to the memory and the at least one transceiver, the at least one processor being configured to: obtain device information of a user equipment; and transmit auxiliary information to the user equipment via the at least one transceiver based on the device information, the auxiliary information indicating one or more candidate machine learning models corresponding to a corresponding set of one or more candidate anchor devices, wherein at least one of the one or more candidate machine learning models can be selected to determine the estimated location of the user equipment.

[0277] Clause 48. A network entity as described in Clause 47, wherein the device information indicates the approximate location of the user equipment.

[0278] Clause 49. A network entity pursuant to any one of Clauses 47 to 48, wherein the auxiliary information is transmitted by the network entity via broadcast, multicast, or unicast.

[0279] Clause 50. A network entity pursuant to any one of Clauses 47 to 49, wherein the auxiliary information indicates a model identifier for the one or more candidate machine learning models.

[0280] Clause 51. A network entity pursuant to any one of Clauses 47 to 50, wherein the at least one processor is further configured to: obtain an indication from the user equipment, the indication indicating one of the one or more candidate machine learning models for determining the estimated location of the user equipment.

[0281] Clause 52. The network entity pursuant to Clause 51, wherein the at least one processor is further configured to: in response to the instruction, transmit the machine learning model among the one or more candidate machine learning models to the user equipment via the at least one transceiver.

[0282] Clause 53. A network entity according to any one of Clauses 51 to 52, wherein the at least one processor is further configured to: receive, via the at least one transceiver, a measurement of a signal between the user equipment and at least a subset of the corresponding set of anchor devices in the one or more candidate anchor device sets that corresponds to the machine learning model; and apply the machine learning model to the measurement to obtain the estimated position of the user equipment.

[0283] Clause 54. A network entity pursuant to any one of Clauses 47 to 53, wherein the set of one or more candidate anchor point devices includes: one or more Transmit / Receive Points (TRPs), one or more User Equipment (UEs), one or more Roadside Units (RSUs), one or more Access Points (APs), or any combination thereof.

[0284] Clause 55. A user equipment comprising: means for transmitting observable anchor information to a network entity, the observable anchor information indicating a set of anchor devices that the user equipment can observe; means for obtaining auxiliary information from the network entity, the auxiliary information indicating a machine learning model corresponding to the set of anchor devices; and means for utilizing at least a subset of the set of anchor devices to participate in a localization process based on the machine learning model to determine an estimated location of the user equipment.

[0285] Clause 56. The user equipment as described in Clause 55, wherein the auxiliary information provides the machine learning model, the model identifier of the machine learning model, or both.

[0286] Clause 57. The user equipment according to any one of Clauses 55 to 56, wherein the observable anchor information indicates a list of anchor device sets, a cell identifier corresponding to the anchor device set, or a group identifier corresponding to the anchor device set.

[0287] Clause 58. The user equipment according to any one of Clauses 55 to 57, wherein the components for participating in the positioning process include: components for measuring signals between the user equipment and at least a subset of the set of anchor devices; and components for applying the machine learning model to the measurement to obtain the estimated position of the user equipment.

[0288] Clause 59. The user equipment according to any one of Clauses 55 to 58, the user equipment further comprising: a component for receiving the machine learning model from the network entity or a server device other than the network entity.

[0289] Clause 60. The user equipment according to any one of Clauses 55 to 57, wherein the components for participating in the positioning process include: components for measuring the signal between the user equipment and the subset of the set of anchor devices; and components for transmitting the measurement to the network entity.

[0290] Clause 61. The user equipment according to any one of Clauses 55 to 60, wherein the set of anchor point devices includes: one or more Transmit / Receive Points (TRPs), one or more User Equipment (UEs), one or more Roadside Units (RSUs), one or more Access Points (APs), or any combination thereof.

[0291] Clause 62. A network entity comprising: means for receiving observable anchor information from a user equipment, the observable anchor information indicating a set of anchor devices that the user equipment can observe; and means for sending auxiliary information to the user equipment based on the availability of a machine learning model corresponding to the set of anchor devices, wherein the machine learning model is capable of determining an estimated location of the user equipment.

[0292] Clause 63. The network entity as described in Clause 62, wherein the observable anchor information indicates a list of anchor device sets, a cell identifier corresponding to the anchor device set, or a group identifier corresponding to the anchor device set.

[0293] Clause 64. A network entity pursuant to any one of Clauses 62 to 63, wherein the auxiliary information provides the machine learning model, a model identifier of the machine learning model, or both.

[0294] Clause 65. The network entity according to any one of Clauses 62 to 64, the network entity further comprising: means for receiving from the user equipment a measurement of a signal between the user equipment and at least a subset of the set of anchor devices; and means for applying the machine learning model to the measurement to obtain the estimated position of the user equipment.

[0295] Clause 66. A network entity pursuant to any one of Clauses 62 to 65, wherein the set of anchor devices includes: one or more Transmit / Receive Points (TRPs), one or more User Equipment (UEs), one or more Roadside Units (RSUs), one or more Access Points (APs), or any combination thereof.

[0296] Clause 67. A user equipment comprising: means for obtaining auxiliary information from a network entity, the auxiliary information indicating one or more candidate machine learning models corresponding to a set of one or more candidate anchor devices; means for selecting a machine learning model from the one or more candidate machine learning models based on a set of anchor devices that the user equipment can observe; and means for utilizing at least a subset of the corresponding set of anchor devices in the one or more candidate anchor devices to participate in a localization process based on the selected machine learning model to determine an estimated location of the user equipment.

[0297] Clause 68. The user equipment as described in Clause 67, wherein the auxiliary information is received from the network entity via broadcast, multicast, or unicast.

[0298] Clause 69. The user equipment pursuant to any one of Clauses 67 to 68, wherein the auxiliary information indicates a model identifier for the one or more candidate machine learning models.

[0299] Clause 70. The user equipment according to any one of Clauses 67 to 69, wherein the components for participating in the positioning process include: components for measuring signals between the user equipment and at least a subset of the corresponding set of anchor point devices; and components for applying a selected machine learning model to the measurement to obtain the estimated position of the user equipment.

[0300] Clause 71. The user equipment according to any one of Clauses 67 to 70, the user equipment further comprising: a component for receiving a selected machine learning model from the network entity or a server device other than the network entity.

[0301] Clause 72. The user equipment according to any one of Clauses 67 to 69, wherein the components for participating in the positioning process include: components for obtaining measurements of signals between the user equipment and at least a subset of the corresponding set of anchor devices; and components for transmitting the measurements to the network entity.

[0302] Clause 73. The user equipment pursuant to any one of Clauses 67 to 72, wherein the set of one or more candidate anchor point devices includes: one or more Transmit / Receive Points (TRPs), one or more User Equipment (UEs), one or more Roadside Units (RSUs), one or more Access Points (APs), or any combination thereof.

[0303] Clause 74. A network entity comprising: a component for obtaining device information of a user equipment; and a component for sending auxiliary information to the user equipment based on the device information, the auxiliary information indicating one or more candidate machine learning models corresponding to a set of one or more candidate anchor devices, wherein at least one of the one or more candidate machine learning models can be selected to determine the estimated location of the user equipment.

[0304] Clause 75. A network entity as described in Clause 74, wherein the device information indicates the approximate location of the user equipment.

[0305] Clause 76. A network entity pursuant to any one of Clauses 74 to 75, wherein the auxiliary information is transmitted by the network entity via broadcast, multicast, or unicast.

[0306] Clause 77. A network entity pursuant to any one of Clauses 74 to 76, wherein the auxiliary information indicates a model identifier for the one or more candidate machine learning models.

[0307] Clause 78. The network entity according to any one of Clauses 74 to 77, the network entity further comprising: a component for obtaining an indication from the user equipment, the indication indicating one or more candidate machine learning models for determining the estimated location of the user equipment.

[0308] Clause 79. The network entity as described in Clause 78, the network entity further comprising: a component for sending the machine learning model, one or more of the candidate machine learning models, to the user equipment in response to the instruction.

[0309] Clause 80. The network entity according to any one of Clauses 78 to 79, the network entity further comprising: means for receiving from the user equipment a measurement of a signal between the user equipment and at least a subset of the corresponding set of anchor devices corresponding to the machine learning model from the one or more sets of candidate anchor devices; and means for applying the machine learning model to the measurement to obtain the estimated position of the user equipment.

[0310] Clause 81. A network entity pursuant to any one of Clauses 74 to 80, wherein the set of one or more candidate anchor point devices includes: one or more Transmit / Receive Points (TRPs), one or more User Equipment (UEs), one or more Roadside Units (RSUs), one or more Access Points (APs), or any combination thereof.

[0311] Clause 82. A non-transitory computer-readable medium storing computer-executable instructions, when executed by a user equipment, causing the user equipment to: send observable anchor information to a network entity, the observable anchor information indicating a set of anchor devices that the user equipment can observe; obtain auxiliary information from the network entity, the auxiliary information indicating a machine learning model corresponding to the set of anchor devices; and utilize at least a subset of the set of anchor devices to participate in a localization process based on the machine learning model to determine an estimated location of the user equipment.

[0312] Clause 83. The non-transitory computer-readable medium as described in Clause 82, wherein the auxiliary information provides the machine learning model, a model identifier of the machine learning model, or both.

[0313] Clause 84. A non-transitory computer-readable medium according to any one of Clauses 82 to 83, wherein the observable anchor information indicates a list of anchor device sets, a cell identifier corresponding to the anchor device set, or a group identifier corresponding to the anchor device set.

[0314] Clause 85. A non-transitory computer-readable medium according to any one of Clauses 82 to 84, wherein the instructions for engaging the user equipment in the positioning process include, when executed by the user equipment, instructions for the user equipment to: obtain a measurement of a signal between the user equipment and at least a subset of the set of anchor devices; and apply the machine learning model to the measurement to obtain the estimated position of the user equipment.

[0315] Clause 86. The non-transitory computer-readable medium according to any one of Clauses 82 to 85, the non-transitory computer-readable medium further comprising, when executed by the user equipment, computer-executable instructions causing the user equipment to: receive the machine learning model from the network entity or a server device other than the network entity.

[0316] Clause 87. A non-transitory computer-readable medium according to any one of Clauses 82 to 84, wherein the instructions for causing the user equipment to participate in the positioning process include instructions, when executed by the user equipment, to cause the user equipment to: obtain a measurement of a signal between the user equipment and the subset of the set of anchor devices; and send the measurement to the network entity.

[0317] Clause 88. A nontransitory computer-readable medium pursuant to any one of Clauses 82 to 87, wherein the set of anchor devices comprises: one or more Transmit / Receive Points (TRPs), one or more User Equipment (UEs), one or more Roadside Units (RSUs), one or more Access Points (APs), or any combination thereof.

[0318] Clause 89. A non-transitory computer-readable medium storing computer-executable instructions, when executed by a network entity, causing the network entity to: receive observable anchor information from a user equipment, the observable anchor information indicating a set of anchor devices that the user equipment can observe; and send auxiliary information to the user equipment based on the availability of a machine learning model corresponding to the set of anchor devices, wherein the machine learning model is capable of determining an estimated location of the user equipment.

[0319] Clause 90. The non-transitory computer-readable medium as described in Clause 89, wherein the observable anchor information indicates a list of anchor device sets, a cell identifier corresponding to the anchor device set, or a group identifier corresponding to the anchor device set.

[0320] Clause 91. A non-transitory computer-readable medium pursuant to any one of Clauses 89 to 90, wherein the auxiliary information provides the machine learning model, a model identifier of the machine learning model, or both.

[0321] Clause 92. The non-transitory computer-readable medium according to any one of Clauses 89 to 91, the non-transitory computer-readable medium further comprising, when executed by the network entity, computer-executable instructions that cause the network entity to: receive from the user equipment a measurement of a signal between the user equipment and at least a subset of the set of anchor devices; and apply the machine learning model to the measurement to obtain the estimated position of the user equipment.

[0322] Clause 93. A nontransitory computer-readable medium pursuant to any one of Clauses 89 to 92, wherein the set of anchor point devices comprises: one or more Transmit / Receive Points (TRPs), one or more User Equipment (UEs), one or more Roadside Units (RSUs), one or more Access Points (APs), or any combination thereof.

[0323] Clause 94. A non-transitory computer-readable medium storing computer-executable instructions, when executed by a user equipment, causing the user equipment to: obtain auxiliary information from a network entity, the auxiliary information indicating one or more candidate machine learning models corresponding to a set of one or more candidate anchor devices; select a machine learning model from the one or more candidate machine learning models based on a set of anchor devices observable by the user equipment; and utilize at least a subset of the corresponding anchor device sets in the one or more candidate anchor device sets to participate in a localization process based on the selected machine learning model to determine the estimated location of the user equipment.

[0324] Clause 95. A non-transitory computer-readable medium as described in Clause 94, wherein the auxiliary information is received from the network entity via broadcast, multicast, or unicast.

[0325] Clause 96. A non-transitory computer-readable medium pursuant to any one of Clauses 94 to 95, wherein the auxiliary information indicates a model identifier for the one or more candidate machine learning models.

[0326] Clause 97. A non-transitory computer-readable medium according to any one of Clauses 94 to 96, wherein the instructions for engaging the user equipment in the positioning process include, when executed by the user equipment, instructions for the user equipment to: obtain a measurement of a signal between the user equipment and at least a subset of the corresponding set of anchor point devices; and apply a selected machine learning model to the measurement to obtain the estimated position of the user equipment.

[0327] Clause 98. The non-transitory computer-readable medium according to any one of Clauses 94 to 97, the non-transitory computer-readable medium further comprising, when executed by the user equipment, computer-executable instructions that cause the user equipment to receive a selected machine learning model from the network entity or a server device other than the network entity.

[0328] Clause 99. A non-transitory computer-readable medium according to any one of Clauses 94 to 96, wherein the instructions for causing the user equipment to participate in the positioning process include instructions, when executed by the user equipment, to cause the user equipment to: obtain a measurement of a signal between the user equipment and at least a subset of the corresponding set of anchor point devices; and send the measurement to the network entity.

[0329] Clause 100. A nontransitory computer-readable medium pursuant to any one of Clauses 94 to 99, wherein the one or more candidate anchor point devices comprise: one or more Transmit / Receive Points (TRPs), one or more User Equipment (UEs), one or more Roadside Units (RSUs), one or more Access Points (APs), or any combination thereof.

[0330] Clause 101. A non-transitory computer-readable medium storing computer-executable instructions, when executed by a network entity, causing the network entity to: obtain device information of a user equipment; and send auxiliary information to the user equipment based on the device information, the auxiliary information indicating one or more candidate machine learning models corresponding to a corresponding set of one or more candidate anchor devices, wherein at least one of the one or more candidate machine learning models can be selected to determine the estimated location of the user equipment.

[0331] Clause 102. The non-transitory computer-readable medium as described in Clause 101, wherein the device information indicates the approximate location of the user equipment.

[0332] Clause 103. A non-transitory computer-readable medium pursuant to any one of Clauses 101 to 102, wherein the auxiliary information is transmitted by the network entity via broadcast, multicast, or unicast.

[0333] Clause 104. A non-transitory computer-readable medium pursuant to any one of Clauses 101 to 103, wherein the auxiliary information indicates a model identifier for the one or more candidate machine learning models.

[0334] Clause 105. The non-transitory computer-readable medium according to any one of Clauses 101 to 104, the non-transitory computer-readable medium further comprising, when executed by the network entity, computer-executable instructions that cause the network entity to: obtain an instruction from the user equipment, the instruction indicating one or more candidate machine learning models for determining the estimated location of the user equipment.

[0335] Clause 106. The non-transitory computer-readable medium according to Clause 105 further includes, when executed by the network entity, computer-executable instructions that cause the network entity to perform the following operation: in response to the instructions, send the machine learning model among the one or more candidate machine learning models to the user equipment.

[0336] Clause 107. The non-transitory computer-readable medium according to any one of Clauses 105 to 106, further comprising, when executed by the network entity, computer-executable instructions that cause the network entity to: receive from the user equipment a measurement of a signal between the user equipment and at least a subset of the corresponding set of anchor devices corresponding to the machine learning model from the one or more sets of candidate anchor devices; and apply the machine learning model to the measurement to obtain the estimated position of the user equipment.

[0337] Clause 108. A nontransitory computer-readable medium pursuant to any one of Clauses 101 to 107, wherein the one or more candidate anchor point devices comprise: one or more Transmit / Receive Points (TRPs), one or more User Equipment (UEs), one or more Roadside Units (RSUs), one or more Access Points (APs), or any combination thereof.

[0338] Those skilled in the art will understand that information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, light fields or optical particles, or any combination thereof.

[0339] Furthermore, those skilled in the art will understand that the various exemplary logic blocks, modules, circuits, and algorithm steps described in connection with the aspects 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 exemplary components, blocks, modules, circuits, and steps have been described above in general terms of their functionality. Whether this functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in different ways for each specific application, but such specific implementation decisions should not be construed as departing from the scope of this disclosure.

[0340] The various exemplary logic blocks, modules, and circuits described in conjunction with the aspects disclosed herein may be implemented or performed using a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic components, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but in alternative embodiments, 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, one or more microprocessors combined with a DSP core, or any other such configuration.

[0341] The methods, sequences, and / or algorithms described in conjunction with the aspects disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or a combination of both. The software module may reside in random access memory (RAM), flash memory, read-only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. Example storage media are coupled to a processor such that the processor can read information from and write information to the storage medium. Alternatively, the storage medium may be integral with the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal (e.g., a UE). Alternatively, the processor and storage medium may reside as discrete components in the user terminal.

[0342] In one or more examples, the described functionality may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functionality may be stored as one or more instructions or code on or transmitted via a computer-readable medium. A computer-readable medium includes both computer storage media and communication media, which includes any medium that facilitates the transfer of a computer program from one place to another. A storage medium may be any available medium 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 disc storage, disk storage or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and is accessible to a computer. Furthermore, any connection is appropriately referred to as a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included within the definition of a medium. As used herein, disks and optical discs include: compact optical discs (CDs), laser discs, optical discs, digital versatile discs (DVDs), floppy disks, and Blu-ray discs. 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.

[0343] While the foregoing disclosure illustrates exemplary aspects of this disclosure, it should be noted that various changes and modifications may be made herein without departing from the scope of this disclosure as defined by the appended claims. Furthermore, the functions, steps, and / or actions of the method claims according to the aspects of this disclosure described herein need not be performed in any particular order. Moreover, although elements of this disclosure may be described or claimed in the singular, the plural form may also be considered unless expressly stated as limited to the singular.

Claims

1. A method for wireless communication performed by a user equipment, the method comprising: Send observable anchor information to network entities, wherein the observable anchor information indicates the set of anchor devices that the user equipment can observe; Auxiliary information is obtained from the network entity, the auxiliary information indicating a machine learning model corresponding to the set of anchor devices; as well as At least a subset of the set of anchor devices is used in the localization process based on the machine learning model to determine the estimated location of the user equipment.

2. The method of claim 1, wherein the auxiliary information provides the machine learning model, the model identifier of the machine learning model, or both.

3. The method of claim 1, wherein the observable anchor information indicates a list of anchor device sets, a cell identifier corresponding to the anchor device set, or a group identifier corresponding to the anchor device set.

4. The method according to claim 1, wherein participating in the positioning process includes: Measurements of signals between the user equipment and at least a subset of the set of anchor devices are obtained; as well as The machine learning model is applied to the measurement to obtain the estimated location of the user equipment.

5. The method according to claim 1, further comprising: The machine learning model is received from the network entity or a server device different from the network entity.

6. The method according to claim 1, wherein participating in the positioning process includes: Measurement of the signal between the user equipment and the subset of the set of anchor devices; as well as The measurement is sent to the network entity.

7. The method of claim 1, wherein the anchor point device set comprises: One or more Transmitter Points (TRPs); One or more user equipment (UE); One or more roadside units (RSUs); One or more access points (APs), or Any combination of them.

8. A method for wireless communication performed by a network entity, the method comprising: Receive observable anchor information from the user equipment, the observable anchor information indicating the set of anchor devices that the user equipment can observe; as well as A machine learning model corresponding to the set of anchor devices is available to send auxiliary information to the user equipment, wherein the machine learning model is capable of determining the estimated location of the user equipment.

9. The method of claim 8, wherein the observable anchor information indicates a list of anchor device sets, a cell identifier corresponding to the anchor device set, or a group identifier corresponding to the anchor device set.

10. The method of claim 8, wherein the auxiliary information provides the machine learning model, a model identifier of the machine learning model, or both.

11. The method according to claim 8, further comprising: Measurements of signals received from the user equipment between at least a subset of the set of anchor devices; as well as The machine learning model is applied to the measurement to obtain the estimated location of the user equipment.

12. The method of claim 8, wherein the anchor point device set comprises: One or more Transmitter Points (TRPs); One or more user equipment (UE); One or more roadside units (RSUs); One or more access points (APs), or Any combination of them.

13. A method for wireless communication performed by a user equipment, the method comprising: Auxiliary information is obtained from network entities, the auxiliary information indicating one or more candidate machine learning models corresponding to a set of one or more candidate anchor devices; A machine learning model is selected from one or more candidate machine learning models based on the set of anchor devices that the user equipment can observe; as well as At least a subset of the corresponding anchor device set from the one or more candidate anchor device sets participates in the localization process based on the selected machine learning model to determine the estimated location of the user equipment.

14. The method of claim 13, wherein the auxiliary information is received from the network entity via broadcast, multicast, or unicast.

15. The method of claim 13, wherein the auxiliary information indicates a model identifier of the one or more candidate machine learning models.

16. The method of claim 13, wherein participating in the positioning process includes: Measurements of signals between the user equipment and at least a subset of the corresponding set of anchor point devices are obtained; as well as The selected machine learning model is applied to the measurement to obtain the estimated location of the user device.

17. The method according to claim 13, further comprising: The selected machine learning model is received from the network entity or a server device different from the network entity.

18. The method of claim 13, wherein participating in the positioning process includes: Measurements of signals between the user equipment and at least a subset of the corresponding set of anchor point devices are obtained; as well as The measurement is sent to the network entity.

19. The method of claim 13, wherein the set of one or more candidate anchor devices comprises: One or more Transmitter Points (TRPs); One or more user equipment (UE); One or more roadside units (RSUs); One or more access points (APs), or Any combination of them.

20. A method for wireless communication performed by a network entity, the method comprising: Obtain device information for the user's device; as well as Based on the device information, auxiliary information is sent to the user equipment, the auxiliary information indicating one or more candidate machine learning models corresponding to a set of one or more candidate anchor devices, wherein at least one of the one or more candidate machine learning models can be selected to determine the estimated location of the user equipment.

21. The method of claim 20, wherein the device information indicates the approximate location of the user equipment.

22. The method of claim 20, wherein the auxiliary information is transmitted by the network entity via broadcast, multicast, or unicast.

23. The method of claim 20, wherein the auxiliary information indicates a model identifier of the one or more candidate machine learning models.

24. The method according to claim 20, further comprising: An indication is obtained from the user equipment, the indication indicating the machine learning model among the one or more candidate machine learning models used to determine the estimated location of the user equipment.

25. The method according to claim 24, further comprising: In response to the instruction, the machine learning model is sent to the user equipment from the one or more candidate machine learning models.

26. The method according to claim 24, further comprising: Measurement of signals received from the user equipment between the user equipment and at least a subset of the corresponding anchor device set in the one or more candidate anchor device sets that corresponds to the machine learning model; as well as The machine learning model is applied to the measurement to obtain the estimated location of the user equipment.

27. The method of claim 20, wherein the set of one or more candidate anchor devices comprises: One or more Transmitter Points (TRPs); One or more user equipment (UE); One or more roadside units (RSUs); One or more access points (APs), or Any combination of them.

28. A user equipment, the user equipment comprising: 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: The observable anchor information is sent to the network entity via the at least one transceiver, the observable anchor information indicating the set of anchor devices that the user equipment can observe; Auxiliary information is obtained from the network entity, the auxiliary information indicating a machine learning model corresponding to the set of anchor devices; as well as At least a subset of the set of anchor devices is used in the localization process based on the machine learning model to determine the estimated location of the user equipment.

29. The user equipment of claim 28, wherein the at least one processor configured to participate in the positioning process is further configured to: Measurements of signals between the user equipment and at least a subset of the set of anchor devices; and The machine learning model is applied to the measurement to obtain the estimated location of the user equipment.

30. The user equipment of claim 28, wherein the at least one processor configured to participate in the positioning process is further configured to: Measurement of the signal between the user equipment and the subset of the anchor point device set; and The measurement is sent to the network entity via the at least one transceiver.

31. A network entity, the network entity comprising: 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 observable anchor information from the user equipment via the at least one transceiver, the observable anchor information indicating a set of anchor devices that the user equipment can observe; and A machine learning model corresponding to the set of anchor devices is available to send auxiliary information to the user equipment via the at least one transceiver, wherein the machine learning model is capable of determining the estimated location of the user equipment.

32. The network entity of claim 31, wherein the at least one processor is further configured to: Measurements of signals received from the user equipment via the at least one transceiver between the user equipment and at least a subset of the set of anchor devices; and The machine learning model is applied to the measurement to obtain the estimated location of the user equipment.

33. A user equipment, the user equipment comprising: 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: Auxiliary information is obtained from network entities, the auxiliary information indicating one or more candidate machine learning models corresponding to a set of one or more candidate anchor devices; A machine learning model is selected from one or more candidate machine learning models based on the set of anchor devices that the user equipment can observe; as well as At least a subset of the corresponding anchor device set from the one or more candidate anchor device sets participates in the localization process based on the selected machine learning model to determine the estimated location of the user equipment.

34. The user equipment of claim 33, wherein the auxiliary information is received from the network entity via broadcast, multicast, or unicast.

35. The user equipment of claim 33, wherein the at least one processor configured to participate in the positioning process is further configured to: Measurements of signals between the user equipment and at least a subset of the corresponding anchor point devices; and The selected machine learning model is applied to the measurement to obtain the estimated location of the user device.

36. The user equipment of claim 33, wherein the at least one processor configured to participate in the positioning process is further configured to: Measurements of signals between the user equipment and at least a subset of the corresponding anchor point devices; and The measurement is sent to the network entity via the at least one transceiver.

37. A network entity, the network entity comprising: 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: Obtain device information for the user's device; as well as Based on the device information, auxiliary information is sent to the user equipment via the at least one transceiver. The auxiliary information indicates one or more candidate machine learning models corresponding to a set of one or more candidate anchor devices, wherein at least one of the one or more candidate machine learning models can be selected to determine the estimated location of the user equipment.

38. The network entity of claim 37, wherein the auxiliary information is transmitted by the network entity via broadcast, multicast, or unicast.

39. The network entity of claim 37, wherein the at least one processor is further configured to: Obtain an indication from the user equipment, the indication indicating a machine learning model among the one or more candidate machine learning models for determining the estimated location of the user equipment; and In response to the instruction, the machine learning model among the one or more candidate machine learning models is sent to the user equipment via the at least one transceiver.

40. The network entity of claim 37, wherein the at least one processor is further configured to: Measurements of signals received from the user equipment via the at least one transceiver between the user equipment and at least a subset of the corresponding anchor device sets from the one or more candidate anchor device sets, corresponding to the machine learning model; and The machine learning model is applied to the measurement to obtain the estimated location of the user equipment.

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