Event-based on-device artificial intelligence / machine learning (AIML) positioning model management

Abstract

In an aspect, a network node may obtain an artificial intelligence / machine learning (AIML) management configuration indicating a set of trigger conditions and corresponding AIML management operations, wherein the AIML management operations are associated with management of one or more positioning AIML models at the network node, and wherein the trigger conditions include handover-based trigger conditions, mobility-based trigger conditions, or a combination thereof. The network node may perform one or more of the AIML management operations based on occurrence of one or more trigger conditions of the set of trigger conditions corresponding to the AIML management operations.

Description

Qualcomm Ref. No. 2404834WO1 / 78EVENT-BASED ON-DEVICE ARTIFICIAL INTELLIGENCE / MACHINE LEARNING (AIML) POSITIONING MODEL MANAGEMENTTECHNICAL FIELD

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

[0002] Wireless communication systems have developed through various generations, including a first-generation analog wireless phone service (1G), a second-generation (2G) digital wireless phone service (including interim 2.5G and 2.75G networks), a third-generation (3G) high speed data, Internet-capable wireless service and a fourth-generation (4G) service (e.g., Long Term Evolution (LTE) or WiMax). There are presently many different types of wireless communication systems in use, including cellular and personal communications service (PCS) systems. Examples of known cellular systems include the cellular analog advanced mobile phone system (AMPS), and digital cellular systems based on code division multiple access (CDMA), frequency division multiple access (FDMA), time division multiple access (TDMA), the Global System for Mobile communications (GSM), etc.

[0003] A fifth generation (5G) wireless standard, referred to as New Radio (NR), enables higher data transfer speeds, greater numbers of connections, and better coverage, among other improvements. The 5G standard, according to the Next Generation Mobile Networks Alliance, is designed to provide higher data rates as compared to previous standards, more accurate positioning (e.g., based on reference signals for positioning (RS-P), such as downlink, uplink, or sidelink positioning reference signals (PRS)), RF sensing, and other technical enhancements. These enhancements, as well as the use of higher frequency bands, enable improved RF sensing and 5G-based positioning.SUMMARY

[0004] The following presents a simplified summary relating to one or more aspects disclosed herein. Thus, the following summary should not be considered an extensive overview relating to all contemplated aspects, nor should the following summary be considered to identify key or critical elements relating to all contemplated aspects or to delineate theQC2404834WOQualcomm Ref. No. 2404834WO2 / 78 scope associated with any particular aspect. Accordingly, the following summary has the sole purpose to present certain concepts relating to one or more aspects relating to the mechanisms disclosed herein in a simplified form to precede the detailed description presented below.

[0005] In an aspect, a method of wireless communication performed by a network node includes obtaining an artificial intelligence / machine learning (AIML) management configuration indicating a set of trigger conditions and corresponding AIML management operations, wherein the AIML management operations are associated with management of one or more positioning AIML models at the network node, and wherein the trigger conditions include handover-based trigger conditions, mobility-based trigger conditions, or a combination thereof; and performing one or more of the AIML management operations based on occurrence of one or more trigger conditions of the set of trigger conditions corresponding to the AIML management operations.

[0006] In an aspect, a network node includes one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to: obtain an artificial intelligence / machine learning (AIML) management configuration indicating a set of trigger conditions and corresponding AIML management operations, wherein the AIML management operations are associated with management of one or more positioning AIML models at the network node, and wherein the trigger conditions include handover-based trigger conditions, mobility-based trigger conditions, or a combination thereof; and perform one or more of the AIML management operations based on occurrence of one or more trigger conditions of the set of trigger conditions corresponding to the AIML management operations.

[0007] In an aspect, a network node includes means for obtaining an artificial intelligence / machine learning (AIML) management configuration indicating a set of trigger conditions and corresponding AIML management operations, wherein the AIML management operations are associated with management of one or more positioning AIML models at the network node, and wherein the trigger conditions include handoverbased trigger conditions, mobility-based trigger conditions, or a combination thereof; and means for performing one or more of the AIML management operations based onQC2404834WOQualcomm Ref. No. 2404834WO3 / 78 occurrence of one or more trigger conditions of the set of trigger conditions corresponding to the AIML management operations.

[0008] In an aspect, a non-transitory computer-readable medium stores computer-executable instructions that, when executed by a network node, cause the network node to: obtain an artificial intelligence / machine learning (AIML) management configuration indicating a set of trigger conditions and corresponding AIML management operations, wherein the AIML management operations are associated with management of one or more positioning AIML models at the network node, and wherein the trigger conditions include handover-based trigger conditions, mobility-based trigger conditions, or a combination thereof; and perform one or more of the AIML management operations based on occurrence of one or more trigger conditions of the set of trigger conditions corresponding to the AIML management operations.

[0009] Other obj ects and advantages associated with the aspects disclosed herein will be apparent to those skilled in the art based on the accompanying drawings and detailed description.BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings are presented to aid in the description of various aspects of the disclosure and are provided solely for illustration of the aspects and not limitation thereof.

[0011] FIG. 1 illustrates an example wireless communications system, according to aspects of the disclosure.

[0012] FIGS. 2A, 2B, and 2C illustrate example wireless network structures, according to aspects of the disclosure.

[0013] FIGS. 3A, 3B, and 3C are simplified block diagrams of several sample aspects of components that may be employed in a user equipment (UE), a base station, and a network entity, respectively, and configured to support communications as taught herein.

[0014] FIG. 4 illustrates examples of various positioning methods supported in New Radio (NR), according to aspects of the disclosure.

[0015] FIG. 5 illustrates an example neural network, according to aspects of the disclosure.

[0016] FIG. 6A is a diagram illustrating an example of direct artificial intelligence / machine learning (AIML) positioning and / or sensing, according to aspects of the disclosure.QC2404834WOQualcomm Ref. No. 2404834WO4 / 78

[0017] FIG. 6B is a diagram illustrating an example of AIML assisted positioning and / or sensing, according to aspects of the disclosure.

[0018] FIG. 6C illustrates various AIML positioning and / or sensing scenarios, according to aspects of the disclosure.

[0019] FIG. 7 illustrates an example of an AIML air interface model, according to aspects of the disclosure.

[0020] FIG. 8 illustrates an example method of wireless communication that may be performed by a network node, according to aspects of the disclosure.DETAILED DESCRIPTION

[0021] Aspects of the disclosure are provided in the following description and related drawings directed to various examples provided for illustration purposes. Alternate aspects may be devised without departing from the scope of the disclosure. Additionally, well-known elements of the disclosure will not be described in detail or will be omitted so as not to obscure the relevant details of the disclosure.

[0022] Various aspects relate generally to managing artificial intelligence / machine learning (AIML) for positioning in new radio (NR) systems. Some aspects more specifically relate to the performance of AIML positioning management operations by a network node (e.g., user equipment (UE), gNodeB (gNB), transmission-reception point (TRP)). In some examples, the network node obtains and AIML management configuration indicating a set of trigger conditions and corresponding AIML management operations. In some examples, the AIML management operations are associated with the management of one or more positioning AIML models at the network node. In some examples, when the network node determines that a trigger condition has occurred, the network node performs one or more AIML management operations corresponding to the trigger condition.

[0023] Particular aspects of the subject matter described in this disclosure can be implemented to realize one or more of the following potential advantages. In some examples, by configuring the network node with such an AIML management configuration, the described techniques can be used to perform AIML management operations at the network node as opposed to the performance of such management operations at a network server thereby providing a more centralized management of the AIML positioning at the network node primarily responsible for implementing the AIML positioning operations.QC2404834WOQualcomm Ref. No. 2404834WO5 / 78

[0024] The words “exemplary” and / or “example” are used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” and / or “example” is not necessarily to be construed as preferred or advantageous over other aspects. Likewise, the term “aspects of the disclosure” does not require that all aspects of the disclosure include the discussed feature, advantage or mode of operation.

[0025] Those of skill in the art will appreciate that the information and signals described below may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the description below may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof, depending in part on the particular application, in part on the desired design, in part on the corresponding technology, etc.

[0026] Further, many aspects are described in terms of sequences of actions to be performed by, for example, elements of a computing device. It will be recognized that various actions described herein can be performed by specific circuits (e.g., application specific integrated circuits (ASICs)), by program instructions being executed by one or more processors, or by a combination of both. Additionally, the sequence(s) of actions described herein can be considered to be embodied entirely within any form of non- transitory computer-readable storage medium having stored therein a corresponding set of computer instructions that, upon execution, would cause or instruct an associated processor of a device to perform the functionality described herein. Thus, the various aspects of the disclosure may be embodied in a number of different forms, all of which have been contemplated to be within the scope of the claimed subject matter. In addition, for each of the aspects described herein, the corresponding form of any such aspects may be described herein as, for example, “logic configured to” perform the described action.

[0027] As used herein, the terms “user equipment” (UE) and “base station” are not intended to be specific or otherwise limited to any particular radio access technology (RAT), unless otherwise noted. In general, a UE may be any wireless communication device (e.g., a mobile phone, router, tablet computer, laptop computer, consumer asset locating device, wearable (e.g., smartwatch, glasses, augmented reality (AR) / virtual reality (VR) headset, etc.), vehicle (e.g., automobile, motorcycle, bicycle, etc.), Internet of Things (loT) device, etc.) used by a user to communicate over a wireless communications network. A UE mayQC2404834WOQualcomm Ref. No. 2404834WO6 / 78 be mobile or may (e.g., at certain times) be stationary, and may communicate with a radio access network (RAN). As used herein, the term “UE” may be referred to interchangeably as an “access terminal” or “AT,” a “client device,” a “wireless device,” a “subscriber device,” a “subscriber terminal,” a “subscriber station,” a “user terminal” or “UT,” a “mobile device,” a “mobile terminal,” a “mobile station,” or variations thereof. Generally, UEs can communicate with a core network via a RAN, and through the core network the UEs can be connected with external networks such as the Internet and with other UEs. Of course, other mechanisms of connecting to the core network and / or the Internet are also possible for the UEs, such as over wired access networks, wireless local area network (WLAN) networks (e.g., based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11 specification, etc.) and so on.

[0028] A base station may operate according to one of several RATs in communication with UEs depending on the network in which it is deployed, and may be alternatively referred to as an access point (AP), a network node, a NodeB, an evolved NodeB (eNB), a next generation eNB (ng-eNB), a New Radio (NR) Node B (also referred to as a gNB or gNodeB), etc. A base station may be used primarily to support wireless access by UEs, including supporting data, voice, and / or signaling connections for the supported UEs. In some systems a base station may provide purely edge node signaling functions while in other systems it may provide additional control and / or network management functions. A communication link through which UEs can send signals to a base station is called an uplink (UL) channel (e.g., a reverse traffic channel, a reverse control channel, an access channel, etc.). A communication link through which the base station can send signals to UEs is called a downlink (DL) or forward link channel (e.g., a paging channel, a control channel, a broadcast channel, a forward traffic channel, etc.). As used herein the term traffic channel (TCH) can refer to either an uplink / reverse or downlink / forward traffic channel.

[0029] The term “base station” may refer to a single physical transmission-reception point (TRP) or to multiple physical TRPs that may or may not be co-located. For example, where the term “base station” refers to a single physical TRP, the physical TRP may be an antenna of the base station corresponding to a cell (or several cell sectors) of the base station. Where the term “base station” refers to multiple co-located physical TRPs, the physical TRPs may be an array of antennas (e.g., as in a multiple-input multiple-output (MEMO)QC2404834WOQualcomm Ref. No. 2404834WO system or where the base station employs beamforming) of the base station. Where the term “base station” refers to multiple non-co-located physical TRPs, the physical TRPs may be a distributed antenna system (DAS) (a network of spatially separated antennas connected to a common source via a transport medium) or a remote radio head (RRH) (a remote base station connected to a serving base station). Alternatively, the non-co-located physical TRPs may be the serving base station receiving the measurement report from the UE and a neighbor base station whose reference radio frequency (RF) signals the UE is measuring. Because a TRP is the point from which a base station transmits and receives wireless signals, as used herein, references to transmission from or reception at a base station are to be understood as referring to a particular TRP of the base station.

[0030] In some implementations that support positioning of UEs, a base station may not support wireless access by UEs (e.g., may not support data, voice, and / or signaling connections for UEs), but may instead transmit reference signals to UEs to be measured by the UEs, and / or may receive and measure signals transmitted by the UEs. Such a base station may be referred to as a positioning beacon (e.g., when transmitting signals to UEs) and / or as a location measurement unit (e.g., when receiving and measuring signals from UEs).

[0031] An “RF signal” comprises an electromagnetic wave of a given frequency that transports information through the space between a transmitter and a receiver. As used herein, a transmitter may transmit a single “RF signal” or multiple “RF signals” to a receiver. However, the receiver may receive multiple “RF signals” corresponding to each transmitted RF signal due to the propagation characteristics of RF signals through multipath channels. The same transmitted RF signal on different paths between the transmitter and receiver may be referred to as a “multipath” RF signal. As used herein, an RF signal may also be referred to as a “wireless signal” or simply a “signal” where it is clear from the context that the term “signal” refers to a wireless signal or an RF signal.

[0032] FIG. 1 illustrates an example wireless communications system 100, according to aspects of the disclosure. The wireless communications system 100 (which may also be referred to as a wireless wide area network (WWAN)) may include various base stations 102 (labeled “BS”) and various UEs 104. The base stations 102 may include macro cell base stations (high power cellular base stations) and / or small cell base stations (low power cellular base stations). In an aspect, the macro cell base stations may include eNBs and / or ng-eNBs where the wireless communications system 100 corresponds to an LTE network,QC2404834WOQualcomm Ref. No. 2404834WO8 / 78 or gNBs where the wireless communications system 100 corresponds to a NR network, or a combination of both, and the small cell base stations may include femtocells, picocells, microcells, etc.

[0033] The base stations 102 may collectively form a RAN and interface with a core network 170 (e.g., an evolved packet core (EPC) or a 5G core (5GC)) through backhaul links 122, and through the core network 170 to one or more location servers 172 (e.g., a location management function (LMF) or a secure user plane location (SUPL) location platform (SLP)). The location server(s) 172 may be part of core network 170 or may be external to core network 170. A location server 172 may be integrated with a base station 102. A UE 104 may communicate with a location server 172 directly or indirectly. For example, a UE 104 may communicate with a location server 172 via the base station 102 that is currently serving that UE 104. A UE 104 may also communicate with a location server 172 through another path, such as via an application server (not shown), via another network, such as via a wireless local area network (WLAN) access point (AP) (e.g., AP 150 described below), and so on. For signaling purposes, communication between a UE 104 and a location server 172 may be represented as an indirect connection (e.g., through the core network 170, etc.) or a direct connection (e.g., as shown via direct connection 128), with the intervening nodes (if any) omitted from a signaling diagram for clarity.

[0034] In addition to other functions, the base stations 102 may perform functions that relate to one or more of transferring user data, radio channel ciphering and deciphering, integrity protection, header compression, mobility control functions (e.g., handover, dual connectivity), inter-cell interference coordination, connection setup and release, load balancing, distribution for non-access stratum (NAS) messages, NAS node selection, synchronization, RAN sharing, multimedia broadcast multicast service (MBMS), subscriber and equipment trace, RAN information management (RIM), paging, positioning, and delivery of warning messages. The base stations 102 may communicate with each other directly or indirectly (e.g., through the EPC / 5GC) over backhaul links 134, which may be wired or wireless.

[0035] The base stations 102 may wirelessly communicate with the UEs 104. Each of the base stations 102 may provide communication coverage for a respective geographic coverage area 110. In an aspect, one or more cells may be supported by a base station 102 in each geographic coverage area 110. A “cell” is a logical communication entity used forQC2404834WOQualcomm Ref. No. 2404834WO9 / 78 communication with a base station (e.g., over some frequency resource, referred to as a carrier frequency, component carrier, carrier, band, or the like), and may be associated with an identifier (e.g., a physical cell identifier (PCI), an enhanced cell identifier (ECI), a virtual cell identifier (VCI), a cell global identifier (CGI), etc.) for distinguishing cells operating via the same or a different carrier frequency. In some cases, different cells may be configured according to different protocol types (e.g., machine-type communication (MTC), narrowband loT (NB-IoT), enhanced mobile broadband (eMBB), or others) that may provide access for different types of UEs. Because a cell is supported by a specific base station, the term “cell” may refer to either or both of the logical communication entity and the base station that supports it, depending on the context. In addition, because a TRP is typically the physical transmission point of a cell, the terms “cell” and “TRP” may be used interchangeably. In some cases, the term “cell” may also refer to a geographic coverage area of a base station (e.g., a sector), insofar as a carrier frequency can be detected and used for communication within some portion of geographic coverage areas 110.

[0036] While neighboring macro cell base station 102 geographic coverage areas 110 may partially overlap (e.g., in a handover region), some of the geographic coverage areas 110 may be substantially overlapped by a larger geographic coverage area 110. For example, a small cell base station 102' (labeled “SC” for “small cell”) may have a geographic coverage area 110' that substantially overlaps with the geographic coverage area 110 of one or more macro cell base stations 102. A network that includes both small cell and macro cell base stations may be known as a heterogeneous network. A heterogeneous network may also include home eNBs (HeNBs), which may provide service to a restricted group known as a closed subscriber group (CSG).

[0037] The communication links 120 between the base stations 102 and the UEs 104 may include uplink (also referred to as reverse link) transmissions from a UE 104 to a base station 102 and / or downlink (DL) (also referred to as forward link) transmissions from a base station 102 to a UE 104. The communication links 120 may use MIMO antenna technology, including spatial multiplexing, beamforming, and / or transmit diversity. The communication links 120 may be through one or more carrier frequencies. Allocation of carriers may be asymmetric with respect to downlink and uplink (e.g., more or less carriers may be allocated for downlink than for uplink).QC2404834WOQualcomm Ref. No. 2404834WO10 / 78

[0038] The wireless communications system 100 may further include a wireless local area network (WLAN) access point (AP) 150 in communication with WLAN stations (STAs) 152 via communication links 154 in an unlicensed frequency spectrum (e.g., 5 GHz). When communicating in an unlicensed frequency spectrum, the WLAN STAs 152 and / or the WLAN AP 150 may perform a clear channel assessment (CCA) or listen before talk (LBT) procedure prior to communicating in order to determine whether the channel is available.

[0039] The small cell base station 102' may operate in a licensed and / or an unlicensed frequency spectrum. When operating in an unlicensed frequency spectrum, the small cell base station 102' may employ LTE or NR technology and use the same 5 GHz unlicensed frequency spectrum as used by the WLAN AP 150. The small cell base station 102', employing LTE / 5G in an unlicensed frequency spectrum, may boost coverage to and / or increase capacity of the access network. NR in unlicensed spectrum may be referred to as NR-U. LTE in an unlicensed spectrum may be referred to as LTE-U, licensed assisted access (LAA), or MULTEFIRE®.

[0040] The wireless communications system 100 may further include a millimeter wave (mmW) base station 180 that may operate in mmW frequencies and / or near mmW frequencies in communication with a UE 182. Extremely high frequency (EHF) is part of the RF in the electromagnetic spectrum. EHF has a range of 30 GHz to 300 GHz and a wavelength between 1 millimeter and 10 millimeters. Radio waves in this band may be referred to as a millimeter wave. Near mmW may extend down to a frequency of 3 GHz with a wavelength of 100 millimeters. The super high frequency (SHF) band extends between 3 GHz and 30 GHz, also referred to as centimeter wave. Communications using the mmW / near mmW radio frequency band have high path loss and a relatively short range. The mmW base station 180 and the UE 182 may utilize beamforming (transmit and / or receive) over a mmW communication link 184 to compensate for the extremely high path loss and short range. Further, it will be appreciated that in alternative configurations, one or more base stations 102 may also transmit using mmW or near mmW and beamforming. Accordingly, it will be appreciated that the foregoing illustrations are merely examples and should not be construed to limit the various aspects disclosed herein.

[0041] Transmit beamforming is a technique for focusing an RF signal in a specific direction. Traditionally, when a network node (e.g., a base station) broadcasts an RF signal, itQC2404834WOQualcomm Ref. No. 2404834WO11 / 78 broadcasts the signal in all directions (omni-directionally). With transmit beamforming, the network node determines where a given target device (e.g., a UE) is located (relative to the transmitting network node) and projects a stronger downlink RF signal in that specific direction, thereby providing a faster (in terms of data rate) and stronger RF signal for the receiving device(s). To change the directionality of the RF signal when transmitting, a network node can control the phase and relative amplitude of the RF signal at each of the one or more transmitters that are broadcasting the RF signal. For example, a network node may use an array of antennas (referred to as a “phased array” or an “antenna array”) that creates a beam of RF waves that can be “steered” to point in different directions, without actually moving the antennas. Specifically, the RF current from the transmitter is fed to the individual antennas with the correct phase relationship so that the radio waves from the separate antennas add together to increase the radiation in a desired direction, while cancelling to suppress radiation in undesired directions.

[0042] Transmit beams may be quasi-co-located, meaning that they appear to the receiver (e.g., a UE) as having the same parameters, regardless of whether or not the transmitting antennas of the network node themselves are physically co-located. In NR, there are four types of quasi-co-location (QCL) relations. Specifically, a QCL relation of a given type means that certain parameters about a second reference RF signal on a second beam can be derived from information about a source reference RF signal on a source beam. Thus, if the source reference RF signal is QCL Type A, the receiver can use the source reference RF signal to estimate the Doppler shift, Doppler spread, average delay, and delay spread of a second reference RF signal transmitted on the same channel. If the source reference RF signal is QCL Type B, the receiver can use the source reference RF signal to estimate the Doppler shift and Doppler spread of a second reference RF signal transmitted on the same channel. If the source reference RF signal is QCL Type C, the receiver can use the source reference RF signal to estimate the Doppler shift and average delay of a second reference RF signal transmitted on the same channel. If the source reference RF signal is QCL Type D, the receiver can use the source reference RF signal to estimate the spatial receive parameter of a second reference RF signal transmitted on the same channel.

[0043] In receive beamforming, the receiver uses a receive beam to amplify RF signals detected on a given channel. For example, the receiver can increase the gain setting and / or adjust the phase setting of an array of antennas in a particular direction to amplify (e.g., toQC2404834WOQualcomm Ref. No. 2404834WO12 / 78 increase the gain level of) the RF signals received from that direction. Thus, when a receiver is said to beamform in a certain direction, it means the beam gain in that direction is high relative to the beam gain along other directions, or the beam gain in that direction is the highest compared to the beam gain in that direction of all other receive beams available to the receiver. This results in a stronger received signal strength (e.g., reference signal received power (RSRP), reference signal received quality (RSRQ), signal-to- interference-plus-noise ratio (SINR), etc.) of the RF signals received from that direction.

[0044] Transmit and receive beams may be spatially related. A spatial relation means that parameters for a second beam (e.g., a transmit or receive beam) for a second reference signal can be derived from information about a first beam (e.g., a receive beam or a transmit beam) for a first reference signal. For example, a UE may use a particular receive beam to receive a reference downlink reference signal (e.g., synchronization signal block (SSB)) from a base station. The UE can then form a transmit beam for sending an uplink reference signal (e.g., sounding reference signal (SRS)) to that base station based on the parameters of the receive beam.

[0045] Note that a “downlink” beam may be either a transmit beam or a receive beam, depending on the entity forming it. For example, if a base station is forming the downlink beam to transmit a reference signal to a UE, the downlink beam is a transmit beam. If the UE is forming the downlink beam, however, it is a receive beam to receive the downlink reference signal. Similarly, an “uplink” beam may be either a transmit beam or a receive beam, depending on the entity forming it. For example, if a base station is forming the uplink beam, it is an uplink receive beam, and if a UE is forming the uplink beam, it is an uplink transmit beam.

[0046] The electromagnetic spectrum is often subdivided, based on frequency / wavelength, into various classes, bands, channels, etc. In 5G NR two initial operating bands have been identified as frequency range designations FR1 (410 MHz - 7.125 GHz) and FR2 (24.25 GHz - 52.6 GHz). It should be understood that although a portion of FR1 is greater than 6 GHz, FR1 is often referred to (interchangeably) as a “Sub-6 GHz” band in various documents and articles. A similar nomenclature issue sometimes occurs with regard to FR2, which is often referred to (interchangeably) as a “millimeter wave” band in documents and articles, despite being different from the extremely high frequency (EHF)QC2404834WOQualcomm Ref. No. 2404834WO13 / 78 band (30 GHz - 300 GHz) which is identified by the INTERNATIONAL TELECOMMUNICATION UNION® as a “millimeter wave” band.

[0047] The frequencies between FR1 and FR2 are often referred to as mid-band frequencies. Recent 5G NR studies have identified an operating band for these mid-band frequencies as frequency range designation FR3 (7.125 GHz - 24.25 GHz). Frequency bands falling within FR3 may inherit FR1 characteristics and / or FR2 characteristics, and thus may effectively extend features of FR1 and / or FR2 into mid-band frequencies. In addition, higher frequency bands are currently being explored to extend 5G NR operation beyond 52.6 GHz. For example, three higher operating bands have been identified as frequency range designations FR4a or FR4-1 (52.6 GHz - 71 GHz), FR4 (52.6 GHz - 114.25 GHz), and FR5 (114.25 GHz - 300 GHz). Each of these higher frequency bands falls within the EHF band.

[0048] With the above aspects in mind, unless specifically stated otherwise, it should be understood that the term “sub-6 GHz” or the like if used herein may broadly represent frequencies that may be less than 6 GHz, may be within FR1, or may include mid-band frequencies. Further, unless specifically stated otherwise, it should be understood that the term “millimeter wave” or the like if used herein may broadly represent frequencies that may include mid-band frequencies, may be within FR2, FR4, FR4-a or FR4-1, and / or FR5, or may be within the EHF band.

[0049] In a multi-carrier system, such as 5G, one of the carrier frequencies is referred to as the “primary carrier” or “anchor carrier” or “primary serving cell” or “PCell,” and the remaining carrier frequencies are referred to as “secondary carriers” or “secondary serving cells” or “SCells.” In carrier aggregation, the anchor carrier is the carrier operating on the primary frequency (e.g., FR1) utilized by a UE 104 / 182 and the cell in which the UE 104 / 182 either performs the initial radio resource control (RRC) connection establishment procedure or initiates the RRC connection re-establishment procedure. The primary carrier carries all common and UE-specific control channels, and may be a carrier in a licensed frequency (however, this is not always the case). A secondary carrier is a carrier operating on a second frequency (e.g., FR2) that may be configured once the RRC connection is established between the UE 104 and the anchor carrier and that may be used to provide additional radio resources. In some cases, the secondary carrier may be a carrier in an unlicensed frequency. The secondary carrier may contain only necessaryQC2404834WOQualcomm Ref. No. 2404834WO14 / 78 signaling information and signals, for example, those that are UE-specific may not be present in the secondary carrier, since both primary uplink and downlink carriers are typically UE-specific. This means that different UEs 104 / 182 in a cell may have different downlink primary carriers. The same is true for the uplink primary carriers. The network is able to change the primary carrier of any UE 104 / 182 at any time. This is done, for example, to balance the load on different carriers. Because a “serving cell” (whether a PCell or an SCell) corresponds to a carrier frequency / component carrier over which some base station is communicating, the term “cell,” “serving cell,” “component carrier,” “carrier frequency,” and the like can be used interchangeably.

[0050] For example, still referring to FIG. 1, one of the frequencies utilized by the macro cell base stations 102 may be an anchor carrier (or “PCell”) and other frequencies utilized by the macro cell base stations 102 and / or the mmW base station 180 may be secondary carriers (“SCells”). The simultaneous transmission and / or reception of multiple carriers enables the UE 104 / 182 to significantly increase its data transmission and / or reception rates. For example, two 20 MHz aggregated carriers in a multi-carrier system would theoretically lead to a two-fold increase in data rate (i.e., 40 MHz), compared to that attained by a single 20 MHz carrier.

[0051] The wireless communications system 100 may further include a UE 164 that may communicate with a macro cell base station 102 over a communication link 120 and / or the mmW base station 180 over a mmW communication link 184. For example, the macro cell base station 102 may support a PCell and one or more SCells for the UE 164 and the mmW base station 180 may support one or more SCells for the UE 164.

[0052] In some cases, the UE 164 and the UE 182 may be capable of sidelink communication. Sidelink-capable UEs (SL-UEs) may communicate with base stations 102 over communication links 120 using the Uu interface (i.e., the air interface between a UE and a base station). SL-UEs (e.g., UE 164, UE 182) may also communicate directly with each other over a wireless sidelink 160 using the PC5 interface (i.e., the air interface between sidelink-capable UEs). A wireless sidelink (or just “sidelink”) is an adaptation of the core cellular (e.g., LTE, NR) standard that allows direct communication between two or more UEs without the communication needing to go through a base station. Sidelink communication may be unicast or multicast, and may be used for device-to-device (D2D) media-sharing, vehi cl e-to- vehicle (V2V) communication, vehicle-to-everything (V2X)QC2404834WOQualcomm Ref. No. 2404834WO15 / 78 communication (e.g., cellular V2X (cV2X) communication, enhanced V2X (eV2X) communication, etc.), emergency rescue applications, etc. One or more of a group of SL- UEs utilizing sidelink communications may be within the geographic coverage area 110 of a base station 102. Other SL-UEs in such a group may be outside the geographic coverage area 110 of a base station 102 or be otherwise unable to receive transmissions from a base station 102. In some cases, groups of SL-UEs communicating via sidelink communications may utilize a one-to-many (1 :M) system in which each SL-UE transmits to every other SL-UE in the group. In some cases, a base station 102 facilitates the scheduling of resources for sidelink communications. In other cases, sidelink communications are carried out between SL-UEs without the involvement of a base station 102.

[0053] In an aspect, the sidelink 160 may operate over a wireless communication medium of interest, which may be shared with other wireless communications between other vehicles and / or infrastructure access points, as well as other RATs. A “medium” may be composed of one or more time, frequency, and / or space communication resources (e.g., encompassing one or more channels across one or more carriers) associated with wireless communication between one or more transmitter / receiver pairs. In an aspect, the medium of interest may correspond to at least a portion of an unlicensed frequency band shared among various RATs. Although different licensed frequency bands have been reserved for certain communication systems (e.g., by a government entity such as the Federal Communications Commission (FCC) in the United States), these systems, in particular those employing small cell access points, have recently extended operation into unlicensed frequency bands such as the Unlicensed National Information Infrastructure (U-NII) band used by wireless local area network (WLAN) technologies, most notably IEEE 802.1 lx WLAN technologies generally referred to as “Wi-Fi.” Example systems of this type include different variants of CDMA systems, TDMA systems, FDMA systems, orthogonal FDMA (OFDMA) systems, single-carrier FDMA (SC-FDMA) systems, and so on.

[0054] Note that although FIG. 1 only illustrates two of the UEs as SL-UEs (i.e., UEs 164 and 182), any of the illustrated UEs may be SL-UEs. Further, although only UE 182 was described as being capable of beamforming, any of the illustrated UEs, including UE 164, may be capable of beamforming. Where SL-UEs are capable of beamforming, they mayQC2404834WOQualcomm Ref. No. 2404834WO16 / 78 beamform towards each other (i.e., towards other SL-UEs), towards other UEs (e.g., UEs 104), towards base stations (e.g., base stations 102, 180, small cell 102’, access point 150), etc. Thus, in some cases, UEs 164 and 182 may utilize beamforming over sidelink 160.

[0055] In the example of FIG. 1, any of the illustrated UEs (shown in FIG. 1 as a single UE 104 for simplicity) may receive signals 124 from one or more Earth orbiting space vehicles (SVs) 112 (e.g., satellites). In an aspect, the S Vs 112 may be part of a satellite positioning system that aUE 104 can use as an independent source of location information. A satellite positioning system typically includes a system of transmitters (e.g., SVs 112) positioned to enable receivers (e.g., UEs 104) to determine their location on or above the Earth based, at least in part, on positioning signals (e.g., signals 124) received from the transmitters. Such a transmitter typically transmits a signal marked with a repeating pseudo-random noise (PN) code of a set number of chips. While typically located in SVs 112, transmitters may sometimes be located on ground-based control stations, base stations 102, and / or other UEs 104. A UE 104 may include one or more dedicated receivers specifically designed to receive signals 124 for deriving geo location information from the SVs 112.

[0056] In a satellite positioning system, the use of signals 124 can be augmented by various satellite-based augmentation systems (SBAS) that may be associated with or otherwise enabled for use with one or more global and / or regional navigation satellite systems. For example an SBAS may include an augmentation system(s) that provides integrity information, differential corrections, etc., such as the Wide Area Augmentation System (WAAS), the European Geostationary Navigation Overlay Service (EGNOS), the Multifunctional Satellite Augmentation System (MSAS), the Global Positioning System (GPS) Aided Geo Augmented Navigation or GPS and Geo Augmented Navigation system (GAGAN), and / or the like. Thus, as used herein, a satellite positioning system may include any combination of one or more global and / or regional navigation satellites associated with such one or more satellite positioning systems.

[0057] In an aspect, SVs 112 may additionally or alternatively be part of one or more nonterrestrial networks (NTNs). In an NTN, an SV 112 is connected to an earth station (also referred to as a ground station, NTN gateway, or gateway), which in turn is connected to an element in a 5G network, such as a modified base station 102 (without a terrestrial antenna) or a network node in a 5GC. This element would in turn provide access to otherQC2404834WOQualcomm Ref. No. 2404834WO nm elements in the 5G network and ultimately to entities external to the 5G network, such as Internet web servers and other user devices. In that way, a UE 104 may receive communication signals (e.g., signals 124) from an SV 112 instead of, or in addition to, communication signals from a terrestrial base station 102.

[0058] The wireless communications system 100 may further include one or more UEs, such as UE 190, that connects indirectly to one or more communication networks via one or more device-to-device (D2D) peer-to-peer (P2P) links (referred to as “sidelinks”). In the example of FIG. 1, UE 190 has a D2D P2P link 192 with one of the UEs 104 connected to one of the base stations 102 (e.g., through which UE 190 may indirectly obtain cellular connectivity) and a D2D P2P link 194 with WLAN STA 152 connected to the WLAN AP 150 (through which UE 190 may indirectly obtain WLAN-based Internet connectivity). In an example, the D2D P2P links 192 and 194 may be supported with any well-known D2D RAT, such as LTE Direct (LTE-D), WI-FI DIRECT®, BLUETOOTH®, and so on.

[0059] FIG. 2A illustrates an example wireless network structure 200. For example, a 5GC 210 (also referred to as a Next Generation Core (NGC)) can be viewed functionally as control plane (C-plane) functions 214 (e.g., UE registration, authentication, network access, gateway selection, etc.) and user plane (U-plane) functions 212, (e.g., UE gateway function, access to data networks, IP routing, etc.) which operate cooperatively to form the core network. User plane interface (NG-U) 213 and control plane interface (NG-C) 215 connect the gNB 222 to the 5GC 210 and specifically to the user plane functions 212 and control plane functions 214, respectively. In an additional configuration, an ng-eNB 224 may also be connected to the 5GC 210 via NG-C 215 to the control plane functions 214 and NG-U 213 to user plane functions 212. Further, ng-eNB 224 may directly communicate with gNB 222 via a backhaul connection 223. In some configurations, a Next Generation RAN (NG-RAN) 220 may have one or more gNBs 222, while other configurations include one or more of both ng-eNBs 224 and gNBs 222. Either (or both) gNB 222 or ng-eNB 224 may communicate with one or more UEs 204 (e.g., any of the UEs described herein).

[0060] Another optional aspect may include a location server 230, which may be in communication with the 5GC 210 to provide location assistance for UE(s) 204. The location server 230 can be implemented as a plurality of separate servers (e.g., physicallyQC2404834WOQualcomm Ref. No. 2404834WO18 / 78 separate servers, different software modules on a single server, different software modules spread across multiple physical servers, etc.), or alternately may each correspond to a single server. The location server 230 can be configured to support one or more location services for UEs 204 that can connect to the location server 230 via the core network, 5GC 210, and / or via the Internet (not illustrated). Further, the location server 230 may be integrated into a component of the core network, or alternatively may be external to the core network (e.g., a third party server, such as an original equipment manufacturer (OEM) server or service server).

[0061] FIG. 2B illustrates another example wireless network structure 240. A 5GC 260 (which may correspond to 5GC 210 in FIG. 2A) can be viewed functionally as control plane functions, provided by an access and mobility management function (AMF) 264, and user plane functions, provided by a user plane function (UPF) 262, which operate cooperatively to form the core network (i.e., 5GC 260). The functions of the AMF 264 include registration management, connection management, reachability management, mobility management, lawful interception, transport for session management (SM) messages between one or more UEs 204 (e.g., any of the UEs described herein) and a session management function (SMF) 266, transparent proxy services for routing SM messages, access authentication and access authorization, transport for short message service (SMS) messages between the UE 204 and the short message service function (SMSF) (not shown), and security anchor functionality (SEAF). The AMF 264 also interacts with an authentication server function (AUSF) (not shown) and the UE 204, and receives the intermediate key that was established as a result of the UE 204 authentication process. In the case of authentication based on a UMTS (universal mobile telecommunications system) subscriber identity module (USIM), the AMF 264 retrieves the security material from the AUSF. The functions of the AMF 264 also include security context management (SCM). The SCM receives a key from the SEAF that it uses to derive access-network specific keys. The functionality of the AMF 264 also includes location services management for regulatory services, transport for location services messages between the UE 204 and a location management function (LMF) 270 (which acts as a location server 230), transport for location services messages between the NG-RAN 220 and the LMF 270, evolved packet system (EPS) bearer identifier allocation for interworking with the EPS, and UE 204 mobility event notification. In addition, the AMFQC2404834WOQualcomm Ref. No. 2404834WO19 / 78264 also supports functionalities for non-3GPP® (Third Generation Partnership Project) access networks.

[0062] Functions of the UPF 262 include acting as an anchor point for intra / inter-RAT mobility (when applicable), acting as an external protocol data unit (PDU) session point of interconnect to a data network (not shown), providing packet routing and forwarding, packet inspection, user plane policy rule enforcement (e.g., gating, redirection, traffic steering), lawful interception (user plane collection), traffic usage reporting, quality of service (QoS) handling for the user plane (e.g., uplink / downlink rate enforcement, reflective QoS marking in the downlink), uplink traffic verification (service data flow (SDF) to QoS flow mapping), transport level packet marking in the uplink and downlink, downlink packet buffering and downlink data notification triggering, and sending and forwarding of one or more “end markers” to the source RAN node. The UPF 262 may also support transfer of location services messages over a user plane between the UE 204 and a location server, such as an SLP 272.

[0063] The functions of the SMF 266 include session management, UE Internet protocol (IP) address allocation and management, selection and control of user plane functions, configuration of traffic steering at the UPF 262 to route traffic to the proper destination, control of part of policy enforcement and QoS, and downlink data notification. The interface over which the SMF 266 communicates with the AMF 264 is referred to as the Ni l interface.

[0064] Another optional aspect may include an LMF 270, which may be in communication with the 5GC 260 to provide location assistance for UEs 204. The LMF 270 can be implemented as a plurality of separate servers (e.g., physically separate servers, different software modules on a single server, different software modules spread across multiple physical servers, etc.), or alternately may each correspond to a single server. The LMF 270 can be configured to support one or more location services for UEs 204 that can connect to the LMF 270 via the core network, 5GC 260, and / or via the Internet (not illustrated). The SLP 272 may support similar functions to the LMF 270, but whereas the LMF 270 may communicate with the AMF 264, NG-RAN 220, and UEs 204 over a control plane (e.g., using interfaces and protocols intended to convey signaling messages and not voice or data), the SLP 272 may communicate with UEs 204 and external clientsQC2404834WOQualcomm Ref. No. 2404834WO20 / 78(e.g., third-party server 274) over a user plane (e.g., using protocols intended to carry voice and / or data like the transmission control protocol (TCP) and / or IP).

[0065] Yet another optional aspect may include a third-party server 274, which may be in communication with the LMF 270, the SLP 272, the 5GC 260 (e.g., via the AMF 264 and / or the UPF 262), the NG-RAN 220, and / or the UE 204 to obtain location information (e.g., a location estimate) for the UE 204. As such, in some cases, the third-party server 274 may be referred to as a location services (LCS) client or an external client. The third- party server 274 can be implemented as a plurality of separate servers (e.g., physically separate servers, different software modules on a single server, different software modules spread across multiple physical servers, etc.), or alternately may each correspond to a single server.

[0066] User plane interface 263 and control plane interface 265 connect the 5GC 260, and specifically the UPF 262 and AMF 264, respectively, to one or more gNBs 222 and / or ng-eNBs 224 in the NG-RAN 220. The interface between gNB(s) 222 and / or ng-eNB(s) 224 and the AMF 264 is referred to as the “N2” interface, and the interface between gNB(s) 222 and / or ng-eNB(s) 224 and the UPF 262 is referred to as the “N3” interface. The gNB(s) 222 and / or ng-eNB(s) 224 of the NG-RAN 220 may communicate directly with each other via backhaul connections 223, referred to as the “Xn-C” interface. One or more of gNBs 222 and / or ng-eNBs 224 may communicate with one or more UEs 204 over a wireless interface, referred to as the “Uu” interface.

[0067] The functionality of a gNB 222 may be divided between a gNB central unit (gNB-CU) 226, one or more gNB distributed units (gNB-DUs) 228, and one or more gNB radio units (gNB-RUs) 229. A gNB-CU 226 is a logical node that includes the base station functions of transferring user data, mobility control, radio access network sharing, positioning, session management, and the like, except for those functions allocated exclusively to the gNB-DU(s) 228. More specifically, the gNB-CU 226 generally host the radio resource control (RRC), service data adaptation protocol (SDAP), and packet data convergence protocol (PDCP) protocols of the gNB 222. A gNB-DU 228 is a logical node that generally hosts the radio link control (RLC) and medium access control (MAC) layer of the gNB 222. Its operation is controlled by the gNB-CU 226. One gNB-DU 228 can support one or more cells, and one cell is supported by only one gNB-DU 228. The interface 232 between the gNB-CU 226 and the one or more gNB-DUs 228 is referred toQC2404834WOQualcomm Ref. No. 2404834WO21 / 78 as the “Fl” interface. The physical (PHY) layer functionality of a gNB 222 is generally hosted by one or more standalone gNB-RUs 229 that perform functions such as power amplification and signal transmission / reception. The interface between a gNB-DU 228 and a gNB-RU 229 is referred to as the “Fx” interface. Thus, a UE 204 communicates with the gNB-CU 226 via the RRC, SDAP, and PDCP layers, with a gNB-DU 228 via the RLC and MAC layers, and with a gNB-RU 229 via the PHY layer.

[0068] Deployment of communication systems, such as 5G NR systems, may be arranged in multiple manners with various components or constituent parts. In a 5G NR system, or network, a network node, a network entity, a mobility element of a network, a RAN node, a core network node, a network element, or a network equipment, such as a base station, or one or more units (or one or more components) performing base station functionality, may be implemented in an aggregated or disaggregated architecture. For example, a base station (such as a Node B (NB), evolved NB (eNB), NR base station, 5G NB, AP, TRP, cell, etc.) may be implemented as an aggregated base station (also known as a standalone base station or a monolithic base station) or a disaggregated base station.

[0069] An aggregated base station may be configured to utilize a radio protocol stack that is physically or logically integrated within a single RAN node. A disaggregated base station may be configured to utilize a protocol stack that is physically or logically distributed among two or more units (such as one or more central or centralized units (CUs), one or more distributed units (DUs), or one or more radio units (RUs)). In some aspects, a CU may be implemented within a RAN node, and one or more DUs may be co-located with the CU, or alternatively, may be geographically or virtually distributed throughout one or multiple other RAN nodes. The DUs may be implemented to communicate with one or more RUs. Each of the CU, DU and RU also can be implemented as virtual units, i.e., a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU).

[0070] Base station-type operation or network design may consider aggregation characteristics of base station functionality. For example, disaggregated base stations may be utilized in an integrated access backhaul (IAB) network, an open radio access network (0-RAN (such as the network configuration sponsored by the 0-RAN ALLIANCE®)), or a virtualized radio access network (vRAN, also known as a cloud radio access network (C- RAN)). Disaggregation may include distributing functionality across two or more units at various physical locations, as well as distributing functionality for at least one unitQC2404834WOQualcomm Ref. No. 2404834WO22 / 78 virtually, which can enable flexibility in network design. The various units of the disaggregated base station, or disaggregated RAN architecture, can be configured for wired or wireless communication with at least one other unit.

[0071] FIG. 2C illustrates an example disaggregated base station architecture 250, according to aspects of the disclosure. The disaggregated base station architecture 250 may include one or more central units (CUs) 280 (e.g., gNB-CU 226) that can communicate directly with a core network 267 (e.g., 5GC 210, 5GC 260) via a backhaul link, or indirectly with the core network 267 through one or more disaggregated base station units (such as a Near-Real Time (Near-RT) RAN Intelligent Controller (RIC) 259 via an E2 link, or a Non-Real Time (Non-RT) RIC 257 associated with a Service Management and Orchestration (SMO) Framework 255, or both). A CU 280 may communicate with one or more DUs 285 (e.g., gNB-DUs 228) via respective midhaul links, such as an Fl interface. The DUs 285 may communicate with one or more radio units (RUs) 287 (e.g., gNB-RUs 229) via respective fronthaul links. The RUs 287 may communicate with respective UEs 204 via one or more radio frequency (RF) access links. In some implementations, the UE 204 may be simultaneously served by multiple RUs 287.

[0072] Each of the units, i.e., the CUs 280, the DUs 285, the RUs 287, as well as the Near-RT RICs 259, the Non-RT RICs 257 and the SMO Framework 255, may include one or more interfaces or be coupled to one or more interfaces configured to receive or transmit signals, data, or information (collectively, signals) via a wired or wireless transmission medium. Each of the units, or an associated processor or controller providing instructions to the communication interfaces of the units, can be configured to communicate with one or more of the other units via the transmission medium. For example, the units can include a wired interface configured to receive or transmit signals over a wired transmission medium to one or more of the other units. Additionally, the units can include a wireless interface, which may include a receiver, a transmitter or transceiver (such as a RF transceiver), configured to receive or transmit signals, or both, over a wireless transmission medium to one or more of the other units.

[0073] In some aspects, the CU 280 may host one or more higher layer control functions. Such control functions can include RRC, PDCP, service data adaptation protocol (SDAP), or the like. Each control function can be implemented with an interface configured to communicate signals with other control functions hosted by the CU 280. The CU 280QC2404834WOQualcomm Ref. No. 2404834WO23 / 78 may be configured to handle user plane functionality (i.e., Central Unit - User Plane (CU- UP)), control plane functionality (i.e., Central Unit - Control Plane (CU-CP)), or a combination thereof. In some implementations, the CU 280 can be logically split into one or more CU-UP units and one or more CU-CP units. The CU-UP unit can communicate bidirectionally with the CU-CP unit via an interface, such as the El interface when implemented in an O-RAN configuration. The CU 280 can be implemented to communicate with the DU 285, as necessary, for network control and signaling.

[0074] The DU 285 may correspond to a logical unit that includes one or more base station functions to control the operation of one or more RUs 287. In some aspects, the DU 285 may host one or more of a RLC layer, a MAC layer, and one or more high PHY layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation and demodulation, or the like) depending, at least in part, on a functional split, such as those defined by the 3rd Generation Partnership Project (3GPP®). In some aspects, the DU 285 may further host one or more low PHY layers. Each layer (or module) can be implemented with an interface configured to communicate signals with other layers (and modules) hosted by the DU 285, or with the control functions hosted by the CU 280.

[0075] Lower-layer functionality can be implemented by one or more RUs 287. In some deployments, an RU 287, controlled by a DU 285, may correspond to a logical node that hosts RF processing functions, or low-PHY layer functions (such as performing fast Fourier transform (FFT), inverse FFT (iFFT), digital beamforming, physical random access channel (PRACH) extraction and filtering, or the like), or both, based at least in part on the functional split, such as a lower layer functional split. In such an architecture, the RU(s) 287 can be implemented to handle over the air (OTA) communication with one or more UEs 204. In some implementations, real-time and non-real-time aspects of control and user plane communication with the RU(s) 287 can be controlled by the corresponding DU 285. In some scenarios, this configuration can enable the DU(s) 285 and the CU 280 to be implemented in a cloud-based RAN architecture, such as a vRAN architecture.

[0076] The SMO Framework 255 may be configured to support RAN deployment and provisioning of non-virtualized and virtualized network elements. For non-virtualizedQC2404834WOQualcomm Ref. No. 2404834WO24 / 78 network elements, the SMO Framework 255 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements which may be managed via an operations and maintenance interface (such as an 01 interface). For virtualized network elements, the SMO Framework 255 may be configured to interact with a cloud computing platform (such as an open cloud (O-Cloud) 269) to perform network element life cycle management (such as to instantiate virtualized network elements) via a cloud computing platform interface (such as an 02 interface). Such virtualized network elements can include, but are not limited to, CUs 280, DUs 285, RUs 287 and Near-RT RICs 259. In some implementations, the SMO Framework 255 can communicate with a hardware aspect of a 4G RAN, such as an open eNB (0-eNB) 261, via an 01 interface. Additionally, in some implementations, the SMO Framework 255 can communicate directly with one or more RUs 287 via an 01 interface. The SMO Framework 255 also may include a Non-RT RIC 257 configured to support functionality of the SMO Framework 255.

[0077] The Non-RT RIC 257 may be configured to include a logical function that enables non- real-time control and optimization of RAN elements and resources, artificial intelligence / machine learning (AI / ML) workflows including model training and updates, or policy-based guidance of applications / features in the Near-RT RIC 259. The Non-RT RIC 257 may be coupled to or communicate with (such as via an Al interface) the Near- RT RIC 259. The Near-RT RIC 259 may be configured to include a logical function that enables near-real-time control and optimization of RAN elements and resources via data collection and actions over an interface (such as via an E2 interface) connecting one or more CUs 280, one or more DUs 285, or both, as well as an O-eNB, with the Near-RT RIC 259.

[0078] In some implementations, to generate AIML models to be deployed in the Near-RT RIC 259, the Non-RT RIC 257 may receive parameters or external enrichment information from external servers. Such information may be utilized by the Near-RT RIC 259 and may be received at the SMO Framework 255 or the Non-RT RIC 257 from non-network data sources or from network functions. In some examples, the Non-RT RIC 257 or the Near-RT RIC 259 may be configured to tune RAN behavior or performance. For example, the Non-RT RIC 257 may monitor long-term trends and patterns for performance and employ AIML models to perform corrective actions through the SMOQC2404834WOQualcomm Ref. No. 2404834WO25HFramework 255 (such as reconfiguration via 01) or via creation of RAN management policies (such as Al policies).

[0079] FIGS. 3A, 3B, and 3C illustrate several example components (represented by corresponding blocks) that may be incorporated into a UE 302 (which may correspond to any of the UEs described herein), a base station 304 (which may correspond to any of the base stations described herein), and a network entity 306 (which may correspond to or embody any of the network functions described herein, including the location server 230 and the LMF 270, or alternatively may be independent from the NG-RAN 220 and / or 5GC 210 / 260 infrastructure depicted in FIGS. 2A and 2B, such as a private network) to support the operations described herein. It will be appreciated that these components may be implemented in different types of apparatuses in different implementations (e.g., in an ASIC, in a system-on-chip (SoC), etc.). The illustrated components may also be incorporated into other apparatuses in a communication system. For example, other apparatuses in a system may include components similar to those described to provide similar functionality. Also, a given apparatus may contain one or more of the components. For example, an apparatus may include multiple transceiver components that enable the apparatus to operate on multiple carriers and / or communicate via different technologies.

[0080] The UE 302 and the base station 304 each include one or more wireless wide area network (WWAN) transceivers 310 and 350, respectively, providing means for communicating (e.g., means for transmitting, means for receiving, means for measuring, means for tuning, means for refraining from transmitting, etc.) via one or more wireless communication networks (not shown), such as an NR network, an LTE network, a GSM network, and / or the like. The WWAN transceivers 310 and 350 may each be connected to one or more antennas 316 and 356, respectively, for communicating with other network nodes, such as other UEs, access points, base stations (e.g., eNBs, gNBs), etc., via at least one designated RAT (e.g., NR, LTE, GSM, etc.) over a wireless communication medium of interest (e.g., some set of time / frequency resources in a particular frequency spectrum). The WWAN transceivers 310 and 350 may be variously configured for transmitting and encoding signals 318 and 358 (e.g., messages, indications, information, and so on), respectively, and, conversely, for receiving and decoding signals 318 and 358 (e.g., messages, indications, information, pilots, and so on), respectively, in accordance with the designated RAT. Specifically, the WWAN transceivers 310 and 350 include one orQC2404834WOQualcomm Ref. No. 2404834WO26 / 78 more transmitters 314 and 354, respectively, for transmitting and encoding signals 318 and 358, respectively, and one or more receivers 312 and 352, respectively, for receiving and decoding signals 318 and 358, respectively.

[0081] The UE 302 and the base station 304 each also include, at least in some cases, one or more short-range wireless transceivers 320 and 360, respectively. The short-range wireless transceivers 320 and 360 may be connected to one or more antennas 326 and 366, respectively, and provide means for communicating (e.g., means for transmitting, means for receiving, means for measuring, means for tuning, means for refraining from transmitting, etc.) with other network nodes, such as other UEs, access points, base stations, etc., via at least one designated RAT (e.g., Wi-Fi, LTE Direct, BLUETOOTH®, ZIGBEE®, Z-WAVE®, PC5, dedicated short-range communications (DSRC), wireless access for vehicular environments (WAVE), near-field communication (NFC), ultra- wideband (UWB), etc.) over a wireless communication medium of interest. The short- range wireless transceivers 320 and 360 may be variously configured for transmitting and encoding signals 328 and 368 (e.g., messages, indications, information, and so on), respectively, and, conversely, for receiving and decoding signals 328 and 368 (e.g., messages, indications, information, pilots, and so on), respectively, in accordance with the designated RAT. Specifically, the short-range wireless transceivers 320 and 360 include one or more transmitters 324 and 364, respectively, for transmitting and encoding signals 328 and 368, respectively, and one or more receivers 322 and 362, respectively, for receiving and decoding signals 328 and 368, respectively. As specific examples, the short-range wireless transceivers 320 and 360 may be Wi-Fi transceivers, BLUETOOTH® transceivers, ZIGBEE® and / or Z-WAVE® transceivers, NFC transceivers, UWB transceivers, or vehi cl e-to- vehicle (V2V) and / or vehicle-to- everything (V2X) transceivers.

[0082] The UE 302 and the base station 304 also include, at least in some cases, satellite signal interfaces 330 and 370, which each include one or more satellite signal receivers 332 and 372, respectively, and may optionally include one or more satellite signal transmitters 334 and 374, respectively. In some cases, the base station 304 may be a terrestrial base station that may communicate with space vehicles (e.g., space vehicles 112) via the satellite signal interface 370. In other cases, the base station 304 may be a space vehicle (or otherQC2404834WOQualcomm Ref. No. 2404834WO27 / 78 non-terrestrial entity) that uses the satellite signal interface 370 to communicate with terrestrial networks and / or other space vehicles.

[0083] The satellite signal receivers 332 and 372 may be connected to one or more antennas 336 and 376, respectively, and may provide means for receiving and / or measuring satellite positioning / communication signals 338 and 378, respectively. Where the satellite signal receiver(s) 332 and 372 are satellite positioning system receivers, the satellite positioning / communication signals 338 and 378 may be global positioning system (GPS) signals, global navigation satellite system (GLONASS) signals, Galileo signals, Beidou signals, Indian Regional Navigation Satellite System (NAVIC), Quasi-Zenith Satellite System (QZSS) signals, etc. Where the satellite signal receiver(s) 332 and 372 are nonterrestrial network (NTN) receivers, the satellite positioning / communication signals 338 and 378 may be communication signals (e.g., carrying control and / or user data) originating from a 5G network. The satellite signal receiver(s) 332 and 372 may comprise any suitable hardware and / or software for receiving and processing satellite positioning / communication signals 338 and 378, respectively. The satellite signal receiver(s) 332 and 372 may request information and operations as appropriate from the other systems, and, at least in some cases, perform calculations to determine locations of the UE 302 and the base station 304, respectively, using measurements obtained by any suitable satellite positioning system algorithm.

[0084] The optional satellite signal transmitter(s) 334 and 374, when present, may be connected to the one or more antennas 336 and 376, respectively, and may provide means for transmitting satellite positioning / communication signals 338 and 378, respectively. Where the satellite signal transmitter(s) 374 are satellite positioning system transmitters, the satellite positioning / communication signals 378 may be GPS signals, GLONASS® signals, Galileo signals, Beidou signals, NAVIC, QZSS signals, etc. Where the satellite signal transmitter(s) 334 and 374 are NTN transmitters, the satellite positioning / communication signals 338 and 378 may be communication signals (e.g., carrying control and / or user data) originating from a 5G network. The satellite signal transmitter(s) 334 and 374 may comprise any suitable hardware and / or software for transmitting satellite positioning / communication signals 338 and 378, respectively. The satellite signal transmitter(s) 334 and 374 may request information and operations as appropriate from the other systems.QC2404834WOQualcomm Ref. No. 2404834WO28 / 78

[0085] The base station 304 and the network entity 306 each include one or more network transceivers 380 and 390, respectively, providing means for communicating (e.g., means for transmitting, means for receiving, etc.) with other network entities (e.g., other base stations 304, other network entities 306). For example, the base station 304 may employ the one or more network transceivers 380 to communicate with other base stations 304 or network entities 306 over one or more wired or wireless backhaul links. As another example, the network entity 306 may employ the one or more network transceivers 390 to communicate with one or more base station 304 over one or more wired or wireless backhaul links, or with other network entities 306 over one or more wired or wireless core network interfaces.

[0086] A transceiver may be configured to communicate over a wired or wireless link. A transceiver (whether a wired transceiver or a wireless transceiver) includes transmitter circuitry (e.g., transmitters 314, 324, 354, 364) and receiver circuitry (e.g., receivers 312, 322, 352, 362). A transceiver may be an integrated device (e.g., embodying transmitter circuitry and receiver circuitry in a single device) in some implementations, may comprise separate transmitter circuitry and separate receiver circuitry in some implementations, or may be embodied in other ways in other implementations. The transmitter circuitry and receiver circuitry of a wired transceiver (e.g., network transceivers 380 and 390 in some implementations) may be coupled to one or more wired network interface ports. Wireless transmitter circuitry (e.g., transmitters 314, 324, 354, 364) may include or be coupled to a plurality of antennas (e.g., antennas 316, 326, 356, 366), such as an antenna array, that permits the respective apparatus (e.g., UE 302, base station 304) to perform transmit “beamforming,” as described herein. Similarly, wireless receiver circuitry (e.g., receivers 312, 322, 352, 362) may include or be coupled to a plurality of antennas (e.g., antennas 316, 326, 356, 366), such as an antenna array, that permits the respective apparatus (e.g., UE 302, base station 304) to perform receive beamforming, as described herein. In an aspect, the transmitter circuitry and receiver circuitry may share the same plurality of antennas (e.g., antennas 316, 326, 356, 366), such that the respective apparatus can only receive or transmit at a given time, not both at the same time. A wireless transceiver (e.g., WWAN transceivers 310 and 350, short-range wireless transceivers 320 and 360) may also include a network listen module (NUM) or the like for performing various measurements.QC2404834WOQualcomm Ref. No. 2404834WO29 / 78

[0087] As used herein, the various wireless transceivers (e.g., transceivers 310, 320, 350, and 360, and network transceivers 380 and 390 in some implementations) and wired transceivers (e.g., network transceivers 380 and 390 in some implementations) may generally be characterized as “a transceiver,” “at least one transceiver,” or “one or more transceivers.” As such, whether a particular transceiver is a wired or wireless transceiver may be inferred from the type of communication performed. For example, backhaul communication between network devices or servers will generally relate to signaling via a wired transceiver, whereas wireless communication between a UE (e.g., UE 302) and a base station (e.g., base station 304) will generally relate to signaling via a wireless transceiver.

[0088] The UE 302, the base station 304, and the network entity 306 also include other components that may be used in conjunction with the operations as disclosed herein. The UE 302, the base station 304, and the network entity 306 include one or more processors 342, 384, and 394, respectively, for providing functionality relating to, for example, wireless communication, and for providing other processing functionality. The processors 342, 384, and 394 may therefore provide means for processing, such as means for determining, means for calculating, means for receiving, means for transmitting, means for indicating, etc. In an aspect, the processors 342, 384, and 394 may include, for example, one or more general purpose processors, multi-core processors, central processing units (CPUs), ASICs, digital signal processors (DSPs), field programmable gate arrays (FPGAs), other programmable logic devices or processing circuitry, or various combinations thereof.

[0089] The UE 302, the base station 304, and the network entity 306 include memory circuitry implementing memories 340, 386, and 396 (e.g., each including a memory device), respectively, for maintaining information (e.g., information indicative of reserved resources, thresholds, parameters, and so on). The memories 340, 386, and 396 may therefore provide means for storing, means for retrieving, means for maintaining, etc. In some cases, the UE 302, the base station 304, and the network entity 306 may include AIML management component 348, 388, and 398, respectively. The AIML management component 348, 388, and 398 may be hardware circuits that are part of or coupled to the processors 342, 384, and 394, respectively, that, when executed, cause the UE 302, the base station 304, and the network entity 306 to perform the functionality described herein.QC2404834WOQualcomm Ref. No. 2404834WO30 / 78In other aspects, the AIML management component 348, 388, and 398 may be external to the processors 342, 384, and 394 (e.g., part of a modem processing system, integrated with another processing system, etc.). Alternatively, the AIML management component 348, 388, and 398 may be memory modules stored in the memories 340, 386, and 396, respectively, that, when executed by the processors 342, 384, and 394 (or a modem processing system, another processing system, etc.), cause the UE 302, the base station 304, and the network entity 306 to perform the functionality described herein. FIG. 3 A illustrates possible locations of the AIML management component 348, which may be, for example, part of the one or more WWAN transceivers 310, the memory 340, the one or more processors 342, or any combination thereof, or may be a standalone component. FIG. 3B illustrates possible locations of the AIML management component 388, which may be, for example, part of the one or more WWAN transceivers 350, the memory 386, the one or more processors 384, or any combination thereof, or may be a standalone component. FIG. 3C illustrates possible locations of the AIML management component 398, which may be, for example, part of the one or more network transceivers 390, the memory 396, the one or more processors 394, or any combination thereof, or may be a standalone component.

[0090] The UE 302 may include one or more sensors 344 coupled to the one or more processors 342 to provide means for sensing or detecting movement and / or orientation information that is independent of motion data derived from signals received by the one or more WWAN transceivers 310, the one or more short-range wireless transceivers 320, and / or the satellite signal interface 330. By way of example, the sensor(s) 344 may include an accelerometer (e.g., a micro-electrical mechanical systems (MEMS) device), a gyroscope, a geomagnetic sensor (e.g., a compass), an altimeter (e.g., a barometric pressure altimeter), and / or any other type of movement detection sensor. Moreover, the sensor(s) 344 may include a plurality of different types of devices and combine their outputs in order to provide motion information. For example, the sensor(s) 344 may use a combination of a multi-axis accelerometer and orientation sensors to provide the ability to compute positions in two-dimensional (2D) and / or three-dimensional (3D) coordinate systems.

[0091] In addition, the UE 302 includes a user interface 346 providing means for providing indications (e.g., audible and / or visual indications) to a user and / or for receiving userQC2404834WOQualcomm Ref. No. 2404834WO31 / 78 input (e.g., upon user actuation of a sensing device such a keypad, a touch screen, a microphone, and so on). Although not shown, the base station 304 and the network entity 306 may also include user interfaces.

[0092] Referring to the one or more processors 384 in more detail, in the downlink, IP packets from the network entity 306 may be provided to the processor 384. The one or more processors 384 may implement functionality for an RRC layer, a packet data convergence protocol (PDCP) layer, a radio link control (RLC) layer, and a medium access control (MAC) layer. The one or more processors 384 may provide RRC layer functionality associated with broadcasting of system information (e.g., master information block (MIB), system information blocks (SIBs)), RRC connection control (e.g., RRC connection paging, RRC connection establishment, RRC connection modification, and RRC connection release), inter-RAT mobility, and measurement configuration for UE measurement reporting; PDCP layer functionality associated with header compression / decompression, security (ciphering, deciphering, integrity protection, integrity verification), and handover support functions; RLC layer functionality associated with the transfer of upper layer PDUs, error correction through automatic repeat request (ARQ), concatenation, segmentation, and reassembly of RLC service data units (SDUs), re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, scheduling information reporting, error correction, priority handling, and logical channel prioritization.

[0093] The transmitter 354 and the receiver 352 may implement Layer- 1 (LI) functionality associated with various signal processing functions. Layer-1, which includes a physical (PHY) layer, may include error detection on the transport channels, forward error correction (FEC) coding / decoding of the transport channels, interleaving, rate matching, mapping onto physical channels, modulation / demodulation of physical channels, and MIMO antenna processing. The transmitter 354 handles mapping to signal constellations based on various modulation schemes (e.g., binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), M-phase-shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The coded and modulated symbols may then be split into parallel streams. Each stream may then be mapped to an orthogonal frequency division multiplexing (OFDM) subcarrier, multiplexed with a reference signal (e.g., pilot) in theQC2404834WOQualcomm Ref. No. 2404834WO32 / 78 time and / or frequency domain, and then combined together using an inverse fast Fourier transform (IFFT) to produce a physical channel carrying a time domain OFDM symbol stream. The OFDM symbol stream is spatially precoded to produce multiple spatial streams. Channel estimates from a channel estimator may be used to determine the coding and modulation scheme, as well as for spatial processing. The channel estimate may be derived from a reference signal and / or channel condition feedback transmitted by the UE 302. Each spatial stream may then be provided to one or more different antennas 356. The transmitter 354 may modulate an RF carrier with a respective spatial stream for transmission.

[0094] At the UE 302, the receiver 312 receives a signal through its respective antenna(s) 316. The receiver 312 recovers information modulated onto an RF carrier and provides the information to the one or more processors 342. The transmitter 314 and the receiver 312 implement Layer- 1 functionality associated with various signal processing functions. The receiver 312 may perform spatial processing on the information to recover any spatial streams destined for the UE 302. If multiple spatial streams are destined for the UE 302, they may be combined by the receiver 312 into a single OFDM symbol stream. The receiver 312 then converts the OFDM symbol stream from the time-domain to the frequency domain using a fast Fourier transform (FFT). The frequency domain signal comprises a separate OFDM symbol stream for each subcarrier of the OFDM signal. The symbols on each subcarrier, and the reference signal, are recovered and demodulated by determining the most likely signal constellation points transmitted by the base station 304. These soft decisions may be based on channel estimates computed by a channel estimator. The soft decisions are then decoded and de-interleaved to recover the data and control signals that were originally transmitted by the base station 304 on the physical channel. The data and control signals are then provided to the one or more processors 342, which implements Layer-3 (L3) and Layer-2 (L2) functionality.

[0095] In the downlink, the one or more processors 342 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, and control signal processing to recover IP packets from the core network. The one or more processors 342 are also responsible for error detection.

[0096] Similar to the functionality described in connection with the downlink transmission by the base station 304, the one or more processors 342 provides RRC layer functionalityQC2404834WOQualcomm Ref. No. 2404834WO33 / 78 associated with system information (e.g., MIB, SIBs) acquisition, RRC connections, and measurement reporting; PDCP layer functionality associated with header compression / decompression, and security (ciphering, deciphering, integrity protection, integrity verification); RLC layer functionality associated with the transfer of upper layer PDUs, error correction through ARQ, concatenation, segmentation, and reassembly of RLC SDUs, re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs onto transport blocks (TBs), demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction through hybrid automatic repeat request (HARQ), priority handling, and logical channel prioritization.

[0097] Channel estimates derived by the channel estimator from a reference signal or feedback transmitted by the base station 304 may be used by the transmitter 314 to select the appropriate coding and modulation schemes, and to facilitate spatial processing. The spatial streams generated by the transmitter 314 may be provided to different antenna(s) 316. The transmitter 314 may modulate an RF carrier with a respective spatial stream for transmission.

[0098] The uplink transmission is processed at the base station 304 in a manner similar to that described in connection with the receiver function at the UE 302. The receiver 352 receives a signal through its respective antenna(s) 356. The receiver 352 recovers information modulated onto an RF carrier and provides the information to the one or more processors 384.

[0099] In the uplink, the one or more processors 384 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover IP packets from the UE 302. IP packets from the one or more processors 384 may be provided to the core network. The one or more processors 384 are also responsible for error detection.

[0100] For convenience, the UE 302, the base station 304, and / or the network entity 306 are shown in FIGS. 3A, 3B, and 3C as including various components that may be configured according to the various examples described herein. It will be appreciated, however, that the illustrated components may have different functionality in different designs. In particular, various components in FIGS. 3A to 3C are optional in alternative configurations and the various aspects include configurations that may vary due to designQC2404834WOQualcomm Ref. No. 2404834WO34 / 78 choice, costs, use of the device, or other considerations. For example, in case of FIG. 3A, a particular implementation of UE 302 may omit the WWAN transceiver(s) 310 (e.g., a wearable device or tablet computer or personal computer (PC) or laptop may have Wi-Fi and / or BLUETOOTH® capability without cellular capability), or may omit the short- range wireless transceiver s) 320 (e.g., cellular-only, etc.), or may omit the satellite signal interface 330, or may omit the sensor(s) 344, and so on. In another example, in case of FIG. 3B, a particular implementation of the base station 304 may omit the WWAN transceiver(s) 350 (e.g., a Wi-Fi “hotspot” access point without cellular capability), or may omit the short-range wireless transceiver(s) 360 (e.g., cellular-only, etc.), or may omit the satellite signal interface 370, and so on. For brevity, illustration of the various alternative configurations is not provided herein, but would be readily understandable to one skilled in the art.

[0101] The various components of the UE 302, the base station 304, and the network entity 306 may be communicatively coupled to each other over data buses 308, 382, and 392, respectively. In an aspect, the data buses 308, 382, and 392 may form, or be part of, a communication interface of the UE 302, the base station 304, and the network entity 306, respectively. For example, where different logical entities are embodied in the same device (e.g., gNB and location server functionality incorporated into the same base station 304), the data buses 308, 382, and 392 may provide communication between them.

[0102] The components of FIGS. 3A, 3B, and 3C may be implemented in various ways. In some implementations, the components of FIGS. 3 A, 3B, and 3C may be implemented in one or more circuits such as, for example, one or more processors and / or one or more ASICs (which may include one or more processors). Here, each circuit may use and / or incorporate at least one memory component for storing information or executable code used by the circuit to provide this functionality. For example, some or all of the functionality represented by blocks 310 to 346 may be implemented by processor and memory component(s) of the UE 302 (e.g., by execution of appropriate code and / or by appropriate configuration of processor components). Similarly, some or all of the functionality represented by blocks 350 to 388 may be implemented by processor and memory component(s) of the base station 304 (e.g., by execution of appropriate code and / or by appropriate configuration of processor components). Also, some or all of the functionality represented by blocks 390 to 398 may be implemented by processor andQC2404834WOQualcomm Ref. No. 2404834WO35 / 78 memory component(s) of the network entity 306 (e.g., by execution of appropriate code and / or by appropriate configuration of processor components). For simplicity, various operations, acts, and / or functions are described herein as being performed “by a UE,” “by a base station,” “by a network entity,” etc. However, as will be appreciated, such operations, acts, and / or functions may actually be performed by specific components or combinations of components of the UE 302, base station 304, network entity 306, etc., such as the processors 342, 384, 394, the transceivers 310, 320, 350, and 360, the memories 340, 386, and 396, the AIML management component 348, 388, and 398, etc.

[0103] In some designs, the network entity 306 may be implemented as a core network component. In other designs, the network entity 306 may be distinct from a network operator or operation of the cellular network infrastructure (e.g., NG RAN 220 and / or 5GC 210 / 260). For example, the network entity 306 may be a component of a private network that may be configured to communicate with the UE 302 via the base station 304 or independently from the base station 304 (e.g., over a non-cellular communication link, such as Wi-Fi).

[0104] NR supports a number of cellular network-based positioning technologies, including downlink-based, uplink-based, and downlink-and-uplink-based positioning methods. Downlink-based positioning methods include observed time difference of arrival (OTDOA) in LTE, downlink time difference of arrival (DL-TDOA) in NR, and downlink angle-of-departure (DL-AoD) in NR. FIG. 4 illustrates examples of various positioning methods, according to aspects of the disclosure. In an OTDOA or DL-TDOA positioning procedure, illustrated by scenario 410, a UE measures the differences between the times of arrival (ToAs) of reference signals (e.g., positioning reference signals (PRS)) received from pairs of base stations, referred to as reference signal time difference (RSTD) or time difference of arrival (TDOA) measurements, and reports them to a positioning entity. More specifically, the UE receives the identifiers (IDs) of a reference base station (e.g., a serving base station) and multiple non-reference base stations in assistance data. The UE then measures the RSTD between the reference base station and each of the non-reference base stations. Based on the known locations of the involved base stations and the RSTD measurements, the positioning entity (e.g., the UE for UE-based positioning or a location server for UE-assisted positioning) can estimate the UE’s location.QC2404834WOQualcomm Ref. No. 2404834WO36 / 78

[0105] For DL-AoD positioning, illustrated by scenario 420, the positioning entity uses a measurement report from the UE of received signal strength measurements of multiple downlink transmit beams to determine the angle(s) between the UE and the transmitting base station(s). The positioning entity can then estimate the location of the UE based on the determined angle(s) and the known location(s) of the transmitting base station(s).

[0106] 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, a UE transmits one or more uplink reference signals that are measured by a reference base station and a plurality of non-reference base stations. Each base station then reports the reception time (referred to as the relative time of arrival (RTOA)) of the reference signal(s) to a positioning entity (e.g., a location server) that knows the locations and relative timing of the involved base stations. Based on the reception-to-reception (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 locations of the base stations, and their known timing offsets, the positioning entity can estimate the location of the UE using TDOA.

[0107] 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 a UE on one or more uplink receive beams. The positioning entity uses the signal strength measurements and the angle(s) of the receive beam(s) to determine the angle(s) between the UE and the base station(s). Based on the determined angle(s) and the known location(s) of the base station(s), the positioning entity can then estimate the location of the UE.

[0108] Downlink-and-uplink-based positioning methods include enhanced cell-ID (E-CID) positioning and multi-round-trip-time (RTT) positioning (also referred to as “multi-cell RTT” and “multi-RTT”). In an RTT procedure, a first entity (e.g., a base station or a UE) transmits a first RTT-related signal (e.g., a PRS or SRS) to a second entity (e.g., a UE or base station), which transmits a second RTT-related signal (e.g., an 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 transmission time of the transmitted RTT-related signal. This time difference is referred to as a reception-to-transmission (Rx- Tx) time difference. The Rx-Tx time difference measurement may be made, or may beQC2404834WOQualcomm Ref. No. 2404834WO37 / 78 adjusted, to include only a time difference between nearest slot boundaries for the received and transmitted signals. Both entities may then send their Rx-Tx time difference measurement to a location server (e.g., an LMF 270), which calculates the round trip propagation time (i.e., RTT) between the two entities from the two Rx-Tx time difference measurements (e.g., as the sum of the two Rx-Tx time difference measurements). Alternatively, one entity may send its Rx-Tx time difference measurement to the other entity, which then calculates the RTT. The distance between the two entities can be determined from the RTT and the known signal speed (e.g., the speed of light). For multi- RTT positioning, illustrated by scenario 430, a first entity (e.g., a UE or base station) performs an RTT positioning procedure with multiple second entities (e.g., multiple base stations or UEs) to enable the location of the first entity to be determined (e.g., using multilateration) based on distances to, and the known locations of, the second entities. RTT and multi-RTT methods can be combined with other positioning techniques, such as UL-AoA and DL-AoD, to improve location accuracy, as illustrated by scenario 440.

[0109] The E-CID positioning method is based on radio resource management (RRM) measurements. In E-CID, the UE reports the serving cell ID, the timing advance (TA), and the identifiers, estimated timing, and signal strength of detected neighbor base stations. The location of the UE is then estimated based on this information and the known locations of the base station(s).

[0110] To assist positioning operations, a location server (e.g., location server 230, LMF 270, SLP 272) may provide assistance data to the UE. For example, the assistance data may include identifiers of the base stations (or the cells / TRPs of the base stations) from which to measure reference signals, the reference signal configuration parameters (e.g., the number of consecutive slots including PRS, periodicity of the consecutive slots including PRS, muting sequence, frequency hopping sequence, reference signal identifier, reference signal bandwidth, etc.), and / or other parameters applicable to the particular positioning method. Alternatively, the assistance data may originate directly from the base stations themselves (e.g., in periodically broadcasted overhead messages, etc.). In some cases, the UE may be able to detect neighbor network nodes itself without the use of assistance data. [OHl] In the case of an OTDOA or DL-TDOA positioning procedure, the assistance data may further include an expected RSTD value and an associated uncertainty, or search window, around the expected RSTD. In some cases, the value range of the expected RSTD mayQC2404834WOQualcomm Ref. No. 2404834WO38 / 78 be + / - 500 microseconds (ps). In some cases, when any of the resources used for the positioning measurement are in FR1, the value range for the uncertainty of the expected RSTD may be + / - 32 ps. In other cases, when all of the resources used for the positioning measurement(s) are in FR2, the value range for the uncertainty of the expected RSTD may be + / - 8 ps.

[0112] A location estimate may be referred to by other names, such as a position estimate, location, position, position fix, fix, or the like. A location estimate may be geodetic and comprise coordinates (e.g., latitude, longitude, and possibly altitude) or may be civic and comprise a street address, postal address, or some other verbal description of a location. A location estimate may further be defined relative to some other known location or defined in absolute terms (e.g., using latitude, longitude, and possibly altitude). A location estimate may include an expected error or uncertainty (e.g., by including an area or volume within which the location is expected to be included with some specified or default level of confidence).

[0113] Machine learning may be used to generate models that may be used to facilitate various aspects associated with processing of data. One specific application of machine learning relates to generation of measurement models for processing of reference signals for positioning (e.g., positioning reference signal (PRS)), such as feature extraction, reporting of reference signal measurements (e.g., selecting which extracted features to report), and so on.

[0114] Machine learning models are generally categorized as either supervised or unsupervised. A supervised model may further be sub-categorized as either a regression or classification model. Supervised learning involves learning a function that maps an input to an output based on example input-output pairs. For example, given a training dataset with two variables of age (input) and height (output), a supervised learning model could be generated to predict the height of a person based on their age. In regression models, the output is continuous. One example of a regression model is a linear regression, which simply attempts to find a line that best fits the data. Extensions of linear regression include multiple linear regression (e.g., finding a plane of best fit) and polynomial regression (e.g., finding a curve of best fit).

[0115] Another example of a machine learning model is a decision tree model. In a decision tree model, a tree structure is defined with a plurality of nodes. Decisions are used to moveQC2404834WOQualcomm Ref. No. 2404834WO39 / 78 from a root node at the top of the decision tree to a leaf node at the bottom of the decision tree (i.e., a node with no further child nodes). Generally, a higher number of nodes in the decision tree model is correlated with higher decision accuracy.

[0116] Another example of a machine learning model is a decision forest. Random forests are an ensemble learning technique that builds off of decision trees. Random forests involve creating multiple decision trees using bootstrapped datasets of the original data and randomly selecting a subset of variables at each step of the decision tree. The model then selects the mode of all of the predictions of each decision tree. By relying on a “majority wins” model, the risk of error from an individual tree is reduced.

[0117] Another example of a machine learning model is a neural network (NN). A neural network is essentially a network of mathematical equations. Neural networks accept one or more input variables, and by going through a network of equations, result in one or more output variables. Put another way, a neural network takes in a vector of inputs and returns a vector of outputs.

[0118] FIG. 5 illustrates an example neural network 500, according to aspects of the disclosure. The neural network 500 includes an input layer ‘i’ that receives ‘n’ (one or more) inputs (illustrated as “Input 1,” “Input 2,” and “Input n”), one or more hidden layers (illustrated as hidden layers ‘hl,’ ‘h2,’ and ‘h3 ’) for processing the inputs from the input layer, and an output layer ‘o’ that provides ‘m’ (one or more) outputs (labeled “Output 1” and “Output m”). The number of inputs ‘n,’ hidden layers ‘h,’ and outputs ‘m’ may be the same or different. In some designs, the hidden layers ‘h’ may include linear function(s) and / or activation function(s) that the nodes (illustrated as circles) of each successive hidden layer process from the nodes of the previous hidden layer.

[0119] In classification models, the output is discrete. One example of a classification model is logistic regression. Logistic regression is similar to linear regression but is used to model the probability of a finite number of outcomes, typically two. In essence, a logistic equation is created in such a way that the output values can only be between ‘0’ and ‘ 1.’ Another example of a classification model is a support vector machine. For example, for two classes of data, a support vector machine will find a hyperplane or a boundary between the two classes of data that maximizes the margin between the two classes. There are many planes that can separate the two classes, but only one plane can maximize the margin or distance between the classes. Another example of a classification model isQC2404834WOQualcomm Ref. No. 2404834WO40 / 78Naive Bayes, which is based on Bayes Theorem. Other examples of classification models include decision tree, random forest, and neural network, similar to the examples described above except that the output is discrete rather than continuous.

[0120] Unlike supervised learning, unsupervised learning is used to draw inferences and find patterns from input data without references to labeled outcomes. Two examples of unsupervised learning models include clustering and dimensionality reduction.

[0121] Clustering is an unsupervised technique that involves the grouping, or clustering, of data points. Clustering is frequently used for customer segmentation, fraud detection, and document classification. Common clustering techniques include k-means clustering, hierarchical clustering, mean shift clustering, and density-based clustering. Dimensionality reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables. In simpler terms, dimensionality reduction is the process of reducing the dimension of a feature set (in even simpler terms, reducing the number of features). Most dimensionality reduction techniques can be categorized as either feature elimination or feature extraction. One example of dimensionality reduction is called principal component analysis (PCA). In the simplest sense, PCA involves projecting higher dimensional data (e.g., three dimensions) to a smaller space (e.g., two dimensions). This results in a lower dimension of data (e.g., two dimensions instead of three dimensions) while keeping all original variables in the model.

[0122] Regardless of which machine learning model is used, at a high-level, a machine learning module (e.g., implemented by a processing system) may be configured to iteratively analyze training input data (e.g., measurements of reference signals to / from various target UEs) and to associate this training input data with an output data set (e.g., a set of possible or likely candidate locations of the various target UEs), thereby enabling later determination of the same output data set when presented with similar input data (e.g., from other target UEs at the same or similar location).

[0123] The artificial intelligence / machine learning (AIML) positioning and / or sensing provided by an AIML model may be “direct” AIML (denoted “D-AIML”) positioning and / or sensing or AIML “assisted” (denoted “A-AIML”) positioning and / or sensing. Note that, as used herein, an AIML model (whether an A-AIML model or a D-AIML model) mayQC2404834WOQualcomm Ref. No. 2404834WO41 / 78 alternatively be referred to as an “ML model,” an “Al model,” an “ML-based model,” an “Al-based model,” and the like.

[0124] FIG. 6A is a diagram 610 illustrating an example of direct AIML positioning and / or sensing, according to aspects of the disclosure. As shown in FIG. 6A, direct AIML positioning and / or sensing is where the AIML model is trained to accept input features (e.g., downlink positioning reference signal (DL-PRS) measurements, sounding reference signal (SRS) measurements, sidelink positioning reference signal (SL-PRS) measurements, sensing signal measurements, beam measurements (e.g., synchronization signal block (SSB) measurements), channel state information reference signal (CSI-RS) measurements, etc.) and output a final result (referred to as a “direct label”), such as a target location (e.g., a UE location for positioning or a target object location for sensing). The measurements of the reference signal(s) may include the channel energy response (CER), channel impulse response (CIR), power delay profile (PDP), delay profile (DP), channel frequency response (CFR), received signal strength indicator (RS SI), reference signal received power (RSRP), path RSRP (RSRPP), reference signal received quality (RSRQ), time of arrival (ToA), relative ToA (RTOA), reference signal time difference (RSTD), angle of departure (AoD), angle of arrival (AoA), and / or the like of the reference signal(s).

[0125] FIG. 6B is a diagram 630 illustrating an example of AIML assisted positioning and / or sensing, according to aspects of the disclosure. As shown in FIG. 6B, AIML assisted positioning and / or sensing is where an AIML model is trained to accept input features (e.g., DL-PRS measurements, SRS measurements, SL-PRS measurements, sensing signal measurements, beam measurements, CSLRS measurements, etc.) and output one or more intermediate results (also referred to as “intermediate label(s)”). In a positioning context, generating the intermediate result may be referred to as “positioning feature extraction,” which may include determining timing / angle information, line of sight (LOS) identification, etc. The intermediate results may include the ToA, RTOA, RSTD, AoD, AoA, LOS indication, and / or the like. The intermediate result(s) may in turn be provided as an input to another AIML model or non-AIML model positioning and / or sensing technique (e.g., Chan’s algorithm, Kalman filtering, etc.) to determine a target location (e.g., a UE location for positioning or a target object location for sensing).QC2404834WOQualcomm Ref. No. 2404834WO42 / 78

[0126] Note that as shown in FIG. 6B, the A-AIML model and the other model / technique may be implemented at the same entity (e.g., UE, base station, location server, sensing server, etc.) or at different entities. For example, for network-assisted positioning, the UE may apply the A-AIML model to compress the measurement data and then report the compressed data to the location server, which may then apply the other position estimation model / technique. As another example, for UE-based positioning, a network component (e.g., a base station, location server, or another UE for sidelink positioning) may apply the A-AIML model to compress the measurement data and report the compressed data to the UE, which then applies the other position estimation model / technique.

[0127] FIG. 6C illustrates various AIML positioning and / or sensing scenarios, according to aspects of the disclosure. As shown in diagram 650, there are three AIML positioning and / or sensing deployment scenarios based on downlink reference signals (e.g., DL-PRS, CSLRS, etc.). The first deployment scenario (labeled “Case 1”) is a UE-based positioning and / or sensing case with a UE-side D-AIML positioning and / or sensing model (labeled “D-AIML”). In this case, the UE applies the D-AIML positioning and / or sensing model (or simply “D-AIML model”) to the downlink reference signal measurements to determine a location of the UE or a target object and reports the target location to the network (e.g., LMF 270).

[0128] The second deployment scenario (labeled “Case 2a”) is UE-assisted / network-based positioning and / or sensing with a UE-side A-AIML positioning and / or sensing model that provides AIML-assisted positioning and / or sensing. That is, the UE inputs measurements of downlink reference signals (e.g., DL-PRS, CSI-RS) received from one or more TRPs into the A-AIML positioning and / or sensing model to obtain intermediate measurements (or quantities) of the downlink reference signals. The UE then reports the intermediate measurements to the network (e.g., LMF 270). The network entity may then apply an AIML model or a non-AIML model technique to the intermediate measurements to determine a target location (e.g., of the UE for positioning scenarios or a target object for sensing scenarios).

[0129] The third deployment scenario (labeled “Case 2b”) is UE-assisted / network-based positioning and / or sensing scenario with a network-side D-AIML positioning and / or sensing model. That is, the UE reports the measurements of the downlink referenceQC2404834WOQualcomm Ref. No. 2404834WO43 / 78 signals received from one or more TRPs to the network (e.g., LMF 270). The network then applies the D-AIML positioning and / or sensing model to the measurements to determine the location of the UE or a target object.

[0130] As shown in diagram 670, there are two AIML positioning and / or sensing deployment scenarios based on uplink reference signals (e.g., SRS). The first deployment scenario (labeled “Case 3a”) is RAN node-assisted positioning and / or sensing with a RAN-side AIML model that provides AIML assisted positioning and / or sensing. In this case, the RAN node (e.g., abase station, TRP, or other base station component) applies an A-AIML positioning and / or sensing model to TRP measurements of one or more uplink reference signals (e.g., SRS) transmitted by a UE to obtain intermediate measurements of the received uplink reference signal(s). The RAN node then reports the intermediate measurements to the core network (e.g., LMF 270), which can use them to locate the UE (for positioning) or a target object (for sensing).

[0131] The second deployment scenario (labeled “Case 3b”) is RAN node-assisted positioning and / or sensing with a network-side AIML positioning and / or sensing model that provides direct AIML positioning and / or sensing. In this case, the RAN node reports measurements of one or more uplink reference signals received from a UE to the core network (e.g., LMF 270). The core network then applies a D-AIML positioning and / or sensing model to the measurements of the uplink reference signal(s) to obtain a target location of the UE (for positioning) or a target object (for sensing).

[0132] Note that there may be other deployment scenarios in which the UE, RAN, or the core network use an AIML positioning and / or sensing model to compute or report a positioning and / or sensing estimate (target location), but these cases are implementationspecific and do not necessarily involve signaling between the UE, RAN, and / or the core network.

[0133] Further note that an AIML model may execute in a training mode or an inferencing mode. In the training mode, the AIML model is provided with pre-validated input data along with pre-validated output data to derive or modify weights of the AIML to increase the reliability of the AIML model to provide new (unvalidated) output data that is similar to the pre-validated output data in response to new (unvalidated) input data that is similar to the pre-validated input data. In the inferencing mode, the AIML model utilizes the weights determined during the training mode to process new (unvalidated) input data soQC2404834WOQualcomm Ref. No. 2404834WO44 / 78 as to generate new (unvalidated) output data (typically, without further adjusting the weights until / unless the AIML model returns to the training mode). The (unvalidated) output data may be characterized as an “inference.” Thus, the “final” positioning or sensing results described above with respect to FIGS. 6 A to 6C may correspond to AIML model weights or inferences depending on whether the respective AIML model is executing in the training mode or the inferencing mode.

[0134] It will be recognized, based on the teachings of the present disclosure, that other use case scenarios in which different AIML models are implemented at more than one entity are also contemplated. For example, other cases in which a network node use AIML to report or compute positioning are not precluded from the teachings of the present disclosure.

[0135] Various potential data content are contemplated for use in AIML positioning, according to aspects of the disclosure. For scenarios employing direct AIML positioning models (e.g., Case 1, Case 2b, and Case 3b), the types of measurements used for model inference input have an impact on the performance of the AIML positioning models and the associated signaling overhead. Existing measurements used for inference include reference signal received power (RSRP), reference signal received path power (RSRPP), and reference signal time difference (RSTD). Additional measurement types include channel impulse response (CIR) and power delay profile (PDP). For scenarios employing ALassisted positioning (Case 2a and Case 3a), measurement reports, including the output of the AIML positioning model, are typically provided to the LMF. The measurement report may include RSTD measurements, line of sight / non-line of sight measurements, and RSRPP measurements. In an aspect, the measurement report may carry model output including ToA, path phase, and soft information / high-resolution RSTD measurements. In an aspect, the measurement report to the LMF may be derived based on measurements different from the model inference output. Further, the assistance signalling and procedures to facilitate model inference may be provided for both UE-side and networkside models (e.g., RS configurations).

[0136] FIG. 7 illustrates an example of an AIML air interface model 700, according to aspects of the disclosure. In the context of performance monitoring, the following metrics / methods for AIML model monitoring in life cycle management (LCM) per use case include 1) monitoring based on inference accuracy, including metrics related to intermediate key performance indicators (KPIs), and 2) monitoring based on systemQC2404834WOQualcomm Ref. No. 2404834WO45 / 78 performance, including metrics related to system performance KPIs. Other monitoring solutions include monitoring based on data distribution and monitoring based on applicable conditions. In an aspect, monitoring based on data distribution may be inputbased (e.g., monitoring the validity of the AIML input, e.g., out-of-distribution detection, drift detection of input data, or SNR, delay spread, etc.) and output-based (e.g., drift detection of output data).

[0137] Methods to assess / monitor the applicability and expected performance of an inactive model / functionality, include the following examples for the purpose of activation / selection / switching of UE-side model s / UE-part of two-sided models / functionalities (if applicable) 1) assessment / monitoring based on the additional conditions associated with the model / functionality, 2) assessment / monitoring based on input / output data distribution, 3) assessment / monitoring using the inactive model / functionality for monitoring purpose and measuring the inference accuracy, and 4) or based on past knowledge of the performance of the same model / functionality (e.g., based on other UEs).

[0138] AIML positioning can provide excellent positioning accuracy in stringent NLOS conditions. In an aspect, AIML model implementation may occur at the network node side (e.g., UE, gNB, TRP). In certain scenarios, the UE side may develop the AIML positioning model. However, in certain scenarios, other entities may develop the AIML model and transfer it to the network node.

[0139] Certain aspects of the disclosure are implemented with a recognition that the AIML positioning model can be sensitive to changes in the wireless environment (e.g., changes due to mobility and movement of the UE from one cell to another cell). As such, multiple AIML positioning models can be developed to cover a geographical area, and a model manager will switch between models in a timely fashion to ensure that the positioning session is not disrupted and that the positioning session runs smoothly and seamlessly. In such situations, the network node side needs signaling or conditions to assist the network node in deciding when to consider monitoring the AIML positioning models or applying model LCM for their AIML positioning models.

[0140] Certain aspects of the disclosure recognize that AIML model management operations can be linked to events (e.g., triggers) associated with mobility and handover occurrences at the network node. As shown in FIG. 7, the responsibility for AIML model managementQC2404834WOQualcomm Ref. No. 2404834WO46 / 78 operations 702 shown in the air interface model 700 may be transferred to the network node 704, which may perform one or more of the AIML model management operations 702 based on mobility / handoff triggers 706. In an aspect, the network node 704 may obtain an intelligence AIML management configuration indicating a set of trigger conditions (e.g., mobility / handoff triggers 706) and corresponding AIML management operations. In an aspect, the AIML management operations are associated with the management of one or more positioning AIML models at the network node 704. In an aspect, the network node 704 may perform one or more of the AIML management operations based on the occurrence of one or more trigger conditions of the set of trigger conditions corresponding to the AIML management operations.

[0141] In an aspect, the AIML management operations may include 1) monitoring the AIML model implemented at the network node and / or LMF, 2) LCM for the AIML model implemented at the network node, 3) updating insulation more fine-tuning the AIML model, 4) measurement reporting (e.g., measurements related to inference, monitoring, data collection, etc.), or 5) any combination thereof. Signaling of the AIML management operations and corresponding triggers may be provided from a network entity to the network node. In an aspect, the signaling indicates mobility and / or handover conditions (e.g., triggers) that the network node is to consider for its AIML positioning model management functions.

[0142] In accordance with aspects of the disclosure, the disclosed AIML positioning management and triggering operations may be used with respect to AIML models running at the network node side (e.g., Casel / 2a / 3a) as well as AIML models running at the network side (e.g., Case2b / 3b). According to various aspects of the disclosure, example network entities include 1) an LMF (e.g., using LTE Positioning Protocol (LPP) and / or New Radio Positioning Protocol A (NRPPa) signaling), 2) an Operations, Administration, and Maintenance (0AM) entity, 3) an Over the Top (OTT) server, 4) an Access and Mobility Management Function (AMF), or 5) any similar network entity.

[0143] In accordance with various aspects of the disclosure, the network node may perform AIML management operations, including 1) one or more LCM operations, 2) one or more AIML monitoring operations, 3) one or more AIML model update operations, 4) one or more AIML model delivery operations, 5) one or more AIML model fine-tuning operations, 6) one or more AIML model monitoring operations, 7) one or more AIMLQC2404834WOQualcomm Ref. No. 2404834WO47 / 78 model measurement operations, or 8) any combination thereof. In an aspect, fine tuning may include tweaking or changing a subset of model parameters (e.g., weights of subset of neurons in the AIML model). In such scenarios, there no substantial model changes or updates. Rather, the structure of the AIML model and / or subset of weights remain same and only some of the model weights are fine tuned (e.g., updated).

[0144] According to various aspects of the disclosure, the measurement reporting based on the quantity (e.g., number) of measurements associated with the one or more trigger conditions may be based on 1) a number of path measurements associated with a given positioning resource to be measured, 2) the number of TRPs to be measured, 3) the number of reference signal resources to be measured, 4) the number of reference signal resources per TRP to be measured, 5) the number of reference signal resources per resource set to be measured, 6) the number of reference signal resources to be measured per slot, or 7) any combination thereof. In an aspect, the measurement reporting based on the size of the measurements associated with the one or more trigger conditions is based on a data size used to report the one or more measurements.

[0145] With respect to the LCM operations, the network node may perform 1) one or more AIML model functionality management operations, 2) one or more AIML model operations, or 3) a combination thereof.

[0146] In an aspect, AIML functionality refers to the practical capabilities or tasks that an AIML system can perform as enabled by one or more AIML models. It is the application of AIML models to solve specific problems or perform specific tasks within a system or product. AIML functionality may encompass not just the AIML model but also the system around which the AIML model operates (e.g., data processing, model inference, decisionmaking processes, user interfaces, and integrations with other systems.)

[0147] In accordance with aspects of the disclosure, the AIML model functionality management operations implemented by the network node may include 1) activation of an AIML functionality, 2) deactivation of the AIML functionality, 3) selection of the AIML functionality, 4) switching of the AIML functionality, 5) selection of a fall back functionality of the AIML functionality, 6) activation of the AIML positioning functionality running at a different entity (e.g., other UE, gNB, and / or TRP), 7) deactivation of the AIML positioning functionality running at the different entity, 8) switching of the AIML positioning functionality running at different entity, 9) selectionQC2404834WOQualcomm Ref. No. 2404834WO48 / 78 of the AIML positioning functionality running at the different entity, or 10) any combination thereof.

[0148] In accordance with aspects of the disclosure, the AIML model management operations implemented by the network node may include 1) activation of an AIML model, 2) deactivation of the AIML model, 3) selection of the AIML model, 4) switching of the AIML model, 5) selection of a fallback AIML model, 6) switching of an AIML positioning model running at a different entity, 7) selection of an AIML positioning model running at the different entity, 8) triggering fallback to a non-AIML positioning method running at the different entity, or 9) any combination thereof. In an aspect, a first entity may have more accurate information needed to apply such change than the other (e.g., different entity). For example, the first UE may have a greater capability to track events and can recommend changes to other UEs (e.g., direct sidelink (SL) communications, and or communications with the other UEs via the LMF). In an aspect, a gNB and / or UE may indicate a need to apply these changes with each other (e.g., UE to gNB or gNB to UE). According to various aspects of the disclosure, the AIML model update operations that may be implemented by the network node include 1) updating the network node with a new AIML model, 2) updating the network node with a portion of the AIML model, 3) requesting an AIML model update, 4) fine-tuning the AIML model at the network node; 4) requesting fine-tuning of the AIML model, or 5) any combination thereof.

[0149] According to various aspects of the disclosure, the AIML monitoring operations that may be implemented by the network node include 1) monitoring based on label-based data or 2) monitoring based on label-free data. In an aspect, the monitoring based on label-based data may include 1) monitoring based on AIML model input statistics, 2) monitoring based on a comparison of the AIML model input statistics with statistics associated with training data used to train the AIML model, 3) monitoring based on AIML model output statistics; 4) monitoring based on a comparison of the AIML model output statistics with statistics associated with the training data used to train the AIML model, 5) transmitting a request of data for monitoring, or 6) any combination thereof. In an aspect, a request of data for monitoring may include data that may be used as model input and / or output (e.g., labels). This can be programmable real-time (PRU) data (e.g., measurement and PRU location information). In an aspect, this data can be labels generated by another entity andQC2404834WOQualcomm Ref. No. 2404834WO49 / 78 used for comparison with the actual AIML model output and determining a delta (e.g., error) corresponding to the accuracy of the AIML model.

[0150] According to various aspects of the disclosure, the AIML model measurement operations implemented by the network node include measurement reporting based on 1) a type of measurement associated with one or more trigger conditions of the set of trigger conditions, 2) a quantity of measurements associated with the one or more trigger conditions of the set of trigger conditions, 3) a size of measurements associated with the one or more trigger conditions of the set of trigger conditions, or 4) any combination thereof. In an aspect, the type of measurement associated with the one or more trigger conditions may include 1) timing measurements, 2) line-of-sight measurements, 3) reference signal power measurements, 4) reference signal quality measurements, 5) reference signal timing measurements, or 5) any combination thereof. In an aspect, the timing-based measurements may correspond to 1) reference signal time difference (RSTD), 2) reference signal received time of arrival (RTOA), 3) RSRPP, 4) RSRP, 5) reference signal time difference-difference (RSTD-diff), 6) RTOA difference, 7) UE transmission-reception (TX-RX) time difference, 8) gNB TX-RX time difference, 9) received signal code power (RSCP), 10) reference carrier phase difference (RSCPD), 11) CIR, 12) power delay profile (PDP), 13) delay profile (DP), etc.

[0151] In accordance with various aspects of the disclosure, the trigger conditions may include mobility-based trigger conditions and / or handover-based trigger conditions. In an aspect, the mobility-based trigger conditions may be based on 1) the speed of the network node, 2) a location change of the network node, 3) a mobility state estimation of the network node, or 4) any combination thereof. In an aspect, the handover-based trigger conditions may be based on signal quality thresholds associated with neighboring cells and / or a change in a serving cell that serves the network node.

[0152] In accordance with aspects of the disclosure, example mobility and handover triggers / conditions may include the following event types:• Event Al (Serving cell becomes better than threshold)• Event A2 (Serving cell becomes worse than threshold)• Event A3 (Neighbor cell becomes offset better than SpCell)• Event A4 (Neighbor cell becomes better than threshold)QC2404834WOQualcomm Ref. No. 2404834WO50 / 78• Event A5 (SpCell becomes worse than threshold 1 and neighbor cell becomes better than threshold2)• Event A6 (Neighbor cell becomes offset better than SCell)• Event Bl (Inter RAT neighbor cell becomes better than threshold)• Event B2 (PCell becomes worse than threshold 1 and inter RAT neighbor cell becomes better than threshold2)• Event II (Interference becomes higher than a threshold)• Event C2 (The NR sidelink channel busy ratio is below a threshold)• Event DI (Distance between UE and referenceLocationl is above thresholdl and distance between UE and referenceLocation2 is below threshold2)• CondEvent T1 (Time measured at UE is within a duration from a threshold)• Event XI (Serving L2 U2N Relay UE becomes worse than thresholdl and NR Cell becomes better than threshold2)

[0153] In accordance with various aspects of the disclosure, the network node may provide an indication (e.g., signal to a network device) of its capability to receive signaling corresponding to the above-noted triggering conditions and apply corresponding reporting, monitoring, and / or LCM tasks based on such triggering conditions. Additionally, or in the alternative, the network node may recommend a handoff to a home (e.g., source) cell when any of the above-noted triggering conditions exist or when management action on AIML positioning takes place. For example, the network node may recommend 1) a handover (HO), 2) a conditional handover (CHO), 3) a dual active protocol stack handover (DAPS HO), or 4) any combination thereof. As an example, when an LCM operation to change the AIML model occurs at the network node, a handoff to the strongest cell currently within the range of the network node may also be triggered.

[0154] According to various aspects of the disclosure, when the network node detects and / or recommends a HO, the network node may implement corresponding HO-based measurement reporting. In an aspect, the network node may track measurements (e.g., RSRP of neighboring cells). When the network node observes the occurrence of a configured mobility event, it may 1) increase measurement size to the AIML model input running at the network node and / or the network entity, 2) reduce the measurement size to the AIML model input running at the network node and / or the network entity, 3) change the measurement types provided to the AIML model input running at the network nodeQC2404834WOQualcomm Ref. No. 2404834WO51 / 78 and / or the network entity, 4) request data collection, and / or 5) collect data. In an aspect, the network node may request the UE to collect data. In an aspect, the data can correspond to CSI measurements (e.g. PMI, CQI, MCS), positioning measurements (RSTD, Rx-Tx, RSRP, RSRPP), sensing measurements (Doppler estimates), beam management measurements (RSRP), SINR, or a combination thereof. In an aspect, the UE may collect data related to model input and output data such as an data related to the reference signal (RS) resources to be configured. In an aspect, the LMF may also help in determining model output (i.e., label, labeling assistance). In an aspect, the request may be focused on requesting RS configurations and labeling assistance.

[0155] According to various aspects of the disclosure, when the network node detects and / or recommends a HO, the network node may implement a corresponding HO-based monitoring. For example, the network node may track measurements (e.g., RSRP of neighboring cells) and observe the occurrence of a configured mobility event. In response to the occurrence of the configured mobility event, the network node may 1) start labelbased monitoring, 2) start label-free monitoring, 3) end label-based monitoring, 4) end label-free monitoring, and / or 5) request data for monitoring (e.g., from a network entity such as the LMF, AMF, network data analytics function (NWDAF), etc.)

[0156] According to various aspects of the disclosure, when the network node detects and / or recommends a HO, the network node may implement corresponding HO-based LCM. For example, the network node may track measurements (e.g., RSRP of neighboring cells) and observe the occurrence of a configured mobility event. In response to the occurrence of the configured mobility event, the network node may 1) (de)activate the AIML model or AIML functionality running at the network node, 2) switch / select the AIML model or AIML functionality running at the network node, 3) fall back to a non- AIML positioning method running at network node, 4) trigger (de)activation of an AIML positioning model or AIML functionality running at a different entity, 5) trigger the switching / selection of an AIML positioning model or AIML functionality running at the different entity, and / or 6) trigger the falling back to a non-AIML positioning method running at the different entity.

[0157] According to various aspects of the disclosure, when the network node detects and / or recommends a HO, the network node may implement corresponding HO-based AIML model update functionality. For example, the network node may track measurementsQC2404834WOQualcomm Ref. No. 2404834WO52 / 78(e.g., RSRP of neighboring cells) and observe the occurrence of a configured mobility event. In response to the occurrence of the configured mobility event, the network node may 1) update the AIML model running at the network node with a new AIML model and / or part of the AIML model, 2) request an AIML model update from another entity, 3) perform a fine-tuning of the AIML model, and / or 4) request a fine-tuning of the AIML model from another entity.

[0158] FIG. 8 illustrates an example method 800 of wireless communication that may be performed by a network node, according to aspects of the disclosure. At operation 802, the network node obtains an artificial intelligence / machine learning (AIML) management configuration indicating a set of trigger conditions and corresponding AIML management operations, wherein the AIML management operations are associated with management of one or more positioning AIML models at the network node, and wherein the trigger conditions include handover-based trigger conditions, mobility-based trigger conditions, or a combination thereof. In an aspect, operation 802 may be performed by the one or more WWAN transceivers 310, the one or more short-range wireless transceivers 320, the one or more processors 342, memory 340, and / or AIML management component 348, any or all of which may be considered means for performing this operation. In an aspect, operation 802 may be performed by the one or more WWAN transceivers 350, the one or more short-range wireless transceivers 360, the one or more processors 384, memory 386, and / or AIML management component 388, any or all of which may be considered means for performing this operation.

[0159] At operation 804, the network node performs one or more of the AIML management operations based on the occurrence of one or more trigger conditions of the set of trigger conditions corresponding to the AIML management operations. In an aspect, operation 804 may be performed by the one or more WWAN transceivers 310, the one or more short-range wireless transceivers 320, the one or more processors 342, memory 340, and / or AIML management component 348, any or all of which may be considered means for performing this operation. In an aspect, operation 804 may be performed by the one or more WWAN transceivers 350, the one or more short-range wireless transceivers 360, the one or more processors 384, memory 386, and / or AIML management component 388, any or all of which may be considered means for performing this operation.QC2404834WOQualcomm Ref. No. 2404834WO53 / 78

[0160] As will be appreciated, a technical advantage of the method 800 is that it provides a method that allows a network node (e.g., UE, gNB, TRP) to perform AIML management operations based on events that can be determined at the network node.

[0161] In the detailed description above it can be seen that different features are grouped together in examples. This manner of disclosure should not be understood as an intention that the example clauses have more features than are explicitly mentioned in each clause. Rather, the various aspects of the disclosure may include fewer than all features of an individual example clause disclosed. Therefore, the following clauses should hereby be deemed to be incorporated in the description, wherein each clause by itself can stand as a separate example. Although each dependent clause can refer in the clauses to a specific combination with one of the other clauses, the aspect(s) of that dependent clause are not limited to the specific combination. It will be appreciated that other example clauses can also include a combination of the dependent clause aspect(s) with the subject matter of any other dependent clause or independent clause or a combination of any feature with other dependent and independent clauses. The various aspects disclosed herein expressly include these combinations, unless it is explicitly expressed or can be readily inferred that a specific combination is not intended (e.g., contradictory aspects, such as defining an element as both an electrical insulator and an electrical conductor). Furthermore, it is also intended that aspects of a clause can be included in any other independent clause, even if the clause is not directly dependent on the independent clause.

[0162] Implementation examples are described in the following numbered clauses:

[0163] Clause 1. A method of wireless communication performed by a network node, comprising: obtaining an artificial intelligence / machine learning (AIML) management configuration indicating a set of trigger conditions and corresponding AIML management operations, wherein the AIML management operations are associated with management of one or more positioning AIML models at the network node, and wherein the trigger conditions include handover-based trigger conditions, mobility-based trigger conditions, or a combination thereof; and performing one or more of the AIML management operations based on occurrence of one or more trigger conditions of the set of trigger conditions corresponding to the AIML management operations.

[0164] Clause 2. The method of clause 1, wherein: the AIML management configuration is obtained from a location management server; an Operations, Administration, andQC2404834WOQualcomm Ref. No. 2404834WO54 / 78Maintenance (OAM) entity; an Over-the-Top (OTT) entity; or an Access and Mobility Management Function (AMF).

[0165] Clause 3. The method of any of clauses 1 to 2, wherein: the AIML management operations include one or more Life Cycle Management (LCM) operations; one or more AIML monitoring operations; one or more AIML model update operations; one or more AIML model delivery operations; one or more AIML model fine-tuning operations; one or more AIML model monitoring operations; one or more AIML model measurement operations; or any combination thereof.

[0166] Clause 4. The method of clause 3, wherein: the one or more LCM operations include one or more AIML model functionality management operations; one or more AIML model operations; or a combination thereof.

[0167] Clause 5. The method of clause 4, wherein: the one or more AIML model functionality management operations include activation of an AIML functionality; deactivation of the AIML functionality; selection of the AIML functionality; switching of the AIML functionality; selection of a fall back functionality of the AIML functionality; activation of the AIML positioning functionality running at a different entity; deactivation of the AIML positioning functionality running at the different entity; switching of the AIML positioning functionality running at the different entity; selection of the AIML positioning functionality running at the different entity; or any combination thereof.

[0168] Clause 6. The method of any of clauses 4 to 5, wherein: the one or more AIML model management operations include activation of an AIML model; deactivation of the AIML model; selection of the AIML model; switching of the AIML model; selection of a fall back AIML model; switching of an AIML positioning model running at a different entity; selection of an AIML positioning model running at the different entity; triggering fallback to a non-AIML positioning method running at the different entity; or any combination thereof.

[0169] Clause 7. The method of any of clauses 3 to 6, wherein: the one or more AIML model update operations include updating the network node with a new AIML model; updating the network node with a portion of the AIML model; requesting an AIML model update; fine tuning the AIML model at the network node; requesting fine tuning of the AIML model; or any combination thereof.QC2404834WOQualcomm Ref. No. 2404834WO55 / 78

[0170] Clause 8. The method of any of clauses 3 to 7, wherein: the one or more AIML monitoring operations include monitoring based on label-based data; monitoring based on label-free data including AIML model input statistics; a comparison of the AIML model input statistics with statistics associated with training data used to train the AIML model; AIML model output statistics; a comparison of the AIML model output statistics with statistics associated with the training data used to train the AIML model; transmitting a request for data monitoring; or any combination thereof.

[0171] Clause 9. The method of any of clauses 3 to 8, wherein: the one or more AIML model measurement operations include measurement reporting based on a type of measurement associated with one or more trigger conditions of the set of trigger conditions; a quantity of measurements associated with the one or more trigger conditions of the set of trigger conditions; a size of measurements associated with the one or more trigger conditions of the set of trigger conditions; or any combination thereof.

[0172] Clause 10. The method of clause 9, wherein: the type of measurement associated with the one or more trigger conditions of the set of trigger conditions include line-of-sight measurements; timing measurements; reference signal power measurements; reference signal quality measurements; reference signal timing measurements; or any combination thereof.

[0173] Clause 11. The method of any of clauses 9 to 10, wherein: the measurement reporting based on the quantity of measurements associated with the one or more trigger conditions is based on a number of path measurements associated with a given positioning resource to be measured; a number of transmission reception points (TRPs) to be measured; a number of reference signal resources to be measured; a number of reference signal resources per TRP to be measured; a number of reference signal resources per resource set to be measured; a number of reference signal resources to be measured per slot; or any combination thereof.

[0174] Clause 12. The method of any of clauses 9 to 11, wherein: the measurement reporting based on the size of the measurements associated with the one or more trigger conditions is based on a data size used to report one or more measurements.

[0175] Clause 13. The method of any of clauses 1 to 12, wherein: the mobility-based trigger conditions are based on a speed of the network node; a location change of the network node; a mobility state estimation of the network node; or any combination thereof.QC2404834WOQualcomm Ref. No. 2404834WO56 / 78

[0176] Clause 14. The method of any of clauses 1 to 13, wherein: the handover-based trigger conditions are based on signal quality thresholds associated with neighboring cells; a change in a serving cell of the network node; or any combination thereof.

[0177] Clause 15. The method of any of clauses 1 to 14, further comprising: providing an indication of the one or more AIML management operations performed at the network node to a location management server; an Operations, Administration, and Maintenance (0AM) entity; an Over-the-Top (OTT) entity; or an Access and Mobility Management Function (AMF).

[0178] Clause 16. A network node, comprising: one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to: obtain an artificial intelligence / machine learning (AIML) management configuration indicating a set of trigger conditions and corresponding AIML management operations, wherein the AIML management operations are associated with management of one or more positioning AIML models at the network node, and wherein the trigger conditions include handover-based trigger conditions, mobility-based trigger conditions, or a combination thereof; and perform one or more of the AIML management operations based on occurrence of one or more trigger conditions of the set of trigger conditions corresponding to the AIML management operations.

[0179] Clause 17. The network node of clause 16, wherein: the AIML management configuration is obtained from a location management server; an Operations, Administration, and Maintenance (0AM) entity; an Over-the-Top (OTT) entity; or an Access and Mobility Management Function (AMF).

[0180] Clause 18. The network node of any of clauses 16 to 17, wherein: the AIML management operations include one or more Life Cycle Management (LCM) operations; one or more AIML monitoring operations; one or more AIML model update operations; one or more AIML model delivery operations; one or more AIML model fine-tuning operations; one or more AIML model monitoring operations; one or more AIML model measurement operations; or any combination thereof.

[0181] Clause 19. The network node of clause 18, wherein: the one or more LCM operations include one or more AIML model functionality management operations; one or more AIML model operations; or a combination thereof.QC2404834WOQualcomm Ref. No. 2404834WO57 / 78

[0182] Clause 20. The network node of clause 19, wherein: the one or more AIML model functionality management operations include activation of an AIML functionality; deactivation of the AIML functionality; selection of the AIML functionality; switching of the AIML functionality; selection of a fall back functionality of the AIML functionality; activation of the AIML positioning functionality running at a different entity; deactivation of the AIML positioning functionality running at the different entity; switching of the AIML positioning functionality running at the different entity; selection of the AIML positioning functionality running at the different entity; or any combination thereof.

[0183] Clause 21. The network node of any of clauses 19 to 20, wherein: the one or more AIML model management operations include activation of an AIML model; deactivation of the AIML model; selection of the AIML model; switching of the AIML model; selection of a fall back AIML model; switching of an AIML positioning model running at a different entity; selection of an AIML positioning model running at the different entity; triggering fallback to a non-AIML positioning method running at the different entity; or any combination thereof.

[0184] Clause 22. The network node of any of clauses 18 to 21, wherein: the one or more AIML model update operations include updating the network node with a new AIML model; updating the network node with a portion of the AIML model; requesting an AIML model update; fine tuning the AIML model at the network node; requesting fine tuning of the AIML model; or any combination thereof.

[0185] Clause 23. The network node of any of clauses 18 to 22, wherein: the one or more AIML monitoring operations include monitoring based on label-based data; monitoring based on label-free data including AIML model input statistics; a comparison of the AIML model input statistics with statistics associated with training data used to train the AIML model; AIML model output statistics; a comparison of the AIML model output statistics with statistics associated with the training data used to train the AIML model; transmitting a request for data monitoring; or any combination thereof.

[0186] Clause 24. The network node of any of clauses 18 to 23, wherein: the one or more AIML model measurement operations include measurement reporting based on a type of measurement associated with one or more trigger conditions of the set of trigger conditions; a quantity of measurements associated with the one or more trigger conditionsQC2404834WOQualcomm Ref. No. 2404834WO58 / 78 of the set of trigger conditions; a size of measurements associated with the one or more trigger conditions of the set of trigger conditions; or any combination thereof.

[0187] Clause 25. The network node of clause 24, wherein: the type of measurement associated with the one or more trigger conditions of the set of trigger conditions include line-of- sight measurements; timing measurements; reference signal power measurements; reference signal quality measurements; reference signal timing measurements; or any combination thereof.

[0188] Clause 26. The network node of any of clauses 24 to 25, wherein: the measurement reporting based on the quantity of measurements associated with the one or more trigger conditions is based on a number of path measurements associated with a given positioning resource to be measured; a number of transmission reception points (TRPs) to be measured; a number of reference signal resources to be measured; a number of reference signal resources per TRP to be measured; a number of reference signal resources per resource set to be measured; a number of reference signal resources to be measured per slot; or any combination thereof.

[0189] Clause 27. The network node of any of clauses 24 to 26, wherein: the measurement reporting based on the size of the measurements associated with the one or more trigger conditions is based on a data size used to report one or more measurements.

[0190] Clause 28. The network node of any of clauses 16 to 27, wherein: the mobility-based trigger conditions are based on a speed of the network node; a location change of the network node; a mobility state estimation of the network node; or any combination thereof.

[0191] Clause 29. The network node of any of clauses 16 to 28, wherein: the handover-based trigger conditions are based on signal quality thresholds associated with neighboring cells; a change in a serving cell of the network node; or any combination thereof.

[0192] Clause 30. The network node of any of clauses 16 to 29, wherein the one or more processors, either alone or in combination, are further configured to: provide an indication of the one or more AIML management operations performed at the network node to a location management server; an Operations, Administration, and Maintenance (0AM) entity; an Over-the-Top (OTT) entity; or an Access and Mobility Management Function (AMF).QC2404834WOQualcomm Ref. No. 2404834WO59 / 78

[0193] Clause 31. A network node, comprising: means for obtaining an artificial intelligence / machine learning (AIML) management configuration indicating a set of trigger conditions and corresponding AIML management operations, wherein the AIML management operations are associated with management of one or more positioning AIML models at the network node, and wherein the trigger conditions include handoverbased trigger conditions, mobility-based trigger conditions, or a combination thereof; and means for performing one or more of the AIML management operations based on occurrence of one or more trigger conditions of the set of trigger conditions corresponding to the AIML management operations.

[0194] Clause 32. The network node of clause 31 , wherein: the AIML management configuration is obtained from a location management server; an Operations, Administration, and Maintenance (0AM) entity; an Over-the-Top (OTT) entity; or an Access and Mobility Management Function (AMF).

[0195] Clause 33. The network node of any of clauses 31 to 32, wherein: the AIML management operations include one or more Life Cycle Management (LCM) operations; one or more AIML monitoring operations; one or more AIML model update operations; one or more AIML model delivery operations; one or more AIML model fine-tuning operations; one or more AIML model monitoring operations; one or more AIML model measurement operations; or any combination thereof.

[0196] Clause 34. The network node of clause 33, wherein: the one or more LCM operations include one or more AIML model functionality management operations; one or more AIML model operations; or a combination thereof.

[0197] Clause 35. The network node of clause 34, wherein: the one or more AIML model functionality management operations include activation of an AIML functionality; deactivation of the AIML functionality; selection of the AIML functionality; switching of the AIML functionality; selection of a fall back functionality of the AIML functionality; activation of the AIML positioning functionality running at a different entity; deactivation of the AIML positioning functionality running at the different entity; switching of the AIML positioning functionality running at the different entity; selection of the AIML positioning functionality running at the different entity; or any combination thereof.QC2404834WOQualcomm Ref. No. 2404834WO60 / 78

[0198] Clause 36. The network node of any of clauses 34 to 35, wherein: the one or more AIML model management operations include activation of an AIML model; deactivation of the AIML model; selection of the AIML model; switching of the AIML model; selection of a fall back AIML model; switching of an AIML positioning model running at a different entity; selection of an AIML positioning model running at the different entity; triggering fallback to a non-AIML positioning method running at the different entity; or any combination thereof.

[0199] Clause 37. The network node of any of clauses 33 to 36, wherein: the one or more AIML model update operations include means for updating the network node with a new AIML model; updating the network node with a portion of the AIML model; requesting an AIML model update; fine tuning the AIML model at the network node; requesting fine tuning of the AIML model; or any combination thereof.

[0200] Clause 38. The network node of any of clauses 33 to 37, wherein: the one or more AIML monitoring operations include monitoring based on label-based data; monitoring based on label-free data including AIML model input statistics; a comparison of the AIML model input statistics with statistics associated with training data used to train the AIML model; AIML model output statistics; a comparison of the AIML model output statistics with statistics associated with the training data used to train the AIML model; transmitting a request for data monitoring; or any combination thereof.

[0201] Clause 39. The network node of any of clauses 33 to 38, wherein: the one or more AIML model measurement operations include measurement reporting based on a type of measurement associated with one or more trigger conditions of the set of trigger conditions; a quantity of measurements associated with the one or more trigger conditions of the set of trigger conditions; a size of measurements associated with the one or more trigger conditions of the set of trigger conditions; or any combination thereof.

[0202] Clause 40. The network node of clause 39, wherein: the type of measurement associated with the one or more trigger conditions of the set of trigger conditions include line-of- sight measurements; timing measurements; reference signal power measurements; reference signal quality measurements; reference signal timing measurements; or any combination thereof.

[0203] Clause 41. The network node of any of clauses 39 to 40, wherein: the measurement reporting based on the quantity of measurements associated with the one or more triggerQC2404834WOQualcomm Ref. No. 2404834WO61 / 78 conditions is based on a number of path measurements associated with a given positioning resource to be measured; a number of transmission reception points (TRPs) to be measured; a number of reference signal resources to be measured; a number of reference signal resources per TRP to be measured; a number of reference signal resources per resource set to be measured; a number of reference signal resources to be measured per slot; or any combination thereof.

[0204] Clause 42. The network node of any of clauses 39 to 41, wherein: the measurement reporting based on the size of the measurements associated with the one or more trigger conditions is based on a data size used to report one or more measurements.

[0205] Clause 43. The network node of any of clauses 31 to 42, wherein: the mobility -based trigger conditions are based on a speed of the network node; a location change of the network node; a mobility state estimation of the network node; or any combination thereof.

[0206] Clause 44. The network node of any of clauses 31 to 43, wherein: the handover-based trigger conditions are based on signaling quality thresholds associated with neighboring cells; a change in a serving cell of the network node; or any combination thereof.

[0207] Clause 45. The network node of any of clauses 31 to 44, further comprising: providing an indication of the one or more AIML management operations performed at the network node to a location management server; an Operations, Administration, and Maintenance (0AM) entity; an Over-the-Top (OTT) entity; or an Access and Mobility Management Function (AMF).

[0208] Clause 46. A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a network node, cause the network node to: obtain an artificial intelligence / machine learning (AIML) management configuration indicating a set of trigger conditions and corresponding AIML management operations, wherein the AIML management operations are associated with management of one or more positioning AIML models at the network node, and wherein the trigger conditions include handover-based trigger conditions, mobility-based trigger conditions, or a combination thereof; and perform one or more of the AIML management operations based on occurrence of one or more trigger conditions of the set of trigger conditions corresponding to the AIML management operations.QC2404834WOQualcomm Ref. No. 2404834WO62 / 78

[0209] Clause 47. The non-transitory computer-readable medium of clause 46, wherein: the AIML management configuration is obtained from a location management server; an Operations, Administration, and Maintenance (0AM) entity; an Over-the-Top (OTT) entity; or an Access and Mobility Management Function (AMF).

[0210] Clause 48. The non-transitory computer-readable medium of any of clauses 46 to 47, wherein: the AIML management operations include one or more Life Cycle Management (LCM) operations; one or more AIML monitoring operations; one or more AIML model update operations; one or more AIML model delivery operations; one or more AIML model fine-tuning operations; one or more AIML model monitoring operations; one or more AIML model measurement operations; or any combination thereof.

[0211] Clause 49. The non-transitory computer-readable medium of clause 48, wherein: the one or more LCM operations include one or more AIML model functionality management operations; one or more AIML model operations; or a combination thereof.

[0212] Clause 50. The non-transitory computer-readable medium of clause 49, wherein: the one or more AIML model functionality management operations include activation of an AIML functionality; deactivation of the AIML functionality; selection of the AIML functionality; switching of the AIML functionality; selection of a fall back functionality of the AIML functionality; activation of the AIML positioning functionality running at a different entity; deactivation of the AIML positioning functionality running at the different entity; switching of the AIML positioning functionality running at the different entity; selection of the AIML positioning functionality running at the different entity; or any combination thereof.

[0213] Clause 51. The non-transitory computer-readable medium of any of clauses 49 to 50, wherein: the one or more AIML model management operations include activation of an AIML model; deactivation of the AIML model; selection of the AIML model; switching of the AIML model; selection of a fall back AIML model; switching of an AIML positioning model running at a different entity; selection of an AIML positioning model running at the different entity; triggering fallback to a non-AIML positioning method running at the different entity; or any combination thereof.

[0214] Clause 52. The non-transitory computer-readable medium of any of clauses 48 to 51, wherein: the one or more AIML model update operations include updating the network node with a new AIML model; updating the network node with a portion of the AIMLQC2404834WOQualcomm Ref. No. 2404834WO63 / 78 model; requesting an AIML model update; fine tuning the AIML model at the network node; requesting fine tuning of the AIML model; or any combination thereof.

[0215] Clause 53. The non-transitory computer-readable medium of any of clauses 48 to 52, wherein: the one or more AIML monitoring operations include monitoring based on labelbased data; monitoring based on label-free data including AIML model input statistics; a comparison of the AIML model input statistics with statistics associated with training data used to train the AIML model; AIML model output statistics; a comparison of the AIML model output statistics with statistics associated with the training data used to train the AIML model; transmitting a request for data monitoring; or any combination thereof.

[0216] Clause 54. The non-transitory computer-readable medium of any of clauses 48 to 53, wherein: the one or more AIML model measurement operations include measurement reporting based on a type of measurement associated with one or more trigger conditions of the set of trigger conditions; a quantity of measurements associated with the one or more trigger conditions of the set of trigger conditions; a size of measurements associated with the one or more trigger conditions of the set of trigger conditions; or any combination thereof.

[0217] Clause 55. The non-transitory computer-readable medium of clause 54, wherein: the type of measurement associated with the one or more trigger conditions of the set of trigger conditions include line-of-sight measurements; timing measurements; reference signal power measurements; reference signal quality measurements; reference signal timing measurements; or any combination thereof.

[0218] Clause 56. The non-transitory computer-readable medium of any of clauses 54 to 55, wherein: the measurement reporting based on the quantity of measurements associated with the one or more trigger conditions is based on a number of path measurements associated with a given positioning resource to be measured; a number of transmission reception points (TRPs) to be measured; a number of reference signal resources to be measured; a number of reference signal resources per TRP to be measured; a number of reference signal resources per resource set to be measured; a number of reference signal resources to be measured per slot; or any combination thereof.

[0219] Clause 57. The non-transitory computer-readable medium of any of clauses 54 to 56, wherein: the measurement reporting based on the size of the measurements associatedQC2404834WOQualcomm Ref. No. 2404834WO64 / 78 with the one or more trigger conditions is based on a data size used to report one or more measurements.

[0220] Clause 58. The non-transitory computer-readable medium of any of clauses 46 to 57, wherein: the mobility-based trigger conditions are based on a speed of the network node; a location change of the network node; a mobility state estimation of the network node; or any combination thereof.

[0221] Clause 59. The non-transitory computer-readable medium of any of clauses 46 to 58, wherein: the handover-based trigger conditions are based on signal quality thresholds associated with neighboring cells; a change in a serving cell of the network node; or any combination thereof.

[0222] Clause 60. The non-transitory computer-readable medium of any of clauses 46 to 59, further comprising computer-executable instructions that, when executed by the network node, cause the network node to: providing an indication of the one or more AIML management operations performed at the network node to a location management server; an Operations, Administration, and Maintenance (0AM) entity; an Over-the-Top (OTT) entity; or an Access and Mobility Management Function (AMF).

[0223] Those of skill in the art will appreciate that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0224] Further, those of skill in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the aspects disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementationQC2404834WOQualcomm Ref. No. 2404834WO65 / 78 decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0225] The various illustrative logical blocks, modules, and circuits described in connection with the aspects disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an ASIC, a field-programable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0226] The methods, sequences and / or algorithms described in connection with the aspects disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in random access memory (RAM), flash memory, read-only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An example storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal (e.g., UE). In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

[0227] In one or more example aspects, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, suchQC2404834WOQualcomm Ref. No. 2404834WO66 / 78 computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0228] While the foregoing disclosure shows illustrative aspects of the disclosure, it should be noted that various changes and modifications could be made herein without departing from the scope of the disclosure as defined by the appended claims. For example, the functions, steps and / or actions of the method claims in accordance with the aspects of the disclosure described herein need not be performed in any particular order. Further, no component, function, action, or instruction described or claimed herein should be construed as critical or essential unless explicitly described as such. Furthermore, as used herein, the terms “set,” “group,” and the like are intended to include one or more of the stated elements. Also, as used herein, the terms “has,” “have,” “having,” “comprises,” “comprising,” “includes,” “including,” and the like does not preclude the presence of one or more additional elements (e.g., an element “having” A may also have B). Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise. Also, as used herein, the term “or” is intended to be inclusive when used in a series and may be used interchangeably with “and / or,” unless explicitly stated otherwise (e.g., if used in combination with “either” or “only one of’) or the alternatives are mutually exclusive (e.g., “one or more” should not be interpreted as “one and more”). Furthermore, although components, functions, actions, and instructions may be described or claimed in the singular, the plural is contemplated unless limitation to the singular isQC2404834WOQualcomm Ref. No. 2404834WO67 / 78 explicitly stated. Accordingly, as used herein, the articles “a,” “an,” “the,” and “said” are intended to include one or more of the stated elements. Additionally, as used herein, the terms “at least one” and “one or more” encompass “one” component, function, action, or instruction performing or capable of performing a described or claimed functionality and also “two or more” components, functions, actions, or instructions performing or capable of performing a described or claimed functionality in combination.QC2404834WO

Claims

Qualcomm Ref. No. 2404834WO68 / 78CLAIMSWhat is claimed is:

1. A method of wireless communication performed by a network node, comprising: obtaining an artificial intelligence / machine learning (AIML) management configuration indicating a set of trigger conditions and corresponding AIML management operations, wherein the AIML management operations are associated with management of one or more positioning AIML models at the network node, and wherein the trigger conditions include handover-based trigger conditions, mobility-based trigger conditions, or a combination thereof; and performing one or more of the AIML management operations based on occurrence of one or more trigger conditions of the set of trigger conditions corresponding to the AIML management operations.

2. The method of claim 1, wherein: the AIML management configuration is obtained from a location management server; an Operations, Administration, and Maintenance (OAM) entity; an Over-the-Top (OTT) entity; or an Access and Mobility Management Function (AMF).

3. The method of claim 1, wherein: the AIML management operations include one or more Life Cycle Management (LCM) operations; one or more AIML monitoring operations; one or more AIML model update operations; one or more AIML model delivery operations; one or more AIML model fine-tuning operations; one or more AIML model monitoring operations; one or more AIML model measurement operations; or any combination thereof.QC2404834WOQualcomm Ref. No. 2404834WO69 / 784. The method of claim 3, wherein: the one or more LCM operations include one or more AIML model functionality management operations; one or more AIML model operations; or a combination thereof.

5. The method of claim 4, wherein: the one or more AIML model functionality management operations include activation of an AIML functionality; deactivation of the AIML functionality; selection of the AIML functionality; switching of the AIML functionality; selection of a fall back functionality of the AIML functionality; activation of the AIML positioning functionality running at a different entity; deactivation of the AIML positioning functionality running at the different entity; switching of the AIML positioning functionality running at the different entity; selection of the AIML positioning functionality running at the different entity; or any combination thereof.

6. The method of claim 4, wherein: the one or more AIML model management operations include activation of an AIML model; deactivation of the AIML model; selection of the AIML model; switching of the AIML model; selection of a fall back AIML model; switching of an AIML positioning model running at a different entity; selection of an AIML positioning model running at the different entity;QC2404834WOQualcomm Ref. No. 2404834WO70 / 78 triggering fallback to a non-AIML positioning method running at the different entity; or any combination thereof.

7. The method of claim 3, wherein: the one or more AIML model update operations include updating the network node with a new AIML model; updating the network node with a portion of the AIML model; requesting an AIML model update; fine tuning the AIML model at the network node; requesting fine tuning of the AIML model; or any combination thereof.

8. The method of claim 3, wherein: the one or more AIML monitoring operations include monitoring based on label-based data; monitoring based on label-free data including AIML model input statistics; a comparison of the AIML model input statistics with statistics associated with training data used to train the AIML model;AIML model output statistics; a comparison of the AIML model output statistics with statistics associated with the training data used to train the AIML model; transmitting a request for data monitoring; or any combination thereof.

9. The method of claim 3, wherein: the one or more AIML model measurement operations include measurement reporting based on a type of measurement associated with one or more trigger conditions of the set of trigger conditions; a quantity of measurements associated with the one or more trigger conditions of the set of trigger conditions;QC2404834WOQualcomm Ref. No. 2404834WO71 / 78 a size of measurements associated with the one or more trigger conditions of the set of trigger conditions; or any combination thereof.

10. The method of claim 9, wherein: the type of measurement associated with the one or more trigger conditions of the set of trigger conditions include line-of-sight measurements; timing measurements; reference signal power measurements; reference signal quality measurements; reference signal timing measurements; or any combination thereof.

11. The method of claim 9, wherein: the measurement reporting based on the quantity of measurements associated with the one or more trigger conditions is based on a number of path measurements associated with a given positioning resource to be measured; a number of transmission reception points (TRPs) to be measured; a number of reference signal resources to be measured; a number of reference signal resources per TRP to be measured; a number of reference signal resources per resource set to be measured; a number of reference signal resources to be measured per slot; or any combination thereof.

12. The method of claim 9, wherein: the measurement reporting based on the size of the measurements associated with the one or more trigger conditions is based on a data size used to report one or more measurements.

13. The method of claim 1, wherein: the mobility -based trigger conditions are based on a speed of the network node;QC2404834WOQualcomm Ref. No. 2404834WO72 / 78 a location change of the network node; a mobility state estimation of the network node; or any combination thereof.

14. A network node, comprising: one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to: obtain an artificial intelligence / machine learning (AIML) management configuration indicating a set of trigger conditions and corresponding AIML management operations, wherein the AIML management operations are associated with management of one or more positioning AIML models at the network node, and wherein the trigger conditions include handover-based trigger conditions, mobility-based trigger conditions, or a combination thereof; and perform one or more of the AIML management operations based on occurrence of one or more trigger conditions of the set of trigger conditions corresponding to the AIML management operations.

15. The network node of claim 14, wherein: the AIML management configuration is obtained from a location management server; an Operations, Administration, and Maintenance (OAM) entity; an Over-the-Top (OTT) entity; or an Access and Mobility Management Function (AMF).

16. The network node of claim 14, wherein: the AIML management operations include one or more Life Cycle Management (LCM) operations; one or more AIML monitoring operations; one or more AIML model update operations;QC2404834WOQualcomm Ref. No. 2404834WO73 / 78 one or more AIML model delivery operations; one or more AIML model fine-tuning operations; one or more AIML model monitoring operations; one or more AIML model measurement operations; or any combination thereof.

17. The network node of claim 16, wherein: the one or more LCM operations include one or more AIML model functionality management operations; one or more AIML model operations; or a combination thereof.

18. The network node of claim 17, wherein: the one or more AIML model functionality management operations include activation of an AIML functionality; deactivation of the AIML functionality; selection of the AIML functionality; switching of the AIML functionality; selection of a fall back functionality of the AIML functionality; activation of the AIML positioning functionality running at a different entity; deactivation of the AIML positioning functionality running at the different entity; switching of the AIML positioning functionality running at the different entity; selection of the AIML positioning functionality running at the different entity; or any combination thereof.

19. The network node of claim 17, wherein: the one or more AIML model management operations include activation of an AIML model; deactivation of the AIML model; selection of the AIML model;QC2404834WOQualcomm Ref. No. 2404834WO74 / 78 switching of the AIML model; selection of a fall back AIML model; switching of an AIML positioning model running at a different entity; selection of an AIML positioning model running at the different entity; triggering fallback to a non- AIML positioning method running at the different entity; or any combination thereof.

20. The network node of claim 16, wherein: the one or more AIML model update operations include updating the network node with a new AIML model; updating the network node with a portion of the AIML model; requesting an AIML model update; fine tuning the AIML model at the network node; requesting fine tuning of the AIML model; or any combination thereof.

21. The network node of claim 16, wherein: the one or more AIML monitoring operations include monitoring based on label-based data; monitoring based on label-free data including AIML model input statistics; a comparison of the AIML model input statistics with statistics associated with training data used to train the AIML model;AIML model output statistics; a comparison of the AIML model output statistics with statistics associated with the training data used to train the AIML model; transmitting a request for data monitoring; or any combination thereof.

22. The network node of claim 16, wherein: the one or more AIML model measurement operations include measurement reporting based onQC2404834WOQualcomm Ref. No. 2404834WO75 / 78 a type of measurement associated with one or more trigger conditions of the set of trigger conditions; a quantity of measurements associated with the one or more trigger conditions of the set of trigger conditions; a size of measurements associated with the one or more trigger conditions of the set of trigger conditions; or any combination thereof.

23. The network node of claim 22, wherein: the type of measurement associated with the one or more trigger conditions of the set of trigger conditions include line-of-sight measurements; timing measurements; reference signal power measurements; reference signal quality measurements; reference signal timing measurements; or any combination thereof.

24. The network node of claim 22, wherein: the measurement reporting based on the quantity of measurements associated with the one or more trigger conditions is based on a number of path measurements associated with a given positioning resource to be measured; a number of transmission reception points (TRPs) to be measured; a number of reference signal resources to be measured; a number of reference signal resources per TRP to be measured; a number of reference signal resources per resource set to be measured; a number of reference signal resources to be measured per slot; or any combination thereof.

25. The network node of claim 22, wherein:QC2404834WOQualcomm Ref. No. 2404834WO76 / 78 the measurement reporting based on the size of the measurements associated with the one or more trigger conditions is based on a data size used to report one or more measurements.

26. The network node of claim 14, wherein: the mobility -based trigger conditions are based on a speed of the network node; a location change of the network node; a mobility state estimation of the network node; or any combination thereof.

27. The network node of claim 14, wherein: the handover-based trigger conditions are based on signal quality thresholds associated with neighboring cells; a change in a serving cell of the network node; or any combination thereof.

28. The network node of claim 14, wherein the one or more processors, either alone or in combination, are further configured to: provide an indication of the one or more AIML management operations performed at the network node to a location management server; an Operations, Administration, and Maintenance (OAM) entity; an Over-the-Top (OTT) entity; or an Access and Mobility Management Function (AMF).

29. A network node, comprising: means for obtaining an artificial intelligence / machine learning (AIML) management configuration indicating a set of trigger conditions and corresponding AIML management operations, wherein the AIML management operations are associated with management of one or more positioning AIML models at the network node, and wherein the trigger conditions include handover-based trigger conditions, mobility-based trigger conditions, orQC2404834WOQualcomm Ref. No. 2404834WO a combination thereof; and means for performing one or more of the AIML management operations based on occurrence of one or more trigger conditions of the set of trigger conditions corresponding to the AIML management operations.

30. A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a network node, cause the network node to: obtain an artificial intelligence / machine learning (AIML) management configuration indicating a set of trigger conditions and corresponding AIML management operations, wherein the AIML management operations are associated with management of one or more positioning AIML models at the network node, and wherein the trigger conditions include handover-based trigger conditions, mobility-based trigger conditions, or a combination thereof; and perform one or more of the AIML management operations based on occurrence of one or more trigger conditions of the set of trigger conditions corresponding to the AIML management operations.QC2404834WO

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