Ai / ML condition signaling for ai / ML-based positioning

Abstract

Disclosed are methods, systems, and computer-readable medium to perform operations including: receiving data specifying artificial intelligence / machine learning (AI / ML) assistance information including an associated identifier (ID) associated with one or more parameters of one or more transmission / reception points (TRPs); and performing one or more AI / ML-based positioning processes based on the AI / ML assistance information.

Description

Attorney Docket No. 56990-0074W01 / P70734WO1AI / ML CONDITION SIGNALING FOR AI / ML-BASED POSITIONING CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to and the benefit of U.S. Provisional Patent Application No. 63 / 756,022, filed February 7, 2025, the entire contents of which is incorporated herein by reference.TECHNICAL FIELD

[0002] The present disclosure relates to wireless communications, and more specifically to techniques for signaling AI / ML conditions and associated data to support AI / ML-based-positioning.BACKGROUND

[0003] Wireless communication networks provide integrated communication platforms and telecommunication services to wireless user devices. Example telecommunication services include telephony, data (e.g., voice, audio, and / or video data), messaging, and / or other services. The wireless communication networks have wireless access nodes that exchange wireless signals with the wireless user devices using one or more wireless network protocols, such as protocols described in various telecommunication standards promulgated by the ETSI Third Generation Partnership Project (3GPP). The wireless communication networks facilitate mobile broadband service using technologies such as orthogonal frequency-division multiple access (OFDMA), multiple input multiple output (MIMO), advanced channel coding, massive MIMO, beamforming, and / or other features.Attorney Docket No. 56990-0074W01 / P70734WO1SUMMARY

[0004] The present disclosure describes techniques for signaling AI / ML conditions and associated data to support AI / ML-based-positioning. Also described is a design for an associated identifier (ID) that uniquely represents a pre-determined or pre-specified set of one or more assistance data parameters, thereby improving security and privacy during assistance information signaling by sending the information implicitly rather than explicitly.

[0005] In general, in a first aspect, a method includes: receiving data specifying artificial intelligence / machine learning (AI / ML) assistance information comprising an associated identifier (ID) associated with one or more parameters of one or more transmission / reception points (TRPs); and performing one or more AI / ML-based positioning processes based on the AI / ML assistance information.

[0006] In a second aspect combinable with the first aspect, the one or more characteristics of the one or more TRPs include geographical characteristics of the one or more TRPs.

[0007] In a third aspect combinable with the first or second aspects, the one or more geographical characteristics include at least one of: a transmission reference location associated with a downlink positioning reference signal (DL-PRS) resource ID, a reference location for a transmitting location of a reference TRP of the one or more TRPs, or a relative location for a transmitting antenna of a TRP other than the reference TRP of the one or more TRPs.

[0008] In a fourth aspect combinable with any of the first through third aspects, the associated ID implicitly indicates the one or more characteristics of the one or more TRPs.

[0009] In a fifth aspect combinable with any of the first through fourth aspects, the AI / ML assistance information includes at least one of network conditions, user equipment (UE) conditions, or base station conditions.

[0010] In a sixth aspect combinable with any of the first through fifth aspects, the network conditions include network assistance data or an associated ID.

[0011] In a seventh aspect combinable with any of the first through sixth aspects, the UE conditions include UE assistance data or an associated ID.

[0012] In an eighth aspect combinable with any of the first through seventh aspects, the base station conditions include base station configuration data, transmission reception point (TRP) configuration data, or an associated ID.Attorney Docket No. 56990-0074W01 / P70734WO1

[0013] In a ninth aspect combinable with any of the first through eighth aspects, the method includes: causing transmission of a LTE Positioning Protocol (LPP) message requesting the AI / ML assistance information; and in response to the LPP message, receiving the data specifying the AI / ML assistance information.

[0014] In a tenth aspect combinable with any of the first through ninth aspects, performing the one or more AI / ML-based positioning processes based on the assistance information includes performing AI / ML-based UE positioning based on the assistance information, and / or performing data collection for an AI / ML-based positioning model based on the assistance information.

[0015] In an eleventh aspect combinable with any of the first through tenth aspects, performing the one or more AI / ML-based positioning processes based on the assistance information includes collecting at least one of training data, inference data, or monitoring data based on the assistance information, the method including signaling the AI / ML assistance information with the at least one of the training data, the inference data, or the monitoring data.

[0016] In a twelfth aspect combinable with any of the first through eleventh aspects, the associated ID represents a pre-determined or pre-specified set of one or more assistance data parameters.

[0017] In a thirteenth aspect combinable with any of the first through twelfth aspects, the associated ID includes a bitmap having a size fixed by a specification, a size signaled during data collection configuration, or a variable size.

[0018] In a fourteenth aspect combinable with any of the first through thirteenth aspects, the associated ID is globally unique, unique within a Public Land Mobile Network (PLMN) or Location Management Function (LMF), unique within a training entity, or unique within a validity area.

[0019] In a fifteenth aspect combinable with any of the first through fourteenth aspects, the validity area includes a cell.

[0020] In a sixteenth aspect combinable with any of the first through fifteenth aspects, the method is performed by a UE.

[0021] In a seventeenth aspect combinable with any of the first through sixteenth aspects, the method is performed by one or more processors.Attorney Docket No. 56990-0074W01 / P70734WO1

[0022] In an eighteenth aspect combinable with any of the first through seventeenth aspects, the method is performed by a base station.

[0023] In general, in a nineteenth aspect, a method includes receiving a request for artificial intelligence or machine learning (AI / ML) assistance information; and causing transmission of a message including data specifying the AI / ML assistance information comprising an associated ID associated with one or more parameters of one or more TRPs.

[0024] In a twentieth aspect combinable with the nineteenth aspect, the one or more characteristics of the one or more TRPs include geographical characteristics of the one or more TRPs.

[0025] In a twenty -first aspect combinable with the nineteenth or twentieth aspects, the one or more geographical characteristics include at least one of: a transmission reference location associated with a DL-PRS resource ID, a reference location for a transmitting location of a reference TRP of the one or more TRPs, or a relative location for a transmitting antenna of a TRP other than the reference TRP of the one or more TRPs.

[0026] In a twenty-second aspect combinable with any of the nineteenth through twenty-first aspects, the AI / ML assistance information includes at least one of network conditions, UE conditions, or base station conditions.

[0027] In a twenty -third aspect combinable with any of the nineteenth through twenty-second aspects, the networks conditions include network assistance data or an associated ID.

[0028] In a twenty-fourth aspect combinable with any of the nineteenth through twenty -third aspects, the UE conditions include UE assistance data or an associated ID.

[0029] In a twenty -fifth aspect combinable with any of the nineteenth through twenty -fourth aspects, the base station conditions include base station configuration data, TRP configuration data, or an associated ID.

[0030] In a twenty-sixth aspect combinable with any of the nineteenth through twenty-fifth aspects, the request includes a LPP message requesting the AI / ML assistance information, or an NR Positioning Protocol A (NRPPa) message requesting the AI / ML assistance information.

[0031] In a twenty-seventh aspect combinable with any of the nineteenth through twenty-sixth aspects, the message includes a LPP message providing the data specifying the AI / MLAttorney Docket No. 56990-0074W01 / P70734WO1assistance information, or an NRPPa message providing the data specifying the AI / ML assistance information.

[0032] In a twenty-eighth aspect combinable with any of the nineteenth through twentyseventh aspects, the associated ID represents a pre-determined or pre-specified set of one or more assistance data parameters.

[0033] In a twenty-ninth aspect combinable with any of the nineteenth through twenty-eighth aspects, the associated ID includes a bitmap having a size fixed by a specification, a size signaled during data collection configuration, or a variable size.

[0034] In a thirtieth aspect combinable with any of the nineteenth through twenty-ninth aspects, the associated ID is globally unique, unique within a PLMN or LMF, unique within a training entity, or unique within a validity area.

[0035] In a thirty-first aspect combinable with any of the nineteenth through thirtieth aspects, the method is performed by a LMF.

[0036] In general, in a thirty-second aspect, a method includes: causing transmission of data specifying a user equipment assistance information (UAI) preference for AI / ML based positioning; receiving one or more on-demand configurations; and causing transmission of an on-demand configuration request.

[0037] In a thirty-third aspect combinable with the thirty-second aspect, the method includes responsive to the on-demand configuration request, receiving data specifying at least one of the one or more on-demand configurations.

[0038] In a thirty-fourth aspect combinable with the thirty-second or thirty-third aspect, the UAI preference includes at least one of: a preferred DL-PRS configuration, a preferred general location information, a preferred implicit assistance information, a preferred angle / beam information, data quality conditions, hardware conditions, or preferred miscellaneous information.

[0039] In general, in a thirty-fifth aspect, a method includes: receiving data specifying a UAI preference for AI / ML based positioning; causing transmission of one or more on-demand configurations; and receiving an on-demand configuration request.Attorney Docket No. 56990-0074W01 / P70734WO1

[0040] In a thirty-sixth aspect combinable with the thirty-fifth aspect, the method includes responsive to the on-demand configuration request, causing transmission of a response specifying at least one of the one or more on-demand configurations.

[0041] In a thirty-seventh aspect combinable with the thirty-fifth or thirty-sixth aspects the UAI preference includes at least one of a preferred DL-PRS configuration, a preferred general location information, a preferred implicit assistance information, a preferred angle / beam information, or a preferred miscellaneous information.

[0042] In general, in a thirty-eighth aspect, one or more processors are configured to, when executing instructions stored in memory, perform the operations of any of the first through thirty-seventh aspects.

[0043] In general, in a thirty-ninth aspect, a non-transitory computer storage medium is encoded with instructions that, when executed by one or more processors, cause the one or more processors to perform the operations of any of the first through thirty-seventh aspects.

[0044] In general, in a fortieth aspect, a system includes one or more processors and one or more storage devices on which are stored instructions that are operable, when executed by the one or more processors, to cause the one or more processors to perform the operations of any of the first through thirty-seventh aspects.

[0045] In general, in a forty-first aspect, an apparatus includes one or more baseband processors configured to perform the operations of any of the first through thirty-seventh aspects.

[0046] In general, in a forty-second aspect, a base station includes one or more processors configured to perform the operations of any of the first through thirty-seventh aspects.Attorney Docket No. 56990-0074W01 / P70734WO1 BRIEF DESCRIPTION OF THE FIGURES

[0047] FIG. 1 illustrates an example wireless network.

[0048] FIG. 2 illustrates an example life cycle management (LCM) framework for artificial intelligence and / or machine learning (AI / ML) in New Radio (NR).

[0049] FIG. 3 illustrates an example on-demand UE assistance information (UAI) procedure.

[0050] FIGS. 4A and 4B illustrate example on-demand user equipment (UE) assistance information procedures.

[0051] FIG. 5 illustrates a flowchart of an example method for AI / ML condition signaling for AI / ML-based positioning.

[0052] FIG. 6 illustrates an example UE.

[0053] FIG. 7 illustrates an example access node.Attorney Docket No. 56990-0074W01 / P70734WO1DETAILED DESCRIPTION

[0054] Wireless communication networks use a variety of techniques to estimate the position of a user equipment (UE). These positioning techniques involve the generation of measurements from configured resources by a UE (or base station), followed by the use of these measurements — either at the UE or another network entity — to estimate the UE’s position. During the measurement phase, the UE receives reference signals (e.g., positioning reference signals (PRS)) from multiple transmission reception points (TRPs) and uses these signals to generate measurements that support positioning methods. Alternatively, in the case of uplink positioning methods, the UE transmits reference signals (e.g., sounding reference signals (SRS)) to multiple TRPs, which then generate measurements to support positioning methods. In some cases, assistance data, such as cell information or reference signal configurations, can guide the process of generating measurements. Once the measurements have been generated, they are processed by the UE (e.g., in UE-based positioning) or another network entity, such as a location management function (LMF) (e.g., in UE-assisted LMF-based positioning), using one or more positioning methods, such as Time of Arrival (TOA), Angle of Arrival (AO A), Angle of Departure (AOD), or Time Difference of Arrival (TDOA).

[0055] To improve positioning accuracy, some wireless communication networks leverage artificial intelligence (Al) and machine learning (ML) techniques to calculate (or assist with calculating) a UE’ s position. AI / ML positioning techniques can be divided into two categories: direct AI / ML positioning, and AI / ML assisted positioning. In direct AI / ML positioning, an AI / ML model processes signal measurements collected by a UE (or TRP) to directly calculate the position of the UE. In AI / ML assisted positioning, an AI / ML model processes the signal measurements to provide intermediate measurements used for positioning, rather than directly outputting the position of the UE. Both direct AI / ML positioning and AI / ML assisted positioning can be implemented in a variety of ways. For example, direct AI / ML positioning can be implemented using UE-based positioning with a UE-side model, UE-assisted positioning with an LMF-side model, or NG-RAN-assisted positioning with an LMF-side model, among others. Similarly, AI / ML assisted positioning can be implemented using UE-assisted / LMF-based positioning with a UE-side model, or NG-RAN assisted positioning with a base station-side model, among others.

[0056] In some examples, a UE (or another network entity) may collect data to develop (e.g., train and / or update) an AI / ML positioning model. To facilitate data collection, it may beAttorney Docket No. 56990-0074W01 / P70734WO1necessary for the network (e.g., the LMF) to provide assistance information about conditions surrounding data collection, such as network conditions, UE conditions, and / or gNB / TRP conditions. These conditions should remain consistent for data collection through each of the LCM stages (e.g., training, inference, monitoring) to ensure proper development of the AI / ML model. Once of the AI / ML model is deployed, assistance information about the current network, UE, and / or gNB / TRP conditions may be needed in order to utilize the model for determining a UE’s position.

[0057] The present disclosure describes techniques for signaling AI / ML conditions and associated data to support AI / ML-based-positioning. In some examples, to assist with AI / ML-based positioning, the information to be signaled to specify network, UE, and gNB / TRP conditions is defined. In addition, techniques for signaling these conditions — including during AI / ML-based positioning and among different LCM stages — are described. Also described is a design for an associated identifier (ID) that uniquely represents a pre-determined or prespecified set of one or more assistance data parameters, thereby improving security and privacy during assistance information signaling by sending the information implicitly rather than explicitly. In addition, an on-demand AI / ML UE assistance information procedure is described to enable a UE to indicate a preferred set of conditions and / or configurations for UE based AI / ML positioning.

[0058] FIG. 1 illustrates an example wireless network 100. The wireless network 100 includes a UE 102 and a base station 104 connected via one or more channels 106A, 106B across an air interface 108. The UE 102 and base station 104 communicate using a system that supports controls for managing the access of the UE 102 to a network via the base station 104.

[0059] In some implementations, the wireless network 100 is a Standalone (SA) network, e.g., that incorporates Fifth Generation (5G) New Radio (NR). In some other implementations, the wireless network 100 is a Non- Standalone (NSA) network that incorporates Long Term Evolution (LTE) and 5G NR. In these implementations, the wireless network 100 may be a E-UTRA (Evolved Universal Terrestrial Radio Access)-NR Dual Connectivity (EN-DC) network, or an NR-EUTRA Dual Connectivity (NE-DC) network. Furthermore, wireless networks implementing one or more other types of communication standards are possible, including future 3GPP systems (e.g., Sixth Generation (6G)), Institute of Electrical and Electronics Engineers (IEEE) 802.11 technology, or the like. While aspects may be describedAttorney Docket No. 56990-0074W01 / P70734WO1herein using terminology commonly associated with 5GNR, aspects of the present disclosure can be applied to other systems, such as systems subsequent to 5G (e.g., 6G).

[0060] In the wireless network 100, the UE 102 and any other UE in the system may be, for example, any of a laptop computer, smartphone, tablet computer, machine-type device (such as smart meters or specialized devices for healthcare), intelligent transportation system, or any other wireless device. In network 100, the base station 104 provides the UE 102 network connectivity to a broader network (not shown). This UE 102 connectivity is provided via the air interface 108 in a base station service area provided by the base station 104. In some implementations, such a broader network may be a wide area network operated by a cellular network provider, or may be the Internet. Each base station service area associated with the base station 104 is supported by one or more antennas integrated with the base station 104. The service areas can be divided into a number of sectors associated with one or more particular antennas. Such sectors may be physically associated with one or more fixed antennas or may be assigned to a physical area with one or more tunable antennas or antenna settings adjustable in a beamforming process used to direct a signal to a particular sector.

[0061] The UE 102 includes control circuitry 110 coupled with transmit circuitry 112 and receive circuitry 114. The transmit circuitry 112 and receive circuitry 114 may each be coupled with one or more antennas. The control circuitry 110 may include application-specific circuitry, baseband circuitry, or any of various combinations thereof. The transmit circuitry 112 and receive circuitry 114 may be adapted to transmit and receive data, respectively, and may include radio frequency (RF) circuitry and / or front-end module (FEM) circuitry.

[0062] In various implementations, aspects of the transmit circuitry 112, receive circuitry 114, and / or control circuitry 110 may be integrated in various ways to implement the operations described herein. The control circuitry 110 may be adapted or configured to perform various operations, such as those described elsewhere in this disclosure related to a UE. For instance, the control circuitry 110 can perform AI / ML-based positioning processes, such as AI / ML model development and AI / ML-based UE positioning.

[0063] The transmit circuitry 112 can perform various operations described in this specification. For example, the transmit circuitry 112 can signal information about AI / ML conditions during positioning and through different LCM stages. The transmit circuitry 112 can also transmit preferences for UE assistance information (UAI) and on-demand UAI requests. Additionally, the transmit circuitry 112 may transmit using a plurality of multiplexedAttorney Docket No. 56990-0074W01 / P70734WO1uplink physical channels. The plurality of uplink physical channels may be multiplexed, e.g., according to time division multiplexing (TDM) or frequency division multiplexing (FDM), and in some implementations, along with carrier aggregation. The transmit circuitry 112 may be configured to receive block data from the control circuitry 110 for transmission on the air interface 108.

[0064] The receive circuitry 114 can perform various operations described in this specification. For instance, the receive circuitry 114 can receive assistance data, associated ID(s), and / or gNB / TRP configurations that make up AI / ML conditions. The receive circuitry 114 can also receive on-demand UAI configurations and on-demand UAI requests. Additionally, the receive circuitry 114 may receive a plurality of multiplexed downlink physical channels from the air interface 108 and relay the physical channels to the control circuitry 110. The plurality of downlink physical channels may be multiplexed, e.g., according to TDM or FDM, e.g., along with carrier aggregation. The transmit circuitry 112 and the receive circuitry 114 may transmit and receive, respectively, both control data and content data (e.g., messages, images, video, etc.) structured within data blocks that are carried by the physical channels.

[0065] FIG. 1 also illustrates the base station 104. In some implementations, the base station 104 may be a 5G radio access network (RAN), a next generation RAN, a E-UTRAN, a nonterrestrial cell, or a legacy RAN, such as a UTRAN. As used herein, the term “5G RAN” or the like may refer to the base station 104 that operates in an NR wireless network 100, and the term “E-UTRAN” or the like may refer to a base station 104 that operates in an LTE wireless network 100. The UE 102 utilizes connections (or channels) 106A, 106B, each of which includes a physical communications interface or layer.

[0066] The base station 104 circuitry may include control circuitry 116 coupled (directly or indirectly) with transmit circuitry 118 and / or receive circuitry 120. The transmit circuitry 118 and receive circuitry 120 may each be coupled (directly or indirectly) with one or more antennas that may be used to enable communications via the air interface 108. The transmit circuitry 118 and receive circuitry 120 may be adapted to transmit and receive data, respectively, addressed to any UE connected to the base station 104. The receive circuitry 120 may receive a plurality of uplink physical channels from one or more UEs, including the UE 102.

[0067] In FIG. 1, the one or more channels 106A, 106B are illustrated as an air interface to enable communicative coupling, and can be consistent with cellular communications protocols,Attorney Docket No. 56990-0074W01 / P70734WO1such as an LTE protocol, Advanced LTE (LTE-A) protocol, LTE-based access to unlicensed spectrum (LTE-U), NR protocol, NR-based access to unlicensed spectrum (NR-U) protocol, and / or any other communications protocol(s). In some implementations, the UE 102 may directly exchange communication data via a ProSe interface. The ProSe interface may alternatively be referred to as a sidelink (SL) interface and may include one or more logical channels, including but not limited to a Physical Sidelink Control Channel (PSCCH), a Physical Sidelink Discovery Channel (PSDCH), and a Physical Sidelink Broadcast Channel (PSBCH).

[0068] Some wireless communication networks, such as those operating in accordance with the 3GPP wireless communication standards, leverage artificial intelligence (Al) and / or machine learning (ML) techniques to calculate (or assist with calculating) a UE’s position with improved accuracy. In particular, for direct AI / ML positioning, the following use cases are supported: UE-based positioning with UE-side model (Case 1); UE-assisted / LMF-based positioning with LMF-side model (Case 2b), and NG-RAN node assisted positioning with LMF-side model (Case 3b). In addition, for AI / ML -assisted positioning, the following use cases are supported: UE-assisted / LMF-based positioning with UE-side model (Case 2a) and NG-RAN node assisted positioning with base station (e.g., gNB) side model (Case 3a).

[0069] As discussed above, a UE (or another network entity) may collect data to develop (e.g., train and / or update) an AI / ML-based positioning model. To facilitate data collection, it may be necessary for the network (e.g., the LMF) to provide assistance information about conditions surrounding data collection, such as network conditions, UE conditions, and / or gNB / TRP conditions. For example, for UE-sided model(s) developed at the UE side (e.g., Case 1, Case 2a), the following procedure may be performed:

[0070] Step A: For data collection, the network signals the data collection related configuration(s) and it / their associated ID(s). For example, the network can signal the associated IDs for each sub use case in relation with additional conditions on the network side.

[0071] Step B: The UE collects the data corresponding to the associated ID(s)

[0072] Step C: AI / ML models are developed (e.g., trained, updated) at the UE side based on the collected data corresponding to the associated ID(s).

[0073] Step D: The UE reports information of its AI / ML models corresponding to associated IDs to the network, and a model ID is determined and / or assigned for each AI / ML model.Attorney Docket No. 56990-0074W01 / P70734WO1

[0074] Once of the AI / ML model is deployed, it may be necessary to obtain assistance information about the current network, UE, and / or gNB / TRP conditions in order to utilize the model for determining a UE’ s position. This disclosure defines the information that is signaled to specify these conditions. In addition, this disclosure describes a manner in which this information is signaled, including during AI / ML-based positioning and during data collection in each of the LCM stages.

[0075] In accordance with an aspect of the present disclosure, to assist with AI / ML-based positioning, the following assistance, configuration, and / or associated ID information may be used to specify the various AI / ML conditions: network assistance data and / or associated ID(s) may be used for network conditions; UE assistance data and / or associated ID(s) may be used for UE conditions; and gNB / TRP configuration data and / or associated ID(s) may be used for gNB / TRP conditions. This information may be identified during data collection and used at different points in the LCM to ensure consistency.

[0076] To request this information, a new information element (IE) within a LTE Positioning Protocol (LPP) RequestAssistanceData message may be introduced. In some examples, a new AI / ML-specific IE (e.g., NR-DL-AI / ML-RequestAssistanceData) is introduced. In some examples, an existing IE in LPP is extended (e.g., DL-TDOA (extend existing NR-On-Demand-DL-PRS-Request-rl7), DL-AOD). Alternatively, a new positioning protocol may be created to send the information.

[0077] In accordance with an aspect of the present disclosure, techniques for signaling network, UE, and / or gNB conditions for data collection during each of the LCM stages (e.g., training, inference, monitoring) are described. During data collection for training data, it may be necessary to indicate the associated data conditions with the training dataset. Accordingly, in some examples, the entire training dataset is signaled / transmitted with the assistance data, the associated ID(s), and / or gNB configurations. This can occur at the data aggregator to the model generator. In some examples, a PLMN-unique associated ID (e.g., signaled by LMF and unique within one LMF) can be used. Alternatively, in some examples, each training dataset entry is signaled / transmitted with the assistance data, the associated ID(s), and / or gNB configurations. Signaling can be between the data collector and the data aggregator (e.g., signaled between gNB and UE). Note that the data aggregator and model generator may be the same entity.Attorney Docket No. 56990-0074W01 / P70734WO1

[0078] During data collection for inference, data can be signaled / transmitted with assistance data, the associated ID(s), and / or gNB configurations. Such signaling can occur between the data collector and the model inference entity. In some examples, data is signaled / transmitted with assistance data, the associated ID(s), and / or gNB configurations for each inference operations. In some examples, data is signaled / transmitted with assistance data, the associated ID(s), and / or gNB configurations only on a change in assistance data, the associated ID(s), and / or gNB configurations.

[0079] During data collection for monitoring, data can signaled / transmitted with assistance data, the associated ID(s), and / or gNB configurations. Such signaling can occur between the monitoring data collector and the modeling entity. In some examples, data is signaled / transmitted with assistance data, the associated ID(s), and / or gNB configurations for each monitoring label. In some examples, data is signaled / transmitted with assistance data, the associated ID(s), and / or gNB configurations for each monitoring metric associated with nonlabel based monitoring. In some examples, data is signaled / transmitted with assistance data, the associated ID(s), and / or gNB configurations only on a change in assistance data, the associated ID(s), and / or gNB configurations. Signaling of a change can indicate a need for an action, which may need an acknowledgement that a model change did or did not occur.

[0080] In some examples, the assistance data, the associated ID(s), and / or gNB configurations are transmitted with the data transmission and / or messages between elements of the LCM framework. FIG. 2 illustrates an example LCM framework for AI / ML in NR depicting the signaling of assistance data, associated ID(s), and / or gNB configurations with the data transmission and / or messages between elements.

[0081] In some examples, the assistance data, the associated ID(s), and / or gNB configurations can be signaled from the LMF to the UE using one or more of the following:

[0082] LTE Positioning Protocol (LPP) Request Assistance Information (UE to LMF)

[0083] LPP Provide Assistance Information (LMF to UE)

[0084] LPP Provide AI / ML-preferred Assistance Information (UE to LMF) (e.g., Case 1, Case 2a)

[0085] LPP Request AI / ML-preferred Assistance Information (LMF to UE) (e.g., Case 1, Case 2a)Attorney Docket No. 56990-0074W01 / P70734WO1

[0086] In some examples, the assistance data, the associated ID(s), and / or gNB configurations can be signaled from the LMF to the gNB using one or more of the following:

[0087] NR Positioning Protocol A (NRPPa) AI / ML Configuration Request (LMF to gNB)

[0088] NRPPa AI / ML Configuration Response (gNB to LMF)

[0089] NRPPa AI / ML Preferred Configuration Request (gNB to LMF) (e.g., Case 3b)

[0090] NRPPa AI / ML Preferred Configuration Response (LMF to gNB) (e.g., Case 3b)

[0091] In some examples, it may be desirable to obfuscate some assistance information. For example, it may be desirable to obfuscate information about the geographical coordinates of the TRPs served by the gNB for to maintain security. To do so, an associated ID that uniquely identifies a pre-determined / pre-specified set of one or more assistance data parameters or assistance data parameter values may be used by the access node to implicitly signal assistance information.

[0092] In accordance with an aspect of the present disclosure, an associated ID design is described. In particular, details on the size, uniqueness, and contents of the associated ID are provided. In some examples, the associated ID is specified using a bitmap with a size fixed according to a specification (e.g., the 3GPP technical specifications). In some examples, the associated ID is specified using a bitmap with a size signaled during the data collection configuration. In some examples, a set of applicable associated ID(s) with variable size can be defined.

[0093] In some examples, the associated ID is or includes a global unique sequence. For example, the associated ID can be a single sequence that is unique across the applicable universe (e.g., globally unique). Alternatively, the associated ID can be unique within, for example, a public land mobile network (PMLN) or LMF. In some examples, the associated ID is or incudes a global non-unique sequence. For example, the associated ID can be unique within a training entity (e.g., a gNB). Alternatively, the associated ID can be unique within a specific validity area (e.g., within one ArealD, such as a logical or physical cell).

[0094] In some examples, the contents of the associated ID includes a pre-determined or prespecified set of one or more assistance data parameters. For example, an associated ID might include only the preferred geographical coordinates of the TRPs served by the gNB (include a transmission reference location for each DL-PRS Resource ID, reference location for theAttorney Docket No. 56990-0074W01 / P70734WO1transmitting antenna of the reference TRP, relative locations for transmitting antennas of other TRPs). As another example, the associated ID might include the preferred geographical coordinates of the TRPs served by the gNB and TRP beam / antenna information (including azimuth angle, zenith angle and relative power between PRS resources per angle per TRP) of preferred beams. Other associated IDs can include, but are not limited to, one or more of the following assistance data parameters:Attorney Docket No. 56990-0074W01 / P70734WO1

[0095] Additional information signaled may include one or more of the following: data quality conditions, such as measurement data quality range (e.g., SNR / SINR range), label data quality range (e.g., mean label positioning error), and time range when data generated, and / or hardware conditions, such as network synchronization error and phase offset error.

[0096] In some examples, only explicit UE assistance information (UAI) and / or assistance data is signaled (e.g., during data collection, positioning). In some examples, a fixed subset of UAI / assistance data are signaled explicitly, and another fixed subset of UAI / assistance data are signaled implicitly (e.g., via associated ID(s)). Alternatively, a variable subset of UAI / assistance data are signaled explicitly, and another variable subset of UAI / assistance data are signaled implicitly (e.g., via associated ID(s)). In some examples, a UE (or another entity) may request all UAI / assistance data and / or associated ID(s), a specific subset of UAI / assistance data and / or associated ID(s), or indicate that a specific subset of UAI / assistance data and / or associated ID(s) are not needed.

[0097] In accordance with an aspect of the present disclosure, an on-demand UAI procedure is described. Such a procedure enables a UE to indicate a preferred set of network conditions and configurations for AI / ML positioning. Referring to FIG. 3, an example of a general on-demand UAI procedure 300 is shown. Initially, the UE requests assistance information using an LPP AI / ML Request Assistance Information message. The LMF responds to the UE with an LPP AI / ML Provide Assistance Information message that includes, for example, explicit and / or implicit assistance information, such as assistance data, associated ID(s), and / or gNB configurations. The UE then transmits information on preferred UAI / associated information for on-demand AI / ML support. In some examples, AI / ML UAI may be divided into various classes X, such as a positioning class (among others). A UE can indicate preferred UAI / associated information within one or more classes. For example, for the positioning class, a UE can indicate preferred DL PRS configuration, preferred general location information, preferred implicit assistance information, preferred angle / beam information, and / or preferred miscellaneous information, among others.

[0098] Referring to FIG. 4A, an example on-demand UAI procedure 400 is shown. Initially, the LMF requests configuration information from a gNB and receives information about on-Attorney Docket No. 56990-0074W01 / P70734WO1Demand reference signal configurations (e.g., PRS configurations) that the gNB can support. The LMF can also receive capability information from the UE. The LMF sends AI / ML assistance data, associated ID(s), and / or gNB configuration(s) to UE. Next, the LMF receives information on preferred UA [ / associated information for on-demand AI / ML support from gNB / UE capability. The LMF then configures on-demand AI / ML configurations and sends pre-defined AI / ML on-demand configurations to the UE. In this example, the UE sends an on-demand AI / ML configuration request to LMF using an LPP Request Assistance Data message. Such a request may be for a specific configuration for data collection or a change to data collection characteristics. The LMF then provides a response or error signal.

[0099] Referring to FIG. 4B, an example on-demand UAI procedure 450 is shown. Initially, the LMF requests configuration information from a gNB and receives information about on-Demand reference signal configurations (e.g., PRS configurations) that the gNB can support. The LMF can also receive capability information from the UE. The LMF sends AI / ML assistance data, associated ID(s), and / or gNB configuration(s) to UE. Next, the LMF receives information on preferred UA [ / associated information for on-demand AI / ML support from gNB / UE capability. The LMF then configures on-demand AI / ML configurations and sends pre-defined AI / ML on-demand configurations to the UE. In this example, the LMF determines that there is a need for data collection or change to data collection parameters for on-going data collection procedure. In response, the LMF requests gNB / TRPs for updated parameters using an NRPPa AI / ML Configuration Request message. The gNB / TRPs update and send an NRPPA configuration response. The LMF sends updated UAI / PRS assistance information to the UE using an LPP Provide Assistance Data / associated ID update. The LMF then provides a response or error signal.

[0100] In some examples, one or more AI / ML on-demand configurations can be predefined. For example, for an AreaValidity UAI, an ArealD-cellList or a sequence of ArealD-cellLists can be predefined. As another example, for geographical coordinates, specific geographical coordinates or a list of specific geographical coordinates can be pre-defined. Alternatively, an associated ID or list of associated IDs linking to specific geographical coordinates can be predefined.

[0101] FIG. 5 illustrates a flowchart of an example method 500 for AI / ML condition signaling for AI / ML-based positioning, according to some implementations. For clarity of presentation, the description that follows generally describes method 500 in the context of the other figuresAttorney Docket No. 56990-0074W01 / P70734WO1in this description. For example, method 500 can be performed by UE 102 and / or base station 104 of FIG. 1. It will be understood that method 500 can be performed, for example, by any suitable system, environment, software, hardware, or a combination of systems, environments, software, and hardware, as appropriate. In some implementations, various steps of method 500 can be run in parallel, in combination, in loops, or in any order.

[0102] Operations of the method 500 include receiving data specifying AI / ML assistance information (502). In some examples, the data specifying the AI / ML assistance data is received in an LPP or NRPPa message in response to a request, such as an LPP or NRPPa request message. In some examples, the AI / ML assistance information includes one or more conditions, such as one or more network conditions (e.g., network assistance data and / or an associated ID), UE conditions (e.g., UE assistance data and / or an associated ID), or BS conditions (e.g., BS / TRP configuration data and / or an associated ID). The associated ID can include a globally or semi -globally unique ID that represents a pre-determined or pre-specified set of one or more assistance data parameters (among other information). In some examples, the associated ID includes a bitmap having a size fixed by a specification, a size signaled during data collection configuration, or a variable size.

[0103] At 504, one or more AEML-based positioning processes are performed based on the AI / ML assistance information. In some examples, performing the one or more AI / ML-based positioning processes includes performing AI / ML-based positioning based on the assistance data to determine a position of a UE (or measurements used to determine the position of a UE), which may then be transmitted to another network entity (e.g., an LMF). In some examples, performing the one or more AI / ML-based positioning processes includes performing data collection (e.g., training data collection, inference data collection, and / or monitoring data collection) for an AI / ML-based positioning model based on the assistance data. In some examples, data resulting from the positioning processes (e.g., the training data, the inference data, and / or the monitoring data) can be transmitted (e.g., among LCM stages and / or to another network entity) with information signaling the AI / ML assistance information.

[0104] FIG. 6 illustrates an example UE 600. The UE 600 may be similar to and substantially interchangeable with UE 102 of FIG. 1.

[0105] The UE 600 may be any mobile or non-mobile computing device, such as, for example, a mobile phone, computer, tablet, industrial wireless sensors, video device (for example,Attorney Docket No. 56990-0074W01 / P70734WO1cameras, video cameras, etc.), wearable devices (for example, a smart watch), relaxed-IoT devices, etc.

[0106] The UE 600 may include any / all of processor 602, RF interface circuitry 604, memory / storage 606, user interface 608, sensors 610, driver circuitry 612, power management integrated circuit (PMIC) 614, one or more antenna(s) 616, and battery 618. The components of the UE 600 may be implemented as integrated circuits (ICs), portions thereof, discrete electronic devices, or other modules, logic, hardware, software, firmware, or a combination thereof. The block diagram of FIG. 6 is intended to show a high-level view of some of the components of the UE 600. However, some of the components shown may be omitted, additional components may be present, and a different arrangement of the components shown may occur in other implementations.

[0107] The components of the UE 600 may be coupled with various other components over one or more interconnects 620, which may represent any type of interface, input / output, bus (local, system, or expansion), transmission line, trace, optical connection, etc., that allows various circuit components (on common or different chips or chipsets) to interact with one another.

[0108] The processor 602 may include one or more processors. For example, the processor 602 may include processor circuitry such as, for example, baseband processor circuitry (BB) 622A, central processor unit circuitry (CPU) 622B, and graphics processor unit circuitry (GPU) 622C. The processor 602 may include any type of circuitry or processor circuitry that executes or otherwise operates computer-executable instructions, such as program code, software modules, or functional processes from memory / storage 606 to cause the UE 600 to perform operations as described herein.

[0109] In some implementations, the baseband processor circuitry 622A may access a communication protocol stack 624 in the memory / storage 606 to communicate over a 3 GPP compatible network. In general, the baseband processor circuitry 622A may access the communication protocol stack to: perform user plane functions at a physical (PHY) layer, medium access control (MAC) layer, radio link control (RLC) layer, packet data convergence protocol (PDCP) layer, service data adaptation protocol (SDAP) layer, and PDU layer; and perform control plane functions at a PHY layer, MAC layer, RLC layer, PDCP layer, RRC layer, and a non-access stratum layer. In some implementations, the PHY layer operations may additionally / altematively be performed by the components of the RF interface circuitry 604.Attorney Docket No. 56990-0074W01 / P70734WO1The baseband processor circuitry 622A may generate or process baseband signals or waveforms that carry information in 3 GPP-compatible networks. In some implementations, the waveforms for NR may be based cyclic prefix orthogonal frequency division multiplexing (OFDM) “CP-OFDM” in the uplink or downlink, and discrete Fourier transform spread OFDM “DFT-S-OFDM” in the uplink.

[0110] The memory / storage 606 may include one or more non -transitory, computer-readable media that includes instructions (for example, communication protocol stack 624) that may be executed by the processor 602 to cause the UE 600 to perform various operations described herein. For instance, the memory / storage 606 can include instructions that may be executed by the processor 602 to perform (or cause the UE 600 to perform) AI / ML-based positioning processes, such as AI / ML model development and / or AI / ML-based UE positioning. The memory / storage 606 include any type of volatile or non-volatile memory that may be distributed throughout the UE 600. In some implementations, some of the memory / storage 606 may be located on the processor 602 itself (for example, LI and L2 cache), while other memory / storage 606 is external to the processor 602 but accessible thereto via a memory interface. The memory / storage 606 may include any suitable volatile or non-volatile memory such as, but not limited to, dynamic random access memory (DRAM), static random access memory (SRAM), erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), Flash memory, solid-state memory, or any other type of memory device technology.[OHl] The RF interface circuitry 604 may include transceiver circuitry and radio frequency front module (RFEM) that allows the UE 600 to communicate with other devices over a radio access network. The RF interface circuitry 604 may include various elements arranged in transmit or receive paths. These elements may include, for example, switches, mixers, amplifiers, filters, synthesizer circuitry, control circuitry, etc.

[0112] In the receive path, the RFEM may receive a radiated signal from an air interface via antenna(s) 616 and proceed to filter and amplify (with a low-noise amplifier) the signal. The signal may be provided to a receiver of the transceiver that downconverts the RF signal into a baseband signal that is provided to the baseband processor. In some examples, the receive path can be used to receive assistance data, associated ID(s), and / or gNB / TRP configurations that make up AI / ML conditions. The receive path can also be used to receive on-demand UAI configurations and / or on-demand UAI requests.Attorney Docket No. 56990-0074W01 / P70734WO1

[0113] In the transmit path, the transmitter of the transceiver up-converts the baseband signal received from the baseband processor and provides the RF signal to the RFEM. The RFEM may amplify the RF signal through a power amplifier prior to the signal being radiated across the air interface via the antenna(s) 616. In various implementations, the RF interface circuitry 604 may be configured to transmit / receive signals in a manner compatible with NR access technologies. In some examples, the transmit path can be used to signal information about AI / ML conditions during positioning and through different LCM stages. The transmit path can also be used to transmit preferences for UAI.

[0114] The antenna(s) 616 may include one or more antenna elements to convert electrical signals into radio waves to travel through the air and to convert received radio waves over the air into electrical signals. In some implementations, the antenna elements may be arranged into one or more antenna panels. The antenna(s) 616 may have antenna panels that are omnidirectional, directional, or a combination thereof, to enable beamforming and multiple input, multiple output communications. The antenna(s) 616 may include any / all of microstrip antennas, printed antennas fabricated on the surface of one or more printed circuit boards, patch antennas, phased array antennas, etc. The antenna(s) 616 may have one or more panels designed for one or more specific frequency bands, such as bands in FR1 or FR2.

[0115] The user interface 608 includes various input / output (VO) devices designed to enable user interaction with the UE 600. The user interface 608 includes input device circuitry and output device circuitry. Input device circuitry includes any physical or virtual means for accepting an input including, inter alia, one or more physical or virtual buttons (for example, a reset button), a physical keyboard, keypad, mouse, touchpad, touchscreen, microphones, scanner, headset, or the like. The output device circuitry includes any physical or virtual means for showing information or otherwise conveying information, such as sensor readings, actuator position(s), or other like information. Output device circuitry may include any number or combinations of audio or visual display, including, inter alia, one or more simple visual outputs / indicators (for example, binary status indicators such as light emitting diodes “LEDs” and multi -character visual outputs), or more complex outputs such as display devices or touchscreens (for example, liquid crystal displays “LCDs,” LED displays, quantum dot displays, projectors, etc.), with the output of characters, graphics, multimedia objects, and the like being generated or produced from the operation of the UE 600.Attorney Docket No. 56990-0074W01 / P70734WO1

[0116] The sensors 610 may include devices, modules, or subsystems whose purpose is to detect events or changes in its environment and send the information (sensor data) about the detected events to some other device, module, subsystem, etc. Examples of such sensors include, inter alia, inertia measurement units including accelerometers, gyroscopes, or magnetometers; microelectromechanical systems or nanoelectromechanical systems including 3-axis accelerometers, 3-axis gyroscopes, or magnetometers; level sensors; temperature sensors (for example, thermistors); pressure sensors; image capture devices (for example, cameras or lensless apertures); light detection and ranging sensors; proximity sensors (for example, infrared radiation detector and the like); depth sensors; ambient light sensors; ultrasonic transceivers; microphones or other like audio capture devices; etc.

[0117] The driver circuitry 612 may include software and hardware elements that operate to control particular devices that are embedded in the UE 600, attached to the UE 600, or otherwise communicatively coupled with the UE 600. The driver circuitry 612 may include individual drivers allowing other components to interact with or control various input / output (EO) devices that may be present within, or connected to, the UE 600. For example, driver circuitry 612 may include a display driver to control and allow access to a display device, a touchscreen driver to control and allow access to a touchscreen interface, sensor drivers to obtain sensor readings of sensors 610 and control and allow access to sensors 610, drivers to obtain actuator positions of electro-mechanic components or control and allow access to the electro-mechanic components, a camera driver to control and allow access to an embedded image capture device, audio drivers to control and allow access to one or more audio devices.

[0118] The PMIC 614 may manage power provided to various components of the UE 600. In particular, with respect to the processor 602, the PMIC 614 may control power-source selection, voltage scaling, battery charging, or DC-to-DC conversion.

[0119] In some implementations, the PMIC 614 may control, or otherwise be part of, various power saving mechanisms of the UE 600. A battery 618 may power the UE 600, although in some examples the UE 600 may be mounted deployed in a fixed location, and may have a power supply coupled to an electrical grid. The battery 618 may be a lithium ion battery, a metal-air battery, such as a zinc-air battery, an aluminum-air battery, a lithium-air battery, and the like. In some implementations, such as in vehicle-based applications, the battery 618 may be a typical lead-acid automotive battery.Attorney Docket No. 56990-0074W01 / P70734WO1

[0120] FIG. 7 illustrates an example access node 700 (e.g., a base station or gNB), according to some implementations. The access node 700 may be similar to and substantially interchangeable with base station 104. The access node 700 may include one or more of processor 702, RF interface circuitry 704, core network (CN) interface circuitry 706, memory / storage circuitry 708, and one or more antenna(s) 710. The processor 702 may include any type of circuitry or processor circuitry that executes or otherwise operates computerexecutable instructions, such as program code, software modules, or functional processes from memory / storage circuitry 708 to cause the access node 700 to perform operations as described herein.

[0121] The components of the access node 700 may be coupled with various other components over one or more interconnects 712. The processor 702, RF interface circuitry 704, memory / storage circuitry 708 (including communication protocol stack 714), antenna(s) 710, and interconnects 712 may be similar to like-named elements shown and described with respect to FIG. 6. For example, the processor 702 may include processor circuitry such as, for example, baseband processor circuitry (BB) 716A, central processor unit circuitry (CPU) 716B, and graphics processor unit circuitry (GPU) 716C.

[0122] In some examples, memory / storage 708 may include one or more non-transitory, computer-readable media that includes instructions that may be executed by the processor 702 to cause the access node 700 to perform various operations described herein. For instance, the memory / storage 708 can include instructions that may be executed by the processor 702 to perform (or cause the access node 700 to perform) AI / ML-based positioning processes, such as AI / ML model development and / or processing of AI / ML-based positioning information received from a UE.

[0123] The RF interface circuitry 704 may include transceiver circuitry and receive circuitry that allows the access node 700 to communicate with other devices over a radio access network In some examples, the transmit path can be used to transmit assistance data, associated ID(s), and / or gNB / TRP configurations that make up AI / ML conditions. The transmit path can also be used to transmit on-demand UAI configurations and / or on-demand UAI requests. In some examples, the receive path can be used to receive information about AI / ML conditions during positioning and through different LCM stages. The receive path can also be used to receive preferences for UAI.Attorney Docket No. 56990-0074W01 / P70734WO1

[0124] The CN interface circuitry 706 may provide connectivity to a core network, for example, a 5th Generation Core network (5GC) using a 5GC -compatible network interface protocol such as carrier Ethernet protocols, or some other suitable protocol. Network connectivity may be provided to / from the access node 700 via a fiber optic or wireless backhaul. The CN interface circuitry 706 may include one or more dedicated processors or FPGAs to communicate using one or more of the aforementioned protocols. In some implementations, the CN interface circuitry 706 may include multiple controllers to provide connectivity to other networks using the same or different protocols.

[0125] As used herein, the terms “access node,” “access point,” or the like may describe equipment that provides the radio baseband functions for data and / or voice connectivity between a network and one or more users. These access nodes can be referred to as BS, gNBs, RAN nodes, eNBs, NodeBs, RSUs, TRxPs or TRPs, and so forth, and can include ground stations (e.g., terrestrial access points) or satellite stations providing coverage within a geographic area (e.g., a cell). As used herein, the term “NG RAN node” or the like may refer to an access node 700 that operates in an NR or 5G system (for example, a gNB), and the term “E-UTRAN node” or the like may refer to an access node 700 that operates in an LTE or 4G system (e.g., an eNB). According to various implementations, the access node 700 may be implemented as one or more of a dedicated physical device such as a macrocell base station, and / or a low power (LP) base station for providing femtocells, picocells or other like cells having smaller coverage areas, smaller user capacity, or higher bandwidth compared to macrocells.

[0126] In some implementations, all or parts of the access node 700 may be implemented as one or more software entities running on server computers as part of a virtual network, which may be referred to as a CRAN and / or a virtual baseband unit pool (vBBUP).

[0127] Various components may be described as performing a task or tasks, for convenience in the description. Such descriptions should be interpreted as including the phrase “configured to.” Reciting a component that is configured to perform one or more tasks is expressly intended not to invoke 35 U.S.C. § 112(f) interpretation for that component.

[0128] For one or more embodiments, at least one of the components set forth in one or more of the preceding figures may be configured to perform one or more operations, techniques, processes, or methods as set forth in the example section below. For example, the baseband circuitry as described above in connection with one or more of the preceding figures may beAttorney Docket No. 56990-0074W01 / P70734WO1configured to operate in accordance with one or more of the examples set forth below. For another example, circuitry associated with a UE, base station, network element, etc., as described above in connection with one or more of the preceding figures may be configured to operate in accordance with one or more of the examples set forth below in the example section.

[0129] Any of the above-described examples may be combined with any other example (or combination of examples), unless explicitly stated otherwise. The foregoing description of one or more implementations provides illustration and description, but is not intended to be exhaustive or to limit the scope of embodiments to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of various embodiments.

[0130] Although the embodiments above have been described in considerable detail, numerous variations and modifications will become apparent to those skilled in the art once the above disclosure is fully appreciated. It is intended that the following claims be interpreted to embrace all such variations and modifications.

[0131] It is well understood that the use of personally identifiable information should follow privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining the privacy of users. In particular, personally identifiable information data should be managed and handled so as to minimize risks of unintentional or unauthorized access or use, and the nature of authorized use should be clearly indicated to users.

Claims

Attorney Docket No. 56990-0074W01 / P70734WO1CLAIMSWe Claim:

1. A method, comprising:receiving data specifying artificial intelligence / machine learning (AI / ML) assistance information comprising an associated identifier (ID) associated with one or more parameters of one or more transmi ssion / recepti on points (TRPs); andperforming one or more AI / ML-based positioning processes based on the AI / ML assistance information.

2. The method of claim 1, wherein the one or more parameters of the one or more TRPs comprise geographical parameters of the one or more TRPs.

3. The method of claim 2, wherein the one or more geographical parameters comprise at least one of: a transmission reference location associated with a downlink positioning reference signal (DL-PRS) resource ID, a reference location for a transmitting location of a reference TRP of the one or more TRPs, or a relative location for a transmitting antenna of a TRP other than the reference TRP of the one or more TRPs.

4. The method of claim 1, wherein the associated ID implicitly indicates the one or more parameters of the one or more TRPs.

5. The method of claim 1, wherein the AI / ML assistance information comprises at least one of network conditions, user equipment (UE) conditions, or base station conditions.

6. The method of claim 1, further comprising:causing transmission of a LTE Positioning Protocol (LPP) message requesting the AI / ML assistance information; andin response to the LPP message, receiving the data specifying the AI / ML assistance information.

7. The method of claim 1, wherein performing the one or more AI / ML-based positioning processes based on the assistance information comprises performing AI / ML-based UE positioning based on the assistance information, or performing data collection for an AI / ML-based positioning model based on the assistance information.Attorney Docket No. 56990-0074W01 / P70734WO18. The method of claim 1, wherein performing the one or more AI / ML-based positioning processes based on the assistance information comprises collecting at least one of training data, inference data, or monitoring data based on the assistance information, the method further comprising:signaling the AI / ML assistance information with the at least one of the training data, the inference data, or the monitoring data.

9. The method of claim 1, wherein the associated ID represents a pre-determined or prespecified set of one or more assistance data parameters.

10. The method of claim 1, wherein the associated ID comprises a bitmap having a size fixed by a specification, a size signaled during data collection configuration, or a variable size.

11. The method of claim 1, wherein the associated ID is unique within a validity area.

12. The method of claim 11, wherein the validity area comprises a cell.

13. A method, comprising:receiving a request for artificial intelligence or machine learning (AI / ML) assistance information; andcausing transmission of a message including data specifying the AI / ML assistance information comprising an associated identifier (ID) associated with one or more parameters of one or more transmi ssion / recepti on points (TRPs).

14. The method of claim 13, wherein the one or more parameters of the one or more TRPs comprise geographical parameters of the one or more TRPs.

15. The method of claim 14, wherein the one or more geographical parameters comprise at least one of: a transmission reference location associated with a downlink positioning reference signal (DL-PRS) resource ID, a reference location for a transmitting location of a reference TRP of the one or more TRPs, or a relative location for a transmitting antenna of a TRP other than the reference TRP of the one or more TRPs.

16. The method of claim 13, wherein the AI / ML assistance information comprises at least one of network conditions, user equipment (UE) conditions, or base station conditions.Attorney Docket No. 56990-0074W01 / P70734WO117. The method of claim 13, wherein the request comprises a LTE Positioning Protocol (LPP) message requesting the AI / ML assistance information, or an NR Positioning Protocol A (NRPPa) message requesting the AI / ML assistance information.

18. The method of claim 13, the message comprises a LTE Positioning Protocol (LPP) message providing the data specifying the AI / ML assistance information, or an NR Positioning Protocol A (NRPPa) message providing the data specifying the AI / ML assistance information.

19. The method of claim 13, wherein the associated ID represents a pre-determined or prespecified set of one or more assistance data parameters.

20. The method of claim 13, wherein the associated ID comprises a bitmap having a size fixed by a specification, a size signaled during data collection configuration, or a variable size.

21. The method of claim 13, wherein the associated ID is unique within a validity area.

22. One or more processors configured to, when executing instructions stored in memory, perform the method of any preceding claim.

23. A non-transitory computer storage medium encoded with instructions that, when executed by one or more processors, cause the one or more processors to perform the method of any of claims 1 to 21.

24. An apparatus comprising one or more baseband processors configured to perform the method of any of claims 1 to 21.

25. A base station comprising one or more processors configured to perform the method of any of any of claims 1 to 21.

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