Quasi-model relationships between machine learning based user equipment positioning estimation models

Abstract

Aspects of the present disclosure relate to signaling of quasi-model relationships (QMLs) between machine learning (ML)-based UE positioning estimation models. In this way, only certain portions of the overall (potentially large) ML-based user equipment (UE) positioning model need to be conveyed (e.g., signaling the difference between a signaled ML-based UE positioning model and a reference ML-based UE positioning model). Such aspects can provide various technical advantages, such as reduced network overhead, fast and simple model relationship indication, fast and simple model lifecycle management (LCM), and the like.

Description

Background Technology

[0001] 1. Technical Field

[0002] All aspects of this disclosure relate to wireless technology.

[0003] 2. Related technical descriptions

[0004] Wireless communication systems have evolved through many generations, including first-generation analog radiotelephone service (1G), second-generation (2G) digital radiotelephone service (including transitional 2.5G and 2.75G networks), third-generation (3G) high-speed data, wireless services with internet capabilities, and fourth-generation (4G) services (e.g., Long Term Evolution (LTE) or WiMax). Currently, many different types of wireless communication systems are in use, including cellular systems and Personal Communication Services (PCS) systems. Known examples of cellular systems include cellular analog Advanced Mobile Phone Systems (AMPS), as well as digital cellular systems based on Code Division Multiple Access (CDMA), Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), Global System for Mobile Communications (GSM), and others.

[0005] The fifth-generation (5G) wireless standard, known as New Radio (NR), delivers higher data transfer speeds, more connections, better coverage, and other improvements. According to the Next Generation Mobile Networks Alliance (NGC), the 5G standard is designed to provide higher data rates, more accurate positioning (e.g., based on Positioning Reference Signals (RS-P), such as downlink, uplink, or sidelink Positioning Reference Signals (PRS)), and other technological enhancements compared to previous standards. These enhancements, along with the use of higher frequency bands, advancements in the PRS process and technology, and the high-density deployment of 5G, enable high-accuracy positioning based on 5G. Summary of the Invention

[0006] The following is a simplified summary of the invention relating to one or more aspects disclosed herein. Therefore, this summary should not be considered an exhaustive overview relating to all conceived aspects, nor should it be considered to identify key or decisive elements relating to all conceived aspects or to depict the scope associated with any particular aspect. Thus, the sole purpose of this summary is to present, in a simplified form, certain concepts relating to one or more aspects involving the mechanisms disclosed herein, prior to the detailed description presented below.

[0007] In one aspect, a method of operating a communication device includes: determining a first quasi-model relationship (QML) between a reference machine learning (ML)-based UE location estimation model and a first ML-based UE location estimation model, the first QML being based on a correspondence between a first set of features associated with the reference ML-based UE location estimation model and a second set of features associated with a second set of features; and sending an instruction to the first QML.

[0008] In one aspect, a method for operating a location estimation entity includes: receiving an indication of a first quasi-model relationship (QML) between a reference machine learning (ML)-based user equipment (UE) location estimation model associated with a first set of characteristics and a first ML-based UE location estimation model associated with a second set of characteristics; and performing one or more actions associated with location estimation of one or more UEs based on the indication of the first QML.

[0009] In one aspect, a communication device includes: one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors being individually or in combination configured to: determine a first quasi-model relationship (QML) between a reference machine learning (ML)-based UE location estimation model and a first ML-based UE location estimation model, the first QML being based on a correspondence between a first set of features associated with the reference ML-based UE location estimation model and a second set of features associated with a second set of features; and transmit an instruction to the first QML via the one or more transceivers.

[0010] In one aspect, a location estimation entity includes: one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors being individually or in combination configured to: receive via the one or more transceivers an indication of a first quasi-model relationship (QML) between a reference machine learning (ML)-based user equipment (UE) location estimation model associated with a first set of features and a first ML-based UE location estimation model associated with a second set of features; and perform one or more actions associated with location estimation of one or more UEs based on the indication of the first QML.

[0011] In one aspect, a communication device includes: a component for determining a first quasi-model relationship (QML) between a reference machine learning (ML)-based UE positioning estimation model and a first ML-based UE positioning estimation model, the first QML being based on a correspondence between a first set of features associated with the reference ML-based UE positioning estimation model and a second set of features associated with a second set of features; and a component for transmitting an instruction to the first QML.

[0012] In one aspect, a location estimation entity includes: a component for receiving an indication of a first quasi-model relationship (QML) between a reference machine learning (ML)-based user equipment (UE) location estimation model associated with a first set of characteristics and a first ML-based UE location estimation model associated with a second set of characteristics; and a component for performing one or more actions associated with the location estimation of one or more UEs based on the indication of the first QML.

[0013] In one aspect, a non-transitory computer-readable medium storing computer-executable instructions, which, when executed by a communication device, cause the communication device to: determine a first quasi-model relationship (QML) between a reference machine learning (ML)-based UE location estimation model and a first ML-based UE location estimation model, the first QML being based on a correspondence between a first set of features associated with the reference ML-based UE location estimation model and a second set of features associated with a second set of features; and send an instruction to the first QML.

[0014] In one aspect, a non-transitory computer-readable medium storing computer-executable instructions that, when executed by a location estimation entity, cause the location estimation entity to: receive an instruction on a first quasi-model relationship (QML) between a reference machine learning (ML)-based user equipment (UE) location estimation model associated with a first set of characteristics and a first ML-based UE location estimation model associated with a second set of characteristics; and perform one or more actions associated with the location estimation of one or more UEs based on the instruction on the first QML.

[0015] 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

[0016] 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 aspects.

[0017] Figure 1 Example wireless communication systems according to various aspects of this disclosure are illustrated.

[0018] Figure 2A , Figure 2B and Figure 2C Example wireless network architectures based on various aspects of this disclosure are illustrated.

[0019] Figure 3A , Figure 3B and Figure 3C It is a simplified block diagram of several examples of components that can be used in user equipment (UE), base stations and network entities and configured to support communications as taught herein.

[0020] Figure 4 This is a diagram illustrating an example frame structure according to various aspects of this disclosure.

[0021] Figure 5A and Figure 5B This is a diagram illustrating example sidelink time slot structures with and without feedback resources according to various aspects of this disclosure.

[0022] Figures 6A to 6D This is a diagram illustrating an example of a resource pool for positioning according to various aspects of this disclosure.

[0023] Figure 7 Examples of various positioning methods supported in new radios (NR) according to various aspects of this disclosure are illustrated.

[0024] Figure 8A and Figure 8B Various scenarios of interest are illustrated according to aspects of this disclosure, including sidelink-only localization or combined Uu and sidelink localization.

[0025] Figure 9 Example neural networks according to various aspects of this disclosure are illustrated.

[0026] Figure 10A AI / ML localization use cases based on various aspects of this disclosure are illustrated.

[0027] Figure 10B AI / ML localization use cases based on various aspects of this disclosure are illustrated.

[0028] Figure 10C AI / ML localization use cases based on various aspects of this disclosure are illustrated.

[0029] Figure 10D AI / ML localization use cases based on various aspects of this disclosure are illustrated.

[0030] Figure 10E AI / ML localization use cases based on various aspects of this disclosure are illustrated.

[0031] Figure 11 An exemplary process of communication according to one aspect of this disclosure is illustrated.

[0032] Figure 12 An exemplary process of communication according to one aspect of this disclosure is illustrated.

[0033] Figure 13 A QML tree hierarchy structure according to various aspects of this disclosure is illustrated. Detailed Implementation

[0034] Various aspects of this disclosure are provided in the following description and accompanying drawings of various examples provided for illustrative purposes. 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.

[0035] The overall picture involves quasi-model relationships (QML) between user equipment (UE) positioning estimation models based on machine learning (ML). Artificial intelligence (AI) / ML positioning is one of the key use cases considered in future network implementations. AI / ML positioning can provide specific benefits under NLOS conditions. In some designs, AI / ML positioning models can be site / area specific, where such models only provide excellent performance in a specific area / region. The challenge of scaling AI / ML positioning can be mitigated by generating multiple AI / ML positioning models that address different site / area / radio characteristics (AI / ML positioning models can overlap and share some radio and / or area characteristics). However, the signaling associated with a large number of AI / ML positioning models can be quite large, leading to high overhead.

[0036] Specific aspects of the subject matter described in this disclosure can be implemented to achieve one or more of the following potential advantages. Aspects of this disclosure relate to signaling quasi-model relationships (QML) between machine learning (ML)-based UE positioning estimation models. In this way, only certain parts of the entire (potentially large) ML-based UE positioning model need to be communicated (e.g., differences between the signaling ML-based UE positioning model and a reference ML-based UE positioning model). Such aspects can provide various technical advantages, such as reduced network overhead, fast and simple model relationship indication, fast and simple model lifecycle management (LCM), and so on.

[0037] 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.

[0038] 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.

[0039] 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 specific circuitry (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, any corresponding form of any such aspect may be described herein as, for example, "logic configured to perform the described actions."

[0040] 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). In general, 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. Overall, 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.).

[0041] 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.

[0042] The term "base station" can refer to a single physical transmit / receive point (TRP) or multiple physical TRPs that may or may not be 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.

[0043] 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).

[0044] 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, an RF signal may also be referred to as a “wireless signal” or simply a “signal” where the context clearly indicates that the term “signal” refers to a wireless signal or an RF signal.

[0045] 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 (wherein the wireless communication system 100 corresponds to an LTE network), or a gNB (wherein 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.

[0046] 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 can be represented as an indirect connection (e.g., via core network 170, etc.) or a direct connection (e.g., as shown via direct connection 128), wherein intermediate nodes (if present) are omitted from the signaling diagram for clarity.

[0047] 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.

[0048] 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, 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" can also refer to the geographical coverage area of ​​a base station (e.g., a sector), as long as the carrier frequency can be detected and used for communication within a portion of the geographical coverage area 110.

[0049] 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 can be referred to as a heterogeneous network. A heterogeneous network may also include a home eNB (HeNB) that can provide service to a restricted group referred to as a Closed Subscriber Group (CSG).

[0050] 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).

[0051] 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.

[0052] 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 5GHz unlicensed spectrum as WLAN AP 150. Small cell base station 102' employing LTE / 5G in unlicensed spectrum can improve the coverage and / or increase the capacity of the access network. 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. ® .

[0053] 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. Furthermore, 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.

[0054] 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.

[0055] Transmit beams can be quasi-co-located, meaning they appear to the receiver (e.g., the 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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 to 7.125GHz) and FR2 (24.25GHz to 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. A similar naming issue sometimes occurs with FR2, which is often (interchangeably) referred to as the "millimeter wave" band in documents and articles, although this differs from the designation used by the International Telecommunication Union.® Extremely high frequency (EHF) bands (30 GHz to 300 GHz) are designated as “millimeter wave” bands.

[0060] The frequencies between FR1 and FR2 are generally referred to as mid-band frequencies. Recent 5G NR studies have identified the operating bands used for these mid-band frequencies as the frequency range designation FR3 (7.125 GHz to 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 mid-band frequencies. Furthermore, higher frequency bands are currently being explored to extend 5G NR operation beyond 52.6 GHz. For example, three higher operating bands have been identified as the frequency range designations FR4a or FR4-1 (52.6 GHz to 71 GHz), FR4 (52.6 GHz to 114.25 GHz), and FR5 (114.25 GHz to 300 GHz). Each of these higher frequency bands falls within the EHF band.

[0061] 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.

[0062] In multi-carrier systems such as 5G, one of the carrier frequencies is referred to as the "primary carrier," "anchor carrier," "primary serving cell," or "PCell," 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, where UE 104 / 182 performs an initial Radio Resource Control (RRC) connection establishment procedure or initiates an RRC connection re-establishment procedure. The primary carrier carries all common and UE-specific control channels and can be a carrier 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 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.

[0063] For example, still refer to Figure 1 One of the frequencies used by macro cell base station 102 can be an anchor carrier (or "PCell"), and the other frequencies used by macro cell base station 102 and / or mmW base station 180 can 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).

[0064] 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.

[0065] 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 used for sidelink communication. In other cases, sidelink communication is performed between the individual SL-UEs without involving base station 102.

[0066] 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 may 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 extended their operation to 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.

[0067] 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.

[0068] exist Figure 1 In the example, the UE shown (for simplicity, in) Figure 1Any UE (shown as a single UE 104) can 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.

[0069] 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 capable of being used with one or more global and / or regional navigation satellite systems. For example, SBAS may include augmentation systems that provide integrity information, differential correction, etc., such as Wide Area Augmentation System (WAAS), European Geostationary Navigation Overlap 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.

[0070] 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 the modified base station 102 (without a ground antenna) or network nodes 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 the ground base station 102, UE 104 can receive communication signals (e.g., signal 124) from SV 112.

[0071] The wireless communication system 100 may also include one or more UEs, such as UE 190, which are indirectly connected to one or more communication networks via one or more device-to-device (D2D) peer-to-peer (P2P) links (referred to as "side links"). Figure 1 In one 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 a 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 utilize any known D2D RAT (such as LTE Direct (LTE-D), Wi-Fi Direct). ® ,Bluetooth ® (etc.) to support.

[0072] 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 can communicate with one or more UEs 204 (e.g., any of the UEs described herein).

[0073] Another optional aspect may include a location server 230, which can communicate with the 5GC 210 to provide location assistance to the UE 204. The location server 230 may be implemented as 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, which may 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).

[0074] Figure 2B Another example wireless network architecture 240.5GC 260 is illustrated (which can be used with...). Figure 2AThe 5GC 210 (corresponding to 5GC 210) can be functionally considered as a control plane function provided by the Access and Mobility Management Function (AMF) 264 and a user plane function provided by the 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 the Session Management Function (SMF) 266, a transparent proxy service for routing SM messages, access authentication and access authorization, transmission of short message service (SMS) messages between UE 204 and the Short Message Service Function (SMSF) (not shown), and Secure Anchoring Functionality (SEAF). AMF 264 also interacts with the Authentication Server Function (AUSF) (not shown) and UE 204 and receives an intermediate key established as a result of the UE 204's authentication process. In the case of UMTS (Universal Mobile Telecommunications System) Subscriber Identity Module (USIM) authentication, AMF 264 retrieves security material from the AMF. AMF 264 also includes Security Context Management (SCM). The SCM receives a key from the SEAF and uses this key to derive an access network-specific key. AMF 264 functionality also includes location service management for regulatory services, transmission of location service messages between UE 204 and Location Management Function (LMF) 270 (which acts as location server 230), transmission of location service messages between NG-RAN 220 and LMF 270, Evolved Packet System (EPS) bearer identifier allocation for EPS interoperability, and UE 204 mobility event notification. Furthermore, AMF 264 also supports non-3GPP... ® (Third Generation Partner Program) Access network functionality.

[0075] The functions of UPF 262 include: acting as an anchor point for intra-RAT / inter-RAT mobility (where applicable), acting as an external Protocol Data Unit (PDU) session point interconnecting to a data network (not shown), providing packet routing and forwarding, packet inspection, user plane policy rule enforcement (e.g., strobing, redirection, traffic steering), lawful eavesdropping (user plane collection), traffic usage reporting, quality of service (QoS) processing for the user plane (e.g., uplink / downlink rate enforcement, reflective QoS marking in the downlink), uplink traffic verification (Service Data Flow (SDF) to QoS flow mapping), transport-level packet marking in the uplink and downlink, downlink packet buffering and downlink data notification triggering, and delivering and forwarding one or more "end markers" to the source RAN node. UPF 262 can also support the delivery of location service messages between UE 204 and location servers (such as SLP 272) on the user plane.

[0076] 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.

[0077] 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 can be implemented as multiple separate servers (e.g., physically separate servers, different software modules on a single server, different software modules distributed across multiple physical servers, etc.), or alternatively, each can correspond to a single server. The LMF 270 can be configured to support one or more location services for the UE 204, which can 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 AMF 264, 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).

[0078] Another optional aspect may include a third-party server 274, which can communicate with LMF 270, SLP 272, 5GC 260 (e.g., via AMF 264 and / or UPF 262), NG-RAN 220, and / or UE 204 to obtain location information (e.g., location estimation) of UE 204. 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.

[0079] 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.

[0080] The functionality of the gNB 222 is 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.

[0081] The deployment of communication systems such as 5G NR systems can be arranged in a variety of ways using various components or parts. In a 5G NR system or network, network nodes, network entities, network mobility elements, RAN nodes, core network nodes, network elements, or network equipment (such as base stations or one or more units (or components) performing base station functions) can be implemented in aggregated or decomposed architectures. For example, base stations (such as Node B (NB), evolved NB (eNB), NR base stations, 5GNB, AP, TRP, cells, etc.) can be implemented as aggregated base stations (also known as standalone base stations or monolithic base stations) or decomposed base stations.

[0082] 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 respects, 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).

[0083] Base station type operation or network design can consider the aggregation characteristics of base station functionality. For example, decomposed base stations can be used in Integrated Access Backhaul (IAB) networks, Open Radio Access Networks (O-RAN) (such as those developed by the O-RAN Alliance), and other similar networks. ® This can be used in proposed network configurations 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 allows for flexibility in network design. Various units in a decomposed base station or decomposed RAN architecture can be configured to communicate wirelessly with at least one other unit.

[0084] 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 duplex 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.

[0085] Each unit in the cells (i.e., CU 280, DU 285, RU 287, and near-RT RIC 259, non-RT RIC 257, and SMO frame 255) may include or be coupled to one or more interfaces configured to receive or transmit signals, data, or information (collectively, signals) via wired or wireless transmission media. Each unit in the cells, 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 signals 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 RF transceivers) configured to receive signals or transmit signals to one or more other units, or both, via wireless transmission media.

[0086] In some aspects, the CU 280 can host one or more higher-level control functions. Such control functions may include RRC, PDCP, Serving Data Adaptation Protocol (SDAP), etc. Each control function can be implemented using an interface configured to communicate signaling with 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 split into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, the CU-UP 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.

[0087] DU 285 may correspond to a logic unit that includes one or more base station functions for controlling the operation of one or more RU 287s. In some aspects, DU 285 may be at least partially based on functional partitioning (such as that provided by the 3rd Generation Partnership Project (3GPP)). ® The DU285 is functionally partitioned to host one or more of the RLC layer, MAC layer, and one or more high-PHY layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation, and demodulation). In some respects, the DU285 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 the DU285 or with control functions hosted by the CU280.

[0088] Lower-layer functionality can be implemented by one or more RU 287s. In some deployments, an RU287 controlled by a DU 285 may correspond to a logical node that hosts RF processing functions or low-PHY layer functions (such as performing Fast Fourier Transform (FFT), Inverse FFT (iFFT), digital beamforming, or Physical Random Access Channel (PRACH) extraction and filtering, or both, at least in part based on functional decomposition (such as lower-layer functional decomposition). In this architecture, 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 implementation of the DU 285 and CU 280 in a cloud-based RAN architecture (such as a vRAN architecture).

[0089] 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 cloud computing platforms such as Open Cloud (O-Cloud) 269 to perform network element lifecycle management (such as instantiating virtualized network elements) via cloud computing platform interfaces 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.

[0090] The non-RT RIC 257 can be configured to include logical functions enabling 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, such as via an A1 interface. The near-RT RIC 259 can be configured to include logical functions enabling near real-time control and optimization of RAN elements and resources via data collection and actions through an interface such as an E2 interface, connecting one or more CU 280s, one or more DU 285s, or both, and O-eNBs to the near-RT RIC 259.

[0091] 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).

[0092] 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 LMF270, or alternatively may be independent of UE 302). Figure 2A and Figure 2BSeveral 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.

[0093] 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, components for blocking transmission, etc.) for communication via one or more wireless communication networks (not shown), such as NR networks, LTE networks, GSM networks, etc. WWAN transceivers 310 and 350 may each be connected to one or more antennas 316 and 356 for communication with other network nodes (such as other UEs, access points, base stations (e.g., eNB, gNB), etc.) via at least one designated RAT (e.g., NR, LTE, GSM, etc.) through a wireless communication medium of interest (e.g., a 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.

[0094] In at least some cases, UE 302 and base station 304 each further include one or more short-range radio transceivers 320 and 360, respectively. Short-range radio transceivers 320 and 360 can be connected to one or more antennas 326 and 366, respectively, and provide access over a wireless communication medium of interest via at least one designated RAT (e.g., Wi-Fi, LTE Direct, Bluetooth). ® ZIGBEE ® Z-WAVE ® Components (e.g., components for transmitting, components for receiving, components for measuring, components for tuning, components for blocking transmission, etc.) that enable 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.) respectively according to a specified RAT, and conversely, to receive and decode signals 328 and 368 (e.g., messages, indications, information, pilots, etc.) respectively. Specifically, short-range wireless transceivers 320 and 360 each include: one or more transmitters 324 and 364 for transmitting and encoding signals 328 and 368, respectively; and one or more receivers 322 and 362 for receiving and decoding signals 328 and 368, respectively. As a specific example, short-range wireless transceivers 320 and 360 can be Wi-Fi transceivers, Bluetooth transceivers, etc. ® Transceiver, Zigbee ® and / or Z-WAVE ® Transceivers, NFC transceivers, UWB transceivers, or vehicle-to-vehicle (V2V) and / or vehicle-to-everything (V2X) transceivers.

[0095] In at least some cases, UE 302 and base station 304 also include satellite signal interfaces 330 and 370, each satellite signal interface including one or more satellite signal receivers 332 and 372, and optionally including one or more satellite signal transmitters 334 and 374, respectively. In some cases, base station 304 may be a terrestrial base station that can communicate with a spacecraft (e.g., spacecraft 112) via satellite signal interface 370. In other cases, base station 304 may be a spacecraft (or other non-terrestrial entity) that uses satellite signal interface 370 to communicate with terrestrial networks and / or other spacecraft.

[0096] Satellite signal receivers 332 and 372 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 332 and 372 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) signals, etc. When satellite signal receivers 332 and 372 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 332 and 372 can include any suitable hardware and / or software for receiving and processing satellite positioning / communication signals 338 and 378, respectively. Satellite signal receivers 332 and 372 may request appropriate information and operations from other systems, and in at least some cases, use measurements obtained by any suitable satellite positioning system algorithm to perform calculations to determine the locations of UE 302 and base station 304, respectively.

[0097] Optional satellite signal transmitters 334 and 374 (when present) can be connected to one or more antennas 336 and 376, respectively, and can be provided with components for transmitting satellite positioning / communication signals 338 and 378, respectively. When satellite signal transmitter 374 is a satellite positioning system transmitter, the satellite positioning / communication signal 378 can be a GPS signal, GLONASS signal, etc. ® Signals include Galileo signals, BeiDou signals, NAVIC signals, and QZSS signals. When satellite signal transmitters 334 and 374 are NTN transmitters, 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 transmitters 334 and 374 can include any suitable hardware and / or software for transmitting satellite positioning / communication signals 338 and 378, respectively. Satellite signal transmitters 334 and 374 can request appropriate information and operations from other systems.

[0098] 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 stations 304, other network entities 306). For example, base station 304 may use one or more network transceivers 380 to communicate with other base stations 304 or network entities 306 via one or more wired or wireless backhaul links. Similarly, 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.

[0099] Transceivers can be configured to communicate via wired or wireless links. A transceiver (whether wired or wireless) includes transmitter circuitry (e.g., transmitters 314, 324, 354, 364) and receiver circuitry (e.g., receivers 312, 322, 352, 362). In some embodiments, the transceiver may be an integrated device (e.g., implementing transmitter and receiver circuitry in a single device), in some embodiments it may include separate transmitter and receiver circuitry, or in other embodiments it may be implemented in a different manner. The transmitter and receiver circuitry of a wired transceiver (e.g., network transceiver 380 and network transceiver 390 in some embodiments) may be coupled to one or more wired network interface ports. Wireless transmitter circuitry (e.g., transmitters 314, 324, 354, 364) may include or be coupled to multiple antennas (e.g., antennas 316, 326, 356, 366), such as an antenna array, which allows the corresponding device (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 an antenna array, which allows the corresponding device (e.g., UE 302, base station 304) to perform receive beamforming, as described herein. In one aspect, the transmitter and receiver circuitry may share the same multiple antennas (e.g., antennas 316, 326, 356, 366), such that the corresponding device 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.

[0100] As used herein, various wireless transceivers (e.g., transceivers 310, 320, 350, and 360 in some specific embodiments, and network transceivers 380 and 390) and wired transceivers (e.g., network transceivers 380 and 390 in some specific 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.

[0101] UE 302, base station 304, and network entity 306 also include other components that can be used in conjunction with the operation disclosed herein. UE 302, base station 304, and network entity 306 each include one or more processors 342, 384, and 394 for providing functionality related to, for example, wireless communication, and for providing other processing functionality. Thus, processors 342, 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 342, 384, and 394 may include, for example, one or more general-purpose processors, multi-core processors, central processing units (CPUs), ASICs, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), other programmable logic devices or processing circuits, or various combinations thereof.

[0102] 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 QML components 348, 388, and 398. QML components 348, 388, and 398 may be hardware circuitry that is part of or coupled to processors 342, 384, and 394, respectively, which, when executed, enable UE 302, base station 304, and network entity 306 to perform the functionality described herein. In other respects, QML components 348, 388, and 398 may be external to processors 342, 384, and 394 (e.g., part of a modem processing system, integrated with another processing system, etc.). Alternatively, QML components 348, 388, and 398 may be memory modules stored in memories 340, 386, and 396, respectively, which, when executed by processors 342, 384, and 394 (or a modem processing system, another processing system, etc.), enable UE 302, base station 304, and network entity 306 to perform the functionality described herein. Figure 3A Possible locations for QML component 348 are illustrated. This CFO component may be, for example, part of one or more WWAN transceivers 310, memory 340, one or more processors 342, or any combination thereof, or may be a standalone component. Figure 3B Possible locations for QML component 388 are illustrated. This PRS component may be, for example, part of one or more WWAN transceivers 350, memory 386, one or more processors 384, or any combination thereof, or may be a standalone component. Figure 3C Possible locations for QML component 398 are illustrated. This PRS component may be, for example, part of one or more network transceivers 390, memory 396, one or more processors 394, or any combination thereof, or may be a standalone component.

[0103] UE 302 may include one or more sensors 344 coupled to one or more processors 342 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 interfaces 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.

[0104] 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.

[0105] 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 processing, and logical channel priority ordering.

[0106] 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 split 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 stream of time-domain OFDM symbols. The OFDM symbol stream is spatially pre-decoded to generate multiple spatial streams. Channel estimates from the channel estimator can be used to determine the decoding and modulation schemes, as well as for spatial processing. The channel estimates can be derived from a reference signal transmitted by UE 302 and / or channel condition feedback. Each spatial stream can then be provided to one or more different antennas 356. The transmitter 354 can utilize the corresponding spatial stream to modulate an RF carrier for transmission.

[0107] 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 342. 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 streams destined for UE 302. If multiple spatial streams are destined for UE 302, they can be combined by receiver 312 into a single OFDM symbol stream. 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 comprises a separate OFDM symbol stream for each subcarrier of the OFDM signal. The symbols on each subcarrier, along with the reference signal, are recovered and demodulated by determining the most probable signal constellation point transmitted by base station 304. These soft decisions can be based on a channel estimate 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 342, which implement layer 3 (L3) and layer 2 (L2) functionality.

[0108] In the downlink, one or more processors 342 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 342 are also responsible for error detection.

[0109] Similar to the functionality described in conjunction with downlink transmissions performed by base station 304, one or more processors 342 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 the delivery of upper-layer PDUs, error correction via ARQ, concatenation, segmentation, and reassembly of RLC SDUs, resegmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality 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 processing, and logical channel priority ordering.

[0110] The channel estimate derived by the channel estimator from the reference signal or feedback transmitted by the base station 304 can be used by the transmitter 314 to select an appropriate decoding and modulation scheme and facilitate spatial processing. The spatial stream generated by the transmitter 314 can be provided to different antennas 316. The transmitter 314 can use the corresponding spatial stream to modulate the RF carrier for transmission.

[0111] 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.

[0112] 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.

[0113] For convenience, UE 302, base station 304 and / or network entity 306 are in Figure 3A , Figure 3B and Figure 3CThe document is shown as including various components that can be configured according to the various examples described herein. However, it should be understood that the illustrated components may have different functionalities in different designs. In particular, Figures 3A to 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, personal computers (PCs), or laptops may have Wi-Fi and / or Bluetooth). ® The short-range wireless transceiver 320 can be omitted (e.g., cellular only), or the satellite signal interface 330 can be omitted, or the sensor 344 can be omitted, 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 interface 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.

[0114] Various components of UE 302, base station 304, and network entity 306 can be communicatively coupled to each other via data buses 308, 382, ​​and 392, respectively. In one aspect, data buses 308, 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 308, 382, ​​and 392 can provide communication between these logical entities.

[0115] 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 can 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 combine 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 blocks 310 to 346 may be implemented by the processor and memory components of UE 302 (e.g., by executing appropriate code and / or by appropriate configuration of the processor components). Similarly, some or all of the functionalities represented by blocks 350 to 388 may be implemented by the processor and memory components of base station 304 (e.g., by executing appropriate code and / or by appropriate configuration of the processor components). Furthermore, some or all of the functionalities represented by blocks 390 to 398 may be implemented by the processor and memory components of network entity 306 (e.g., by executing appropriate code and / or by appropriate configuration of the processor components). For simplicity, various operations, actions, and / or functions are described herein as being performed "by the UE," "by the base station," "by the network entity," etc. However, it should be understood that such operations, actions and / or functions can actually be performed by specific components or combinations of components of the UE 302, base station 304, network entity 306, etc., such as processors 342, 384, 394, transceivers 310, 320, 350 and 360, memory 340, 386 and 396, QML components 348, 388 and 398, etc.

[0116] 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 can be configured to communicate with UE 302 via base station 304 or independently of base station 304 (e.g., via a non-cellular communication link such as Wi-Fi).

[0117] Various frame structures can be used to support downlink and uplink transmission between network nodes (e.g., base stations and UEs). Figure 4 Figure 400 illustrates an example frame structure according to various aspects of this disclosure. The frame structure may be a downlink or uplink frame structure. Other wireless communication technologies may have different frame structures and / or different channels.

[0118] LTE (and in some cases NR) uses Orthogonal Frequency Division Multiplexing (OFDM) on the downlink and Single-Carrier Frequency Division Multiplexing (SC-FDM) on the uplink. However, unlike LTE, NR also has the option to use OFDM on the uplink. OFDM and SC-FDM divide the system bandwidth into multiple (K) orthogonal subcarriers, which are often referred to as tones, frequency slots, etc. Each subcarrier can be modulated using data. Generally, modulation symbols are transmitted using OFDM in the frequency domain and SC-FDM in the time domain. The spacing between adjacent subcarriers can be fixed, and the total number of subcarriers (K) can depend on the system bandwidth. For example, the subcarrier spacing can be 15 kHz, and the minimum resource allocation (resource block) can be 12 subcarriers (or 180 kHz). Therefore, for system bandwidths of 1.25 MHz, 2.5 MHz, 5 MHz, 10 MHz, or 20 MHz, the nominal Fast Fourier Transform (FFT) size can be equal to 128, 256, 512, 1024, or 2048, respectively. The system bandwidth can also be divided into subbands. For example, a subband can cover 1.08 MHz (i.e., 6 resource blocks), and for system bandwidths of 1.25 MHz, 2.5 MHz, 5 MHz, 10 MHz, or 20 MHz, there can be 1, 2, 4, 8, or 16 subbands, respectively.

[0119] LTE supports a single set of parameters (subcarrier spacing (SCS), symbol length, etc.). In contrast, NR can support multiple sets of parameters (µ), for example, subcarrier spacings of 15kHz (µ=0), 30kHz (µ=1), 60kHz (µ=2), 120kHz (µ=3), and 240kHz (µ=4) or larger can be available. Within each subcarrier spacing, there are 14 symbols per time slot. For a 15kHz SCS (µ=0), there is one time slot per subframe, 10 time slots per frame, a time slot duration of 1 millisecond (ms), a symbol duration of 66.7 microseconds (µs), and a maximum nominal system bandwidth (in MHz) of 4K FFT size. For a 30kHz SCS (µ=1), there are two time slots per subframe, 20 time slots per frame, a time slot duration of 0.5ms, a symbol duration of 33.3µs, and a maximum nominal system bandwidth (in MHz) of 4K FFT size. For a 60kHz SCS (µ=2), there are four time slots per subframe, 40 time slots per frame, a time slot duration of 0.25ms, a symbol duration of 16.7µs, and a maximum nominal system bandwidth (in MHz) of 4K FFT size. For a 120kHz SCS (µ=3), there are eight time slots per subframe, 80 time slots per frame, a time slot duration of 0.125ms, a symbol duration of 8.33µs, and a maximum nominal system bandwidth (in MHz) of 4K FFT size. For a 240kHz SCS (µ=4), there are 16 time slots per subframe, 160 time slots per frame, a time slot duration of 0.0625ms, a symbol duration of 4.17µs, and a maximum nominal system bandwidth (in MHz) of 4K FFT size.

[0120] exist Figure 4 In the example, a parameter set of 15kHz is used. Therefore, in the time domain, a 10ms frame is divided into 10 equal-sized subframes, each 1ms long, and each subframe includes one time slot. Figure 4 In the diagram, time is represented horizontally (on the X-axis), with time increasing from left to right, while frequency is represented vertically (on the Y-axis), with frequency increasing (or decreasing) from bottom to top.

[0121] A resource grid can be used to represent time slots, each of which includes one or more time-concurrent resource blocks (RBs) (also known as physical RBs (PRBs)) in the frequency domain. The resource grid is further divided into multiple resource elements (REs). An RE corresponds to a symbol length in the time domain and a subcarrier in the frequency domain. Figure 4In the parameter set, for a normal cyclic prefix, the RB can contain 12 consecutive subcarriers in the frequency domain and seven consecutive symbols in the time domain, for a total of 84 REs. For an extended cyclic prefix, the RB can contain 12 consecutive subcarriers in the frequency domain and six consecutive symbols in the time domain, for a total of 72 REs. The number of bits carried by each RE depends on the modulation scheme.

[0122] Some REs may carry reference (pilot) signals (RS). These reference signals may include positioning reference signals (PRS), tracking reference signals (TRS), phase tracking reference signals (PTRS), cell-specific reference signals (CRS), channel state information reference signals (CSI-RS), demodulation reference signals (DMRS), primary synchronization signals (PSS), secondary synchronization signals (SSS), synchronization signal blocks (SSB), sounding reference signals (SRS), etc., depending on whether the illustrated frame structure is used for uplink or downlink communication. Figure 4 An example location (labeled "R") of an RE carrying a reference signal is shown.

[0123] Sidelink communication occurs within transmit or receive resource pools. In the frequency domain, the smallest unit of resource allocation is a subchannel (e.g., a set of consecutive PRBs in the frequency domain). In the time domain, resource allocation is performed within a time slot interval. However, some time slots are unavailable for sidelinks, and some time slots contain feedback resources. Furthermore, sidelink resources can be (pre-)configured to occupy fewer than 14 symbols in a time slot.

[0124] Configure sidelink resources at the Radio Resource Control (RRC) layer. RRC configuration can be pre-configured (e.g., pre-loaded on the UE) or configured (e.g., from the serving base station).

[0125] The set of resource elements (REs) used for PRS transmission is called a "PRS resource". The set of resource elements can span multiple PRBs in the frequency domain and span "N" (such as one or more) consecutive symbols within a time slot in the time domain. In a given OFDM symbol in the time domain, the PRS resource occupies a consecutive PRB in the frequency domain.

[0126] The transmission of PRS resources within a given PRB has a specific comb size (also known as "comb density"). The comb size "N" represents the subcarrier spacing (or frequency / tone spacing) within each symbol of the PRS resource configuration. Specifically, for a comb size "N", the PRS is transmitted in every Nth subcarrier of a symbol within the PRB. For example, for comb size-4, for each symbol of the PRS resource configuration, the RE corresponding to every fourth subcarrier (such as subcarrier 0, 4, 8) is used to transmit the PRS resource. Currently, for DL-PRS, comb sizes-2, comb size-4, comb size-6, and comb size-12 are supported. Figure 4 An example PRS resource configuration for Comb-4 (which spans four symbols) is shown. That is, the location of the shaded RE (marked as "R") indicates the Comb-4 PRS resource configuration.

[0127] Currently, DL-PRS resources can span 2, 4, 6, or 12 consecutive symbols within a time slot using a full-frequency-domain interleaved mode. DL-PRS resources can be configured in any downlink or flexible (FL) symbol configured by a higher layer within a time slot. For all REs of a given DL-PRS resource, there may be a constant energy per resource element (EPRE). The following are the per-symbol frequency offsets for comb sizes 2, 4, 6, and 12 on 2, 4, 6, and 12 symbols. 2-symbol comb-2: {0, 1}; 4-symbol comb-2: {0, 1, 0, 1}; 6-symbol comb-2: {0, 1, 0, 1, 0, 1}; 12-symbol comb-2: {0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1}; 4-symbol comb-4: {0, 2, 1, 3} (as in...). Figure 4 (In the examples); 12-symbol comb-4: {0,2, 1, 3, 0, 2, 1, 3, 0, 2, 1, 3}; 6-symbol comb-6: {0, 3, 1, 4, 2, 5}; 12-symbol comb-6: {0, 3, 1, 4, 2, 5, 0, 3, 1, 4, 2, 5}; and 12-symbol comb-12: {0, 6, 3, 9, 1, 7, 4, 10, 2, 8, 5, 11}.

[0128] A “PRS resource set” is a collection of PRS resources used to transmit PRS signals, where each PRS resource has a PRS resource ID. Furthermore, PRS resources in a PRS resource set are associated with the same TRP. A PRS resource set is identified by a PRS resource set ID and associated with a specific TRP (identified by the TRP ID). Additionally, PRS resources in a PRS resource set have the same periodicity, common silence mode configuration, and the same repetition factor (such as “PRS-ResourceRepetitionFactor”) across time slots. Periodicity is the time from the first repetition of the first PRS resource in the first PRS instance to the same first repetition of the same first PRS resource in the next PRS instance. Periodicity can have a length selected from: 2^µ The time slots are {4, 5, 8, 10, 16, 20, 32, 40, 64, 80, 160, 320, 640, 1280, 2560, 5120, 10240}, where µ = 0, 1, 2, 3. The repetition factor can have a length selected from {1, 2, 4, 6, 8, 16, 32} time slots.

[0129] In a PRS resource set, a PRS resource ID is associated with a single beam (or beam ID) transmitted from a single TRP (where one TRP can transmit one or more beams). That is, each PRS resource in a PRS resource set can be transmitted on a different beam, and therefore, a "PRS resource" (or simply "resource") can also be referred to as a "beam." It should be noted that this does not imply whether the UE knows the TRP and beam on which it transmits the PRS.

[0130] A “PRS instance” or “PRS timing” is an instance of a periodically repeating time window (such as a group of one or more consecutive time slots) in which a PRS is expected to be sent. A PRS timing may also be referred to as a “PRS positioning timing,” “PRS positioning instance,” “positioning timing,” “positioning instance,” “positioning repetition,” or simply “timing,” “instance,” or “repetition.”

[0131] A “positioning frequency layer” (also simply “frequency layer”) is a collection of one or more PRS resource sets with identical values ​​for certain parameters across one or more TRPs. Specifically, the collection of PRS resource sets has the same subcarrier spacing and cyclic prefix (CP) type (meaning that all parameter sets supported for the Physical Downlink Shared Channel (PDSCH) are also supported by the PRS), the same point A, the same downlink PRS bandwidth, the same starting PRB (and center frequency), and the same comb size. The point A parameter takes the value of the parameter “ARFCN-ValueNR” (where “ARFCN” stands for “Absolute Radio Channel Number”) and is an identifier / code specifying a pair of physical radio channels used for transmission and reception. The downlink PRS bandwidth can have a granularity of four PRBs, with a minimum of 24 PRBs and a maximum of 272 PRBs. Currently, up to four frequency layers have been defined, and up to two PRS resource sets can be configured per frequency layer per TRP.

[0132] The concept of a frequency layer is somewhat similar to that of component carriers and bandwidth portions (BWPs), but the difference is that component carriers and BWPs are used by a single base station (or macrocell base station and small cell base station) to transmit data channels, while a frequency layer is used by several (usually three or more) base stations to transmit PRS. A UE can indicate the number of frequency layers it can support when transmitting its positioning capabilities to the network (such as during an LTE Positioning Protocol (LPP) session). For example, a UE can indicate whether it can support one or four positioning frequency layers.

[0133] It should be noted that the terms "location reference signal" and "PRS" generally refer to specific reference signals used for positioning in NR and LTE systems. However, as used herein, the terms "location reference signal" and "PRS" can also refer to any type of reference signal that can be used for positioning, such as, but not limited to, PRS, TRS, PTRS, CRS, CSI-RS, DMRS, PSS, SSS, SSB, SRS, UL-PRS, etc., as defined in LTE and NR. Furthermore, the terms "location reference signal" and "PRS" can refer to downlink positioning reference signals, uplink positioning reference signals, or sidelink positioning reference signals, unless otherwise indicated by the context. If further distinction is required regarding the type of PRS, downlink positioning reference signals can be referred to as "DL-PRS," uplink positioning reference signals (e.g., positioning SRS, i.e., PTRS) as "UL-PRS," and sidelink positioning reference signals as "SL-PRS." Furthermore, for signals that can be transmitted in the downlink, uplink, and / or sidelink (e.g., DMRS), these signals may be preceded by "DL", "UL", or "SL" to distinguish the direction. For example, "UL-DMRS" may be different from "DL-DMRS".

[0134] On the one hand, Figure 4 The reference signal carried on the RE marked "R" can be the SRS. The SRS transmitted by the UE can be used by the base station to obtain the Channel State Information (CSI) used to transmit the UE. The CSI describes how the RF signal propagates from the UE to the base station and represents the combined effects of scattering, attenuation, and power decay with distance. The system uses the SRS for resource scheduling, link adaptation, massive MIMO, beam management, etc.

[0135] The set of REs used for SRS transmission is called an "SRS resource" and is identified by the parameter "SRS-ResourceId". The set of resource elements can span multiple PRBs in the frequency domain and span "N" (e.g., one or more) consecutive symbols within a time slot in the time domain. In a given OFDM symbol, SRS resources occupy one or more consecutive PRBs. An "SRS resource set" is a group of SRS resources used for SRS signal transmission and is identified by the SRS resource set ID ("SRS-ResourceSetId").

[0136] The transmission of SRS resources within a given PRB has a specific comb size (also known as "comb density"). The comb size "N" represents the subcarrier spacing (or frequency / tone spacing) within each symbol of the SRS resource configuration. Specifically, for a comb size "N", SRS is transmitted in every Nth subcarrier of a symbol within the PRB. For example, for comb size -4, for each symbol of the SRS resource configuration, the RE corresponding to every fourth subcarrier (such as subcarriers 0, 4, 8) is used to transmit the SRS of the SRS resource. Figure 4 In the example, the illustrated SRS is comb tooth-4 spanning four symbols. That is, the position of the shaded SRS RE indicates the SRS resource configuration of comb tooth-4.

[0137] Currently, SRS resources with comb tooth sizes of 2, 4, or 8 can span 1, 2, 4, 8, or 12 consecutive symbols within a time slot. The following are the symbol-by-symbol frequency offsets for the currently supported SRS comb tooth patterns: 1-symbol comb tooth-2: {0}; 2-symbol comb tooth-2: {0, 1}; 2-symbol comb tooth-4: {0, 2}; 4-symbol comb tooth-2: {0, 1, 0, 1}; 4-symbol comb tooth-4: {0, 2, 1, 3} (as in...). Figure 4 (In the examples); 8-symbol comb-4: {0, 2, 1, 3, 0, 2, 1, 3}; 12-symbol comb-4: {0, 2, 1, 3, 0, 2, 1, 3, 0, 2, 1, 3}; 4-symbol comb-8: {0, 4, 2, 6}; 8-symbol comb-8: {0, 4, 2, 6, 1, 5, 3, 7}; and 12-symbol comb-8: {0, 4, 2, 6, 1, 5, 3, 7, 0, 4, 2, 6}.

[0138] Generally, as mentioned above, the UE transmits an SRS so that the receiving base station (serving base station or neighboring base station) can measure the channel quality (i.e., CSI) between the UE and the base station. However, the SRS can also be specifically configured as an uplink positioning reference signal for uplink-based positioning procedures, such as uplink time difference of arrival (UL-TDOA), round-trip time (RTT), uplink angle of arrival (UL-AoA), etc. As used herein, the term "SRS" can refer to an SRS configured for channel quality measurement or an SRS configured for positioning purposes. When it is necessary to distinguish between the two types of SRS, the former may be referred to herein as "SRS for communication" and / or the latter as "SRS for positioning" or "positioning SRS".

[0139] Several enhancements to the previously defined SRS may be available for “SRS for Positioning” (also known as “UL-PRS”), such as new interleaving patterns within SRS resources (other than single symbol / comb-2), new comb types for SRS, new sequences of SRS, a larger set of SRS resources per component carrier, and a larger number of SRS resources per component carrier. Furthermore, the parameters “SpatialRelationInfo” and “PathLossReference” are configured based on downlink reference signals or SSBs from adjacent TRPs. Further, an SRS resource can be transmitted outside the active BWP, and an SRS resource can span multiple component carriers. Moreover, SRS can be configured in RRC connected state and transmitted only within the active BWP. Additionally, there may be no frequency hopping, no repetition factor, a single antenna port, and new SRS lengths (e.g., 8 and 12 symbols). Open-loop power control may also exist, but closed-loop power control is not possible, and comb-8 (i.e., SRS transmitted every eighth subcarrier in the same symbol) can be used. Finally, the UE can transmit from multiple SRS resources using the same transmit beam for UL-AoA. These features can be configured via higher-layer RRC signaling (and potentially triggered or activated via MAC control elements (MAC-CE) or downlink control information (DCI)).

[0140] The NR side link supports Hybrid Automatic Repeat Request (HARQ) retransmission. Figure 5A This is a diagram 500 illustrating an example time slot structure without feedback resources based on various aspects of this disclosure. Figure 5AIn the example, time is represented horizontally and frequency vertically. In the time domain, the length of each block is one Orthogonal Frequency Division Multiplexing (OFDM) symbol, and 14 symbols constitute a time slot. In the frequency domain, the height of each block is a subchannel. Currently, the (pre-)configured subchannel size can be selected from a set of {10, 15, 20, 25, 50, 75, 100} Physical Resource Blocks (PRBs).

[0141] For side-link time slots, the first symbol is a repetition of the previous symbol and is used for automatic gain control (AGC) settings. This is in Figure 5A This is illustrated using vertical and horizontal hashing. For example... Figure 5A As shown, for sidelinks, the Physical Sidelink Control Channel (PSCCH) and the Physical Sidelink Shared Channel (PSSCH) are transmitted in the same time slot. Similar to the Physical Downlink Control Channel (PDCCH), the PSCCH carries control information about sidelink resource allocation and a description of the sidelink data sent to the UE. Likewise, similar to the Physical Downlink Shared Channel (PDSCH), the PSSCH carries the UE's user data. Figure 5A In the example, the PSCCH occupies half the bandwidth of the sub-channel and only takes up three symbols. Finally, the gap symbol appears after the PSSCH.

[0142] Figure 5B This is a diagram 550 illustrating an example time-slot structure with feedback resources based on various aspects of this disclosure. Figure 5B In the example, time is represented horizontally and frequency is represented vertically. In the time domain, the length of each block is one OFDM symbol, and 14 symbols constitute a time slot. In the frequency domain, the height of each block is a subchannel.

[0143] Figure 5B The illustrated time slot structure and Figure 5A The illustrated time slot structures are similar, but the difference is... Figure 5B The illustrated time slot structure includes feedback resources. Specifically, the last two symbols of the time slot are dedicated to the Physical Side Link Feedback Channel (PSFCH). The first PSFCH symbol is a repetition of the second PSFCH symbol used for AGC setup. In addition to the gap symbol following the PSFCH, there is a gap symbol after the two PSFCH symbols. Currently, the resources used for the PSFCH can be configured using a periodicity selected from the set of {0, 1, 2, 4} time slots.

[0144] Figure 6A Figure 600 illustrates an example of a location-based resource pool configured within a sidelink resource pool (i.e., a shared resource pool) for communication, according to various aspects of this disclosure. Figure 6AIn the example, time is represented horizontally and frequency is represented vertically. In the time domain, the length of each block is one Orthogonal Frequency Division Multiplexing (OFDM) symbol, and 14 symbols constitute a time slot. In the frequency domain, the height of each block is a subchannel.

[0145] exist Figure 6A In the example, the entire time slot (excluding the first and last symbols) can be a resource pool for sidelink communication. That is, any symbol other than the first and last can be allocated for sidelink communication. However, the resource pool for positioning (RP-P) is allocated in the last four pre-slot symbols of the time slot. Therefore, non-sidelink positioning data (such as User Data (PSSCH), Channel State Information Reference Signal (CSI-RS), and control information) can only be transmitted in the first eight post-AGC symbols, not in the last four pre-slot symbols, to prevent conflicts with the configured RP-P. Non-sidelink positioning data that would otherwise be transmitted in the last four pre-slot symbols can be punctured or silenced, or rate-matched non-sidelink data that typically spans more than eight post-AGC symbols can be used to accommodate these eight post-AGC symbols.

[0146] Sidelink Positioning Reference Signal (SL-PRS) has been defined to support the sidelink positioning process between UEs. Similar to the downlink PRS (DL-PRS), an SL-PRS resource consists of one or more resource elements (i.e., an OFDM symbol in the time domain and a subcarrier in the frequency domain). SL-PRS resources are designed with a comb-based pattern to enable Fast Fourier Transform (FFT) based processing at the receiver. SL-PRS resources consist of uninterleaved or only partially interleaved resource elements in the frequency domain to provide small time-of-arrival (TOA) uncertainties and reduced overhead for each SL-PRS resource. SL-PRS can also be associated with a specific RP-P (e.g., some SL-PRS can be allocated in some RP-Ps). SL-PRS is also defined as having intra-slot repetition ( Figure 6A (not shown in the image) to allow for combined gain (if needed). RP-P inter-UE coordination may also exist to provide dynamic SL-PRS and data multiplexing while minimizing SL-PRS collisions.

[0147] Figures 6B to 6C Figures 630 to 650 are additional examples illustrating a location resource pool configured within a sidelink resource pool used for communication. Similar to Figure 6, Figures 6B to 6C The example illustrates a shared resource pool structure. For Figure 6B and Figure 6CIn some designs, the following parameters can be defined, such as: the Physical Side Link Control Channel (PSCCH) and SL-PRS are time-division multiplexed only; the PSSCH and SL-PRS are time-division multiplexed only (e.g., the maximum comb size is 4); the PSSCH carries both Type 2 Side Link Control Information (SCI-2) and Side Link Shared Channel (SL-SCH) (e.g., introducing a new SCI-2 format); the SL-PRS is mapped on consecutive symbols; the SL-PRS is not mapped on symbols with PSSCH demodulation reference signals (DMRS); and / or the SL-PRS transmit power is the same as the PSSCH transmit power (e.g., this implies that per-resource element power boost will be applied for comb-2 and comb-4).

[0148] Figure 6D Figure 670 is another example illustrating a location-based resource pool configured within a sidelink resource pool used for communication. Figure 6D The example depicts a dedicated resource pool structure. For Figure 6D In some designs, the following parameters can be defined, such as: SL-PRS immediately following the AGC symbol, SL-PRS immediately following the gap symbol (at least when the gap symbol is the last sidelink symbol in the time slot), PSCCH and SL-PRS can only be time-division multiplexed, different comb sizes (N) and SL-PRS durations (M) can be supported in the same resource pool (e.g., a set of SL-PRS resources can only have a single (M, N) combination), PSSCH is mapped to the first few sidelink symbols in the time slot, the number of PSCCH symbols is (pre)configured to 1, 2 or 3, the number of physical resource blocks is (pre)configured using sidelink communication values, and / or there is a one-to-one implicit mapping between PSCCH and SL-PRS.

[0149] In some designs, within a shared resource pool, the fields in SCI Format 2-D may include, for example: SL-PRS resource information indication for the current time slot (ceiling(log2(number of (pre-)configured SL-PRS resources in the resource pool) bits)), SL-PRS request (0 or 1 bit), and / or embedded SCI format ([X] bit). If the "embedded SCI format" field is set to [0], the SCI 2-A field and necessary padding are included. If the "embedded SCI format" field is set to [1], the SCI 2-B field is included.

[0150] In some designs, for a shared resource pool, there may be explicit (pre)configuration of SL-PRS resources in the time slots, applicable to the indicated frequency domain allocation, including, for example, SL-PRS resource ID, (M, N) mode, and / or comb offset. In some designs, for a given "M" value, SL-PRS resources are mapped to the last consecutive "M" sidelink symbols available for SL-PRS in the time slot, taking into account multiplexing with PSSCH DMRS, Phase Tracking Reference Signal (PT-RS), CSI-RS, PSFCH, gap symbols, AGC symbols, and / or PSCCH in the time slot. In some designs, the maximum number of SL-PRS resources in the time slots of the shared resource pool can be (pre)configured.

[0151] In some designs, within a dedicated resource pool, regarding the process for determining the subset of resources to be reported to the higher layer, when the resource (re)selection process is triggered, the higher layer provides the following parameters to the candidate SL-PRS to send, such as: the resource pool from which it reports the SL-PRS resources, priority, delay budget, reserved time period, list of resources for preemption and re-evaluation, and / or a set of SL-PRS resource identifiers that may include all (pre)configured SL-PRS resource identifiers.

[0152] 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 7 Examples of various positioning methods according to aspects of this disclosure are illustrated. In the OTDOA or DL-TDOA positioning process illustrated in scenario 710, 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.

[0153] For the DL-AoD positioning illustrated in scenario 720, the positioning entity uses measurement reports from the UE regarding the received signal strength measurements of multiple downlink transmitted 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.

[0154] 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.

[0155] For UL-AoA positioning, one or more base stations measure the received signal strength of one or more uplink reference signals (e.g., SRS) received from the UE on one or more uplink receive beams. The positioning entity uses the signal strength measurement and the angle of the receive beam to determine the angle between the UE and the base station. Based on the determined angle and the known location of the base station, the positioning entity can then estimate the location of the UE.

[0156] 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 signal and the transmitted signal. 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 730, 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 740.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Location estimates can be referred to by other names, such as location estimation, location, positioning, fixed location, etc. Location estimates can be geodesic and include coordinates (e.g., latitude, longitude, and possible elevation), or they can be municipal and include street addresses, postal addresses, or some other verbal description of the location. Location estimates can be further defined relative to some other known location or in absolute terms (e.g., using latitude, longitude, and possible elevation). Location estimates 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).

[0161] NR supports or enables various sidelink positioning technologies. Figure 8AVarious scenarios of interest, including sidelink-only positioning or combined Uu and sidelink positioning, are illustrated according to various aspects of this disclosure. In scenario 810, at least one peer UE with a known location can improve the Uu-based positioning of a target UE by providing additional anchors (e.g., using sidelink round-trip time (RTT) (SL-RTT)). In scenario 820, a low-end (e.g., low-capacity or “RedCap”) target UE can obtain assistance from a high-end UE to determine its location using, for example, a sidelink positioning and ranging process with the high-end UE. Compared to the low-end UE, the high-end UE may have more capabilities, such as more sensors, a faster processor, more memory, more antenna elements, higher transmit power capabilities, access to additional frequency bands, or any combination thereof. In scenario 830, a relay UE (e.g., with a known location) participates in the positioning estimation of a remote UE without performing uplink positioning reference signal (PRS) transmission via the Uu interface. Scenario 840 illustrates joint positioning of multiple UEs. Specifically, in scenario 840, two UEs with unknown locations can co-locate under non-line-of-sight (NLOS) conditions by utilizing constraints from nearby UEs.

[0162] Figure 8B Additional scenarios of interest are illustrated, including sidelink-only or combined Uu and sidelink positioning according to various aspects of this disclosure. In scenario 850, a UE used for public safety (e.g., by police, firefighters, etc.) may perform peer-to-peer (P2P) positioning and ranging for public safety and other purposes. For example, in scenario 850, a public safety UE may be outside network coverage and use sidelink positioning techniques to determine the location or relative distance and relative positioning between public safety UEs. Similarly, scenario 860 illustrates multiple UEs outside coverage and using sidelink positioning techniques such as SL-RTT to determine their location or relative distance and relative positioning.

[0163] 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)).

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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 receives a vector of inputs and returns a vector of outputs.

[0168] Figure 9 An example neural network 900 according to various aspects of this disclosure is illustrated. The neural network 900 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.

[0169] 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 value 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.

[0170] 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.

[0171] 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.

[0172] Regardless of which machine learning model is used, at a high level, the machine learning module (e.g., implemented by the processing system) can be configured to iteratively analyze the training input data (e.g., measurements of reference signals to / from various target UEs) and correlate the training input data with the output dataset (e.g., a set of possible or highly probable candidate locations for various target UEs), thereby enabling the same output dataset to be determined later when similar input data (e.g., from other target UEs at the same or similar locations) is provided.

[0173] In some designs, the location estimation entity (e.g., UE, gNB, LMF, etc.) can utilize direct AI / ML location estimation techniques. In this case, a direct (D)-AI / ML model is trained to accept input data (e.g., DL-PRS measurements, UL-PRS measurements, SL-PRS measurements, etc.), which is processed to provide a location estimate of the UE as the output (i.e., direct labeling).

[0174] In some designs, auxiliary (or indirect) AI / ML localization estimation techniques are utilized. In this case, an auxiliary (A)-AI / ML model is trained to accept input data (e.g., DL-PRS measurements, UL-PRS measurements, SL-PRS measurements, etc.), which is processed to provide intermediate data as output (i.e., intermediate labels, sometimes referred to as localization feature extraction, such as timing / angle information, LOS markers, etc.), which is then provided as input to another localization estimation model. It should be noted that the other localization estimation model can be another AI / ML model or a non-AI / ML model (e.g., the Chan algorithm, the Kalman filter (KF) algorithm, etc.). Furthermore, the A-AI / ML model and another model can be implemented in the same entity (e.g., UE, LMF, etc.) or different entities (e.g., for network-assisted positioning, the UE applies the A-AI / ML model to compress measurement data, which is then reported to the LMF, which then applies another positioning estimation model; for UE-based positioning, network components (such as gNB or LMF or another UE for sidelinks) apply the A-AI / ML model to compress measurement data, which is then reported to the UE, which then applies another positioning estimation model).

[0175] It should be noted that, as used in this article, AI / ML models (e.g., A-AI / ML models or D-AI / ML models) may alternatively be referred to as “ML models” or “AI models” or “ML-based models” or “AI-based models”, etc.

[0176] Figure 10A AI / ML positioning use case 1010 according to various aspects of this disclosure is illustrated. AI / ML positioning use case 1010 can be characterized as Case 1 and includes UE-based positioning with direct AI / ML or AI / ML-assisted positioning and with a UE-side AI / ML model.

[0177] Figure 10B AI / ML positioning use case 1020 according to various aspects of this disclosure is illustrated. AI / ML positioning use case 1020 can be characterized as Case 2a and includes UE-assisted / LMF-based positioning with AI / ML-assisted positioning and UE-side AI / ML models. Figure 10CAI / ML positioning use case 1030 according to various aspects of this disclosure is illustrated. AI / ML positioning use case 1030 can be characterized as case 2b and includes UE-assisted / LMF-based positioning with direct AI / ML positioning and with an LMF-side AI / ML model.

[0178] Figure 10D AI / ML localization use case 1040 according to various aspects of this disclosure is illustrated. AI / ML localization use case 1040 can be characterized as case 3a and includes NG-RAN node assisted localization with AI / ML assisted localization and a gNB-side AI / ML model.

[0179] Figure 10E AI / ML localization use case 1050 according to various aspects of this disclosure is illustrated. AI / ML localization use case 1050 can be characterized as case 3b and includes NG-RAN node-assisted localization with direct AI / ML localization and with an LMF-side AI / ML model.

[0180] It should be noted that Figures 10A to 10E Example AI / ML model use cases are described, and other AI / ML model use cases are also possible.

[0181] AI / ML localization is one of the key use cases considered in future network implementations. AI / ML localization can provide specific benefits under NLOS conditions. In some designs, AI / ML localization models can be site / area specific, where such models only provide superior performance in a particular area / region. The challenge of scaling AI / ML localization can be mitigated by generating multiple AI / ML localization models that address different site / area / radio characteristics (AI / ML localization models can overlap and share some radio and / or area characteristics). However, the signaling associated with a large number of AI / ML localization models can be quite large, leading to high overhead.

[0182] Various aspects of this disclosure relate to signaling of quasi-model relationships (QML) between machine learning (ML)-based UE positioning estimation models. In this way, only certain parts of the entire (potentially large) ML-based UE positioning model need to be communicated (e.g., differences between the signaling ML-based UE positioning model and a reference ML-based UE positioning model). Such aspects offer various technical advantages, such as reduced network overhead, fast and simple model relationship indication, fast and simple model lifecycle management (LCM), and so on.

[0183] Figure 11An exemplary process 1100 of communication according to one aspect of this disclosure is illustrated. Process 1100 of FIG. 16 is performed by a communication device. In some designs, the communication device may correspond to a network component (e.g., an LMF integrated at a gNB / BS 304 or O-RAN component, or a remote location server such as network entity 306). In other designs, the communication device may correspond to a UE. In scenarios where the communication device is integrated with another device (e.g., a location estimation entity, etc.), any reference to any Rx / Tx operation between the communication device and the device in which the communication device is integrated may correspond to information transmission between different logical components of the device via a data bus, etc.

[0184] refer to Figure 11 At 1110, the communication device (e.g., processor 342, 384, or 394, QML component 348, 388, or 398, etc.) determines a first quasi-model relationship (QML) between a reference machine learning (ML)-based UE positioning estimation model and a first ML-based UE positioning estimation model. This first QML is based on a correspondence between a first set of features associated with the reference ML-based UE positioning estimation model and a second set of features associated with a second set of features. In some designs, the components used to perform the determination at 1110 include... Figures 3A to 3C Processors 342, 384, or 394, QML components 348, 388, or 398, etc.

[0185] refer to Figure 11 At 1120, a communication device (e.g., transmitter 314, 324, 354, or 364, network transceiver 380 or 390, etc.) (e.g., to a location estimation entity) sends an instruction for the first QML. In some designs, the components used to perform the transmission at 1120 include Figures 3A to 3C Transmitters such as 314, 324, 354, or 364, and network transceivers such as 380 or 390.

[0186] Figure 12 An exemplary process 1200 of communication according to one aspect of this disclosure is illustrated. Figure 12The process 1200 is performed by a location estimation entity. In some designs, this location estimation entity may correspond to a network component (e.g., an LMF integrated in a gNB / BS304 or O-RAN component, or a remote location server such as network entity 306). In other designs, the location estimation entity may correspond to another UE (e.g., a sidelink anchor UE or a sidelink server UE) or to the target UE itself. In scenarios where the location estimation entity is integrated with another device (e.g., a UE, gNB, location server, etc.), any reference to any Rx / Tx operation between the location estimation entity and the device in which the location estimation entity is integrated may correspond to information transmission between different logical components of the device via a data bus, etc. In some designs, process 1200 may be combined with... Figure 11 The process is executed at 1100.

[0187] refer to Figure 12 At 1210, the location estimation entity (e.g., receiver 312 or 322 or 352 or 362, network transceiver 380 or 390, etc.) receives an indication of a first quasi-model relationship (QML) between a reference machine learning (ML)-based user equipment (UE) location estimation model associated with a first set of characteristics and a first ML-based UE location estimation model associated with a second set of characteristics. In some designs, the components for performing the receiving at 1210 include Figures 3A to 3C Receivers such as 312, 322, 352, or 362, and network transceivers such as 380 or 390.

[0188] refer to Figure 12 At 1220, the location estimation entity (e.g., transmitter 314 or 324 or 354 or 364, network transceiver 380 or 390, processor 342 or 384 or 394, QML component 348 or 388 or 398, etc.) performs one or more actions associated with the location estimation of one or more UEs based on an instruction to the first QML. In some designs, the components used to perform the actions at 1220 include Figures 3A to 3C Transmitters 314, 324, 354, or 364; network transceivers 380 or 390; processors 342, 384, or 394; QML components 348, 388, or 398, etc.

[0189] refer to Figures 11 to 12 In some designs, communication equipment corresponds to network components or UEs.

[0190] refer to Figures 11 to 12 In some designs, the first ML model and the reference ML model each correspond to the physical model or the logical model.

[0191] refer to Figures 11 to 12In some designs, the first ML-based UE positioning estimation model and the reference ML-based UE positioning estimation model are associated with different location regions that are in the same location region or at least partially overlap with each other.

[0192] refer to Figures 11 to 12 In some designs, a reference ML-based UE location estimation model is trained on first training data, and a first ML-based UE location estimation model is trained on second training data. In one aspect, the communication device further determines a second QML between the second ML-based UE location estimation model and the reference ML-based UE location estimation model, the second QML being based on a correspondence between a first set of features and a third set of features associated with the second ML-based UE location estimation model, and the communication device further transmits (and the location estimation entity also receives) the second ML-based UE location estimation model and an indication of the second QML. In another aspect, the second ML-based UE location estimation model is trained on third training data.

[0193] refer to Figures 11 to 12 In some designs, the communication device further determines a second QML between the second ML-based UE location estimation model and the first ML-based UE location estimation model. This second QML is based on the correspondence between a second set of features and a third set of features associated with the second ML-based UE location estimation model. The communication device also transmits (and the location estimation entity receives) the second ML-based UE location estimation model and an indication of the second QML. In one aspect, the second ML-based UE location estimation model is trained based on third training data.

[0194] refer to Figures 11 to 12 In some designs, the first QML indicates the similarity or difference between a first value or a first range of values ​​of an attribute type associated with a first set of features and a second value or a second range of values ​​of the same attribute type associated with the first set of features. In one aspect, attribute types include: location region information, or timing information, or model complexity, or Doppler shift, or Doppler spread, or average delay, or delay spread, or spatial receive filter, or spatial transmit filter, or any combination thereof.

[0195] refer to Figures 11 to 12 In some designs, the transmission of the indication for the first QML is performed via a capability exchange process, or via Long Term Evolution (LTE) Location Protocol (LPP) Location Request Signalling, or via LPP Auxiliary Data Signalling, or via LPP Broadcast Location Signalling, or any combination thereof.

[0196] refer to Figures 11 to 12 In some designs, the communication device also receives (and the positioning estimation entity also sends) a request for the first QML. In one aspect, signaling indicating the first QML is in response to that request.

[0197] refer to Figures 11 to 12 In some designs, one or more actions performed at 1220 include activating, deactivating, or selecting a first ML-based UE positioning estimation model, a reference ML-based UE positioning estimation model, or another ML-based UE positioning estimation model, or one or more actions include switching between ML-based UE positioning estimation models, or a combination thereof.

[0198] refer to Figures 11 to 12 In specific examples, the LMF can indicate a Quasi-Model (QML) relationship to the UE, allowing the UE to run the model provided by the LMF. On one hand, this relationship information can help the UE autonomously select the correct model and apply appropriate LCM operations (Case 1 / Case 2a).

[0199] refer to Figures 11 to 12 In specific examples, the UE may indicate the QML relationship of its AI / ML model (or an AI / ML model provided by the UE and running on the LMF side (Case 2b), if applicable to future 3GPP releases) to the LMF. In one aspect, this allows the LMF to manage and apply lifecycle management (LCM) of the model running on the UE side (Case 1 / Case 2a). In another aspect, this allows the LMF to manage and apply LCM of the model provided by the UE and running on the LMF side (Case 2b). As used herein, the AI / ML positioning model may refer to an AI / ML positioning physical model or an AI / ML positioning logical model.

[0200] refer to Figures 11 to 12 In a specific example, QML can indicate whether the first AI / ML positioning model and the second AI / ML positioning model share common large-scale wireless properties (e.g., average delay, delay spread, Doppler shift, Doppler spread, etc.), the same RX spatial filter (i.e., they support the same receiving direction), the same TX spatial filter (i.e., they support the same transmitting direction), the same spatial / regional information (the same or overlapping regions), timing information (applicable operating time), and complexity information (i.e., the same model complexity size, computation, memory, latency, etc.).

[0201] refer to Figures 11 to 12 In a specific example, a QML type may include any of the following, for example: QML type: QML Type A: Regional Information Relationships QML Type B: Timing Information Relationship QML Type C: Model Complexity Relationship QML Type D: Average Delay, Delay Spread, Doppler Shift, Doppler Spread QML Type E: Doppler Shift, Doppler Spread QML type F: Average delay, delay spread QML type G: Spatial RX relation QML type H: Spatial TX relationship See Figures 11 to 12 In a specific example, the UE may indicate to the LMF a pointer to the relationship between a first AI / ML positioning model and a second AI / ML positioning model; the LMF uses this pointer to further apply model lifecycle management (e.g., activation, deactivation, selection, switching, rollback) to the first AI / ML positioning model, the second AI / ML positioning model, or both the first and second AI / ML positioning models. In one aspect, the first AI / ML model may (or will) run on the UE side or on the LMF side. In another aspect, the second AI / ML model may (or will) run on the UE side or on the LMF side.

[0202] refer to Figures 11 to 12 In a specific example, the UE indicator can be a QML relationship that shows the type of equivalence / similarity between the characteristics of a first AI / ML localization model and a second AI / ML localization model, for example: QML Type A: Regional Information Relationships QML Type B: Timing Information Relationship QML Type C: Model Complexity Relationship QML Type D: Average Delay, Delay Spread, Doppler Shift, Doppler Spread QML Type E: Doppler Shift, Doppler Spread QML type G: Mean delay, Doppler offset QML type H: Spatial RX relationship QML Type I: Spatial TX Relationship refer to Figures 11 to 12 In certain examples, the UE indication can be signaled as part of the LPP UE capability exchange (e.g., AI / ML location features or component / conditional part of feature groups).

[0203] refer to Figures 11 to 12 In a specific example, the UE may receive a quasi-model relationship indicator from the LMF indicating the relationship between a first AI / ML positioning model and a second AI / ML positioning model. The UE uses this indicator to further apply model lifecycle management (e.g., activation, deactivation, selection, switching, rollback) to the first AI / ML positioning model, the second AI / ML positioning model, or both the first and second AI / ML positioning models. In one aspect, the first AI / ML positioning model may (or will) operate on the UE side or on the LMF side. In another aspect, the second AI / ML positioning model may (or will) operate on the UE side or on the LMF side.

[0204] refer to Figures 11 to 12 In a specific example, the LMF indicator can be a QML relation that indicates the type of equivalence / similarity between the characteristics of a first AI / ML localization model and a second AI / ML localization model, for example: QML Type A: Regional Information Relationships QML Type B: Timing Information Relationship QML Type C: Model Complexity Relationship QML Type D: Average Delay, Delay Spread, Doppler Shift, Doppler Spread QML Type E: Doppler Shift, Doppler Spread QML type G: Mean delay, Doppler offset QML type H: Spatial RX relationship QML Type I: Spatial TX Relationship refer to Figures 11 to 12 In specific examples, this indication can be signaled as part of the transmission of an LPP location request message, an LPP auxiliary data message, or an LPP broadcast location message. On one hand, the UE can transmit a request to the LMF, requesting the LMF to provide a QML relationship.

[0205] Figure 13 A QML tree hierarchy structure 1300 according to various aspects of this disclosure is illustrated. Figure 13In this framework, AI / ML localization model 1 is defined as the "base model." In other words, AI / ML localization model 1 is defined as the complete AI / ML model, not relative to any other AI / ML model. AI / ML localization model 1 includes three sub-AI / ML localization models 2, 3, and 4, such that AI / ML localization models 2, 3, and 4 are defined via QML relative to AI / ML localization model 1. AI / ML localization model 2 is a sub-AI / ML localization model 4, such that AI / ML localization model 4 is defined via QML relative to AI / ML localization model 2. AI / ML localization model 3 includes two sub-AI / ML localization models 5 and 6, such that AI / ML localization models 5 and 6 are defined via QML relative to AI / ML localization model 3. AI / ML localization model 6 is a sub-AI / ML localization model 7, such that AI / ML localization model 7 is defined via QML relative to AI / ML localization model 6. AI / ML localization model 4 includes two sub-AI / ML localization models 8 and 9, such that AI / ML localization models 8 and 9 are defined via QML relative to AI / ML localization model 4.

[0206] refer to Figure 13 Each parent node is connected to its corresponding child node by an arrow marked with a QML type indicator. This QML type indicator indicates a QML type that distinguishes the correspondence (difference or similarity) between a child node and its parent node for that specific QML type. Therefore, as an example, the QML identifier of AI / ML positioning model 3 identifies whether QML type E is different / same between AI / ML positioning model 3 and AI / ML positioning model 1. Consequently, other QML types not identified by the QML of AI / ML positioning model 3 are assumed to be the same as AI / ML positioning model 1. As long as AI / ML positioning model 1 is known, the differences (if any) in QML type E of AI / ML positioning model 3 can be known from the QML of AI / ML positioning model 3, allowing the complete AI / ML positioning model 3 to be constructed at the device receiving the QML of AI / ML positioning model 3.

[0207] 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 have more features than those explicitly 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 explicitly include these combinations unless explicitly 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.

[0208] Specific implementation examples are described in the following numbered clauses: Clause 1. A method of operating a communication device, the method comprising: determining a first quasi-model relation (QML) between a reference machine learning (ML)-based UE location estimation model and a first ML-based UE location estimation model, the first quasi-model relation (QML) being based on a correspondence between a first set of features associated with the reference ML-based UE location estimation model and a second set of features associated with a second set of features; and sending an instruction to the first QML.

[0209] Clause 2. The method described in Clause 1, wherein the communication device corresponds to a network component or a UE.

[0210] Clause 3. The method according to any one of Clauses 1 to 2, wherein the first ML model and the reference ML model each correspond to a physical model or a logical model.

[0211] Clause 4. The method according to any one of Clauses 1 to 3, wherein the first ML-based UE positioning estimation model and the reference ML-based UE positioning estimation model are associated with the same location region or different location regions that at least partially overlap with each other.

[0212] Clause 5. The method according to any one of Clauses 1 to 4, wherein the reference ML-based UE positioning estimation model is trained on first training data, and the first ML-based UE positioning estimation model is trained on second training data.

[0213] Clause 6. The method according to Clause 5, the method further comprising: determining a second QML between a second ML-based UE positioning estimation model and the reference ML-based UE positioning estimation model, the second QML being based on a first set of features and a third set of features associated with the second ML-based UE positioning estimation model; and sending the second ML-based UE positioning estimation model and an instruction to the second QML.

[0214] Clause 7. The method described in Clause 6, wherein the second ML-based UE positioning estimation model is trained based on third training data.

[0215] Clause 8. The method according to any one of Clauses 5 to 7, the method further comprising: determining a second QML between a second ML-based UE positioning estimation model and a first ML-based UE positioning estimation model, the second QML being based on a second set of features and a third set of features associated with the second ML-based UE positioning estimation model; and sending the second ML-based UE positioning estimation model and an instruction to the second QML.

[0216] Clause 9. The method described in Clause 8, wherein the second ML-based UE positioning estimation model is trained based on third training data.

[0217] Clause 10. The method according to any one of Clauses 1 to 9, wherein the first QML indicates the similarity or difference between a first value or a first range of values ​​of an attribute type associated with the first set of properties and a second value or a second range of values ​​of the attribute type associated with the first set of properties.

[0218] Clause 11. The method according to Clause 10, wherein the attribute type includes: location area information, or timing information, or model complexity, or Doppler shift, or Doppler spread, or average delay, or delay spread, or spatial receiving filter, or spatial transmitting filter, or any combination thereof.

[0219] Clause 12. The method according to any one of Clauses 1 to 11, wherein the transmission of the indication to the first QML is performed via a capability exchange procedure, or wherein the transmission of the indication to the first QML is performed via Long Term Evolution (LTE) Positioning Protocol (LPP) Location Request Signaling, or wherein the transmission of the indication to the first QML is performed via LPP Auxiliary Data Signaling, or wherein the transmission of the indication to the first QML is performed via LPP Broadcast Positioning Signaling, or any combination thereof.

[0220] Clause 13. The method according to any one of Clauses 1 to 12, the method further comprising: receiving a request for the first QML, wherein the transmission of the indication to the first QML is in response to the request.

[0221] Clause 14. A method of operating a location estimation entity, the method comprising: receiving an indication of a first quasi-model relationship (QML) between a reference machine learning (ML)-based user equipment (UE) location estimation model associated with a first set of characteristics and a first ML-based UE location estimation model associated with a second set of characteristics; and performing one or more actions associated with location estimation of one or more UEs based on the indication of the first QML.

[0222] Clause 15. The method according to Clause 14, wherein the reference ML-based UE positioning estimation model is trained on first training data, and the first ML-based UE positioning estimation model is trained on second training data.

[0223] Clause 16. The method according to any one of Clauses 14 to 15, wherein the one or more actions include activating, deactivating, or selecting the first ML-based UE positioning estimation model, the reference ML-based UE positioning estimation model, or another ML-based UE positioning estimation model, or wherein the one or more actions include switching between ML-based UE positioning estimation models, or a combination thereof.

[0224] Clause 17. The method according to any one of Clauses 14 to 16, wherein the location estimation entity corresponds to a network component or a UE.

[0225] Clause 18. The method according to any one of Clauses 14 to 17, wherein the first ML model and the reference ML model each correspond to a physical model or a logical model.

[0226] Clause 19. The method according to any one of Clauses 14 to 18, wherein the first ML-based UE positioning estimation model and the reference ML-based UE positioning estimation model are associated with the same location region or different location regions that at least partially overlap with each other.

[0227] Clause 20. The method according to any one of Clauses 14 to 19, the method further comprising: receiving an indication of a second QML between a second ML-based UE positioning estimation model associated with a third set of features and a reference ML-based UE positioning estimation model, the second QML being based on a correspondence between the first set of features and the third set of features associated with the second ML-based UE positioning estimation model, wherein one or more actions are further based on the indication of the second QML.

[0228] Clause 21. The method according to Clause 20, wherein the second ML-based UE positioning estimation model is trained based on third training data.

[0229] Clause 22. The method according to any one of Clauses 14 to 21, the method further comprising: receiving an indication of a second QML between a second ML-based UE location estimation model associated with a third set of features and a first ML-based UE location estimation model, the second QML being based on a correspondence between the second set of features and the third set of features associated with the second ML-based UE location estimation model, wherein one or more actions are further based on the indication of the second QML.

[0230] Clause 23. The method according to Clause 22, wherein the second ML-based UE positioning estimation model is trained based on third training data.

[0231] Clause 24. The method according to any one of Clauses 14 to 23, wherein the first QML indicates the similarity or difference between a first value or a first range of values ​​of an attribute type associated with the first set of properties and a second value or a second range of values ​​of the attribute type associated with the first set of properties.

[0232] Clause 25. The method according to Clause 24, wherein the attribute type includes: location area information, or timing information, or model complexity, or Doppler shift, or Doppler spread, or average delay, or delay spread, or spatial receiving filter, or spatial transmitting filter, or any combination thereof.

[0233] Clause 26. The method according to any one of Clauses 14 to 25, wherein the reception of the indication to the first QML is performed via a capability exchange procedure, or wherein the reception of the indication to the first QML is performed via Long Term Evolution (LTE) Location Protocol (LPP) Location Request Signalling, or wherein the reception of the indication to the first QML is performed via LPP Auxiliary Data Signalling, or wherein the reception of the indication to the first QML is performed via LPP Broadcast Location Signalling, or any combination thereof.

[0234] Clause 27. The method according to any one of Clauses 14 to 26, the method further comprising: sending a request for the first QML, wherein the receipt of the indication to the first QML is in response to the request.

[0235] Clause 28. A communication device comprising: one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors being individually or in combination configured to: determine a first quasi-model relationship (QML) between a reference machine learning (ML)-based UE location estimation model and a first ML-based UE location estimation model, the first quasi-model relationship (QML) being based on a correspondence between a first set of features associated with the reference ML-based UE location estimation model and a second set of features associated with a second set of features; and transmit an indication of the first QML via the one or more transceivers.

[0236] Clause 29. The communication device as described in Clause 28, wherein the communication device corresponds to a network component or a UE.

[0237] Clause 30. A communication device pursuant to any one of Clauses 28 to 29, wherein the first ML model and the reference ML model each correspond to a physical model or a logical model.

[0238] Clause 31. The communication device according to any one of Clauses 28 to 30, wherein the first ML-based UE positioning estimation model and the reference ML-based UE positioning estimation model are associated with the same location area or different location areas that at least partially overlap with each other.

[0239] Clause 32. The communication device according to any one of Clauses 28 to 31, wherein the reference ML-based UE positioning estimation model is trained on first training data, and the first ML-based UE positioning estimation model is trained on second training data.

[0240] Clause 33. The communication device according to Clause 32, wherein the one or more processors are further configured individually or in combination to: determine a second QML between a second ML-based UE location estimation model and the reference ML-based UE location estimation model, the second QML being based on a first set of features and a third set of features associated with the second ML-based UE location estimation model; and transmit the second ML-based UE location estimation model and an indication of the second QML via the one or more transceivers.

[0241] Clause 34. The communication device pursuant to Clause 33, wherein the second ML-based UE positioning estimation model is trained based on third training data.

[0242] Clause 35. A communication device according to any one of Clauses 32 to 34, wherein the one or more processors are further configured individually or in combination to: determine a second QML between a second ML-based UE location estimation model and a first ML-based UE location estimation model, the second QML being based on a second set of features and a third set of features associated with the second ML-based UE location estimation model; and transmit the second ML-based UE location estimation model and an indication of the second QML via the one or more transceivers.

[0243] Clause 36. The communication device pursuant to Clause 35, wherein the second ML-based UE positioning estimation model is trained based on third training data.

[0244] Clause 37. A communication device according to any one of Clauses 28 to 36, wherein the first QML indicates the similarity or difference between a first value or a first range of values ​​of an attribute type associated with the first set of characteristics and a second value or a second range of values ​​of the attribute type associated with the first set of characteristics.

[0245] Clause 38. The communication device pursuant to Clause 37, wherein the attribute type includes: location area information, or timing information, or model complexity, or Doppler shift, or Doppler spread, or average delay, or delay spread, or spatial receive filter, or spatial transmit filter, or any combination thereof.

[0246] Clause 39. A communication device pursuant to any one of Clauses 28 to 38, wherein the transmission of the indication to the first QML is performed via a capability exchange procedure, or wherein the transmission of the indication to the first QML is performed via Long Term Evolution (LTE) Positioning Protocol (LPP) Location Request Signalling, or wherein the transmission of the indication to the first QML is performed via LPP Auxiliary Data Signalling, or wherein the transmission of the indication to the first QML is performed via LPP Broadcast Positioning Signalling, or any combination thereof.

[0247] Clause 40. A communication device according to any one of Clauses 28 to 39, wherein the one or more processors are further configured individually or in combination to receive a request for the first QML via the one or more transceivers, wherein the transmission of the indication of the first QML is in response to the request.

[0248] Clause 41. A location estimation entity comprising: one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors being individually or in combination configured to: receive via the one or more transceivers an indication of a first quasi-model relation (QML) between a reference machine learning (ML)-based user equipment (UE) location estimation model associated with a first set of features and a first ML-based UE location estimation model associated with a second set of features; and to perform one or more actions associated with location estimation of one or more UEs based on the indication of the first QML.

[0249] Clause 42. The positioning estimation entity as described in Clause 41, wherein the reference ML-based UE positioning estimation model is trained on first training data, and the first ML-based UE positioning estimation model is trained on second training data.

[0250] Clause 43. The location estimation entity according to any one of Clauses 41 to 42, wherein the one or more actions include activating, deactivating, or selecting the first ML-based UE location estimation model, the reference ML-based UE location estimation model, or another ML-based UE location estimation model, or wherein the one or more actions include switching between ML-based UE location estimation models, or a combination thereof.

[0251] Clause 44. A location estimation entity pursuant to any one of Clauses 41 to 43, wherein the location estimation entity corresponds to a network component or a UE.

[0252] Clause 45. The location estimation entity according to any one of Clauses 41 to 44, wherein the first ML model and the reference ML model each correspond to a physical model or a logical model.

[0253] Clause 46. The location estimation entity according to any one of Clauses 41 to 45, wherein the first ML-based UE location estimation model and the reference ML-based UE location estimation model are associated with the same location region or different location regions that at least partially overlap with each other.

[0254] Clause 47. The location estimation entity according to any one of Clauses 41 to 46, wherein the one or more processors are further configured individually or in combination to: receive via the one or more transceivers an indication of a second QML between a second ML-based UE location estimation model associated with a third set of features and a reference ML-based UE location estimation model, the second QML being based on a correspondence between the first set of features and the third set of features associated with the second ML-based UE location estimation model, wherein the one or more actions are further based on the indication of the second QML.

[0255] Clause 48. The positioning estimation entity as described in Clause 47, wherein the second ML-based UE positioning estimation model is trained based on third training data.

[0256] Clause 49. The location estimation entity according to any one of Clauses 41 to 48, wherein the one or more processors are further configured individually or in combination to: receive via the one or more transceivers an indication of a second QML between a second ML-based UE location estimation model associated with a third set of features and a first ML-based UE location estimation model, the second QML being based on a correspondence between the second set of features and the third set of features associated with the second ML-based UE location estimation model, wherein the one or more actions are further based on the indication of the second QML.

[0257] Clause 50. The positioning estimation entity as described in Clause 49, wherein the second ML-based UE positioning estimation model is trained based on third training data.

[0258] Clause 51. A location estimation entity according to any one of Clauses 41 to 50, wherein the first QML indicates the similarity or difference between a first value or a first range of values ​​of an attribute type associated with the first set of features and a second value or a second range of values ​​of the attribute type associated with the first set of features.

[0259] Clause 52. The positioning estimation entity as described in Clause 51, wherein the attribute type includes: location area information, or timing information, or model complexity, or Doppler frequency shift, or Doppler spread, or average delay, or delay spread, or spatial receiving filter, or spatial transmitting filter, or any combination thereof.

[0260] Clause 53. A location estimation entity pursuant to any one of Clauses 41 to 52, wherein the receipt of the indication to the first QML is performed via a capability exchange procedure, or wherein the receipt of the indication to the first QML is performed via Long Term Evolution (LTE) Location Protocol (LPP) Location Request Signaling, or wherein the receipt of the indication to the first QML is performed via LPP Auxiliary Data Signaling, or wherein the receipt of the indication to the first QML is performed via LPP Broadcast Location Signaling, or any combination thereof.

[0261] Clause 54. The location estimation entity according to any one of Clauses 41 to 53, wherein the one or more processors are further configured individually or in combination to: transmit a request for the first QML via the one or more transceivers, wherein the reception of the indication of the first QML is in response to the request.

[0262] Clause 55. A communication device comprising: a component for determining a first quasi-model relationship (QML) between a reference machine learning (ML)-based UE positioning estimation model and a first ML-based UE positioning estimation model, the first quasi-model relationship (QML) being based on a correspondence between a first set of features associated with the reference ML-based UE positioning estimation model and a second set of features associated with a second set of features; and a component for transmitting an indication of the first QML.

[0263] Clause 56. The communication device as described in Clause 55, wherein the communication device corresponds to a network component or a UE.

[0264] Clause 57. A communication device according to any one of Clauses 55 to 56, wherein the first ML model and the reference ML model each correspond to a physical model or a logical model.

[0265] Clause 58. The communication device according to any one of Clauses 55 to 57, wherein the first ML-based UE positioning estimation model and the reference ML-based UE positioning estimation model are associated with the same location area or different location areas that at least partially overlap with each other.

[0266] Clause 59. The communication device according to any one of Clauses 55 to 58, wherein the reference ML-based UE positioning estimation model is trained on first training data, and the first ML-based UE positioning estimation model is trained on second training data.

[0267] Clause 60. The communication device according to Clause 59, the communication device further comprising: components for determining a second QML between a second ML-based UE positioning estimation model and the reference ML-based UE positioning estimation model, the second QML being based on a first set of features and a third set of features associated with the second ML-based UE positioning estimation model; and components for transmitting the second ML-based UE positioning estimation model and an indication to the second QML.

[0268] Clause 61. The communication device according to Clause 60, wherein the second ML-based UE positioning estimation model is trained based on third training data.

[0269] Clause 62. The communication device according to any one of Clauses 59 to 61, the communication device further comprising: a component for determining a second QML between a second ML-based UE positioning estimation model and a first ML-based UE positioning estimation model, the second QML being based on a second set of features and a third set of features associated with the second ML-based UE positioning estimation model; and a component for transmitting the second ML-based UE positioning estimation model and an indication to the second QML.

[0270] Clause 63. The communication device according to Clause 62, wherein the second ML-based UE positioning estimation model is trained based on third training data.

[0271] Clause 64. A communication device according to any one of Clauses 55 to 63, wherein the first QML indicates the similarity or difference between a first value or a first range of values ​​of an attribute type associated with the first set of characteristics and a second value or a second range of values ​​of the attribute type associated with the first set of characteristics.

[0272] Clause 65. The communication device pursuant to Clause 64, wherein the attribute type includes: location area information, or timing information, or model complexity, or Doppler shift, or Doppler spread, or average delay, or delay spread, or spatial receive filter, or spatial transmit filter, or any combination thereof.

[0273] Clause 66. A communication device pursuant to any one of Clauses 55 to 65, wherein the transmission of the indication to the first QML is performed via a capability exchange procedure, or wherein the transmission of the indication to the first QML is performed via Long Term Evolution (LTE) Positioning Protocol (LPP) Location Request Signaling, or wherein the transmission of the indication to the first QML is performed via LPP Auxiliary Data Signaling, or wherein the transmission of the indication to the first QML is performed via LPP Broadcast Positioning Signaling, or any combination thereof.

[0274] Clause 67. The communication device according to any one of Clauses 55 to 66, the communication device further comprising: a component for receiving a request for the first QML, wherein the transmission of the instruction to the first QML is in response to the request.

[0275] Clause 68. A location estimation entity, the location estimation entity comprising: a component for receiving an indication of a first quasi-model relationship (QML) between a first set of reference machine learning (ML)-based user equipment (UE) location estimation models associated with a first set of characteristics and a first ML-based UE location estimation model associated with a second set of characteristics; and a component for performing one or more actions associated with location estimation of one or more UEs based on the indication of the first QML.

[0276] Clause 69. The positioning estimation entity as described in Clause 68, wherein the reference ML-based UE positioning estimation model is trained on first training data, and the first ML-based UE positioning estimation model is trained on second training data.

[0277] Clause 70. A location estimation entity pursuant to any one of Clauses 68 to 69, wherein the one or more actions include activating, deactivating, or selecting the first ML-based UE location estimation model, the reference ML-based UE location estimation model, or another ML-based UE location estimation model, or wherein the one or more actions include switching between ML-based UE location estimation models, or a combination thereof.

[0278] Clause 71. A location estimation entity pursuant to any one of Clauses 68 to 70, wherein the location estimation entity corresponds to a network component or a UE.

[0279] Clause 72. The location estimation entity according to any one of Clauses 68 to 71, wherein the first ML model and the reference ML model each correspond to a physical model or a logical model.

[0280] Clause 73. The location estimation entity according to any one of Clauses 68 to 72, wherein the first ML-based UE location estimation model and the reference ML-based UE location estimation model are associated with the same location region or different location regions that at least partially overlap with each other.

[0281] Clause 74. The location estimation entity according to any one of Clauses 68 to 73, the location estimation entity further comprising: a component for receiving an indication of a second QML between a second ML-based UE location estimation model associated with a third set of features and a reference ML-based UE location estimation model, the second QML being based on a correspondence between the first set of features and the third set of features associated with the second ML-based UE location estimation model, wherein one or more actions are further based on the indication of the second QML.

[0282] Clause 75. The positioning estimation entity as described in Clause 74, wherein the second ML-based UE positioning estimation model is trained based on third training data.

[0283] Clause 76. The location estimation entity according to any one of Clauses 68 to 75, the location estimation entity further comprising: a component for receiving an indication of a second QML between a second ML-based UE location estimation model associated with a third set of features and a first ML-based UE location estimation model, the second QML being based on a correspondence between the second set of features and the third set of features associated with the second ML-based UE location estimation model, wherein one or more actions are further based on the indication of the second QML.

[0284] Clause 77. The positioning estimation entity as described in Clause 76, wherein the second ML-based UE positioning estimation model is trained based on third training data.

[0285] Clause 78. A location estimation entity according to any one of Clauses 68 to 77, wherein the first QML indicates the similarity or difference between a first value or a first range of values ​​of an attribute type associated with the first set of features and a second value or a second range of values ​​of the attribute type associated with the first set of features.

[0286] Clause 79. The positioning estimation entity as described in Clause 78, wherein the attribute type includes: location area information, or timing information, or model complexity, or Doppler frequency shift, or Doppler spread, or average delay, or delay spread, or spatial receiving filter, or spatial transmitting filter, or any combination thereof.

[0287] Clause 80. A location estimation entity pursuant to any one of Clauses 68 to 79, wherein the receipt of the indication to the first QML is performed via a capability exchange procedure, or wherein the receipt of the indication to the first QML is performed via Long Term Evolution (LTE) Location Protocol (LPP) Location Request Signalling, or wherein the receipt of the indication to the first QML is performed via LPP Auxiliary Data Signalling, or wherein the receipt of the indication to the first QML is performed via LPP Broadcast Location Signalling, or any combination thereof.

[0288] Clause 81. The location estimation entity according to any one of Clauses 68 to 80, the location estimation entity further comprising: a component for sending a request for the first QML, wherein the receipt of the indication of the first QML is in response to the request.

[0289] Clause 82. A non-transitory computer-readable medium storing computer-executable instructions, which, when executed by a communication device, cause the communication device to: determine a first quasi-model relation (QML) between a reference machine learning (ML)-based UE location estimation model and a first ML-based UE location estimation model, the first quasi-model relation (QML) being based on a correspondence between a first set of features associated with the reference ML-based UE location estimation model and a second set of features associated with a second set of features; and send an instruction to the first QML.

[0290] Clause 83. The non-transitory computer-readable medium as described in Clause 82, wherein the communication device corresponds to a network component or a UE.

[0291] Clause 84. A non-transitory computer-readable medium according to any one of Clauses 82 to 83, wherein the first ML model and the reference ML model each correspond to a physical model or a logical model.

[0292] Clause 85. The non-transitory computer-readable medium according to any one of Clauses 82 to 84, wherein the first ML-based UE positioning estimation model and the reference ML-based UE positioning estimation model are associated with the same location region or different location regions that at least partially overlap with each other.

[0293] Clause 86. The non-transitory computer-readable medium according to any one of Clauses 82 to 85, wherein the reference ML-based UE positioning estimation model is trained on first training data, and the first ML-based UE positioning estimation model is trained on second training data.

[0294] Clause 87. The non-transitory computer-readable medium according to Clause 86, further comprising computer-executable instructions, which, when executed by the communication device, cause the communication device to: determine a second QML between a second ML-based UE location estimation model and a reference ML-based UE location estimation model, the second QML being based on a first set of features and a third set of features associated with the second ML-based UE location estimation model; and transmit the second ML-based UE location estimation model and an instruction to the second QML.

[0295] Clause 88. The non-transitory computer-readable medium as described in Clause 87, wherein the second ML-based UE positioning estimation model is trained based on third training data.

[0296] Clause 89. A nontransitory computer-readable medium according to any one of Clauses 86 to 88, the nontransitory computer-readable medium further comprising computer-executable instructions, which, when executed by the communication device, cause the communication device to: determine a second QML between a second ML-based UE location estimation model and a first ML-based UE location estimation model, the second QML being based on a second set of features and a third set of features associated with the second ML-based UE location estimation model; and transmit the second ML-based UE location estimation model and an instruction to the second QML.

[0297] Clause 90. The non-transitory computer-readable medium as described in Clause 89, wherein the second ML-based UE positioning estimation model is trained based on third training data.

[0298] Clause 91. A non-transitory computer-readable medium according to any one of Clauses 82 to 90, wherein the first QML indicates a similarity or difference between a first value or a first range of values ​​of an attribute type associated with the first set of properties and a second value or a second range of values ​​of the attribute type associated with the first set of properties.

[0299] Clause 92. The non-transitory computer-readable medium as described in Clause 91, wherein the attribute type includes: location area information, or timing information, or model complexity, or Doppler shift, or Doppler spread, or average delay, or delay spread, or spatial receiving filter, or spatial transmitting filter, or any combination thereof.

[0300] Clause 93. A non-transitory computer-readable medium pursuant to any one of Clauses 82 to 92, wherein the transmission of the indication to the first QML is performed via a capability exchange procedure, or wherein the transmission of the indication to the first QML is performed via Long Term Evolution (LTE) Location Protocol (LPP) Location Request Signalling, or wherein the transmission of the indication to the first QML is performed via LPP Auxiliary Data Signalling, or wherein the transmission of the indication to the first QML is performed via LPP Broadcast Location Signalling, or any combination thereof.

[0301] Clause 94. A nontransitory computer-readable medium according to any one of Clauses 82 to 93, the nontransitory computer-readable medium further comprising computer-executable instructions that, when executed by the communication device, cause the communication device to: receive a request for the first QML, wherein the transmission of the instruction for the first QML is in response to the request.

[0302] Clause 95. A non-transitory computer-readable medium storing computer-executable instructions, which, when executed by a location estimation entity, cause the location estimation entity to: receive an instruction for a first quasi-model relation (QML) between a reference machine learning (ML)-based user equipment (UE) location estimation model associated with a first set of characteristics and a first ML-based UE location estimation model associated with a second set of characteristics; and perform one or more actions associated with location estimation of one or more UEs based on the instruction for the first QML.

[0303] Clause 96. The non-transitory computer-readable medium according to Clause 95, wherein the reference ML-based UE positioning estimation model is trained on first training data, and the first ML-based UE positioning estimation model is trained on second training data.

[0304] Clause 97. A non-transitory computer-readable medium pursuant to any one of Clauses 95 to 96, wherein the one or more actions comprise activation, deactivation, or selection of the first ML-based UE location estimation model, the reference ML-based UE location estimation model, or another ML-based UE location estimation model, or wherein the one or more actions comprise switching between ML-based UE location estimation models, or a combination thereof.

[0305] Clause 98. A non-transitory computer-readable medium pursuant to any one of Clauses 95 to 97, wherein the location estimation entity corresponds to a network component or a UE.

[0306] Clause 99. A non-transitory computer-readable medium pursuant to any one of Clauses 95 to 98, wherein the first ML model and the reference ML model each correspond to a physical model or a logical model.

[0307] Clause 100. A non-transitory computer-readable medium according to any one of Clauses 95 to 99, wherein the first ML-based UE positioning estimation model and the reference ML-based UE positioning estimation model are associated with the same location region or different location regions that at least partially overlap with each other.

[0308] Clause 101. A non-transitory computer-readable medium according to any one of Clauses 95 to 100, the non-transitory computer-readable medium further comprising computer-executable instructions, which, when executed by the positioning estimation entity, cause the positioning estimation entity to: receive an instruction for a second QML between a second ML-based UE positioning estimation model associated with a third set of features and a reference ML-based UE positioning estimation model, the second QML being based on a correspondence between the first set of features and the third set of features associated with the second ML-based UE positioning estimation model, wherein the one or more actions are further based on the instruction for the second QML.

[0309] Clause 102. The non-transitory computer-readable medium as described in Clause 101, wherein the second ML-based UE positioning estimation model is trained based on third training data.

[0310] Clause 103. A non-transitory computer-readable medium according to any one of Clauses 95 to 102, the non-transitory computer-readable medium further comprising computer-executable instructions, which, when executed by the positioning estimation entity, cause the positioning estimation entity to: receive an instruction for a second QML between a second ML-based UE positioning estimation model associated with a third set of features and a first ML-based UE positioning estimation model, the second QML being based on a correspondence between the second set of features and the third set of features associated with the second ML-based UE positioning estimation model, wherein the one or more actions are further based on the instruction for the second QML.

[0311] Clause 104. The non-transitory computer-readable medium as described in Clause 103, wherein the second ML-based UE positioning estimation model is trained based on third training data.

[0312] Clause 105. A non-transitory computer-readable medium according to any one of Clauses 95 to 104, wherein the first QML indicates a similarity or difference between a first value or a first range of values ​​of an attribute type associated with the first set of properties and a second value or a second range of values ​​of the attribute type associated with the first set of properties.

[0313] Clause 106. The non-transitory computer-readable medium as described in Clause 105, wherein the attribute type includes: location area information, or timing information, or model complexity, or Doppler shift, or Doppler spread, or average delay, or delay spread, or spatial receiving filter, or spatial transmitting filter, or any combination thereof.

[0314] Clause 107. A non-transitory computer-readable medium pursuant to any one of Clauses 95 to 106, wherein the reception of the indication to the first QML is performed via a capability exchange procedure, or wherein the reception of the indication to the first QML is performed via Long Term Evolution (LTE) Location Protocol (LPP) Location Request Signalling, or wherein the reception of the indication to the first QML is performed via LPP Auxiliary Data Signalling, or wherein the reception of the indication to the first QML is performed via LPP Broadcast Location Signalling, or any combination thereof.

[0315] Clause 108. The non-transitory computer-readable medium according to any one of Clauses 95 to 107, the non-transitory computer-readable medium further comprising computer-executable instructions, which, when executed by the positioning estimation entity, cause the positioning estimation entity to: send a request for the first QML, wherein the receipt of the indication of the first QML is in response to the request.

[0316] 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.

[0317] 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 such 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; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0318] 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.

[0319] 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.

[0320] 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.

[0321] 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. For example, the functions, steps, and / or actions of the method claims according to aspects of this disclosure described herein need not be performed in any particular order. Furthermore, no component, function, action, or instruction described or claimed herein should be construed as critical or essential unless explicitly stated otherwise. Additionally, as used herein, the terms “set,” “group,” etc., are intended to include one or more of the stated elements. Furthermore, as used herein, the terms “having,” “comprising,” “including,” etc., do not exclude the presence of one or more additional elements (e.g., element “having” A may also have B). Furthermore, the phrase “based on” is intended to mean “at least partially based on” unless otherwise explicitly stated. Furthermore, as used herein, the term “or” is intended to be open-ended when used in a series and is interchangeable with “and / or” unless otherwise explicitly stated (e.g., if used in conjunction with “any” or “only one”), or these alternatives are mutually exclusive (e.g., “one or more” should not be interpreted as “one and more”). Additionally, although components, functions, actions, and instructions may be described or claimed in the singular, plural forms may also be considered unless explicitly stated to be limited to the singular. Therefore, as used herein, the articles “a,” “an,” “the,” and “described” are intended to include one or more of the stated elements. Additionally, as used herein, the terms “at least one” and “one or more” include “one” component, function, action, or instruction that performs or is capable of performing the described or claimed functionality, and also include “two or more” components, functions, actions, or instructions that perform or are capable of performing the described or claimed functionality in combination.

Claims

1. A method for operating a communication device, the method comprising: A first quasi-model relationship (QML) is determined between a reference machine learning (ML)-based UE positioning estimation model and a first ML-based UE positioning estimation model. The first quasi-model relationship (QML) is based on the correspondence between a first set of features associated with the reference ML-based UE positioning estimation model and a second set of features associated with a second set of features. as well as Send an instruction to the first QML.

2. The method according to claim 1, wherein the communication device corresponds to a network component or a UE.

3. The method according to claim 1, wherein the first ML model and the reference ML model each correspond to a physical model or a logical model.

4. The method of claim 1, wherein the first ML-based UE positioning estimation model and the reference ML-based UE positioning estimation model are associated with the same location region or different location regions that at least partially overlap with each other.

5. The method according to claim 1, wherein the reference ML-based UE positioning estimation model is trained based on the first training data, and the first ML-based UE positioning estimation model is trained based on the second training data.

6. The method according to claim 5, further comprising: Determine a second QML between the second ML-based UE positioning estimation model and the reference ML-based UE positioning estimation model, and the second QML is based on the correspondence between the first set of features and the third set of features associated with the second ML-based UE positioning estimation model; as well as Send the second ML-based UE positioning estimation model and instructions for the second QML.

7. The method according to claim 6, wherein the second ML-based UE positioning estimation model is trained based on the third training data.

8. The method according to claim 5, further comprising: Determine a second QML between the second ML-based UE positioning estimation model and the first ML-based UE positioning estimation model, and the second QML is based on the correspondence between a second set of features and a third set of features associated with the second ML-based UE positioning estimation model; as well as Send the second ML-based UE positioning estimation model and instructions for the second QML.

9. The method according to claim 8, wherein the second ML-based UE positioning estimation model is trained based on the third training data.

10. The method of claim 1, wherein the first QML indicates the similarity or difference between a first value or a first range of values ​​of an attribute type associated with the first set of features and a second value or a second range of values ​​of the attribute type associated with the first set of features.

11. The method of claim 10, wherein the attribute type includes: Location area information, or Scheduled information, or Model complexity, or Doppler shift, or Doppler extension, or Average delay, or Delayed spread, or Space receiving filter, or Spatial transmission filter, or Any combination of them.

12. The method according to claim 1, The transmission of the instruction to the first QML is performed via a capability exchange process, or The transmission of the indication to the first QML is performed via Long Term Evolution (LTE) Location Protocol (LPP) location request signaling, or The transmission of the indication to the first QML is performed via LPP auxiliary data signaling, or The transmission of the indication to the first QML is performed via LPP broadcast positioning signaling, or Any combination of them.

13. The method according to claim 1, further comprising: Receive the request for the first QML. The sending of the instruction to the first QML is in response to the request.

14. A method for locating and estimating an entity, the method comprising: Receive an indication of a first quasi-model relationship (QML) between a reference machine learning (ML)-based user equipment (UE) positioning estimation model for a first set of features and a first ML-based UE positioning estimation model for a second set of features; and Perform one or more actions associated with the location estimation of one or more UEs based on the instructions given in the first QML.

15. The method of claim 14, wherein the reference ML-based UE positioning estimation model is trained based on the first training data, and the first ML-based UE positioning estimation model is trained based on the second training data.

16. The method according to claim 14, The one or more actions mentioned include activating, deactivating, or selecting the first ML-based UE positioning estimation model, the reference ML-based UE positioning estimation model, or another ML-based UE positioning estimation model. The one or more actions mentioned include switching between ML-based UE positioning estimation models, or Their combination.

17. The method of claim 14, wherein the location estimation entity corresponds to a network component or a UE.

18. The method of claim 14, wherein the first ML model and the reference ML model each correspond to a physical model or a logical model.

19. The method of claim 14, wherein the first ML-based UE positioning estimation model and the reference ML-based UE positioning estimation model are associated with the same location region or different location regions that at least partially overlap with each other.

20. The method of claim 14, further comprising: Receives an indication of a second QML between a second ML-based UE location estimation model and a reference ML-based UE location estimation model, based on a third set of features, wherein the second QML is based on a correspondence between the first set of features and the third set of features associated with the second ML-based UE location estimation model. The one or more actions thereon are further based on the instructions given to the second QML.

21. The method of claim 20, wherein the second ML-based UE positioning estimation model is trained based on the third training data.

22. The method according to claim 14, further comprising: Receives an indication of a second QML between a second ML-based UE location estimation model and a first ML-based UE location estimation model, based on a third set of features, wherein the second QML is based on the correspondence between the second set of features and the third set of features associated with the second ML-based UE location estimation model. The one or more actions thereon are further based on the instructions given to the second QML.

23. The method of claim 22, wherein the second ML-based UE positioning estimation model is trained based on the third training data.

24. The method of claim 14, wherein the first QML indicates the similarity or difference between a first value or a first range of values ​​of an attribute type associated with the first set of features and a second value or a second range of values ​​of the attribute type associated with the first set of features.

25. The method of claim 24, wherein the attribute type includes: Location area information, or Scheduled information, or Model complexity, or Doppler shift, or Doppler extension, or Average delay, or Delayed spread, or Space receiving filter, or Spatial transmission filter, or Any combination of them.

26. The method according to claim 14, The receipt of the instruction in the first QML is performed via a capability exchange process, or The reception of the indication to the first QML is performed via Long Term Evolution (LTE) Location Protocol (LPP) location request signaling, or The receipt of the indication to the first QML is performed via LPP auxiliary data signaling, or The reception of the indication to the first QML is performed via LPP broadcast positioning signaling, or Any combination of them.

27. The method of claim 14, further comprising: Send a request for the first QML. The receipt of the instruction in the first QML is in response to the request.

28. A communication device, the communication device comprising: One or more memory units; One or more transceivers; and One or more processors, communicatively coupled to one or more memories and one or more transceivers, wherein the one or more processors are configured individually or in combination to: A first quasi-model relationship (QML) is determined between a reference machine learning (ML)-based UE positioning estimation model and a first ML-based UE positioning estimation model. The first quasi-model relationship (QML) is based on the correspondence between a first set of features associated with the reference ML-based UE positioning estimation model and a second set of features associated with a second set of features. as well as Instructions to the first QML are sent via the one or more transceivers.

29. The communication device of claim 28, wherein the communication device corresponds to a network component or a UE.

30. A location estimation entity, the location estimation entity comprising: One or more memory units; One or more transceivers; and One or more processors, communicatively coupled to one or more memories and one or more transceivers, wherein the one or more processors are configured individually or in combination to: Receive, via the one or more transceivers, an indication of a first quasi-model relationship (QML) between a reference machine learning (ML)-based user equipment (UE) positioning estimation model associated with a first set of features and a first ML-based UE positioning estimation model associated with a second set of features; and Perform one or more actions associated with the location estimation of one or more UEs based on the instructions given in the first QML.

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