Concurrent use of multiple machine learning models of different functionalities

Abstract

Disclosed are techniques for wireless communication. In an aspect, a network node may transmit a capability message indicating one or more capabilities of the network node to support a plurality of machine learning (ML) models for a plurality of functions simultaneously, wherein the plurality of ML models include at least a first ML model and a second ML model. The network node may apply the first ML model to a first set of input signals and the second ML model to a second set of input signals based on the one or more capabilities, wherein at least one of the ML models for the plurality of functions that are supported simultaneously is a positioning ML model.

Description

Qualcomm Ref. No.2402402WO CONCURRENT USE OF MULTIPLE MACHINE LEARNING MODELS OF DIFFERENT FUNCTIONALITIES TECHNICAL 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)), radio frequency (RF) sensing, and other technical enhancements. These enhancements, as well as the use of higher frequency bands, advances in PRS processes and technology, and high-density deployments for 5G, enable highly accurate 5G-based sensing and positioning. SUMMARY

[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 1 QC2402402WOQualcomm Ref. No.2402402WO identify key or critical elements relating to all contemplated aspects or to delineate the scope associated with any particular aspect. Accordingly, the following summary has the sole purpose to present certain concepts relating to one or more aspects relating to the mechanisms disclosed herein in a simplified form to precede the detailed description presented below.

[0005] In an aspect, a method of wireless communication includes transmitting a capability message indicating one or more capabilities of the network node to support a plurality of machine learning (ML) models for a plurality of functions simultaneously, wherein the plurality of ML models include at least a first ML model and a second ML model; and applying the first ML model to a first set of input signals and the second ML model to a second set of input signals based on the one or more capabilities, wherein at least one of the ML models for the plurality of functions that are supported simultaneously is a positioning ML model.

[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: transmit, via the one or more transceivers, a capability message indicating one or more capabilities of the network node to support a plurality of machine learning (ML) models for a plurality of functions simultaneously, wherein the plurality of ML models include at least a first ML model and a second ML model; and apply the first ML model to a first set of input signals and the second ML model to a second set of input signals based on the one or more capabilities, wherein at least one of the ML models for the plurality of functions that are supported simultaneously is a positioning ML model.

[0007] In an aspect, a network node includes means for transmitting a capability message indicating one or more capabilities of the network node to support a plurality of machine learning (ML) models for a plurality of functions simultaneously, wherein the plurality of ML models include at least a first ML model and a second ML model; and means for applying the first ML model to a first set of input signals and the second ML model to a second set of input signals based on the one or more capabilities, wherein at least one of the ML models for the plurality of functions that are supported simultaneously is a positioning ML model. QC2402402WOQualcomm Ref. No.2402402WO

[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: transmit a capability message indicating one or more capabilities of the network node to support a plurality of machine learning (ML) models for a plurality of functions simultaneously, wherein the plurality of ML models include at least a first ML model and a second ML model; and apply the first ML model to a first set of input signals and the second ML model to a second set of input signals based on the one or more capabilities, wherein at least one of the ML models for the plurality of functions that are supported simultaneously is a positioning ML model.

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

[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 an example neural network, according to aspects of the disclosure.

[0015] FIG. 5A is a diagram illustrating an example of direct artificial intelligence / machine learning (AI / ML) positioning, according to aspects of the disclosure.

[0016] FIG.5B is a diagram illustrating an example of AI / ML assisted positioning, according to aspects of the disclosure.

[0017] FIG. 6 illustrates various AI / ML positioning scenarios, according to aspects of the disclosure.

[0018] FIG.7 illustrates examples of AI / ML models which may be supported at a network node, according to aspects of the disclosure. QC2402402WOQualcomm Ref. No.2402402WO

[0019] FIG. 8 illustrates an examples of switching between a beam management AI / ML model and a positioning AI / ML model, according to aspects of the disclosure.

[0020] FIG. 9 illustrates an example of direct associations between beam management AI / ML models and positioning AI / ML models, according to aspects of the disclosure.

[0021] FIG. 10 illustrates an example of indirect associations between a beam management AI / ML model and multiple positioning AI / ML models, according to aspects of the disclosure.

[0022] FIG. 11 illustrates an example method of wireless communication, according to aspects of the disclosure. DETAILED DESCRIPTION

[0023] 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.

[0024] Various aspects relate generally to data communications and positioning. Some aspects more specifically relate to artificial intelligence / machine learning (AI / ML) models for data and positioning. In some examples, a network node (e.g., a user equipment (UE), a base station, or a network entity) may support multiple AI / ML models for multiple functions (e.g., data AI / ML functions and positioning AI / ML functions) concurrently based on processing capabilities of AI / ML models at the network node.

[0025] 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 supporting multiple AI / ML models for multiple functions, the described techniques can be used by the network node to signal the applicability of AI / ML models to the network in a quick and simple yet efficient manner.

[0026] 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. 4 QC2402402WOQualcomm Ref. No.2402402WO

[0027] 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.

[0028] 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.

[0029] As used herein, the terms “user equipment” (UE) and “base station” are not intended to be specific or otherwise limited to any particular radio access technology (RAT), unless otherwise noted. In general, a UE may be any wireless communication device (e.g., a mobile phone, router, tablet computer, laptop computer, consumer asset locating device, wearable (e.g., smartwatch, glasses, augmented reality (AR) / virtual reality (VR) headset, etc.), vehicle (e.g., automobile, motorcycle, bicycle, etc.), Internet of Things (IoT) device, etc.) used by a user to communicate over a wireless communications network. A UE may be mobile or may (e.g., at certain times) be stationary, and may communicate with a radio access network (RAN). As used herein, the term “UE” may be referred to interchangeably as an “access terminal” or “AT,” a “client device,” a “wireless device,” a “subscriber device,” a “subscriber terminal,” a “subscriber station,” a “user terminal” or “UT,” a “mobile device,” a “mobile terminal,” a “mobile station,” or variations thereof. QC2402402WOQualcomm Ref. No.2402402WO 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.

[0030] 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.

[0031] The term “base station” may refer to a single physical transmission-reception point (TRP) or to multiple physical TRPs that may or may not be co-located. For example, where the term “base station” refers to a single physical TRP, the physical TRP may be an antenna of the base station corresponding to a cell (or several cell sectors) of the base station. Where the term “base station” refers to multiple co-located physical TRPs, the physical TRPs may be an array of antennas (e.g., as in a multiple-input multiple-output (MIMO) system or where the base station employs beamforming) of the base station. Where the term “base station” refers to multiple non-co-located physical TRPs, the physical TRPs may be a distributed antenna system (DAS) (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 6 QC2402402WOQualcomm Ref. No.2402402WO 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.

[0032] 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).

[0033] 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.

[0034] 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 some aspects, the macro cell base stations may include eNBs and / or ng-eNBs where the wireless communications system 100 corresponds to an LTE network, or gNBs where the wireless communications system 100 corresponds to a NR network, or a combination of both, and the small cell base stations may include femtocells, picocells, microcells, etc.

[0035] 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, QC2402402WOQualcomm Ref. No.2402402WO 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.

[0036] 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.

[0037] 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 some aspects, one or more cells may be supported by a base station 102 in each geographic coverage area 110. A “cell” is a logical communication entity used for communication with a base station (e.g., over some frequency resource, referred to as a carrier frequency, component carrier, carrier, band, or the like), and may be associated with an identifier (e.g., a physical cell identifier (PCI), an enhanced cell identifier (ECI), a virtual cell identifier (VCI), a cell global identifier (CGI), etc.) for distinguishing cells operating via the same or a different carrier frequency. In some cases, different cells may 8 QC2402402WOQualcomm Ref. No.2402402WO be configured according to different protocol types (e.g., machine-type communication (MTC), narrowband IoT (NB-IoT), enhanced mobile broadband (eMBB), or others) that may provide access for different types of UEs. Because a cell is supported by a specific base station, the term “cell” may refer to either or both of the logical communication entity and the base station that supports it, depending on the context. In addition, because a TRP is typically the physical transmission point of a cell, the terms “cell” and “TRP” may be used interchangeably. In some cases, the term “cell” may also refer to a geographic coverage area of a base station (e.g., a sector), insofar as a carrier frequency can be detected and used for communication within some portion of geographic coverage areas 110.

[0038] 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).

[0039] 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).

[0040] 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 9 QC2402402WOQualcomm Ref. No.2402402WO (LBT) procedure prior to communicating in order to determine whether the channel is available.

[0041] 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®.

[0042] 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.

[0043] Transmit beamforming is a technique for focusing an RF signal in a specific direction. Traditionally, when a network node (e.g., a base station) broadcasts an RF signal, it broadcasts the signal in all directions (omni-directionally). With transmit beamforming, the network node determines where a given target device (e.g., a UE) is located (relative to the transmitting network node) and projects a stronger downlink RF signal in that specific direction, thereby providing a faster (in terms of data rate) and stronger RF signal for the receiving device(s). To change the directionality of the RF signal when QC2402402WOQualcomm Ref. No.2402402WO 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.

[0044] 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.

[0045] In receive beamforming, the receiver uses a receive beam to amplify RF signals detected on a given channel. For example, the receiver can increase the gain setting and / or adjust the phase setting of an array of antennas in a particular direction to amplify (e.g., to increase the gain level of) the RF signals received from that direction. Thus, when a receiver is said to beamform in a certain direction, it means the beam gain in that direction is high relative to the beam gain along other directions, or the beam gain in that direction is the highest compared to the beam gain in that direction of all other receive beams available to the receiver. This results in a stronger received signal strength (e.g., reference QC2402402WOQualcomm Ref. No.2402402WO 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.

[0046] 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.

[0047] 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.

[0048] The electromagnetic spectrum is often subdivided, based on frequency / wavelength, into various classes, bands, channels, etc. In 5G NR two initial operating bands have been identified as frequency range designations FR1 (410 MHz – 7.125 GHz) and FR2 (24.25 GHz – 52.6 GHz). It should be understood that although a portion of FR1 is greater than 6 GHz, FR1 is often referred to (interchangeably) as a “Sub-6 GHz” band in various documents and articles. A similar nomenclature issue sometimes occurs with regard to FR2, which is often referred to (interchangeably) as a “millimeter wave” band in documents and articles, despite being different from the extremely high frequency (EHF) band (30 GHz – 300 GHz) which is identified by the INTERNATIONAL TELECOMMUNICATION UNION® as a “millimeter wave” band.

[0049] 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 QC2402402WOQualcomm Ref. No.2402402WO 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.

[0050] 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.

[0051] In a multi-carrier system, such as 5G, one of the carrier frequencies is referred to as the “primary carrier” or “anchor carrier” or “primary serving cell” or “PCell,” and the remaining carrier frequencies are referred to as “secondary carriers” or “secondary serving cells” or “SCells.” In carrier aggregation, the anchor carrier is the carrier operating on the primary frequency (e.g., FR1) utilized by a UE 104 / 182 and the cell in which the UE 104 / 182 either performs the initial radio resource control (RRC) connection establishment procedure or initiates the RRC connection re-establishment procedure. The primary carrier carries all common and UE-specific control channels, and may be a carrier in a licensed frequency (however, this is not always the case). A secondary carrier is a carrier operating on a second frequency (e.g., FR2) that may be configured once the RRC connection is established between the UE 104 and the anchor carrier and that may be used to provide additional radio resources. In some cases, the secondary carrier may be a carrier in an unlicensed frequency. The secondary carrier may contain only necessary signaling information and signals, for example, those that are UE-specific may not be present in the secondary carrier, since both primary uplink and downlink carriers are typically UE-specific. This means that different UEs 104 / 182 in a cell may have different downlink primary carriers. The same is true for the uplink primary carriers. The network is able to change the primary carrier of any UE 104 / 182 at any time. This is done, for example, to balance the load on different carriers. Because a “serving cell” (whether a QC2402402WOQualcomm Ref. No.2402402WO 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.

[0052] 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.

[0053] 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.

[0054] In some cases, the UE 164 and the UE 182 may be capable of sidelink communication. Sidelink-capable UEs (SL-UEs) may communicate with base stations 102 over communication links 120 using the Uu interface (i.e., the air interface between a UE and a base station). SL-UEs (e.g., UE 164, UE 182) may also communicate directly with each other over a wireless sidelink 160 using the PC5 interface (i.e., the air interface between sidelink-capable UEs). A wireless sidelink (or just “sidelink”) is an adaptation of the core cellular (e.g., LTE, NR) standard that allows direct communication between two or more UEs without the communication needing to go through a base station. Sidelink communication may be unicast or multicast, and may be used for device-to-device (D2D) media-sharing, vehicle-to-vehicle (V2V) communication, vehicle-to-everything (V2X) communication (e.g., cellular V2X (cV2X) communication, enhanced V2X (eV2X) communication, etc.), emergency rescue applications, etc. One or more of a group of SL- UEs utilizing sidelink communications may be within the geographic coverage area 110 of a base station 102. Other SL-UEs in such a group may be outside the geographic coverage area 110 of a base station 102 or be otherwise unable to receive transmissions from a base station 102. In some cases, groups of SL-UEs communicating via sidelink QC2402402WOQualcomm Ref. No.2402402WO 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.

[0055] In some aspects, 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 some aspects, the medium of interest may correspond to at least a portion of an unlicensed frequency band shared among various RATs. Although different licensed frequency bands have been reserved for certain communication systems (e.g., by a government entity such as the Federal Communications Commission (FCC) in the United States), these systems, in particular those employing small cell access points, have recently extended operation into unlicensed frequency bands such as the Unlicensed National Information Infrastructure (U-NII) band used by wireless local area network (WLAN) technologies, most notably IEEE 802.11x WLAN technologies generally referred to as “Wi-Fi.” Example systems of this type include different variants of CDMA systems, TDMA systems, FDMA systems, orthogonal FDMA (OFDMA) systems, single-carrier FDMA (SC-FDMA) systems, and so on.

[0056] Note that although FIG. 1 only illustrates two of the UEs as SL-UEs (i.e., UEs 164 and 182), any of the illustrated UEs may be SL-UEs. Further, although only UE 182 was described as being capable of beamforming, any of the illustrated UEs, including UE 164, may be capable of beamforming. Where SL-UEs are capable of beamforming, they may beamform towards each other (i.e., towards other SL-UEs), towards other UEs (e.g., UEs 104), towards base stations (e.g., base stations 102, 180, small cell 102’, access point 150), etc. Thus, in some cases, UEs 164 and 182 may utilize beamforming over sidelink 160.

[0057] 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 QC2402402WOQualcomm Ref. No.2402402WO (SVs) 112 (e.g., satellites). In some aspects, the SVs 112 may be part of a satellite positioning system that a UE 104 can use as an independent source of location information. A satellite positioning system typically includes a system of transmitters (e.g., SVs 112) positioned to enable receivers (e.g., UEs 104) to determine their location on or above the Earth based, at least in part, on positioning signals (e.g., signals 124) received from the transmitters. Such a transmitter typically transmits a signal marked with a repeating pseudo-random noise (PN) code of a set number of chips. While typically located in SVs 112, transmitters may sometimes be located on ground-based control stations, base stations 102, and / or other UEs 104. A UE 104 may include one or more dedicated receivers specifically designed to receive signals 124 for deriving geo location information from the SVs 112.

[0058] In a satellite positioning system, the use of signals 124 can be augmented by various satellite-based augmentation systems (SBAS) that may be associated with or otherwise enabled for use with one or more global and / or regional navigation satellite systems. For example an SBAS may include an augmentation system(s) that provides integrity information, differential corrections, etc., such as the Wide Area Augmentation System (WAAS), the European Geostationary Navigation Overlay Service (EGNOS), the Multi- functional Satellite Augmentation System (MSAS), the Global Positioning System (GPS) Aided Geo Augmented Navigation or GPS and Geo Augmented Navigation system (GAGAN), and / or the like. Thus, as used herein, a satellite positioning system may include any combination of one or more global and / or regional navigation satellites associated with such one or more satellite positioning systems.

[0059] In some aspects, SVs 112 may additionally or alternatively be part of one or more non- terrestrial networks (NTNs). In an NTN, an SV 112 is connected to an earth station (also referred to as a ground station, NTN gateway, or gateway), which in turn is connected to an element in a 5G network, such as a modified base station 102 (without a terrestrial antenna) or a network node in a 5GC. This element would in turn provide access to other elements in the 5G network and ultimately to entities external to the 5G network, such as Internet web servers and other user devices. In that way, a UE 104 may receive communication signals (e.g., signals 124) from an SV 112 instead of, or in addition to, communication signals from a terrestrial base station 102. QC2402402WOQualcomm Ref. No.2402402WO

[0060] 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.

[0061] 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).

[0062] Another optional aspect may include a location server 230, which may be in communication with the 5GC 210 to provide location assistance for UE(s) 204. The location server 230 can be implemented as a plurality of separate servers (e.g., physically separate servers, different software modules on a single server, different software modules spread across multiple physical servers, etc.), or alternately may each correspond to a single server. The location server 230 can be configured to support one or more location services for UEs 204 that can connect to the location server 230 via the core QC2402402WOQualcomm Ref. No.2402402WO 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).

[0063] FIG.2B illustrates another example wireless network structure 240. A 5GC 260 (which may correspond to 5GC 210 in FIG. 2A) can be viewed functionally as control plane functions, provided by an access and mobility management function (AMF) 264, and user plane functions, provided by a user plane function (UPF) 262, which operate cooperatively to form the core network (i.e., 5GC 260). The functions of the AMF 264 include registration management, connection management, reachability management, mobility management, lawful interception, transport for session management (SM) messages between one or more UEs 204 (e.g., any of the UEs described herein) and a session management function (SMF) 266, transparent proxy services for routing SM messages, access authentication and access authorization, transport for short message service (SMS) messages between the UE 204 and the short message service function (SMSF) (not shown), and security anchor functionality (SEAF). The AMF 264 also interacts with an authentication server function (AUSF) (not shown) and the UE 204, and receives the intermediate key that was established as a result of the UE 204 authentication process. In the case of authentication based on a UMTS (universal mobile telecommunications system) subscriber identity module (USIM), the AMF 264 retrieves the security material from the AUSF. The functions of the AMF 264 also include security context management (SCM). The SCM receives a key from the SEAF that it uses to derive access-network specific keys. The functionality of the AMF 264 also includes location services management for regulatory services, transport for location services messages between the UE 204 and a location management function (LMF) 270 (which acts as a location server 230), transport for location services messages between the NG-RAN 220 and the LMF 270, evolved packet system (EPS) bearer identifier allocation for interworking with the EPS, and UE 204 mobility event notification. In addition, the AMF 264 also supports functionalities for non-3GPP® (Third Generation Partnership Project) access networks.

[0064] 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 QC2402402WOQualcomm Ref. No.2402402WO 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.

[0065] The functions of the SMF 266 include session management, UE Internet protocol (IP) address allocation and management, selection and control of user plane functions, configuration of traffic steering at the UPF 262 to route traffic to the proper destination, control of part of policy enforcement and QoS, and downlink data notification. The interface over which the SMF 266 communicates with the AMF 264 is referred to as the N11 interface.

[0066] Another optional aspect may include an LMF 270, which may be in communication with the 5GC 260 to provide location assistance for UEs 204. The LMF 270 can be implemented as a plurality of separate servers (e.g., physically separate servers, different software modules on a single server, different software modules spread across multiple physical servers, etc.), or alternately may each correspond to a single server. The LMF 270 can be configured to support one or more location services for UEs 204 that can connect to the LMF 270 via the core network, 5GC 260, and / or via the Internet (not illustrated). The SLP 272 may support similar functions to the LMF 270, but whereas the LMF 270 may communicate with the AMF 264, NG-RAN 220, and UEs 204 over a control plane (e.g., using interfaces and protocols intended to convey signaling messages and not voice or data), the SLP 272 may communicate with UEs 204 and external clients (e.g., third-party server 274) over a user plane (e.g., using protocols intended to carry voice and / or data like the transmission control protocol (TCP) and / or IP).

[0067] 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 QC2402402WOQualcomm Ref. No.2402402WO 20 (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.

[0068] 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.

[0069] The functionality of a gNB 222 may be divided between a gNB central unit (gNB-CU) 226, one or more gNB distributed units (gNB-DUs) 228, and one or more gNB radio units (gNB-RUs) 229. A gNB-CU 226 is a logical node that includes the base station functions of transferring user data, mobility control, radio access network sharing, positioning, session management, and the like, except for those functions allocated exclusively to the gNB-DU(s) 228. More specifically, the gNB-CU 226 generally host the radio resource control (RRC), service data adaptation protocol (SDAP), and packet data convergence protocol (PDCP) protocols of the gNB 222. A gNB-DU 228 is a logical node that generally hosts the radio link control (RLC) and medium access control (MAC) layer of the gNB 222. Its operation is controlled by the gNB-CU 226. One gNB-DU 228 can support one or more cells, and one cell is supported by only one gNB-DU 228. The interface 232 between the gNB-CU 226 and the one or more gNB-DUs 228 is referred to as the “F1” interface. The physical (PHY) layer functionality of a gNB 222 is generally hosted by one or more standalone gNB-RUs 229 that perform functions such as power amplification and signal transmission / reception. The interface between a gNB-DU 228 and a gNB-RU 229 is referred to as the “Fx” interface. Thus, a UE 204 communicates QC2402402WOQualcomm Ref. No.2402402WO 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.

[0070] 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.

[0071] 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).

[0072] Base station-type operation or network design may consider aggregation characteristics of base station functionality. For example, disaggregated base stations may be utilized in an integrated access backhaul (IAB) network, an open radio access network (O-RAN (such as the network configuration sponsored by the O-RAN 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 unit virtually, which can enable flexibility in network design. The various units of the disaggregated base station, or disaggregated RAN architecture, can be configured for wired or wireless communication with at least one other unit. 21 QC2402402WOQualcomm Ref. No.2402402WO

[0073] FIG. 2C illustrates an example disaggregated base station architecture 250, according to aspects of the disclosure. The disaggregated base station architecture 250 may include one or more central units (CUs) 280 (e.g., gNB-CU 226) that can communicate directly with a core network 267 (e.g., 5GC 210, 5GC 260) via a backhaul link, or indirectly with the core network 267 through one or more disaggregated base station units (such as a Near-Real Time (Near-RT) RAN Intelligent Controller (RIC) 259 via an E2 link, or a Non-Real Time (Non-RT) RIC 257 associated with a Service Management and Orchestration (SMO) Framework 255, or both). A CU 280 may communicate with one or more DUs 285 (e.g., gNB-DUs 228) via respective midhaul links, such as an F1 interface. The DUs 285 may communicate with one or more radio units (RUs) 287 (e.g., gNB-RUs 229) via respective fronthaul links. The RUs 287 may communicate with respective UEs 204 via one or more radio frequency (RF) access links. In some implementations, the UE 204 may be simultaneously served by multiple RUs 287.

[0074] 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.

[0075] In some aspects, the CU 280 may host one or more higher layer control functions. Such control functions can include RRC, PDCP, service data adaptation protocol (SDAP), or the like. Each control function can be implemented with an interface configured to communicate signals with other control functions hosted by the CU 280. The CU 280 may be configured to handle user plane functionality (i.e., Central Unit – User Plane (CU- UP)), control plane functionality (i.e., Central Unit – Control Plane (CU-CP)), or a combination thereof. In some implementations, the CU 280 can be logically split into QC2402402WOQualcomm Ref. No.2402402WO 23 one or more CU-UP units and one or more CU-CP units. The CU-UP unit can communicate bidirectionally with the CU-CP unit via an interface, such as the E1 interface when implemented in an O-RAN configuration. The CU 280 can be implemented to communicate with the DU 285, as necessary, for network control and signaling.

[0076] 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.

[0077] 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.

[0078] The SMO Framework 255 may be configured to support RAN deployment and provisioning of non-virtualized and virtualized network elements. For non-virtualized network elements, the SMO Framework 255 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements which may be managed via an operations and maintenance interface (such as an O1 interface). For QC2402402WOQualcomm Ref. No.2402402WO 24 virtualized network elements, the SMO Framework 255 may be configured to interact with a cloud computing platform (such as an open cloud (O-Cloud) 269) to perform network element life cycle management (such as to instantiate virtualized network elements) via a cloud computing platform interface (such as an O2 interface). Such virtualized network elements can include, but are not limited to, CUs 280, DUs 285, RUs 287 and Near-RT RICs 259. In some implementations, the SMO Framework 255 can communicate with a hardware aspect of a 4G RAN, such as an open eNB (O-eNB) 261, via an O1 interface. Additionally, in some implementations, the SMO Framework 255 can communicate directly with one or more RUs 287 via an O1 interface. The SMO Framework 255 also may include a Non-RT RIC 257 configured to support functionality of the SMO Framework 255.

[0079] The Non-RT RIC 257 may be configured to include a logical function that enables non- real-time control and optimization of RAN elements and resources, artificial intelligence / machine learning (AI / ML) workflows including model training and updates, or policy-based guidance of applications / features in the Near-RT RIC 259. The Non-RT RIC 257 may be coupled to or communicate with (such as via an A1 interface) the Near- RT RIC 259. The Near-RT RIC 259 may be configured to include a logical function that enables near-real-time control and optimization of RAN elements and resources via data collection and actions over an interface (such as via an E2 interface) connecting one or more CUs 280, one or more DUs 285, or both, as well as an O-eNB, with the Near-RT RIC 259.

[0080] In some implementations, to generate AI / ML models to be deployed in the Near-RT RIC 259, the Non-RT RIC 257 may receive parameters or external enrichment information from external servers. Such information may be utilized by the Near-RT RIC 259 and may be received at the SMO Framework 255 or the Non-RT RIC 257 from non-network data sources or from network functions. In some examples, the Non-RT RIC 257 or the Near-RT RIC 259 may be configured to tune RAN behavior or performance. For example, the Non-RT RIC 257 may monitor long-term trends and patterns for performance and employ AI / ML models to perform corrective actions through the SMO Framework 255 (such as reconfiguration via O1) or via creation of RAN management policies (such as A1 policies). QC2402402WOQualcomm Ref. No.2402402WO

[0081] 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.

[0082] The UE 302 and the base station 304 each include one or more wireless wide area network (WWAN) transceivers 310 and 350, respectively, providing means for communicating (e.g., means for transmitting, means for receiving, means for measuring, means for tuning, means for refraining from transmitting, etc.) via one or more wireless communication networks (not shown), such as an NR network, an LTE network, a GSM network, and / or the like. The WWAN transceivers 310 and 350 may each be connected to one or more antennas 316 and 356, respectively, for communicating with other network nodes, such as other UEs, access points, base stations (e.g., eNBs, gNBs), etc., via at least one designated RAT (e.g., NR, LTE, GSM, etc.) over a wireless communication medium of interest (e.g., some set of time / frequency resources in a particular frequency spectrum). The WWAN transceivers 310 and 350 may be variously configured for transmitting and encoding signals 318 and 358 (e.g., messages, indications, information, and so on), respectively, and, conversely, for receiving and decoding signals 318 and 358 (e.g., messages, indications, information, pilots, and so on), respectively, in accordance with the designated RAT. Specifically, the WWAN transceivers 310 and 350 include one or more transmitters 314 and 354, respectively, for transmitting and encoding signals 318 QC2402402WOQualcomm Ref. No.2402402WO and 358, respectively, and one or more receivers 312 and 352, respectively, for receiving and decoding signals 318 and 358, respectively.

[0083] 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 vehicle-to-vehicle (V2V) and / or vehicle-to- everything (V2X) transceivers.

[0084] The UE 302 and the base station 304 also include, at least in some cases, satellite signal interfaces 330 and 370, which each include one or more satellite signal receivers 332 and 372, respectively, and may optionally include one or more satellite signal transmitters 334 and 374, respectively. In some cases, the base station 304 may be a terrestrial base station that may communicate with space vehicles (e.g., space vehicles 112) via the satellite signal interface 370. In other cases, the base station 304 may be a space vehicle (or other non-terrestrial entity) that uses the satellite signal interface 370 to communicate with terrestrial networks and / or other space vehicles. QC2402402WOQualcomm Ref. No.2402402WO 27

[0085] The satellite signal receivers 332 and 372 may be connected to one or more antennas 336 and 376, respectively, and may provide means for receiving and / or measuring satellite positioning / communication signals 338 and 378, respectively. Where the satellite signal receiver(s) 332 and 372 are satellite positioning system receivers, the satellite positioning / communication signals 338 and 378 may be global positioning system (GPS) signals, global navigation satellite system (GLONASS) signals, Galileo signals, Beidou signals, Indian Regional Navigation Satellite System (NAVIC), Quasi-Zenith Satellite System (QZSS) signals, etc. Where the satellite signal receiver(s) 332 and 372 are non- terrestrial network (NTN) receivers, the satellite positioning / communication signals 338 and 378 may be communication signals (e.g., carrying control and / or user data) originating from a 5G network. The satellite signal receiver(s) 332 and 372 may comprise any suitable hardware and / or software for receiving and processing satellite positioning / communication signals 338 and 378, respectively. The satellite signal receiver(s) 332 and 372 may request information and operations as appropriate from the other systems, and, at least in some cases, perform calculations to determine locations of the UE 302 and the base station 304, respectively, using measurements obtained by any suitable satellite positioning system algorithm.

[0086] 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.

[0087] 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 QC2402402WOQualcomm Ref. No.2402402WO 28 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.

[0088] 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 some aspects, the transmitter circuitry and receiver circuitry may share the same plurality of antennas (e.g., antennas 316, 326, 356, 366), such that the respective apparatus can only receive or transmit at a given time, not both at the same time. A wireless transceiver (e.g., WWAN transceivers 310 and 350, short-range wireless transceivers 320 and 360) may also include a network listen module (NLM) or the like for performing various measurements.

[0089] 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 QC2402402WOQualcomm Ref. No.2402402WO 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.

[0090] 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 some aspects, 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.

[0091] 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 AI / ML component 348, 388, and 398, respectively. The AI / ML component 348, 388, and 398 may be hardware circuits that are part of or coupled to the processors 342, 384, and 394, respectively, that, when executed, cause the UE 302, the base station 304, and the network entity 306 to perform the functionality described herein. In other aspects, the AI / ML 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, QC2402402WOQualcomm Ref. No.2402402WO etc.). Alternatively, the AI / ML component 348, 388, and 398 may be memory modules stored in the memories 340, 386, and 396, respectively, that, when executed by the processors 342, 384, and 394 (or a modem processing system, another processing system, etc.), cause the UE 302, the base station 304, and the network entity 306 to perform the functionality described herein. FIG. 3A illustrates possible locations of the AI / ML 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 AI / ML 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 AI / ML 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.

[0092] 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.

[0093] In addition, the UE 302 includes a user interface 346 providing means for providing indications (e.g., audible and / or visual indications) to a user and / or for receiving user input (e.g., upon user actuation of a sensing device such a keypad, a touch screen, a microphone, and so on). Although not shown, the base station 304 and the network entity 306 may also include user interfaces. QC2402402WOQualcomm Ref. No.2402402WO

[0094] 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.

[0095] The transmitter 354 and the receiver 352 may implement Layer-1 (L1) functionality associated with various signal processing functions. Layer-1, which includes a physical (PHY) layer, may include error detection on the transport channels, forward error correction (FEC) coding / decoding of the transport channels, interleaving, rate matching, mapping onto physical channels, modulation / demodulation of physical channels, and MIMO antenna processing. The transmitter 354 handles mapping to signal constellations based on various modulation schemes (e.g., binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), M-phase-shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The coded and modulated symbols may then be split into parallel streams. Each stream may then be mapped to an orthogonal frequency division multiplexing (OFDM) subcarrier, multiplexed with a reference signal (e.g., pilot) in the time and / or frequency domain, and then combined together using an inverse fast Fourier transform (IFFT) to produce a physical channel carrying a time domain OFDM symbol stream. The OFDM symbol stream is spatially precoded to produce multiple spatial QC2402402WOQualcomm Ref. No.2402402WO 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.

[0096] 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.

[0097] 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.

[0098] Similar to the functionality described in connection with the downlink transmission by the base station 304, the one or more processors 342 provides RRC layer functionality associated with system information (e.g., MIB, SIBs) acquisition, RRC connections, and measurement reporting; PDCP layer functionality associated with header compression / decompression, and security (ciphering, deciphering, integrity protection, QC2402402WOQualcomm Ref. No.2402402WO 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] For convenience, the UE 302, the base station 304, and / or the network entity 306 are shown in FIGS.3A, 3B, and 3C as including various components that may be configured according to the various examples described herein. It will be appreciated, however, that the illustrated components may have different functionality in different designs. In particular, various components in FIGS. 3A to 3C are optional in alternative configurations and the various aspects include configurations that may vary due to design choice, costs, use of the device, or other considerations. For example, in case of FIG.3A, a particular implementation of UE 302 may omit the WWAN transceiver(s) 310 (e.g., a wearable device or tablet computer or personal computer (PC) or laptop may have Wi-Fi QC2402402WOQualcomm Ref. No.2402402WO 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.

[0103] 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 some aspects, 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.

[0104] The components of FIGS.3A, 3B, and 3C may be implemented in various ways. In some implementations, the components of FIGS. 3A, 3B, and 3C may be implemented in one or more circuits such as, for example, one or more processors and / or one or more ASICs (which may include one or more processors). Here, each circuit may use and / or incorporate at least one memory component for storing information or executable code used by the circuit to provide this functionality. For example, some or all of the functionality represented by blocks 310 to 346 may be implemented by processor and memory component(s) of the UE 302 (e.g., by execution of appropriate code and / or by appropriate configuration of processor components). Similarly, some or all of the functionality represented by blocks 350 to 388 may be implemented by processor and memory component(s) of the base station 304 (e.g., by execution of appropriate code and / or by appropriate configuration of processor components). Also, some or all of the functionality represented by blocks 390 to 398 may be implemented by processor and memory component(s) of the network entity 306 (e.g., by execution of appropriate code and / or by appropriate configuration of processor components). For simplicity, various operations, acts, and / or functions are described herein as being performed “by a UE,” “by QC2402402WOQualcomm Ref. No.2402402WO 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 AI / ML component 348, 388, and 398, etc.

[0105] 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).

[0106] 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. In an OTDOA or DL-TDOA positioning procedure, 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.

[0107] For DL-AoD positioning, 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). QC2402402WOQualcomm Ref. No.2402402WO

[0108] 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.

[0109] 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.

[0110] 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 be 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). QC2402402WOQualcomm Ref. No.2402402WO 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, 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.

[0111] 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).

[0112] 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.

[0113] 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 may be + / - 500 microseconds (μs). 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 μs. 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 μs. QC2402402WOQualcomm Ref. No.2402402WO

[0114] 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).

[0115] A collection of resource elements (REs) that are used for transmission of PRS is referred to as a “PRS resource.” The collection of resource elements can span multiple PRBs in the frequency domain and ‘N’ (such as 1 or more) consecutive symbol(s) within a slot in the time domain. In a given OFDM symbol in the time domain, a PRS resource occupies consecutive PRBs in the frequency domain.

[0116] The transmission of a PRS resource within a given PRB has a particular comb size (also referred to as the “comb density”). A comb size ‘N’ represents the subcarrier spacing (or frequency / tone spacing) within each symbol of a PRS resource configuration. Specifically, for a comb size ‘N,’ PRS are transmitted in every Nth subcarrier of a symbol of a PRB. For example, for comb-4, for each symbol of the PRS resource configuration, REs corresponding to every fourth subcarrier (such as subcarriers 0, 4, 8) are used to transmit PRS of the PRS resource. Currently, comb sizes of comb-2, comb-4, comb-6, and comb-12 are supported for DL-PRS.

[0117] Currently, a DL-PRS resource may span 2, 4, 6, or 12 consecutive symbols within a slot with a fully frequency-domain staggered pattern. A DL-PRS resource can be configured in any higher layer configured downlink or flexible (FL) symbol of a slot. There may be a constant energy per resource element (EPRE) for all REs of a given DL-PRS resource. The following are the frequency offsets from symbol to symbol for comb sizes 2, 4, 6, and 12 over 2, 4, 6, and 12 symbols. 2-symbol comb-2: {0, 1}; 4-symbol comb-2: {0, 1, 0, 1}; 6-symbol comb-2: {0, 1, 0, 1, 0, 1}; 12-symbol comb-2: {0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1}; 4-symbol comb-4: {0, 2, 1, 3}; 12-symbol comb-4: {0, 2, 1, 3, 0, 2, 1, 3, 0, 2, 1, 3}; 6-symbol comb-6: {0, 3, 1, 4, 2, 5}; 12-symbol comb-6: {0, 3, 1, 4, 2, 5, 0, 3, 1, 4, 2, 5}; and 12-symbol comb-12: {0, 6, 3, 9, 1, 7, 4, 10, 2, 8, 5, 11}. QC2402402WOQualcomm Ref. No.2402402WO

[0118] A “PRS resource set” is a set of PRS resources used for the transmission of PRS signals, where each PRS resource has a PRS resource ID. In addition, the PRS resources in a PRS resource set are associated with the same TRP. A PRS resource set is identified by a PRS resource set ID and is associated with a particular TRP (identified by a TRP ID). In addition, the PRS resources in a PRS resource set have the same periodicity, a common muting pattern configuration, and the same repetition factor (such as “PRS- ResourceRepetitionFactor”) across slots. The periodicity is the time from the first repetition of the first PRS resource of a first PRS instance to the same first repetition of the same first PRS resource of the next PRS instance. The periodicity may have a length selected from 2^μ*{4, 5, 8, 10, 16, 20, 32, 40, 64, 80, 160, 320, 640, 1280, 2560, 5120, 10240} slots, with μ = 0, 1, 2, 3. The repetition factor may have a length selected from {1, 2, 4, 6, 8, 16, 32} slots.

[0119] A PRS resource ID in a PRS resource set is associated with a single beam (or beam ID) transmitted from a single TRP (where a TRP may transmit one or more beams). That is, each PRS resource of a PRS resource set may be transmitted on a different beam, and as such, a “PRS resource,” or simply “resource,” also can be referred to as a “beam.” Note that this does not have any implications on whether the TRPs and the beams on which PRS are transmitted are known to the UE.

[0120] A “PRS instance” or “PRS occasion” is one instance of a periodically repeated time window (such as a group of one or more consecutive slots) where PRS are expected to be transmitted. A PRS occasion also may be referred to as a “PRS positioning occasion,” a “PRS positioning instance, a “positioning occasion,” “a positioning instance,” a “positioning repetition,” or simply an “occasion,” an “instance,” or a “repetition.”

[0121] A “positioning frequency layer” (also referred to simply as a “frequency layer”) is a collection of one or more PRS resource sets across one or more TRPs that have the same values for certain parameters. Specifically, the collection of PRS resource sets has the same subcarrier spacing and cyclic prefix (CP) type (meaning all numerologies supported for the physical downlink shared channel (PDSCH) are also supported for PRS), the same Point A, the same value of the downlink PRS bandwidth, the same start PRB (and center frequency), and the same comb-size. The Point A parameter takes the value of the parameter “ARFCN-ValueNR” (where “ARFCN” stands for “absolute radio-frequency channel number”) and is an identifier / code that specifies a pair of physical radio channel QC2402402WOQualcomm Ref. No.2402402WO used for transmission and reception. The downlink PRS bandwidth may have a granularity of four PRBs, with a minimum of 24 PRBs and a maximum of 272 PRBs. Currently, up to four frequency layers have been defined, and up to two PRS resource sets may be configured per TRP per frequency layer.

[0122] The concept of a frequency layer is somewhat like the concept of component carriers and bandwidth parts (BWPs), but different in that component carriers and BWPs are used by one base station (or a macro cell base station and a small cell base station) to transmit data channels, while frequency layers are used by several (usually three or more) base stations to transmit PRS. A UE may indicate the number of frequency layers it can support when it sends the network its positioning capabilities, such as during an LTE positioning protocol (LPP) session. For example, a UE may indicate whether it can support one or four positioning frequency layers.

[0123] In some aspects, the reference signal carried on the REs may be SRS. SRS transmitted by a UE may be used by a base station to obtain the channel state information (CSI) for the transmitting UE. CSI describes how an RF signal propagates from the UE to the base station and represents the combined effect of scattering, fading, and power decay with distance. The system uses the SRS for resource scheduling, link adaptation, massive MIMO, beam management, etc.

[0124] A collection of REs that are used for transmission of SRS is referred to as an “SRS resource,” and may be identified by the parameter “SRS-ResourceId.” The collection of resource elements can span multiple PRBs in the frequency domain and ‘N’ (e.g., one or more) consecutive symbol(s) within a slot in the time domain. In a given OFDM symbol, an SRS resource occupies one or more consecutive PRBs. An “SRS resource set” is a set of SRS resources used for the transmission of SRS signals, and is identified by an SRS resource set ID (“SRS-ResourceSetId”).

[0125] The transmission of SRS resources within a given PRB has a particular comb size (also referred to as the “comb density”). A comb size ‘N’ represents the subcarrier spacing (or frequency / tone spacing) within each symbol of an SRS resource configuration. Specifically, for a comb size ‘N,’ SRS are transmitted in every Nth subcarrier of a symbol of a PRB. For example, for comb-4, for each symbol of the SRS resource configuration, REs corresponding to every fourth subcarrier (such as subcarriers 0, 4, 8) are used to transmit SRS of the SRS resource. In an example, the SRS may be comb-4 over four QC2402402WOQualcomm Ref. No.2402402WO symbols. That is, the locations of the shaded SRS REs indicate a comb-4 SRS resource configuration.

[0126] Currently, an SRS resource may span 1, 2, 4, 8, or 12 consecutive symbols within a slot with a comb size of comb-2, comb-4, or comb-8. The following are the frequency offsets from symbol to symbol for the SRS comb patterns that are currently supported.1-symbol comb-2: {0}; 2-symbol comb-2: {0, 1}; 2-symbol comb-4: {0, 2}; 4-symbol comb-2: {0, 1, 0, 1}; 4-symbol comb-4: {0, 2, 1, 3}; 8-symbol comb-4: {0, 2, 1, 3, 0, 2, 1, 3}; 12- symbol comb-4: {0, 2, 1, 3, 0, 2, 1, 3, 0, 2, 1, 3}; 4-symbol comb-8: {0, 4, 2, 6}; 8-symbol comb-8: {0, 4, 2, 6, 1, 5, 3, 7}; and 12-symbol comb-8: {0, 4, 2, 6, 1, 5, 3, 7, 0, 4, 2, 6}.

[0127] Generally, as noted above, a UE transmits SRS to enable the receiving base station (either the serving base station or a neighboring base station) to measure the channel quality (i.e., CSI) between the UE and the base station. However, SRS can also be specifically configured as uplink positioning reference signals for uplink-based positioning procedures, such as uplink time difference of arrival (UL-TDOA), round-trip-time (RTT), uplink angle-of-arrival (UL-AoA), etc. As used herein, the term “SRS” may refer to SRS configured for channel quality measurements or SRS configured for positioning purposes. The former may be referred to herein as “SRS-for-communication” and / or the latter may be referred to as “SRS for positioning” or “positioning SRS” when needed to distinguish the two types of SRS.

[0128] Several enhancements over the previous definition of SRS may be available for SRS for positioning (also referred to as “UL-PRS”), such as a new staggered pattern within an SRS resource (except for single-symbol / comb-2), a new comb type for SRS, new sequences for SRS, a higher number of SRS resource sets per component carrier, and a higher number of SRS resources per component carrier. In addition, the parameters “SpatialRelationInfo” and “PathLossReference” are to be configured based on a downlink reference signal or SSB from a neighboring TRP. Further still, one SRS resource may be transmitted outside the active bandwidth part (BWP), and one SRS resource may span across multiple component carriers. Also, SRS may be configured in RRC connected state and only transmitted within an active BWP. Further, there may be no frequency hopping, no repetition factor, a single antenna port, and new lengths for SRS (e.g., 8 and 12 symbols). There also may be open-loop power control and not closed-loop power control, and comb-8 (i.e., an SRS transmitted every eighth subcarrier in the same symbol) 41 QC2402402WOQualcomm Ref. No.2402402WO may be used. Lastly, the UE may transmit through the same transmit beam from multiple SRS resources for UL-AoA. These features may be configured through RRC higher layer signaling (and potentially triggered or activated through a MAC control element (MAC- CE) or downlink control information (DCI)).

[0129] Note that the terms “positioning reference signal” and “PRS” generally refer to specific reference signals that are used for positioning in NR and LTE systems. However, as used herein, the terms “positioning reference signal” and “PRS” may also refer to any type of reference signal that can be used for positioning, such as but not limited to, PRS as defined in LTE and NR, TRS, PTRS, CRS, CSI-RS, DMRS, PSS, SSS, SSB, SRS, UL-PRS, etc. In addition, the terms “positioning reference signal” and “PRS” may refer to downlink, uplink, or sidelink positioning reference signals, unless otherwise indicated by the context. If needed to further distinguish the type of PRS, a downlink positioning reference signal may be referred to as a “DL-PRS,” an uplink positioning reference signal (e.g., an SRS-for-positioning, PTRS) may be referred to as an “UL-PRS,” and a sidelink positioning reference signal may be referred to as an “SL-PRS.” In addition, for signals that may be transmitted in the downlink, uplink, and / or sidelink (e.g., DMRS), the signals may be prepended with “DL,” “UL,” or “SL” to distinguish the direction. For example, “UL-DMRS” is different from “DL-DMRS.”

[0130] 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.

[0131] 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 QC2402402WOQualcomm Ref. No.2402402WO include multiple linear regression (e.g., finding a plane of best fit) and polynomial regression (e.g., finding a curve of best fit).

[0132] Another example of a machine learning model is a decision tree model. In a decision tree model, a tree structure is defined with a plurality of nodes. Decisions are used to move from a root node at the top of the decision tree to a leaf node at the bottom of the decision tree (i.e., a node with no further child nodes). Generally, a higher number of nodes in the decision tree model is correlated with higher decision accuracy.

[0133] 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.

[0134] 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.

[0135] FIG.4 illustrates an example neural network 400, according to aspects of the disclosure. The neural network 400 includes an input layer ‘i’ that receives ‘n’ (one or more) inputs (illustrated as “Input 1,” “Input 2,” and “Input n”), one or more hidden layers (illustrated as hidden layers ‘h1,’ ‘h2,’ and ‘h3’) for processing the inputs from the input layer, and an output layer ‘o’ that provides ‘m’ (one or more) outputs (labeled “Output 1” and “Output m”). The number of inputs ‘n,’ hidden layers ‘h,’ and outputs ‘m’ may be the same or different. In some designs, the hidden layers ‘h’ may include linear function(s) and / or activation function(s) that the nodes (illustrated as circles) of each successive hidden layer process from the nodes of the previous hidden layer.

[0136] 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 QC2402402WOQualcomm Ref. No.2402402WO two classes of data, a support vector machine will find a hyperplane or a boundary between the two classes of data that maximizes the margin between the two classes. There are many planes that can separate the two classes, but only one plane can maximize the margin or distance between the classes. Another example of a classification model is Naïve 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.

[0137] 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.

[0138] Clustering is an unsupervised technique that involves the grouping, or clustering, of data points. Clustering is frequently used for customer segmentation, fraud detection, and document classification. Common clustering techniques include k-means clustering, hierarchical clustering, mean shift clustering, and density-based clustering. Dimensionality reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables. In simpler terms, dimensionality reduction is the process of reducing the dimension of a feature set (in even simpler terms, reducing the number of features). Most dimensionality reduction techniques can be categorized as either feature elimination or feature extraction. One example of dimensionality reduction is called principal component analysis (PCA). In the simplest sense, PCA involves project higher dimensional data (e.g., three dimensions) to a smaller space (e.g., two dimensions). This results in a lower dimension of data (e.g., two dimensions instead of three dimensions) while keeping all original variables in the model.

[0139] 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). 44 QC2402402WOQualcomm Ref. No.2402402WO

[0140] The artificial intelligence / machine learning (AI / ML) positioning provided by an AI / ML positioning and / or sensing model may be direct AI / ML positioning / sensing or AI / ML assisted positioning / sensing. FIG. 5A is a diagram 500 illustrating an example of direct AI / ML positioning / sensing, according to aspects of the disclosure. As shown in FIG.5A, direct AI / ML positioning / sensing is where the input features (or simply “inputs” or “features”) to the AI / ML positioning / sensing model are measurements of one or more reference signals (e.g., DL-PRS or SRS) and the output label (or simply “output” or “label”) of the AI / ML positioning / sensing model is the target (estimated) location of the UE or target object. The measurements of the reference signal(s) may include the channel energy response (CER), channel impulse response (CIR), channel frequency response (CFR), received signal strength indicator (RSSI), reference signal received power (RSRP), path RSRP (RSRPP), reference signal received quality (RSRQ), time of arrival (ToA), relative ToA (RTOA), reference signal time difference (RSTD), angle of departure (AoD), angle of arrival (AoA), and / or the like of the reference signal(s).

[0141] FIG. 5B is a diagram 550 illustrating an example of AI / ML assisted positioning and / or sensing, according to aspects of the disclosure. As shown in FIG. 5B, AI / ML assisted positioning / sensing is where the input features to the AI / ML positioning / sensing model are measurements of one or more reference signals (e.g., DL-PRS, SRS) and the output labels of the AI / ML positioning / sensing model are intermediate measurements (or quantities) of the reference signal(s). The location of the UE or target object is then determined using non-artificial intelligence techniques (e.g., Chan’s algorithm, Kalman filtering, etc.) or a different machine learning model. In this case, the measurements of the reference signal(s) may include the CER, CIR, CFR, RSSI, RSRP, RSRPP, RSRQ, delay, Doppler, angle spectrum, and / or the like of the reference signal(s). The intermediate measurements of the reference signal(s) may include the ToA, RTOA, RSTD, AoD, AoA, line-of-sight (LOS) indication, and / or the like of the reference signal(s).

[0142] FIG. 6 illustrates various artificial intelligence / machine learning (AI / ML) positioning and / or sensing scenarios, according to aspects of the disclosure. As shown in diagram 600, there are three AI / ML positioning / sensing deployment scenarios based on downlink reference signals (e.g., DL-PRS). The first deployment scenario (labeled “Case 1”) is a UE-based positioning / sensing case with a UE-side AI / ML positioning / sensing model QC2402402WOQualcomm Ref. No.2402402WO (labeled “AI / ML”). In this case, the UE applies the AI / ML positioning / sensing model (or simply “AI / ML model”) to determine a location of the UE or target object and reports the location to the network (e.g., LMF 270). The AI / ML positioning / sensing provided by the AI / ML positioning / sensing model may be direct AI / ML (D-AI / ML) positioning / sensing.

[0143] The second deployment scenario (labeled “Case 2a”) is UE-assisted / network-based positioning / sensing with a UE-side AI / ML positioning / sensing model that provides AI / ML assisted positioning / sensing. That is, the UE inputs measurements of downlink reference signals (e.g., DL-PRS) received from one or more TRPs into the AI / ML positioning / 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). As noted above, for AI / ML assisted positioning / sensing, the measurements of the downlink reference signals may include the CER, CIR, CFR, RSSI, RSRP, RSRPP, RSRQ, delay, Doppler, angle spectrum (which shows a strength or a probability of each value (e.g., the x-axis is delay and the y-axis is the probability of the delay), and / or the like of the downlink reference signals. The intermediate measurements of the downlink reference signals may include the ToA, RSTD, AoD, AoA, line-of-sight (LOS) indication, and / or the like of the downlink reference signals.

[0144] The third deployment scenario (labeled “Case 2b”) is UE-assisted / network-based positioning / sensing with a network-side model that provides direct AI / ML positioning / sensing. That is, the UE reports the measurements of the downlink reference signals received from one or more TRPs to the core network (e.g., LMF 270). The core network then applies the AI / ML positioning / sensing model to the measurements to determine the location of the UE or target object. In this case, the measurements of the downlink reference signals reported to the network may include the CER, CIR, CFR, RSSI, RSRP, RSRPP, RSRQ, ToA, RSTD, AoD, AoA, LOS indication, delay, Doppler, angle spectrum, and / or the like of the downlink reference signals.

[0145] As shown in diagram 650, there are two AI / ML positioning / sensing deployment scenarios based on uplink reference signals (e.g., SRS). The first deployment scenario (labeled “Case 3a”) is NG-RAN node-assisted positioning / sensing with an NG RAN-side model that provides AI / ML assisted positioning / sensing. In this case, the NG-RAN applies an AI / ML positioning / 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 46 QC2402402WOQualcomm Ref. No.2402402WO received uplink reference signal(s). The NG-RAN then reports the intermediate measurements to the core network (e.g., LMF 270). As noted above, for AI / ML assisted positioning / sensing, the measurements of the uplink reference signal(s) may include the CER, CIR, CFR, RSSI, RSRP, RSRPP, RSRQ, delay, Doppler, angle spectrum, and / or the like of the uplink reference signal(s). The intermediate measurements of the uplink reference signal(s) may include the RTOA, RSTD, AoD, AoA, LOS indication, and / or the like of the uplink reference signal(s).

[0146] The second deployment scenario (labeled “Case 3b”) is NG-RAN node-assisted positioning / sensing with a network-side AI / ML positioning / sensing model that provides direct AI / ML positioning / sensing. In this case, the NG-RAN 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 an AI / ML positioning / sensing model to the measurements of the uplink reference signal(s) to obtain a target location of the UE. The measurements of the uplink reference signal(s) may include the CER, CIR, CFR, RSSI, RSRP, RSRPP, RSRQ, RTOA, RSTD, AoD, AoA, LOS indication, delay, Doppler, angle spectrum, and / or the like of the uplink reference signal(s).

[0147] Note that there may be other deployment scenarios in which the UE, NG-RAN, or the core network use an AI / ML positioning / sensing model to compute or report a positioning / sensing estimate (target location), but these cases are implementation-specific and do not necessarily involve signaling between the UE, NG-RAN, and / or the core network.

[0148] In some aspects, AI / ML models may be trained and used for various use cases, including, for example, CSI feedback, beam management, and positioning. AI / ML models may be trained by the UE or the network, or any node or component of the network. In some aspects, the AI / ML model may be UE-sided, network-sided, or two-sided (for example, encoder on one side (e.g., UE or network) and decoder on the other side (e.g., network or UE) for CSI feedback compression).

[0149] Some AI / ML models may learn solutions that map to specific radio characteristics and / or site-specific features relating to one or more network nodes (e.g., UE 302, base station 304, or network entity 306). For example, some AI / ML models may be specific to certain radio characteristics (e.g., delay spread, Doppler frequency, spatial relation, etc.). QC2402402WOQualcomm Ref. No.2402402WO

[0150] In some situations, a UE that utilizes AI / ML models may be a mobile device in a dynamically changing environment. For example, the UE may be in a vehicle or part of a vehicle driving between different cells or base stations in a network. In such dynamic situations, the UE and the network (e.g., base station / gNB) may need to signal the applicability of AI / ML models to each other based on observed reference signal radio characteristics in a quick and simple yet efficient manner.

[0151] In some situations, while it is possible to explicitly indicate a range of radio characteristics for an AI / ML model (e.g., area information), it still may be cumbersome to map these ranges to an actual deployment or operation because this would require extensive signaling and exchange of information between the UE and the network (e.g., base station / gNB), thus incurring a significant amount of signaling overhead.

[0152] In some aspects, multiple AI / ML models may be configured in a network node (e.g., UE 302, base station 304, or network entity 306) to perform multiple functions. Examples of various functions to which AI / ML models may be applicable include a positioning function, a beam management function, and a CSI function. Other types of functions may be supported by other types of AI / ML models according to aspects of the disclosure.

[0153] FIG.7 illustrates examples of AI / ML models which may be supported at a network node (e.g., UE), according to aspects of the disclosure. In FIG. 7, a network node may be provided with capabilities to support a CSI feedback AI / ML model 702, a beam management AI / ML model 704, and a positioning AI / ML model 706.

[0154] In the example shown in FIG. 7, the network node may apply the CSI feedback AI / ML model 702 to a set of CSI inputs 710 and generate a set of CSI outputs 712 based on the CSI feedback AI / ML model 702, apply the beam management AI / ML model 704 to a set of beam inputs 714 and generate a set of beam outputs 716 based on the beam management AI / ML model 704, and apply the positioning AI / ML model 706 to a set of positioning inputs 718 and generate a set of positioning outputs 720 based on the positioning AI / ML model 706.

[0155] In some aspects, the network node (e.g., UE 302, base station 304, or network entity 306) may have capabilities to support multiple AI / ML models simultaneously. In some aspects, multiple AI / ML models may be active simultaneously or one at time, depending on the capabilities and processing power of the network node. In some situations, at a network node, data AI / ML models (e.g., CSI feedback AI / ML models or beam QC2402402WOQualcomm Ref. No.2402402WO management AI / ML models) may be running more frequently than positioning AI / ML models, or vice versa.

[0156] In some aspects, some types of data AI / ML models (e.g., beam management AI / ML model) may be associated with positioning AI / ML models. In some aspects, the output of at least some types of data AI / ML models (e.g., beam management AI / ML model) may be used to configure the positioning AI / ML models or at least some of the parameters of the positioning AI / ML models.

[0157] In some situations, where a network node (e.g., UE 302, base station 304, or network entity 306) may have a limited processing capability for running multiple AI / ML models, priorities may be given to different types of AI / ML models. For example, data AI / ML models may be given higher priorities than positioning AI / ML models, or vice versa. In some aspects, different priorities may be accorded to different types of data AI / ML models.

[0158] In some situations, one or more of the AI / ML models with lower priorities may be deactivated while one or more AI / ML models with higher priorities are running. In some situations, one or more AI / ML models with lower priorities may be activated when one or more AI / ML models with higher priorities become inactive.

[0159] In some aspects, a network node (e.g., UE 302, base station 304, or network entity 306) may be provided with capabilities to support multiple AI / ML models for multiple functions. For example, a network node may have one or more capabilities to support a positioning AI / ML model and two data AI / ML models, including a CSI model and a beam management model, under one or more of the following example options:

[0160] Option 1: The network node supports a standalone positioning model;

[0161] Option 2: The network node supports a positioning AI / ML model and a CSI feedback AI / ML model in parallel;

[0162] Option 3: The network node supports a positioning AI / ML model and a beam management AI / ML model in parallel;

[0163] Option 4: The network node supports a positioning AI / ML model, a CSI feedback AI / ML model, and a beam management AI / ML model in parallel.

[0164] In some situations, there may be a maximum number of standalone AI / ML models that the network node is capable of supporting, due to the processing constraints of the network node. In some situations, where the network node is capable of supporting at QC2402402WOQualcomm Ref. No.2402402WO least a positioning AI / ML model and a data AI / ML model, there may be a maximum total number AI / ML models that the network node can support simultaneously, due to the processing constraints of the network node.

[0165] In some aspects, the network node (e.g., UE 302, base station 304, or network entity 306) may transmit a capability message to another network node (e.g., base station 304, network entity 306, or UE 302) indicating one or more capabilities of the network node to support multiple AI / ML models for multiple functions simultaneously. This capability message may be reported in various manners according to aspects of the disclosure. For example, the network node may transmit a capability message that reports its AI / ML capabilities on a per-model per-band basis, on a basis of multiple models per band, or on a basis of multiple models across a combination of multiple bands.

[0166] In some situations, given the processing constraints at a network node (e.g., UE 302, base station 304, or network entity 306), some types of AI / ML models may be accorded higher priorities over other types of AI / ML models. For example, in some cases, a positioning AI / ML model may be given a higher priority than data AI / ML models. In such cases, a data AI / ML model may not be activated while the network node is in a positioning session.

[0167] In some cases, the positioning AI / ML model may be accorded a lower priority than data AI / ML models. In such cases, the network node (e.g., UE 302, base station 304, or network entity 306) may switch its positioning operation from a positioning AI / ML model to non-AI / ML positioning technique (e.g., traditional or classic positioning technique) when a higher-priority data AI / ML model is activated at the network node.

[0168] FIG. 8 illustrates an examples of switching between a beam management AI / ML model and a positioning AI / ML model, according to aspects of the disclosure. In the example shown in FIG. 8, the positioning AI / ML model has a lower priority than the beam management AI / ML model.

[0169] As illustrated in FIG. 8, the positioning AI / ML model may be initially activated, as indicated by block 802, when the beam management AI / ML model is inactive. When the beam management AI / ML model is activated, as indicated by arrow 804, the network node switches its AI / ML operations to the beam management AI / ML model, as indicated by block 806. Meanwhile, the positioning operations of the network node may switch to QC2402402WOQualcomm Ref. No.2402402WO a non-AI / ML positioning technique (e.g., traditional or classic positioning technique), as indicated by block 808.

[0170] After the beam management AI / ML model has been active at the network node for a period of time, the beam management AI / ML model may be deactivated, as indicated by arrow 810. After the beam management AI / ML model has been deactivated, the positioning AI / ML model may be activated again at the network node for positioning operations, as indicated by block 812.

[0171] In some aspects, switching between the positioning AI / ML model and the non-AI / ML model positioning technique (e.g., traditional or classic positioning technique) may have an impact on the measurement period for positioning which may be reported to the network. For example, if the positioning AI / ML model is deactivated from time to time due to limited processing capabilities of the network node, then the measurement period for the AI / ML measurement report may be extended to account for both the measurement period based on the positioning AI / ML model and the measurement period based on the non-AI / ML positioning technique.

[0172] In some aspects, the measurement period may be a weighted measurement period between the positioning AI / ML model and the non-AI / ML model positioning technique. For example, the measurement period TRSTD,ML of positioning measurements based on the positioning AI / ML model may be given as follows:

[0173] In some aspects, the measurement period TRSTD,NON-MLof positioning measurements based on the non-AI / ML model positioning technique (e.g., traditional or classic positioning technique) may be given as follows: T,

[0174] In the above equations: - is the index of the positioning frequency layer; - is the total number of positioning frequency layers; -T , is the periodicity of the PRS RSTD measurement in positioning frequencylayerQC2402402WOQualcomm Ref. No.2402402WO -T , is the measurement period for PRS RSTD measurement in positioningfrequency layer i as specified below: -T corresponds to the “durationOfPRS-ProcessingSymbolsInEveryTms”LPP IE; -T _ ,i LCM T ,i, MGRPi , the least common multiple between T ,iand MGRPi; -iis the repetition periodicity of the measurement gap applicable for measurement in the PRS frequency layer i; -is the periodicity of DL-PRS resource on frequency layer i.

[0175] In some aspects, the weighted measurement period for positioning measurements may be given as follows: T= T , * + T , * (1- )

[0176] where is the percentage of time the network node (e.g., UE 302, base station 304, or network entity 306) performs positioning measurements based on the non-AI / ML model positioning technique, and 1- is the percentage of time the network node performspositioning measurements based on the positioning AI / ML model. In some situations, a network node may need to perform positioning measurements based on the non-AI / ML model positioning technique during time periods in which the network node needs to use its processing capabilities for running one or more other AI / ML models, for example, data AI / ML models.

[0177] In some aspects, signaling may be provided between different nodes in a network such that the network nodes may share lists of AI / ML models for training and / or inference, for example. In some aspects, signaling may be provided between a network function (e.g., LMF) and a base station (e.g., gNB) such that the base station may share the list of data AI / ML models given to one or more UEs for training and / or inference, for example.

[0178] In some aspects, signaling may be provided between a UE and a network function (e.g., LMF) such that the UE may transmit its list of data AI / ML models to the network and share them with other network nodes for training and / or inference, for example.

[0179] In some aspects, a network function (e.g., LMF) may define an association between one or more data AI / ML models and one or more positioning AI / ML models. Some types of data AI / ML models, for example, beam management AI / ML models, may be associated with positioning AI / ML models. The association between one or more data AI / ML QC2402402WOQualcomm Ref. No.2402402WO models and one or more positioning AI / ML models may be direct or indirect, according to aspects of the disclosure.

[0180] FIG. 9 illustrates an example of direct associations between beam management AI / ML models and positioning AI / ML models, according to aspects of the disclosure. In the example shown in FIG. 9, a first beam management AI / ML model 902 (denoted “Beam Management Model 1”) may be directly associated with a first positioning AI / ML model 904 (denoted “Positioning Model 1”). Likewise, a second beam management AI / ML model 906 (denoted “Beam Management Model 2”) may be directly associated with a second positioning AI / ML model 908 (denoted “Positioning Model 2”), and a third beam management AI / ML model 910 (denoted “Beam Management Model 3”) may be directly associated with a third positioning AI / ML model 912 (denoted “Positioning Model 3”).

[0181] In the example illustrated in FIG.9, there is a one-to-one direct association between each data AI / ML model (e.g., beam management AI / ML model) and a corresponding positioning AI / ML model. In some aspects, the network (e.g., base station / gNB) may transmit to the UE a configuration of a direct association between a data ML model (e.g., beam management AI / ML model) and a corresponding positioning AI / ML model. Upon receipt, the UE may change its positioning AI / ML model based on the configuration of direct association.

[0182] In some situations, the network may initiate a change to the data AI / ML model used by the UE, and transmit a new configuration of a direct association between a new data AI / ML model and a different corresponding positioning AI / ML model to the UE. Upon receipt, the UE may change its positioning AI / ML model based on the new configuration. In some aspects, a network function (e.g., LMF) may configure a set of multiple positioning AI / ML models, where each positioning AI / ML model is directly associated with a distinct data AI / ML model.

[0183] FIG. 10 illustrates an example of indirect associations between a beam management AI / ML model and multiple positioning AI / ML models, according to aspects of the disclosure. In the example shown in FIG. 10, a beam management AI / ML model 1002 (denoted “Beam Management Model 1”) may generate different output sets at different times. For example, as shown in FIG.10, the beam management AI / ML model 1002 may generate a first output set 1004 (denoted “Output Set 1”), a second output set 1006 (denoted “Output Set 2”), and a third output set 1008 (denoted “Output Set 3”) at different QC2402402WOQualcomm Ref. No.2402402WO times, depending on changes to the network environment in which the network node (e.g., UE) operates.

[0184] For example, a change to the serving cell for the UE (e.g., a mobile device in a vehicle or as part of a vehicle) may result in a change to the output set generated by the beam management AI / ML model 1002. In another example, activation and / or deactivation of one or more secondary cells for the UE also may result in a change to the output set generated by the beam management AI / ML model 1002. Thus, different output sets 1004, 1006 and 1008 may be generated by the beam management AI / ML model 1002 depending on the location of the UE. In some aspects, changing the serving cell and / or activating and / or deactivating one or more secondary cells may result in a change to the positioning AI / ML model for the UE.

[0185] In the example shown in FIG. 10, the first output set 1004 of the beam management AI / ML model 1002 may be associated with a first positioning AI / ML model 1010 (denoted “Positioning Model 1”). Likewise, the second output set 1006 of the beam management AI / ML model 1002 may be associated with a second positioning AI / ML model 1012 (denoted “Positioning Model 2”), and the third output set 1008 of the beam management AI / ML model 1002 may be associated with a third positioning AI / ML model 1014 (denoted “Positioning Model 3”). In some aspects, a network function (e.g., LMF) may configure a set of multiple positioning AI / ML models having indirect associations with one or more data AI / ML models (e.g., beam management AI / ML models).

[0186] In some aspects, an indirect association between the data AI / ML model (e.g., beam management AI / ML model) and positioning may be made by changing the one or more positioning model parameters, instead of changing the positioning AI / ML models, based on a change to the output set of the data AI / ML model. In some aspects, the positioning AI / ML model for the UE may remain the same while one or more positioning parameters are changed as a result of a change to the output set generated by the data AI / ML model. In some aspects, a network function (e.g., LMF) may configure indirect associations between output sets of the data AI / ML model and one or more positioning model parameters.

[0187] FIG. 11 illustrates an example method 1100, according to aspects of the disclosure. In some aspects, method 1100 may be performed by a network node, such as a UE (e.g., UE QC2402402WOQualcomm Ref. No.2402402WO 302 described herein), a base station (e.g., base station 304 described herein), or a network entity (e.g., network entity 306 described herein).

[0188] At 1110, the network node (e.g., UE 302, base station 304, or network entity 306) may transmit a capability message indicating one or more capabilities of the network node to support a plurality of machine learning (ML) models for a plurality of functions simultaneously, wherein the plurality of ML models include at least a first ML model and a second ML model.

[0189] In some aspects, where the network node is a UE, operation 1110 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 AI / ML component 348, any or all of which may be considered means for performing this operation.

[0190] In some aspects, where the network node is a base station or base station component, operation 1110 may be performed by the one or more WWAN transceivers 350, the one or more short-range wireless transceivers 360, the one or more network transceivers 380, the one or more processors 384, memory 386, and / or AI / ML component 388, any or all of which may be considered means for performing this operation.

[0191] In some aspects, where the network node is a network entity, operation 1110 may be performed the one or more network transceivers 390, the one or more processors 394, memory 396, and / or AI / ML component 398, any or all of which may be considered means for performing this operation.

[0192] At 1120, the network node (e.g., UE 302, base station 304, or network entity 306) may apply the first ML model to a first set of input signals and the second ML model to a second set of input signals based on the one or more capabilities, wherein at least one of the ML models for the plurality of functions that are supported simultaneously is a positioning ML model.

[0193] In some aspects, where the network node is a UE, operation 1120 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 AI / ML component 348, any or all of which may be considered means for performing this operation.

[0194] In some aspects, where the network node is a base station or base station component, operation 1120 may be performed by the one or more WWAN transceivers 350, the one or more short-range wireless transceivers 360, the one or more network transceivers 380, QC2402402WOQualcomm Ref. No.2402402WO the one or more processors 384, memory 386, and / or AI / ML component 388, any or all of which may be considered means for performing this operation.

[0195] In some aspects, where the network node is a network entity, operation 1120 may be performed the one or more network transceivers 390, the one or more processors 394, memory 396, and / or AI / ML component 398, any or all of which may be considered means for performing this operation.

[0196] Method 1100 may include additional implementations, such as any single implementation or any combination of implementations described below and / or in connection with one or more other operations described elsewhere herein.

[0197] In some aspects, the first ML model is the positioning ML model, and the first set of input signals comprises one or more positioning reference signals (PRS), or one or more sounding reference signals (SRS).

[0198] In some aspects, the second ML model is a channel state information (CSI) ML model, and the second set of input signals comprises one or more CSI input signals.

[0199] In some aspects, the second ML model is a beam management ML model.

[0200] In some aspects, the plurality of ML models further include a third ML model, and method 1100 further includes applying the third ML model to a third set of input signals based on the one or more capabilities.

[0201] In some aspects, the third ML model is a CSI ML model, and the third set of input signals comprises one or more CSI input signals.

[0202] In some aspects, the first ML model is the positioning ML model, and the second ML model is a data ML model having a priority different from the positioning ML model.

[0203] In some aspects, the data ML model is not activated when the network node is in a positioning session based on the positioning ML model having a higher priority than the data ML model.

[0204] In some aspects, method 1100 includes obtaining a plurality of positioning measurements based on the positioning ML model and a non-ML model positioning technique based on the data ML model having a higher priority than the positioning ML model.

[0205] In some aspects, the plurality of positioning measurements are obtained over a measurement period that is weighted between a first measurement period of one or more positioning measurements based on the positioning ML model and a second measurement QC2402402WOQualcomm Ref. No.2402402WO period of one or more positioning measurements based on the non-ML model positioning technique.

[0206] In some aspects, method 1100 includes transmitting the data ML model to a second network node, wherein the second network node is a base station or a location management function (LMF).

[0207] In some aspects, method 1100 includes receiving a configuration of a direct association between the data ML model and the positioning ML model, and changing the positioning ML model based on the configuration of the direct association.

[0208] In some aspects, method 1100 includes receiving a configuration of an indirect association between the data ML model and the positioning ML model, changing the positioning ML model or one or more positioning parameters based on the configuration of the indirect association.

[0209] In some aspects, the network node is a user equipment (UE), a base station, or a network entity.

[0210] Although FIG. 11 shows example blocks of method 1100, in some implementations, method 1100 may include additional blocks, fewer blocks, different blocks, or differently arranged blocks than those depicted in FIG. 11. Additionally, or alternatively, two or more of the blocks of method 1100 may be performed in parallel, or performed in a sequence that is different from the sequence listed in FIG.11.

[0211] As will be appreciated, a technical advantage of method 1100 is that a network node (e.g., UE 302, base station 304, or network entity 306) may transmit a capability message indicating its capabilities of running AI / ML models for various functions (e.g., data and / or positioning AI / ML models) to the network in a quick and simple yet efficient manner. The network node may then apply its AI / ML models to input signals for various types of operations (e.g., CSI feedback, beam management, and / or positioning).

[0212] 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 QC2402402WOQualcomm Ref. No.2402402WO 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.

[0213] Implementation examples are described in the following numbered clauses:

[0214] Clause 1. A method of wireless communication, performed at a network node, comprising: transmitting a capability message indicating one or more capabilities of the network node to support a plurality of machine learning (ML) models for a plurality of functions simultaneously, wherein the plurality of ML models include at least a first ML model and a second ML model; and applying the first ML model to a first set of input signals and the second ML model to a second set of input signals based on the one or more capabilities, wherein at least one of the ML models for the plurality of functions that are supported simultaneously is a positioning ML model.

[0215] Clause 2. The method of clause 1, wherein the first ML model is the positioning ML model, and wherein the first set of input signals comprises: one or more positioning reference signals (PRS), or one or more sounding reference signals (SRS).

[0216] Clause 3. The method of any of clauses 1 to 2, wherein the second ML model is a channel state information (CSI) ML model, and wherein the second set of input signals comprises one or more CSI input signals.

[0217] Clause 4. The method of any of clauses 1 to 3, wherein the second ML model is a beam management ML model.

[0218] Clause 5. The method of clause 4, wherein the plurality of ML models further include a third ML model, further comprising: applying the third ML model to a third set of input signals based on the one or more capabilities.

[0219] Clause 6. The method of clause 5, wherein the third ML model is a CSI ML model, and wherein the third set of input signals comprises one or more CSI input signals. QC2402402WOQualcomm Ref. No.2402402WO

[0220] Clause 7. The method of any of clauses 1 to 6, wherein the first ML model is the positioning ML model, and wherein the second ML model is a data ML model having a priority different from the positioning ML model.

[0221] Clause 8. The method of clause 7, wherein the data ML model is not activated when the network node is in a positioning session based on the positioning ML model having a higher priority than the data ML model.

[0222] Clause 9. The method of any of clauses 7 to 8, further comprising obtaining a plurality of positioning measurements based on the positioning ML model and a non-ML model positioning technique based on the data ML model having a higher priority than the positioning ML model.

[0223] Clause 10. The method of clause 9, wherein the plurality of positioning measurements are obtained over a measurement period that is weighted between a first measurement period of one or more positioning measurements based on the positioning ML model and a second measurement period of one or more positioning measurements based on the non- ML model positioning technique.

[0224] Clause 11. The method of any of clauses 7 to 10, further comprising: transmitting the data ML model to a second network node, wherein the second network node is a base station or a location management function (LMF).

[0225] Clause 12. The method of any of clauses 7 to 11, further comprising: receiving a configuration of a direct association between the data ML model and the positioning ML model; and changing the positioning ML model based on the configuration of the direct association.

[0226] Clause 13. The method of any of clauses 7 to 12, further comprising: receiving a configuration of an indirect association between the data ML model and the positioning ML model; changing the positioning ML model or one or more positioning parameters based on the configuration of the indirect association.

[0227] Clause 14. The method of any of clauses 1 to 13, wherein the network node is a user equipment (UE), a base station, or a network entity.

[0228] Clause 15. 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: transmit, via the one or more transceivers, a capability message indicating QC2402402WOQualcomm Ref. No.2402402WO one or more capabilities of the network node to support a plurality of machine learning (ML) models for a plurality of functions simultaneously, wherein the plurality of ML models include at least a first ML model and a second ML model; and apply the first ML model to a first set of input signals and the second ML model to a second set of input signals based on the one or more capabilities, wherein at least one of the ML models for the plurality of functions that are supported simultaneously is a positioning ML model.

[0229] Clause 16. The network node of clause 15, wherein the first ML model is the positioning ML model, and wherein the first set of input signals comprises: one or more positioning reference signals (PRS), or one or more sounding reference signals (SRS).

[0230] Clause 17. The network node of any of clauses 15 to 16, wherein the second ML model is a channel state information (CSI) ML model, and wherein the second set of input signals comprises one or more CSI input signals.

[0231] Clause 18. The network node of any of clauses 15 to 17, wherein the second ML model is a beam management ML model.

[0232] Clause 19. The network node of clause 18, wherein the plurality of ML models further include a third ML model, wherein the one or more processors, either alone or in combination, are further configured to: apply the third ML model to a third set of input signals based on the one or more capabilities.

[0233] Clause 20. The network node of clause 19, wherein the third ML model is a CSI ML model, and wherein the third set of input signals comprises one or more CSI input signals.

[0234] Clause 21. The network node of any of clauses 15 to 20, wherein the first ML model is the positioning ML model, and wherein the second ML model is a data ML model having a priority different from the positioning ML model.

[0235] Clause 22. The network node of clause 21, wherein the data ML model is not activated when the network node is in a positioning session based on the positioning ML model having a higher priority than the data ML model.

[0236] Clause 23. The network node of any of clauses 21 to 22, wherein the one or more processors, either alone or in combination, are further configured to obtain a plurality of positioning measurements based on the positioning ML model and a non-ML model positioning technique based on the data ML model having a higher priority than the positioning ML model. QC2402402WOQualcomm Ref. No.2402402WO

[0237] Clause 24. The network node of clause 23, wherein the plurality of positioning measurements are obtained over a measurement period that is weighted between a first measurement period of one or more positioning measurements based on the positioning ML model and a second measurement period of one or more positioning measurements based on the non-ML model positioning technique.

[0238] Clause 25. The network node of any of clauses 21 to 24, wherein the one or more processors, either alone or in combination, are further configured to: transmit, via the one or more transceivers, the data ML model to a second network node, wherein the second network node is a base station or a location management function (LMF).

[0239] Clause 26. The network node of any of clauses 21 to 25, wherein the one or more processors, either alone or in combination, are further configured to: receive, via the one or more transceivers, a configuration of a direct association between the data ML model and the positioning ML model; and change the positioning ML model based on the configuration of the direct association.

[0240] Clause 27. The network node of any of clauses 21 to 26, wherein the one or more processors, either alone or in combination, are further configured to: receive, via the one or more transceivers, a configuration of an indirect association between the data ML model and the positioning ML model; change the positioning ML model or one or more positioning parameters based on the configuration of the indirect association.

[0241] Clause 28. The network node of any of clauses 15 to 27, wherein the network node is a user equipment (UE), a base station, or a network entity.

[0242] Clause 29. A network node, comprising: means for transmitting a capability message indicating one or more capabilities of the network node to support a plurality of machine learning (ML) models for a plurality of functions simultaneously, wherein the plurality of ML models include at least a first ML model and a second ML model; and means for applying the first ML model to a first set of input signals and the second ML model to a second set of input signals based on the one or more capabilities, wherein at least one of the ML models for the plurality of functions that are supported simultaneously is a positioning ML model.

[0243] Clause 30. The network node of clause 29, wherein the first ML model is the positioning ML model, and wherein the first set of input signals comprises: one or more positioning reference signals (PRS), or one or more sounding reference signals (SRS). QC2402402WOQualcomm Ref. No.2402402WO

[0244] Clause 31. The network node of any of clauses 29 to 30, wherein the second ML model is a channel state information (CSI) ML model, and wherein the second set of input signals comprises one or more CSI input signals.

[0245] Clause 32. The network node of any of clauses 29 to 31, wherein the second ML model is a beam management ML model.

[0246] Clause 33. The network node of clause 32, wherein the plurality of ML models further include a third ML model, further comprising: means for applying the third ML model to a third set of input signals based on the one or more capabilities.

[0247] Clause 34. The network node of clause 33, wherein the third ML model is a CSI ML model, and wherein the third set of input signals comprises one or more CSI input signals.

[0248] Clause 35. The network node of any of clauses 29 to 34, wherein the first ML model is the positioning ML model, and wherein the second ML model is a data ML model having a priority different from the positioning ML model.

[0249] Clause 36. The network node of clause 35, wherein the data ML model is not activated when the network node is in a positioning session based on the positioning ML model having a higher priority than the data ML model.

[0250] Clause 37. The network node of any of clauses 35 to 36, further comprising means for obtaining a plurality of positioning measurements based on the positioning ML model and a non-ML model positioning technique based on the data ML model having a higher priority than the positioning ML model.

[0251] Clause 38. The network node of clause 37, wherein the plurality of positioning measurements are obtained over a measurement period that is weighted between a first measurement period of one or more positioning measurements based on the positioning ML model and a second measurement period of one or more positioning measurements based on the non-ML model positioning technique.

[0252] Clause 39. The network node of any of clauses 35 to 38, further comprising: means for transmitting the data ML model to a second network node, wherein the second network node is a base station or a location management function (LMF).

[0253] Clause 40. The network node of any of clauses 35 to 39, further comprising: means for receiving a configuration of a direct association between the data ML model and the positioning ML model; and means for changing the positioning ML model based on the configuration of the direct association. QC2402402WOQualcomm Ref. No.2402402WO

[0254] Clause 41. The network node of any of clauses 35 to 40, further comprising: means for receiving a configuration of an indirect association between the data ML model and the positioning ML model; means for changing the positioning ML model or one or more positioning parameters based on the configuration of the indirect association.

[0255] Clause 42. The network node of any of clauses 29 to 41, wherein the network node is a user equipment (UE), a base station, or a network entity.

[0256] Clause 43. A non-transitory computer-readable medium stores computer-executable instructions that, when executed by a network node, cause the network node to: transmit a capability message indicating one or more capabilities of the network node to support a plurality of machine learning (ML) models for a plurality of functions simultaneously, wherein the plurality of ML models include at least a first ML model and a second ML model; and apply the first ML model to a first set of input signals and the second ML model to a second set of input signals based on the one or more capabilities, wherein at least one of the ML models for the plurality of functions that are supported simultaneously is a positioning ML model.

[0257] Clause 44. The non-transitory computer-readable medium of clause 43, wherein the first ML model is the positioning ML model, and wherein the first set of input signals comprises: one or more positioning reference signals (PRS), or one or more sounding reference signals (SRS).

[0258] Clause 45. The non-transitory computer-readable medium of any of clauses 43 to 44, wherein the second ML model is a channel state information (CSI) ML model, and wherein the second set of input signals comprises one or more CSI input signals.

[0259] Clause 46. The non-transitory computer-readable medium of any of clauses 43 to 45, wherein the second ML model is a beam management ML model.

[0260] Clause 47. The non-transitory computer-readable medium of clause 46, wherein the plurality of ML models further include a third ML model, further comprising computer- executable instructions that, when executed by the network node, cause the network node to: apply the third ML model to a third set of input signals based on the one or more capabilities.

[0261] Clause 48. The non-transitory computer-readable medium of clause 47, wherein the third ML model is a CSI ML model, and wherein the third set of input signals comprises one or more CSI input signals. QC2402402WOQualcomm Ref. No.2402402WO

[0262] Clause 49. The non-transitory computer-readable medium of any of clauses 43 to 48, wherein the first ML model is the positioning ML model, and wherein the second ML model is a data ML model having a priority different from the positioning ML model.

[0263] Clause 50. The non-transitory computer-readable medium of clause 49, wherein the data ML model is not activated when the network node is in a positioning session based on the positioning ML model having a higher priority than the data ML model.

[0264] Clause 51. The non-transitory computer-readable medium of any of clauses 49 to 50, further comprising computer-executable instructions that, when executed by the network node, cause the network node to obtain a plurality of positioning measurements based on the positioning ML model and a non-ML model positioning technique based on the data ML model having a higher priority than the positioning ML model.

[0265] Clause 52. The non-transitory computer-readable medium of clause 51, wherein the plurality of positioning measurements are obtained over a measurement period that is weighted between a first measurement period of one or more positioning measurements based on the positioning ML model and a second measurement period of one or more positioning measurements based on the non-ML model positioning technique.

[0266] Clause 53. The non-transitory computer-readable medium of any of clauses 49 to 52, further comprising computer-executable instructions that, when executed by the network node, cause the network node to: transmit the data ML model to a second network node, wherein the second network node is a base station or a location management function (LMF).

[0267] Clause 54. The non-transitory computer-readable medium of any of clauses 49 to 53, further comprising computer-executable instructions that, when executed by the network node, cause the network node to: receive a configuration of a direct association between the data ML model and the positioning ML model; and change the positioning ML model based on the configuration of the direct association.

[0268] Clause 55. The non-transitory computer-readable medium of any of clauses 49 to 54, further comprising computer-executable instructions that, when executed by the network node, cause the network node to: receive a configuration of an indirect association between the data ML model and the positioning ML model; change the positioning ML model or one or more positioning parameters based on the configuration of the indirect association. QC2402402WOQualcomm Ref. No.2402402WO

[0269] Clause 56. The non-transitory computer-readable medium of any of clauses 43 to 55, wherein the network node is a user equipment (UE), a base station, or a network entity.

[0270] 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.

[0271] Further, those of skill in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the aspects disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0272] 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.

[0273] 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 QC2402402WOQualcomm Ref. No.2402402WO 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.

[0274] In one or more example aspects, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media. QC2402402WOQualcomm Ref. No.2402402WO

[0275] While the foregoing disclosure shows illustrative aspects of the disclosure, it should be noted that various changes and modifications could be made herein without departing from the scope of the disclosure as defined by the appended claims. For example, the functions, steps and / or actions of the method claims in accordance with the aspects of the disclosure described herein need not be performed in any particular order. Further, no component, function, action, or instruction described or claimed herein should be construed as critical or essential unless explicitly described as such. Furthermore, as used herein, the terms “set,” “group,” and the like are intended to include one or more of the stated elements. Also, as used herein, the terms “has,” “have,” “having,” “comprises,” “comprising,” “includes,” “including,” and the like does not preclude the presence of one or more additional elements (e.g., an element “having” A may also have B). Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise. Also, as used herein, the term “or” is intended to be inclusive when used in a series and may be used interchangeably with “and / or,” unless explicitly stated otherwise (e.g., if used in combination with “either” or “only one of”) or the alternatives are mutually exclusive (e.g., “one or more” should not be interpreted as “one and more”). Furthermore, although components, functions, actions, and instructions may be described or claimed in the singular, the plural is contemplated unless limitation to the singular is explicitly stated. Accordingly, as used herein, the articles “a,” “an,” “the,” and “said” are intended to include one or more of the stated elements. Additionally, as used herein, the terms “at least one” and “one or more” encompass “one” component, function, action, or instruction performing or capable of performing a described or claimed functionality and also “two or more” components, functions, actions, or instructions performing or capable of performing a described or claimed functionality in combination. QC2402402WO

Claims

Qualcomm Ref. No.2402402WO CLAIMS What is claimed is:

1. 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: transmit, via the one or more transceivers, a capability message indicating one or more capabilities of the network node to support a plurality of machine learning (ML) models for a plurality of functions simultaneously, wherein the plurality of ML models include at least a first ML model and a second ML model; and apply the first ML model to a first set of input signals and the second ML model to a second set of input signals based on the one or more capabilities, wherein at least one of the ML models for the plurality of functions that are supported simultaneously is a positioning ML model.

2. The network node of claim 1, wherein the first ML model is the positioning ML model, and wherein the first set of input signals comprises: one or more positioning reference signals (PRS), or one or more sounding reference signals (SRS).

3. The network node of claim 1, wherein the second ML model is a channel state information (CSI) ML model, and wherein the second set of input signals comprises one or more CSI input signals.

4. The network node of claim 1, wherein the second ML model is a beam management ML model. QC2402402WOQualcomm Ref. No.2402402WO 5. The network node of claim 4, wherein the plurality of ML models further include a third ML model, wherein the one or more processors, either alone or in combination, are further configured to: apply the third ML model to a third set of input signals based on the one or more capabilities.

6. The network node of claim 5, wherein the third ML model is a CSI ML model, and wherein the third set of input signals comprises one or more CSI input signals.

7. The network node of claim 1, wherein the first ML model is the positioning ML model, and wherein the second ML model is a data ML model having a priority different from the positioning ML model.

8. The network node of claim 7, wherein the data ML model is not activated when the network node is in a positioning session based on the positioning ML model having a higher priority than the data ML model.

9. The network node of claim 7, wherein the one or more processors, either alone or in combination, are further configured to obtain a plurality of positioning measurements based on the positioning ML model and a non-ML model positioning technique based on the data ML model having a higher priority than the positioning ML model.

10. The network node of claim 9, wherein the plurality of positioning measurements are obtained over a measurement period that is weighted between a first measurement period of one or more positioning measurements based on the positioning ML model and a second measurement period of one or more positioning measurements based on the non-ML model positioning technique.

11. The network node of claim 7, wherein the one or more processors, either alone or in combination, are further configured to: QC2402402WOQualcomm Ref. No.2402402WO transmit, via the one or more transceivers, the data ML model to a second network node, wherein the second network node is a base station or a location management function (LMF).

12. The network node of claim 7, wherein the one or more processors, either alone or in combination, are further configured to: receive, via the one or more transceivers, a configuration of a direct association between the data ML model and the positioning ML model; and change the positioning ML model based on the configuration of the direct association.

13. The network node of claim 7, wherein the one or more processors, either alone or in combination, are further configured to: receive, via the one or more transceivers, a configuration of an indirect association between the data ML model and the positioning ML model; change the positioning ML model or one or more positioning parameters based on the configuration of the indirect association.

14. The network node of claim 1, wherein the network node is a user equipment (UE), a base station, or a network entity.

15. A method of wireless communication, performed at a network node, comprising: transmitting a capability message indicating one or more capabilities of the network node to support a plurality of machine learning (ML) models for a plurality of functions simultaneously, wherein the plurality of ML models include at least a first ML model and a second ML model; and applying the first ML model to a first set of input signals and the second ML model to a second set of input signals based on the one or more capabilities, wherein at least one of the ML models for the plurality of functions that are supported simultaneously is a positioning ML model. QC2402402WOQualcomm Ref. No.2402402WO 16. The method of claim 15, wherein the first ML model is the positioning ML model, and wherein the first set of input signals comprises: one or more positioning reference signals (PRS), or one or more sounding reference signals (SRS).

17. The method of claim 15, wherein the second ML model is a channel state information (CSI) ML model, and wherein the second set of input signals comprises one or more CSI input signals.

18. The method of claim 15, wherein the second ML model is a beam management ML model.

19. The method of claim 18, wherein the plurality of ML models further include a third ML model, further comprising: applying the third ML model to a third set of input signals based on the one or more capabilities.

20. A network node, comprising: means for transmitting a capability message indicating one or more capabilities of the network node to support a plurality of machine learning (ML) models for a plurality of functions simultaneously, wherein the plurality of ML models include at least a first ML model and a second ML model; and means for applying the first ML model to a first set of input signals and the second ML model to a second set of input signals based on the one or more capabilities, wherein at least one of the ML models for the plurality of functions that are supported simultaneously is a positioning ML model. QC2402402WO

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