Positioning training and data collection with channel estimation error

Abstract

A wireless device (1002) may receive a set of positioning signals (1024). The wireless device may measure (1026) a set of positioning signals based on at least one of the plurality of sparse pilot masks or the plurality of artificial noise signals. The wireless device may output (1036) the measured set of positioning signals for training a positioning model. The wireless device may output the measured set of positioning signals for training a positioning model by training a positioning model at the wireless device based on the measured set of positioning signals, or may send the measured set of positioning signals to a training entity for training a positioning model. The training entity may train a positioning model based on the measured set of positioning signals. The wireless device may include a user equipment (UE) or a network node.

Description

Cross Reference to Related Applications

[0001] This application claims the benefit of U.S. Nonprovisional Patent Application Serial No. 18 / 324,052 entitled “POSITIONING TRAINING AND DATA COLLECTION WITH CHANNEL ESTIMATION ERRORS” and filed on May 25, 2023, which is expressly incorporated by reference herein in its entirety. TECHNICAL FIELD

[0002] The present disclosure relates generally to communication systems, and more specifically to a system for performing positioning using radio frequency (RF) signals. BACKGROUND

[0003] Wireless communication systems are widely deployed to provide various telecommunication services such as telephony, video, data, messaging, and broadcasts. Typical wireless communication systems can 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.

[0004] These multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that enables different wireless devices to communicate on a municipal, national, regional, and even global level. One example of a telecommunication standard is 5G New Radio (NR). 5G NR is a continuing SUMMARY

[0005] The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects. It is intended to neither identify key or critical elements of all aspects nor delineate 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 description that is presented later.

[0006] In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus can include a wireless device. The wireless device can be a user equipment (UE) and a network node. The apparatus can receive a set of positioning signals. The apparatus can measure the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. The apparatus can output the measured set of positioning signals for training a positioning model. The apparatus can output the measured set of positioning signals for training a positioning model by training a positioning model at the wireless device based on the measured set of positioning signals. The apparatus can output the measured set of positioning signals for training a positioning model by sending the measured set of positioning signals to a training entity for training a positioning model.

[0007] In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus can include a network entity. The network entity can include a location management function (LMF). The apparatus can send a first configuration for receiving a set of positioning signals. The apparatus can send a second configuration for measuring the set of positioning signals for training a positioning model. The second configuration can include a configuration to measure the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals.

[0008] To the accomplishment of the foregoing and related aspects, one or more aspects can include the features recited in the following description and illustrated in the accompanying drawings. The following description and accompanying drawings provide illustrative examples of the various aspects. However, various changes can be made and equivalents employed. BRIEF DESCRIPTION OF DRAWINGS

[0009] Figure 1 is a diagram illustrating an example of a wireless communications system and an access network.

[0010] Figure 2A is a diagram illustrating an example of a first frame, in accordance with aspects of the present disclosure.

[0011] Figure 2B is a diagram illustrating an example of a downlink (DL) channel within a subframe, in accordance with aspects of the present disclosure.

[0012] Figure 2Cis a diagram illustrating an example of a second frame in accordance with various aspects of the present disclosure.

[0013] Figure 2D is a diagram illustrating an example of an uplink (UL) channel within a subframe in accordance with various aspects of the present disclosure.

[0014] Figure 3 is a diagram illustrating an example of a base station and user equipment (UE) in an access network.

[0015] Figure 4 is a diagram illustrating an example of positioning based on positioning signal measurements.

[0016] Figures 5A to 5E is a diagram illustrating an example of a positioning signal pattern.

[0017] Figures 6A to 6C is a diagram illustrating an example of a positioning signal pattern.

[0018] Figures 7A to 7F is a diagram illustrating an example of a positioning signal pattern.

[0019] Figures 8A to 8C is a diagram illustrating an example of a positioning signal pattern.

[0020] Figure 9 is a diagram illustrating an example of a network entity coordinating multiple base stations to perform positioning with a wireless device.

[0021] Figure 10 is a connection flow diagram illustrating an example of communications between a positioning target wireless device, a set of neighboring wireless devices, and a positioning network entity configured to train a positioning model.

[0022] Figure 11 is a flowchart of a method of wireless communication.

[0023] Figure 12 is a flowchart of a method of wireless communication.

[0024] Figure 13 is a flowchart of a method of wireless communication.

[0025] Figure 14 is a flowchart of a method of wireless communication.

[0026] Figure 15 is a flowchart of a method of wireless communication.

[0027] Figure 16 is a flowchart of a method of wireless communication.

[0028] Figure 17 is a flowchart of a method of wireless communication.

[0029] Figure 18 is a diagram illustrating an example of a hardware implementation for an example apparatus and / or network entity.

[0030] Figure 19 is a diagram illustrating an example of a hardware implementation for an example network entity.

[0031] Figure 20 is a diagram illustrating an example of a hardware implementation for an example network entity. DETAILED DESCRIPTION

[0032] The following description relates to examples intended to describe innovations of the present disclosure. However, one of ordinary skill in the art will recognize that the teachings herein can be applied in a number of ways. Some or all of the described examples can be implemented in any device, system or network that is capable of transmitting and receiving radio frequency (RF) signals according to one or more of the Institute of Electrical and Electronics Engineers (IEEE) 1402.11 standards, the IEEE 1402.15 standards, the Bluetooth ® standards as defined by the Bluetooth Special Interest Group (SIG), or the Long Term Evolution (LTE), 3G, 4G or 5G (New Radio (NR)) standards promulgated by the Third Generation Partnership Project (3GPP), among others. The described examples can be implemented in any device, system or network that is 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- spreading multiple access (RSMA), multi-user shared access (MUSA), single-user (SU) multiple-input multiple-output (MIMO) and multi-user (MU) MIMO. The described examples can also be implemented using other wireless communication protocols or RF signals suitable for use in one or more of a wireless personal area network (WPAN), a wireless local area network (WLAN), a wireless wide area network (WWAN), a wireless metropolitan area network (WMAN), or an Internet of Things (IoT) network.

[0033] Various aspects generally relate to wireless communication and more specifically to positioning of wireless devices. Some aspects more specifically relate to training a positioning model for positioning of a wireless device. In some examples, a wireless device can be used to collect data for training a positioning model. The model can be an artificial intelligence (AI) / machine learning (ML) (AI / ML or AIML) positioning model trained using a set of inputs and a set of expected outputs or labels. Such a positioning model can be used to compute a new set of outputs based on a new set of inputs. The wireless device can comprise a user equipment (UE), a network node, or a positioning reference unit (PRU). In some aspects, a wireless device that collects measurements for training a positioning model can simulate channel estimation errors by changing how it measures a set of positioning signals received by the wireless device. For example, the wireless device can measure the set of positioning signals based on a plurality of sparse pilot masks, such as by measuring every other symbol, or by measuring every fourth symbol. In another example, the wireless device can measure the set of positioning signals based on a plurality of artificial noise signals, such as by adding, subtracting, or multiplying artificial noise with the set of positioning signals. By training the positioning model using positioning signals that have experienced simulated channel estimation error scenarios, the positioning model can more accurately compute outputs when the positioning model performs computations based on measurements of positioning signals that have been measured under conditions in which channel estimation errors have occurred.

[0034] In some examples, a wireless device can receive a set of positioning signals (e.g., sounding reference signals (SRS), positioning reference signals (PRS), or channel state information (CSI) reference signals (CSI-RS)). The wireless device can measure the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. The wireless device can output the measured set of positioning signals for training a positioning model. For example, the wireless device can train a positioning model at the wireless device based on the measured set of positioning signals, or can send the measured set of positioning signals to a training entity for training a positioning model. In some examples, a network entity can send a first configuration for receiving the set of positioning signals. The network entity can send a second configuration for measuring the set of positioning signals for training the positioning model. The second configuration can include a configuration to measure the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. In some aspects, the first configuration and the second configuration can be sent to the wireless device within a single configuration message.

[0035] Target wireless devices and network nodes (such as next generation (NG) radio access network (NG-RAN) nodes) can use channel estimation by transmitting and measuring PRS and / or SRS. Measurements of such positioning signals can be used to train positioning models. Wireless devices can be configured to consider different issues of channel estimation when training positioning models, including noise, interference from neighboring cells, and / or use of different channel estimation techniques by different targets. For example, one wireless device can use a minimum mean square error (MMSE) channel estimation implementation, while another wireless device uses a least squares (LS) channel estimation implementation or a likelihood-based channel estimation implementation. In one aspect, a network entity (such as a location management function (LMF)) can emulate different non-ideal channel estimation scenarios by configuring one or more training data collection entities (e.g., UEs, PRUs, next generation NodeBs (gNBs), transmission reception points (TRPs)) to implement different non-ideal channel estimation instances. Wireless devices that receive and measure a set of positioning signals can be referred to as training data collection entities. Multiple training data collection entities can come from different vendors and can use different channel estimation implementations, such as different sparse positioning signal patterns. In one aspect, a network node can configure a positioning signal with a frequency comb pattern and / or a symbol pattern. A training data collection entity can consider different subsets of pilots (i.e., sparse pattern pilots) in the pattern when estimating a channel. In some aspects, the subsets of pilots to consider can be configured by a network entity. For example, via a long term evolution (LTE) positioning protocol (LPP) message or LPP annex (LPPa) message for a UE, via an LPP message or LPPa message for a PRU, via a new radio (NR) positioning protocol (NRPP) message or NRPP annex (NRPPa) message for an NG-RAN node, or via a different protocol or procedure. In one aspect, a wireless device (e.g., a UE / PRU / NG-RAN node) can indicate its capabilities with respect to using subsets of pilots to collect for different instances / implementations of non-ideal channel estimation to a network entity (e.g., an LMF) as part of a capability exchange procedure (for LPP messages or LPPa protocol) or a TRP information exchange (for NRPP messages or NRPPa protocol). The capability message can indicate (1) a number of channel estimation implementations that the device can support, (2) sparse patterns that can be supported for positioning signals, and / or (3) any measurement gap conditions / requirements. In some aspects, the number of channel estimation implementations can depend on an expected signal-to-noise ratio (SNR) setting at the data collection entity. In some aspects, a network entity can configure a wireless device to add artificial noise when estimating a channel. The noise can be configured by the network entity, for example, via an LPP / LPPa message for a UE, via an LPP / LPPa message for a PRU, via an NRPP / NRPPa message for an NG-RAN node, or via a different protocol or procedure.In some aspects, the network entity can configure the wireless entity to jointly add artificial noise and also consider a sparse positioning signal pattern when estimating a channel between the positioning target device and the positioning network node.

[0036] Particular aspects of the subject matter described in this disclosure can be implemented to realize one or more of the following potential advantages. In some examples, by configuring a wireless device to measure a positioning signal based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals, a positioning model can consider channel estimation impairments when collecting training data and applying the training data to the positioning model for training. The wireless device can collect training data with a plurality of model input instances / realizations, which can be obtained with non-ideal channel estimation inputs. The network entity can implement different non-ideal channel estimation instances by configuring the wireless device (e.g., UE, PRU, gNB, TRP) to construct a plurality of channel estimates for a given positioning resource. In this way, the wireless device can emulate different non-ideal channel estimation scenarios. The wireless device can consider a sparse pilot representation when estimating a channel. The wireless device can add artificial noise when estimating a channel. The wireless device can consider a sparse pilot representation and can add artificial noise when estimating a channel. The network entity can consider each permutation of estimation instances with a large number of wireless entities from different vendors with potentially different channel estimation implementations. This can enrich the training data for the positioning model and can ensure that the model is robust to channel estimation errors and can generalize to measurements received from wireless entities from different vendors.

[0037] The detailed description set forth below, in connection with the appended drawings, is a description of various configurations and does not represent the only configurations in which the concepts described herein can be practiced. The detailed description includes specific details for the purpose of providing a thorough understanding of various concepts. However, it will be apparent to those skilled in the art that these concepts can be practiced without these specific details. In some instances, well-known structures and components are shown in block diagram form, in order to avoid obscuring such concepts.

[0038] Several aspects of telecommunication systems will now be presented with reference to various apparatus and methods. These apparatus and methods will be described in the following detailed description and illustrated in the accompanying drawings by various blocks, components, circuits, processes, algorithms, etc. (collectively referred to as “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 particular application and design constraints imposed on the overall system.

[0039] As an example, an element, or any portion of an element, or any combination of elements can be implemented as a "processing system" that includes one or more processors. 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, systems on a chip (SoC), baseband processors, field programmable gate arrays (FPGAs), programmable logic devices (PLDs), state machines, gated logic, discrete hardware circuits, and other suitable hardware configured to perform the various functionality described throughout this disclosure. One or more processors in the processing system can execute software. Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software components, applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, functions, or any combination of them deemed useful by one of ordinary skill in the art, regardless of the particular nomenclature used.

[0040] Thus, in one or more example aspects, implementations, and / or use cases, the described functions can be implemented in hardware, software, or any combination thereof. If implemented in software, the functions can be stored on or encoded as one or more instructions or code on a computer-readable medium. Computer-readable media includes computer storage media. Storage media can be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise a random-access memory (RAM), a read-only memory (ROM), an electrically erasable programmable ROM (EEPROM), optical disk storage, magnetic disk storage, other magnetic storage devices, combinations of the aforementioned 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 that can be accessed by a computer.

[0041] While aspects, implementations, and / or use cases are described herein by way of example, other 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 variety of devices, chip-level components, systems, distributed arrangements, aggregated or decomposed components, end-user equipment, etc., of various sizes, shapes, and configurations.

[0042] 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.

[0043] A disaggregated base station can be configured to utilize a radio protocol stack that is physically or logically integrated within a single RAN node. A disaggregated base station can be configured to utilize a protocol stack that is distributed, physically or logically, between two or more units, such as one or more central or centralized units (CUs), one or more distributed units (DUs), or one or more radio units (RUs). In some aspects, a CU can be implemented within a RAN node, and one or more DUs can be co-located with the CU, or alternatively, can be geographically or virtually distributed in one or more other RAN nodes. A DU can be implemented to be in communication with one or more RUs. Each of the CU, DU, and RU can be implemented as virtual units, a virtual central unit (VCU), virtual distributed unit (VDU), or virtual radio unit (VRU).

[0044] Base station operations or network designs can take into account the disaggregated nature of base station functionality. For example, a disaggregated base station can be utilized in an integrated access backhaul (IAB) network, an open radio access network (O-RAN (such as network configurations initiated by the O-RAN Alliance)), or a virtualized radio access network (vRAN, also referred to as a cloud radio access network (C-RAN)). Disaggregation can include distributing functionality across two or more units at various physical locations, as well as virtually distributing functionality of at least one unit, which can enable flexibility in network design. Various units of a disaggregated base station or disaggregated RAN architecture can be configured for wired or wireless communication with at least one other unit.

[0045] Figure 1 FIG. 1 is a diagram 100 illustrating an example of a wireless communication system and access network. The illustrated wireless communication system includes a disaggregated base station architecture. The disaggregated base station architecture can include one or more CUs 110 that can communicate directly with a core network 120 via a backhaul link, or indirectly with the core network 120 through one or more disaggregated 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. The CUs 110 can communicate with one or more DUs 130 via respective fronthaul links, such as Fl interfaces. The DUs 130 can communicate with one or more RUs 140 via respective front-haul links. The RUs 140 can communicate with respective UEs 104 via one or more radio frequency (RF) access links. In some implementations, a UE 104 can be simultaneously served by multiple RUs 140.

[0046] Each of the units (i.e., CU 110, DU 130, RU 140, and near-RT RIC 125, non-RT RIC 115, and SMO framework 105) can include or be coupled to one or more interfaces configured to receive or transmit signals, data, or information (collectively referred to as signals) via a wired or wireless transmission medium. Each of the units or an associated processor or controller providing instructions to the communication interfaces of these units can be configured to communicate with one or more of the other units via the transmission medium. For example, the units can include wired interfaces configured to receive or transmit signals to one or more of the other units over a wired transmission medium. Additionally, the units can include wireless interfaces that can include receivers, transmitters, or transceivers (such as RF transceivers) configured to receive and / or transmit signals to one or more of the other units over a wireless transmission medium.

[0047] In some aspects, CU 110 can host one or more higher layer control functions. Such control functions can include radio resource control (RRC), packet data convergence protocol (PDCP), service data adaptation protocol (SDAP), etc. Each control function can be implemented with an interface configured to communicate signals with other control functions hosted by CU 110. 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, CU 110 can be logically split into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, CU-UP units can communicate bi-directionally with CU-CP units via an interface, such as an El interface. CU 110 can be implemented to communicate with DU 130 as needed for network control and signaling.

[0048] DU 130 can correspond to a logical unit that includes one or more base station functions for controlling operation of one or more RUs 140. In some aspects, DU 130 can host one or more of a radio link control (RLC) layer, a medium 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.) in accordance with a functional split, such as those defined by 3GPP. In some aspects, DU 130 can also host one or more low PHY layers. Each layer (or module) can be implemented with an interface configured to communicate signals with other layers (and modules) hosted by DU 130 or with control functions hosted by CU 110.

[0049] Lower layer functionality can be implemented by one or more RUs 140. In some deployments, RUs 140 controlled by a DU 130 can correspond to logical nodes that host RF processing functions or low PHY layer functions (such as performing fast Fourier transforms (FFTs), inverse FFTs (iFFTs), digital beamforming, physical random access channel (PRACH) extraction and filtering, etc.) or both based at least in part on a functional split, such as a lower layer functional split. In such an architecture, RUs 140 can be implemented to handle over-the-air (OTA) communications with one or more UEs 104. In some implementations, real-time and non-real-time aspects of control plane and user plane communications with RUs 140 can be controlled by a corresponding DU 130. In some scenarios, this configuration can enable DUs 130 and CUs 110 to be implemented in a cloud-based RAN architecture, such as a vRAN architecture.

[0050] The SMO framework 105 can be configured to support RAN deployment and provisioning of non-virtualized network elements and virtualized network elements. For non-virtualized network elements, the SMO framework 105 can be configured to support deployment of dedicated physical resources for RAN coverage requirements, which can be managed via an operations and maintenance interface, such as an Ol interface. For virtualized network elements, the SMO framework 105 can be configured to interact with a cloud computing platform, such as an Open Cloud (O-Cloud) 190 to perform network element lifecycle management, such as to instantiate a virtualized network element, via a cloud computing platform interface, such as an 02 interface. Such virtualized network elements can include, but are not limited to, CUs 110, DUs 130, RUs 140, and near-RT RICs 125. In some implementations, the SMO framework 105 can communicate with hardware aspects of a 4G RAN, such as an Open eNB (O-eNB) 111, via an Ol interface. Additionally, in some implementations, the SMO framework 105 can communicate directly with one or more RUs 140 via an Ol interface. The SMO framework 105 can also include a non-RT RIC 115 configured to support functionality of the SMO framework 105.

[0051] The non-RT RIC 115 can be configured to include logical functions that enable non-real-time control and optimization of RAN elements and resources, artificial intelligence (AI) / machine learning (ML) (AI / ML) workflows including model training and updating, or policy-based steering of applications / features in the near-RT RIC 125. The non-RT RIC 115 can be coupled to, or in communication with, the near-RT RIC 125, such as via an Al interface. The near-RT RIC 125 can be configured to include logical functions that enable near-real-time control and optimization of RAN elements and resources via data collection and actions via an interface, such as via an E2 interface, that connects one or more CUs 110, one or more DUs 130, or both, and an O-eNB with the near-RT RIC 125.

[0052] In some implementations, to generate AI / ML models to be deployed in the near-RT RIC 125, the non-RT RIC 115 can receive parameters or external enrichment information from an external server. Such information can be utilized by the near-RT RIC 125 and can be received at the SMO framework 105 or the non-RT RIC 115 from non-network data sources or from network functions. In some examples, the non-RT RIC 115 or the near-RT RIC 125 can be configured to tune RAN behavior or performance. For example, the non-RT RIC 115 can monitor long-term trends and patterns of performance and employ AI / ML models to perform corrective actions via the SMO framework 105, such as via reconfiguration of Ol, or via creation of RAN management policies, such as Al policies.

[0053] At least one of the CUs 110, the DUs 130, and the RUs 140 can be referred to as a base station 102. Thus, the base station 102 can include one or more of the CU 110, the DU 130, and the RU 140 (each component is indicated in dashed line to represent that each component can or can not be included in the base station 102). The base station 102 provides wireless access to the core network 120 for the UEs 104. The base station 102 can include a macro cell (high power cellular base station) and / or a small cell (low power cellular base station). The small cell includes a femto cell, a pico cell, and a micro cell. A network that includes both small cells and macro cells can be known as a heterogeneous network. A heterogeneous network can also include home evolved node Bs (eNBs) (HeNBs), which can provide service to a restricted group known as a closed subscriber group (CSG). The communication links between the RUs 140 and the UEs 104 can include uplink (UL) (also referred to as reverse link) transmissions from a UE 104 to a RU 140 and / or downlink (DL) (also referred to as forward link) transmissions from a RU 140 to a UE 104. The communication links can use multiple-input and multiple-output (MIMO) antenna technology, including spatial multiplexing, beamforming, and / or transmit diversity. The communication links can be through one or more carriers, where a carrier can be a set of resource blocks that can be used to transmit data. Each carrier can be on the same frequency channel and can be a location to wireless devices on the core network 120. A carrier can include a portion of a radio frequency spectrum band. The carrier can be associated with a frequency channel (e.g., 1.9, 2.1, 2.4, 2.6, 3.1, 4, 5, 7.25, 8, 38, 39, 40 GHz, etc.). In some examples, carrier can also be referred to as a frequency channel. A spectrum band can include a plurality of carriers, and each carrier can be a location on the spectrum band. A UE 104 can to communicate with a RU 140 via the DU 130. The UE 104 can include, for example, a smartphone, a tablet, a machine, vehicle, etc. A RU 140 can include, for example, a base station, a Node-B, a Site

[0054] Certain UEs 104 can communicate with each other using device-to-device (D2D) communication link 158. The D2D communication link 158 can use the DL / UL WWAN spectrum. The D2D communication link 158 can use one or more sidelink channels, such as a physical sidelink broadcast channel (PSBCH), a physical sidelink discovery channel (PSDCH), a physical sidelink shared channel (PSSCH), and a physical sidelink control channel (PSCCH). D2D communication can be through a variety of wireless D2D communications systems, such as for example, Bluetooth, Wi-Fi based on Institute of Electrical and Electronics Engineers (IEEE) 1402.11 standards, LTE, or NR.

[0055] The wireless communications system can also include a Wi-Fi AP 150 in communication with UEs 104 (also referred to as Wi-Fi stations (STAs)) via communication links 154, e.g., in a 5 GHz unlicensed frequency spectrum or the like. When communicating in an unlicensed frequency spectrum, the UEs 104 / AP 150 can perform a clear channel assessment (CCA) prior to communicating in order to determine whether the channel is available.

[0056] The electromagnetic spectrum is often subdivided based on frequency / wavelength into various classes, bands, channels, etc. In 5G NR, two initial operating bands have been identified as frequency range designations FR1 (410 MHz to 7. 125 GHz) and FR2 (24.25 GHz to 52.6 GHz). Despite a portion of FR1 being greater than 6 GHz, FR1 is often referred to (interchangeably) as a “Sub- 6 GHz” band in various documents and articles. A similar naming confusion occurs with regard to FR2, which is often referred to (interchangeably) as a “millimeter wave” band in documents and articles, despite such a band being different from the extremely high frequency (EHF) band (30 GHz to 300 GHz) which is designated as a “millimeter wave” band by the International Telecommunications Union (ITU).

[0057] The frequencies between FR1 and FR2 are often referred to as mid-band frequencies. Recent 5G NR studies have identified operating bands for these mid-band frequencies as frequency range designation FR3 (7. 125 GHz to 24.25 GHz). Bands falling within FR3 can inherit FR1 characteristics and / or FR2 characteristics, and thus can effectively extend features of FR1 and / or FR2 to mid-band frequencies. Moreover, even higher bands are currently under exploration to extend 5G NR operation beyond 52.6 GHz. For example, three higher operating bands have been identified as 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 bands fall within the EHF band.

[0058] With the above in mind, unless specifically stated otherwise, if the term “Sub-6 GHz” or the like is used herein, this can broadly represent frequencies that can be less than 6 GHz, can be within FR1, or can include mid-band frequencies. Further, unless specifically stated otherwise, if the term “millimeter wave” or the like is used herein, this can broadly represent frequencies that can include mid-band frequencies, can be within FR2, FR4, FR2-2, and / or FR5, or can be within the EHF band.

[0059] The base stations 102 and the UEs 104 can each include multiple antennas (such as antenna elements, antenna panels, and / or antenna arrays) to facilitate beamforming. The base stations 102 can transmit to UEs 104 in one or more transmit directions using beamforming. The UEs 104 can receive from the base stations 102 in one or more receive directions using beamforming. The UEs 104 can also transmit to the base stations 102 in one or more transmit directions using beamforming. The base stations 102 can receive from the UEs 104 in one or more receive directions using beamforming. The base station 102 / UE 104 can perform beam training to determine the best receive and transmit directions for each of the base station 102 / UE 104. The transmit and receive directions of the base station 102 can or can not be the same. The transmit and receive directions of the UE 104 can or can not be the same.

[0060] The base stations 102 can include and / or be referred to as a gNB, NodeB, eNB, an access point, a transceiver base station, a radio base station, a radio transceiver, a transceiver function, a basic service set (BSS), an extended service set (ESS), a TRP, a network node, a network entity, a network equipment, or some other suitable terminology. The base station 102 can be implemented as an integrated access and backhaul (IAB) node, a relay node, a sidelink node, an aggregated (monolithic) base station with a baseband unit (BBU) (including a CU and a DU) and a RU, or as a disaggregated base station including one or more of a CU, a DU, and / or a RU. A set of base stations that can include disaggregated base stations and / or aggregated base stations can be referred to as a next generation (NG) RAN (NG-RAN).

[0061] The core network 120 can include an access and mobility management function (AMF) 161, a session management function (SMF) 162, a user plane function (UPF) 163, a unified data management (UDM) 164, one or more location servers 168, and other functional entities. The AMF 161 is a control node that handles signaling between the UEs 104 and the core network 120. The AMF 161 supports registration management, connection management, mobility management, and other functions. The SMF 162 supports session management and other functions. The UPF 163 supports packet routing, packet forwarding, and other functions. The UDM 164 supports generation of authentication and key agreement (AKA) credentials, user identification handling, access authorization, and subscription management. The one or more location servers 168 are illustrated as including a gateway mobile location center (GMLC) 165 and a location management function (LMF) 166. However, generally speaking, the one or more location servers 168 can include one or more location / determination servers, which can include one or more of a GMLC 165, an LMF 166, a position determining entity (PDE), a serving mobile location center (SMLC), a mobile positioning center (MPC), and the like. The GMLC 165 and the LMF 166 support UE location services. The GMLC 165 provides an interface for clients / applications (e.g., emergency services) to access UE positioning information. The LMF 166 receives measurements and assistance information from the NG-RAN and the UE 104 via the AMF 161 to compute a position of the UE 104. The NG-RAN can utilize one or more positioning methods to determine a location of the UE 104. Positioning the UE 104 can involve signal measurements, position estimation, and optional velocity calculations based on these measurements. The signal measurements can be made by the UE 104 and / or the base stations 102 serving the UE 104. The measured signals can be based on one or more of a satellite positioning system (SPS) 170 (e.g., Global Navigation Satellite System (GNSS), Global Positioning System (GPS), Non-Terrestrial Network (NTN), or other satellite positioning / location system), LTE signals, Wireless Local Area Network (WLAN) signals, Bluetooth signals, Terrestrial Beacon System (TBS), sensor-based information (e.g., barometric pressure sensors, motion sensors), NR Enhanced Cell ID (NR E-CID) methods, NR signals (e.g., multi-round trip time (multi-RTT), DL angle of departure (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.

[0062] Examples of UEs 104 include cellular phones, smart phones, session initiation protocol (SIP) phones, laptop computers, personal digital assistants (PDAs), satellite radios, global positioning systems, multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, tablet devices, smart devices, wearable devices, vehicles, electric meters, pumps, large or small kitchen appliances, healthcare devices, implants, sensors / actuators, displays, or any other similar functional devices. Some of the UEs 104 can be referred to as IoT devices (e.g., parking meters, gas pumps, toaster, vehicles, heart monitors, etc.). The UEs 104 can also be referred to as stations, mobile stations, subscriber stations, mobile units, subscriber units, wireless units, remote units, mobile devices, wireless devices, wireless communication devices, remote devices, mobile subscriber stations, access terminals, mobile terminals, wireless terminals, remote terminals, handheld devices, user agents, mobile clients, clients, or some other suitable terminology. In some scenarios, the term UE can also apply to one or more companion devices such as in a device constellation arrangement. One or more of these devices can collectively or individually access a network.

[0063] Referring again to Figure 1 In certain aspects, the UE 104 and / or the base station 102 can have a measurement error component 198 that can be configured to receive a set of positioning signals. The measurement error component 198 can be configured to measure the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. The measurement error component 198 can be configured to output the measured set of positioning signals for training a positioning model. In certain aspects, the base station 102 can have a measurement error configuration component 199 that can be configured to transmit a first configuration for receiving a set of positioning signals. The measurement error configuration component 199 can be configured to transmit a second configuration for measuring the set of positioning signals for training a positioning model. The second configuration can include a configuration to measure the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. One or more wireless devices configured to perform positioning, such as the UE 104, and a set of network nodes, such as the base station 102, can have a measurement error component 198 configured to measure a received set of positioning signals with a certain level of simulated channel estimation error artifacts, such as sparse pilot masks or artificial noise signals. The measurement error component 198 can use the measured positioning signals with simulated channel estimation error artifacts to train a positioning model to minimize errors in positioning measurements when actual channel estimation errors occur in live, non-test environments. The measurement error configuration component 199 can configure both the positioning occasions and the simulated channel estimation error artifacts.

[0064] Figure 2Ais a diagram 200 illustrating an example of a first subframe within a 5G NR frame structure. Figure 2B is a diagram 230 illustrating an example of DL channels within a 5G NR subframe. Figure 2C is a diagram 250 illustrating an example of a second subframe within a 5G NR frame structure. Figure 2D is a diagram 280 illustrating an example of UL channels within a 5G NR subframe. The 5G NR frame structure can be frequency-division duplexed (FDD) in which particular subcarrier sets (carrier system bandwidths) are dedicated to DL or UL, or can be time-division duplexed (TDD) in which particular subcarrier sets (carrier system bandwidths) are dedicated to both DL and UL. In Figure 2A , Figure 2C In the examples provided, the 5G NR frame structure is assumed to be TDD, where subframe 4 is configured with slot format 28 (with mostly DL), where D is DL, U is UL, and F is flexible to use between DL / UL, and subframe 3 is configured with slot format 1 (with all UL). While subframes 3, 4 are shown with slot formats 1, 28, respectively, any particular subframe can be configured with any of the various available slot formats 0-61. Slot formats 0, 1 are all DL, UL, respectively. Other slot formats 2-61 include a mix of DL, UL, and flexible symbols. UEs are configured with the slot format by a received slot format indicator (SFI) (dynamically through DL control information (DCI) or semi-statically / statically through radio resource control (RRC) signaling). Note that the following description also applies for 5G NR frame structures that are TDD.

[0065] Figures 2A to 2DA frame structure is illustrated, and aspects of the disclosure can be applicable to other wireless communication technologies that can have different frame structures and / or different channels. One frame (10 ms) can be divided into 10 equal sized subframes (1 ms). Each subframe can include one or more slots. A subframe can also include mini-slots, which can contain 7, 4, or 2 symbols. Each slot can include 14 or 12 symbols depending on whether a cyclic prefix (CP) is normal or extended. For a normal CP, each slot can include 14 symbols, and for an extended CP, each slot can include 12 symbols. A symbol on the DL can be a CP-orthogonal frequency division multiplexing (OFDM) (CP-OFDM) symbol. A symbol on the UL can be a CP-OFDM symbol (for high throughput scenarios) or a discrete Fourier transform (DFT) spread OFDM (DFT-s-OFDM) symbol (for power limited scenarios; limited to single stream transmission). The number of slots within a subframe is based on the CP and the numerology. The numerology defines the subcarrier spacing (SCS) (see Table 1). The symbol length / duration can scale with 1 / SCS.

[0066]

[0067] Table 1: Parameter Set, SCS, and CP

[0068] For a normal CP (14 symbols / slot), different numerologies m 0 to 4 allow for 1, 2, 4, 8, and 16 slots per subframe, respectively. For an extended CP, numerology 2 allows for 4 slots per subframe. Thus, for a normal CP and numerology m, there are 14 symbols / slot and 2 µ slots / subframe. The subcarrier spacing can be equal to where is the numerology 0 to 4. Thus, the subcarrier spacing for numerology m = 0 is 15 kHz, and the subcarrier spacing for numerology m = 4 is 240 kHz. The symbol length / duration is inversely related to the subcarrier spacing. Figures 2A to 2D An example of a normal CP with 14 symbols per slot and numerology m = 2 with 4 slots per subframe is provided. The slot duration is 0.25 ms, the subcarrier spacing is 60 kHz, and the symbol duration is approximately 16.67 ps. Within a frame collection, there can be one or more different bandwidth parts (BWPs) that are frequency division multiplexed (see Figure 2B ). Each BWP can have a particular numerology and CP (normal or extended).

[0069] A resource grid can be used to represent the frame structure. Each time slot includes a resource block (RB) (also referred to as a physical RB (PRB)) that extends 12 consecutive subcarriers. The resource grid is divided into multiple resource elements (REs). The number of bits carried by each RE depends on the modulation scheme.

[0070] As Figure 2A illustrated, some of the REs carry reference (pilot) signals (RS) for the UE. The RS can include demodulation RS (DM-RS) (indicated as R for one particular configuration, but other DM-RS configurations are possible) and channel state information reference signals (CSI-RS) for channel estimation at the UE. The RS can also include beam measurement RS (BRS), beam refinement RS (BRRS), and phase tracking RS (PT-RS).

[0071] Figure 2B An example of various DL channels are illustrated. The physical downlink control channel (PDCCH) carries DCI within one or multiple control channel elements (CCEs) (e.g., 1, 2, 4, 8, or 16 CCEs), each CCE including six RE groups (REGs), each REG including 12 consecutive REs in an OFDM symbol of an RB. A PDCCH within one BWP can be referred to as a control resource set (CORESET). A UE is configured to monitor PDCCH candidates in a PDCCH search space (e.g., common search space, UE-specific search space) during PDCCH monitoring occasions on the CORESET, where the PDCCH candidates have different DCI formats and different aggregation levels. Additional BWPs can be located at greater and / or lower frequencies on the channel bandwidth. A primary synchronization signal (PSS) can be within symbol 2 of particular subframes of a frame. The PSS is used by a UE 104 to determine subframe / symbol timing and physical layer identity. A secondary synchronization signal (SSS) can be within symbol 4 of particular subframes of a frame. The SSS is used by a UE to determine physical layer cell identity group number and radio frame timing. Based on the physical layer identity and the physical layer cell identity group number, the UE can determine a physical cell identifier (PCI). Based on the PCI, the UE can determine the locations of DM-RS. The physical broadcast channel (PBCH), which carries a master information block (MIB) that provides system bandwidth and a

[0072] As Figure 2CSome of the REs carry DM-RS (indicated by 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 one or two symbols of the PUSCH. The PUCCH DM-RS can be transmitted in different configurations depending on whether short or long PUCCHs are transmitted and depending on the particular PUCCH format used. The UE can transmit sounding reference signals (SRS). The SRS can be transmitted in the last symbol in a subframe. The SRS can have a comb- type structure, and a UE can transmit SRS on one of the combs. The SRS can be used by a base station for channel quality estimation to enable frequency-dependent scheduling for the UL.

[0073] Figure 2D Examples of various UL channels within a subframe of a frame are illustrated. The PUCCH can be located as indicated in one configuration. The PUCCH carries uplink control information (UCI), such as scheduling requests, a channel quality indicator (CQI), a precoding matrix indicator (PMI), a rank indicator (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 PUSCH carries data, and can additionally be used to carry buffer status reports (BSRs), power headroom reports (PHRs), and / or UCI.

[0074] Figure 3is a block diagram of the base station 310 and the UE 350 communicating in an access network. In the DL, Internet Protocol (IP) packets can be provided to a controller / processor 375. The controller / processor 375 implements layer 3 and layer 2 functionality. Layer 3 includes a radio resource control (RRC) layer, and layer 2 includes a service data adaptation protocol (SDAP) layer, a packet data convergence protocol (PDCP) layer, a radio link control (RLC) layer, and a medium access control (MAC) layer. The controller / processor 375 provides RRC layer functionality associated with broadcasting of system information (e.g., MIB, SIBs), 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 associated with a serving cell and a neighbor cell; PDCP layer functionality associated with header compression / decompression, security (ciphering, deciphering, integrity protection, integrity verification), and handover support functions; RLC layer functionality associated with the transfer of upper layer packet data units (PDUs), error correction through ARQ, concatenation, segmentation, and reassembly of upper layer SDUs, re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, multiplexing / de-multiplexing of MAC SDUs onto / from transport blocks (TBs), scheduling information reporting, error correction through HARQ, priority handling, and logical channel prioritization.

[0075] The transmit (TX) processor 316 and the receive (RX) processor 370 implement layer 1 functionality associated with various signal processing functions. Layer 1, which includes a physical (PHY) layer, can include error detection on the transport channels, forward error correction (FEC) coding / decoding of the transport channels, interleaving, rate matching, mapping to physical channels, modulation / demodulation of physical channels, and MIMO antenna processing. The TX processor 316 handles mapping to signal constellations based on various modulation schemes (e.g., binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), M-phase-shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM)). The coded and modulated symbols can then be split into parallel streams. Each stream can then be mapped to an OFDM subcarrier, multiplexed with a reference signal (e.g., pilot) in the time and / or frequency domain, and then combined together using an inverse fast Fourier transform (IFFT) to produce a physical channel carrying a time domain OFDM symbol stream. The OFDM stream is spatially precoded to produce multiple spatial streams if multiple spatial streams are

[0076] At the UE 350, each receiver 354Rx receives a signal through its respective antenna 352. Each receiver 354Rx recovers information modulated onto an RF carrier and provides the information to the receive (RX) processor 356. The TX processor 368 and the 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 streams destined for the UE 350. If multiple spatial streams are destined for the UE 350, they can be combined by the RX processor 356 into a single OFDM symbol stream. The RX processor 356 then converts the OFDM symbol stream from the time domain to the frequency domain using a fast Fourier transform (FFT). The frequency domain signal includes a separate OFDM symbol stream for each subcarrier of the OFDM signal. The symbols on each subcarrier, and the reference signal, are recovered and demodulated by determining the most likely signal constellation points transmitted by the base station 310. These soft decisions can be based on channel estimates computed by the channel estimator 358. The soft decisions are then decoded and deinterleaved to recover the data and control signals that were originally transmitted by the base station 310 on the physical channel. The data and control signals are then provided to the controller / processor 359, which implements layer 3 and layer 2 functionality.

[0077] The controller / processor 359 can be associated with a memory 360 that stores program codes and data. The memory 360 can be referred to as a computer-readable medium. In the UL, the controller / processor 359 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, and control signal processing to recover IP packets from the core network. The controller / processor 359 is also responsible for error detection using an ACK and / or NACK protocol to support HARQ operations.

[0078] Similar to the functionality described in connection with the DL transmission by the base station 310, the controller / processor 359 provides RRC layer functionality associated with system information (e.g., MIB, SIBs) acquisition, RRC connections, and measurement reporting; PDCP layer functionality associated with header compression / decompression, and security (ciphering, deciphering, integrity protection, integrity verification); RLC layer functionality associated with the transfer of upper layer PDUs, error correction through ARQ, concatenation, segmentation, and reassembly of RLC SDUs, re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs onto TBs, demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction through HARQ, priority handling, and logical channel prioritization.

[0079] The TX processor 368 can use channel estimates from the channel estimator 358 to select an appropriate coding and modulation scheme to use for the data streams. The channel estimates can be performed in response to the reference signals or sounding feedback transmitted by the base stations 310. The spatial streams generated by the TX processor 368 can be provided to different antenna 352 via separate transmitters 354. Each transmitter 354 can modulate an RF carrier with a respective spatial stream for transmission.

[0080] The UL transmission is processed at the base station 310 in a manner similar to that described in connection with the receiver function at the UE 350. Each receiver 318 receives a signal through its respective antenna 320. Each receiver 318 recovers information modulated onto an RF carrier and provides the information to a RX processor 370.

[0081] The controller / processor 375 can be associated with a memory 376 that stores program codes and data. The memory 376 can be referred to as a computer-readable medium. In the UL, the controller / processor 375 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover IP packets from the core network. The controller / processor 375 is also responsible for error detection using an ACK and / or NACK protocol to support HARQ operations.

[0082] At least one of the TX processor 368, RX processor 356, and controller / processor 359 can be configured to combine Figure 1 The measurement error component 198 is used to perform various aspects.

[0083] At least one of the TX processor 316, RX processor 370, and controller / processor 375 can be configured to combine Figure 1 The measurement error component 198 is used to perform various aspects.

[0084] At least one of the TX processor 316, RX processor 370, and controller / processor 375 can be configured to combine Figure 1 The measurement error configuration component 199 is used to perform various aspects.

[0085] Figure 4 Figure 400 illustrates an example of positioning based on reference signal measurements. Wireless device 402 can be a UE, base station, or positioning reference unit (PRU). Wireless device 404 can be a UE, base station, or PRU. Wireless device 406 can be a UE, base station, or PRU. Wireless device 402 can be referred to as a positioning target wireless device, the location of which can be calculated based on measurements of one or more reference signals. Wireless devices 404 and 406 can be referred to as positioning neighboring wireless devices, the locations of which can be known and used to calculate the location of wireless device 402. Wireless device 404 can be positioned at time T. SRS_TX Send SRS 412 to wireless device 404. Wireless device 404 can do so at time T. PRS_RX Location Reference Signal (PRS) 410 is received from radio device 406. SRS 412 may be UL-SRS. PRS 410 may be DL-PRS. In some aspects, radio device 402 may be a TRP, and radio device 406 may be a TRP, both of which may be configured to transmit DL-PRS to radio device 404. Radio device 404 may be a UE configured to transmit UL-SRS to both radio device 402 and radio device 406.

[0086] Wireless device 406 can be in time T SRS_RX Receive SRS 412 from wireless device 404, and at time T PRS_TXPRS 410 to wireless device 404. Wireless device 404 can receive PRS 410 prior to transmitting SRS 412. Wireless device 404 can transmit SRS 412 prior to receiving PRS 410. Wireless device 404 can transmit SRS 412 in response to receiving PRS 410. Wireless device 406 can transmit PRS 410 in response to receiving SRS 412. A positioning server (e.g., location server 168), wireless device 404, or wireless device 406 can determine a round trip time (RTT) 414 based on ||T SRS_RX – T PRS_TX | – |T SRS_TX – T PRS_RX |. Multi-RTT positioning can utilize Rx-Tx time difference measurements (i.e., |T SRS_TX – T PRS_RX |) and PRS reference signal received power (RSRP) (PRS-RSRP) of PRS signals received from multiple wireless devices (such as wireless device 402 and wireless device 406) measured by wireless device 404, and Rx-Tx time difference measurements (i.e., |T SRS_RX – T PRS_TX |) and SRS-RSRP of SRS transmitted from wireless device 404 measured at multiple wireless devices, such as at wireless device 402 and at wireless device 406. Wireless device 404 can measure the Rx-Tx time difference measurements and / or PRS-RSRP of received signals using assistance data received from a positioning server, wireless device 402, and / or wireless device 406. Wireless device 402 and wireless device 406 can measure the Rx-Tx time difference measurements and / or SRS-RSRP of received signals using assistance data received from a positioning server. These measurements can be used at the positioning server or wireless device 404 to determine an RTT, which can be used to estimate the location of wireless device 404. Other methods for determining RTT are possible, such as, for example, using time difference of arrival (TDOA) measurements, such as DL-TDOA and / or UL-TDOA measurements.

[0087] DL-AoD positioning can utilize measured PRS-RSRP of signals transmitted from multiple wireless devices, such as wireless device 402 and wireless device 406, and received at wireless device 404. AoD positioning can also be referred to as DL-AoD positioning, where the PRS is a DL signal. Wireless device 404 can measure PRS-RSRP of received signals using assistance data received from a positioning server, and the resulting measurements can be used to position wireless device 404 relative to neighboring wireless devices that transmitted the PRS, such as wireless device 402 and wireless device 406, along with the azimuth angle of departure (A-AoD), zenith angle of departure (Z-AoD), and other configuration information.

[0088] DL-TDOA positioning can utilize DL reference signal time difference (RSTD) and / or PRS-RSRP of signals received at wireless device 404 from multiple wireless devices, such as wireless device 402 and wireless device 406. Wireless device 404 can measure RSTD and / or PRS-RSRP of received PRS signals using assistance data received from a positioning server, and the resulting measurements can be used to position wireless device 404 relative to neighboring wireless devices that transmitted the PRS, such as wireless device 402 and wireless device 406, along with other configuration information.

[0089] UL-TDOA positioning can utilize UL relative time of arrival (RTOA) and / or SRS-RSRP of signals transmitted from wireless device 404 at multiple wireless devices, such as wireless device 402 and wireless device 406. Wireless devices, such as wireless device 402 and wireless device 406, can measure RTOA and / or SRS-RSRP of received signals using assistance data received from a positioning server, and the resulting measurements can be used to estimate the position of wireless device 404 along with other configuration information.

[0090] UL-AoA positioning can utilize measured angle of arrival azimuth (A-AoA) and angle of arrival zenith (Z-AoA) of signals transmitted from wireless device 404 at multiple wireless devices, such as wireless device 402 and wireless device 406. Wireless devices 402 and wireless device 406 can measure A-AoA and Z-AoA of received signals using assistance data received from a positioning server, and the resulting measurements can be used to estimate the position of wireless device 404 along with other configuration information.

[0091] Additional positioning methods can be used to estimate the position of wireless device 404, such as, for example, UL-AoD and / or DL-AoA at wireless device 404. Note that data / measurement from various techniques can be combined in various ways to increase accuracy, determine and / or enhance certainty, supplement / complement measurements, and / or replace / provide missing information.

[0092] Within a slot, a PRS resource can be configured to span multiple OFDM symbols (e.g., 2, 4, 6, or 12). With respect to the frequency domain pattern, a PRS resource can have a comb-like pattern. The potential combinations of comb type and number of symbols within a slot can be represented by the time / frequency patterns shown in Table 2.

[0093]

[0094] Table 2: PRS time / frequency patterns within time slots

[0095] Figures 5A to 5E and Figures 6A to 6C is a diagram illustrating examples of PRS patterns. Each block in a PRS pattern can represent an information element (IE) in a resource set. Figure 5A is a diagram 500 of a PRS signal with a time / frequency pattern of comb-2 with 2 symbols. Figure 5B is a diagram 510 of a PRS signal with a time / frequency pattern of comb-2 with 6 symbols. Figure 5C is a diagram 520 of a PRS signal with a time / frequency pattern of comb-2 with 12 symbols. Figure 5D is a diagram 530 of a PRS signal with a time / frequency pattern of comb-4 with 4 symbols. Figure 5E is a diagram 540 of a PRS signal with a time / frequency pattern of comb-2 with 12 symbols. Figure 6A is a diagram 600 of a PRS signal with a time / frequency pattern of comb-6 with 6 symbols. Figure 6B is a diagram 610 of a PRS signal with a time / frequency pattern of comb-6 with 12 symbols. Figure 6C is a diagram 620 of a PRS signal with a time / frequency pattern of comb-12 with 12 symbols.

[0096] Within a slot, an SRS resource can also be configured to span multiple OFDM symbols (e.g., 2, 4, 6, or 12). With respect to the frequency domain pattern, an SRS resource can also have a comb-like pattern. The potential combinations of comb type and number of symbols within a slot can be represented by the time / frequency patterns shown in Table 3.

[0097]

[0098] Table 2: PRS time / frequency patterns within time slots

[0099] Figures 7A to 7F and Figures 8A to 8C is a diagram illustrating examples of SRS patterns. Each block in the SRS pattern can represent an IE in a set of resources. Figure 7A is a diagram 700 of an SRS signal with a comb-2 time / frequency pattern with 1 symbol. Figure 7B is a diagram 710 of an SRS signal with a comb-2 time / frequency pattern with 2 symbols. Figure 7C is a diagram 720 of an SRS signal with a comb-2 time / frequency pattern with 4 symbols. Figure 7D is a diagram 730 of an SRS signal with a comb-4 time / frequency pattern with 2 symbols. Figure 7E is a diagram 740 of an SRS signal with a comb-4 time / frequency pattern with 4 symbols. Figure 7F is a diagram 750 of an SRS signal with a comb-4 time / frequency pattern with 8 symbols. Figure 8A is a diagram 800 of an SRS signal with a comb-8 time / frequency pattern with 4 symbols. Figure 8B is a diagram 810 of an SRS signal with a comb-4 time / frequency pattern with 8 symbols. Figure 8C is a diagram 820 of an SRS signal with a comb-4 time / frequency pattern with 12 symbols.

[0100] Figure 9 is a diagram 900 illustrating a network entity 908 that can be configured to coordinate wireless devices 902 and 906 to perform positioning with wireless device 904. The locations of wireless devices 902 and 906 can be known. Wireless device 902 can be a base station, gNB, or TRP. Wireless device 906 can be a base station, gNB, or TRP. Wireless device 904 can be a UE or PRU. Network entity 908 can be connected to wireless devices 902 and 906 via physical links (e.g., backhaul or midhaul links) or via wireless links such as an air interface (Uu) link. Network entity 908 can be part of a core network, such as an LMF or set of location servers. Network entity 908 can configure positioning occasions between wireless devices 902, 904, and 906.

[0101] To perform positioning, the network entity 908 can configure the wireless devices to transmit positioning signals at one another. For example, the wireless device 904 can transmit a set of positioning signals 912 at the wireless device 902. The set of positioning signals 912 can be a set of SRSs. The wireless device 902 can measure the set of positioning signals 912. The wireless device 902 can transmit a set of positioning signals 916 at the wireless device 904. The set of positioning signals 916 can be a set of PRSs, such as the PRS patterns illustrated in FIGs. 6A through Figures 5A to 5E or 8A through Figure 6C The wireless device 904 can measure the set of positioning signals 916. The wireless device 904 can transmit a set of positioning signals 914 at the wireless device 906. The set of positioning signals 914 can be a set of SRSs, such as the SRS patterns illustrated in FIGs. 6A through Figures 7A to 7E or 8A through Figure 8C The wireless device 906 can measure the set of positioning signals 914. The wireless device 906 can transmit a set of positioning signals 918 at the wireless device 904. The set of positioning signals 918 can be a set of PRSs. The wireless device 904 can measure the set of positioning signals 918. One or more of the wireless devices can measure the received positioning signals to compute positioning measurements that can be used to compute a position of the wireless device 904, or that can be used to compute a position or location of the wireless device 904. For example, if the position of the wireless device 902 and the position of the wireless device 906 are known, the position of the wireless device 904 can be computed based on the RTT between the wireless device 902 and the wireless device 904 and the RTT between the wireless device 904 and the wireless device 906. In another example, the wireless device 904 can compute an angle of arrival (AoA) or an angle of departure (AoD) for the set of positioning signals 916, and can compute an AoA or an AoD for the set of positioning signals 918. If the position of the wireless device 902 and the position of the wireless device 906 are also known, the computed AoA and / or AoD can be used to compute a position of the wireless device 904. Other measurements can be performed, such as RTOA, line of sight (LOS) identification (identifying whether a direct line of sight path exists between the wireless devices), or multi-cell round trip time (multi-RTT) computation, to compute a position of the wireless device 904 or to compute measurements that can be used to compute a position of the wireless device 904.

[0102] In some aspects, a positioning model can be used to compute one or more positioning metrics based on measurements. For example, based on measurements of positioning signals, the positioning model can compute a position of the wireless device 904 or an intermediate measurement that can be used to compute a position of the wireless device 904. 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, assistance information associated with the positioning signals) and a set of labels. The positioning signals can include PRS, SRS, CSI-RS, or SSB. The measurements can be channel impulse responses (CIRs) or other measurements used to perform positioning on a target wireless device. The labels can be computed, derived, or given (i.e., known) expected results associated with the set of inputs, such as a position of the wireless device 904 or an intermediate measurement that can be used to compute a position of the wireless device 904 (e.g., a timing measurement, an angle measurement, an LOS identification). The set of inputs and the set of labels can be used to generate and / or train the positioning model using AI / ML.

[0103] When training a positioning model, measurements of positioning signals are input, clean or noisy labels (clean labels can have a quality metric greater than or equal to a threshold, noisy labels can have a quality metric less than or equal to a threshold) are expected output, and training data assistance information is input or expected output. A positioning model can operate on any wireless device based on the set of inputs. For example, wireless device 904 can have a positioning model configured to accept a set of positioning measurements and generate an estimate of a location of wireless device 904. In another example, wireless device 904 can have a positioning model configured to accept a set of positioning measurements and generate intermediate measurements (e.g., timing measurements, angle measurements, LOS identification) that can be used (by wireless device 904 or another entity, such as network entity 908, wireless device 902, or wireless device 906) to compute a location of wireless device 904. In another example, wireless device 902 or wireless device 906 can have a positioning model configured to accept a set of positioning measurements and generate an estimate of a location of wireless device 904. In another example, wireless device 902 or wireless device 906 can have a positioning model configured to accept a set of positioning measurements and generate intermediate measurements that can be used to compute a location of wireless device 904. In another example, network entity 908 can have a positioning model configured to accept a set of positioning measurements and generate an estimate of a location of wireless device 904. In some aspects, positioning measurements can be measured by an entity with a positioning model, for example, wireless device 904 can measure set of positioning signals 916 and can measure set of positioning signals 918, and can use those measurements as input to a positioning model at wireless device 904. In some aspects, positioning measurements can be measured and aggregated by an entity with a positioning model, for example, wireless device 902 can measure set of positioning signals 912, and can aggregate measurements from wireless device 906 and / or wireless device 904 for use as input to a positioning model. In some aspects, positioning measurements can be aggregated by an entity with a positioning model. For example, network entity 908 can aggregate measurements from wireless device 902, wireless device 904, and wireless device 906 for use as input to a positioning model.

[0104] The positioning model can be trained on a wireless device performing positioning, such as wireless device 902, wireless device 904, wireless device 906, and / or network entity 908, or can be trained on an offline device, such as an over-the-top (OTT) server. The input to the positioning model can include measurements of positioning signals, such as measurements of SRS, PRS, CSI-RS, and / or SSB. The input to the positioning model can include assistance information associated with the measured positioning signals, such as the BWP of the positioning signal resource, the number of TRPs, beam information, positioning signal configuration. The label / output of the positioning model can include an estimated position, such as an estimated position of wireless device 904, or an intermediate measurement, such as an indication of whether there is a LOS path between wireless device 902 and wireless device 904.

[0105] To construct the input measurements for the positioning model, a wireless device can estimate the channel between the positioning target wireless device and the positioning neighboring wireless device. The wireless device can estimate the channel based on positioning signals, such as PRS, SRS, CSI-RS, and / or SSB. Due to implementation, the channel estimation can be non-ideal and noisy. The channel estimation can be affected by interference from neighboring cells. Different wireless devices can also implement different channel estimation techniques. For example, a first wireless device performing positioning with wireless device 902 and wireless device 906 can perform a different channel estimation technique than a second wireless device performing positioning with wireless device 902 and wireless device 906. This can create heterogeneity in the training data collected. Additionally, channel estimation performed under ideal conditions can be different from channel estimation performed under conditions where the channel estimation can be impaired. In some aspects, simulating channel estimation impairments can be used to train the positioning model to account for such non-ideal, noisy, and / or impaired positioning signal measurements. Channel estimation impairments can be simulated by measuring the positioning signals using a plurality of sparse pilot masks that mask at least some of the received positioning signals, and / or by measuring the positioning signals using a plurality of artificial noise signals combined with the received positioning signals.

[0106] A wireless device can send assistance information and measurements and / or labels for training a positioning model to an LMF or a training entity. The assistance information can include, for example, a BWP for PRS, a number of TRPs that sent a set of PRS at the wireless device, beam information, and / or PRS configuration information. The assistance information can include an indication of a reference signal resource used by the wireless device to derive and / or compute a positioning signal measurement (e.g., frame number, slot index, orthogonal frequency-division multiplexing (OFDM) symbol, superframe number). The assistance information can include an enhanced timestamp of a reference signal resource used by the wireless device to derive and / or compute a positioning signal measurement (e.g., coordinated universal time (UTC) timing). The assistance information can include proprietary information associated with the wireless device, such as UE-side beam information used to obtain measurements and / or compute labels. The assistance information can include an indication of a reference signal resource used to obtain, derive, and / or compute a location of the wireless device using a RAT method (e.g., frame number, slot index, OFDM symbol, superframe number). The assistance information can include an enhanced timestamp of a reference signal resource used to obtain, derive, and / or compute a location of the wireless device using any RAT or non-RAT method (e.g., UTC timing). The wireless device can send such assistance information in response to a request to provide assistance information for training a positioning model.

[0107] A network entity (e.g., LMF) can send assistance information and measurements and / or labels for training a positioning model to a wireless device or a training entity. Such assistance information can include an indication of a reference signal resource used by the network entity or another wireless device to obtain, derive, and / or compute a location or intermediate labels of the wireless device using a RAT method (e.g., frame number, slot index, OFDM symbol, superframe number). The assistance information can include an enhanced timestamp of a reference signal resource used by the network entity or another wireless device to obtain, derive, and / or compute a location or intermediate labels of the wireless device using a RAT method or a non-RAT method (e.g., UTC timing). The assistance information can include network node (e.g., gNB, TRP) proprietary information (e.g., TRP-side beam information) used by the network entity or another wireless device to obtain, derive, and / or compute a location or intermediate labels of the wireless device. The network entity can obtain labels (locations or intermediate labels of the wireless device) by computing labels or by receiving a transmission from another wireless device that includes at least some of the labels. The network entity can send such assistance information in response to a request to provide assistance information for training a positioning model.

[0108] The labels can include known locations of the wireless device (e.g., locations of PRUs known by the LMF), locations of the wireless device obtained using non-RAT methods (e.g., using LIDAR sensors, GNSS positioning, WLAN positioning methods, other sensors at the wireless device), locations of the wireless device computed using RAT methods (e.g., based on DL-TDoA, DL-AoD, multi-RTT), and / or an intermediate set of labels computed based on RAT measurements. The labels can be computed / obtained by the wireless device, or can be received from a network entity such as the LMF. The LMF can compute the labels based on measurements / labels received from the wireless device, can compute the labels based on measurements / labels received from other wireless devices (e.g., other TRPs in a multi-RTT session and / or other network nodes), or can obtain the labels from another wireless device and send them to the training entity for training the positioning model.

[0109] Some wireless devices can not support signaling to collect training data to train a positioning model based on measurements made on downlink reference signals. Some wireless devices can also not support sending certain tag assistance information due to privacy restrictions (e.g., a base station can not be configured to send its location or beam information to a first set of wireless devices, but can be configured to send its location or beam information to a second set of wireless devices). In some aspects, wireless devices can be configured to report training data measurements, subsets of assistance information, and / or tags to each other to support training of a positioning model based on downlink signals. In some aspects, a wireless device can request another wireless device to assist in training data collection. For example, wireless device 904 can send a request to network entity 908 to assist in training data collection as part of an LPPa framework. In some aspects, wireless device 904 can request network entity 908 to configure a positioning procedure (e.g., DL-TDoA, DL-AoD, multi-RTT), and in response, network entity 908 can provide tags and / or assistance information as part of configuring the requested positioning procedure. In some aspects, wireless device 904 can request network entity 908 to perform a dedicated procedure for collecting training data (e.g., as part of an LPPa framework for the wireless device). For example, wireless device 904 can send an indication to network entity 908 to train a positioning model for use by wireless device 904 to calculate a location of wireless device 904 or to calculate intermediate measurements that can be used to calculate a location of wireless device 904. In another example, network entity 908 can request wireless device 904 to assist in training data collection as part of an LPPa framework. In some aspects, network entity 908 can request wireless device 904 to perform a positioning procedure (e.g., DL-TDoA, DL-AoD, multi-RTT), and in response, wireless device 904 can provide tags and / or assistance information as part of performing the requested positioning procedure. In some aspects, network entity 908 can request wireless device 904 to perform a dedicated procedure for collecting training data (e.g., as part of an LPPa framework for the wireless device). For example, network entity 908 can send an indication to wireless device 904 to provide measurements and assistance data to network entity 908 or another training entity (e.g., an over-the-top (OTT) server) to train a positioning model for use by network entity 908 or wireless device 904 to calculate a location of wireless device 904 or to calculate intermediate measurements that can be used to calculate a location of wireless device 904. In other words, a training data collection session between a wireless device and a network entity can be initiated by the wireless device (e.g., a UE or PRU can initiate training data collection as part of LPPa signaling) or can be initiated by the network entity (e.g., an LMF can initiate training data collection as part of LPPa signaling).A training data collection session between a wireless device and a network entity can be configured to enable the wireless device and the network entity to exchange assistance information as part of a session capability exchange, a session configuration exchange, a session initiation message, a session error message, a session pause message, and / or a session termination message.

[0110] In one aspect, the system can be configured to train a positioning model at the wireless device 904. For example, the wireless device 904 can have a positioning model configured to compute a location of the wireless device 904 based on a set of inputs. The wireless device 904 can train the positioning model based on a computed location of the wireless device 904 and the set of inputs, such as measurements of the set of positioning signals 916 received at the wireless device 904, measurements of the set of positioning signals 918 received at the wireless device 904, intermediate measurements computed at the wireless device 904, intermediate measurements computed at the wireless device 902, intermediate measurements computed at the wireless device 906, intermediate measurements computed at the network entity 908, assistance information at the wireless device 902 associated with the set of positioning signals 916 or the set of positioning signals 912, assistance information at the wireless device 902 associated with the set of positioning signals 918 or the set of positioning signals 914, and / or assistance information at the network entity 908 associated with the set of positioning signals 916, the set of positioning signals 912, the set of positioning signals 918, or the set of positioning signals 914. The location of the wireless device 904 can be computed at the network entity 908, the wireless device 902, the wireless device 906, or the wireless device 904. For example, the network entity 908 can receive measurements from the wireless device 902, the wireless device 906, and the wireless device 904 and can compute a position of the wireless device 904, or the wireless device 904 can compute its position using a set of signals received by a LIDAR device, a GNSS device, or a WLAN antenna. After training the positioning model, the wireless device 902 can transmit the set of positioning signals 916 at the wireless device 904, and the wireless device 906 can transmit the set of positioning signals 918 at the wireless device 904. The wireless device 904 can measure the set of positioning signals 916 and the set of positioning signals 918. The wireless device 904 can receive assistance information from the wireless device 902, the wireless device 904, and / or the network entity 908. The wireless device 904 can compute its location using the positioning model and can transmit its location to the network entity 908. In some aspects, the wireless device 904 can be used to generate a positioning model that can be used by another UE or PRU in an area (e.g., a sector, a similar environment) around the location at which the wireless device 904 was trained.

[0111] In another example, the wireless device 904 can have a positioning model configured to compute intermediate measurements that can be used to compute a position of the wireless device 904 based on a set of inputs. The wireless device 904 can train the positioning model based on the computed intermediate measurements (e.g., timing measurements, angle measurements, LOS identification) and the set of inputs such as measurements of the set of positioning signals 916 received at the wireless device 904, measurements of the set of positioning signals 918 received at the wireless device 904, intermediate measurements computed at the wireless device 904, intermediate measurements computed at the wireless device 902, intermediate measurements computed at the wireless device 906, intermediate measurements computed at the network entity 908, assistance information (e.g., BWP, number of TRPs, beam information, PRS configuration information) associated with the set of positioning signals 916 or the set of positioning signals 912 at the wireless device 902, assistance information associated with the set of positioning signals 918 or the set of positioning signals 914 at the wireless device 902, assistance information associated with the set of positioning signals 916, the set of positioning signals 912, the set of positioning signals 918, or the set of positioning signals 914 at the network entity 908, and / or a location of the wireless device 904. After training the positioning model, the wireless device 902 can transmit the set of positioning signals 916 at the wireless device 904, and the wireless device 906 can transmit the set of positioning signals 918 at the wireless device 904. The wireless device 904 can measure the set of positioning signals 916 and the set of positioning signals 918. The wireless device 904 can receive assistance information from the wireless device 902, the wireless device 904, and / or the network entity 908. The wireless device 904 can use the positioning model to compute a set of intermediate measurements, the wireless device 904 can use the set of intermediate measurements to compute its position, and can transmit its position to the network entity 908. In another aspect, the wireless device 904 can transmit the set of intermediate measurements to the network entity 908, and the network entity 908 can compute the position of the wireless device 904. The network entity 908 can not transmit as much assistance information to the wireless device 904 as when the wireless device 904 computes its own position using the intermediate measurements from the positioning model, thereby minimizing the amount of assistance information transmitted to the wireless device 904 for its positioning model. In some aspects, the wireless device 904 can be configured to generate a positioning model that can be used by another UE or PRU in an area around the location at which the wireless device 904 was trained.

[0112] In another aspect, the system can be configured to train a positioning model at the network entity 908. For example, the network entity 908 can have a positioning model configured to compute a location of the wireless device 904 based on a set of inputs. The network entity 908 can train the positioning model based on the computed location of the wireless device 904 and the set of inputs, such as measurements of the set of positioning signals 916 received at the wireless device 904, measurements of the set of positioning signals 918 received at the wireless device 904, measurements of the set of positioning signals 912 received at the wireless device 902, measurements of the set of positioning signals 914 received at the wireless device 906, intermediate measurements computed at the wireless device 904, intermediate measurements computed at the wireless device 902, intermediate measurements computed at the wireless device 906, intermediate measurements computed at the network entity 908, assistance information at the wireless device 902 associated with the set of positioning signals 916 or the set of positioning signals 912, assistance information at the wireless device 902 associated with the set of positioning signals 918 or the set of positioning signals 914, and / or assistance information at the network entity 908 associated with the set of positioning signals 916, the set of positioning signals 912, the set of positioning signals 918, or the set of positioning signals 914. The location of the wireless device 904 can be computed at the network entity 908, the wireless device 902, the wireless device 906, or the wireless device 904. After training the positioning model, the wireless device 902 can transmit the set of positioning signals 916 at the wireless device 904, and the wireless device 906 can transmit the set of positioning signals 918 at the wireless device 904. The wireless device 904 can measure the set of positioning signals 916 and the set of positioning signals 918. The wireless device 904 can transmit its measurements to the network entity 908. The network entity can then compute the location of the wireless device 904 based on the received measurements and any assistance information and / or measurements received by other devices, such as measurements of the set of positioning signals 912 from the wireless device 904 measured by the wireless device 902 or the set of positioning signals 914 from the wireless device 904 measured by the wireless device 906. The network entity 908 can use the positioning model to compute the location of the wireless device 904. In some aspects, the wireless device 904 can be used to generate a positioning model that can be used by the network entity 908 to compute the location of another UE or PRU in an area around the location at which the wireless device 904 was trained.

[0113] In some aspects, the wireless device 904 can initiate training collection. For example, the wireless device 904 can send a request to the network entity 908 to provide its capability to participate in a training data collection session and to provide label assistance and / or other network-side related assistance information (e.g., TRP beam configuration, location information, mapping of PRS resources to TRP locations, and / or resource beams). The request message can include an indication of the request for the type of label assistance that the network entity 908 can provide (e.g., labels that provide a location of the wireless device 904, labels that provide intermediate measurements that can be used to compute a location of the wireless device 904). In response to the request message, the network entity 908 can send an indication of its capability to assist the wireless device 904 in collecting training data and providing label assistance (e.g., the network entity 908 can utilize an estimated / computed / known location of the wireless device 904 to compute / derive intermediate labels based on its knowledge of TRP locations) or other assistance information (e.g., TRP beam configuration). In response to receiving the indication of the capability of the network entity 908, the wireless device 904 can send a set of properties of a requested DL positioning signal configuration to the network entity 908. The set of properties of the requested DL positioning signal configuration can include a periodicity of a label assistance report and / or whether the network entity 908 should report enhanced timing or resource indications to be used as label assistance information. The network entity 908 can send labels and / or assistance information to the wireless device 904 for training a positioning model. The network entity 908 can periodically send such data according to a request from the wireless device 904. The network entity 908 can send any suitable assistance information that can be used to train a positioning model, such as TRP beam information or enhanced timing and indications of resources used to generate labels. In some aspects, the wireless device 904 can send measurements and / or its location (e.g., obtained using a non-RAT method) to the network entity 908 for use by the network entity 908 to compute intermediate measurement labels, which can be sent to the wireless device 904 for training a positioning model.

[0114] In some aspects, the network entity 908 can initiate training collection. For example, the network entity 908 can transmit a request to the wireless device 904 to provide its capability to participate in a training data collection session and to provide label assistance and / or other device-side related assistance information (e.g., CIR, CFR, PDP, ToA, RSTD, RSRP, RSRPP, AoD). The request message can include an indication of a request for a type of training data (e.g., CIR, CFR, PDP, ToA, RSTD, RSRP, RSRPP, AoD) that the wireless device 904 can provide. In response to the request message, the wireless device 904 can transmit an indication of its capability to assist the network entity 908 in collecting training data and / or to provide label assistance (e.g., to compute a location of the wireless device 904 using a non-RAT method or a RAT method). In response to receiving the indication of the capability of the wireless device 904, the network entity 908 can transmit assistance data to the wireless device 904 to assist in data collection (e.g., as part of an LPPa assistance data exchange). The assistance data can include PRS configuration information, a type of measurements that the wireless device 904 is to report, a periodicity at which the wireless device 904 is to report measurements and / or assistance data, and / or whether the wireless device 904 should report enhanced timing or resource indications. The wireless device 904 can receive positioning signals (such as positioning signal set 916 and positioning signal set 918), collect measurements of the positioning signals, and feed back any measurements, reports, labels, and / or assistance data to the network entity 908 for training a positioning model.

[0115] In some aspects, the network entity 908 can use a given frequency / time structure (e.g., a given comb / symbol structure, such as Figures 5A to 5E , 6A to Figure 6C , 7A to Figure 7F or 8A to Figure 8CThe positioning signals (e.g., PRS or SRS) can be configured with different pilot subsets (e.g., different comb-tooth / symbol structures as disclosed in the present disclosure). A training data collection entity, such as wireless device 902, wireless device 904, or wireless device 906, can consider different pilot subsets in the positioning signal pattern when estimating the channel between the transmitting wireless device and the receiving wireless device. For each pilot subset, the data collection entity can estimate the channel and report the corresponding measurements to a training entity or data repository for training the positioning model. Network entity 908 can configure (e.g., via LPP or NRPP messages) the pilot subsets to be considered by the data collection entity for channel estimation. For example, wireless device 904 can be a UE or PRU that receives the configuration from network entity 908 as part of an LPPa protocol (e.g., an assistance data exchange procedure) or another standardized procedure for enabling wireless device 904 to collect training data for training the positioning model. In another example, wireless device 902 can be a NG-RAN node that receives the configuration from network entity 908 as part of an NRPPa protocol (e.g., a measurement information transfer procedure) or another standardized procedure for enabling wireless device 902 to collect training data for training the positioning model. In some aspects, the data collection entity can indicate its capabilities to the network entity with respect to using pilot subsets to collect different implementations / instances for non-ideal channel estimation. For example, wireless device 904 can indicate its capabilities to network entity 908 as part of a capabilities exchange procedure in the LPPa protocol. In another example, wireless device 902 can indicate its capabilities to network entity 908 as part of a TRP information exchange procedure in the NRPPa protocol. Such a capabilities message can indicate the number of instances that the device can support. Such a capabilities message can indicate one or more sparse patterns that can be supported for masking the received positioning signals. Such a capabilities message can be any measurement gap conditions that can be associated with enabling the data collection entity to compute multiple channel estimates. The number of channel estimate instances can be configured by network entity 908. The number of channel estimate instances can depend on the expected SNR settings at the data collection entity. For example, network entity 908 can indicate a mapping of the number of instances for different SNR ranges, where a 10 dB SNR can be associated with at least 5 instances for that number of channel instances (e.g., every fifth pilot is picked and two offsets are considered), and a 20 dB SNR can be associated with at least 2 instances for that number of channel estimate instances (e.g., every other pilot is picked and two offsets are considered).

[0116] In some aspects, a training data collection entity, such as wireless device 902, wireless device 904, or wireless device 906, can consider different artificial noise subsets when estimating a channel between a transmitting wireless device and a receiving wireless device. For each artificial noise subset, the data collection entity can estimate the channel and report corresponding measurements to a training entity or data repository for training a positioning model. For example, the data collection entity can consider multiple additive noise implementations / instances, and can report multiple measurement instances to the training entity. Network entity 908 can configure (e.g., via LPP or NRPP messages) the statistics and distribution of the additive noise. For example, wireless device 904 can be a UE or PRU that receives the configuration from network entity 908 as part of an LPPa protocol (e.g., an assistance data exchange procedure) or another standardized procedure for enabling wireless device 904 to collect training data for training a positioning model. In another example, wireless device 902 can be a NG-RAN node that receives the configuration from network entity 908 as part of an NRPPa protocol (e.g., a measurement information transfer procedure) or another standardized procedure for enabling wireless device 902 to collect training data for training a positioning model. In some aspects, the data collection entity can indicate its capabilities to the network entity with respect to using artificial additive noise to collect different implementations / instances for non-ideal channel estimation. For example, wireless device 904 can indicate its capabilities to network entity 908 as part of a capabilities exchange procedure in an LPPa protocol. In another example, wireless device 902 can indicate its capabilities to network entity 908 as part of a TRP information exchange procedure in an NRPPa protocol. Such a capabilities message can indicate a number of instances that the device can support. Such a capabilities message can indicate one or more noise distributions that can be added to a received positioning signal that can be supported. Such a capabilities message can be any measurement gap conditions that can be associated with enabling the data collection entity to compute multiple channel estimates with different noise statistics. The number of additive noise instances and / or noise settings can be configured by network entity 908. The number of additive noise instances and / or noise settings can depend on the expected SNR settings at the data collection entity. For example, network entity 908 can indicate a mapping of noise properties for different SNR ranges, where an SNR less than or equal to a first threshold value can be associated with an instance number greater than or equal to a second threshold value and / or a noise variance greater than or equal to a third threshold value, and an SNR greater than or equal to the first threshold value can be associated with an instance number less than or equal to the second threshold value and / or a noise variance less than or equal to the third threshold value.

[0117] In some aspects, the network entity can configure the data collection entity to jointly add artificial noise and consider a sparsity pattern mask when estimating a channel between the target wireless device and the network node. In some aspects, a training entity that receives reported measurement instances for a given target location can train a positioning model. The training entity can cycle over other target locations used for training to build a robust positioning model, or can design different positioning models for different properties of the positioning scenario.

[0118] Figure 10 FIG. 10 is a connection flow diagram 1000 illustrating an example of communications between a positioning target wireless device 1002, a set of positioning neighbor wireless devices 1004, and a positioning network entity 1006. The wireless devices can be configured to collect training data for training a positioning model. For example, the positioning target wireless device 1002 can collect training data for training a positioning model. One of the set of positioning neighbor wireless devices 1004 can be used to train the positioning model. The positioning model can be configured to compute a position of the positioning target wireless device 1002 and / or a set of measurements that can be used to compute a position of the positioning target wireless device 1002. The positioning target wireless device 1002 can be a UE or a PRU. The PRU can have a set of sensors that can be used to compute a position of the PRU with high accuracy, such as a LIDAR sensor, a GNSS device, or a WLAN system configured to compute a position of the positioning target wireless device 1002 based on received WLAN signals. The PRU can be a fixed PRU with a known position or can be a mobile PRU placed at a known position for a duration of collecting measurements for training the positioning model. The set of positioning neighbor wireless devices 1004 can include a set of base stations and / or TRPs configured to transmit positioning signals at the positioning target wireless device 1002. The positioning network entity 1006 can include an LMF or can include one or more location servers. The positioning network entity 1006 can be configured to configure positioning occasions between the positioning target wireless device 1002 and the set of positioning neighbor wireless devices 1004. The positioning network entity 1006 can be configured to configure error measurement instances for collecting training data during the positioning occasions between the positioning target wireless device 1002 and the set of positioning neighbor wireless devices 1004.

[0119] The positioning target wireless device 1002 can transmit a capability communication 1008 to the positioning network entity 1006. The positioning network entity 1006 can receive the capability communication 1008 from the positioning target wireless device 1002. The positioning network entity 1006 can transmit the capability communication 1008 to the positioning target wireless device 1002. The positioning target wireless device 1002 can receive the capability communication 1008 from the positioning network entity 1006. The capability communication 1008 can include an indication of a capability of the positioning target wireless device 1002 to collect different implementations / instances for non-ideal channel estimation. The capability communication 1008 can be part of a capability exchange procedure via LPP messages. The capability communication 1008 can be transmitted using the LPPa protocol. The capability communication 1008 can indicate a number of instances that the positioning target wireless device 1002 can support. The capability communication 1008 can indicate a noise profile that the positioning target wireless device 1002 can add. The capability communication 1008 can indicate a set of sparse patterns that the positioning target wireless device 1002 can use to measure a positioning signal (e.g., PRS, CSI-RS). The capability communication 1008 can indicate a set of measurement gap conditions for the positioning target wireless device 1002 to use to compute multiple channel estimates with different noise and / or sparse patterns. In some aspects, the positioning network entity 1006 can configure, via the capability communication 1008 or the set of configurations 1018, a number of channel estimation instances for the positioning target wireless device 1002 to use based on a signal-to-noise ratio (SNR) setting at the positioning target wireless device 1002. For example, the positioning network entity 1006 can indicate a mapping of implementation / instance numbers for different SNR ranges. Each SNR range can correspond to a number of instances for the positioning target wireless device 1002 to measure. For example, for an SNR of 10 dB, the mapping can indicate to the positioning target wireless device 1002 to use at least 5 instances (e.g., pick every 5 pilots and consider 5 offsets). Conversely, for an SNR of 20 dB, the mapping can indicate to the positioning target wireless device 1002 to use at least 2 instances (e.g., pick every one pilot and consider 2 offsets). In another example, for an SNR estimated or expected to be at or below a first threshold amount, the mapping can indicate to the positioning target wireless device 1002 to use an implementation number at or above a second threshold amount and / or to use a noise variance at or above a third threshold amount. For an SNR estimated or expected to be at or above a first threshold amount, the mapping can indicate to the positioning target wireless device 1002 to use an implementation number at or below a second threshold amount and / or to use a noise variance at or below a third threshold amount.

[0120] The at least one positioning neighbor wireless device in the set of positioning neighbor wireless devices 1004 can transmit a capability communication 1010 to the positioning network entity 1006. The positioning network entity 1006 can receive the capability communication 1010 from the at least one positioning neighbor wireless device in the set of positioning neighbor wireless devices 1004. The positioning network entity 1006 can transmit the capability communication 1010 to the at least one positioning neighbor wireless device in the set of positioning neighbor wireless devices 1004. The at least one positioning neighbor wireless device in the set of positioning neighbor wireless devices 1004 can receive the capability communication 1010 from the positioning network entity 1006. The capability communication 1010 can include an indication of a capability of the at least one positioning neighbor wireless device in the set of positioning neighbor wireless devices 1004 to collect different implementations / instances for non-ideal channel estimation. The capability communication 1010 can be part of a TRP information exchange procedure via a NRPP message. The capability communication 1010 can be transmitted using the NRPPa protocol. The capability communication 1010 can indicate a number of instances that the at least one positioning neighbor wireless device in the set of positioning neighbor wireless devices 1004 can support. The capability communication 1010 can indicate a noise profile that the at least one positioning neighbor wireless device in the set of positioning neighbor wireless devices 1004 can add. The capability communication 1010 can indicate a set of sparse patterns that the at least one positioning neighbor wireless device in the set of positioning neighbor wireless devices 1004 can use to measure a positioning signal (e.g., SRS, CSI-RS). The capability communication 1010 can indicate a set of measurement gap conditions for the at least one positioning neighbor wireless device in the set of positioning neighbor wireless devices 1004 to use to compute multiple channel estimates with different noise and / or sparse patterns. In some aspects, the positioning network entity 1006 can configure, via the capability communication 1010 or the set of configurations 1014, a number of channel estimation instances used by the at least one positioning neighbor wireless device in the set of positioning neighbor wireless devices 1004 based on a signal-to-noise ratio (SNR) setting at the at least one positioning neighbor wireless device in the set of positioning neighbor wireless devices 1004.

[0121] At 1012, the positioning network entity 1006 can configure positioning signals for transmission between the positioning target wireless device 1002 and the set of positioning neighbor wireless devices 1004. In other words, the positioning network entity 1006 can configure a set of positioning occasions. At 1012, the positioning network entity 1006 can configure error measurements for a set of positioning signals 1024 to be measured by the positioning target wireless device 1002 and / or the set of positioning neighbor wireless devices 1004. In other words, the positioning network entity 1006 can configure a set of channel estimation error instances / implementations to be generated by the training data collection device based on the set of positioning signals 1024 received thereby.

[0122] The positioning network entity 1006 can transmit a set of configurations 1014 to the set of positioning neighboring wireless devices 1004. The set of positioning neighboring wireless devices 1004 can receive the set of configurations 1014 from the positioning network entity 1006. At least one of the set of positioning neighboring wireless devices 1004 can transmit at least some of a set of configurations 1016 to the positioning target wireless device 1002 based on the set of configurations 1014. For example, a network node serving the positioning target wireless device 1002 can transmit the set of configurations 1016 for configuring transmission of or measurement of at least some of a set of positioning signals 1024. In some aspects, the positioning network entity 1006 can directly configure the positioning target wireless device 1002 by transmitting a set of configurations 1018 to the positioning target wireless device 1002. The positioning target wireless device 1002 can receive the set of configurations 1018.

[0123] At 1020, the positioning target wireless device 1002 can apply the set of configurations 1018 and / or the set of configurations 1016. At 1022, the set of positioning neighboring wireless devices 1004 can apply the set of configurations 1014. The set of configurations can configure the wireless devices to transmit a set of positioning signals 1024. The set of configurations can configure the wireless devices to measure the set of positioning signals 1024 based on a plurality of sparse pilot masks and / or a plurality of artificial noise signals.

[0124] The positioning target wireless device 1002 can transmit a set of positioning signals 1024 at the set of positioning neighbor wireless devices 1004 based on at least some of the set of configurations 1018 and / or the set of configurations 1016. The set of positioning signals 1024 can include a set of SRSs, a set of CSI-RSs, or a set of SSBs. At 1028, the set of positioning neighbor wireless devices 1004 can measure the set of positioning signals 1024 based on the set of configurations 1014. The set of configurations 1014 can indicate at least some of the estimation error measurements made by the set of positioning neighbor wireless devices 1004 in measuring the set of positioning signals 1024, such as an identifier of a sparse pilot mask to be applied to the measurements and / or an identifier of an artificial noise signal to be added to the measurements. At least one of the set of positioning neighbor wireless devices 1004 can receive positioning feedback 1030 from the positioning target wireless device 1002 and / or positioning feedback 1032 from the positioning network entity 1006. The positioning feedback can include assistance information or a computed label, such as a computed position of the positioning target wireless device 1002. At 1038, at least one of the set of positioning neighbor wireless devices 1004 can output the measured positioning signals to a positioning model. For example, at least one of the set of positioning neighbor wireless devices 1004 can train a positioning model at at least one of the set of positioning neighbor wireless devices 1004 based on the measured positioning signals with channel error estimates, or at least one of the set of positioning neighbor wireless devices 1004 can transmit training data to a training entity, such as the positioning network entity 1006, the positioning target wireless device 1002, or an OTT server.

[0125] The set of positioning neighbor wireless devices 1004 can transmit a set of positioning signals 1024 at the positioning target wireless device 1002 based on at least some of the set of configurations 1014. The set of positioning signals 1024 can include a set of PRSs or a set of CSI-RSs. At 1026, the positioning target wireless device 1002 can measure the set of positioning signals 1024 based on at least some of the set of configurations 1018 and / or the set of configurations 1016. The set of configurations 1018 and / or the set of configurations 1016 can indicate at least some of the error measurements made by the positioning target wireless device 1002 in measuring the set of positioning signals 1024, such as an identifier of a sparse pilot mask to be applied to the measurements and / or an identifier of an artificial noise signal to be added to the measurements. The positioning target wireless device 1002 can receive positioning feedback 1034 from the positioning network entity 1006. The positioning feedback can include assistance information or a computed label, such as a computed position of the positioning target wireless device 1002. At 1036, the positioning target wireless device 1002 can output the measured positioning signals to a positioning model. For example, the positioning target wireless device 1002 can train a positioning model at the positioning target wireless device 1002 based on the measured positioning signals with channel error estimates, or the positioning target wireless device 1002 can send training data to a training entity, such as the positioning network entity 1006, at least one of the set of positioning neighbor wireless devices 1004, or an OTT server.

[0126] Figure 11 FIG. 11 is a flow diagram 1100 of a method of wireless communication. The method can be performed by a wireless device (e.g., the UE 104, the UE 350; the base station 102, the base station 310; the wireless device 402, the wireless device 404, the wireless device 406, the wireless device 902, the wireless device 904, the wireless device 906; the positioning target wireless device 1002; one of the set of positioning neighbor wireless devices 1004; the apparatus 1804; the network entity 1802, the network entity 1902). At 1102, the wireless device can receive a set of positioning signals. For example, 1102 can be performed by the positioning target wireless device 1002 in FIG. 10 receiving the set of positioning signals 1024 from the set of positioning neighbor wireless devices 1004. The set of positioning signals 1024 can include a set of PRSs, a set of CSI-RSs, or a set of SSBs. For example, 1102 can be performed by one of the set of positioning neighbor wireless devices 1004 in FIG. 10 receiving the set of positioning signals 1024 from the set of positioning target wireless devices 1002. The set of positioning signals 1024 can include a set of SRSs, a set of CSI-RSs, or a set of SSBs. Also, 1102 can be performed by the network entity 1802 in FIG. 18 receiving the set of positioning signals 1024 from the set of positioning target wireless devices 1002. Figure 10 Figure 10 Figure 18 or​​ Figure 19 by the component 198 in FIG. 19.

[0127] At 1104, the wireless device can measure a set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. For example, 1104 can be performed by the positioning target wireless device 1002, which can measure the set of positioning signals 1024 based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals indicated in the configuration set 1016 or the configuration set 1018, at 1026. In another example, 1104 can be performed by one of the set of positioning neighboring wireless devices 1004, which can measure the set of positioning signals 1024 based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals indicated in the configuration set 1014, at 1028. Further, 1104 can be performed by the component 198 in FIG. 19. Figure 10 by the component 198 in FIG. 19. Figure 10 by the component 198 in FIG. 19. Figure 18 or Figure 19 by the component 198 in FIG. 19.

[0128] At 1106, the wireless device can output the measured set of positioning signals for training a positioning model. For example, 1106 can be performed by the positioning target wireless device 1002, which can output the measured set of positioning signals measured at 1026 for training a positioning model, at 1036. In another example, 1106 can be performed by one of the set of positioning neighboring wireless devices 1004, which can output the measured set of positioning signals measured at 1026 for training a positioning model, at 1038. Further, 1106 can be performed by the component 198 in FIG. 19. Figure 10 by the component 198 in FIG. 19. Figure 10 by the component 198 in FIG. 19. Figure 18 or Figure 19 by the component 198 in FIG. 19.

[0129] Figure 12 is a flow diagram of a method of wireless communication 1200. The method can be performed by a wireless device (e.g., the UE 104, the UE 350; the base station 102, the base station 310; the wireless device 402, the wireless device 404, the wireless device 406, the wireless device 902, the wireless device 904, the wireless device 906; the positioning target wireless device 1002; one of the set of positioning neighboring wireless devices 1004; the apparatus 1804; the network entity 1802, the network entity 1902). At 1202, the wireless device can receive a set of positioning signals. For example, 1202 can be performed by the component 198 in FIG. 19. Figure 10The positioning target wireless device 1002 in the set of positioning neighboring wireless devices 1004 can receive the set of positioning signals 1024 from the set of positioning target wireless devices 1002. The set of positioning signals 1024 can include a set of PRS, a set of CSI-RS, or a set of SSB. For example, 1202 can be performed by the positioning target wireless device 1002 in Figure 10 The one of the set of positioning neighboring wireless devices 1004 in the set of positioning target wireless devices 1002 can receive the set of positioning signals 1024 from the set of positioning neighboring wireless devices 1004. The set of positioning signals 1024 can include a set of SRS, a set of CSI-RS, or a set of SSB. Further, 1202 can be performed by the one of the set of positioning neighboring wireless devices 1004 in Figure 18 or Figure 19 component 198 in FIG. 18.

[0130] At 1204, the wireless device can measure the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. For example, 1204 can be performed by the positioning target wireless device 1002 in Figure 10 The positioning target wireless device 1002 in the set of positioning neighboring wireless devices 1004 can receive the set of positioning signals 1024 from the set of positioning target wireless devices 1002. The set of positioning signals 1024 can include a set of PRS, a set of CSI-RS, or a set of SSB. For example, 1202 can be performed by the positioning target wireless device 1002 in Figure 10 The one of the set of positioning neighboring wireless devices 1004 in the set of positioning target wireless devices 1002 can receive the set of positioning signals 1024 from the set of positioning neighboring wireless devices 1004. The set of positioning signals 1024 can include a set of SRS, a set of CSI-RS, or a set of SSB. Further, 1202 can be performed by the one of the set of positioning neighboring wireless devices 1004 in Figure 18 or Figure 19 component 198 in FIG. 18.

[0131] At 1206, the wireless device can output the measured set of positioning signals for training a positioning model. For example, 1206 can be performed by the positioning target wireless device 1002 in Figure 10 The positioning target wireless device 1002 in the set of positioning neighboring wireless devices 1004 can receive the set of positioning signals 1024 from the set of positioning target wireless devices 1002. The set of positioning signals 1024 can include a set of PRS, a set of CSI-RS, or a set of SSB. For example, 1202 can be performed by the positioning target wireless device 1002 in Figure 10 The one of the set of positioning neighboring wireless devices 1004 in the set of positioning target wireless devices 1002 can receive the set of positioning signals 1024 from the set of positioning neighboring wireless devices 1004. The set of positioning signals 1024 can include a set of SRS, a set of CSI-RS, or a set of SSB. Further, 1202 can be performed by the one of the set of positioning neighboring wireless devices 1004 in Figure 18 or Figure 19 component 198 in FIG. 18.

[0132] At 1208, the wireless device can simulate a first set of compromised location signals by masking the location signal set based on a first sparse pilot mask among multiple sparse pilot masks. For example, 1208 can be derived from... Figure 10 The positioning target wireless device 1002 performs this action, which can simulate a first set of damaged positioning signals by masking a set of positioning signals based on a first sparse pilot mask among a plurality of sparse pilot masks. In another example, 1208 can be performed by... Figure 10 The location neighbor wireless device in the set of 1004 performs the operation, which simulates a first set of damaged location signals by masking the location signal set based on a first sparse pilot mask among a plurality of sparse pilot masks. Furthermore, 1208 can be performed by... Figure 18 or Figure 19 Component 198 is executed.

[0133] At point 1210, the wireless device can measure the first set of damaged location signals. For example, 1210 can be determined by... Figure 10 The positioning target wireless device 1002 performs this action, which can measure a first set of damaged positioning signals. In another example, 1210 can be performed by... Figure 10 The positioning neighbor wireless device in the set of 1004 performs the measurement of a first set of damaged positioning signals. Additionally, 1210 can be performed by... Figure 18 or Figure 19 Component 198 is executed.

[0134] At 1212, the wireless device can simulate a second set of compromised location signals by masking the location signal set based on a second sparse pilot mask among multiple sparse pilot masks. For example, 1212 can be derived from... Figure 10 The positioning target wireless device 1002 performs this action, which can simulate a second set of damaged positioning signals by masking the positioning signal set based on a second sparse pilot mask among multiple sparse pilot masks. In another example, 1212 can be performed by... Figure 10 The location neighbor wireless device in the set of 1004 performs the operation, which simulates a second set of damaged location signals by masking the location signal set based on a second sparse pilot mask among multiple sparse pilot masks. Furthermore, 1212 can be performed by... Figure 18 or Figure 19 Component 198 is executed.

[0135] At point 1214, the wireless device can measure a second set of damaged location signals. For example, 1214 can be determined by... Figure 10the positioning target wireless device 1002 in the set of positioning neighbor wireless devices 1004 can measure the second set of impaired positioning signals. Further, 1214 can be performed by the component 198 in the apparatus 200. Figure 10 the one of the set of positioning neighbor wireless devices 1004 can measure the second set of impaired positioning signals. Further, 1214 can be performed by the component 198 in the apparatus 198. Figure 18 or Figure 19 the component 198 in the apparatus 200.

[0136] At 1216, the wireless device can simulate a first set of impaired positioning signals by combining the set of positioning signals with a first artificial noise signal of the plurality of artificial noise signals. For example, 1216 can be performed by the positioning target wireless device 1002 in the apparatus 1000. Figure 10 the positioning target wireless device 1002 in the apparatus 1000 can simulate the first set of impaired positioning signals by combining the set of positioning signals with a first artificial noise signal of the plurality of artificial noise signals. In another example, 1216 can be performed by the set of positioning neighbor wireless devices 1004 in the apparatus 1000. Figure 10 the one of the set of positioning neighbor wireless devices 1004 in the apparatus 1000 can simulate the first set of impaired positioning signals by combining the set of positioning signals with a first artificial noise signal of the plurality of artificial noise signals. Further, 1216 can be performed by the component 198 in the apparatus 198. Figure 18 or Figure 19 the component 198 in the apparatus 200.

[0137] At 1218, the wireless device can measure the first set of impaired positioning signals. For example, 1218 can be performed by the positioning target wireless device 1002 in the apparatus 1000. Figure 10 the positioning target wireless device 1002 in the apparatus 1000 can measure the first set of impaired positioning signals. In another example, 1218 can be performed by the set of positioning neighbor wireless devices 1004 in the apparatus 1000. Figure 10 the one of the set of positioning neighbor wireless devices 1004 in the apparatus 1000 can measure the first set of impaired positioning signals. Further, 1218 can be performed by the component 198 in the apparatus 198. Figure 18 or Figure 19 the component 198 in the apparatus 200.

[0138] At 1220, the wireless device can simulate a second set of impaired positioning signals by combining the set of positioning signals with a second artificial noise signal of the plurality of artificial noise signals. For example, 1220 can be performed by the positioning target wireless device 1002 in the apparatus 1000. Figure 10 the positioning target wireless device 1002 in the apparatus 1000 can simulate the second set of impaired positioning signals by combining the set of positioning signals with a second artificial noise signal of the plurality of artificial noise signals. In another example, 1220 can be performed by the set of positioning neighbor wireless devices 1004 in the apparatus 1000. Figure 10one of the set of positioning neighbor wireless devices 1004 in the set of positioning neighbor wireless devices 1004 can measure the second set of impaired positioning signals. Further, 1222 can be performed by the component 198 in the apparatus 198. Figure 18 or Figure 19 Further, 1222 can be performed by the component 198 in the apparatus 198.

[0139] At 1224, the wireless device can train a positioning model at the wireless device based on the set of measured positioning signals. For example, 1224 can be performed by the positioning target wireless device 1002 in the apparatus 1002, which can train a positioning model at the wireless device based on the set of measured positioning signals. In another example, 1224 can be performed by one of the set of positioning neighbor wireless devices 1004 in the set of positioning neighbor wireless devices 1004, which can train a positioning model at the wireless device based on the set of measured positioning signals. Further, 1224 can be performed by the component 198 in the apparatus 198. Figure 10 Figure 10 Further, 1224 can be performed by the component 198 in the apparatus 198. Figure 18 Figure 19 Further, 1224 can be performed by the component 198 in the apparatus 198.

[0140] At 1226, the wireless device can transmit the set of measured positioning signals to a training entity for training a positioning model. For example, 1226 can be performed by the positioning target wireless device 1002 in the apparatus 1002, which can transmit the set of measured positioning signals to a training entity for training a positioning model. In another example, 1226 can be performed by one of the set of positioning neighbor wireless devices 1004 in the set of positioning neighbor wireless devices 1004, which can transmit the set of measured positioning signals to a training entity for training a positioning model. Further, 1226 can be performed by the component 198 in the apparatus 198. Figure 10 Figure 10 Further, 1226 can be performed by the component 198 in the apparatus 198. Figure 18 Figure 19 Further, 1226 can be performed by the component 198 in the apparatus 198.

[0141] Figure 10 Figure 10 Further, 1226 can be performed by the component 198 in the apparatus 198. Figure 18 Figure 19 Further, 1226 can be performed by the component 198 in the apparatus 198.

[0142] Figure 13 ​​​​​​​is a flowchart 1300 of a method of wireless communication. The method can be performed by a wireless device (e.g., the UE 104, the UE 350; the base station 102, the base station 310; the wireless device 402, the wireless device 404, the wireless device 406, the wireless device 902, the wireless device 904, the wireless device 906; the positioning target wireless device 1002; one of the set of positioning neighbor wireless devices 1004; the apparatus 1804; the network entity 1802, the network entity 1902). At 1301, the wireless device can transmit a capability message that can include an indication of a capability to measure a set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. For example, 1301 can be performed by the positioning target wireless device 1002 in the system 1000 transmitting a capability message that can include an indication of a capability to measure a set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. In another example, 1301 can be performed by one of the set of positioning neighbor wireless devices 1004 in the system 1000 transmitting a capability message that can include an indication of a capability to measure a set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. Further, 1301 can be performed by the component 198 in the apparatus 1900 or the apparatus 2000. Figure 10 Figure 10 Figure 18 Figure 19

[0143] At 1303, the wireless device can receive, from a network entity, a configuration message that can include a configuration of at least some of the plurality of sparse pilot masks or at least some of the plurality of artificial noise signals. For example, 1303 can be performed by the positioning target wireless device 1002 in the system 1000 receiving, from a network entity, a configuration message that can include a configuration of at least some of the plurality of sparse pilot masks or at least some of the plurality of artificial noise signals. In another example, 1303 can be performed by one of the set of positioning neighbor wireless devices 1004 in the system 1000 receiving, from a network entity, a configuration message that can include a configuration of at least some of the plurality of sparse pilot masks or at least some of the plurality of artificial noise signals. Further, 1303 can be performed by the component 198 in the apparatus 1900 or the apparatus 2000. Figure 10 Figure 10 Figure 18 Figure 19

[0144] At 1302, the wireless device can receive a set of positioning signals. For example, 1302 can be performed by the positioning target wireless device 1002 in the system 1000 receiving a set of positioning signals. In another example, 1302 can be performed by one of the set of positioning neighbor wireless devices 1004 in the system 1000 receiving a set of positioning signals. Further, 1302 can be performed by the component 198 in the apparatus 1900 or the apparatus 2000. Figure 10 ​​​​​​​​the positioning target wireless device 1002 in 1302 can receive a set of positioning signals 1024 from a set of positioning neighbor wireless devices 1004. The set of positioning signals 1024 can include a set of PRSs, a set of CSI-RSs, or a set of SSBs. For example, 1302 can be performed by a positioning target wireless device 1002 in Figure 10 one of the set of positioning neighbor wireless devices 1004 in 1302 can receive a set of positioning signals 1024 from a set of positioning target wireless devices 1002. The set of positioning signals 1024 can include a set of SRSs, a set of CSI-RSs, or a set of SSBs. Further, 1302 can be performed by a positioning neighbor wireless device in Figure 18 or Figure 19 component 198 in 1302.

[0145] At 1304, the wireless device can measure the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. For example, 1304 can be performed by a positioning target wireless device 1002 in Figure 10 the positioning target wireless device 1002 in 1302 can measure the set of positioning signals 1024 at 1026 based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals indicated in the set of configurations 1016 or the set of configurations 1018. In another example, 1304 can be performed by a positioning neighbor wireless device in Figure 10 one of the set of positioning neighbor wireless devices 1004 in 1302 can measure the set of positioning signals 1024 at 1028 based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals indicated in the set of configurations 1014. Further, 1304 can be performed by a positioning neighbor wireless device in Figure 18 or Figure 19 component 198 in 1304.

[0146] At 1306, the wireless device can output the measured set of positioning signals for training a positioning model. For example, 1306 can be performed by a positioning target wireless device 1002 in Figure 10 the positioning target wireless device 1002 in 1302 can output the measured set of positioning signals measured at 1026 for training a positioning model at 1036. In another example, 1306 can be performed by a positioning neighbor wireless device in Figure 10 one of the set of positioning neighbor wireless devices 1004 in 1302 can output the measured set of positioning signals measured at 1026 for training a positioning model at 1038. Further, 1306 can be performed by a positioning neighbor wireless device in Figure 18 or Figure 19 component 198 in 1306.

[0147] At 1308, the wireless device can receive an SNR mapping that relates at least one SNR value to at least one quantity of channel estimates. For example, 1308 can be performed by a positioning target wireless device 1002 in Figure 10 The positioning target wireless device 1002 performs this action, and the positioning target wireless device can receive an SNR map that correlates at least one SNR value with at least one channel estimate. In another example, 1308 can be performed by... Figure 10 The location neighbor radio device in the set of 1004 performs the operation, which can receive an SNR map that correlates at least one SNR value with at least one channel estimate. Furthermore, 1308 can be performed by... Figure 18 or Figure 19 Component 198 is executed.

[0148] At 1310, the wireless device can estimate the SNR value associated with the reception of the location signal set. For example, 1310 can be determined by... Figure 10 The positioning target wireless device 1002 performs this function, which can estimate the SNR value associated with the reception of the positioning signal set. In another example, 1310 can be performed by... Figure 10 The location neighbor radio device in the set of 1004 performs the function of estimating the SNR value associated with the reception of the location signal set. Furthermore, 1310 can be performed by... Figure 18 or Figure 19 Component 198 is executed.

[0149] At 1312, the wireless device can select the number of channel estimation instances from the SNR map based on the estimated SNR value, wherein the indication of the ability to measure a set of location signals based on multiple sparse pilot masks may include the number of channel estimation instances associated with the multiple sparse pilot masks. For example, 1312 may be... Figure 10 The location target wireless device 1002 performs this action, which can select a number of channel estimation instances from an SNR map based on the estimated SNR value, wherein the indication of the ability to measure a set of location signals based on multiple sparse pilot masks may include the number of channel estimation instances associated with the multiple sparse pilot masks. In another example, 1312 may be performed by... Figure 10 The location neighbor radio device in the set of 1004 performs the selection of a number of channel estimation instances from the SNR map based on the estimated SNR value, wherein the indication of the ability to measure the set of location signals based on multiple sparse pilot masks may include the number of channel estimation instances associated with the multiple sparse pilot masks. Furthermore, 1312 may be performed by... Figure 18 or Figure 19 Component 198 is executed.

[0150] At 1314, the wireless device can estimate the SNR value associated with the reception of the location signal set. For example, 1314 can be determined by... Figure 10 The positioning target wireless device 1002 performs this function, which can estimate the SNR value associated with the reception of the positioning signal set. In another example, 1314 can be performed by... Figure 10 The location neighbor radio device in the set of 1004 performs the function of estimating the SNR value associated with the reception of the location signal set. Furthermore, 1314 can be performed by... Figure 18 or Figure 19 Component 198 is executed.

[0151] At 1316, the wireless device can calculate at least one of the number of channel estimation instances or the noise variance value based on the estimated SNR value, wherein the indication of the ability to measure a set of location signals based on multiple artificial noise signals may include at least one of the number of channel estimation instances or the noise variance value associated with the multiple artificial noise signals. For example, 1316 may be derived from... Figure 10 The positioning target wireless device 1002 performs this action, which can calculate at least one of the number of channel estimation instances or the noise variance value based on the estimated SNR value, wherein the indication of the ability to measure a set of positioning signals based on multiple artificial noise signals may include at least one of the number of channel estimation instances or the noise variance value associated with the multiple artificial noise signals. In another example, 1316 may be performed by... Figure 10 The location neighbor radio device in the set of 1004 performs the calculation based on the estimated SNR value, which can calculate at least one of the number of channel estimation instances or the noise variance value. The indication of the ability to measure the set of location signals based on multiple artificial noise signals may include at least one of the number of channel estimation instances or the noise variance value associated with the multiple artificial noise signals. Furthermore, 1316 may be performed by... Figure 18 or Figure 19 Component 198 is executed.

[0152] Figure 14 This is a flowchart 1400 of a wireless communication method. This method can be performed by network entities (e.g., base station 102, base station 310; wireless device 402, wireless device 406, network entity 908; positioning network entity 1006; network entity 1802, network entity 1002, network entity 1160). At 1402, the network entity can send a first configuration for receiving a set of positioning signals. For example, 1402 can be performed by… Figure 10by the positioning network entity 1006 in 1402, which can transmit, to the set of positioning neighboring wireless devices 1004, the first configuration for receiving the set of positioning signals 1024 as the set of configurations 1014, or to the positioning target wireless device 1002 as the set of configurations 1018. Further, 1402 can be performed by the component 199 in Figure 19 or Figure 20 .

[0153] At 1404, the network entity can transmit a second configuration for measuring the set of positioning signals for training the positioning model, where the second configuration can include a configuration to measure the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. For example, 1404 can be performed by the positioning network entity 1006 in 1402, which can transmit, to the set of positioning neighboring wireless devices 1004, the second configuration for measuring the set of positioning signals 1024 for training the positioning model as the set of configurations 1014, or to the positioning target wireless device 1002 as the set of configurations 1018. The second configuration can include a configuration to measure the set of positioning signals 1024 based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. Further, 1404 can be performed by the component 199 in Figure 10 or Figure 19 . Figure 20

[0154] Figure 15 is a flow diagram of a method of wireless communication 1500. The method can be performed by a network entity (e.g., the base station 102, the base station 310; the wireless device 402, the wireless device 406, the network entity 908; the positioning network entity 1006; the network entity 1802, the network entity 1002, the network entity 1160). At 1502, the network entity can transmit a first configuration for receiving a set of positioning signals. For example, 1502 can be performed by the positioning network entity 1006 in 1502, which can transmit, to the set of positioning neighboring wireless devices 1004, the first configuration for receiving the set of positioning signals 1024 as the set of configurations 1014, or to the positioning target wireless device 1002 as the set of configurations 1018. Further, 1502 can be performed by the component 199 in Figure 10 or Figure 19 . Figure 20

[0155] At 1504, the network entity can transmit a second configuration for measuring the set of positioning signals for training the positioning model, where the second configuration can include a configuration to measure the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. For example, 1504 can be performed by the positioning network entity 1006 in 1502, which can transmit, to the set of positioning neighboring wireless devices 1004, the second configuration for measuring the set of positioning signals 1024 for training the positioning model as the set of configurations 1014, or to the positioning target wireless device 1002 as the set of configurations 1018. The second configuration can include a configuration to measure the set of positioning signals 1024 based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. Further, 1404 can be performed by the component 199 in Figure 10 ​​by the positioning network entity 1006 in FIG. 10A, which can transmit the second configuration for receiving the set of positioning signals 1024 for training the positioning model as the set of configurations 1014 to the set of positioning neighboring wireless devices 1004 or as the set of configurations 1018 to the positioning target wireless device 1002. The second configuration can include a configuration to measure the set of positioning signals 1024 based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. Further, 1504 can be performed by the component 199 in FIG. 19. Figure 19 or Figure 20 in FIG. 19.

[0156] At 1506, the network entity can receive the measured set of positioning signals in response to the transmission of the second configuration. For example, 1506 can be performed by the positioning network entity 1006 in FIG. 10A, which can receive the measured set of positioning signals in response to the transmission of the second configuration. Further, 1506 can be performed by the component 199 in FIG. 19. Figure 10 or Figure 19 in FIG. 19. Figure 20 At 1508, the network entity can output the measured set of positioning signals for training the positioning model. For example, 1508 can be performed by the positioning network entity 1006 in FIG. 10A, which can output the measured set of positioning signals for training the positioning model. Further, 1508 can be performed by the component 199 in FIG. 19.

[0157] Figure 10 At 1510, the network entity can transmit a configuration message that can include the first configuration and the second configuration. For example, 1510 can be performed by the positioning network entity 1006 in FIG. 10A, which can transmit a configuration message that can include the first configuration and the second configuration. Further, 1510 can be performed by the component 199 in FIG. 19. Figure 19 or Figure 20 in FIG. 19.

[0158] At 1512, the network entity can transmit a first configuration message that can include the first configuration. For example, 1512 can be performed by the positioning network entity 1006 in FIG. 10A, which can transmit a first configuration message that can include the first configuration. Further, 1512 can be performed by the component 199 in FIG. 19. Figure 10 or Figure 19 in FIG. 19. Figure 20 At 1514, the network entity can transmit a second configuration message that can include the second configuration. For example, 1514 can be performed by the positioning network entity 1006 in FIG. 10A, which can transmit a second configuration message that can include the second configuration. Further, 1514 can be performed by the component 199 in FIG. 19.

[0159] Figure 10 At 1514, the network entity can transmit a second configuration message that can include the second configuration. For example, 1514 can be performed by the positioning network entity 1006 in FIG. 10A, which can transmit a second configuration message that can include the second configuration. Further, 1514 can be performed by the component 199 in FIG. 19. Figure 19 or Figure 20 in FIG. 19.

[0160] At 1514, the network entity can transmit a second configuration message that can include the second configuration. For example, 1514 can be performed by the positioning network entity 1006 in FIG. 10A, which can transmit a second configuration message that can include the second configuration. Further, 1514 can be performed by the component 199 in FIG. 19. Figure 10 ​​The location network entity 1006 in the system performs this action, and the location network entity can send a second configuration message that may include a second configuration. Furthermore, 1514 can be... Figure 19 or Figure 20 Component 199 is executed.

[0161] At point 1516, the network entity can train a localization model at that point based on the measured set of localization signals. For example, point 1516 can be generated by... Figure 10 The positioning network entity 1006 in the middle performs the task, which can train a positioning model at the network entity based on the measured set of positioning signals. Furthermore, 1516 can be executed by... Figure 19 or Figure 20 Component 199 is executed.

[0162] At point 1518, the network entity can send the measured set of localization signals to the training entity for training the localization model. For example, 1518 can be... Figure 10 The positioning network entity 1006 in the middle executes this, which can send the measured set of positioning signals to the training entity for training the positioning model. Furthermore, 1518 can be... Figure 19 or Figure 20 Component 199 is executed.

[0163] Figure 16 This is a flowchart 1600 of a wireless communication method. This method can be performed by network entities (e.g., base station 102, base station 310; wireless device 402, wireless device 406, network entity 908; positioning network entity 1006; network entity 1802, network entity 1002, network entity 1160). At 1602, the network entity can send a first configuration for receiving a set of positioning signals. For example, 1602 can be performed by… Figure 10 The positioning network entity 1006 executes this, which can send a first configuration for receiving a set of positioning signals 1024 as a configuration set 1014 to a set of neighboring wireless devices 1004, or as a configuration set 1018 to a target wireless device 1002. Furthermore, 1602 can be... Figure 19 or Figure 20 Component 199 is executed.

[0164] At 1604, the network entity may send a second configuration for measuring a set of positioning signals to train a positioning model, wherein the second configuration may include a configuration for measuring the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. For example, 1604 may be provided by Figure 10by the positioning network entity 1006 in the positioning network entity 1006, which can transmit the second configuration for receiving the set of positioning signals 1024 for training the positioning model as the set of configurations 1014 to the set of positioning neighboring wireless devices 1004 or as the set of configurations 1018 to the positioning target wireless device 1002. The second configuration can include a configuration to measure the set of positioning signals 1024 based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. Further, 1604 can be performed by the component 199 in the apparatus 199. Figure 19 or Figure 20 in the apparatus 199.

[0165] At 1606, the network entity can transmit the second configuration to the first wireless device. For example, 1606 can be performed by the positioning network entity 1006 in the positioning network entity 1006, which can transmit the second configuration to the first wireless device. Further, 1606 can be performed by the component 199 in the apparatus 199. Figure 10 Figure 19 or Figure 20 in the apparatus 199.

[0166] At 1608, the network entity can receive the first measured set of positioning signals in response to the transmission of the second configuration to the first wireless device. For example, 1608 can be performed by the positioning network entity 1006 in the positioning network entity 1006, which can receive the first measured set of positioning signals in response to the transmission of the second configuration to the first wireless device. Further, 1608 can be performed by the component 199 in the apparatus 199. Figure 10 Figure 19 or Figure 20 in the apparatus 199.

[0167] At 1610, the network entity can output the first measured set of positioning signals and the second measured set of positioning signals for training the positioning model. For example, 1610 can be performed by the positioning network entity 1006 in the positioning network entity 1006, which can output the first measured set of positioning signals and the second measured set of positioning signals for training the positioning model. Further, 1610 can be performed by the component 199 in the apparatus 199. Figure 10 Figure 19 or Figure 20 in the apparatus 199.

[0168] At 1612, the network entity can transmit the second configuration to the second wireless device. For example, 1612 can be performed by the positioning network entity 1006 in the positioning network entity 1006, which can transmit the second configuration to the second wireless device. Further, 1612 can be performed by the component 199 in the apparatus 199. Figure 10 Figure 19 or Figure 20 in the apparatus 199.

[0169] At 1614, the network entity can receive the second measured set of positioning signals in response to the transmission of the second configuration to the second wireless device. For example, 1614 can be performed by the positioning network entity 1006 in the positioning network entity 1006, which can receive the second measured set of positioning signals in response to the transmission of the second configuration to the second wireless device. Further, 1614 can be performed by the component 199 in the apparatus 199. Figure 10 ​​​​The positioning network entity 1006 in the middle is executed, which can receive the second measured set of positioning signals in response to the transmission of the second configuration to the second wireless device. Furthermore, 1614 can be... Figure 19 or Figure 20 Component 199 is executed.

[0170] At point 1616, network entities can train a localization model for that network entity based on the measured set of localization signals. For example, point 1616 can be derived from... Figure 10 The positioning network entity 1006 in the middle executes this, and the positioning network entity can train a positioning model at the network entity based on the measured set of positioning signals. Furthermore, 1616 can be... Figure 19 or Figure 20 Component 199 is executed.

[0171] At point 1618, the network entity can send the measured set of localization signals to the training entity for training the localization model. For example, 1618 can be generated by... Figure 10 The positioning network entity 1006 in the middle executes this, which can send the measured set of positioning signals to the training entity for training the positioning model. Furthermore, 1618 can be... Figure 19 or Figure 20 Component 199 is executed.

[0172] At 1620, the network entity may send a configuration message to the radio device, which may include a second configuration, wherein the radio device may include a UE or a PRU, and wherein the configuration message may include an LPP message. For example, 1620 may be... Figure 10 The positioning network entity 1006 performs this action, and this positioning network entity can send a configuration message, which may include a second configuration, to a radio device, wherein the radio device may include a UE or a PRU, and wherein the configuration message may include an LPP message. Furthermore, 1620 may be... Figure 19 or Figure 20 Component 199 is executed.

[0173] At point 1622, a network entity may send a configuration message to a wireless device that may include a second configuration, wherein the wireless device may include a network node, and wherein the configuration message may include an NRPP message. For example, 1622 may be... Figure 10 The positioning network entity 1006 performs this action, and the positioning network entity can send a configuration message, which may include a second configuration, to a wireless device, wherein the wireless device may include a network node, and the configuration message may include an NRPP message. Furthermore, 1622 may be... Figure 19 or Figure 20 Component 199 is executed.

[0174] Figure 17is a flowchart 1700 of a method of wireless communication. The method can be performed by a network entity (e.g., the base station 102, the base station 310; the wireless device 402, the wireless device 406, the network entity 908; the positioning network entity 1006; the network entity 1802, the network entity 1002, the network entity 1160). At 1702, the network entity can transmit an SNR mapping that relates at least one SNR value to at least one number of channel estimates, where a number of channel estimate instances associated with a plurality of sparse pilot masks can be based on the SNR mapping and an SNR value associated with a wireless device. For example, 1702 can be performed by the positioning network entity 1006 in FIG. 13D that can transmit an SNR mapping that relates at least one SNR value to at least one number of channel estimates, where a number of channel estimate instances associated with a plurality of sparse pilot masks can be based on the SNR mapping and an SNR value associated with a wireless device. Further, 1702 can be performed by the component 199 in FIG. 13E or in FIG. 13F. Figure 10 is a flowchart 1700 of a method of wireless communication. The method can be performed by a network entity (e.g., the base station 102, the base station 310; the wireless device 402, the wireless device 406, the network entity 908; the positioning network entity 1006; the network entity 1802, the network entity 1002, the network entity 1160). At 1702, the network entity can transmit an SNR mapping that relates at least one SNR value to at least one number of channel estimates, where a number of channel estimate instances associated with a plurality of sparse pilot masks can be based on the SNR mapping and an SNR value associated with a wireless device. For example, 1702 can be performed by the positioning network entity 1006 in FIG. 13D that can transmit an SNR mapping that relates at least one SNR value to at least one number of channel estimates, where a number of channel estimate instances associated with a plurality of sparse pilot masks can be based on the SNR mapping and an SNR value associated with a wireless device. Further, 1702 can be performed by the component 199 in FIG. 13E or in FIG. 13F. Figure 19 Figure 20 is a flowchart 1700 of a method of wireless communication. The method can be performed by a network entity (e.g., the base station 102, the base station 310; the wireless device 402, the wireless device 406, the network entity 908; the positioning network entity 1006; the network entity 1802, the network entity 1002, the network entity 1160). At 1702, the network entity can transmit an SNR mapping that relates at least one SNR value to at least one number of channel estimates, where a number of channel estimate instances associated with a plurality of sparse pilot masks can be based on the SNR mapping and an SNR value associated with a wireless device. For example, 1702 can be performed by the positioning network entity 1006 in FIG. 13D that can transmit an SNR mapping that relates at least one SNR value to at least one number of channel estimates, where a number of channel estimate instances associated with a plurality of sparse pilot masks can be based on the SNR mapping and an SNR value associated with a wireless device. Further, 1702 can be performed by the component 199 in FIG. 13E or in FIG. 13F.

[0175] At 1704, the network entity can receive a capability message that can include an indication of a capability of the wireless device to measure a set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals, where the indication of the capability to measure the set of positioning signals based on the plurality of sparse pilot masks can include a number of channel estimate instances. For example, 1704 can be performed by the positioning network entity 1006 in FIG. 13D that can receive a capability message that can include an indication of a capability of the wireless device to measure a set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals, where the indication of the capability to measure the set of positioning signals based on the plurality of sparse pilot masks can include a number of channel estimate instances. Further, 1704 can be performed by the component 199 in FIG. 13E or in FIG. 13F. Figure 10 is a flowchart 1700 of a method of wireless communication. The method can be performed by a network entity (e.g., the base station 102, the base station 310; the wireless device 402, the wireless device 406, the network entity 908; the positioning network entity 1006; the network entity 1802, the network entity 1002, the network entity 1160). At 1702, the network entity can transmit an SNR mapping that relates at least one SNR value to at least one number of channel estimates, where a number of channel estimate instances associated with a plurality of sparse pilot masks can be based on the SNR mapping and an SNR value associated with a wireless device. For example, 1702 can be performed by the positioning network entity 1006 in FIG. 13D that can transmit an SNR mapping that relates at least one SNR value to at least one number of channel estimates, where a number of channel estimate instances associated with a plurality of sparse pilot masks can be based on the SNR mapping and an SNR value associated with a wireless device. Further, 1702 can be performed by the component 199 in FIG. 13E or in FIG. 13F. Figure 19 Figure 20 At 1706, the network entity can configure a second configuration to measure the set of positioning signals based on at least one of the plurality of sparse pilot masks or the plurality of artificial noise signals based on the indication of the capability. For example, 1706 can be performed by the positioning network entity 1006 in FIG. 13D that can configure a second configuration to measure the set of positioning signals based on at least one of the plurality of sparse pilot masks or the plurality of artificial noise signals based on the indication of the capability. Further, 1706 can be performed by the component 199 in FIG. 13E or in FIG. 13F.

[0176] is a flowchart 1700 of a method of wireless communication. The method can be performed by a network entity (e.g., the base station 102, the base station 310; the wireless device 402, the wireless device 406, the network entity 908; the positioning network entity 1006; the network entity 1802, the network entity 1002, the network entity 1160). At 1702, the network entity can transmit an SNR mapping that relates at least one SNR value to at least one number of channel estimates, where a number of channel estimate instances associated with a plurality of sparse pilot masks can be based on the SNR mapping and an SNR value associated with a wireless device. For example, 1702 can be performed by the positioning network entity 1006 in FIG. 13D that can transmit an SNR mapping that relates at least one SNR value to at least one number of channel estimates, where a number of channel estimate instances associated with a plurality of sparse pilot masks can be based on the SNR mapping and an SNR value associated with a wireless device. Further, 1702 can be performed by the component 199 in FIG. 13E or in FIG. 13F. Figure 10 Figure 19 At 1706, the network entity can configure a second configuration to measure the set of positioning signals based on at least one of the plurality of sparse pilot masks or the plurality of artificial noise signals based on the indication of the capability. For example, 1706 can be performed by the positioning network entity 1006 in FIG. 13D that can configure a second configuration to measure the set of positioning signals based on at least one of the plurality of sparse pilot masks or the plurality of artificial noise signals based on the indication of the capability. Further, 1706 can be performed by the component 199 in FIG. 13E or in FIG. 13F. Figure 20

[0177] ​​​​At 1708, the network entity can transmit a first configuration for receiving a set of positioning signals. For example, 1708 can be performed by Figure 10 the positioning network entity 1006 in FIG. 10, which can transmit the first configuration for receiving the set of positioning signals 1024 to the set of positioning neighboring wireless devices 1004 as the set of configurations 1014 or to the positioning target wireless device 1002 as the set of configurations 1018. Further, 1708 can be performed by Figure 19 or Figure 20 component 199 in FIG. 19.

[0178] At 1710, the network entity can transmit a second configuration for measuring the set of positioning signals for training a positioning model. For example, 1710 can be performed by Figure 10 the positioning network entity 1006 in FIG. 10, which can transmit the second configuration for receiving the set of positioning signals 1024 for training a positioning model to the set of positioning neighboring wireless devices 1004 as the set of configurations 1014 or to the positioning target wireless device 1002 as the set of configurations 1018. The second configuration can include a configuration to measure the set of positioning signals 1024 based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. Further, 1710 can be performed by Figure 19 or Figure 20 component 199 in FIG. 19.

[0179] Figure 18is a diagram 1800 that illustrates an example of a hardware implementation for an apparatus 1804. The apparatus 1804 can be a UE, a component of a UE, or can implement UE functionality. In some aspects, the apparatus 1804 can include a cellular baseband processor 1824 (also referred to as a modem) coupled with one or more transceivers 1822 (e.g., cellular RF transceivers). The cellular baseband processor 1824 can include on-chip memory 1824'. In some aspects, the apparatus 1804 can further include one or more Subscriber Identity Modules (SIM) cards 1820, and an application processor 1806 coupled with a secure digital (SD) card 1808 and a screen 1810. The application processor 1806 can include on-chip memory 1806'. In some aspects, the apparatus 1804 can further include a Bluetooth module 1812, a WLAN module 1814, a SPS module 1816 (e.g., a GNSS module), one or more sensor modules 1818 (e.g., a barometric pressure sensor / altimeter; a motion sensor such as an inertial measurement unit (IMU), a gyroscope, and / or an accelerometer; a light detection and ranging (LIDAR), a radio detection and ranging (RADAR), a sound navigation and ranging (SONAR), a magnetometer, audio, and / or other technology for positioning), an additional memory module 1826, a power source 1830, and / or a camera 1832. The Bluetooth module 1812, the WLAN module 1814, and the SPS module 1816 can include on-chip transceivers (TRXs) (or in some cases, only receivers (RXs)). The Bluetooth module 1812, the WLAN module 1814, and the SPS module 1816 can include their own dedicated antennas and / or communicate using the antennas 1880. The cellular baseband processor 1824 communicates with the UE 104 and / or with a RU associated with the network entity 1802 via the one or more antennas 1880 through the transceiver 1822. The cellular baseband processor 1824 and the application processor 1806 can each include computer-readable media / memory 1824', 1806', respectively. The additional memory module 1826 can also be considered a computer-readable medium / memory. Each computer-readable medium / memory 1824', 1806', 1826 can be non-transitory. The cellular baseband processor 1824 and the application processor 1806 each are responsible for general processing, including the execution of software stored on the computer-readable media / memory. The software, when executed by the cellular baseband processor 1824 / application processor 1806, causes the cellular baseband processor 1824 / application processor 1806 to perform the various functions described supra. The computer-readable media / memory can also be used for storing data that is manipulated by the cellular baseband processor 1824 / application processor 1806 when executing software.The cellular baseband processor 1824 / application processor 1806 can be a component of the UE 350 and can include the memory 360 and / or at least one of the TX processor 368, the RX processor 356, and the controller / processor 359. In one configuration, the apparatus 1804 can be a processor chip (modem and / or application) and only include the cellular baseband processor 1824 and / or the application processor 1806, while in another configuration, the apparatus 1804 can be the entire UE (e.g., see FIG. 3.1) and include the additional modules of the UE 350. Figure 3 In either configuration, the apparatus 1804 can be a single

[0180] As discussed above, the component 198 can be configured to receive a set of positioning signals. The component 198 can be configured to measure the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. The component 198 can be configured to output the measured set of positioning signals for training a positioning model. The component 198 can be within the cellular baseband processor 1824, the application processor 1806, or both the cellular baseband processor 1824 and the application processor 1806. The component 198 can be one or more hardware components specifically configured to carry out the stated processes / algorithm, implemented by one or more processors configured to perform the stated processes / algorithm, stored within a computer-readable medium for implementation by one or more processors, or some combination thereof. As shown, the apparatus 1804 can include a variety of components configured for various functions. In one configuration, the apparatus 1804 (and in particular the cellular baseband processor 1824 and / or the application processor 1806) can include means for receiving a set of positioning signals. The apparatus 1804 can include means for measuring the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. The apparatus 1804 can include means for outputting the measured set of positioning signals for training a positioning model. The apparatus 1804 can include means for outputting the measured set of positioning signals by training a positioning model at the wireless device based on the measured set of positioning signals. The apparatus 1804 can include means for outputting the measured set of positioning signals by sending the measured set of positioning signals to a training entity for training a positioning model. The apparatus 1804 can include means for receiving a configuration message from a network entity, the configuration message including a configuration of at least some of a plurality of sparse pilot masks or at least some of a plurality of artificial noise signals. The network entity can include an LMF. The apparatus 1804 can include a UE or a PRU. The configuration message can include an LPP message. The apparatus 1804 can include means for sending a capability message including an indication of a capability to measure a set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. Receiving the configuration can include receiving the configuration based on the indication of the capability to measure the set of positioning signals based on at least one of the plurality of sparse pilot masks or the plurality of artificial noise signals. The indication of the capability to measure the set of positioning signals based on the plurality of sparse pilot masks can include at least one of a number of channel estimation instances, a set of sparse patterns including the plurality of sparse pilot masks, or a set of measurement gap conditions for computing a plurality of channel estimates associated with the plurality of sparse pilot masks. The apparatus 1804 can include means for receiving an SNR mapping relating at least one SNR value to at least one number of channel estimates. The apparatus 1804 can include means for estimating an SNR value associated with reception of the set of positioning signals.The apparatus 1804 can include means for selecting a number of channel estimation instances from an SNR map based on the estimated SNR value. The indication of the capability to measure the set of positioning signals based on the plurality of artificial noise signals can include at least one of the number of channel estimation instances, a noise variance value, a set of noise distribution patterns including the plurality of artificial noise signals, or a set of measurement gap conditions for computing a plurality of channel estimates associated with the plurality of artificial noise signals. The apparatus 1804 can include means for estimating an SNR value associated with reception of the set of positioning signals. The apparatus 1804 can include means for computing at least one of the number of channel estimation instances or the noise variance value based on the estimated SNR value. The apparatus 1804 can include a UE or a PRU. The set of positioning signals can include a set of PRSs. The apparatus 1804 can include means for measuring the set of positioning signals based on a plurality of sparse pilot masks by: simulating a first set of impaired positioning signals by masking the set of positioning signals based on a first sparse pilot mask of the plurality of sparse pilot masks, simulating a second set of impaired positioning signals by masking the set of positioning signals based on a second sparse pilot mask of the plurality of sparse pilot masks, measuring the first set of impaired positioning signals, and measuring the second set of impaired positioning signals. The apparatus 1804 can include means for measuring the set of positioning signals based on a plurality of artificial noise signals by: simulating a first set of impaired positioning signals by combining the set of positioning signals with a first artificial noise signal of the plurality of artificial noise signals, simulating a second set of impaired positioning signals by combining the set of positioning signals with a second artificial noise signal of the plurality of artificial noise signals, measuring the first set of impaired positioning signals, and measuring the second set of impaired positioning signals. The means can be the components 198 of the apparatus 1804 configured to perform the functions recited by the means. As described above, the apparatus 1804 can include the TX processor 368, the RX processor 356, and the controller / processor 359. Accordingly, in one configuration, the means can be the TX processor 368, the RX processor 356, and / or the controller / processor 359 configured to perform the functions recited by the means.

[0181] Figure 19is a diagram 1900 that is an example of a hardware implementation for an example network entity 1902. The network entity 1902 can be a BS, a component of a BS, or can implement BS functionality. The network entity 1902 can include at least one of a CU 1910, a DU 1930, or a RU 1940. For example, depending on the layer functionality handled by the component 199, the network entity 1902 can include the CU 1910; both the CU 1910 and the DU 1930; each of the CU 1910, the DU 1930, and the RU 1940; the DU 1930; both the DU 1930 and the RU 1940; or the RU 1940. The CU 1910 can include a CU processor 1912. The CU processor 1912 can include on-chip memory 1912'. In some aspects, the CU 1910 can also include an additional memory module 1914 and a communication interface 1918. The CU 1910 communicates with the DU 1930 over a backhaul link, such as an Fl interface. The DU 1930 can include a DU processor 1932. The DU processor 1932 can include on-chip memory 1932'. In some aspects, the DU 1930 can also include an additional memory module 1934 and a communication interface 1938. The DU 1930 communicates with the RU 1940 over a front-haul link. The RU 1940 can include a RU processor 1942. The RU processor 1942 can include on-chip memory 1942'. In some aspects, the RU 1940 can also include an additional memory module 1944, one or more transceivers 1946, antennas 1980, and a communication interface 1948. The RU 1940 communicates with the UE 104. The on-chip memories 1912', 1932', 1942' and the additional memory modules 1914, 1934, 1944 can each be considered a computer- readable medium / memory. Each computer-readable medium / memory can be non-transitory. Each of the processors 1912, 1932, 1942 is responsible for general processing, including the execution of software stored on the computer-readable medium / memory. The software, when executed by the corresponding processor, causes the processor to perform the various functions described supra. The computer-readable medium / memory can also be used for storing data that is manipulated by the processor when executing software.

[0182] As discussed above, the component 198 can be configured to receive a set of positioning signals. The component 198 can be configured to measure the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. The component 198 can be configured to output the measured set of positioning signals for training a positioning model. The component 198 can be within one or more processors of one or more of the CU 1910, the DU 1930, and the RU 1940. The component 198 can be one or more hardware components specifically configured to perform the stated processes / algorithms, implemented by one or more processors configured to perform the stated processes / algorithms, stored within a computer-readable medium for implementation by one or more processors, or some combination thereof. The network entity 1902 can include multiple components that are configured to perform various functions. In one configuration, the network entity 1902 can include means for receiving a set of positioning signals. The network entity 1902 can include means for measuring the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. The network entity 1902 can include means for outputting the measured set of positioning signals for training a positioning model. The network entity 1902 can include means for outputting the measured set of positioning signals by training a positioning model at a wireless device based on the measured set of positioning signals. The network entity 1902 can include means for outputting the measured set of positioning signals by sending the measured set of positioning signals to a training entity for training a positioning model. The network entity 1902 can include means for receiving a configuration message from another network entity, the configuration message including a configuration of at least some of a plurality of sparse pilot masks or at least some of a plurality of artificial noise signals. The other network entity can include an LMF. The network entity 1902 can include a network node. The configuration message can include an NRPP message. The network entity 1902 can include means for sending a capability message including an indication of a capability to measure a set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. The receiving the configuration can include receiving the configuration based on the indication of the capability to measure the set of positioning signals based on at least one of the plurality of sparse pilot masks or the plurality of artificial noise signals. The indication of the capability to measure the set of positioning signals based on the plurality of sparse pilot masks can include at least one of a number of channel estimation instances, a set of sparse patterns including the plurality of sparse pilot masks, or a set of measurement gap conditions for computing a plurality of channel estimates associated with the plurality of sparse pilot masks. The network entity 1902 can include means for receiving an SNR mapping relating at least one SNR value to at least one number of channel estimates. The network entity 1902 can include means for estimating an SNR value associated with reception of the set of positioning signals.The network entity 1902 can include means for selecting a number of channel estimation instances from an SNR map based on an estimated SNR value. The indication of the capability to measure the set of positioning signals based on the plurality of artificial noise signals can include at least one of: the number of channel estimation instances, a noise variance value, a set of noise profile patterns including the plurality of artificial noise signals, or a set of measurement gap conditions for computing a plurality of channel estimates associated with the plurality of artificial noise signals. The network entity 1902 can include means for estimating an SNR value associated with reception of the set of positioning signals. The network entity 1902 can include means for computing at least one of the number of channel estimation instances or the noise variance value based on the estimated SNR value. The network entity 1902 can include a network node. The set of positioning signals can include a set of SRSs. The network entity 1902 can include means for measuring the set of positioning signals based on a plurality of sparse pilot masks by: simulating a first set of impaired positioning signals by masking the set of positioning signals based on a first sparse pilot mask of the plurality of sparse pilot masks, simulating a second set of impaired positioning signals by masking the set of positioning signals based on a second sparse pilot mask of the plurality of sparse pilot masks, measuring the first set of impaired positioning signals, and measuring the second set of impaired positioning signals. The network entity 1902 can include means for measuring the set of positioning signals based on a plurality of artificial noise signals by: simulating a first set of impaired positioning signals by combining the set of positioning signals with a first artificial noise signal of the plurality of artificial noise signals, simulating a second set of impaired positioning signals by combining the set of positioning signals with a second artificial noise signal of the plurality of artificial noise signals, measuring the first set of impaired positioning signals, and measuring the second set of impaired positioning signals. The means can be the components 198 of the network entity 1902 configured to perform the functions recited by the means. As described above, the network entity 1902 can include the TX processor 316, the RX processor 370, and the controller / processor 375. Accordingly, in one configuration, the means can be the TX processor 316, the RX processor 370, and / or the controller / processor 375 configured to perform the functions recited by the means.

[0183] As discussed above, the component 199 can be configured to transmit a first configuration for receiving a set of positioning signals. The measurement error configuration component 199 can be configured to transmit a second configuration for measuring the set of positioning signals for training a positioning model. The second configuration can include a configuration to measure the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. The component 199 can be within one or more processors of one or more of the CU 1910, the DU 1930, and the RU 1940. The component 199 can be one or more hardware components specifically configured to perform the stated processes / algorithms, implemented by one or more processors configured to perform the stated processes / algorithms, stored within a computer-readable medium for implementation by one or more processors, or some combination thereof. The network entity 1902 can include a variety of components configured for various functions. In one configuration, the network entity 1902 can include means for transmitting a first configuration for receiving a set of positioning signals. The network entity 1902 can include means for transmitting a second configuration for measuring the set of positioning signals for training a positioning model. The second configuration can include a configuration to measure the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. The network entity 1902 can include means for transmitting the first configuration and transmitting the second configuration by transmitting a configuration message including the first configuration and the second configuration. The network entity 1902 can include means for transmitting the first configuration by transmitting a first configuration message including the first configuration. The network entity 1902 can include means for transmitting the second configuration by transmitting a second configuration message including the second configuration. The network entity 1902 can include means for receiving a measured set of positioning signals in response to transmission of the second configuration. The network entity 1902 can include means for outputting the measured set of positioning signals for training a positioning model. The network entity 1902 can include means for outputting the measured set of positioning signals based on the measured set of positioning signals training a positioning model at the network entity. The network entity 1902 can include means for outputting the measured set of positioning signals by transmitting the measured set of positioning signals to a training entity for training a positioning model. The network entity 1902 can include means for transmitting the second configuration by transmitting the second configuration to a first wireless device and a second wireless device. The network entity 1902 can include means for receiving a first measured set of positioning signals from the first wireless device in response to transmission of the second configuration to the first wireless device. The network entity 1902 can include means for receiving a second measured set of positioning signals from the second wireless device in response to transmission of the second configuration to the second wireless device. The network entity 1902 can include means for outputting the first measured set of positioning signals and the second measured set of positioning signals for training a positioning model. The first measured set of positioning signals can be associated with a first channel estimation implementation of a plurality of channel estimation implementations.The second measured set of positioning signals can be associated with a second channel estimation implementation of the plurality of channel estimation implementations. The plurality of channel estimation implementations can include at least one of a minimum mean square error (MMSE) channel estimation implementation, a least squares (LS) channel estimation implementation, or a likelihood-based channel estimation implementation. The network entity 1902 can include an LMF. The network entity 1902 can include means for transmitting the second configuration by transmitting, to the wireless device, a configuration message including the second configuration. The wireless device can include a UE or a PRU. The configuration message can include an LPP message. The wireless device can include a network node. The configuration message can include an NRPP message. The network entity 1902 can include means for receiving a capability message including an indication of a capability of the wireless device to measure a set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. The network entity 1902 can include means for configuring the second configuration based on the indication of the capability to measure the set of positioning signals based on at least one of the plurality of sparse pilot masks or the plurality of artificial noise signals. The indication of the capability to measure the set of positioning signals based on the plurality of sparse pilot masks can include at least one of a number of channel estimation instances, a set of sparse patterns including the plurality of sparse pilot masks, or a set of measurement gap conditions for computing a plurality of channel estimates associated with the plurality of sparse pilot masks. The network entity 1902 can include means for transmitting an SNR map relating at least one SNR value to at least one number of channel estimation instances. The number of channel estimation instances can be based on the SNR map and an SNR value associated with the wireless device. The indication of the capability to measure the set of positioning signals based on the plurality of artificial noise signals can include at least one of a number of channel estimation instances, a noise variance value, a set of noise distribution patterns including the plurality of artificial noise signals, or a set of measurement gap conditions for computing a plurality of channel estimates associated with the plurality of artificial noise signals. At least one of the number of channel estimation instances or the noise variance value can be based on an SNR value associated with the wireless device. The wireless device can include a UE, a PRU, or a network node. The set of positioning signals can include a set of SRS or a set of PRS. The means can be a component of the network entity 1902 configured to perform the function(s) recited by the means. As described above, the network entity 1902 can include the TX processor 316, the RX processor 370, and the controller / processor 375. Accordingly, in one configuration, the means can be the TX processor 316, the RX processor 370, and / or the controller / processor 375 configured to perform the functions recited by the means.

[0184] Figure 20is a diagram 2000 that is an example of an example hardware implementation of the network entity 2060. In one example, the network entity 2060 can be within the core network 120. The network entity 2060 can include a network processor 2012. The network processor 2012 can include on-chip memory 2012'. In some aspects, the network entity 2060 can also include an additional memory module 2014. The network entity 2060 communicates with the CU 2002 directly (e.g., a backhaul link) or indirectly (e.g., through a RIC) via a network interface 2080. The on-chip memory 2012' and the additional memory module 2014 can each be considered a computer- readable medium / memory. Each computer-readable medium / memory can be non-transitory. The processor 2012 is responsible for general processing, including the execution of software stored on the computer-readable medium / memory. The software, when executed by the corresponding processor, causes the processor to perform the various functions described supra. The computer-readable medium / memory can also be used for storing data that is manipulated by the processor when executing software.

[0185] As discussed above, the component 199 can be configured to transmit a first configuration for receiving a set of positioning signals. The component 199 can be configured to transmit a second configuration for measuring the set of positioning signals for training a positioning model. The second configuration can include a configuration to measure the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. The component 199 can be within the processor 2012. The component 199 can be one or more hardware components specifically configured to perform the stated processes / algorithms, implemented by one or more processors configured to perform the stated processes / algorithms, stored within a computer-readable medium for implementation by one or more processors, or some combination thereof. The network entity 2060 can include a variety of components configured for various functions. In one configuration, the network entity 2060 can include means for transmitting a first configuration for receiving a set of positioning signals. The network entity 2060 can include means for transmitting a second configuration for measuring the set of positioning signals for training a positioning model. The second configuration can include a configuration to measure the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. The network entity 2060 can include means for transmitting the first configuration and transmitting the second configuration by transmitting a configuration message including the first configuration and the second configuration. The network entity 2060 can include means for transmitting the first configuration by transmitting a first configuration message including the first configuration. The network entity 2060 can include means for transmitting the second configuration by transmitting a second configuration message including the second configuration. The network entity 2060 can include means for receiving a measured set of positioning signals in response to transmission of the second configuration. The network entity 2060 can include means for outputting the measured set of positioning signals for training a positioning model. The network entity 2060 can include means for outputting the measured set of positioning signals based on the measured set of positioning signals training a positioning model at the network entity. The network entity 2060 can include means for outputting the measured set of positioning signals by transmitting the measured set of positioning signals to a training entity for training a positioning model. The network entity 2060 can include means for transmitting the second configuration by transmitting the second configuration to a first wireless device and a second wireless device. The network entity 2060 can include means for receiving a first measured set of positioning signals in response to transmission of the second configuration to the first wireless device. The network entity 2060 can include means for receiving a second measured set of positioning signals in response to transmission of the second configuration to the second wireless device. The network entity 2060 can include means for outputting the first measured set of positioning signals and the second measured set of positioning signals for training a positioning model. The first measured set of positioning signals can be associated with a first channel estimation implementation of a plurality of channel estimation implementations. The second measured set of positioning signals can be associated with a second channel estimation implementation of the plurality of channel estimation implementations.The multiple channel estimation implementations can include at least one of: an MMSE channel estimation implementation, an LS channel estimation implementation, or a likelihood-based channel estimation implementation. The network entity 2060 can comprise an LMF. The network entity 2060 can comprise a means for transmitting a second configuration by transmitting, to a wireless device, a configuration message comprising the second configuration. The wireless device can comprise a UE or a PRU. The configuration message can comprise an LPP message. The wireless device can comprise a network node. The configuration message can comprise an NRPP message. The network entity 2060 can comprise a means for receiving a capability message comprising an indication of a capability of the wireless device to measure a set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. The network entity 2060 can comprise a means for configuring the second configuration based on the indication of the capability to measure the set of positioning signals based on at least one of the plurality of sparse pilot masks or the plurality of artificial noise signals. The indication of the capability to measure the set of positioning signals based on the plurality of sparse pilot masks can comprise at least one of: a number of channel estimation instances, a set of sparse patterns comprising the plurality of sparse pilot masks, or a set of measurement gap conditions for computing a plurality of channel estimates associated with the plurality of sparse pilot masks. The network entity 2060 can comprise a means for transmitting an SNR map relating at least one SNR value to at least one number of channel estimation instances. The number of channel estimation instances can be based on the SNR map and an SNR value associated with the wireless device. The indication of the capability to measure the set of positioning signals based on the plurality of artificial noise signals can comprise at least one of: a number of channel estimation instances, a noise variance value, a set of noise distribution patterns comprising the plurality of artificial noise signals, or a set of measurement gap conditions for computing a plurality of channel estimates associated with the plurality of artificial noise signals. At least one of the number of channel estimation instances or the noise variance value can be based on an SNR value associated with the wireless device. The wireless device can comprise a UE, a PRU, or a network node. The set of positioning signals can comprise a set of SRS or a set of PRS. The means can be a component 199 of the network entity 2060 configured to perform the functions recited by the means.

[0186] It should be understood that the particular order or hierarchy of steps in the processes / flow diagrams disclosed are merely examples. It should be appreciated that a particular order or hierarchy of steps can be rearranged, so long as the steps involve the disclosed functions. Also, a person having ordinary skill in the art would

[0187] The preceding 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 readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other aspects. Thus, the claims are not to be limited to the aspects described herein, but are to be given the full scope defined by the language of the claims. Unless otherwise defined, a reference to a singular element includes “one or more” thereof. Terms such as “if,” “when,” and “while” do not imply direct temporal relationships or reactions. That is, the phrases “when,” “if,” and “while” do not necessarily mean that the action occurs immediately upon the occurrence of the condition or during the occurrence of the condition, but simply that the action will occur if the condition is met, without a specific or immediate temporal limitation on 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 advantageous over other aspects. Unless specifically stated otherwise, 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 the group consisting of A, B, and C,” “one or more of the group consisting of A, B, and C,” and “A, B, and / or C or any combination thereof” include number one (1) or more. Specifically, combinations such as “at least one of A, B, or C,” “one or more of A, B, or C,” “at least one of the group consisting of A, B, and C,” “one or more of the group consisting of A, B, and C,” and “A, B, and / or C or any combination thereof” can be one or more A, one or more B, one or more C, one or more A and one or more B, one or more A and one or more C, one or more B and one or more C, or one or more A, one or more B, and one or more C, where any such A, B, or C can include one or more members. A set should be interpreted to be a collection of elements that can be one or more. Thus, for a set of X, X will include one or more elements. If a first device receives data or sends data to a second device, the data can be received / sent directly between the first and second device or indirectly through a set of devices between the first and second device. A device configured to “output” data, such as a transmission, signal, or message, may, for example, transmit the data with a transceiver, communicate the data to a device that transmits the data, or output the data to a component of the device. A device configured to “obtain” data, such as a transmission, signal, or message, may, for example, receive the data with a transceiver, obtain the data from a device that receives the data, or obtain the data from a component of the device. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims.Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly identified as being "dedicated to the public." Neither is the word "module," "mechanism," "element," "device," or "means" intended to refer to a "means plus function" unless such a phrase is explicitly recited. Thus, no claim element is to be construed as a means plus function unless the element is expressly recited using the phrases "means for" or "step for."

[0188] As used herein, the phrase "based on" shall not be construed as a surrender of the equivalent phrase "based at least in part on." In other words, a phrase according to the equivalent phrase "based at least in part on" should be interpreted "based on" and / or "based at least in part on."

[0189] The following aspects are merely exemplary and can be combined with other aspects or teachings described herein without limitation.

[0190] Aspect 1 is a method of wireless communication at a wireless device, where the method includes receiving a set of positioning signals. The method also includes measuring the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals. The method also includes outputting the measured set of positioning signals for training a positioning model.

[0191] Aspect 2 is the method of aspect 1, where outputting the measured set of positioning signals includes training the positioning model at the wireless device based on the measured set of positioning signals.

[0192] Aspect 3 is the method of any of aspect 1, where outputting the measured set of positioning signals includes sending the measured set of positioning signals to a training entity for training the positioning model.

[0193] Aspect 4 is the method of any of aspects 1-3, where the method further includes receiving a configuration message from a network entity, the configuration message including a configuration of at least some of the plurality of sparse pilot masks or at least some of the plurality of artificial noise signals.

[0194] Aspect 5 is the method of any of aspect 4, where the network entity includes a location management function (LMF).

[0195] Aspect 6 is the method of any of aspects 4 or 5, where the wireless device includes a user equipment (UE) or a positioning reference unit (PRU), where the configuration message includes a long term evolution (LTE) positioning protocol (LPP) message.

[0196] Aspect 7 is the method of any of aspects 4 or 5, wherein the wireless device comprises a network node, wherein the configuration message comprises a New Radio (NR) Positioning Protocol (NRPP) message.

[0197] Aspect 8 is the method of any of aspects 4-7, wherein the method further comprises transmitting a capability message comprising an indication of a capability to measure the set of positioning signals based on at least one of the plurality of sparse pilot masks or the plurality of artificial noise signals, wherein receiving the configuration comprises receiving the configuration based on the indication of the capability to measure the set of positioning signals based on at least one of the plurality of sparse pilot masks or the plurality of artificial noise signals.

[0198] Aspect 9 is the method of aspect 8, wherein the indication of the capability to measure the set of positioning signals based on the plurality of sparse pilot masks comprises at least one of a number of channel estimation instances, a set of sparse patterns comprising the plurality of sparse pilot masks, or a set of measurement gap conditions for computing a plurality of channel estimates associated with the plurality of sparse pilot masks.

[0199] Aspect 10 is the method of aspect 9, wherein the method further comprises receiving a signal-to-noise ratio (SNR) mapping relating at least one SNR value to at least one number of channel estimates. The method further comprises estimating an SNR value associated with the reception of the set of positioning signals. The method further comprises selecting the number of channel estimation instances from the SNR mapping based on the estimated SNR value.

[0200] Aspect 11 is the method of aspect 8, wherein the indication of the capability to measure the set of positioning signals based on the plurality of artificial noise signals comprises at least one of a number of channel estimation instances, a noise variance value, a set of noise distribution patterns comprising the plurality of artificial noise signals, or a set of measurement gap conditions for computing a plurality of channel estimates associated with the plurality of artificial noise signals.

[0201] Aspect 12 is the method of aspect 11, wherein the method further comprises estimating a signal-to-noise ratio (SNR) value associated with the reception of the set of positioning signals. The method further comprises computing at least one of the number of channel estimation instances or the noise variance value based on the estimated SNR value.

[0202] Aspect 13 is the method of any of aspects 1-12, wherein the wireless device comprises a user equipment (UE), a positioning reference unit (PRU), or a network node.

[0203] Aspect 14 is the method of any one of aspects 1 through 13, wherein the set of positioning signals comprises a set of sounding reference signals (SRS) or a set of positioning reference signals (PRS).

[0204] Aspect 15 is the method of any one of aspects 1 through 14, wherein measuring the set of positioning signals based on the plurality of sparse pilot masks comprises simulating a first set of impaired positioning signals by masking the set of positioning signals based on a first sparse pilot mask of the plurality of sparse pilot masks, simulating a second set of impaired positioning signals by masking the set of positioning signals based on a second sparse pilot mask of the plurality of sparse pilot masks, measuring the first set of impaired positioning signals, and measuring the second set of impaired positioning signals.

[0205] Aspect 16 is the method of any one of aspects 1 through 15, wherein measuring the set of positioning signals based on the plurality of artificial noise signals comprises simulating a first set of impaired positioning signals by combining the set of positioning signals with a first artificial noise signal of the plurality of artificial noise signals, simulating a second set of impaired positioning signals by combining the set of positioning signals with a second artificial noise signal of the plurality of artificial noise signals, measuring the first set of impaired positioning signals, and measuring the second set of impaired positioning signals.

[0206] Aspect 17 is a method of wireless communication at a network entity, wherein the method comprises transmitting a first configuration for receiving a set of positioning signals. The method further comprises transmitting a second configuration for measuring the set of positioning signals for training a positioning model, wherein the second configuration comprises a configuration to measure the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals.

[0207] Aspect 18 is the method of aspect 17, wherein transmitting the first configuration and transmitting the second configuration comprises transmitting a configuration message comprising the first configuration and the second configuration.

[0208] Aspect 19 is the method of any one of aspects 17 or 18, wherein transmitting the first configuration comprises transmitting a first configuration message comprising the first configuration, and wherein transmitting the second configuration comprises transmitting a second configuration message comprising the second configuration.

[0209] Aspect 20 is the method of any one of aspects 17 through 19, wherein the method further comprises receiving a measured set of positioning signals in response to the transmission of the second configuration. The method further comprises outputting the measured set of positioning signals for training the positioning model.

[0210] Aspect 21 is the method of aspect 20, wherein outputting the set of measured positioning signals comprises training the positioning model at the network entity based on the set of measured positioning signals.

[0211] Aspect 22 is the method of aspect 20, wherein outputting the set of measured positioning signals comprises sending the set of measured positioning signals to a training entity for training the positioning model.

[0212] Aspect 23 is the method of any of aspects 17 through 22, wherein sending the second configuration comprises sending the second configuration to a first wireless device and a second wireless device. The method further comprises receiving a first set of measured positioning signals in response to the sending of the second configuration to the first wireless device. The method further comprises receiving a second set of measured positioning signals in response to the sending of the second configuration to the second wireless device. The method further comprises outputting the first set of measured positioning signals and the second set of measured positioning signals for training the positioning model.

[0213] Aspect 24 is the method of aspect 23, wherein the first set of measured positioning signals is associated with a first channel estimation implementation of a plurality of channel estimation implementations, wherein the second set of measured positioning signals is associated with a second channel estimation implementation of the plurality of channel estimation implementations.

[0214] Aspect 25 is the method of aspect 24, wherein the plurality of channel estimation implementations comprises at least one of a minimum mean square error (MMSE) channel estimation implementation, a least squares (LS) channel estimation implementation, or a likelihood-based channel estimation implementation.

[0215] Aspect 26 is the method of any of aspects 17 through 25, wherein the network entity comprises a location management function (LMF).

[0216] Aspect 27 is the method of any of aspects 17 through 26, wherein sending the second configuration comprises sending a configuration message comprising the second configuration to a wireless device, wherein the wireless device comprises a user equipment (UE) or a positioning reference unit (PRU), wherein the configuration message comprises a long term evolution (LTE) positioning protocol (LPP) message.

[0217] Aspect 28 is the method of any of aspects 17 through 27, wherein sending the second configuration comprises sending a configuration message comprising the second configuration to a wireless device, wherein the wireless device comprises a network node, wherein the configuration message comprises a new radio (NR) positioning protocol (NRPP) message.

[0218] Aspect 29 is the method of any of aspects 17 through 28, wherein the method further comprises: receiving a capability message comprising an indication of a capability of a wireless device to measure the set of positioning signals based on at least one of the plurality of sparse pilot masks or the plurality of artificial noise signals. The method further comprises: configuring the second configuration to measure the set of positioning signals based on at least one of the plurality of sparse pilot masks or the plurality of artificial noise signals based on the indication of the capability.

[0219] Aspect 30 is the method of claim 29, wherein the indication of the capability to measure the set of positioning signals based on the plurality of sparse pilot masks comprises at least one of: a number of channel estimation instances, a set of sparse patterns comprising the plurality of sparse pilot masks, or a set of measurement gap conditions for computing a plurality of channel estimates associated with the plurality of sparse pilot masks.

[0220] Aspect 31 is the method of aspect 30, wherein the method further comprises: transmitting a signal-to-noise ratio (SNR) mapping relating at least one SNR value to at least one number of channel estimation instances, wherein the number of channel estimation instances is based on the SNR mapping and an SNR value associated with the wireless device.

[0221] Aspect 32 is the method of aspect 29, wherein the indication of the capability to measure the set of positioning signals based on the plurality of artificial noise signals comprises at least one of: a number of channel estimation instances, a noise variance value, a set of noise distribution patterns comprising the plurality of artificial noise signals, or a set of measurement gap conditions for computing a plurality of channel estimates associated with the plurality of artificial noise signals.

[0222] Aspect 33 is the method of aspect 32, wherein at least one of the number of channel estimation instances or the noise variance value is based on a signal-to-noise ratio (SNR) value associated with the wireless device.

[0223] Aspect 34 is the method of any of aspects 29 through 33, wherein the wireless device comprises a user equipment (UE), a positioning reference unit (PRU), or a network node.

[0224] Aspect 35 is the method of any of aspects 17 through 34, wherein the set of positioning signals comprises a set of sounding reference signals (SRS) or a set of positioning reference signals (PRS).

[0225] Aspect 36 is an apparatus for wireless communication, comprising at least one memory; and at least one processor coupled to the at least one memory and configured as, alone or in any combination, to implement any of aspects 1 to 35 based at least in part on information stored in the at least one memory.

[0226] Aspect 37 is the apparatus of aspect 36, further comprising at least one of an antenna or a transceiver coupled to the at least one processor.

[0227] Aspect 38 is an apparatus for wireless communication, comprising means for implementing any of aspects 1 to 35.

[0228] Aspect 39 is a computer-readable medium (for example, a non-transitory computer- readable medium) storing computer executable code, where the code when executed by a processor causes the processor to implement any of aspects 1 to 35.

Claims

1. An apparatus for performing wireless communication at a wireless device, 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 location signal set; The location signal set is measured based on at least one of multiple sparse pilot masks or multiple artificial noise signals; and The measured set of positioning signals is output for use in training the positioning model.

2. The apparatus according to claim 1, wherein, In order to output the measured set of positioning signals, the at least one processor is configured individually or in any combination to: The localization model at the wireless device is trained based on the measured set of localization signals.

3. The apparatus of claim 1, further comprising a transceiver coupled to the at least one processor, wherein, In order to output the measured set of positioning signals, the at least one processor is configured individually or in any combination to: The measured set of positioning signals is transmitted to the training entity via the transceiver to train the positioning model.

4. The apparatus of claim 1, wherein the at least one processor is further configured, alone or in any combination, to: Receive a configuration message from a network entity, the configuration message including the configuration of at least some of the plurality of sparse pilot masks or at least some of the plurality of artificial noise signals.

5. The apparatus of claim 4, wherein the network entity includes a location management function (LMF).

6. The apparatus of claim 4, wherein the wireless device includes a user equipment (UE) or a positioning reference unit (PRU), and wherein the configuration message includes a Long Term Evolution (LTE) Positioning Protocol (LPP) message.

7. The apparatus of claim 4, wherein the wireless device includes a network node, and wherein the configuration message includes a New Radio (NR) Positioning Protocol (NRPP) message.

8. The apparatus of claim 4, wherein the at least one processor is further configured, alone or in any combination, to: Send a capability message, the capability message including an indication of the ability to measure the set of positioning signals based on at least one of the plurality of sparse pilot masks or the plurality of artificial noise signals, wherein, In order to receive the configuration, the at least one processor is configured individually or in any combination to receive the configuration based on the indication of the ability to measure the set of positioning signals based on at least one of the plurality of sparse pilot masks or the plurality of artificial noise signals.

9. The apparatus of claim 8, wherein the indication of the ability to measure the set of location signals based on the plurality of sparse pilot masks comprises at least one of: a number of channel estimation instances, a set of sparse patterns including the plurality of sparse pilot masks, or a set of measurement gap conditions for calculating a plurality of channel estimates associated with the plurality of sparse pilot masks.

10. The apparatus of claim 9, wherein the at least one processor is further configured, alone or in any combination, to: Receive signal-to-noise ratio (SNR) mapping, which correlates at least one SNR value with at least one channel estimate quantity; Estimate the SNR value associated with the reception of the location signal set; as well as The number of channel estimation instances is selected from the SNR mapping based on the estimated SNR value.

11. The apparatus of claim 8, wherein the indication of the ability to measure the set of positioning signals based on the plurality of artificial noise signals includes at least one of: the number of channel estimation instances, the noise variance value, a set of noise distribution patterns including the plurality of artificial noise signals, or a set of measurement gap conditions for calculating a plurality of channel estimates associated with the plurality of artificial noise signals.

12. The apparatus of claim 11, wherein the at least one processor is further configured, alone or in any combination, to: Estimate the signal-to-noise ratio (SNR) value associated with the received signals in the set of location signals; and The number of channel estimation instances or the noise variance value is calculated based on the estimated SNR value.

13. The apparatus of claim 1, wherein the wireless device includes a user equipment (UE), a positioning reference unit (PRU), or a network node.

14. The apparatus of claim 1, wherein the set of positioning signals includes a set of detection reference signals (SRS) or a set of positioning reference signals (PRS).

15. The apparatus according to claim 1, wherein, In order to measure the set of positioning signals based on the plurality of sparse pilot masks, the at least one processor is configured individually or in any combination to: The first set of damaged positioning signals is simulated by masking the positioning signal set based on the first sparse pilot mask among the plurality of sparse pilot masks; The second set of damaged positioning signals is simulated by masking the positioning signal set based on the second sparse pilot mask among the plurality of sparse pilot masks; The first set of measured damaged location signals; and The second set of damaged location signals is measured.

16. The apparatus according to claim 1, wherein, In order to measure the set of positioning signals based on the plurality of artificial noise signals, the at least one processor is configured individually or in any combination to: The first set of damaged positioning signals is simulated by combining the set of positioning signals with a first artificial noise signal from the plurality of artificial noise signals; A second set of damaged positioning signals is simulated by combining the set of positioning signals with a second artificial noise signal from the plurality of artificial noise signals. The first set of measured damaged location signals; and The second set of damaged location signals is measured.

17. 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: Send a first configuration for receiving a set of positioning signals; and Send a second configuration for measuring the set of positioning signals to train a positioning model, wherein the second configuration includes a configuration for measuring the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals.

18. The apparatus of claim 17, further comprising a transceiver coupled to the at least one processor, wherein, In order to send the first configuration and the second configuration, the at least one processor is configured individually or in any combination to: A configuration message including the first configuration and the second configuration is sent via the transceiver.

19. The apparatus according to claim 17, wherein, In order to send the first configuration, the at least one processor is configured individually or in any combination to send a first configuration message including the first configuration, and wherein, in order to send the second configuration, the at least one processor is configured individually or in any combination to send a second configuration message including the second configuration.

20. The apparatus of claim 17, wherein the at least one processor is further configured, alone or in any combination, to: In response to the transmission of the second configuration, a set of measured positioning signals is received; and The measured set of positioning signals is output for use in training the positioning model.

21. The apparatus according to claim 20, wherein, In order to output the measured set of positioning signals, the at least one processor is configured individually or in any combination to: The localization model at the network entity is trained based on the measured set of localization signals.

22. The apparatus according to claim 20, wherein, In order to output the measured set of positioning signals, the at least one processor is configured individually or in any combination to: The measured set of positioning signals is sent to the training entity for training the positioning model.

23. The apparatus according to claim 17, wherein, In order to transmit the second configuration, the at least one processor is configured individually or in any combination to transmit the second configuration to the first wireless device and the second wireless device, wherein the at least one processor is also configured individually or in any combination to: In response to the second configuration sending to the first wireless device, a first measured set of positioning signals is received; In response to the second configuration sending to the second wireless device, a second measured set of positioning signals is received; as well as The first set of positioning signals and the second set of positioning signals are output for training the positioning model.

24. The apparatus of claim 23, wherein the first measured set of positioning signals is associated with a first channel estimation implementation in a plurality of channel estimation implementations, and wherein the second measured set of positioning signals is associated with a second channel estimation implementation in the plurality of channel estimation implementations.

25. The apparatus of claim 24, wherein the plurality of channel estimation embodiments includes at least one of: minimum mean square error (MMSE) channel estimation embodiment, least squares (LS) channel estimation embodiment, or likelihood-based channel estimation embodiment.

26. The apparatus of claim 17, wherein the network entity includes a location management function (LMF).

27. The apparatus according to claim 17, wherein, In order to send the second configuration, the at least one processor is configured individually or in any combination to send a configuration message including the second configuration to a wireless device, wherein the wireless device includes a user equipment (UE) or a positioning reference unit (PRU), and wherein the configuration message includes a Long Term Evolution (LTE) Positioning Protocol (LPP) message.

28. The apparatus according to claim 17, wherein, In order to send the second configuration, the at least one processor is configured individually or in any combination to send a configuration message including the second configuration to a wireless device, wherein the wireless device includes a network node, and wherein the configuration message includes a New Radio (NR) Positioning Protocol (NRPP) message.

29. A method for performing wireless communication at a wireless device, the method comprising: Receive location signal set; The location signal set is measured based on at least one of multiple sparse pilot masks or multiple artificial noise signals; as well as The measured set of positioning signals is output for use in training the positioning model.

30. A method for wireless communication at a network entity, the method comprising: Send the first configuration for receiving a set of positioning signals; as well as Send a second configuration for measuring the set of positioning signals to train a positioning model, wherein the second configuration includes a configuration for measuring the set of positioning signals based on at least one of a plurality of sparse pilot masks or a plurality of artificial noise signals.

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