Positioning measurement data filtering

Abstract

A user equipment (UE) may receive a configuration for collecting measurements to at least one of train or validate a positioning model. The UE may receive a set of positioning signals. The UE may measure the set of positioning signals. The UE may output a subset of the set of measured positioning signals to at least one of train or verify the positioning model based on the configuration. The UE may output the subset of the set of measured positioning signals by training at the UE to validate the positioning model based on the subset of the set of measured positioning signals or by at least one of transmitting the subset of the set of measured positioning signals to train or validate the positioning model.

Description

[0001] Cross-reference to related applications

[0002] This application claims the benefit of U.S. non-provisional patent application No. 18 / 461,970, filed on September 6, 2023, entitled “POSITIONING MEASUREMENT DATAFILTERING,” the entire contents of which are expressly incorporated herein by reference. Technical Field

[0003] This disclosure relates generally to communication systems, and more specifically to wireless positioning systems. Background Technology

[0004] Wireless communication systems are widely deployed to provide a variety of telecommunications services, such as telephone, video, data, messaging, and broadcasting. Typical wireless communication systems may employ multiple access technologies capable of supporting communication with multiple users by sharing available system resources. Examples of such multiple access technologies include Code Division Multiple Access (CDMA) systems, Time Division Multiple Access (TDMA) systems, Frequency Division Multiple Access (FDMA) systems, Orthogonal Frequency Division Multiple Access (OFDMA) systems, Single Carrier Frequency Division Multiple Access (SC-FDMA) systems, and Time Division Synchronous Code Division Multiple Access (TD-SCDMA) systems.

[0005] These multiple access technologies have been adopted in various telecommunications standards to provide a common protocol that enables different wireless devices to communicate at the city, national, regional, and even global levels. An example telecommunications standard is 5G New Radio (NR). 5G NR is part of the Continuous Evolution of Mobile Broadband (CEM) program issued by the 3rd Generation Partnership Project (3GPP) to meet new requirements associated with latency, reliability, security, scalability (e.g., with the Internet of Things (IoT),) and other requirements. 5G NR includes services associated with enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). Some aspects of 5G NR can be based on the 4G Long Term Evolution (LTE) standard. Further improvements to 5G NR technology are needed. Furthermore, these improvements can also be applied to other multiple access technologies and telecommunications standards that adopt these technologies. Summary of the Invention

[0006] The following is a simplified summary of one or more aspects to provide a basic understanding of these aspects. This summary is not a comprehensive overview of all conceived aspects. It neither identifies key or essential elements of all aspects nor describes the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed descriptions that follow.

[0007] In one aspect of this disclosure, a method, computer-readable medium, and apparatus are provided. The apparatus may include user equipment (UE). The UE may include a positioning reference unit (PRU). The apparatus may receive a conditional configuration for collecting measurements to train or validate at least one of a positioning model. Model validation may include validation of the positioning model before or after activation. The apparatus may receive a set of positioning signals. The apparatus may measure the set of positioning signals. The apparatus may output a subset of the measured set of positioning signals to train or validate at least one of the positioning models based on at least one condition of the conditional configuration. The apparatus may output the subset of the measured set of positioning signals by training the positioning model at the apparatus based on at least one condition of the conditional configuration. The apparatus may output the subset of the measured set of positioning signals by validating the positioning model at the apparatus based on at least one condition of the conditional configuration. The device can output the subset of the measured location signals to a network entity by sending the subset of the measured location signals to the network entity based on at least one condition configured by the condition, to train or validate at least one of the location models.

[0008] In one aspect of this disclosure, a method, computer-readable medium, and apparatus are provided. The apparatus may include a collection of base stations, network entities, network nodes, core networks, location servers, or location management functions (LMFs). The apparatus may be configured with conditional configurations for selecting a subset of measurements of a set of location signals to train or validate at least one of the location models. The apparatus may transmit the conditional configurations to train or validate at least one of the location models.

[0009] To achieve the foregoing and related objectives, one or more aspects may include the features fully described below and specifically pointed out in the claims. The following description and drawings set forth some exemplary features of one or more aspects in detail. However, these features indicate only a few of the various ways in which the principles of the various aspects may be employed. Attached Figure Description

[0010] Figure 1 This is a diagram illustrating an example of a wireless communication system and an access network.

[0011] Figure 2A This is an illustration of an example of the first frame according to various aspects of this disclosure.

[0012] Figure 2B This is a diagram illustrating examples of downlink (DL) channels within a subframe according to various aspects of this disclosure.

[0013] Figure 2CThis is an illustration of an example of a second frame according to various aspects of this disclosure.

[0014] Figure 2D This is a diagram illustrating examples of uplink (UL) channels within a subframe according to various aspects of this disclosure.

[0015] Figure 3 This is a diagram illustrating examples of base stations and user equipment (UEs) in an access network.

[0016] Figure 4 This is a diagram illustrating an example of positioning based on positioning signal measurements.

[0017] Figure 5 This is a diagram illustrating an example of positioning based on positioning signal measurements.

[0018] Figure 6 This is a connection flowchart illustrating an example of data collection entity filtering for training and / or validating localization models using measurements and / or labels.

[0019] Figure 7 This is a flowchart of a wireless communication method.

[0020] Figure 8 This is a flowchart of a wireless communication method.

[0021] Figure 9 This is a flowchart of a wireless communication method.

[0022] Figure 10 This is a flowchart of a wireless communication method.

[0023] Figure 11 This is a flowchart of a wireless communication method.

[0024] Figure 12 This is a flowchart of a wireless communication method.

[0025] Figure 13 These are illustrations illustrating specific hardware implementations used for example devices and / or network entities.

[0026] Figure 14 This is a diagram illustrating an example of a hardware implementation used for an example network entity.

[0027] Figure 15 This is a diagram illustrating an example of a hardware implementation used for an example network entity. Detailed Implementation

[0028] The following description relates to examples intended to illustrate the innovative aspects of this disclosure. However, those skilled in the art will recognize that the teachings herein can be applied in numerous ways. Some or all of the examples described may be applicable to Bluetooth systems that meet the requirements of the Institute of Electrical and Electronics Engineers (IEEE) 1002.11, IEEE 1002.15, or Bluetooth as defined by the Bluetooth Special Interest Group (SIG). ® The described examples can be implemented in any device, system, or network that transmits and receives radio frequency (RF) signals using one or more of the following standards, or standards such as Long Term Evolution (LTE), 3G, 4G, or 5G (New Radio (NR)) published by the 3rd Generation Partnership Project (3GPP). The examples described can be implemented in any device, system, or network capable of transmitting and receiving RF signals according to one or more of the following technologies or techniques: Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal FDMA (OFDMA), Single Carrier FDMA (SC-FDMA), Space Division Multiple Access (SDMA), Rate Split Multiple Access (RSMA), Multi-User Shared Access (MUSA), Single-User (SU) Multiple-Input Multiple-Output (MIMO), and Multi-User (MU) MIMO. The examples described can also be implemented using other wireless communication protocols or RF signals suitable for use in one or more of the following wireless personal area networks (WPAN), wireless local area networks (WLAN), wireless wide area networks (WWAN), wireless metropolitan area networks (WMAN), or Internet of Things (IoT) networks.

[0029] Various aspects generally relate to positioning systems. Some aspects more specifically relate to wireless positioning systems that use input data and tags to train positioning models. In some examples, a user equipment (UE) may receive a conditional configuration for collecting measurements to train or validate at least one of the positioning models. The UE may receive a set of positioning signals. The UE may measure the set of positioning signals. The UE may output a subset of the measured set of positioning signals to train or validate at least one of the positioning models based on at least one condition of the conditional configuration. The conditional configuration may include a set of conditions that assist the UE in identifying the subset of the measured set of positioning signals to be used. The UE may output the subset of the measured set of positioning signals by training the positioning model at the UE based on the subset of the measured set of positioning signals. The UE may output the subset of the measured set of positioning signals by validating the positioning model at the UE based on the subset of the measured set of positioning signals. The UE can output the subset of measured location signals to train or validate at least one of the location models by sending the subset to a network entity (e.g., a training entity, a validation entity). In other examples, the network entity can configure conditional settings for selecting a subset of measurements of the location signals to train or validate at least one of the location models. The network entity can send the conditional settings to train or validate at least one of the location models. The training entity can train the location model by providing it with a set of inputs and labels, enabling the location model to reproduce the labels based on the set of inputs. The validation entity can validate the accuracy of the location model by providing it with a set of inputs and validating that the model's output is within the accuracy range of the labels. The validation entity can validate the reliability of the location model by checking the distribution / statistics of the model input measurements and comparing them with the training model inputs, or by checking the consistency of the model output over time. The verification entity can verify the accuracy / reliability of the positioning model before activation (e.g., to confirm whether the model will work as expected) or after activation (e.g., to confirm whether the model continues to work as expected). The verification entity can be configured to verify the positioning model periodically, such as every ten minutes or every hour.

[0030] In some aspects, techniques targeting data collection entities and data source entities (e.g., UEs) can be used to ensure high-quality measurements and labels for training / validating localization models (e.g., artificial intelligence / machine learning (AI / ML) localization models). In some aspects, network entities (such as LMFs, core entities (CNs), or over-the-top (OTT) servers) can configure data quality and conditions at the UE. The UE can use the configuration to collect measurements accordingly and apply labels to the measurements. In some aspects, the UE can be configured using one or more conditions related to labeling the localization model. For example, a condition could be that the UE receives labels from other wireless devices (e.g., network entities) without labeling. A condition could be that the UE generates labels based on Radio Access Network (RAT) localization methods and / or non-RAT localization methods. A condition could be that the UE uses a localization method to generate labels if the associated error rate of the localization method is below a threshold. A condition could be that the UE uses multiple label generation methods to generate labels and provides indicators of multiple labels in some way (e.g., grouping labels in a set, averaging a weighted set of labels, averaging labels). The conditions may include the UE being configured to associate tags with quality indicators (e.g., error estimation, signal-to-noise ratio (SNR)). The conditions may also include the UE collecting and reporting a set of location data that meets the conditions. In some aspects, the UE may be configured to prioritize location measurements (e.g., measurements of location reference signals (PRS), sounding reference signals (SRS), synchronization signal blocks (SSBs)) based on thresholds for one or more factors. In some aspects, the UE may request assistance (e.g., auxiliary data) from network entities to ensure high-quality data and tags. For example, the UE may request configuration attributes for location dialogues, and / or the UE may request labeled auxiliary data that lists prioritization criteria for tags. The UE may receive configuration to generate multiple tags and measurements accordingly. The UE may provide such collected data (e.g., measurements and tags) to network entities (e.g., LMF, CN, OTT server, data collection entity, data repository, training entity, validation entity).

[0031] Specific aspects of the subject matter described in this disclosure can be implemented to achieve one or more of the following potential advantages. In some examples, by configuring the UE to filter its data used for training or validating a localization model based on a set of conditions configured, the described techniques can be used to improve the quality or reliability of training or validating a localization model. Improving the quality of the input and / or labels used for training or validating a localization model improves the performance of the localization model.

[0032] The detailed descriptions following, illustrated with reference to the accompanying drawings, describe various configurations and do not represent the only configurations in which the concepts described herein can be practiced. To provide a thorough understanding of the various concepts, the detailed descriptions include specific details. However, these concepts can be practiced without these specific details. In some instances, well-known structures and components are shown in block diagram form to avoid obscuring these concepts.

[0033] Various apparatuses and methods are presented with reference to several aspects of a telecommunications system. These apparatuses and methods are described in detail below and illustrated in the accompanying drawings by various blocks, components, circuits, processes, algorithms, etc. (collectively, “elements”). These elements can be implemented using electronic hardware, computer software, or any combination thereof. Whether such elements are implemented as hardware or software depends on the specific application and the design constraints imposed on the system as a whole.

[0034] As an example, an element, any part of an element, or any combination of elements may be implemented as a "processing system" including one or more processors. When multiple processors are implemented, the multiple processors may perform functions individually or in combination. Examples of processors include microprocessors, microcontrollers, graphics processing units (GPUs), central processing units (CPUs), application processors, digital signal processors (DSPs), reduced instruction set computing (RISC) processors, system-on-a-chip (SoCs), baseband processors, field-programmable gate arrays (FPGAs), programmable logic devices (PLDs), state machines, gate logic, discrete hardware circuits, and other suitable hardware configured to perform the various functionalities described throughout this disclosure. One or more processors in a processing system may execute software. Whether referred to as software, firmware, middleware, microcode, hardware description language, or other terms, software should be broadly interpreted as instructions, instruction sets, code, code segments, program code, programs, subroutines, software components, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, or any combination thereof.

[0035] Therefore, in one or more example aspects, specific implementations, and / or use cases, the described functionality may be implemented in hardware, software, or any combination thereof. If implemented in software, the functionality may be stored or encoded as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media. Storage media can be any available medium that can be accessed by a computer. By way of example, such computer-readable media may include random access memory (RAM), read-only memory (ROM), electrically erasable programmable ROM (EEPROM), optical disc storage devices, magnetic disk storage devices, other magnetic storage devices, combinations of these types of computer-readable media, or any other medium that can be used to store computer-executable code in the form of instructions or data structures accessible by a computer.

[0036] While aspects, implementations, and / or use cases are described herein by way of example, additional or different aspects, implementations, and / or use cases may arise in many different arrangements and scenarios. The aspects, implementations, and / or use cases described herein can be implemented across many different platform types, devices, systems, shapes, sizes, and package arrangements. For example, aspects, implementations, and / or use cases may arise via integrated chip implementations and other devices based on non-modular components (e.g., end-user devices, vehicles, communication devices, computing devices, industrial equipment, retail / purchasing devices, medical devices, AI-enabled devices, etc.). While some examples may or may not be specific to a use case or application, the described examples may exhibit broad applicability. Aspects, implementations, and / or use cases can range from chip-level or modular components to non-modular, non-chip-level implementations, and further to aggregated, distributed, or original equipment manufacturer (OEM) devices or systems incorporating one or more of the technologies described herein. In some practical settings, devices incorporating the described aspects and features may also include additional components and features for implementing and practicing the claimed and described aspects. For example, the transmission and reception of wireless signals necessarily involve multiple components for analog and digital purposes (e.g., hardware components including antennas, RF chains, power amplifiers, modulators, buffers, processors, interleavers, adders / summers, etc.). The techniques described herein can be practiced in a wide variety of devices, chip-level components, systems, distributed arrangements, aggregated or decomposed components, end-user equipment, etc., of various sizes, shapes, and configurations.

[0037] Communication systems, such as 5G NR systems, can be deployed in various ways with a variety of components or parts. In a 5G NR system or network, network nodes, network entities, network mobility elements, radio access network (RAN) nodes, core network nodes, network elements or network equipment (such as base stations (BS)), or one or more units (or components) performing base station functions can be implemented in aggregated or decomposed architectures. For example, BSs (such as Node B (NB), evolved NB (eNB), NR BS, 5G NB, access point (AP), transmit / receive point (TRP), or cell, etc.) can be implemented as aggregated base stations (also known as standalone BS or monolithic BS) or decomposed base stations.

[0038] Aggregated base stations can be configured to utilize a radio protocol stack that is physically or logically integrated within a single RAN node. Decentralized base stations can be configured to utilize a protocol stack that is physically or logically distributed across two or more units, such as one or more central or centralized units (CUs), one or more distributed units (DUs), or one or more radio units (RUs). In some respects, the CU may be implemented within a RAN node, and one or more DUs may co-located with the CU, or alternatively, may be geographically or virtually distributed across one or more other RAN nodes. DUs may be implemented to communicate with one or more RUs. Each of the CUs, DUs, and RUs may be implemented as a virtual unit, namely a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU).

[0039] Base station operation or network design can take into account the aggregation characteristics of base station functionality. For example, decomposed base stations can be utilized in Integrated Access Backhaul (IAB) networks, Open Radio Access Networks (O-RAN (such as network configurations initiated by the O-RAN Alliance)), or Virtualized Radio Access Networks (vRAN, also known as Cloud Radio Access Networks (C-RAN)). Decomposition can include distributing functionality across two or more units in various physical locations, as well as virtually distributing the functionality of at least one unit, which enables flexibility in network design. The various units of a decomposed base station or decomposed RAN architecture can be configured to communicate wirelessly with at least one other unit.

[0040] Figure 1Figure 100 illustrates an example of a wireless communication system and access network. The illustrated wireless communication system includes a decomposed base station architecture. The decomposed base station architecture may include one or more CUs 110, which may communicate directly with the core network 120 via a backhaul link, or indirectly with the core network 120 via one or more decomposed base station units, such as a near real-time (near-RT) RAN Intelligent Controller (RIC) 125 via an E2 link, or a non-real-time (non-RT) RIC 115 associated with a Service Management and Orchestration (SMO) framework 105, or both. CUs 110 may communicate with one or more DUs 130 via a corresponding midhaul link (such as an F1 interface). DUs 130 may communicate with one or more RUs 140 via a corresponding fronthaul link. RUs 140 may communicate with a corresponding UE 104 via one or more radio frequency (RF) access links. In some implementations, a UE 104 may be served simultaneously by multiple RUs 140.

[0041] Each of the units (i.e., CU 110, DU 130, RU 140, and near-RT RIC 125, non-RT RIC 115, and SMO frame 105) may include or be coupled to one or more interfaces configured to receive or transmit signals, data, or information (collectively, signals) via wired or wireless transmission media. Each of the units, or an associated processor or controller providing instructions to the communication interfaces of these units, may be configured to communicate with one or more other units via transmission media. For example, these units may include wired interfaces configured to receive signals or transmit signals to one or more other units via wired transmission media. Additionally, these units may include wireless interfaces that may include receivers, transmitters, or transceivers (such as RF transceivers) configured to receive signals via wireless transmission media or transmit signals to one or more other units, or both.

[0042] In some aspects, the CU 110 can host one or more higher-level control functions. Such control functions may include Radio Resource Control (RRC), Packet Data Convergence Protocol (PDCP), Serving Data Adaptation Protocol (SDAP), etc. Each control function can be implemented using an interface configured to signal to other control functions hosted by the CU 110. The CU 110 can be configured to handle user plane functionality (i.e., Central Unit-User Plane (CU-UP)), control plane functionality (i.e., Central Unit-Control Plane (CU-CP)), or a combination thereof. In some implementations, the CU 110 may be logically split into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, the CU-UP units can communicate bidirectionally with the CU-CP units via an interface such as an E1 interface. The CU 110 may be implemented to communicate with the DU 130 for network control and signaling, as needed.

[0043] DU 130 may correspond to a logic unit that includes one or more base station functions for controlling the operation of one or more RU 140s. In some aspects, DU 130 may at least partially host one or more of the Radio Link Control (RLC) layer, Media Access Control (MAC) layer, and one or more high physical (PHY) layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation and demodulation, etc.) according to functional splits (such as those defined by 3GPP). In some aspects, DU 130 may further host one or more low PHY layers. Each layer (or module) may be implemented using an interface configured to communicate signaling with other layers (and modules) hosted by DU 130 or with control functions hosted by CU 110.

[0044] Lower-layer functionality can be implemented by one or more RU 140s. In some deployments, an RU140 controlled by a DU 130 may correspond to a logical node that hosts RF processing functions or low-PHY layer functions (such as performing Fast Fourier Transform (FFT), Inverse FFT (iFFT), digital beamforming, Physical Random Access Channel (PRACH) extraction and filtering, or both, based at least in part on functional decomposition (such as lower-layer functional decomposition). In such architectures, the RU 140 may be implemented to handle over-the-air (OTA) communications with one or more UE 104s. In some specific implementations, the real-time and non-real-time aspects of control plane and user plane communications with the RU 140 may be controlled by the corresponding DU 130. In some scenarios, this configuration enables the implementation of the DU 130 and CU 110 in cloud-based RAN architectures such as vRAN architectures.

[0045] SMO framework 105 can be configured to support RAN deployment and provisioning of both non-virtualized and virtualized network elements. For non-virtualized network elements, SMO framework 105 can be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which can be managed via operation and maintenance interfaces such as the O1 interface. For virtualized network elements, SMO framework 105 can be configured to interact with a cloud computing platform such as Open Cloud (O-Cloud) 190 to perform network element lifecycle management (such as instantiating virtualized network elements) via a cloud computing platform interface such as the O2 interface. Such virtualized network elements may include, but are not limited to, CU 110, DU 130, RU 140, and near-RT RIC 125. In some implementations, SMO framework 105 can communicate with the hardware aspects of the 4G RAN (such as Open eNB (O-eNB) 111) via the O1 interface. Additionally, in some implementations, SMO framework 105 can communicate directly with one or more RU 140s via the O1 interface. SMO framework 105 may also include a non-RT RIC 115 configured to support the functionality of SMO framework 105.

[0046] The non-RT RIC 115 can be configured to include logical functions enabling non-real-time control and optimization of RAN elements and resources, including artificial intelligence (AI) / machine learning (ML) workflows for model training and updates, or policy-based guidance for applications / features in the near-RT RIC 125. The non-RT RIC 115 can be coupled to or communicate with the near-RT RIC 125, such as via an A1 interface. The near-RT RIC 125 can be configured to include logical functions enabling near real-time control and optimization of RAN elements and resources via an interface, such as an E2 interface, through data collection and actions, connecting one or more CU 110s, one or more DU 130s, or both, and O-eNBs to the near-RT RIC 125.

[0047] In some implementations, to generate AI / ML models to be deployed in the near-RT RIC 125, the non-RT RIC 115 may receive parameters or external enrichment information from an external server. This information can be utilized by the near-RT RIC 125 and may be received from non-network data sources or network functions at the SMO framework 105 or the non-RT RIC 115. In some examples, the non-RT RIC 115 or the near-RT RIC 125 may be configured to tune RAN behavior or performance. For example, the non-RT RIC 115 may monitor long-term trends and patterns in performance and employ AI / ML models to perform corrective actions via the SMO framework 105 (such as reconfiguration via O1) or by creating RAN management policies (such as A1 policies).

[0048] At least one of CU 110, DU 130, and RU 140 may be referred to as base station 102. Therefore, base station 102 may include one or more of CU 110, DU 130, and RU 140 (each component is indicated by a dashed line to indicate that each component may or may not be included in base station 102). Base station 102 provides UE 104 with an access point to core network 120. Base station 102 may include macro cells (high-power cellular base stations) and / or small cells (low-power cellular base stations). Small cells include femtocells, picocells, and microcells. A network that includes both small cells and macro cells may be referred to as a heterogeneous network. A heterogeneous network may also include an evolved home node B (eNB) (HeNB), which can provide service to a restricted group referred to as a closed subscriber group (CSG). The communication link between RU 140 and UE 104 may include uplink (UL) transmission (also known as reverse link) from UE 104 to RU 140 and / or downlink (DL) transmission (also known as forward link) transmission from RU 140 to UE 104. The communication link may utilize multiple-input multiple-output (MIMO) antenna techniques, including spatial multiplexing, beamforming, and / or transmit diversity. The communication link may use one or more carriers. For each direction, the total number of carriers used for transmission can be up to [number missing]. Yx MHz ( x For each carrier allocated in carrier aggregation (of component carriers), base station 102 / UE 104 can use up to [number] carriers. Y A spectrum with a bandwidth of MHz (e.g., 5MHz, 10MHz, 15MHz, 20MHz, 100MHz, 400MHz, etc.). Carriers may be adjacent to each other or may not be adjacent to each other. Carrier allocation may be asymmetric with respect to DL and UL (e.g., more or fewer carriers may be allocated to DL compared to UL). Component carriers may include primary component carriers and one or more secondary component carriers. The primary component carrier may be referred to as the primary cell (PCell) and the secondary component carrier may be referred to as the secondary cell (SCell).

[0049] Some UEs 104 can communicate with each other using device-to-device (D2D) communication link 158. D2D communication link 158 can use DL / UL wireless wide area network (WWAN) spectrum. D2D communication link 158 can use one or more sidelink channels, such as Physical Sidelink Broadcast Channel (PSBCH), Physical Sidelink Discovery Channel (PSDCH), Physical Sidelink Shared Channel (PSSCH), and Physical Sidelink Control Channel (PSCCH). D2D communication can be performed through various wireless D2D communication systems, such as Bluetooth. ™ (Bluetooth is a trademark of the Bluetooth Special Interest Group (SIG), and is based on the IEEE 1002.11 standard for Wi-Fi.) ™ (Wi-Fi is a trademark of the Wi-Fi Alliance), LTE, or NR.

[0050] The wireless communication system may also include a Wi-Fi AP 150, which communicates with the UE 104 (also referred to as a Wi-Fi station (STA)) via a communication link 154, for example, in an unlicensed spectrum such as 5 GHz. When communicating in unlicensed spectrum, the UE 104 / AP 150 may perform a free channel assessment (CCA) to determine whether a channel is available before communication.

[0051] The electromagnetic spectrum is typically subdivided into various categories, bands, channels, etc., based on frequency / wavelength. In 5G NR, two initial operating bands have been designated as frequency ranges FR1 (410MHz to 7.125GHz) and FR2 (24.25GHz to 52.6GHz). Although a portion of FR1 is greater than 6GHz, in various documents and articles, FR1 is often (interchangeably) referred to as the "sub-6GHz" band. Similar naming issues sometimes occur with FR2, which is often (interchangeably) referred to as the "millimeter wave" band in documents and articles, although this is distinct from the Extremely High Frequency (EHF) band (30GHz to 300GHz) designated as "millimeter wave" by the International Telecommunication Union (ITU).

[0052] The frequencies between FR1 and FR2 are generally referred to as intermediate frequency (IF) bands. Recent 5G NR studies have designated the operating bands for these IF bands as the frequency range designation FR3 (7.125 GHz to 24.25 GHz). Bands falling within FR3 can inherit FR1 and / or FR2 characteristics, thus effectively extending the features of FR1 and / or FR2 to IF band frequencies. Furthermore, higher frequency bands are currently being explored to extend 5G NR operation beyond 52.6 GHz. For example, three higher operating bands have been designated as the frequency range designations FR2-2 (52.6 GHz to 71 GHz), FR4 (71 GHz to 114.25 GHz), and FR5 (114.25 GHz to 300 GHz). Each of these higher frequency bands falls within the EHF band.

[0053] In view of the above, unless otherwise specified, the term "below 6 GHz" as used herein can broadly refer to frequencies less than 6 GHz, within FR1, or including intermediate frequency band frequencies. Furthermore, unless otherwise specified, the term "millimeter wave" as used herein can broadly refer to frequencies that can include intermediate frequency band frequencies, within FR2, FR4, FR2-2 and / or FR5, or within the EHF band.

[0054] Base station 102 and UE 104 may each include multiple antennas (such as antenna elements, antenna panels, and / or antenna arrays) to facilitate beamforming. Base station 102 may transmit beamformed signals 182 to UE 104 in one or more transmit directions. UE 104 may receive beamformed signals from base station 102 in one or more receive directions. UE 104 may also transmit beamformed signals 184 to base station 102 in one or more transmit directions. Base station 102 may receive beamformed signals from UE 104 in one or more receive directions. Base station 102 / UE 104 may perform beamforming training to determine the optimal receive and transmit directions for each of base station 102 / UE 104. The transmit and receive directions of base station 102 may be the same or different. The transmit and receive directions of UE 104 may be the same or different.

[0055] Base station 102 may include and / or be referred to as gNB, Node B, eNB, access point, base transceiver, radio base station, radio transceiver, transceiver function, basic service set (BSS), extended service set (ESS), TRP, network node, network entity, network equipment, or some other suitable terminology. Base station 102 may be implemented as an integrated access and backhaul (IAB) node, relay node, sidelink node, aggregated (monolithic) base station with baseband units (BBU) (including CU and DU) and RU, or may be implemented as a decomposed base station including one or more of CU, DU, and / or RU. A collection of base stations that may include decomposed base stations and / or aggregated base stations may be referred to as Next Generation (NG) RAN (NG-RAN).

[0056] The core network 120 may include Access and Mobility Management Function (AMF) 161, Session Management Function (SMF) 162, User Plane Function (UPF) 163, Unified Data Management (UDM) 164, one or more location servers 168, and other functional entities. AMF 161 is the control node that processes signaling between UE 104 and the core network 120. AMF 161 supports registration management, connection management, mobility management, and other functions. SMF 162 supports session management and other functions. UPF 163 supports packet routing, packet forwarding, and other functions. UDM 164 supports authentication and key agreement (AKA) credential generation, user identity processing, access authorization, and subscription management. One or more location servers 168 are exemplified as including a Gateway Mobile Location Center (GMLC) 165 and a Location Management Function (LMF) 166. However, generally, one or more location servers 168 may include one or more location / positioning servers, which may include one or more of GMLC 165, LMF 166, Position Determination Entity (PDE), Serving Mobile Location Center (SMLC), Mobile Location Center (MPC), etc. GMLC 165 and LMF 166 support UE location services. GMLC 165 provides an interface for clients / applications (e.g., emergency services) to access UE location information. LMF 166 receives measurement and auxiliary information from NG-RAN and UE 104 via AMF 161 to calculate the location of UE 104. NG-RAN may use one or more positioning methods to determine the location of UE 104. Positioning UE 104 may involve signal measurement, location estimation, and optional speed calculation based on these measurements. Signal measurement may be performed by UE 104 and / or base station 102 serving UE 104. The measured signals may be based on one or more of the following: Satellite Positioning System (SPS) 170 (e.g., one or more of Global Navigation Satellite System (GNSS), Global Positioning System (GPS), Non-Terrestrial Network (NTN) or other satellite positioning / location systems), LTE signals, Wireless Local Area Network (WLAN) signals, Bluetooth signals, Terrestrial Beacon System (TBS), sensor-based information (e.g., barometric pressure sensor, motion sensor), NR Enhanced Cell ID (NR E-CID) method, NR signals (e.g., multiple round-trip time (multiple RTT), DL departure angle (DL-AoD), DL time difference of arrival (DL-TDOA), UL time difference of arrival (UL-TDOA), and UL angle of arrival (UL-AoA) positioning) and / or other systems / signals / sensors.

[0057] Examples of UE 104 include cellular phones, smartphones, Session Initiation Protocol (SIP) phones, laptops, personal digital assistants (PDAs), satellite radios, GPS devices, multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, tablets, smart devices, wearable devices, vehicles, electricity meters, air pumps, large or small kitchen appliances, healthcare devices, implants, sensors / actuators, displays, or any other similarly functional device. Some UEs in UE 104 may be referred to as IoT devices (e.g., parking meters, air pumps, toasters, vehicles, heart monitors, etc.). UE 104 may also be referred to as a station, mobile station, subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, mobile phone, user agent, mobile client, client, or some other suitable terminology. In some scenarios, the term UE may also be applied to one or more companion devices, such as in a device constellation arrangement. One or more of these devices may access the network together and / or individually.

[0058] Refer again Figure 1In some aspects, UE 104 may have a data collection component 198, which is configured to receive a conditional configuration for collecting measurements to train or validate at least one of the positioning models. The data collection component 198 may be configured to receive a set of positioning signals. The data collection component 198 may be configured to measure the set of positioning signals. The data collection component 198 may be configured to output a subset of the measured set of positioning signals to train or validate at least one of the positioning models based on the conditional configuration. The data collection component 198 may be configured to output the subset of the measured set of positioning signals to train or validate at least one of the positioning models based on at least one condition of the conditional configuration. The data collection component 198 may be configured to output the subset of the measured set of positioning signals to validate the positioning model at UE 104 by means of the subset of the measured set of positioning signals. Data collection component 198 may be configured to output the subset of measured location signals to a network entity (e.g., base station 102, core network 120, LMF 166, over-the-top (OTT) server) for training or validating at least one of the location models. In some aspects, base station 102 may have a data collection configuration component 199 configured to configure conditional configurations for selecting a subset of the measured location signals for training or validating at least one of the location models. Data collection configuration component 199 may be configured to send these conditional configurations for training or validating at least one of the location models. In other words, data collection configuration component 199 may configure conditional configurations for selecting a subset of the measured location signals for training or validating at least one of the location models. The data collection configuration component 199 can send conditional configurations to the data collection component 198, which then selects a subset of the measured positioning signals based on the set of conditions specified in the conditional configuration. This ensures that the data collected by the data collection component 198 maintains a minimum standard to optimize the use of the collected data for training or validation of the positioning model.

[0059] Figure 2A Figure 200 illustrates an example of the first subframe within a 5G NR frame structure. Figure 2B Figure 230 illustrates an example of a DL channel within a 5G NR subframe. Figure 2C Figure 250 is an example of a second subframe within a 5G NR frame structure. Figure 2DFigure 280 illustrates an example of a UL channel within a 5G NR subframe. The 5G NR frame structure can be Frequency Division Duplex (FDD) (where subframes within a specific set of subcarriers (carrier system bandwidth) are dedicated to either DL or UL) or Time Division Duplex (TDD) (where subframes within a specific set of subcarriers (carrier system bandwidth) are dedicated to both DL and UL). Figure 2A , Figure 2C In the provided example, the 5G NR frame structure is assumed to be TDD, where subframe 4 is configured using slot format 28 (most of which are DL), where D is DL, U is UL, and F is flexible and can be used between DL / UL, and subframe 3 is configured with slot format 1 (all of which are UL). Although subframes 3 and 4 are shown as having slot formats 1 and 28 respectively, any particular subframe can be configured using any of the various available slot formats 0-61. Slot formats 0 and 1 are both DL and UL, respectively. Other slot formats 2-61 include a mixture of DL, UL, and flexible symbols. The UE is configured using the slot format (dynamically configured via DL control information (DCI) or semi-statically / statically configured via Radio Resource Control (RRC) signaling) via the received Slot Format Indicator (SFI). Note that the following description also applies to the 5G NR frame structure as TDD.

[0060] Figures 2A to 2D The frame structure is illustrated, and aspects of this disclosure are applicable to other wireless communication technologies that may have different frame structures and / or different channels. A frame (10 ms) can be divided into 10 equal-sized subframes (1 ms). Each subframe may include one or more time slots. Subframes may also include micro-time slots, which may include 7, 4, or 2 symbols. Each time slot may include 14 or 12 symbols, depending on whether the cyclic prefix (CP) is normal or extended. For normal CP, each time slot may include 14 symbols, and for extended CP, each time slot may include 12 symbols. Symbols on the DL may be CP Orthogonal Frequency Division Multiplexing (OFDM) (CP-OFDM) symbols. Symbols on the UL may be CP-OFDM symbols (for high-throughput scenarios) or Discrete Fourier Transform (DFT) Extended OFDM (DFT-s-OFDM) symbols (for power-constrained scenarios; limited to single-stream transmission). The number of time slots within a subframe is based on the CP and a parameter set. The parameter set defines the subcarrier spacing (SCS) (see Table 1). Symbol length / duration can be scaled using 1 / SCS.

[0061]

[0062] Table 1: Parameter Set, SCS, and CP

[0063] For a normal CP (14 symbols / slot), different parameter sets µ 0 through 4 allow 1, 2, 4, 8, and 16 slots per subframe, respectively. For the extended CP, parameter set 2 allows 4 slots per subframe. Therefore, for a normal CP and parameter set µ, there are 14 symbols per slot and 2 slots per subframe. µ One time slot. The subcarrier spacing can be equal to ,in The parameter sets are 0 to 4. Therefore, parameter set µ=0 has a subcarrier spacing of 15 kHz, and parameter set µ=4 has a subcarrier spacing of 240 kHz. The symbol length / duration is negatively correlated with the subcarrier spacing. Figures 2A to 2D Examples of a normal frequency division multiplexing (CP) with 14 symbols per time slot and a parameter set of µ=2 with 4 time slots per subframe are provided. The time slot duration is 0.25 ms, the subcarrier spacing is 60 kHz, and the symbol duration is approximately 16.67 μs. Within the frame set, there may be one or more distinct bandwidth portions (BWPs) of frequency division multiplexing (see [link to relevant documentation]). Figure 2B Each BWP can have a specific set of parameters and CP (normal or extended).

[0064] A resource grid can be used to represent the frame structure. Each time slot consists of a resource block (RB) extending for 12 consecutive subcarriers (also known as a physical RB (PRB)). The resource grid is divided into multiple resource elements (REs). The number of bits carried by each RE depends on the modulation scheme.

[0065] like Figure 2A As illustrated, some of the REs carry reference (pilot) signals (RS) for the UE. RS may include demodulation RS (DM-RS) (indicated as R for a particular configuration, but other DM-RS configurations are possible) and channel state information reference signals (CSI-RS) for channel estimation at the UE. RS may also include beam measurement RS (BRS), beam refinement RS (BRRS), and phase tracking RS (PT-RS).

[0066] Figure 2BExamples of various DL channels within a subframe of a frame are illustrated. The Physical Downlink Control Channel (PDCCH) carries the DCI within one or more Control Channel Elements (CCEs) (e.g., 1, 2, 4, 8, or 16 CCEs), each CCE comprising six RE Groups (REGs), each REG comprising 12 consecutive REs in the OFDM symbol of the RB. A PDCCH within a BWP can be referred to as a Control Resource Set (CORESET). The UE is configured to monitor PDCCH candidates in the PDCCH search space (e.g., the common search space, the UE-specific search space) during PDCCH monitoring timing on the CORESET, where the PDCCH candidates have different DCI formats and different aggregation levels. Additional BWPs may be located at higher and / or lower frequencies on the channel bandwidth. The Primary Synchronization Signal (PSS) may be located within symbol 2 of a specific subframe of the frame. The PSS is used by the UE 104 to determine subframe / symbol timing and physical layer identification. The Secondary Synchronization Signal (SSS) may be located within symbol 4 of a specific subframe of the frame. The SSS is used by the UE to determine the Physical Layer Cell Identifier Group Number and radio frame timing. Based on the Physical Layer Identifier and the Physical Layer Cell Identifier Group Number, the UE can determine the Physical Cell Identifier (PCI). Based on the PCI, the UE can determine the location of the DM-RS. The Physical Broadcast Channel (PBCH), carrying the Master Information Block (MIB), can be logically grouped with the PSS and SSS to form a Synchronization Signal (SS) / PBCH block (also known as an SS block (SSB)). The MIB provides the number of RBs in the system bandwidth and the System Frame Number (SFN). The Physical Downlink Shared Channel (PDSCH) carries user data, broadcast system information not transmitted via the PBCH (such as System Information Block (SIB)), and paging messages.

[0067] like Figure 2C As illustrated, some REs in the REs carry DM-RS (indicated as R for one particular configuration, but other DM-RS configurations are possible) for channel estimation at the base station. The UE can transmit DM-RS for the Physical Uplink Control Channel (PUCCH) and DM-RS for the Physical Uplink Shared Channel (PUSCH). The PUSCH DM-RS can be transmitted in the first or first two symbols of the PUSCH. Depending on whether a short or long PUCCH is transmitted and depending on the specific PUCCH format used, the PUCCH DM-RS can be transmitted in different configurations. The UE can transmit a Sounding Reference Signal (SRS). The SRS can be transmitted in the last symbol of a subframe. The SRS can have a comb structure, and the UE can transmit the SRS on one of the comb teeth. The SRS can be used by the base station for channel quality estimation to enable frequency-dependent scheduling of the UL.

[0068] Figure 2DExamples of various UL channels within a subframe of a frame are illustrated. The PUCCH may be located as indicated in one configuration. The PUCCH carries uplink control information (UCI), such as scheduling requests, channel quality indicators (CQI), pre-decoding matrix indicators (PMI), rank indicators (RI), and hybrid automatic repeat request (HARQ) acknowledgment (ACK) (HARQ-ACK) feedback (i.e., one or more HARQ ACK bits indicating one or more ACKs and / or negative ACKs (NACKs)). The PUCCH carries data and may additionally be used to carry buffer status reports (BSR), power clearance reports (PHR), and / or UCIs.

[0069] Figure 3 This is a block diagram illustrating communication between base station 310 and UE 350 in the access network. In the DL, Internet Protocol (IP) packets can be provided to controller / processor 375. Controller / processor 375 implements Layer 3 and Layer 2 functionality. Layer 3 includes the Radio Resource Control (RRC) layer, and Layer 2 includes the Service Data Adaptation Protocol (SDAP) layer, Packet Data Convergence Protocol (PDCP) layer, Radio Link Control (RLC) layer, and Media Access Control (MAC) layer. The controller / processor 375 provides RRC layer functionality associated with broadcasting system information (e.g., MIB, SIB), RRC connection control (e.g., RRC connection paging, RRC connection establishment, RRC connection modification, and RRC connection release), inter-Radio Access Technology (RAT) mobility, and measurement configuration for UE measurement reporting; PDCP layer functionality associated with header compression / decompression, security (encryption, decryption, integrity protection, integrity verification), and handover support functions; RLC layer functionality associated with the delivery of upper-layer packet data units (PDUs), error correction via ARQ, concatenation, segmentation, and reassembly of RLC service data units (SDUs), resegmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs onto transport blocks (TBs), demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction via HARQ, priority handling, and logical channel priority ordering.

[0070] Transmit (TX) processor 316 and receive (RX) processor 370 implement Layer 1 functionality associated with various signal processing functions. Layer 1 (which includes the physical (PHY) layer) may include error detection on the transport channel, forward error correction (FEC) decoding / decoding of the transport channel, interleaving, rate matching, mapping to the physical channel, modulation / demodulation of the physical channel, and MIMO antenna processing. TX processor 316 processes the mapping to the signal constellation based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-order phase shift keying (M-PSK), M-order quadrature amplitude modulation (M-QAM)). The decoded and modulated symbols can then be divided into parallel streams. Each stream can then be mapped to OFDM subcarriers, multiplexed with a reference signal (e.g., a pilot) in the time and / or frequency domains, and then combined using inverse fast Fourier transform (IFFT) to produce a physical channel carrying a stream of time-domain OFDM symbols. The OFDM stream undergoes spatial pre-decoding to generate multiple spatial streams. A channel estimate from channel estimator 374 can be used to determine the decoding and modulation scheme, as well as for spatial processing. This channel estimate can be derived from a reference signal transmitted by UE 350 and / or channel condition feedback. Each spatial stream can then be provided to a different antenna 320 via a separate transmitter 318Tx. Each transmitter 318Tx can utilize the corresponding spatial stream to modulate a radio frequency (RF) carrier for transmission.

[0071] At UE 350, each receiver 354Rx receives signals via its corresponding antenna 352. Each receiver 354Rx recovers the information modulated onto the RF carrier and provides that information to the receive (RX) processor 356. The TX processor 368 and RX processor 356 implement Layer 1 functionality associated with various signal processing functions. The RX processor 356 can perform spatial processing on the information to recover any spatial stream destined for UE 350. If multiple spatial streams are destined for UE 350, the RX processor 356 can combine them into a single OFDM symbol stream. The RX processor 356 then uses a Fast Fourier Transform (FFT) to transform the OFDM symbol stream from the time domain to the frequency domain. The frequency domain signal consists of a separate OFDM symbol stream for each subcarrier of the OFDM signal. The symbols on each subcarrier, along with the reference signal, are recovered and demodulated by determining the most probable signal constellation points transmitted by base station 310. These soft decisions can be based on a channel estimate calculated by channel estimator 358. The soft decision is then decoded and deinterleaved to recover the data and control signals originally transmitted by base station 310 on the physical channel. The data and control signals are then provided to controller / processor 359, which implements layer 3 and layer 2 functionality.

[0072] The controller / processor 359 may be associated with at least one memory 360 storing program code and data. The at least one memory 360 may be referred to as a computer-readable medium. In the UL, the controller / processor 359 provides demultiplexing, packet reassembly, decryption, header decompression, and control signal processing between transport and logical channels to recover IP packets. The controller / processor 359 is also responsible for error detection using ACK and / or NACK protocols to support HARQ operation.

[0073] Similar to the functionality described in conjunction with DL transmission performed by base station 310, controller / processor 359 provides RRC layer functionality associated with system information (e.g., MIB, SIB) acquisition, RRC connectivity, and measurement reporting; PDCP layer functionality associated with header compression / decompression and security (encryption, decryption, integrity protection, integrity verification); RLC layer functionality associated with upper-layer PDU delivery, error correction via ARQ, concatenation, segmentation, and reassembly of RLC SDUs, resegmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs onto TBs, demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction via HARQ, priority handling, and logical channel priority ordering.

[0074] The TX processor 368 can use the channel estimate derived from the reference signal or feedback transmitted by the channel estimator 358 from the base station 310 to select an appropriate decoding and modulation scheme and facilitate spatial processing. The spatial stream generated by the TX processor 368 can be provided to different antennas 352 via individual transmitters 354Tx. Each transmitter 354Tx can use the corresponding spatial stream to modulate an RF carrier for transmission.

[0075] UL transmission is processed at base station 310 in a manner similar to that described in conjunction with the receiver function at UE 350. Each receiver 318Rx receives signals via its corresponding antenna 320. Each receiver 318Rx recovers the information modulated onto the RF carrier and provides that information to RX processor 370.

[0076] The controller / processor 375 may be associated with at least one memory 376 storing program code and data. The at least one memory 376 may be referred to as a computer-readable medium. In the UL, the controller / processor 375 provides demultiplexing, packet reassembly, decryption, header decompression, and control signal processing to recover IP packets between transport and logical channels. The controller / processor 375 is also responsible for error detection using ACK and / or NACK protocols to support HARQ operation.

[0077] At least one of the TX processor 368, RX processor 356, and controller / processor 359 can be configured to combine Figure 1 The data collection component 198 performs various aspects.

[0078] At least one of the TX processor 316, RX processor 370, and controller / processor 375 can be configured to combine Figure 1 The data collection configuration component 199 performs various aspects.

[0079] Figure 4 Figure 400 illustrates an example of positioning based on location signal measurements. The location signal can be any reference signal that can be measured to calculate the positioning or location attributes of a wireless device, such as a Positioning Reference Signal (PRS), Sounding Reference Signal (SRS), Channel State Information (CSI) Reference Signal (CSI-RS), or Synchronization and Signal Block (SSB). Wireless device 402 can be a base station, such as a TRP, or a UE with a known location / position, such as a Positioning Reference Unit (PRU), or a UE with a high-accuracy sensor that can identify the UE's location, such as a GNSS sensor or a GPS sensor. Wireless device 406 can be a base station or a UE with a known location / position. Wireless device 404 can be a UE or TRP configured to perform positioning to collect data (e.g., to collect data to train an artificial intelligence machine learning (AI / ML or AIML) model, test the strength of the location signal, or test the location noise attributes in an area). In some aspects, the UE can be a Positioning Reference Unit (PRU) with a known location for training or validating the accuracy of the positioning model.

[0080] Wireless device 404 can be in time T SRS_TX Send UL-SRS 412, and at time T PRS_RX Receives DL Positioning Reference Signal (PRS) (DL-PRS) 410. Wireless device 406 can receive the DL positioning reference signal (PRS) at time T. SRS_Rx Receive UL-SRS 412, and at time T PRS_Tx Send DL-PRS 410. Wireless device 404 may receive DL-PRS 410 before sending UL-SRS 412, or may send UL-SRS 412 before receiving DL-PRS 410. In both cases, the location server (e.g., location server 168, LMF 166) or wireless device 404 may base its signal on ||T. SRS_RX -T PRS_TX |-|T SRS_TX -T PRS_RX|| to determine RTT 414. Therefore, multi-RTT positioning can utilize the UE Rx-Tx time difference measurement (i.e., |T) of downlink signals received from multiple radio devices 402, 406 and measured by radio device 404. SRS_TX -T PRS_RX |) and DL-PRS reference signal received power (RSRP) (DL-PRS-RSRP), and the TRP Rx-Tx time difference measurement of the uplink signal transmitted from wireless device 404 at multiple wireless devices 402, 406 (i.e., |T) SRS_RX -T PRS_TX |) and UL-SRS-RSRP. Wireless device 404 can use auxiliary data received from the positioning server to measure the UE Rx-Tx time difference (and optionally, the DL-PRS-RSRP of the received signal), and wireless devices 402 and 406 can use auxiliary data received from the positioning server to measure the gNB Rx-Tx time difference (and optionally, the UL-SRS-RSRP of the received signal). Measurements can be used at the positioning server or wireless device 404 to determine the RTT. The RTT can be used to estimate the location of wireless device 404. Other methods for determining the RTT are possible, such as, for example, using DL-TDOA and / or UL-TDOA measurements.

[0081] DL-AoD positioning can utilize the measured DL-PRS-RSRP of downlink signals received at wireless device 404 from multiple wireless devices 402, 406. Wireless device 404 can use auxiliary data received from a positioning server to measure the DL-PRS-RSRP of the received signals, and the resulting measurement, along with the azimuth departure (A-AoD), zenith departure (Z-AoD), and other configuration information, can be used to locate wireless device 404 relative to neighboring wireless devices 402, 406.

[0082] DL-TDOA positioning can utilize the DL Reference Signal Time Difference (RSTD) (and optionally, DL-PRS-RSRP) of downlink signals received at wireless device 404 from multiple wireless devices 402, 406. Wireless device 404 can use auxiliary data received from a positioning server to measure the DL RSTD (and optionally, DL-PRS-RSRP) of the received signals, and the resulting measurement, along with other configuration information, can be used to locate the position / location of wireless device 404 relative to neighboring wireless devices 402, 406.

[0083] UL-TDOA positioning can utilize the UL relative time of arrival (RTOA) (and optionally, UL-SRS-RSRP) of the uplink signal transmitted from wireless device 404 at multiple wireless devices 402, 406. Wireless devices 402, 406 can use auxiliary data received from a positioning server to measure the UL-RTOA (and optionally, UL-SRS-RSRP) of the received signal, and the resulting measurement can be used, along with other configuration information, to estimate the location of wireless device 404.

[0084] UL-AoA positioning utilizes the azimuth angle (A-AoA) and zenith angle (Z-AoA) of the uplink signal transmitted from wireless device 404, measured at multiple wireless devices 402 and 406. Wireless devices 402 and 406 can use auxiliary data received from a positioning server to measure the A-AoA and Z-AoA of the received signal, and the resulting measurements, along with other configuration information, can be used to estimate the position of wireless device 404.

[0085] Additional positioning methods can be used to estimate the location of the wireless device 404, such as, for example, UE-side UL-AoD and / or DL-AoA. It should be noted that data / measurements from various technologies can be combined in various ways to increase accuracy, determine and / or enhance certainty, supplement / improve measurements, and / or replace / provide missing information.

[0086] Figure 5 This is an illustration 500 of a network entity 508 that can be configured to coordinate wireless devices 502 and 506 to perform location with wireless device 504. The locations of wireless devices 502 and 506 may be known to at least one device (such as wireless device 502, wireless device 504, wireless device 506, and / or network entity 508). Wireless device 502 may be a base station, gNB, or TRP. Wireless device 506 may be a base station, gNB, or TRP. Wireless device 504 may be a UE. In some aspects, the UE may be a PRU. The PRU may be a UE with a known location. For example, the PRU may be fixed in a known location or may be placed in a known location for a period of time, or the PRU may have a collection of sensors (e.g., high-accuracy GNSS sensors) that can be used to accurately calculate the PRU's location. Network entity 508 may be connected to wireless devices 502 and 506 via a physical link (e.g., a backhaul link or a midhaul link) or via a wireless link (such as an air interface (UE-UTRAN(Uu)) link). Network entity 508 can be part of the core network, such as an LMF or a collection of location servers. Network entity 508 can configure the timing of location between wireless devices 502, 504, and 506.

[0087] To perform location tracking, network entity 508 may configure one or more wireless devices to transmit location signals to each other. For example, wireless device 504 may transmit a set of location signals 512 to wireless device 502. The set of location signals 512 may be a set of SRS, SSB, or CSI-RS. Wireless device 502 may measure the set of location signals 512. Wireless device 502 may transmit a set of location signals 516 to wireless device 504. The set of location signals 516 may be a set of PRS, SSB, or CSI-RS. Wireless device 504 may measure the set of location signals 516. Wireless device 504 may transmit a set of location signals 514 to wireless device 506. The set of location signals 514 may be a set of SRS, SSB, or CSI-RS. Wireless device 506 may measure the set of location signals 514. Wireless device 506 may transmit a set of location signals 518 to wireless device 504. The set of location signals 518 can be a set of PRS, SSB, or CSI-RS. Wireless device 504 can measure the set of location signals 518. One or more wireless devices can measure the received location signals to calculate a location measurement, which can be used to calculate the location / position of wireless device 504, or can be used to calculate the location / position of wireless device 504. For example, if the locations of wireless devices 502 and 506 are known, the location of wireless device 504 can be calculated based on the RTT between wireless devices 502 and 504 and the RTT between wireless devices 504 and 506. In another example, wireless device 504 can calculate the angle of arrival (AoA) or angle of departure (AoD) of the set of location signals 516, and can calculate the AoA or AoD of the set of location signals 518. If the locations of wireless devices 502 and 506 are also known, the calculated AoA and / or AoD can be used to calculate the location of wireless device 504. Other measurements (such as RTOA, line-of-sight (LOS) identification (identifying whether there is a direct line-of-sight path between wireless devices)) or multi-cell round-trip time (multi-RTT) calculations can be performed to calculate the location of wireless device 504, or to calculate measurements that can be used to calculate the location of wireless device 504.

[0088] In some aspects, the positioning model can be used to calculate one or more positioning metrics based on measurements. For example, the location / position of wireless device 504 can be calculated or estimated based on measurements of a set of positioning signals 512 and / or a set of positioning signals 514 transmitted by wireless device 504, or intermediate measurements that can be calculated or estimated for use in calculating the location / position of wireless device 504. The positioning model can be trained using artificial intelligence (AI) / machine learning (ML) (AI / ML or AIML) based on a set of inputs (e.g., measurements of positioning signals, auxiliary information associated with the positioning signals) and a set of tags. Positioning signals can include any reference signal transmitted from the wireless device, such as PRS, SRS, SSB, or CSI-RS. RS transmitted from a UE (such as a PRU) can be referred to as an uplink positioning signal or UL positioning signal. RS transmitted from a base station or TRP can be referred to as a downlink positioning signal or DL ​​positioning signal. Measurements can be channel impulse response (CIR), channel frequency response (CFR), or other measurements used to perform positioning on the target wireless device. Labels can be computed, derived, or given (i.e., known) expected results associated with the set of inputs, such as the location / position of wireless device 504 or intermediate measurements (e.g., timing measurements, angle measurements, LOS markers) that can be used to compute the location / position of wireless device 504. The set of inputs and the set of labels can be used to generate and / or train a localization model using AI / ML.

[0089] When training the localization model, measurements of the localization signal serve as input, clean or noisy labels (clean labels may have a quality metric greater than or equal to a threshold, and noisy labels may have a quality metric less than or equal to a threshold) serve as the expected output, and training data auxiliary information serves as either input or the expected output. The localization model can operate on any wireless device based on the set of inputs. For example, wireless device 502 may have a localization model configured to accept a set of localization measurements and produce an estimate of the localization / position of wireless device 504. In another example, wireless device 502 may have a localization model configured to accept a set of localization measurements and produce intermediate measurements (e.g., timing measurements, angle measurements, LOS identifiers) that can be used by wireless device 502 or another entity, such as network entity 508, wireless device 504, or wireless device 506, to calculate the localization / position of wireless device 504. In another example, network entity 508 may have a localization model configured to accept a set of localization measurements and produce an estimate of the localization / position of wireless device 504, or produce intermediate measurements that can be used to calculate the localization / position of wireless device 504. In another example, wireless device 504 may have a positioning model configured to receive a set of positioning measurements and produce an estimate of the location / position of wireless device 504, or to produce intermediate measurements that can be used to calculate the location / position of wireless device 504. In some aspects, positioning measurements may be aggregated by an entity having a positioning model; for example, network entity 508 may aggregate measurements of a set of positioning signals 512 from wireless device 502, measurements of a set of positioning signals 514 used by wireless device 506 as input to the positioning model, measurements of a set of positioning signals 516 from wireless device 504, and / or measurements of a set of positioning signals 518 from wireless device 504.

[0090] The positioning model can be trained on wireless devices performing positioning (such as wireless devices 502, 504, 506, and / or network entity 508) or on offline devices (such as over-the-top (OTT) servers). Inputs to the positioning model may include measurements of positioning signals, such as SRS, PRS, SSB, and / or CSI-RS. Inputs to the measurements may include auxiliary information associated with the measured positioning signals, such as the BWP of the positioning signal resource, the number of TRPs, beam information, and positioning signal configuration. The positioning model's labels / outputs may include location or intermediate measurements.

[0091] In some aspects, the positioning model can be configured to calculate the location of wireless device 504 using measurements of UL positioning signals transmitted from the wireless device, or to calculate intermediate measurements that can be used to calculate the location of wireless device 504. The positioning model can be trained via a training entity and can be used at wireless device 502, wireless device 504, wireless device 506, or network entity 508. For example, the positioning model at wireless device 504 can be configured to calculate the location of wireless device 504 based on measurements of a set of positioning signals 516 and a set of positioning signals 518. In another example, wireless device 502 can transmit a set of intermediate measurements to network entity 508, such that network entity 508 can calculate the location of wireless device 504 based on that set of intermediate measurements. In another example, wireless device 504 can transmit measurements of a set of positioning signals 516 and a set of positioning signals 518 to network entity 508. The positioning model can be at network entity 508. The positioning model at network entity 508 can calculate the location of wireless device 504 based on measurements of the set of positioning signals 516 and / or the set of positioning signals 518 transmitted from wireless device 504, the set of positioning signals 512 transmitted from wireless device 502, and / or the set of positioning signals 514 transmitted from wireless device 506. In other words, any of wireless device 502, wireless device 504, and / or wireless device 506 can assist network entity 508 in performing positioning using a trained positioning model.

[0092] In some respects, the positioning model can be site-specific. For example, a first positioning model can be trained in locations or sets of locations associated with a first set of sites having boundaries, and a second positioning model can be trained in locations or sets of locations associated with a second set of sites having boundaries. A wireless device can be configured to use one of a plurality of site-specific positioning models. For example, the wireless device can select a site-specific positioning model based on its location, or it can select a site-specific positioning model based on an indicator (e.g., a signal indicating selection of a specific site-specific positioning model from a plurality of site-specific positioning models sent from network entity 508).

[0093] Location signal measurement can be performed via a channel between the target device (e.g., wireless device 504) and a set of network nodes (e.g., wireless devices 502 and 506). Wireless device 504 can transmit location signals such as SRS, SSB, or CSI-RS. Wireless device 502 and / or wireless device 506 can measure location signals for data collection purposes to train a location model. Wireless device 504 and / or wireless device 506 can transmit location signals such as PRS, SSB, or CSI-RS. Wireless device 504 can measure location signals for data collection purposes to train a location model. Wireless devices 502, 504, and / or 506 can measure location signal resources in various ways, such as channel impulse response (CIR), channel frequency response (CFR), power delay distribution (PDP), set of reflection paths, receive-transmit (Rx-Tx) time difference, received signal strength indicator (RSSI), reference signal received power (RSRP), reference signal received power per path (RSRPP), reference signal received quality (RSRQ), time of arrival (ToA), reference signal time difference (RSTD), or angle of departure (AoD).

[0094] Although Figure 500 illustrates two location-adjacent wireless devices (wireless devices 502 and 506) configured to perform location calculations using a target wireless device (wireless device 504) to determine the location / position of wireless device 504, any number of location-adjacent wireless devices can be configured to perform location calculations using any number of target wireless devices. For example, four location-adjacent wireless devices can be configured to calculate the location / position of two target wireless devices, three location-adjacent wireless devices can be configured to calculate the location / position of one target wireless device, or two location-adjacent wireless devices can be configured to calculate the location / position of one target wireless device.

[0095] In some respects, measurements collected by wireless devices may be insufficient to train a positioning model. For example, a barrier blocking wireless signals may be placed between wireless devices 504 and 502, which could lead to a significant degradation in the quality of the measured signals. In some respects, the data collection entity may be configured to request data (e.g., measurements and positioning tags) with specified quality and / or conditions. In some respects, the data source entity (e.g., UE, PRU) may be configured to filter out measurements and / or tags that do not comply with specified quality and / or conditions. Thus, the data source entity may report clean positioning measurements and / or tags for use in training the positioning entity. In other respects, such clean positioning measurements and / or tags can be used to validate the accuracy of the positioning model. In some respects, the quality of the collected data may be improved at the UE using conditions and / or criteria for data collection, selection, and / or prioritization to train and / or validate the accuracy of the positioning model.

[0096] For example, network entity 508 may configure data quality and / or conditions for wireless device 504. In some aspects, a training entity (such as an OTT server) may send a request to network entity 508 to collect high-quality data for training and / or validating a localization model, and in response, network entity 508 may configure data collection for wireless device 504 to ensure the requested quality and / or conditions are met. In another example, wireless device 504 may request network entity 508 to ensure that the collected data has specified data quality and / or conditions. In other words, wireless device 504 may be a training entity, or may collect data for a training entity, and may request certain configurations for localization signals transmitted to wireless device 504 and / or certain auxiliary data or annotation aids from network entity 508. Wireless device 504 may send a request for localization signal configurations that meet a set of conditions (i.e., having specified data quality and / or conditions). Wireless device 504 may send a request for annotation aids from network entity 508 based on a specified set of data quality and / or conditions. Wireless device 504 may also be referred to as a data collection entity or a data source entity.

[0097] Figure 6This is a connection flowchart 600 illustrating an example of communication between a target wireless device 602, a set of neighboring wireless devices 604, and a location network entity 606. The target wireless device 602 may be a UE (User Equipment). The UE may be a PRU (Programmer's Unit) with a known location. The set of neighboring wireless devices 604 may include a set of base stations and / or a set of TRPs configured to send location signals to the target wireless device 602. The location network entity 606 may include an LMF (Location Management Function), one or more location servers, a core entity (CN), or an over-the-top (OTT) server. The location network entity 606 may be configured to configure the timing of location between the target wireless device 602 and the set of neighboring wireless devices 604. The location network entity 606 may be configured to configure data collection configuration settings for the target wireless device 602 to measure, select, and output measurements and / or tags for training and / or validating a location model. The target wireless device 602 may be configured to ensure that the measurements and / or tags collected by the target wireless device 602 comply with a set of quality and / or conditions configured according to the settings.

[0098] In some aspects, the target wireless device 602 may send a configuration request 608 to the positioning network entity 606. The positioning network entity 606 may receive the configuration request 608 from the target wireless device 602. The configuration request 608 may include indicators for the positioning network entity 606 to configure positioning signal configuration to ensure that measurements collected by the target wireless device 602 and / or tags calculated based on the collected measurements comply with a set of quality conditions for data collection. The configuration request 608 may include a set of positioning signal configuration attributes. Positioning signal configuration attributes may include an indicator of bandwidth portion (BWP), the number of resources, a range of the number of resources, the number of TRPs, a range of the number of TRPs, the number of positioning frequency layers (PFLs), a range of the number of PFLs, a set of positioning signal silence masks, and / or a set of processing gap conditions. The range may be open-ended or may be defined by a threshold. For example, the range may include values ​​greater than or equal to a certain value, less than or equal to a certain value, or between two values.

[0099] Configuration request 608 may include indicators for locating annotation assistance provided by network entity 606. The indicators may include a list of criteria for prioritizing annotation assistance. Configuration request 608 may include indicators for locating network entity 606 using a set of criteria that meet a threshold range based on the Radio Access Technology (RAT) method (e.g., The location measurement is used to calculate an indicator for the set of tags. Threshold ranges may include RSRP threshold ranges, signal-to-noise ratio (SNR) threshold ranges, signal-to-interference and noise ratio (SINR) threshold ranges, delay spread threshold ranges, Rice factor threshold ranges, Doppler spread threshold ranges, and / or multipath component threshold ranges (e.g., the number of multipath components that meet the threshold). In one aspect, the multipath component threshold range may be a minimum number of detected paths (i.e., the number of detected paths > 1). N Configuration request 608 may include indicators for locating network entity 606 to ensure that the tag error rate meets a threshold range (e.g., does not exceed a threshold). The tag error rate may be calculated as a single ratio, a set of tag error rates, or a statistical value of the set of tag error rates (e.g., the average, median, or weighted average of tag error rates). Configuration request 608 may include indicators for locating network entity 606 to calculate the set of tags using multiple calculation methods (e.g., calculating the location of the target wireless device 602 based on RTT, RSTD, and multi-RTT measurements). Configuration request 608 may include indicators for locating network entity 606 to individually report the set of tags (e.g., a tag array) or the statistical value of the set of tags (e.g., the average, median, or weighted average). Configuration request 608 may include indicators for locating network entity 606 to calculate tag quality for each calculated tag. In some aspects, configuration request 608 may include a set of tag quality criteria for each calculated tag (e.g., tag error estimates, statistics for the set of tags, residual error rate from the localization solution). In some aspects, the residual error rate may be the residual loss after solving the localization equation using an iterative optimization algorithm. For example, residual error after Kalman estimation / fitting. In some aspects, configuration request 608 may instruct localization network entity 606 to report tag error rates via representative indicators, such as a low error rate indicator for error rates meeting a first threshold range, a medium error rate indicator for error rates meeting a second threshold range, and a high error rate indicator for error rates meeting a third threshold range. In some aspects, localization network entity 606 and target radio device 602 may perform a Long Term Evolution (LTE) Localization Protocol (LPP) capability exchange procedure including configuration request 608. In other words, target radio device 602 may send an LPP message including configuration request 608 (e.g., as part of an LPP Annex (LPPa) protocol). Configuration request 608 may include an indicator for configuring a set of data collection configurations for locating network entity 606 to locate target wireless device 602.

[0100] At 610, the positioning network entity 606 can configure data collection for the positioning target wireless device 602. In some aspects, the positioning network entity 606 can configure data collection for the positioning target wireless device 602 based on a set of data quality conditions provided by the network (e.g., compliance with network standards, computation based on network entities). In some aspects, the positioning network entity 606 can configure data collection for the positioning target wireless device 602 based on a configuration request 608 received from the positioning target wireless device 602. The positioning network entity 606 can send a set of data collection configurations 612 to the positioning target wireless device 602 based on the configuration performed at 610. The positioning target wireless device 602 can receive the set of data collection configurations 612 from the positioning network entity 606.

[0101] The set of data collection configurations 612 may include indicators for a set of conditions that assist the target wireless device 602 in selecting a subset of measured location signals for / reporting location. In some aspects, the set of conditions may assist the target wireless device 602 in selecting a subset of tags for / reporting location. The set of data collection configurations 612 may include a single condition configuration with a single set of conditions for both measurements and tags, or multiple condition configurations with multiple sets of conditions, wherein some sets of conditions are associated with conditions for measurements, and some sets of conditions are associated with conditions for tags.

[0102] The set of conditions may include a set of measurement types collected by the target wireless device 602. The set of measurement types may include, for example, RSTD, RSRP, RSRPP, path-associated timing measurements, additional path information, delay distribution (DP), power delay distribution (PDP), channel impulse response (CIR) timing measurements, CIR power measurements, CIR phase measurements, channel frequency response (CFR) timing measurements, CFR power measurements, CFR phase measurements, CIR timing information, CIR power information, CIR phase information, CFR frequency information, CFR power information, and / or CFR phase information. Measurements may be path-associated, for example, timing measurements may be generated for each path detected by the target wireless device 602. The set of conditions may include indicators of a set of non-RAT positioning methods used by the target wireless device 602 to calculate the location / position of the target wireless device 602. The set of non-RAT positioning methods may include GNSS positioning methods, GPS positioning methods, LiDAR positioning methods, BLE positioning methods, WLAN positioning methods, and / or motion sensor (e.g., inertial motion unit (IMU)) positioning methods. This set may include an indicator of the set of RAT positioning methods used by the target wireless device 602 to calculate the location / position of the target wireless device 602. The set of RAT positioning methods may include DL-TDoA positioning methods, UL-AoD positioning methods, multi-RTT positioning methods, UL-TDoA positioning methods, and / or UL-AoA positioning methods. This set may include an indicator of how the target wireless device 602 combines methods (e.g., a combination of RAT methods, a combination of non-RAT methods, or a combination of RAT and non-RAT methods) to estimate the location / position of the target wireless device 602, for example, by averaging the set of calculated positions or by calculating a weighted average of the set of calculated positions. The set of conditions may include indicators for the target wireless device 602 to prioritize measurements according to the set of conditions. The set of conditions for the measurements may include, for example, threshold ranges for the measurements (e.g., RSRP threshold range, SNR threshold range, SINR threshold range, delay spread threshold range, Rice factor threshold range, Doppler spread threshold range, multipath component threshold range). The set of conditions may include indicators for the target wireless device 602 to prioritize tags according to the set of conditions.The set of conditions for tags may include indicators for which the target wireless device 602 does not calculate tags, indicators for which the target wireless device 602 calculates tags based on a set of RAT-based methods, indicators for which the target wireless device 602 calculates tags based on a set of non-RAT-based methods, indicators for which the target wireless device 602 calculates tags using measurements that meet a threshold range, indicators for which the target wireless device 602 calculates tags with a tag error rate (or a statistical calculation of a set of tag error rates) that meets a threshold range, indicators for which the target wireless device 602 calculates tags using multiple tag calculation methods, indicators for which the target wireless device 602 reports the set of tags individually or as a statistical calculation of the set of tags, and / or indicators for which the target wireless device 602 reports the set of tags with tag quality indicators (e.g., tag error estimation, average SNR, residual error from the positioning solution, low, medium, high). The set of data collection configurations 612 may include multiple configurations, each of which is associated with a set of quality conditions for measurements and / or tags. The location target wireless device 602 may be configured to select a subset of the multiple configurations, or the location target wireless device 602 may be configured to collect and report each of the multiple configurations. In some aspects, the location network entity 606 may transmit a set of signals to the location target wireless device 602 to tune / update the configurations among the multiple configurations. In some aspects, the location network entity 606 may transmit a set of signals to the location target wireless device 602 to activate / deactivate the configurations among the multiple configurations.

[0103] The positioning network entity 606 may send a set of positioning configurations 614 to the positioning target wireless device 602. The positioning target wireless device 602 may receive the set of positioning configurations 614. The set of positioning configurations 614 may include an indicator of a set of BWPs corresponding to a set of positioning signals 618, multiple positioning signal resources corresponding to the set of positioning signals 618, multiple TRPs for transmitting the set of positioning signals 618, multiple PFLs corresponding to the set of positioning signals 618, a set of positioning signal silence masks corresponding to the set of positioning signals 618, and / or a set of processing gap conditions corresponding to the set of positioning signals 618. The set of positioning configurations 614 may indicate to the positioning target wireless device 602 a schedule for receiving the set of positioning signals 618.

[0104] At 616, the target wireless device 602 can apply a set of data collection configurations 612 and a set of positioning configurations 614. A set of neighboring wireless devices 604 can send a set of positioning signals 618 to the target wireless device 602. In some aspects, the positioning network entity 606 can be configured to transmit the set of positioning signals 618 to the set of neighboring wireless devices 604.

[0105] At 620, the target wireless device 602 can measure a set of positioning signals 618 based on a set of positioning configurations 614. The target wireless device 602 can measure a set of positioning signals 618 based on a set of data collection configurations 612. The target wireless device 602 can select a subset of measurements to be output at 620 based on a set of data collection configurations 612.

[0106] In some aspects, the target wireless device 602 may send a set of measurement reports 622 to the location network entity 606. The location network entity 606 may receive the set of measurement reports 622 from the target wireless device 602. At 624, the location network entity 606 may calculate a set of tags based on at least some of the measurement reports in the set of measurement reports 622. The location network entity 606 may calculate this set of tags based on additional measurements received from other devices (such as the target wireless device 602 or other UEs). The location network entity 606 may send a set of tags 626 to the target wireless device 602. The target wireless device 602 may receive the set of tags 626 from the location network entity 606.

[0107] At 628, the target wireless device 602 can compute a set of tags. The computed set of tags may be based on measurements collected at 620. The computed set of tags may be based on a subset of measurements collected at 620 based on the set of data collection configuration 612. The computed set of tags may be based on a subset of the set of tags 626 received from the location network entity 606. At 630, the target wireless device 602 may output a set of tags 626 as a subset of measurements performed at 620 and / or (a) computed at 628 and / or (b) tags received from the location network entity 606. The target wireless device 602 may use the subset of measurements and / or the subset of tags to train a location model at the target wireless device 602. The target wireless device 602 may use the subset of measurements and / or the subset of tags to validate the location model at the target wireless device 602. In some aspects, the target wireless device 602 may validate the location model via a tag-based method or a tag-free method. For a tag-based method, the positioning target wireless device 602 can use both tags and a subset of measurements collected at 620 to verify whether the positioning model outputs a calculated location, localization, or intermediate measurement to the tags with a certain accuracy (i.e., verifying the accuracy of the positioning model). For a tag-less method, the positioning target wireless device 602 can compare the subset of measurements with the distribution of the inputs used to train the positioning model (i.e., verifying that the inputs are within the tolerance range of the training data for the positioning model). Alternatively, the positioning target wireless device 602 can compare the subset of measurements with statistical data (e.g., mean, median) of the inputs used to train the positioning model (i.e., verifying that the inputs are within the tolerance range of the training data for the positioning model). Furthermore, the positioning target wireless device 602 can check the consistency of the positioning model's output over time to determine if the values ​​deviate significantly from previous outputs using similar inputs (i.e., verifying the consistency of the positioning model's output). In some aspects, the positioning target wireless device 602 can verify the positioning model before activation (e.g., to confirm whether the positioning model will work as expected). In some respects, the target wireless device 602 can verify the positioning model after it has been activated (e.g., to verify whether the positioning model continues to function as expected).

[0108] The target wireless device 602 may send a subset of measurements and / or a subset of tags to a network entity (e.g., a location network entity 606, a training entity, or an OTT server) to train or validate a location model using the measurements from the target wireless device 602. The network entity receiving the subset of measurements may train or validate the location model in a similar manner to the aspects described above.

[0109] Figure 7This is a flowchart 700 of a wireless communication method. This method can be performed by a UE (e.g., UE 104, UE 350; wireless device 404, wireless device 504, location target wireless device 602; device 1304). At 702, the UE can receive conditional configurations for collecting measurements to train or validate at least one of the positioning models. For example, 702 can be performed by the location target wireless device 602, which can receive a set of data collection configurations 612 from the positioning network entity 606. The set of data collection configurations 612 may include a set of conditions that assist the location target wireless device 602 in selecting a subset of positioning signal measurements to train or validate at least one of the positioning models at 630. Furthermore, 702 can be performed by… Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0110] At 704, the UE can receive a set of location signals. For example, 704 can be performed by a target wireless device 602, which can receive a set of location signals 618 from a set of neighboring wireless devices 604. Furthermore, 704 can be performed by… Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0111] At 706, the UE can measure the set of location signals. For example, 706 can be performed by a location target wireless device 602, which can measure the set of location signals 618 at 620. Furthermore, 706 can be performed by... Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0112] At 708, the UE can output a subset of the measured location signals to train, monitor, or validate at least one of the location models based on at least one condition configured by the condition. For example, 708 can be performed by a location target wireless device 602, which can output a subset of measurements collected at 620 at 630 based on configuration 614. Location target wireless device 602 can output the subset of measurements to train or validate at least one of the location models based on the configuration. Furthermore, 708 can be performed by… Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0113] At 710, the UE can train the positioning model at the UE based on a subset of the measured positioning signals to output the subset of the measured positioning signals. For example, 710 can be performed by a positioning target wireless device 602, which can train a positioning model at 630 based on a subset of measurements collected at 620. The subset can be based on a set of conditions indicated in the data collection configuration 612. Furthermore, 710 can be performed by... Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0114] At 712, the UE can output the subset of the measured positioning signals by validating the positioning model at the UE based on the subset of the measured positioning signals. For example, 712 can be performed by a positioning target wireless device 602, which can validate the positioning model (e.g., accuracy, input tolerance, output consistency, predictability) at 630 based on the subset of measurements collected at 620. The subset can be based on a set of conditions indicated in the data collection configuration 612. Furthermore, 712 can be performed by... Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0115] At 714, the UE can output the subset of the measured location signals to a network entity for training or validating at least one of the location models by sending the subset of the measured location signals to the network entity. For example, 714 can be performed by a location target wireless device 602, which can send the subset of measurements collected at 620 to a network entity (e.g., location network entity 606, training entity) at 630 for training or validating at least one of the location models. The subset can be a set of conditions indicated in the set of data collection configuration 612. Furthermore, 714 can be performed by... Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0116] Figure 8 This is a flowchart 800 of a wireless communication method. The method can be performed by a UE (e.g., UE 104, UE 350; wireless device 404, wireless device 504, target location wireless device 602; device 1304).

[0117] At point 801, the UE can send a request for conditional configuration. For example, 801 can be performed by a location target radio device 602, which can send a configuration request 608 to a location network entity 606. The configuration request 608 may include a request for conditional configuration from the location network entity 606. Furthermore, 801 can be performed by... Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0118] At 802, the UE may receive conditional configurations for collecting measurements to train or validate at least one of the positioning models. For example, 802 may be performed by a positioning target radio device 602, which may receive a set of data collection configurations 612 from a positioning network entity 606. The set of data collection configurations 612 may include a set of conditions assisting the positioning target radio device 602 in selecting a subset of positioning signal measurements to train or validate at least one of the positioning models at 630. Furthermore, 802 may be performed by… Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0119] At point 804, the UE can receive a set of location signals. For example, 804 can be performed by a target wireless device 602, which can receive a set of location signals 618 from a set of neighboring wireless devices 604. Furthermore, 804 can be performed by... Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0120] At 806, the UE can measure the set of location signals. For example, 806 can be performed by a location target wireless device 602, which can measure the set of location signals 618 at 620. Furthermore, 806 can be performed by... Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0121] At 807, the UE can select the subset of the measured location signals based on a threshold range. For example, 807 can be performed by a location target radio device 602, which can select the subset of measurements at 620 based on a threshold range indicated by the set specified by data collection configuration 612. Furthermore, 807 can be performed by… Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0122] At 808, the UE may output a subset of the measured location signals to train, monitor, or validate at least one of the location models based on at least one condition configured by the condition. For example, 808 may be performed by a location target radio device 602, which may output a subset of measurements collected at 620 at 630 based on configuration 614. Location target radio device 602 may output a subset of measurements collected at 620 at 630 to train or validate at least one of the location models based on the configuration. Furthermore, 808 may be performed by… Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0123] At point 810, the UE can obtain a set of tags based on the request. For example, point 810 can be performed by a location target radio device 602, which can obtain a set of tags based on the request. For example, location target radio device 602 can receive a set of tags 626 from location network entity 606 based on the request. Alternatively, location target radio device 602 can calculate the set of tags at point 628. Furthermore, point 810 can be performed by... Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0124] At point 812, the UE can output the set of tags for training or validating at least one of the positioning models. For example, 812 can be performed by a positioning target radio device 602, which can output the set of tags at point 630 for training or validating at least one of the positioning models. Furthermore, 812 can be performed by… Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0125] At point 814, the UE can obtain the set of tags based on the request by receiving a first subset of the set of tags. For example, 814 can be performed by a location target radio device 602, which can receive a set of tags 626 from a location network entity 606 as a first subset of the set of tags. Furthermore, 814 can be performed by... Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0126] At point 816, the UE can obtain the set of tags based on the request by calculating a second subset of the set of tags. For example, 816 can be performed by a target radio device 602, which can calculate the second subset of the set of tags at point 628. Furthermore, 816 can be performed by... Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0127] At point 818, the UE can output the set of tags to train or validate the positioning model at the UE using the set of tags. For example, 818 can be performed by a positioning target radio device 602, which can train the positioning model at the positioning target radio device 602 at point 630 based on the set of tags. Furthermore, 818 can be performed by... Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0128] At point 820, the UE may output the set of tags to train or validate at least one of the positioning models by verifying the positioning model at the UE based on the set of tags. For example, point 820 may be performed by a positioning target wireless device 602, which may validate the positioning model at point 602 based on the set of tags at point 630. Furthermore, point 820 may be performed by... Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0129] At point 822, the UE can output the set of tags to a network entity for training or validating at least one of the positioning models. For example, point 822 can be performed by a target radio device 602, which can send the set of tags to a network entity (e.g., a positioning network entity 606 or an OTT server) at point 630 for training or validating at least one of the positioning models. Furthermore, point 822 can be performed by... Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0130] At 824, the UE can measure the set of location signals by measuring the set of location signals based on the measurement type. For example, 824 can be executed by the location target wireless device 602, which can measure the set of location signals at 620 based on the measurement type indicated by the set of data collection configuration 612. Furthermore, 824 can be performed by... Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0131] Figure 9This is a flowchart 900 of a wireless communication method. The method can be performed by a UE (e.g., UE 104, UE 350; wireless device 404, wireless device 504, target location wireless device 602; device 1304).

[0132] At 902, the UE may receive a second conditional configuration to obtain a set of tags. For example, 902 may be performed by a location-targeting radio device 602, which may receive a set of data collection configurations 612 from a location network entity 606. The set of data collection configurations 612 may include the second conditional configuration to obtain a set of tags. Furthermore, 902 may be performed by... Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0133] At 904, the UE can obtain the set of tags based on at least one second condition configured by the second condition. For example, 904 can be performed by a location target radio device 602, which can obtain the set of tags based on at least one second condition configured by the second condition indicated by the set in data collection configuration 612. For example, location target radio device 602 can receive a set of tags 626 from location network entity 606. In another example, location target radio device 602 can calculate the set of tags at 628 based on measurements collected at 620. Furthermore, 904 can be performed by… Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0134] At 906, the UE can output the set of tags to train or validate at least one of the positioning models based on the second conditional configuration. For example, 906 can be performed by a positioning target radio device 602, which can output the set of tags at 630 to train or validate at least one of the positioning models based on the second conditional configuration indicated by the set in data collection configuration 612. Furthermore, 906 can be performed by… Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0135] At 908, the UE can obtain the set of tags by receiving a first subset of that set of tags. For example, 908 can be performed by a location target radio device 602, which can receive a set of tags 626 from a location network entity 606. The set of tags 626 can be a first subset of that set of tags. Furthermore, 908 can be performed by… Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0136] At 910, the UE can obtain the set of tags by calculating a second subset of that set. For example, 910 can be performed by a target location radio device 602, which can calculate the second subset of the set of tags at 628. Furthermore, 910 can be performed by... Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0137] At 912, the UE can train the positioning model at the UE using the set of tags to output the set of tags. For example, 912 can be performed by a positioning target radio device 602, which can train the positioning model at the positioning target radio device 602 based on the set of tags at 630. Furthermore, 912 can be performed by... Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0138] At 914, the UE can output the set of tags to verify the positioning model at its location based on the set of tags. For example, 914 can be performed by the target wireless device 602, which can verify the positioning model at its location based on the set of tags at 630. Furthermore, 914 can be performed by... Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0139] At 916, the UE can output the set of tags to train or validate at least one of the positioning models 88 by sending the set of tags to a network entity via 9. For example, 916 can be performed by a positioning target radio device 602, which can send the set of tags to a network entity (e.g., positioning network entity 606 or training entity) at 630 to train or validate at least one of the positioning models. Furthermore, 916 can be performed by... Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0140] At 918, the UE can output the set of labels by outputting its known location as a label to train or validate at least one of the positioning models. For example, 918 can be performed by a positioning target wireless device 602, which can output its known location (e.g., if the UE is a PRU with a known location) as a label at 630 to train or validate at least one of the positioning models. Furthermore, 918 can be performed by... Figure 1 , Figure 3 or Figure 13 Component 198 is executed.

[0141] Figure 10 This is a flowchart 1000 of a wireless communication method. This method can be performed by network entities (e.g., base station 102, base station 310; core network 120; one or more location servers 168; LMF 166; wireless devices 402, 406, 502, 506; positioning network entity 606; network entity 1302, 1402, 1560). At 1002, the network entity can be configured with conditional configurations for selecting a subset of measurements of the set of positioning signals to train or validate at least one of the positioning models. For example, 1002 can be performed by… Figure 6 The positioning network entity 606 in the middle performs the operation, which can be configured at 610 to select a subset of measurements of the set of positioning signals 618 for training or validating at least one of the positioning models. Furthermore, 1002 can be performed by... Figure 1 , Figure 3 , Figure 14 or Figure 15 Component 199 is executed.

[0142] At position 1004, the network entity can send the conditional configuration to train or validate at least one of the localization models. For example, position 1004 can be... Figure 6 The positioning network entity 606 performs this function, which can send a set of data collection configurations 612 to the positioning target wireless device 602. The set of data collection configurations 612 may include conditional configurations for selecting a subset of measurements of the set of positioning signals 618 for training or validating at least one of the positioning models. Furthermore, 1004 may be... Figure 1 , Figure 3 , Figure 14 or Figure 15 Component 199 is executed.

[0143] Figure 11 This is a flowchart 1100 of a wireless communication method. The method can be performed by network entities (e.g., base station 102, base station 310; core network 120; one or more location servers 168; LMF 166; wireless devices 402, 406, 502, 506; location network entity 606; network entity 1302, 1402, 1560).

[0144] At point 1101, the network entity can receive requests for condition configuration. For example, 1101 can be configured by... Figure 6The location network entity 606 performs this action, and this location network entity may receive a configuration request 608 from the location target wireless device 602. The configuration request 608 may include a request for conditional configuration from the location network entity 606. Furthermore, 1101 may be... Figure 1 , Figure 3 , Figure 14 or Figure 15 Component 199 is executed.

[0145] At 1102, the network entity can be configured with a conditional configuration for selecting a subset of measurements of the set of localization signals, for at least one of training or validating a localization model. For example, 1102 can be configured by... Figure 6 The positioning network entity 606 in the middle performs the conditional configuration at 610 for selecting a subset of measurements of the set of positioning signals 618, for training or validating at least one of the positioning models. Furthermore, 1102 can be performed by... Figure 1 , Figure 3 , Figure 14 or Figure 15 Component 199 is executed.

[0146] At point 1104, the network entity can send the conditional configuration to train or validate at least one of the localization models. For example, 1104 can be... Figure 6 The positioning network entity 606 performs this action, which can send a set of data collection configurations 612 to the positioning target wireless device 602. The set of data collection configurations 612 may include conditional configurations for selecting a subset of measurements of the set of positioning signals 618 for training or validating at least one of the positioning models. Furthermore, 1104 may be... Figure 1 , Figure 3 , Figure 14 or Figure 15 Component 199 is executed.

[0147] At point 1106, the network entity can configure the conditional configuration by configuring the conditional configuration based on a threshold range. For example, 1106 can be configured by... Figure 6 The location network entity 606 performs this operation, and this location network entity can configure the conditional configuration at 610 based on a threshold range. Furthermore, 1106 can be... Figure 1 , Figure 3 , Figure 14 or Figure 15 Component 199 is executed.

[0148] At point 1108, the network entity can be configured with a second conditional configuration to obtain a set of labels based on at least one of a threshold range or a label calculation condition. For example, 1108 can be configured by... Figure 6The location network entity 606 in the middle performs the operation, which can be configured at 610 with a second conditional configuration to obtain a set of tags based on at least one of a threshold range or a tag calculation condition. Furthermore, 1108 can be performed by... Figure 1 , Figure 3 , Figure 14 or Figure 15 Component 199 is executed.

[0149] At 1110, the network entity can send the second conditional configuration to obtain the set of labels for training or validating at least one of the localization models. For example, 1110 can be... Figure 6 The location network entity 606 performs this action, which can send a set of data collection configurations 612 to the location target wireless device 602. The set of data collection configurations 612 may include the second conditional configuration for obtaining the set of tags for at least one of training or validating the location model. Furthermore, 1110 may be... Figure 1 , Figure 3 , Figure 14 or Figure 15 Component 199 is executed.

[0150] Figure 12 This is a flowchart 1200 of a wireless communication method. The method can be performed by network entities (e.g., base station 102, base station 310; core network 120; one or more location servers 168; LMF 166; wireless devices 402, 406, 502, 506; location network entity 606; network entity 1302, 1402, 1560).

[0151] At 1202, the network entity can be configured with a second conditional configuration to obtain a set of labels for training or validating at least one of the localization models. For example, 1202 can be configured by... Figure 6 The localization network entity 606 in the model performs the operation, and this localization network entity can be configured at 610 with a second conditional configuration to obtain a set of labels for training or validating at least one of the localization models. Furthermore, 1202 can be... Figure 1 , Figure 3 , Figure 14 or Figure 15 Component 199 is executed.

[0152] At point 1204, the network entity can send the conditional configuration to train or validate at least one of the localization models. For example, point 1204 can be... Figure 6The location network entity 606 performs this action, and can send a set of data collection configurations 612 to the target wireless device 602. The set of data collection configurations 612 may include at least one of the conditional configurations for training or validating the location model. In some aspects, the location network entity 606 can send the configuration to other wireless devices, such as a set of neighboring wireless devices 604 or other UEs / PRUs related to the target wireless device 602, which may be configured to collect / compute tags and report tags to the location network entity 606. Furthermore, 1204 may be... Figure 1 , Figure 3 , Figure 14 or Figure 15 Component 199 is executed.

[0153] At point 1206, the network entity can receive a second set of measurements of the location signals. For example, 1206 can be... Figure 6 The positioning network entity 606 performs this action, and can receive a set of measurement reports 622 from the target wireless device 602. The set of measurement reports 622 may include a set of measurements of a set of positioning signals 618. In some aspects, the positioning network entity 606 can receive measurements from, for example, a set of neighboring wireless devices 604 or other devices of other UEs / PRUs in a shared area of ​​the target wireless device 602. Furthermore, 1206 can be performed by... Figure 1 , Figure 3 , Figure 14 or Figure 15 Component 199 is executed.

[0154] At point 1208, the network entity can compute a subset of the set of labels based on the measured set. For example, 1208 can be derived from... Figure 6 The positioning network entity 606 performs this function, which can calculate a subset of the tag's set at 624 based on the set of measurement reports 622 and / or measurements received from other devices. Furthermore, 1208 can be... Figure 1 , Figure 3 , Figure 14 or Figure 15 Component 199 is executed.

[0155] At 1210, the network entity can send the subset of the set of tags based on at least one second condition configured by the second condition. For example, 1210 can be... Figure 6 The location network entity 606 performs this action, which can send a set of tags 626 to a location target wireless device 602. The set of tags 626 may include a first subset of the set of tags. The transmission of the set of tags 626 may be based on at least one second condition configured by the second condition. Furthermore, 1210 may be... Figure 1 , Figure 3 , Figure 14 or Figure 15 Component 199 is executed.

[0156] At position 1212, the network entity can receive a subset of that set of tags. For example, 1212 can be... Figure 6 The location network entity 606 performs this action, and this location network entity can receive a subset of that set of tags, for example, from a set of neighboring radio devices 604 or from other devices of other UEs / PRUs regarding the target radio device 602. In other words, the location network entity 606 can aggregate tags from multiple radio devices. Furthermore, 1212 can be performed by... Figure 1 , Figure 3 , Figure 14 or Figure 15 Component 199 is executed.

[0157] At 1214, the network entity can send that subset of the set of tags based on at least one second condition configured by the second condition. For example, 1214 can be... Figure 6 The location network entity 606 performs this action, which can send a set of tags 626 to the location target wireless device 602. The transmission of the set of tags 626 can be based on at least one second condition configured by the second condition. Furthermore, 1214 can be... Figure 1 , Figure 3 , Figure 14 or Figure 15 Component 199 is executed.

[0158] Figure 13Figure 1300 illustrates an example of a hardware implementation for device 1304. Device 1304 may be a UE, a component of a UE, or implement UE functionality. In some aspects, device 1304 may include at least one cellular baseband processor 1324 (also referred to as a modem) coupled to one or more transceivers 1322 (e.g., cellular RF transceivers). Cellular baseband processor 1324 may include at least one on-chip memory 1324'. In some aspects, device 1304 may also include one or more Subscriber Identity Module (SIM) cards 1320 and at least one application processor 1306 coupled to a Secure Digital Card (SD) card 1308 and a screen 1310. Application processor 1306 may include on-chip memory 1306'. In some aspects, device 1304 may also include a Bluetooth module 1312, a WLAN module 1314, an SPS module 1316 (e.g., a GNSS module), one or more sensor modules 1318 (e.g., barometric pressure sensor / altimeter; motion sensors such as inertial measurement unit (IMU), gyroscope and / or accelerometer; light detection and ranging (LIDAR), radio-assisted detection and ranging (RADAR), sound navigation and ranging (SONAR), magnetometer, audio and / or other technologies for positioning), an additional memory module 1326, a power source 1330, and / or a camera 1332. Bluetooth module 1312, WLAN module 1314, and SPS module 1316 may include an on-chip transceiver (TRX) (or in some cases, only a receiver (RX)). Bluetooth module 1312, WLAN module 1314, and SPS module 1316 may include their own dedicated antennas and / or use antenna 1380 for communication. Cellular baseband processor 1324 communicates with UE 104 and / or RU associated with network entity 1302 via transceiver 1322 through one or more antennas 1380. Cellular baseband processor 1324 and application processor 1306 may each include computer-readable media / memory 1324', 1306' respectively. Additional memory module 1326 may also be considered as computer-readable media / memory. Each computer-readable media / memory 1324', 1306', 1326 may be non-transitory. Cellular baseband processor 1324 and application processor 1306 are each responsible for general processing, including executing software stored on the computer-readable media / memory. When executed by cellular baseband processor 1324 / application processor 1306, the software causes cellular baseband processor 1324 / application processor 1306 to perform the various functions described above. Cellular baseband processor 1324 and application processor 1306 are configured to perform the various functions described above based at least in part on information stored in memory.In other words, the cellular baseband processor 1324 and application processor 1306 can be configured to perform a first subset of the various functions described above without information stored in memory, and can be configured to perform a second subset of the various functions described above based on information stored in memory. The computer-readable medium / memory can also be used to store data manipulated by the cellular baseband processor 1324 / application processor 1306 during software execution. The cellular baseband processor 1324 / application processor 1306 can be a component of the UE 350 and can include at least one of a memory 360 and / or at least one of a TX processor 368, an RX processor 356, and a controller / processor 359. In one configuration, the device 1304 can be at least one processor chip (modem and / or application) and includes only the cellular baseband processor 1324 and / or application processor 1306, while in another configuration, the device 1304 can be the entire UE (e.g., see [link]). Figure 3 The UE350 includes an additional module of the device 1304.

[0159] As discussed above, component 198 can be configured to receive a conditional configuration for collecting measurements to train or validate at least one of the positioning models. Component 198 can be configured to receive a set of positioning signals. Component 198 can be configured to measure the set of positioning signals. Component 198 can be configured to output a subset of the measured set of positioning signals to train or validate at least one of the positioning models based on the conditional configuration. Component 198 can be configured to train the positioning model at device 1304 by using the subset of the measured set of positioning signals to output the subset of the measured set of positioning signals. Component 198 can be configured to validate the positioning model at device 1304 by using the subset of the measured set of positioning signals to output the subset of the measured set of positioning signals. Component 198 can be configured to output the subset of the measured set of positioning signals to train or validate at least one of the positioning models by sending the subset of the measured set of positioning signals to a network entity. Component 198 may be located within cellular baseband processor 1324, application processor 1306, or both cellular baseband processor 1324 and application processor 1306. Component 198 may be one or more hardware components specifically configured to implement the stated process / algorithm, implemented by one or more processors configured to execute the stated process / algorithm, stored in a computer-readable medium for implementation by one or more processors, or some combination thereof. When multiple processors are implemented, the multiple processors may execute the stated process / algorithm individually or in combination. As shown, device 1304 may include a variety of components configured for various functions. In one configuration, device 1304, and in particular cellular baseband processor 1324 and / or application processor 1306, may include components for receiving conditional configurations for collecting measurements to train or validate at least one of the positioning models. Device 1304 may include components for performing: receiving a set of positioning signals; measuring the set of positioning signals. The device 1304 may include components for outputting a subset of the measured positioning signals to train or validate at least one of the positioning models based on at least one condition of the conditional configuration. The conditional configuration may include an indicator of the set of conditions. The set of conditions may include a threshold range. The device 1304 may include components for selecting the subset of the measured positioning signals based on the threshold range. The threshold range may include at least one of: (a) an RSRP threshold range, (b) an SNR threshold range, (c) a SINR threshold range, (d) a delay spread threshold range, (e) a Rice factor threshold range, (f) a Doppler spread threshold range, or (g) a multipath component threshold range. The conditional configuration may include an indicator of the set of conditions. The set of conditions may include a measurement type.Apparatus 1304 may include components for measuring the set of positioning signals by measuring the set of positioning signals based on the measurement type. The measurement type may include at least one of the following: (a) RSTD, (b) RSRP, (c) RSRPP, (d) path-associated timing measurement, (e) CIR timing measurement, (f) CIR power measurement, (g) CIR phase measurement, (h) CFR frequency measurement, (i) CFR power measurement, or (j) CFR phase measurement. Apparatus 1304 may include components for receiving a second conditional configuration to obtain a set of tags. Apparatus 1304 may include components for obtaining the set of tags based on at least one second condition of the second conditional configuration. Apparatus 1304 may include components for outputting the set of tags to train or validate at least one of the positioning models based on the second conditional configuration. The second configuration may include a second set of conditions. The second set of conditions may include at least one of the following: (a) a first threshold range associated with a second subset of the set of measured positioning signals used to calculate the position of device 1304; (b) a second threshold range associated with a tag error rate associated with a method for calculating the position of device 1304; (c) a first indicator of a set of calculation methods used to calculate the position of device 1304; (d) a second indicator of a statistical representation method used to summarize the calculated position of device 1304; or (e) a third indicator of a set of tag quality criteria associated with the set of tags. Device 1304 may include components for outputting the set of tags by at least one of: training the positioning model at device 1304 based on the set of tags; or components for outputting the set of tags by validating the positioning model at device 1304 based on the set of tags. Apparatus 1304 may include components for outputting the set of tags for training or validating at least one of the localization model by sending the set of tags to a network entity. Apparatus 1304 may include components for obtaining the set of tags by receiving a first subset of the set of tags. Apparatus 1304 may include components for sending a request for the conditional configuration prior to the reception of the conditional configuration. Apparatus 1304 may include components for obtaining the set of tags by calculating a second subset of the set of tags. The request may include an indicator of a threshold range. Apparatus 1304 may include components for selecting the subset of the set of measured localization signals based on the threshold range. The request may include at least one of a first indicator of a threshold range associated with the calculation of the set of tags or a second indicator of tag calculation conditions associated with the calculation of the set of tags.Apparatus 1304 may include components for obtaining the set of labels based on the request. Apparatus 1304 may include components for outputting the set of labels for training or validating at least one of the localization model. Apparatus 1304 may include components for obtaining the set of labels based on the request by receiving a first subset of the set of labels. Apparatus 1304 may include components for obtaining the set of labels based on the request by calculating a second subset of the set of labels. The request may include at least one of the following: (a) a second threshold range associated with a set of measured positioning signals used to calculate the position of device 1304; (b) a third threshold range associated with a tag error rate associated with a method for calculating the position of device 1304; (c) a third indicator of a set of calculation methods used to calculate the position of device 1304; (d) a fourth indicator of a statistical representation method used to summarize the calculated position of device 1304; or (e) a fifth indicator of a set of tag quality criteria associated with the set of tags. Device 1304 may include components for training the positioning model at device 1304 based on the subset of the measured positioning signals to output the subset of the measured positioning signals. Apparatus 1304 may include components for outputting a subset of the measured location signals to validate the location model at apparatus 1304 based on a subset of the measured location signals. Apparatus 1304 may include components for outputting a subset of the measured location signals to a network entity to train or validate at least one of the location models. Apparatus 1304 may include a PRU with a known location. Apparatus 1304 may include components for outputting the known location as a label to train or validate at least one of the location models. The component may be component 198 of apparatus 1304 configured to perform the functions described therein. As described above, apparatus 1304 may include a TX processor 368, an RX processor 356, and a controller / processor 359. Thus, in one configuration, these components may be the TX processor 368, the RX processor 356, and / or the controller / processor 359 configured to perform the functions described therein.

[0160] Figure 14Figure 1400 illustrates an example of a hardware implementation for network entity 1402. Network entity 1402 may be a BS, a component of a BS, or implement BS functionality. Network entity 1402 may include at least one of CU 1410, DU 1430, or RU 1440. For example, depending on the layer functionality handled by component 199, network entity 1402 may include CU 1410; both CU 1410 and DU 1430; each of CU 1410, DU 1430, and RU 1440; DU 1430; both DU 1430 and RU 1440; or RU 1440. CU 1410 may include at least one CU processor 1412. CU processor 1412 may include on-chip memory 1412'. In some aspects, CU 1410 may also include an additional memory module 1414 and a communication interface 1418. CU1410 communicates with DU 1430 via a midhaul link, such as an F1 interface. DU 1430 may include at least one DU processor 1432. DU processor 1432 may include on-chip memory 1432'. In some aspects, DU 1430 may also include an additional memory module 1434 and a communication interface 1438. DU 1430 communicates with RU 1440 via a fronthaul link. RU 1440 may include at least one RU processor 1442. RU processor 1442 may include on-chip memory 1442'. In some aspects, RU 1440 may also include an additional memory module 1444, one or more transceivers 1446, an antenna 1480, and a communication interface 1448. RU 1440 communicates with UE 104. On-chip memories 1412', 1432', 1442' and additional memory modules 1414, 1434, 1444 may each be considered as computer-readable media / memory. Each computer-readable medium / memory can be non-transitory. Each of processors 1412, 1432, and 1442 is responsible for general processing, including executing software stored on the computer-readable medium / memory. When executed by the corresponding processor, the software causes the processor to perform the various functions described above. The computer-readable medium / memory can also be used to store data manipulated by the processor while executing the software.

[0161] As discussed above, component 199 may be configured to configure conditional configurations for selecting a subset of measurements of a set of positioning signals to train or validate at least one of the positioning models. Component 199 may be configured to send the conditional configurations to train or validate at least one of the positioning models. Component 199 may be located within one or more processors of one or more of CU 1410, DU 1430, and RU1440. Component 199 may be one or more hardware components specifically configured to implement the stated process / algorithm, implemented by one or more processors configured to execute the stated process / algorithm, stored in a computer-readable medium for implementation by one or more processors, or some combination thereof. When multiple processors are implemented, the multiple processors may execute the stated process / algorithm individually or in combination. Network entity 1402 may include a variety of components configured for various functions. In one configuration, network entity 1402 may include components for configuring conditional configurations for selecting a subset of measurements of a set of positioning signals to train or validate at least one of the positioning models. Network entity 1402 may include components for transmitting the conditional configuration to train or validate at least one of the localization models. The conditional configuration may include an indicator of a set of conditions. The set of conditions may include a threshold range. Device 1304 may include components for selecting a subset of the set of measured localization signals based on the threshold range. The threshold range may include at least one of the following: (a) an RSRP threshold range, (b) an SNR threshold range, (c) a SINR threshold range, (d) a delay spread threshold range, (e) a Rice factor threshold range, (f) a Doppler spread threshold range, or (g) a multipath component threshold range. The conditional configuration may include an indicator of a set of conditions. The set of conditions may include a measurement type. Device 1304 may include components for measuring the set of localization signals by measuring the set of localization signals based on the measurement type. The measurement type may include at least one of the following: (a) RSTD, (b) RSRP, (c) RSRPP, (d) path-associated timing measurement, (e) CIR timing measurement, (f) CIR power measurement, (g) CIR phase measurement, (h) CFR frequency measurement, (i) CFR power measurement, or (j) CFR phase measurement. Network entity 1402 may include components for configuring a second conditional configuration to obtain a set of labels for training or validating at least one of the localization models. Network entity 1402 may include components for sending the second conditional configuration to obtain the set of labels for training or validating at least one of the localization models. The second conditional configuration may include a set of conditions.The set of conditions may include at least one of the following: (a) a first threshold range for a second selection of a second subset of the measurements of the set of location signals used for calculating the location of the UE (e.g., UE 104); (b) a second threshold range for a tag error rate associated with the calculation of the UE's location; (c) a first indicator of a set of calculation methods used for the calculation of the UE's location; (d) a second indicator of a statistical representation method used for the calculation of the UE's location; or (e) a third indicator of a set of tag quality criteria used for the calculation of the UE's location. Network entity 1402 may include components for receiving a subset of the set of tags. Network entity 1402 may include components for training the location model at network entity 1402 based on the set of tags. Network entity 1402 may include components for validating the location model at network entity 1402 based on the set of tags. Network entity 1402 may include components for receiving a set of measurements of a second set of location signals. Network entity 1402 may include components for calculating a subset of the set of tags based on the set of measurements. Network entity 1402 may include components for transmitting the subset of the set of tags based on the second conditional configuration. Network entity 1402 may include components for receiving a request for the conditional configuration. The transmission of the conditional configuration may be based on the request. The request may include an indicator of a threshold range. Network entity 1402 may include components for configuring the conditional configuration for the selected subset of measurements of the set of location signals to train or validate at least one of the location model by configuring the conditional configuration based on the threshold range. The request may include at least one of a first indicator of a threshold range associated with the calculation of the set of tags or a second indicator of tag calculation conditions associated with the calculation of the set of tags. Network entity 1402 may include components for configuring the second conditional configuration to obtain the set of tags based on at least one of the threshold range or the tag calculation conditions. Network entity 1402 may include components for sending the second conditional configuration to obtain the set of labels for training or validating at least one of the localization models. Network entity 1402 may include components for receiving a subset of the set of labels. Network entity 1402 may include components for training the localization model at network entity 1402 based on the set of labels. Network entity 1402 may include components for validating the localization model at network entity 1402 based on the set of labels. Network entity 1402 may include components for receiving a set of measurements of the second set of localization signals. Network entity 1402 may include components for calculating a subset of the set of labels based on the set of measurements.Network entity 1402 may include components for transmitting a subset of the set of tags based on the second condition configuration. The request may include at least one of the following: (a) a first threshold range for a second selection of a second subset of the measurements of the set of location signals used for calculating the location of the UE (e.g., UE 104); (b) a second threshold range for a tag error rate associated with the calculation of the UE's location; (c) a first indicator of a set of calculation methods used for the calculation of the UE's location; (d) a second indicator of a statistical representation method used for the calculation of the UE's location; or (e) a third indicator of a set of tag quality standards used for the calculation of the UE's location. Network entity 1402 may include components for receiving the subset of measurements of the set of location signals. Network entity 1402 may include components for training the location model at network entity 1402 based on the subset of measurements of the set of location signals. Network entity 1402 may include components for validating the positioning model at network entity 1402 based on a subset of measurements of the set of positioning signals. Network entity 1402 may include an LMF. The component may be a component 199 of network entity 1402 configured to perform the functions described therein. As described above, network entity 1402 may include a TX processor 316, an RX processor 370, and a controller / processor 375. Thus, in one configuration, these components may be the TX processor 316, the RX processor 370, and / or the controller / processor 375 configured to perform the functions described therein.

[0162] Figure 15 Figure 1500 illustrates an example of a hardware implementation for network entity 1560. In one example, network entity 1560 may be within core network 120. Network entity 1560 may include at least one network processor 1512. Network processor 1512 may include on-chip memory 1512'. In some aspects, network entity 1560 may also include an additional memory module 1514. Network entity 1560 communicates with CU 1502 directly (e.g., via a backhaul link) or indirectly (e.g., via RIC) through network interface 1580. On-chip memory 1512' and additional memory module 1514 may each be considered as computer-readable media / memory. Each computer-readable media / memory may be non-transitory. Network processor 1512 is responsible for general processing, including executing software stored on the computer-readable media / memory. The software, when executed by the corresponding processor, causes the processor to perform the various functions described above. The computer-readable media / memory may also be used to store data manipulated by the processor when executing the software.

[0163] As discussed above, component 199 may be configured to configure conditional configurations for selecting a subset of measurements of a set of location signals to train or validate at least one of the location models. Component 199 may be configured to send the conditional configurations to train or validate at least one of the location models. Component 199 may be within network processor 1512. Component 199 may be one or more hardware components specifically configured to implement the stated process / algorithm, implemented by one or more processors configured to execute the stated process / algorithm, stored in a computer-readable medium for implementation by one or more processors, or some combination thereof. When multiple processors are implemented, the multiple processors may execute the stated process / algorithm individually or in combination. Network entity 1560 may include a variety of components configured for various functions. In one configuration, network entity 1560 may include components for configuring conditional configurations for selecting a subset of measurements of a set of location signals to train or validate at least one of the location models. Network entity 1560 may include components for sending the conditional configurations to train or validate at least one of the location models. The condition configuration may include an indicator of a set of conditions. The set of conditions may include a threshold range. Device 1304 may include components for selecting a subset of the set of measured positioning signals based on the threshold range. The threshold range may include at least one of the following: (a) an RSRP threshold range, (b) an SNR threshold range, (c) a SINR threshold range, (d) a delay spread threshold range, (e) a Rice factor threshold range, (f) a Doppler spread threshold range, or (g) a multipath component threshold range. The condition configuration may include an indicator of a set of conditions. The set of conditions may include a measurement type. Device 1304 may include components for measuring the set of positioning signals by measuring the set of positioning signals based on the measurement type. The measurement type may include at least one of the following: (a) RSTD, (b) RSRP, (c) RSRPP, (d) path-associated timing measurement, (e) CIR timing measurement, (f) CIR power measurement, (g) CIR phase measurement, (h) CFR frequency measurement, (i) CFR power measurement, or (j) CFR phase measurement. Network entity 1560 may include components for configuring a second conditional configuration to obtain a set of labels for training or validating at least one of the localization models. Network entity 1560 may include components for sending the second conditional configuration to obtain the set of labels for training or validating at least one of the localization models. The second conditional configuration may include a set of conditions.The set of conditions may include at least one of the following: (a) a first threshold range for a second selection of a second subset of the measurements of the set of location signals used for calculating the location of the UE (e.g., UE 104); (b) a second threshold range for a tag error rate associated with the calculation of the location of the UE; (c) a first indicator of a set of calculation methods used for the calculation of the location of the UE; (d) a second indicator of a statistical representation method used for the calculation of the location of the UE; or (e) a third indicator of a set of tag quality criteria used for the calculation of the location of the UE. Network entity 1560 may include components for receiving a subset of the set of tags. Network entity 1560 may include components for training the location model at network entity 1560 based on the set of tags. Network entity 1560 may include components for validating the location model at network entity 1560 based on the set of tags. Network entity 1560 may include components for receiving a set of measurements of a second set of location signals. Network entity 1560 may include components for calculating a subset of the set of tags based on the set of measurements. Network entity 1560 may include components for transmitting the subset of the set of tags based on the second conditional configuration. Network entity 1560 may include components for receiving a request for the conditional configuration. The transmission of the conditional configuration may be based on the request. The request may include an indicator of a threshold range. Network entity 1560 may include components for configuring the conditional configuration for the selected subset of measurements of the set of location signals to train or validate at least one of the location model by configuring the conditional configuration based on the threshold range. The request may include at least one of a first indicator of a threshold range associated with the calculation of the set of tags or a second indicator of tag calculation conditions associated with the calculation of the set of tags. Network entity 1560 may include components for configuring the second conditional configuration to obtain the set of tags based on at least one of the threshold range or the tag calculation conditions. Network entity 1560 may include components for sending the second conditional configuration to obtain the set of labels for training or validating at least one of the localization models. Network entity 1560 may include components for receiving a subset of the set of labels. Network entity 1560 may include components for training the localization model at network entity 1560 based on the set of labels. Network entity 1560 may include components for validating the localization model at network entity 1560 based on the set of labels. Network entity 1560 may include components for receiving a set of measurements of the second set of localization signals. Network entity 1560 may include components for calculating a subset of the set of labels based on the set of measurements.Network entity 1560 may include components for transmitting a subset of the set of tags based on the second condition configuration. The request may include at least one of the following: (a) a first threshold range for a second selection of a second subset of the measurements of the set of location signals used for calculating the location of the UE (e.g., UE 104); (b) a second threshold range for a tag error rate associated with the calculation of the UE's location; (c) a first indicator of a set of calculation methods used for the calculation of the UE's location; (d) a second indicator of a statistical representation method used for the calculation of the UE's location; or (e) a third indicator of a set of tag quality criteria used for the calculation of the UE's location. Network entity 1560 may include components for receiving the subset of measurements of the set of location signals. Network entity 1560 may include components for training the location model at network entity 1560 based on the subset of measurements of the set of location signals. Network entity 1560 may include components for validating the positioning model at network entity 1560 using a subset of measurements based on the set of positioning signals. Network entity 1560 may include an LMF. The component may be a component 199 of network entity 1560 configured to perform the functions described therein.

[0164] It should be understood that the specific order or hierarchy of the boxes in the disclosed process / flowcharts is merely an example of the exemplary method. It should be understood that the specific order or hierarchy of the boxes in the process / flowcharts may be rearranged based on design preferences. Furthermore, some boxes may be combined or omitted. The appended method claims present the elements of various boxes in a sample order, but are not limited to the given specific order or hierarchy.

[0165] The foregoing description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects. Therefore, the claims are not limited to the aspects described herein but should be given the full scope consistent with the language of the claims. Unless specifically stated otherwise, references to elements in the singular form do not mean “one and only one” but rather “one or more.” Terms such as “if,” “when,” and “simultaneously” do not imply a direct temporal relationship or reaction. That is, these phrases, such as “when,” do not imply an immediate action in response to the occurrence of an action or during the occurrence of an action, but simply suggest that if a condition is met, then the action will occur, without requiring a specific or immediate time limit for the occurrence of the action. The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” is not necessarily to be construed as preferred or superior to other aspects. Unless otherwise specifically stated, the term “some” refers to one or more. Combinations such as "at least one of A, B, or C", "one or more of A, B, or C", "at least one of A, B, and C", "one or more of A, B, and C", and "A, B, C, or any combination thereof" include any combination of A, B, and / or C, and may include multiple A, multiple B, or multiple C. Specifically, combinations such as "at least one of A, B, or C", "one or more of A, B, or C", "at least one of A, B, and C", "one or more of A, B, and C", and "A, B, C, or any combination thereof" can be only A, only B, only C, A and B, A and C, B and C, or A and B and C, where any such combination may contain one or more members of A, B, or C. A set should be interpreted as a group of elements, where the elements are numbered one or more. Therefore, for a set of X, X will include one or more elements. When at least one processor is configured to execute a set of functions, that at least one processor is configured to execute that set of functions individually or in any combination. Therefore, each of the at least one processor can be configured to perform a specific subset of the set of functions, wherein the subset is the complete set, a suitable subset of the set, or an empty subset of the set. If the first device receives data from or sends data to the second device, data can be received / sent directly between the first and second devices, or indirectly between the first and second devices through a set of devices. A device configured to "output" data (such as transmission, signal, or message) can, for example, use a transceiver to transmit the data, transmit the data to the device that sent the data, or output the data to a component of the device.A device configured to "acquire" data (such as transmission, signaling, or messaging) may, for example, utilize a transceiver to receive the data, obtain the data from a device receiving the data, or obtain the data from a component of the receiving device. Information stored in memory includes instructions and / or data. All structural and functional equivalents of the elements throughout the various aspects described herein that are known to those skilled in the art or will later be known are expressly incorporated herein by reference and are covered by the claims. Furthermore, nothing disclosed herein is intended to be offered to the public, whether or not such disclosure is explicitly recited in the claims. The words "module," "mechanism," "element," "device," etc., cannot replace the word "component." Therefore, no claim element will be construed as a functional component unless the element is explicitly recited using the phrase "component for..."

[0166] As used in this article, the phrase “based on” should not be interpreted as referring to a closed set of information, one or more conditions, one or more factors, etc. In other words, the phrase “based on A” (where “A” can be information, conditions, factors, etc.) should be interpreted as “based on at least A”, unless specifically stated differently.

[0167] The following aspects are merely illustrative and may be combined with other aspects or teachings described herein without limitation.

[0168] Aspect 1 is a method for wireless communication at a user equipment (UE), the method comprising: receiving a conditional configuration for collecting measurements to train or validate at least one of a positioning model; receiving a set of positioning signals; measuring the set of positioning signals; and outputting a subset of the measured set of positioning signals to train or validate at least one of the positioning models based on at least one condition of the conditional configuration.

[0169] Aspect 2 is the method according to aspect 1, wherein the condition configuration includes an indicator of a set of conditions, wherein the set of conditions includes a threshold range, and the method further includes selecting a subset of the set of measured positioning signals based on the threshold range.

[0170] Aspect 3 is the method according to aspect 2, wherein the threshold range includes at least one of the following: a reference signal received power (RSRP) threshold range; a signal-to-noise ratio (SNR) threshold range; a signal-to-interference and noise ratio (SINR) threshold range; a delay spread threshold range; a Rice factor threshold range; a Doppler spread threshold range; or a multipath component threshold range.

[0171] Aspect 4 is a method according to any one of Aspects 1 to 3, wherein the condition configuration includes an indicator of a set of conditions, wherein the set of conditions includes a measurement type, and wherein the set of measured positioning signals includes the set of positioning signals measured based on the measurement type.

[0172] Aspect 5 is the method according to aspect 4, wherein the measurement type includes at least one of the following: reference signal time difference (RSTD); reference signal received power (RSRP); reference signal received power per path (RSRPP); timing measurement associated with the path; channel impulse response (CIR) timing measurement; CIR power measurement; CIR phase measurement; channel frequency response (CFR) frequency measurement; CFR power measurement; or CFR phase measurement.

[0173] Aspect 6 is a method according to any one of Aspects 1 to 5, the method further comprising: receiving a second conditional configuration to obtain a set of labels; obtaining the set of labels based on at least one second condition of the second conditional configuration; and outputting the set of labels to train or validate at least one of the localization models based on the second conditional configuration.

[0174] Aspect 7 is the method according to aspect 6, wherein the second configuration includes a second set of conditions, wherein the second set of conditions includes at least one of: a first threshold range associated with a second subset of a set of measured location signals used to calculate the location of the UE; a second threshold range associated with a tag error rate associated with a calculation method used to calculate the location of the UE; a first indicator of a set of calculation methods used to calculate the location of the UE; a second indicator of a statistical representation method used to summarize a set of calculated locations of the UE; or a third indicator of a set of tag quality criteria associated with the set of tags.

[0175] Aspect 8 is the method according to any one of Aspect 6 or 7, wherein the set of output tags includes at least one of: training the localization model at the UE based on the set of tags; or validating the localization model at the UE based on the set of tags.

[0176] Aspect 9 is the method according to any one of Aspects 6 to 8, wherein the set of output labels includes sending the set of labels to a network entity for training or validating at least one of the localization models.

[0177] Aspect 10 is the method according to any one of aspects 6 to 9, wherein obtaining the set of tags includes at least one of: receiving a first subset of the set of tags; or calculating a second subset of the set of tags.

[0178] Aspect 11 is a method according to any one of aspects 1 to 10, the method further comprising sending a request for the condition configuration prior to the receipt of the condition configuration.

[0179] Aspect 12 is the method according to aspect 11, wherein the request includes an indicator of a threshold range, and the method further includes selecting the subset of the set of measured positioning signals based on the threshold range.

[0180] Aspect 13 is a method according to any one of Aspects 11 or 12, wherein the request includes at least one of a first indicator of a threshold range associated with the calculation of the set of labels or a second indicator of label calculation conditions associated with the calculation of the set of labels, the method further comprising: obtaining the set of labels based on the request; and outputting the set of labels for training or validating at least one of the localization model.

[0181] Aspect 14 is the method according to aspect 13, wherein obtaining the set of tags based on the request includes at least one of: receiving a first subset of the set of tags; or calculating a second subset of the set of tags.

[0182] Aspect 15 is a method according to any one of Aspects 13 or 14, wherein the request includes at least one of the following: a second threshold range associated with a set of location signals measured for calculating the location of the UE; a third threshold range associated with a tag error rate associated with a calculation method for calculating the location of the UE; a third indicator of a set of calculation methods used to calculate the location of the UE; a fourth indicator of a statistical representation method used to summarize the set of calculated locations of the UE; or a fifth indicator of a set of tag quality criteria associated with the set of tags.

[0183] Aspect 16 is a method according to any one of aspects 1 to 15, wherein outputting the subset of the set of measured positioning signals comprises: training the positioning model at the UE based on the subset of the set of measured positioning signals; or validating the positioning model at the UE based on the subset of the set of measured positioning signals.

[0184] Aspect 17 is a method according to any one of aspects 1 to 16, wherein outputting a subset of the set of measured positioning signals includes sending the subset of the set of measured positioning signals to a network entity for training or validating at least one of the positioning models.

[0185] Aspect 18 is a method according to any one of Aspects 1 to 17, wherein the UE includes a localization reference unit (PRU) having a known location; the method further includes outputting the known location as a label for training or validating at least one of the localization models.

[0186] Aspect 19 is a method for wireless communication at a network entity, the method comprising: configuring a conditional configuration for selecting a subset of measurements of a set of location signals to train or validate at least one of the location models; and transmitting the conditional configuration to train or validate at least one of the location models.

[0187] Aspect 20 is the method according to aspect 19, wherein the condition configuration includes an indicator of a set of conditions, wherein the set of conditions includes a threshold range for the selected subset of measurements of the set of location signals, wherein the subset of measurements of the set of location signals is within the threshold range.

[0188] Aspect 21 is the method according to aspect 20, wherein the threshold range includes at least one of the following: a reference signal received power (RSRP) threshold range; a signal-to-noise ratio (SNR) threshold range; a signal-to-interference and noise ratio (SINR) threshold range; a delay spread threshold range; a Rice factor threshold range; a Doppler spread threshold range; or a multipath component threshold range.

[0189] Aspect 22 is a method according to any one of aspects 19 to 21, wherein the condition configuration includes an indicator of a set of conditions, wherein the set of conditions includes a measurement type, and wherein the subset of measurements of the set of positioning signals includes the measurement type.

[0190] Aspect 23 is the method according to aspect 22, wherein the measurement type includes at least one of the following: reference signal time difference (RSTD); reference signal received power (RSRP); reference signal received power per path (RSRPP); timing measurement associated with the path; channel impulse response (CIR) timing measurement; CIR power measurement; CIR phase measurement; channel frequency response (CFR) frequency measurement; CFR power measurement; or CFR phase measurement.

[0191] Aspect 24 is a method according to any one of aspects 19 to 23, the method further comprising: configuring a second conditional configuration to obtain a set of labels for training or validating at least one of the localization models; and sending the second conditional configuration to obtain the set of labels for training or validating at least one of the localization models.

[0192] Aspect 25 is the method according to aspect 24, wherein the second condition configuration includes a set of conditions, wherein the set of conditions includes at least one of: a first threshold range for a second selection of a second subset of measurements of the set of location signals for calculating the location of a user equipment (UE); a second threshold range for a tag error rate associated with the calculation of the location of the UE; a first indicator of a set of calculation methods for the calculation of the location of the UE; a second indicator of a statistical representation method for the calculation of the location of the UE; or a third indicator of a set of tag quality standards for the calculation of the location of the UE.

[0193] Aspect 26 is the method according to any one of aspects 24 or 25, the method further comprising: receiving a subset of the set of tags; and training the localization model at the network entity based on the set of tags or validating the localization model at the network entity based on the set of tags.

[0194] Aspect 27 is a method according to any one of aspects 24 to 26, the method further comprising: receiving a set of measurements of a second set of positioning signals; calculating a subset of the set of tags based on the set of measurements; and transmitting the subset of the set of tags based on the second conditional configuration.

[0195] Aspect 28 is a method according to any one of aspects 19 to 27, the method further comprising: receiving a request for the condition configuration, wherein the sending of the condition configuration is based on the request.

[0196] Aspect 29 is the method according to aspect 28, wherein the request includes an indicator of a threshold range, wherein configuring the conditional configuration for training or validating at least one of the localization models for the selection of a subset of measurements of the set of localization signals includes configuring the conditional configuration based on the threshold range.

[0197] Aspect 30 is a method according to any one of Aspects 28 or 29, wherein the request includes at least one of a first indicator of a threshold range associated with the calculation of the set of labels or a second indicator of label calculation conditions associated with the calculation of the set of labels, the method further comprising: configuring a second conditional configuration to obtain the set of labels based on at least one of the threshold range or the label calculation conditions; and sending the second conditional configuration to obtain the set of labels for training or validating at least one of the localization model.

[0198] Aspect 31 is the method according to aspect 30, the method further comprising: receiving a subset of the set of tags; and training the localization model at the network entity based on the set of tags or validating the localization model at the network entity based on the set of tags.

[0199] Aspect 32 is a method according to any one of aspects 30 or 31, the method further comprising: receiving a set of measurements of a second set of positioning signals; calculating a subset of the set of tags based on the set of measurements; and transmitting the subset of the set of tags based on the second conditional configuration.

[0200] Aspect 33 is a method according to any one of Aspects 28 to 32, wherein the request includes at least one of: a first threshold range for a second selection of a second subset of the measurements of the set of location signals for calculating the location of a user equipment (UE); a second threshold range for a tag error rate associated with the calculation of the location of the UE; a first indicator of a set of calculation methods for calculating the location of the UE; a second indicator of a statistical representation method for calculating the location of the UE; or a third indicator of a set of tag quality standards for calculating the location of the UE.

[0201] Aspect 34 is a method according to any one of aspects 19 to 33, the method further comprising: receiving a subset of measurements of the set of positioning signals; and training the positioning model at the network entity based on the subset of measurements of the set of positioning signals or validating the positioning model at the network entity based on the subset of measurements of the set of positioning signals.

[0202] Aspect 35 is the method according to any one of aspects 19 to 34, wherein the network entity includes a location management function (LMF).

[0203] Aspect 36 is an apparatus for wireless communication, the apparatus comprising: at least one memory; and at least one processor coupled to the at least one memory and based at least in part on information stored in the at least one memory, the at least one processor being configured individually or in any combination to perform the method according to any one of aspects 1 to 35.

[0204] Aspect 37 is an apparatus for wireless communication, the apparatus comprising components for performing each step of the method according to any one of aspects 1 to 35.

[0205] Aspect 38 is an apparatus according to any one of aspects 36 to 37, the apparatus further comprising a transceiver (e.g., a transceiver coupled to the at least one processor in aspect 36), the transceiver being configured to receive or transmit in association with the method according to any one of aspects 1 to 35.

[0206] Aspect 39 is a computer-readable medium (e.g., a non-transitory computer-readable medium) storing computer-executable code that, when executed by at least one processor, causes the at least one processor to perform the method according to any one of aspects 1 to 35.

Claims

1. An apparatus for wireless communication at a user equipment (UE), the apparatus comprising: At least one memory; and At least one processor, coupled to the at least one memory, and configured individually or in any combination, based at least in part on information stored in the at least one memory, to: Receive conditional configurations for collecting measurements to train or validate at least one of the localization models; A collection of received positioning signals; The set of measurement positioning signals; as well as A subset of the measured positioning signals is output to train or validate at least one of the positioning models based on at least one condition configured by the conditions.

2. The apparatus of claim 1, wherein the condition configuration includes an indicator of a set of conditions, wherein the set of conditions includes a threshold range, wherein the at least one processor is further configured individually or in any combination to: The subset of the set of measured positioning signals is selected based on the threshold range.

3. The apparatus of claim 2, wherein the threshold range includes at least one of the following: Reference signal received power (RSRP) threshold range; Signal-to-noise ratio (SNR) threshold range; Signal-to-interference-and-noise ratio (SINR) threshold range; Delay extension threshold range; Rice factor threshold range; Doppler extended threshold range; or Multipath component threshold range.

4. The apparatus of claim 1, wherein the condition configuration includes an indicator of a set of conditions, wherein the set of conditions includes measurement types, wherein, In order to measure the set of positioning signals, the at least one processor is configured individually or in any combination to: The set of positioning signals is measured based on the measurement type.

5. The apparatus of claim 4, wherein the measurement type includes at least one of the following: Reference Signal Time Difference (RSTD); Reference signal received power (RSRP); Reference signal received power per path (RSRPP); Timing measurements associated with the path; Channel impulse response (CIR) timing measurement; CIR power measurement; CIR phase measurement; Channel frequency response (CFR) frequency measurement; CFR power measurement; or CFR phase measurement.

6. The apparatus of claim 1, wherein the at least one processor is further configured, alone or in any combination, to: Receive the second condition configuration to obtain the set of tags; The set of tags is obtained based on at least one second condition configured according to the second condition; and The set of output labels is used to train or validate at least one of the localization models based on the second conditional configuration.

7. The apparatus of claim 6, wherein the second condition configuration comprises a second set of conditions, wherein the second set of conditions comprises at least one of the following: A first threshold range, which is associated with a second subset of the set of measured positioning signals used to calculate the location of the UE; A second threshold range, the second threshold range being associated with a tag error rate, the tag error rate being associated with a calculation method used to calculate the location of the UE; A first indicator of a set of calculation methods, the set of calculation methods being used to calculate the position of the UE; A second indicator of the statistical representation method, which is used to summarize the set of locations calculated by the UE; or A third indicator of the set of label quality standards, which is associated with the set of labels.

8. The apparatus according to claim 6, wherein, In order to output the set of tags, the at least one processor is configured individually or in any combination as follows: The localization model is trained at the UE based on the set of labels; or The location model is validated at the UE based on the set of tags.

9. The apparatus according to claim 6, wherein, In order to output the set of tags, the at least one processor is configured individually or in any combination as follows: The set of tags is sent to network entities to train or validate at least one of the localization models.

10. The apparatus according to claim 6, wherein, In order to obtain the set of tags, the at least one processor is configured individually or in any combination as follows: The first subset of the set of receiving tags; or Calculate the second subset of the set of labels.

11. The apparatus of claim 1, wherein the at least one processor is further configured, alone or in any combination, to: A request for the condition configuration is sent before the reception of the condition configuration.

12. The apparatus of claim 11, wherein the request includes an indicator of a threshold range, and wherein the at least one processor is further configured, individually or in any combination, to: The subset of the set of measured positioning signals is selected based on the threshold range.

13. The apparatus of claim 11, wherein the request includes at least one of a first indicator of a threshold range associated with the calculation of the set of tags or a second indicator of tag calculation conditions associated with the calculation of the set of tags, wherein the at least one processor is further configured individually or in any combination to: To obtain the set of tags based on the request; and The set of output labels is used to train or validate at least one of the localization models.

14. The apparatus according to claim 13, wherein, In order to obtain the set of tags based on the request, the at least one processor is configured individually or in any combination to: The first subset of the set of receiving tags; or Calculate the second subset of the set of labels.

15. The apparatus of claim 13, wherein the request comprises at least one of the following: A second threshold range, which is associated with a set of location signals measured for calculating the location of the UE; A third threshold range, the third threshold range being associated with a tag error rate, the tag error rate being associated with a calculation method used to calculate the location of the UE; A third indicator of the set of calculation methods, the set of calculation methods being used to calculate the position of the UE; A fourth indicator of the statistical representation method, which is used to summarize the set of locations calculated by the UE; or The fifth indicator of the set of label quality standards, which is associated with the set of labels.

16. The apparatus according to claim 1, wherein, In order to output the subset of the measured positioning signals, the at least one processor is configured individually or in any combination to: The positioning model is trained at the UE based on a subset of the measured positioning signals; or The positioning model is validated at the UE based on a subset of the measured positioning signals.

17. The apparatus of claim 1, further comprising a transceiver coupled to the at least one processor, wherein, In order to output the subset of the measured positioning signals, the at least one processor is configured individually or in any combination to: The subset of the measured location signals is transmitted to the network entity via the transceiver for training or validation of at least one of the location models.

18. The apparatus of claim 1, wherein the UE includes a positioning reference unit (PRU) having a known location, wherein the at least one processor is further configured, individually or in any combination, to: The known locations are output as labels to train or validate at least one of the localization models.

19. An apparatus for wireless communication at a network entity, the apparatus comprising: At least one memory; and At least one processor, coupled to the at least one memory, and configured individually or in any combination, based at least in part on information stored in the at least one memory, to: The configuration of conditions for selecting a subset of measurements from the set of localization signals is configured for training or validating at least one of the localization models; and Send the conditional configuration to train or validate at least one of the localization models.

20. The apparatus of claim 19, wherein the condition configuration includes an indicator of a set of conditions, wherein the set of conditions includes a threshold range for the selection of a subset of measurements of the set of location signals, wherein the subset of measurements of the set of location signals is within the threshold range.

21. The apparatus of claim 19, wherein the condition configuration includes an indicator of a set of conditions, wherein the set of conditions includes a measurement type, and wherein the subset of measurements of the set of positioning signals includes the measurement type.

22. The apparatus of claim 19, wherein the at least one processor is further configured, alone or in any combination, to: Configure a second conditional configuration to obtain a set of labels for training or validating at least one of the localization models; and Send the second conditional configuration to obtain the set of labels for training or validating at least one of the localization models.

23. The apparatus of claim 22, wherein the second condition configuration comprises a set of conditions, wherein the set of conditions includes at least one of the following: A first threshold range, the first threshold range being used for a second selection of a second subset of the measurements of the set of location signals for calculating the location of the user equipment (UE); A second threshold range, the second threshold range being used to associate the tag error rate with the calculation of the location of the UE; A first indicator of a set of calculation methods, the set of calculation methods being used for the calculation of the position of the UE; A second indicator of a statistical representation method used for calculating the location of the UE; or A third indicator of the set of tag quality standards used for the calculation of the location of the UE.

24. The apparatus of claim 22, wherein the at least one processor is further configured, alone or in any combination, to: The set of measurements of the second set of received positioning signals; A subset of the set of tags is calculated based on the set of measurements; and The subset of the set of tags is sent based on at least one second condition configured according to the second condition.

25. The apparatus of claim 19, wherein the at least one processor is further configured, alone or in any combination, to: A request for the condition configuration is received, wherein the sending of the condition configuration is based on the request.

26. The apparatus of claim 25, wherein the request includes an indicator of a threshold range, wherein, In order to configure the conditional configuration for selecting the subset of measurements of the set of positioning signals for training or validating at least one of the positioning models, the at least one processor is configured individually or in any combination to: Configure the condition settings based on the threshold range.

27. The apparatus of claim 25, further comprising a transceiver coupled to the at least one processor, wherein the request includes at least one of a first indicator of a threshold range associated with computation of the set of tags or a second indicator of tag computation conditions associated with computation of the set of tags, wherein the at least one processor is further configured individually or in any combination to: Configure a second condition to obtain the set of tags based on at least one of the threshold range or the tag calculation condition; and The second conditional configuration is sent via the transceiver to obtain the set of labels for training or validating at least one of the localization models.

28. The apparatus of claim 25, wherein the request comprises at least one of the following: A first threshold range, the first threshold range being used for a second selection of a second subset of the measurements of the set of location signals for calculating the location of the user equipment (UE); A second threshold range, the second threshold range being used to associate the tag error rate with the calculation of the location of the UE; A first indicator of a set of calculation methods, the set of calculation methods being used for the calculation of the position of the UE; A second indicator of a statistical representation method used for calculating the location of the UE; or A third indicator of the set of tag quality standards used for the calculation of the location of the UE.

29. A method for conducting wireless communication at a user equipment (UE), the method comprising: Receive conditional configurations for collecting measurements to train or validate at least one of the localization models; A collection of received positioning signals; The set of measurement positioning signals; as well as A subset of the measured positioning signals is output to train or validate at least one of the positioning models based on at least one condition configured by the conditions.

30. A method for wireless communication at a network entity (LMF), the method comprising: Configure conditions for selecting a subset of measurements of the set of localization signals to train or validate at least one of the localization models. as well as Send the conditional configuration to train or validate at least one of the localization models.

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