System and method for navigation model enhancement

Abstract

Aspects presented herein may improve performance of mobile / computer applications. Aspects presented herein may enable a mobile / computer application to distinguish entities associated with a UE or entity running a navigation application, thereby enabling the mobile / computer application (or its associated server) to have more accurate knowledge of conditions around the UE and its user. In an aspect, a network node obtains first information including at least one feature associated with a plurality of devices. The network node selects a first subset of the plurality of devices for measurement based on the at least one feature associated with the plurality of devices (or the network node may exclude a second subset of the plurality of devices from the measurement).

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of U.S. non-provisional patent application serial number 17 / 804,976, filed on June 1, 2022, entitled “SYSTEMS AND METHODS FOR NAVIGATION MODEL ENHANCEMENT,” which is expressly incorporated herein by reference in its entirety. Technical Field

[0003] The present disclosure relates generally to positioning systems, and more particularly to positioning systems related to navigation. Background Art

[0004] Wireless communication systems are widely deployed to provide a variety of telecommunication services, such as telephony, video, data, messaging, and broadcast. Typical wireless communication systems may employ multiple access technologies capable of supporting communication with multiple users by sharing available system resources. Examples of such multiple access technologies include code division multiple access (CDMA) systems, time division multiple access (TDMA) systems, frequency division multiple access (FDMA) systems, orthogonal frequency division multiple access (OFDMA) systems, single carrier frequency division multiple access (SC-FDMA) systems, and time division synchronous code division multiple access (TD-SCDMA) systems.

[0005] These multiple access technologies have been adopted in various telecommunication standards to provide a common protocol that enables different wireless devices to communicate at city, country, region, and even global levels. An example telecommunication standard is 5G New Radio (NR). 5G NR is part of the continuous mobile broadband evolution promulgated by the Third Generation Partnership Project (3GPP) to meet new requirements associated with latency, reliability, security, scalability (e.g., with the Internet of Things (IoT)) and other requirements. 5GNR includes services associated with enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra-reliable low latency communication (URLLC). Certain aspects of 5G NR may be based on 4G Long Term Evolution (LTE) standards. It is necessary to further improve 5G NR technology. In addition, these improvements may also be applicable to other multiple access technologies and telecommunication standards that employ these technologies. Summary of the invention

[0006] The following presents a summary of one or more aspects in order to provide a basic understanding of these aspects. This summary is not an extensive review of all contemplated aspects. This summary is neither intended to identify the key or important elements of all aspects, nor to describe 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 a more detailed description presented later.

[0007] In one aspect of the present disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus obtains first information including at least one characteristic associated with a plurality of devices. The apparatus selects a first subset of the plurality of devices for measurement based on the at least one characteristic associated with the plurality of devices.

[0008] To achieve the aforementioned and related purposes, one or more aspects include the features described in full below and particularly pointed out in the claims. The following description and the accompanying drawings set forth in detail some exemplary features of one or more aspects. However, these features are only indicative of only some of the various ways in which the principles of the various aspects can be employed. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0010] Figure 2A is a diagram illustrating an example of a first frame according to various aspects of the present disclosure.

[0011] Figure 2B is a diagram illustrating an example of downlink (DL) channels within a subframe according to aspects of the present disclosure.

[0012] Figure 2C is a diagram illustrating an example of a second frame according to various aspects of the present disclosure.

[0013] Figure 2D is a diagram illustrating an example of uplink (UL) channels within a subframe according to aspects of the present disclosure.

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

[0015] Figure 4 is a diagram illustrating an example of UE positioning based on reference signal measurement results according to various aspects of the present disclosure.

[0016] Figure 5 is a diagram illustrating an example of a navigation application that predicts traffic based on real-time data from a user according to various aspects of the present disclosure.

[0017] Figure 6 is a diagram illustrating an example of using infrastructure to identify locations and numbers of UEs in an area according to various aspects of the present disclosure.

[0018] Figure 7 is a diagram illustrating an example of identifying a cluster of UEs according to various aspects of the present disclosure.

[0019] Figure 8 is a diagram illustrating an example of identifying categories or clusters of UEs based on travel path area usage according to various aspects of the present disclosure.

[0020] Fig. 9 is a diagram illustrating an example of multiple wireless devices exchanging sensor data according to various aspects of the present disclosure.

[0021] Fig.10 is a flow chart of a method of wireless communication.

[0022] Fig.11 is a flow chart of a method of wireless communication.

[0023] Fig.12 are diagrams illustrating examples of hardware implementations for example apparatuses and / or network entities.

[0024] Fig.13 are diagrams illustrating examples of hardware implementations for example apparatuses and / or network entities.

[0025] Fig.14 are diagrams illustrating examples of hardware implementations for example apparatuses and / or network entities. DETAILED DESCRIPTION

[0026] The aspects presented herein can improve the performance of mobile applications (e.g., navigation applications, advertising applications, and / or messaging applications). The aspects presented herein can enable mobile applications to distinguish entities associated with the UE / entity running the mobile application, thereby enabling the mobile application (or their associated servers) to have a more accurate understanding of the conditions around the UE and provide a better user experience for the user.

[0027] The specific embodiments described below in conjunction with the accompanying drawings are descriptions of various configurations and do not represent the only configurations in which the concepts described herein can be practiced. In order to provide a thorough understanding of the various concepts, the specific embodiments include specific details. However, these concepts can be practiced without these specific details. In some cases, well-known structures and components are shown in block diagram form to avoid blurring such concepts.

[0028] Several aspects of the telecommunication system are also presented with reference to various apparatuses and methods. These apparatuses and methods are described in the following detailed description and are illustrated in the accompanying drawings by various blocks, components, circuits, processes, algorithms, etc. (collectively referred to as "elements"). These elements may be implemented using electronic hardware, computer software, or any combination thereof. Whether these elements are implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system.

[0029] As an example, an element, or any part of an element, or any combination of elements can be implemented as a "processing system", which 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 chip (SoCs), baseband processors, field programmable gate arrays (FPGAs), programmable logic devices (PLDs), state machines, gating logic, discrete hardware circuits, and other suitable hardware configured to perform various functionalities described throughout the present disclosure. One or more processors in a processing system can execute software. Whether referred to as software, firmware, middleware, microcode, hardware description language, or other terms, software should be broadly interpreted as meaning instructions, instruction sets, codes, code segments, program codes, programs, subroutines, software components, applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, functions, or any combination thereof.

[0030] Thus, in one or more example aspects, specific implementations and / or use cases, the described functionality may be implemented in hardware, software, or any combination thereof. If implemented in software, the functionality may be stored or encoded as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media. Storage media may be any available media that can be accessed by a computer. For example, such computer-readable media may include random access memory (RAM), read-only memory (ROM), electrically erasable programmable ROM (EEPROM), optical disk storage, magnetic disk storage, other magnetic storage devices, combinations of these types of computer-readable media, or any other medium that can be used to store computer-executable code in the form of instructions or data structures that can be accessed by a computer.

[0031] Although various aspects, specific implementations and / or use cases are described in this application by the illustration of some examples, additional or different aspects, specific implementations and use cases may be generated in many different arrangements and scenarios. The various aspects, specific implementations and / or use cases described herein can be implemented across many different platform types, devices, systems, shapes, sizes and packaging arrangements. For example, various aspects, specific implementations and / or use cases can be generated via integrated chip specific implementations and other devices based on non-module components (e.g., end-user devices, vehicles, communication equipment, computing equipment, industrial equipment, retail / purchase equipment, medical equipment, artificial intelligence (AI) enabled devices, etc.). Although some examples may or may not be specifically for use cases or applications, the described examples may have a wide range of applicability. Various aspects, specific implementations and / or use cases can be in the range from chip-level or modular components to non-modular, non-chip-level specific implementations, and further to the range of aggregated, distributed or original equipment manufacturer (OEM) devices or systems in conjunction with one or more technologies herein. In some actual settings, the equipment combined with the various aspects and features described 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 include several components for analog and digital purposes (e.g., hardware components including antennas, RF chains, power amplifiers, modulators, buffers, processors, interleavers, adders / summers, etc.) The techniques described herein can be practiced in a wide variety of devices of various sizes, shapes, and configurations, chip-level components, systems, distributed arrangements, aggregated or disaggregated components, end-user devices, etc.

[0032] The deployment of a communication system (such as a 5G NR system) can be arranged with various components or constituent parts in a variety of ways. In a 5G NR system or network, a network node, a network entity, a mobility element of a network, a radio access network (RAN) node, a core network node, a network element or a network equipment (such as a base station (BS), or one or more units (or one or more components) that perform base station functionality can be implemented in an aggregated or decomposed architecture. For example, a BS (such as a Node B (NB), an evolved NB (eNB), an NR BS, a 5G NB, an access point (AP), a transmit receive point (TRP) or a cell, etc.) can be implemented as an aggregated base station (also referred to as an independent BS or a monolithic BS) or a decomposed base station.

[0033] A clustered base station may be configured to utilize a radio protocol stack physically or logically integrated within a single RAN node. A decomposed base station may be configured to utilize a protocol stack physically or logically distributed 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 may be implemented within a RAN node, and one or more DUs may be co-located with the CU, or alternatively, may be geographically or virtually distributed in one or more other RAN nodes. A DU may be implemented to communicate with one or more RUs. Each of a CU, a DU, and a RU may be implemented as a virtual unit, i.e., a virtual central unit (VCU), a virtual distributed unit (VDU), or a virtual radio unit (VRU).

[0034] Base station operation or network design may take into account the aggregated nature of base station functionality. For example, a disaggregated base station may be used in an integrated access backhaul (IAB) network, an open radio access network (O-RAN (such as a network configuration sponsored by the O-RAN Alliance)), or a virtualized radio access network (vRAN, also known as a cloud radio access network (C-RAN)). Decomposition may include distributing functionality across two or more units at various physical locations, as well as virtually distributing functionality of at least one unit, which may enable flexibility in network design. Individual units of a disaggregated base station or disaggregated RAN architecture may be configured for wired or wireless communication with at least one other unit.

[0035] Figure 1 1 is a diagram 100 illustrating an example of a wireless communication system and an access network. The illustrated wireless communication system includes a disaggregated base station architecture. The disaggregated base station architecture may include one or more CUs 110 that may 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 CU 110 may communicate with one or more DUs 130 via corresponding midhaul links, such as an F1 interface. The DU 130 may communicate with one or more RUs 140 via corresponding fronthaul links. The RU 140 may communicate with corresponding UEs 104 via one or more radio frequency (RF) access links. In some implementations, a UE 104 may be served simultaneously by multiple RUs 140.

[0036] 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) may include or be coupled to one or more interfaces configured to receive or send signals, data, or information (collectively, signals) via a wired or wireless transmission medium. Each of the units or an associated processor or controller that provides instructions to the communication interfaces of these units may be configured to communicate with one or more of the other units via a transmission medium. For example, these units may include a wired interface that is configured to receive signals or send signals to one or more of the other units via a wired transmission medium. Additionally, these units may include a wireless interface that may include a receiver, transmitter, or transceiver (such as an RF transceiver) configured to receive and / or send signals to one or more of the other units via a wireless transmission medium.

[0037] In some aspects, CU 110 may host one or more higher layer control functions. Such control functions may include radio resource control (RRC), packet data convergence protocol (PDCP), service data adaptation protocol (SDAP), etc. Each control function may be implemented using an interface that is configured to communicate signals with other control functions hosted by CU 110. CU 110 may be configured to handle user plane functionality (i.e., central unit-user plane (CU-UP)), control plane functionality (i.e., central unit-control plane (CU-CP)), or a combination thereof. In some specific implementations, CU 110 may be logically split into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, the CU-UP unit may communicate bidirectionally with the CU-CP unit via an interface (such as an E1 interface). As needed, CU 110 may be implemented to communicate with DU 130 for network control and signaling.

[0038] DU 130 may correspond to a logical unit that includes one or more base station functions for controlling the operation of one or more RUs 140. In some aspects, DU 130 may 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.) at least in part according to a functional split such as those defined by 3GPP. In some aspects, DU 130 may also host one or more low PHY layers. Each layer (or module) may be implemented using an interface that is configured to communicate signals with other layers (and modules) hosted by DU 130 or with control functions hosted by CU 110.

[0039] The lower layer functionality may be implemented by one or more RUs 140. In some deployments, the RUs 140 controlled by the DUs 130 may 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 functional splits (such as lower layer functional splits). In such an architecture, the RUs 140 may 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 the RUs 140 may be controlled by the corresponding DUs 130. In some scenarios, such a configuration may enable the DUs 130 and CUs 110 to be implemented in a cloud-based RAN architecture (such as a vRAN architecture).

[0040] The SMO framework 105 may be configured to support RAN deployment and configuration of non-virtualized network elements and virtualized network elements. For non-virtualized network elements, the SMO framework 105 may be configured to support the deployment of dedicated physical resources for RAN coverage requirements, which may be managed via an operation and maintenance interface (such as an O1 interface). For virtualized network elements, the SMO framework 105 may 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 instantiating virtualized network elements) via a cloud computing platform interface (such as an O2 interface). Such virtualized network elements may include, but are not limited to, CU 110, DU 130, RU 140, and near-RT RIC 125. In some specific implementations, the SMO framework 105 may communicate with hardware aspects of the 4G RAN (such as an open eNB (O-eNB) 111) via the O1 interface. Additionally, in some specific implementations, the SMO framework 105 may communicate directly with one or more RUs 140 via the O1 interface. The SMO framework 105 may also include a non-RT RIC 115 configured to support the functionality of the SMO framework 105 .

[0041] The non-RT RIC 115 may be configured to include logic 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 guidance of applications / features in the near-RT RIC 125. The non-RT RIC 115 may be coupled to or in communication with the near-RT RIC 125 (such as via an A1 interface). The near-RT RIC 125 may be configured to include logic functions that enable near-real-time control and optimization of RAN elements and resources via data collection and actions through an interface (such as via an E2 interface) that connects one or more CUs 110, one or more DUs 130, or both, and the O-eNB with the near-RT RIC 125.

[0042] In some implementations, in order to generate an AI / ML model to be deployed in the near-RT RIC 125, the non-RT RIC 115 may receive parameters or external enrichment information from an external server. Such information may be utilized by the near-RT RIC 125 and may be received from a non-network data source or from a network function at the SMO framework 105 or the non-RT RIC 115. In some examples, the non-RT RIC 115 or the near-RT RIC 125 may be configured to tune RAN behavior or performance. For example, the non-RT RIC 115 may monitor long-term trends and patterns for performance and employ AI / ML models to perform corrective actions through the SMO framework 105 (such as via reconfiguration of O1) or via creation of RAN management policies (such as A1 policies).

[0043] At least one of the CU 110, the DU 130, and the RU 140 may be referred to as a base station 102. Therefore, the base station 102 may include one or more of the CU 110, the DU 130, and the RU 140 (each component is indicated by a dotted line to indicate that each component may be included in the base station 102 or may not be included in the base station). The base station 102 provides an access point to the core network 120 for the UE 104. The base station 102 may include a macro cell (a high-power cellular base station) and / or a small cell (a low-power cellular base station). Small cells include femto cells, pico cells, and micro cells. A network including both small cells and macro cells may be referred to as a heterogeneous network. A heterogeneous network may also include a home evolved Node B (eNB) (HeNB), which may provide services to a restricted group referred to as a closed subscriber group (CSG). The communication link between RU 140 and UE 104 may include uplink (UL) (also known as reverse link) transmission from UE 104 to RU 140 and / or downlink (DL) (also known as forward link) transmission from RU 140 to UE 104. The communication link may use multiple-input multiple-output (MIMO) antenna technology, including spatial multiplexing, beamforming and / or transmit diversity. The communication link may pass through one or more carriers. For each carrier allocated in a carrier aggregation of up to YxMHz (x component carriers) for transmission in each direction, the base station 102 / UE 104 may use a spectrum of up to YMHz (e.g., 5MHz, 10MHz, 15MHz, 20MHz, 100MHz, 400MHz, etc.) bandwidth. The carriers may or may not be adjacent to each other. The allocation of carriers may be asymmetric with respect to DL and UL (e.g., more or fewer carriers may be allocated to DL compared to UL). The component carrier may include a primary component carrier and one or more secondary component carriers. The primary component carrier may be referred to as a primary cell (PCell) and the secondary component carrier may be referred to as a secondary cell (SCell).

[0044] Some UEs 104 may communicate with each other using a device-to-device (D2D) communication link 158. The D2D communication link 158 may use DL / UL wireless wide area network (WWAN) spectrum. The D2D communication link 158 may 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). The D2D communication may be through a variety of wireless D2D communication systems, such as, for example, Bluetooth, Wi-Fi based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard, LTE, or NR.

[0045] The wireless communication system may also include a Wi-Fi AP 150 that communicates with a UE 104 (also referred to as a Wi-Fi station (STA)) via a communication link 154, for example, in a 5 GHz unlicensed spectrum, etc. When communicating in an unlicensed spectrum, the UE 104 / AP 150 may perform a clear channel assessment (CCA) to determine whether a channel is available prior to communication.

[0046] The electromagnetic spectrum is typically subdivided into various categories, bands, channels, etc. based on frequency / wavelength. In 5GNR, two initial operating bands have been identified as frequency range designations FR1 (410 MHz-7.125 GHz) and FR2 (24.25 GHz-52.6 GHz). Although a portion of FR1 is greater than 6 GHz, FR1 is often (interchangeably) referred to as the "below 6 GHz" band in various documents and articles. A similar naming problem sometimes occurs with respect to FR2, which is often (interchangeably) referred to as the "millimeter wave" band in documents and articles, although it is different from the extremely high frequency (EHF) band (30 GHz-300 GHz) identified as the "millimeter wave" band by the International Telecommunication Union (ITU).

[0047] Frequencies between FR1 and FR2 are generally referred to as mid-band frequencies. Recent 5G NR research has identified the operating bands for these mid-band frequencies as frequency range designation FR3 (7.125GHz–24.25GHz). The bands falling within FR3 can inherit FR1 characteristics and / or FR2 characteristics, and therefore the features of FR1 and / or FR2 can be effectively extended to mid-band frequencies. In addition, higher frequency bands are currently being explored to extend 5G NR operations to more than 52.6GHz. For example, three higher operating bands have been identified as frequency range designations FR2-2 (52.6GHz-71GHz), FR4 (71GHz-114.25GHz), and FR5 (114.25GHz-300GHz). Each of these higher frequency bands falls within the EHF band.

[0048] In view of the above, unless otherwise specified, if the term "6 GHz or less" is used herein, it may broadly refer to frequencies that may be less than 6 GHz, may be within FR1, or may include mid-band frequencies. In addition, unless otherwise specified, if the term "millimeter wave" is used herein, it may broadly refer to frequencies that may include mid-band frequencies, may be within FR2, FR4, FR2-2, and / or FR5, or may be within the EHF band.

[0049] The base station 102 and the UE 104 may each include multiple antennas (such as antenna elements, antenna panels, and / or antenna arrays) to facilitate beamforming. The base station 102 may send a beamformed signal 182 to the UE 104 in one or more transmit directions. The UE 104 may receive the beamformed signal from the base station 102 in one or more receive directions. The UE 104 may also send a beamformed signal 184 to the base station 102 in one or more transmit directions. The base station 102 may receive the beamformed signal from the UE 104 in one or more receive directions. The base station 102 / UE 104 may perform beam training to determine the best receive direction and transmit direction for each of the base station 102 / UE 104. The transmit direction and receive direction of the base station 102 may be the same or different. The transmit direction and receive direction of the UE 104 may be the same or different.

[0050] The base station 102 may include and / or be referred to as a gNB, a Node B, an eNB, an access point, a base transceiver station, a radio base station, a radio transceiver, a transceiver function, a basic service set (BSS), an extended service set (ESS), a transmit receive point (TRP), a network node, a network entity, a network equipment, or some other suitable term. The base station 102 may be implemented as an integrated access and backhaul (IAB) node, a relay node, a sidelink node, a clustered (monolithic) base station with a baseband unit (BBU) (including a CU and a DU) and a RU, or as a decomposed base station including one or more of a CU, a DU, and / or a RU. A set of base stations that may include decomposed base stations and / or aggregated base stations may be referred to as a next generation (NG) RAN (NG-RAN).

[0051] The core network 120 may 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. AMF 161 is a control node that processes signaling between UE 104 and the core network 120. AMF 161 supports registration management, connection management, mobility management, and other functions. SMF 162 supports session management and other functions. UPF 163 supports packet routing, packet forwarding, and other functions. UDM 164 supports the generation of authentication and key agreement (AKA) credentials, user identity processing, access authorization, and subscription management. 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, in general, the one or more location servers 168 may include one or more location / positioning servers, which may include one or more of the GMLC 165, the LMF 166, the position determination entity (PDE), the serving mobile location center (SMLC), the mobile positioning center (MPC), etc. 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 calculate the location of the UE 104. The NG-RAN may determine the location of the UE 104 using one or more positioning methods. Positioning the UE 104 may involve signal measurements, position estimation, and optional speed calculations based on these measurements. Signal measurements may be performed by the UE 104 and / or the serving base station 102. The measured signal can be based on a satellite positioning system (SPS) 170 (e.g., one or more of a global navigation satellite system (GNSS), a global positioning system (GPS), a non-terrestrial network (NTN), or other satellite positioning / positioning systems), LTE signals, wireless local area network (WLAN) signals, Bluetooth signals, a terrestrial beacon system (TBS), sensor-based information (e.g., an atmospheric pressure sensor, a motion sensor), an NR enhanced cell ID (NR E-CID) method, an NR signal (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 one or more of other systems / signals / sensors.

[0052] Examples of UE 104 include cellular phones, smart phones, session initiation protocol (SIP) phones, laptops, personal digital assistants (PDAs), satellite radios, global positioning systems, multimedia devices, video devices, digital audio players (e.g., MP3 players), cameras, game consoles, tablet computers, smart devices, wearable devices, vehicles, electric meters, gas pumps, large or small kitchen appliances, healthcare equipment, implants, sensors / actuators, displays, or any other similarly functional devices. Some of the UEs 104 may be referred to as IoT devices (e.g., parking meters, gas pumps, toasters, vehicles, heart monitors, etc.). UE 104 may 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, mobile phones, user agents, mobile clients, clients, or some other suitable terminology. In some scenarios, the term UE may also be applied to one or more supporting devices, such as in a device constellation arrangement. One or more of these devices may access the network collectively and / or individually.

[0053] Reference again Figure 1 In some aspects, the UE 104 may be configured to obtain first information including at least one characteristic associated with a plurality of devices, and select a first subset of the plurality of devices for measurement based on the at least one characteristic associated with the plurality of devices via the sensing data processing component 198. In some aspects, the base station 102 may be configured to obtain first information including at least one characteristic associated with a plurality of devices, and select a first subset of the plurality of devices for measurement based on the at least one characteristic associated with the plurality of devices via the sensing data processing component 199.

[0054] Figure 2A FIG200 is a diagram illustrating an example of a first subframe within a 5G NR frame structure. Figure 2B FIG230 is a diagram illustrating an example of DL channels within a 5G NR subframe. Figure 2C FIG250 is a diagram illustrating an example of a second subframe within a 5G NR frame structure. Figure 2D FIG280 is a diagram illustrating an example of UL channels within a 5G NR subframe. The 5G NR frame structure may be frequency division duplex (FDD), where for a particular set of subcarriers (carrier system bandwidth), subframes within that subcarrier set are dedicated to either DL or UL, or may be time division duplex (TDD), where for a particular set of subcarriers (carrier system bandwidth), subframes within that subcarrier set are dedicated to both DL and UL. Figure 2A , Figure 2CIn the example provided, the 5G NR frame structure is assumed to be TDD, where subframe 4 is configured with slot format 28 (most of which are DL), where D is DL, U is UL, and F is flexible between DL / UL, and subframe 3 is configured with slot format 1 (all of which are UL). Although subframes 3 and 4 are shown as having slot formats 1 and 28, respectively, any particular subframe may be configured with any of the various available slot formats 0-61. Slot formats 0 and 1 are full DL and full UL, respectively. Other slot formats 2-61 include a mix of DL, UL and flexible symbols. The slot format is configured for the UE via a received slot format indicator (SFI) (dynamically configured via DL control information (DCI) or semi-statically / statically configured via radio resource control (RRC) signaling). Note that the following description also applies to the 5G NR frame structure as TDD.

[0055] FIG. 2A to FIG. 2D The frame structure is illustrated, and various aspects of the present disclosure may be applicable to other wireless communication technologies that may have different frame structures and / or different channels. A frame (10ms) may be divided into 10 subframes of equal size (1ms). Each subframe may include one or more time slots. A subframe may also include a microslot, which may include 7, 4, or 2 symbols. Each time slot may include 14 or 12 symbols, depending on whether the cyclic prefix (CP) is normal or extended. For a normal CP, each time slot may include 14 symbols, and for an extended CP, each time slot may include 12 symbols. The symbol on the DL may be a CP orthogonal frequency division multiplexing (OFDM) (CP-OFDM) symbol. The symbol on the UL may be a CP-OFDM symbol (for high throughput scenarios) or a discrete Fourier transform (DFT) extended OFDM (DFT-s-OFDM) symbol (also known as a single carrier frequency division multiple access (SC-FDMA) symbol) (for power-limited scenarios; limited to single-stream transmission). The number of slots within a subframe is based on the CP and the parameter set. The parameter set defines the subcarrier spacing (SCS) and effectively defines the symbol length / duration, which is equal to 1 / SCS.

[0056]

[0057] For normal CP (14 symbols / slot), different parameter sets μ0 to 4 allow 1, 2, 4, 8, and 16 slots per subframe, respectively. For extended CP, parameter set 2 allows 4 slots per subframe. Thus, for normal CP and parameter set μ, there are 14 symbols / slot and 2 μ timeslots / subframe. The subcarrier spacing can be equal to 2 μ*15kHz, where μ is parameter set 0 to 4. Therefore, the subcarrier spacing for parameter set μ=0 is 15kHz, and the subcarrier spacing for parameter set μ=4 is 240kHz. The symbol length / duration is inversely related to the subcarrier spacing. FIG. 2A to FIG. 2D An example of a parameter set μ=2 with a normal CP of 14 symbols per slot and 4 slots per subframe is provided. The slot duration is 0.25 ms, the subcarrier spacing is 60 kHz, and the symbol duration is about 16.67 μs. Within a frame set, there may be one or more different bandwidth parts (BWPs) that are frequency-division multiplexed (see Figure 2B ). Each BWP may have a specific parameter set and CP (normal or extended).

[0058] A resource grid may be used to represent the frame structure. Each slot includes a resource block (RB) (also called a physical RB (PRB)) extending over 12 consecutive subcarriers. The resource grid is divided into a number of resource elements (REs). The number of bits carried by each RE depends on the modulation scheme.

[0059] like Figure 2A As illustrated, some of the REs carry reference (pilot) signals (RS) for the UE. The RS may include a demodulation RS (DM-RS) (indicated as R for one particular configuration, but other DM-RS configurations are possible) and a channel state information reference signal (CSI-RS) for channel estimation at the UE. The RS may also include a beam measurement RS (BRS), a beam refinement RS (BRRS), and a phase tracking RS (PT-RS).

[0060] Figure 2BExamples of various DL channels within a subframe of a frame are illustrated. A physical downlink control channel (PDCCH) carries DCI within one or more 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 a BWP may be referred to as a control resource set (CORESET). The UE is configured to monitor PDCCH candidates in a PDCCH search space (e.g., a common search space, a UE-specific search space) during a PDCCH monitoring opportunity on a CORESET, wherein the PDCCH candidates have different DCI formats and different aggregation levels. Additional BWPs may be located at higher and / or lower frequencies on the channel bandwidth. A primary synchronization signal (PSS) may be within symbol 2 of a particular subframe of a frame. The PSS is used by UE 104 to determine subframe / symbol timing and physical layer identification. A secondary synchronization signal (SSS) may be within symbol 4 of a particular subframe of a frame. The SSS is used by the UE to determine the physical layer cell identity group number and the radio frame timing. Based on the physical layer identity and the physical layer cell identity group number, the UE can determine the physical cell identifier (PCI). Based on the PCI, the UE can determine the location of the DM-RS. The physical broadcast channel (PBCH) carrying the master information block (MIB) can be logically grouped with the PSS and SSS to form a synchronization signal (SS) / PBCH block (also referred to as an SS block (SSB)). The MIB provides the number of RBs in the system bandwidth and the system frame number (SFN). The physical downlink shared channel (PDSCH) carries user data, broadcast system information not sent via the PBCH (such as a system information block (SIB)), and paging messages.

[0061] like Figure 2C As illustrated, some of the REs carry DM-RS (indicated as R for a particular configuration, but other DM-RS configurations are possible) for channel estimation at the base station. The UE may send a DM-RS for a physical uplink control channel (PUCCH) and a DM-RS for a physical uplink shared channel (PUSCH). The PUSCH DM-RS may be sent in the first or first two symbols of the PUSCH. Depending on whether a short PUCCH or a long PUCCH is sent and depending on the specific PUCCH format used, the PUCCH DM-RS may be sent in different configurations. The UE may send a sounding reference signal (SRS). The SRS may be sent in the last symbol of a subframe. The SRS may have a comb structure, and the UE may send the SRS on one of the comb teeth. The SRS may be used by the base station for channel quality estimation to achieve frequency-dependent scheduling of the UL.

[0062] Figure 2DExamples of various UL channels within a subframe of a frame are illustrated. The PUCCH may be located at a position as indicated in one configuration. The PUCCH carries uplink control information (UCI), such as scheduling requests, channel quality indicators (CQI), precoding matrix indicators (PMI), rank indicators (RI), and hybrid automatic repeat request (HARQ) acknowledgement (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 may additionally be used to carry buffer status reports (BSRs), power headroom reports (PHRs), and / or UCI.

[0063] Figure 3 375. The Controller / Processor 375 is a block diagram of a base station 310 in an access network communicating with a UE 350. In the DL, Internet Protocol (IP) packets may 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, SIB), RRC connection control (e.g., RRC connection paging, RRC connection establishment, RRC connection modification, and RRC connection release), inter-radio access technology (RAT) mobility, and measurement configuration for UE measurement reporting; PDCP layer functionality associated with header compression / decompression, security (encryption, decryption, integrity protection, integrity verification), and handover support functions; RLC layer functionality associated with transmission of upper layer packet data units (PDUs), error correction through ARQ, concatenation, segmentation and reassembly of RLC service data units (SDUs), re-segmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs onto transport blocks (TBs), demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction through HARQ, priority handling, and logical channel prioritization.

[0064] 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 the physical (PHY) layer) may include error detection on the transport channel, forward error correction (FEC) coding / decoding of the transport channel, interleaving, rate matching, mapping to the physical channel, modulation / demodulation of the physical channel, and MIMO antenna processing. The TX processor 316 processes the mapping to the signal constellation based on various modulation schemes (e.g., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), M-order phase shift keying (M-PSK), M-order quadrature amplitude modulation (M-QAM)). The coded and modulated symbols may then be separated into parallel streams. Each stream may 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 pre-coded to generate multiple spatial streams. Channel estimates from a channel estimator 374 may be used to determine the coding and modulation schemes, as well as for spatial processing. Channel estimates may be derived from reference signals and / or channel condition feedback sent by the UE 350. Each spatial stream may then be provided to a different antenna 320 via a separate transmitter 318Tx. Each transmitter 318Tx may modulate a radio frequency (RF) carrier with a corresponding spatial stream for transmission.

[0065] At the UE 350, each receiver 354Rx receives a signal through its corresponding antenna 352. Each receiver 354Rx recovers the information modulated onto the RF carrier and provides the information to a 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 stream destined for the UE 350. If multiple spatial streams are destined for the UE 350, they can be combined into a single OFDM symbol stream by the RX processor 356. 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 symbol on each subcarrier and the reference signal are recovered and demodulated by determining the signal constellation point most likely to be sent by the base station 310. These soft decisions can be based on the channel estimate calculated by the channel estimator 358. The soft decisions are then decoded and deinterleaved to recover the data and control signals originally sent on the physical channel by the base station 310. The data and control signals are then provided to the controller / processor 359, which implements layer 3 and layer 2 functionality.

[0066] The controller / processor 359 may be associated with a memory 360 that stores program codes and data. The memory 360 may be referred to as a computer readable medium. In the UL, the controller / processor 359 provides demultiplexing between transport channels and logical channels, packet reassembly, decryption, header decompression, and control signal processing to recover IP packets. The controller / processor 359 is also responsible for error detection using ACK and / or NACK protocols to support HARQ operations.

[0067] Similar to the functionality described in conjunction with DL transmissions performed by the base station 310, the controller / processor 359 provides RRC layer functionality associated with system information (e.g., MIB, SIB) acquisition, RRC connection, and measurement reporting; PDCP layer functionality associated with header compression / decompression and security (encryption, decryption, integrity protection, integrity verification); RLC layer functionality associated with delivery of upper layer PDUs, error correction through ARQ, concatenation, segmentation and reassembly of RLC SDUs, resegmentation of RLC data PDUs, and reordering of RLC data PDUs; and MAC layer functionality associated with mapping between logical channels and transport channels, multiplexing of MAC SDUs onto TBs, demultiplexing of MAC SDUs from TBs, scheduling information reporting, error correction through HARQ, priority handling, and logical channel prioritization.

[0068] The TX processor 368 may use channel estimates derived by the channel estimator 358 from a reference signal or feedback transmitted by the base station 310 to select appropriate coding and modulation schemes and to facilitate spatial processing. The spatial streams generated by the TX processor 368 may be provided to different antennas 352 via separate transmitters 354Tx. Each transmitter 354Tx may modulate an RF carrier with a corresponding spatial stream for transmission.

[0069] UL transmissions are processed at the base station 310 in a manner similar to that described in conjunction with the receiver functionality at the UE 350. Each receiver 318Rx receives a signal through its corresponding antenna 320. Each receiver 318Rx recovers information modulated onto an RF carrier and provides the information to the RX processor 370.

[0070] The controller / processor 375 may be associated with a memory 376 that stores program codes and data. The memory 376 may be referred to as a computer readable medium. In the UL, the controller / processor 375 provides demultiplexing between transport channels and logical channels, packet reassembly, decryption, header decompression, control signal processing to recover IP packets. The controller / processor 375 is also responsible for error detection using ACK and / or NACK protocols to support HARQ operations.

[0071] At least one of the TX processor 368, the RX processor 356, and the controller / processor 359 may be configured to combine Figure 1 The sensor data processing component 198 is used to perform various aspects.

[0072] At least one of the TX processor 316, the RX processor 370, and the controller / processor 375 may be configured to combine Figure 1 The sensor data processing component 199 is used to perform various aspects.

[0073] Figure 4 4 is a diagram illustrating an example of UE positioning based on reference signal measurements (which may also be referred to as “network-based positioning”) according to various aspects of the present disclosure. UE 404 may be at time T SRS_TX UL-SRS 412 is sent and at time T PRS_RX A DL positioning reference signal (PRS) (DL-PRS) 410 is received. TRP 406 may be at time T SRS_RX Receive UL-SRS 412 and at time T PRS_TX 410. UE 404 may receive DL-PRS 410 before transmitting UL-SRS 412, or may transmit UL-SRS 412 before receiving DL-PRS 410. In both cases, a positioning server (e.g., location server 168) or UE 404 may determine the UL-SRS 412 based on the received DL-PRS 410. SRS_RX –T PRS_TX |–|T SRS_TX –T PRS_RX |) to determine RTT 414. Thus, multi-RTT positioning can utilize UE Rx-Tx time difference measurements (i.e., |T SRS_TX –T PRS_RX |) and DL-PRS reference signal received power (RSRP) (DL-PRS-RSRP), and the measured TRP Rx-Tx time difference measurement (ie, |T SRS_RX –T PRS_TX|) and UL-SRS-RSRP. The UE 404 measures the UE Rx-Tx time difference measurement (and / or the DL-PRS-RSRP of the received signal) using the assistance data received from the positioning server, and the TRP 402, 406 measures the gNB Rx-Tx time difference measurement (and / or the UL-SRS-RSRP of the received signal) using the assistance data received from the positioning server. These measurements may be used at the positioning server or the UE 404 to determine the RTT, which is used to estimate the position of the UE 404. Other methods for determining the RTT are possible, such as, for example, using DL-TDOA and / or UL-TDOA measurements.

[0074] DL-AoD positioning may utilize the measured DL-PRS-RSRP of downlink signals received from multiple TRPs 402, 406 at the UE 404. The UE 404 measures the DL-PRS-RSRP of the received signals using assistance data received from a positioning server, and the resulting measurements, along with the departure azimuth angle (A-AoD), departure zenith angle (Z-AoD), and other configuration information, are used to position the UE 404 relative to neighboring TRPs 402, 406.

[0075] DL-TDOA positioning may utilize DL reference signal time difference (RSTD) (and / or DL-PRS-RSRP) of downlink signals received from multiple TRPs 402, 406 at the UE 404. The UE 404 measures the DL RSTD (and / or DL-PRS-RSRP) of the received signals using assistance data received from a positioning server, and the resulting measurements, along with other configuration information, are used to position the UE 404 relative to neighboring TRPs 402, 406.

[0076] UL-TDOA positioning may utilize the UL relative time of arrival (RTOA) (and / or UL-SRS-RSRP) at multiple TRPs 402, 406 of uplink signals sent from the UE 404. The TRPs 402, 406 measure the UL-RTOA (and / or UL-SRS-RSRP) of the received signals using assistance data received from a positioning server, and the resulting measurements are used along with other configuration information to estimate the position of the UE 404.

[0077] UL-AoA positioning may utilize the measured azimuth angle of arrival (A-AoA) and zenith angle of arrival (Z-AoA) of uplink signals sent from the UE 404 at multiple TRPs 402, 406. The TRPs 402, 406 measure the A-AoA and Z-AoA of the received signals using assistance data received from a positioning server, and the resulting measurements are used along with other configuration information to estimate the position of the UE 404.

[0078] Additional positioning methods may be used to estimate the position of the UE 404, such as, for example, UE-side UL-AoD and / or DL-AoA. Note that data / measurements from various techniques may be combined in various ways to increase accuracy, determine and / or enhance certainty, supplement / complete measurements, and / or replace / provide missing information.

[0079] A navigation application may refer to an application in a user equipment (UE) (e.g., a smart phone) that provides navigation directions in real time. In the past few years, users have become increasingly dependent on navigation applications because they provide various benefits. For example, navigation applications provide convenience to users because they enable users to find a path to their destination, and also allow users to contribute information and mark important places, thereby generating the most accurate description of a location. In some examples, navigation applications can also provide professional guidance to users, wherein the navigation application can guide users to their destination via the best, most direct, or most time-saving route. For example, a navigation application can obtain the current state of traffic, and then locate the shortest and fastest path for users to reach their destination, and also provide users with approximately how long it will take to reach their destination. Therefore, a navigation application can use an Internet connection and a GPS / GNSS navigation system to provide turn-by-turn guidance instructions on how to reach a given destination.

[0080] In some scenarios, the information provided by the navigation application may be based on historical data and / or real-time data from devices (e.g., UEs) that have opted in to sharing real-time data. For example, users using the navigation application may be specified to provide their location, direction, and / or speed of travel. Then, based at least in part on the real-time data provided by these users, the navigation application (or a server associated with the navigation application) may be able to determine the traffic around the user. However, sometimes, navigation applications may not be able to provide accurate information or predictions because they may not have the ability to distinguish or identify situations in which users are using the navigation application. For example, the navigation application may not be able to determine whether the user is using the navigation application in a vehicle, on the street, or on other types of transportation devices, etc. This may cause the navigation application to provide inaccurate traffic data.

[0081] Figure 5500 is a diagram illustrating an example of a navigation application that predicts traffic based on real-time data from a user according to various aspects of the present disclosure. As shown at 502 and 504, a pedestrian group may be walking on a bridge area, and the pedestrian group may be using a navigation application via their UE (e.g., mobile phone, smart watch, etc.). If the pedestrian group has opted in to share their real-time data, such as their location, speed, and / or orientation, the navigation application (or a server associated with the navigation application) can collect these real-time data and predict traffic around the bridge area. However, as shown at 506, if the navigation application (or an algorithm run by the navigation application) cannot distinguish whether the UE (or real-time data) represents a vehicle, a cyclist, or a pedestrian, the navigation application / server may predict that there is a heavy vehicle traffic load on the bridge area when there are a certain number of UEs moving below a speed threshold on the bridge area. Therefore, the navigation application may provide an inaccurate prediction of traffic, and thereby affect the accuracy of route planning (e.g., finding the fastest and shortest route) performed by the navigation application. In some examples, in addition to being unable to distinguish / identify entities (e.g., vehicles, pedestrians, etc.) associated with the UE running the navigation application, the algorithm run by the navigation application may also be limited to making traffic predictions when there are more than a certain amount of UEs providing data. For example, the algorithm may specify that X UEs (e.g., X=30, 50, etc.) are considered sufficient data for traffic prediction. Thus, if there are not enough UEs providing their real-time data, or if the UEs have not opted in to sharing their real-time data, the algorithm may avoid making traffic predictions in an area.

[0082] The aspects presented herein can improve the performance of mobile / computer applications. The aspects presented herein can enable mobile / computer applications to distinguish entities associated with the UE / entity running the mobile / computer application, thereby enabling the mobile / computer application (or their associated servers) to have a more accurate understanding of the conditions around these UEs and their users. It should be noted that although the diagrams may use navigation applications as examples to illustrate the present disclosure, various aspects of the present disclosure may also be applied to other types of mobile / computer applications, such as advertising applications and messaging applications.

[0083] In one aspect of the present disclosure, a mobile / computer application (or a server associated with a mobile / computer application) may use one or more infrastructures to identify the approximate location of UEs in an area and / or the number of UEs in an area. For the purposes of the present disclosure, the approximate location of a UE may include the absolute location of the UE and the relative location of the UE. For example, determining the approximate location of a UE may include determining a relative distance, measuring a relative distance between UEs, or estimating a relative distance between UEs, etc. The mobile / computer application may include a navigation application, an advertising application, or an application including advertising, a messaging application, etc. The server may include a location server, a crowdsourcing server, or a place statistics server, etc. For example, a base station (or a component of a base station) or a roadside unit (RSU) may identify the number of UEs in an area based on UEs connected to a base station or RSU from an area or via certain beam directions associated with the area. In some examples, the base station or RSU may also determine the speed of the detected UEs based on the Doppler information of the UE, and the base station or RSU may classify whether a particular UE is likely to be a pedestrian UE or a vehicle UE. For purposes of this disclosure, a UE held by or co-located with a pedestrian (e.g., a user walking on a road) may be referred to as a "pedestrian UE," and a UE used by a user in a vehicle or as part of a vehicle (e.g., part of an onboard communication system of a vehicle) may be referred to as a "vehicle UE." Based at least in part on the number of UEs in an area and their identities (e.g., pedestrian UEs, vehicle UEs, other types of UEs, etc.), mobile / computer applications may have a better understanding of the UEs and conditions in the area, and the mobile / computer applications may use this information to make more accurate predictions, advertising, messaging, and / or navigation for their users.

[0084] Figure 6600 is an example of using infrastructure to identify the location and number of UEs in an area according to various aspects of the present disclosure. Infrastructures 602a and 602b (collectively referred to as infrastructure 602), which may each be a base station, a component of a base station, or an RSU, may detect the number of UEs accessing or communicating with the infrastructure 602 in certain directions, for example, based on the direction of a communication beam between the infrastructure 602 and the UE. For example, the infrastructure 602 may detect that there are X UEs in an intersection area. The infrastructure 602 may then further measure or obtain Doppler information for the X UEs to determine the speeds of these UEs. Based on the detected speed or the average speed detected over a period of time, the infrastructure 602 may identify or predict whether the UE is a pedestrian UE, a vehicle UE, or another type of UE. For example, the infrastructure 602 may classify a UE that moves between three (3) miles and four (4) miles per hour as a pedestrian UE 604, a UE that moves more than fifteen miles as a vehicle UE 606, and a UE that does not move over a period of time as a fixed device, etc. In some examples, the UE's Doppler information may also be used to identify the UE's orientation, such as whether the measured Doppler is shifting toward infrastructure 602 or moving away from infrastructure 602. Thus, infrastructure 602 may also identify or predict the UE's direction of travel.

[0085] In one example, based on knowing the number of UEs in the area, the directions of these UEs, and / or the potential classification of these UEs, the navigation application can use this information to have a better understanding of the traffic in this area. For example, if the infrastructure 602 identifies that there are ten pedestrian UEs 604 and two vehicle UEs 606 in the intersection, the navigation application can determine that the vehicle traffic at the intersection is light, and if the infrastructure 602 identifies that there are twelve vehicle UEs 606 moving in one direction in the intersection, the navigation application can determine that the vehicle traffic at the intersection in that direction is heavy, etc. Therefore, the navigation application can make more accurate predictions and navigation for this intersection area without specifying that users in the area share their real-time information.

[0086] In another example, based on knowing the classification and / or movement of UE, the advertising application (or application including the advertisement) and / or the advertiser may be able to provide more targeted or appropriate advertisements at different areas. For example, based on the number of vehicle UEs and pedestrian UEs detected in a period of time, advertisements using larger billboards (e.g., which are dynamic) may be focused on areas with more vehicle UEs (e.g., targeting passengers in vehicles), while advertisements using smaller signs / monitors may be more focused on areas with more pedestrian UEs (e.g., targeting pedestrians), because people walking may be more inclined to stop what they are doing and enjoy food, entertainment, shopping, etc. In another example, dynamic advertisements may be displayed to passengers / cyclists stopped at intersections instead of static advertisements. In some examples, advertisers may be charged by the amount of consumers who view the advertisements and / or based on the type of people who view the advertisements (e.g., exercisers, parents pushing strollers, people wearing suits, etc.).

[0087] In another example, based on knowing the classification and / or movement of the UE, the messaging application may be configured to display messages and communications in different ways. For example, a customized message (similar to a targeted advertisement) may be provided to a user based on the movement of the user (or his UE). For example, a scrolling message may be more suitable for a stationary person, but less suitable for a person moving over a certain speed. In another example, a vehicle-related message (such as a speeding warning or a "no drinking and driving" warning) may be displayed to a vehicle UE but not to a pedestrian UE. In another example, if both a vehicle UE and a pedestrian UE are detected, a warning message about the presence of a pedestrian (e.g., watch out for pedestrians) may be provided to the vehicle UE and / or a similar warning message about the presence of a vehicle (e.g., watch out for the vehicle) may be provided to the pedestrian UE, etc.

[0088] In some examples, infrastructure 602 may be configured to periodically and / or randomly sample UEs in one or more areas and perform positioning for these UEs. Infrastructure 602 may also use other available positioning means to detect the location of the UE. For example, infrastructure 602 may use radio frequency (RF) sensing and / or network positioning methods to identify the location of the UE. At least one benefit of using infrastructure to identify the number of UEs, the location of the UE, and / or the classification of the UE is that this information can be collected by an entity without specifying that the UE opts in to sharing the UE information, thereby enabling the entity to obtain more accurate and complete information than information obtained based on opt-in. In other words, the infrastructure can collect this information passively. Note that although Figure 6An example of using infrastructure to identify the number of UEs in an area is illustrated, but this is for illustration purposes only. The same mechanism can also be used to identify the number of UEs in certain areas (such as in a shopping mall, parking garage, or building). The application can then use this information to derive other information associated with these areas, such as the approximate number of users in these areas, the degree of congestion of these users, and / or the movement / direction of these users, etc.

[0089] In some scenarios, although combined with Figure 6 The described aspects may enable an infrastructure or mobile / computer application to identify the approximate number of UEs in an area, the locations of these UEs, and / or the types of these UEs, but the infrastructure or mobile / computer application may not be able to determine whether groups of UEs are associated with each other or co-located (e.g., multiple UEs are used by users in the same vehicle). In some examples, the infrastructure or mobile / computer application may also not be able to accurately determine the mobility type of the UE (e.g., pedestrian UE, vehicle UE, or other type of UE, etc.), such as when multiple vehicle UEs are moving at a low speed or are stationary (e.g., the vehicle is in a traffic jam or parked).

[0090] In another aspect of the present disclosure, a device, a mobile / computer application / server, or an infrastructure may be configured to identify whether UE clusters are associated or co-located with each other based on at least one feature associated with the UE clusters. In one example, by knowing whether UE clusters are associated with each other in an area, a navigation application may further make more accurate predictions of traffic in the area. For example, a navigation application may treat a UE cluster as one UE instead of multiple UEs (e.g., some UEs may be excluded from measurement by the navigation application), thereby improving the accuracy of traffic prediction and navigation.

[0091] Figure 7 700 is a diagram illustrating an example of identifying a cluster of UEs according to various aspects of the present disclosure. Although the example illustrated by diagram 700 shows a group of passengers / UEs in a vehicle, the aspects presented herein may also be applied to users / UEs in other types of intervals or areas. For example, the aspects presented herein may also be applied to various types of UEs, such as wireless devices used in association with bicycling, autonomous vehicles, autonomous delivery devices / transporters, drones, etc.

[0092] In one aspect, a mobile / computer application, a server (e.g., a location server, a crowdsourcing server, a network server, etc.), a UE, or a network device / node may be able to identify whether a cluster of UEs is associated or co-located with each other based on the location of the UEs. For example, as shown at 706, in conjunction with Figure 4As described, one or more network-based positioning methods may be used to identify the location of the UE. Thus, if the location server detects that the UEs 702 are in the same location or within a distance threshold of each other, the location server may determine that the UEs 702 are associated with each other. The determination may be further based on one or more additional factors, such as the speed and / or orientation of the UEs 702. In addition, as shown at 706, 708, and 710, control plane Uu positioning together with thresholded total received / radiated power (e.g., group / associated UEs 702 based on total received power), indoor positioning methods (such as positioning methods based on ultra-wideband (UWB) or WiFi), and / or positioning methods based on vehicle-to-vehicle (V2V) and / or vehicle-to-everything (V2X) may also be used to identify the location of the UE 702, depending on the location of the UE 702 (e.g., in a vehicle, in a building, in a certain terrain, etc.).

[0093] On the other hand, a mobile / computer application, server, UE, or network device / node may be able to identify whether UE clusters are associated with each other or co-located based on measurements of nearby devices by the UE clusters. For example, as shown at 712, a UE group may be configured to measure reference signals or other types of signals sent by other devices and / or UEs (e.g., UEs / devices within the reception range of the UE group), and these UEs may report the measurements of these UEs to a location server or base station, such as via a channel state information (CSI) report and / or a received signal strength indicator (RSSI) report. Based on CSI reports and / or RSSI reports received from multiple UEs, the location server of the base station may be able to identify which UE clusters are associated with each other. For example, when UE 702 is located in the same vehicle, they may receive signals with similar signal strengths and signatures from other devices (or from each other). Therefore, a location server or base station may use such measurements and / or signatures to identify whether UE 702 are co-located with each other.

[0094] In another aspect, a mobile / computer application, server, UE, or network device / node may be able to identify whether a cluster of UEs is associated with or co-located with each other based on the acceleration pattern and / or speed of the cluster of UEs. For example, as shown at 714, a group of UEs may be configured to report to a location server or base station the acceleration and / or speed of the group of UEs obtained via the accelerometer of the group of UEs over a period of time. Since UEs in the same vehicle are likely to have similar acceleration and / or speed patterns over the same period of time, the location server of the base station may identify whether the UEs are co-located based on the acceleration and / or speed measurements / patterns of UE 702.

[0095] In another aspect, a mobile / computer application, server, UE, or network device / node may be able to identify whether a cluster of UEs is associated or co-located with each other based on sounds detected by the cluster of UEs. For example, as shown at 716, a group of UEs may be configured to report to a location server or base station the sounds (or specific types of sounds) that it detects or records within a time period. Since UEs in the same neighborhood (e.g., a building, in a vehicle, etc.) will likely record / receive similar sounds within the same time period, the location server of the base station may identify whether UEs 702 are co-located with each other based on the sounds recorded by the UEs. The sounds may include tire hum, engine noise, air turbulence, passenger speech, music, or a combination thereof.

[0096] In another aspect, a mobile / computer application, server, UE, or network device / node may be able to identify whether a cluster of UEs are associated or co-located with each other based on the Doppler of signals received by nearby devices. For example, a group of UEs may be configured to measure the Doppler of signals received by them (e.g., such as beacons in a vehicle). Since UEs in the same vehicle or other type of transportation device will likely obtain similar Doppler measurements from nearby devices, the location server of the base station may identify whether UEs 702 are co-located with each other based on the Doppler information provided by UE 702.

[0097] After identifying that the UE clusters are associated or co-located with each other, the mobile / computer application, server, UE, or network device / node may treat the UE cluster as one UE. Thus, some UEs from the UE cluster may be excluded from measurement. For example, a navigation application may identify that UE 702 is within the same vehicle on a road, and the navigation application may treat UE 702 as one UE (or avoid including some of UE 702 for measurement), such as for the purpose of predicting traffic on the road to improve the accuracy of traffic prediction (e.g., UE 702 is treated as one vehicle UE instead of four separate vehicle UEs).

[0098] Figure 8 800 is a diagram illustrating an example of identifying a category or cluster of UEs based on the use of travel path areas in accordance with various aspects of the present disclosure. In another aspect of the present disclosure, a mobile / computer application, a server, a UE, or a network device / node may be able to classify the type of UE and / or identify whether a cluster of UEs is associated with or co-located with each other based on the travel path. For example, an area may include a bicycle lane 802 designated for cyclists, a car lane 804 designated for cars, and a pedestrian lane 806 designated for pedestrians (which may include a crosswalk). Based on the UE being detected therein (e.g., using a combination of Figure 4Based on the travel path of the UE using the network-based positioning method or GNSS-based positioning described above, the mobile / computer application, server, UE or network device / node can determine or be inclined to determine that the UE is using the corresponding travel path.

[0099] For example, as shown at 810, if infrastructure 814 detects that UE 812 is on bicycle lane 802, infrastructure 814 may determine or be biased to determine that UE 812 is used by a cyclist (hereinafter referred to as a cyclist UE). As shown at 816, if infrastructure 814 detects that UE 818 is on pedestrian lane 806, infrastructure 814 may determine or be biased to determine that UE 818 is used by a pedestrian (e.g., a pedestrian UE). Similarly, as shown at 820, if infrastructure 814 detects that UE 822 is on automobile lane 804, infrastructure 814 may determine or be biased to determine that UE 822 is used in a vehicle (e.g., a vehicle UE). Therefore, infrastructure 814 may be able to classify UEs based on their corresponding positions on a specified path of travel. In addition, infrastructure 814 may also determine whether UE groups are associated or co-located based on the positions of UE groups on the path of travel. For example, if multiple UEs are detected on the automobile lane 804 and they are close to each other (e.g., within 2 meters of each other), the infrastructure 814 may be biased towards determining that the UEs are located on one vehicle, while if the UEs are detected on the pedestrian lane 806, the infrastructure 814 may be less biased towards determining that the UEs are associated or co-located. Figure 7 The described aspects are used to further improve the accuracy of the determination.

[0100] In some scenarios, it may be helpful to distinguish between UEs using different areas of a path of travel. For example, different vehicle paths of travel may be categorized based on traffic control markings (e.g., crossing lanes, turns, parking spaces, temporary turn lanes, parking lanes for emergency stops (e.g., to secure tires), temporary loading / unloading areas, fire truck entrances and exits, etc.). Motorcycles may be allowed to use non-marked areas (e.g., between lanes), and UEs at crosswalks (during pedestrian "walk" signals) or on sidewalks may be less likely to be vehicle UEs, etc. These distinctions may be important for public areas that are adjacent and have different uses. For example, a park may have bike lanes, a street may have shared bike / vehicle lanes, a public park may have vehicle lanes, etc. The different uses of areas in a path of travel may be identified based on local laws, traffic markings, sidewalks, etc.

[0101] In some examples, UE behavior can also be used to define how the space is being used (e.g., this may be useful for areas that do not have strict lane markings and path of travel usage, etc.). For example, a UE in a parked car in a designated area for parking may have two categories: 1) as a potential pedestrian leaving a vehicle, or 2) as a potential vehicle leaving a parking space. However, if the position of the UE estimated over time is sufficiently accurate, the movement of the UE can be used to classify the UE (e.g., to identify whether the UE is a pedestrian UE, a vehicle UE, a cyclist UE, or another type of UE, etc.). For example, if the UE makes a movement that a vehicle UE or a cyclist UE would not be able to make (e.g., making a short sharp right turn, going up stairs, walking between bollards / gates, etc.), the UE may be a pedestrian UE. Therefore, UE classification based on UE movement can further improve the accuracy of UE classification.

[0102] By enabling a location server, base station, or mobile / computer application to classify UEs and / or determine whether UE clusters are associated or co-located with each other, the location server, base station, or mobile / computer application can eliminate redundant UEs (e.g., UEs with similar signatures) or combine redundant UEs into one UE or exclude (e.g., avoid including) some redundant UEs from measurement. For example, a mobile / computer application can treat four UEs (e.g., UE 702) in a vehicle as one UE traveling on a road rather than four different UEs traveling on the same road, which can enable the mobile / computer application to have a more accurate understanding or prediction of road conditions.

[0103] In another aspect of the present disclosure, the Figures 6 to 8 Information or measurements collected by UEs, base stations, RSUs and / or network devices / nodes as described above (such as the location of UEs, the types of these UEs and / or clustering of these UEs) are sent to a server for analysis. The server can be a central server, a crowdsourcing server, a cloud server, an edge device, etc. For example, multiple entities in an area (e.g., UEs, multiple radio access technologies (RATs) and / or RSUs, etc.)) can be configured to detect the location of the UE or perform location-related measurements (e.g., Doppler, speed, orientation, etc.) on the UEs in the area. Multiple entities can then report the location information and / or location-related measurements of the UE to the server for the server to aggregate and eliminate redundant links. For example, a server can collect location measurements performed by multiple base stations (or components of base stations) for UEs in an area. Based on the collected location measurements, the server can aggregate or eliminate / exclude UEs that are likely to be associated with or co-located with each other.

[0104] In some examples, the server may also direct the entity / device to perform measurements with nearby entities / devices for verification. Figure 6, after the infrastructure 602 identifies the number of vehicle UEs 606 and pedestrian UEs 604 in the area, the infrastructure 602 may report the information to the server. The server may then request another infrastructure (not shown), pedestrian UE, vehicle UE, or RSU to perform the same measurement on the area to verify whether the information collected by the infrastructure 602 is accurate. In another example, the server may implement or provide a single interface of traffic data from the server to one or more cloud providers or users, so that the cloud providers and / or users may have an easier and more convenient means to access the traffic data.

[0105] In another aspect of the present disclosure, a hierarchy may be defined for information (e.g., sensor data) collected by different types of sensors. For example, sensors on a vehicle (e.g., advanced driver assistance system (ADAS) sensors, or vehicle sensors with a certain degree of functionality, authentication, and / or anti-spoofing capabilities) may have higher accuracy than sensors on a smartphone (e.g., microphones, cameras, etc.). Therefore, sensors on a vehicle may be given a higher hierarchy than sensors on a smartphone. Therefore, if a server collects sensed data from sensors of a vehicle (e.g., via a car to cloud / server / infrastructure configuration) and sensed data from sensors of a smartphone, the sensed data from the vehicle may take precedence over sensed data from the smartphone (if there is a conflict between the sensed data). In other words, if there are multiple sensors (by a server) used to provide traffic data, information from sensors with a higher hierarchy may take precedence over information from sensors with a lower hierarchy. In some examples, the sensed data provided by the sensor may also be augmented with device-specific data. For example, data provided by a vehicle's sensors to a cloud server (eg, via a car-to-cloud configuration) may be augmented with data collected by the cloud server (eg, a car-to-cloud platform) from sensors of other vehicles / entities.

[0106] In another aspect of the present disclosure, the sensing data obtained from different sensors can also be used to verify traffic data. For example, RF sensing technology (e.g., based on UWB, WiFi, Bluetooth, etc.) and / or image recognition technology (e.g., based on a camera) can be used to identify whether there are humans in an area or environment (e.g., in a vehicle, in a building) and / or the number of humans in the area or environment. Then, based on the identification, the mobile / computer application (or location server) can make more accurate predictions about traffic and / or provide more accurate navigation. For example, return to reference Figure 7, a device (e.g., a RAT) may perform RF sensing on a vehicle to identify whether there are any humans in the vehicle and / or the number of humans in the vehicle. Thus, when the vehicle is not moving, such as when the vehicle is stopped at a red light, the location server (or mobile / computer application) may determine that the vehicle is unlikely to be a parked vehicle because humans were detected in the vehicle. On the other hand, if the vehicle is not moving and no humans are detected in the vehicle, the location server (or mobile / computer application) may consider the vehicle to be a parked vehicle.

[0107] In another aspect of the present disclosure, different devices / entities can share their sensor data associated with an area and uniquely identify objects within the area. These devices / entities can then communicate with each other or other devices regarding the objects being identified and / or the uniqueness associated with these objects.

[0108] Fig. 9 900 is a diagram illustrating an example of multiple wireless devices exchanging sensor data in accordance with various aspects of the present disclosure. Infrastructure 902 (e.g., a base station or a component of the base station), RSU 904, and vehicle 906 may be configured to detect the number of UEs in an area (e.g., an intersection), the location / orientation of each UE, and / or its classification / classification (e.g., vehicle UE, pedestrian UE, stationary UE, etc.) (collectively, “sensed data”). For example, infrastructure 902 may detect the presence of three UEs in the area based on the approximate location of the UEs and the number of attached UEs from the area, such as in combination with Figure 6 As described. The RSU 904 may identify the presence of two vehicle UEs 908 moving in the same direction based on sidelink ranging / positioning. The vehicle 906 may detect the presence of two vehicles and one pedestrian moving in the area, such as based on ADAS sensors (e.g., cameras, sonar, lidar, etc.) on the vehicle 906. For example, ADAS sensors may accurately identify the location and orientation of objects over time, which may confirm that a vehicle is driving on the road (vs. parking) or that a pedestrian with a mobile device is walking on the road (vs. mistaking it for a vehicle), etc. Moreover, camera data may be used to distinguish between devices juxtaposed in a single vehicle, pedestrians walking together, etc. The infrastructure 902, RSU 904, and vehicle 906 may then exchange their sensing data with each other or provide their sensing data to a server. Based on the exchanged / collected sensing data, the infrastructure 902, RSU 904, vehicle 906, and / or server may determine that there are likely to be two vehicle UEs 908 and one pedestrian UE 910 in the area. In another example, as combined with Figure 8As described, UEs detected at an intersection may be classified based on their travel paths. Thus, infrastructure 902 may determine that there are four vehicle UEs and one pedestrian based on detecting four UEs in the car lane and one UE in the pedestrian lane (or at a crosswalk), etc.

[0109] In one example, infrastructure 902, RSU 904, vehicle 906 and / or server can share this information with each other or provide this information to another entity. For example, the server can share this information with another vehicle 912 (or with a mobile / computer application associated with the vehicle 912) so that the vehicle 912 (or mobile / computer application) understands that there are two vehicles and a pedestrian at the upcoming intersection. If the vehicle 912 is turning at the intersection and only two vehicles are observed, the vehicle 912 (or mobile / computer application) can be configured to be cautious to deal with potential pedestrians who may be in the blind spot / hidden field of vision of the vehicle 912. Similarly, positioning, location and / or UE classification information can also be very useful for traffic control devices because they can enable traffic control devices to have a more accurate understanding of current traffic. For example, when the camera on the traffic light cannot see all UEs in the area, information related to the number, positioning, location and / or type of UE can be used to predict traffic instead.

[0110] Thus, while sensing performed by one entity may only provide partial information about UEs / objects in an area, the combination of sensing data from multiple entities may provide a more complete view and identification of UEs / objects in the area. Additionally, sensing data from sensors or different entities may be managed by a server, and based on the managed sensing data, the server may provide predictive behavior and / or object classification (e.g., via a machine learning algorithm) for identified objects / UEs.

[0111] In another aspect of the present disclosure, as in Figure 7 As shown at 712 of , a mobile device (e.g., UE 702) can be configured to determine or estimate measurements of nearby devices (e.g., other UEs 702) and send an indication of the proximity of the nearby devices or whether the nearby devices are likely to correspond to a single device (e.g., in the same car or transportation vehicle) to a server. In response, the server can cluster the UEs 702 based on ranging information (e.g., timing or RSSI), and the server can consider UE locations, such as in combination with Figure 8 The UE position detected based on its corresponding travel path is described as such (eg, on a car lane, bicycle lane, sidewalk, pedestrian path, venue, etc.).

[0112] In some scenarios, there may be multiple different UEs associated with the same user (e.g., a user carrying a smart phone and a smart watch, etc.). Therefore, in another example, UEs used by the same user or on the same entity (e.g., the same vehicle) may be configured to provide an indication of which UE(s) are associated with the user. For example, a first UE (e.g., a smart watch) may indicate to a server that a second UE (e.g., a smart phone) is associated with the same user or vice versa. Therefore, if both UEs report their measurements (e.g., measurements associated with the user) to the server, the server may discard one of the measurements (e.g., which may be considered redundant information).

[0113] In another aspect of the present disclosure, when different UEs or services of UEs (e.g., different applications on the same UE) are associated with or used by the same user (e.g., a user carrying a smart phone and a smart watch or a user who installs multiple navigation applications on the same device, etc.), measurements from different UEs / services may be compared to further improve the accuracy of UE classification and clustering. For example, the UE may obtain measurement data from a first application provider and a second application provider, and the UE (or the user of the UE) may determine the correspondence between the two services or how to offset the measurement data. For example, the UE (or the user of the UE) may notice that the first application / UE is more reliable than the second application / UE in a specific location or area (e.g., it may be region-specific or region-type specific). The user may then also have a third UE / application that is able to determine the correspondence between the first UE / application and the second UE / application on the data provided by the third UE / application. Therefore, the third UE / application may receive data from the second UE / application and determine how much the data corresponds to what is seen from the UE in the area, and adjust the data accordingly without providing the offset back to the second UE / application. This offset may be more specific to a particular area or provide a higher granularity than that provided by the second UE / application (e.g., lane specific). The third UE / application may also determine when an alert provided by the second UE / application is relevant or not relevant (e.g., the UE / application or its server may be integrated with a real-time database of traffic conflicts). Thus, comparisons of measurements from different devices or applications may be used to derive filtering or pre-filtering for alerts provided by a third party. Note that aspects described herein may also occur at a server, or may occur at a mobile device.

[0114] In another example, if the third UE / application is aware of the correspondence between the first UE / application and the second UE / application, the third UE / application may also indicate to the server potential duplication in the data reported by the first UE / application and the second UE / application, so that the server may be made aware of the "duplicated" UE. For example, there may be multiple asset trackers in a transportation vehicle. If the network operator indicates that an area is "busy" (e.g., as in Figure 5 506 of the example), then if the server or UE knows that a subset of those asset trackers all correspond to the same transportation vehicle, the server or UE may disregard or ignore the indication, and thus the network busy signal may be ignored and not extrapolated to the transportation data for that particular area.

[0115] Fig.10 1000 is a flow chart of a method of wireless communication. The method may be performed by a network node (e.g., UE 104, 404, 702; base station 102; TRP 402, 406; infrastructure 602, 902; vehicle UE 606, 822, 908; pedestrian UE 604, 818, 910; cyclist UE 812, vehicle 906; device 1204; network entity 1202). The method may enable the network node to improve the performance and accuracy of mobile / computer applications by distinguishing / classifying entities associated with UEs and / or entities running navigation applications, thereby enabling the mobile / computer applications to have a more accurate understanding of the situation around the UE and provide better navigation for the user.

[0116] At 1002, a network node may obtain first information including at least one characteristic associated with a plurality of devices, such as a combination of Figure 6 For example, the infrastructure 602 may obtain information such as the approximate location of the plurality of devices, the number of the plurality of devices, Doppler information associated with the plurality of devices, the orientation of the plurality of devices, and / or the speed of the plurality of devices. The first information may be obtained by, for example Fig.12 The sensory data processing component 198 of the device 1204 and / or Fig.12 and Fig.13 The sensing data processing component 199 of the network entity 1202 / 1302 is executed.

[0117] In one example, the network node may be a UE, a component of the UE, a base station, a component of the base station, a network entity, or a location server.

[0118] In another example, the first information may be obtained from a plurality of devices, at least one base station or a component of the at least one base station, at least one RSU, or a combination thereof.

[0119] In another example, the at least one feature may correspond to an approximate location of the plurality of devices, a number of the plurality of devices, Doppler information associated with the plurality of devices, an orientation of the plurality of devices, a velocity of the plurality of devices, or a combination thereof.

[0120] In another example, the at least one feature may correspond to positioning of the plurality of devices, and wherein the positioning of the plurality of devices is based on V2X positioning, UWB positioning, Wi-Fi positioning, control plane positioning, or a combination thereof.

[0121] In another example, the at least one characteristic may correspond to an acceleration of each of the plurality of devices.

[0122] In another example, at least one feature may correspond to sound captured by multiple devices.

[0123] In another example, the at least one feature may correspond to an identification of the plurality of devices or one or more objects associated with the plurality of devices. In such an example, the identification of the plurality of devices or one or more objects associated with the plurality of devices may be based on an advanced driver assistance system.

[0124] At 1004, the network entity may perform classification of the plurality of devices based on the first information, such as in combination with Figure 6 For example, the infrastructure 602 may determine whether the UE group is a vehicle UE 606 or a pedestrian UE 604 based on the speed and / or location of the UE group over a period of time. The classification of multiple devices may be performed by, for example Fig.12 The sensory data processing component 198 of the device 1204 and / or Fig.12 and Fig.13 The sensing data processing component 199 of the network entity 1202 / 1302 is executed.

[0125] In one example, at 1008, to perform classification of the plurality of devices based on the first information, the network node may identify or determine that a subset of the plurality of devices is associated with the first information as redundant information or overlapping information, and the network node may classify the first subset of the plurality of devices into the same category based on the first subset of the plurality of devices being associated with the first information as redundant information or overlapping information. In such an example, at 1010, the network node may direct the first subset of the plurality of devices to perform at least one measurement with one or more other devices to verify the accuracy of the first information.

[0126] In another example, at 1012, the network node may augment the first information with data received from at least one other platform, wherein the classification of the plurality of devices may be further based on the data.

[0127] In another example, at 1014, the network node may verify or confirm the accuracy of the classification of the plurality of devices based on the RF sensing.

[0128] At 1006, the network node may select a first subset of the plurality of devices for measurement based on at least one characteristic associated with the plurality of devices, such as in conjunction with Figure 6 and Figure 7 For example, Figure 7 As shown, the infrastructure 602 can distinguish whether clusters of UEs 702 are associated / co-located with each other based on one or more positioning mechanisms, based on acceleration of the UEs 702, and / or based on sounds detected by the UEs 702, etc. The infrastructure 602 can then perform measurements for the cluster of UEs (e.g., measurements associated with an application or server) or avoid performing measurements for the cluster of UEs, etc. The selection of the first subset of the plurality of devices can be performed by, for example Fig.12 The sensory data processing component 198 of the device 1204 and / or Fig.12 and Fig.13 The sensing data processing component 199 of the network entity 1202 / 1302 is executed.

[0129] In one example, the application may include a navigation application, a location location application, an advertising application, or a messaging application.

[0130] In another example, at 1016, to select the first subset of the plurality of devices for measurement, the network node may exclude a second subset of the plurality of devices from the measurement based on the classification of the plurality of devices.

[0131] Fig.11 1100 is a flow chart of a method of wireless communication. The method may be performed by a network node (e.g., UE 104, 404, 702; base station 102, TRP 402, 406; infrastructure 602, 902; vehicle UE 606, 822, 908; pedestrian UE 604, 818, 910; cyclist UE 812, vehicle 906; device 1204; network entity 1202). The method may enable a network node to improve the performance and accuracy of a mobile / computer application by distinguishing / classifying entities associated with a UE and / or entity running a navigation application, thereby enabling the mobile / computer application to have a more accurate understanding of the situation around the UE and provide better navigation for the user.

[0132] At 1102, a network node may obtain first information including at least one characteristic associated with a plurality of devices, such as a combination of Figure 6For example, the infrastructure 602 may obtain information such as the approximate location of the plurality of devices, the number of the plurality of devices, Doppler information associated with the plurality of devices, the orientation of the plurality of devices, and / or the speed of the plurality of devices. The first information may be obtained by, for example Fig.12 The sensory data processing component 198 of the device 1204 and / or Fig.12 and Fig.13 The sensing data processing component 199 of the network entity 1202 / 1302 is executed.

[0133] In one example, the network node may be a UE, a component of the UE, a base station, a component of the base station, a network entity, or a location server.

[0134] In another example, the first information may be obtained from a plurality of devices, at least one base station or a component of the at least one base station, at least one RSU, or a combination thereof.

[0135] In another example, the at least one feature may correspond to an approximate location of the plurality of devices, a number of the plurality of devices, Doppler information associated with the plurality of devices, an orientation of the plurality of devices, a velocity of the plurality of devices, or a combination thereof.

[0136] In another example, the at least one feature may correspond to positioning of the plurality of devices, and wherein the positioning of the plurality of devices is based on V2X positioning, UWB positioning, Wi-Fi positioning, control plane positioning, or a combination thereof.

[0137] In another example, the at least one characteristic may correspond to an acceleration of each of the plurality of devices.

[0138] In another example, at least one feature may correspond to sound captured by multiple devices.

[0139] In another example, the at least one feature may correspond to an identification of the plurality of devices or one or more objects associated with the plurality of devices. In such an example, the identification of the plurality of devices or one or more objects associated with the plurality of devices may be based on an advanced driver assistance system.

[0140] In another example, the network entity may perform classification of the plurality of devices based on the first information, such as in combination with Figure 6 For example, the infrastructure 602 may determine whether the UE group is a vehicle UE 606 or a pedestrian UE 604 based on the speed and / or location of the UE group over a period of time. The classification of multiple devices may be performed by, for example Fig.12 The sensory data processing component 198 of the device 1204 and / or Fig.12 and Fig.13The sensing data processing component 199 of the network entity 1202 / 1302 is executed.

[0141] In another example, to perform classification of the plurality of devices based on the first information, the network node may identify or determine that a first subset of the plurality of devices is associated with the first information as redundant information or overlapping information, and the network node may classify the first subset of the plurality of devices into the same category based on the first subset of the plurality of devices being associated with the first information as redundant information or overlapping information. In such an example, the network node may direct the first subset of the plurality of devices to perform at least one measurement with one or more other devices to verify the accuracy of the first information.

[0142] In another example, the network node may augment the first information with data received from at least one other platform, wherein the classification of the plurality of devices may be further based on the data.

[0143] In another example, the network node may verify or confirm the accuracy of the classification of the plurality of devices based on RF sensing.

[0144] At 1106, the network node may select a first subset of the plurality of devices for measurement based on at least one characteristic associated with the plurality of devices, such as in conjunction with Figure 6 and Figure 7 For example, Figure 7 As shown, the infrastructure 602 can distinguish whether clusters of UEs 702 are associated / co-located with each other based on one or more positioning mechanisms, based on acceleration of the UEs 702, and / or based on sounds detected by the UEs 702, etc. The infrastructure 602 can then perform measurements for the cluster of UEs (e.g., measurements associated with an application or server) or avoid performing measurements for the cluster of UEs, etc. The selection of the first subset of the plurality of devices can be performed by, for example Fig.12 The sensory data processing component 198 of the device 1204 and / or Fig.12 and Fig.13 The sensing data processing component 199 of the network entity 1202 / 1302 is executed.

[0145] In one example, the application may include a navigation application, a location location application, an advertising application, or a messaging application.

[0146] In another example, to select a first subset of the plurality of devices for measurement, the network node may exclude a second subset of the plurality of devices from the measurement based on a classification of the plurality of devices.

[0147] Fig.121200 is an example of a hardware implementation of an exemplary apparatus 1204. The apparatus 1204 may be a UE, a component of a UE, or may implement UE functionality. In some aspects, the apparatus 1204 may include a cellular baseband processor 1224 (also referred to as a modem) coupled to one or more transceivers 1222 (e.g., a cellular RF transceiver). The cellular baseband processor 1224 may include on-chip memory 1224'. In some aspects, the apparatus 1204 may also include one or more subscriber identity module (SIM) cards 1220 and an application processor 1206 coupled to a secure digital (SD) card 1208 and a screen 1210. The application processor 1206 may include on-chip memory 1206'. In some aspects, the device 1204 may also include a Bluetooth module 1212, a WLAN module 1214, an SPS module 1216 (e.g., a GNSS module), one or more sensor modules 1218 (e.g., an atmospheric pressure sensor / altimeter; a motion sensor such as an inertial management unit (IMU), a gyroscope, and / or an accelerometer; light detection and ranging (LIDAR), radio-aided detection and ranging (RADAR), sound navigation and ranging (SONAR), a magnetometer, audio, and / or other technologies for positioning), an additional memory module 1226, a power source 1230, and / or a camera 1232. The Bluetooth module 1212, the WLAN module 1214, and the SPS module 1216 may include an on-chip transceiver (TRX) (or in some cases, only a receiver (RX)). The Bluetooth module 1212, the WLAN module 1214, and the SPS module 1216 may include their own dedicated antennas and / or communicate using an antenna 1280. The cellular baseband processor 1224 communicates with the UE 104 and / or with the RU associated with the network entity 1202 through the transceiver 1222 via one or more antennas 1280. The cellular baseband processor 1224 and the application processor 1206 can each include a computer-readable medium / memory 1224', 1206', respectively. The additional memory module 1226 can also be considered as a computer-readable medium / memory. Each computer-readable medium / memory 1224', 1206', additional memory module 1226 can be non-transitory. The cellular baseband processor 1224 and the application processor 1206 are each responsible for general processing, including executing software stored on the computer-readable medium / memory. The software enables the cellular baseband processor 1224 / application processor 1206 to perform the various functions described above when executed by the cellular baseband processor 1224 / application processor 1206. The computer-readable medium / memory can also be used to store data manipulated by the cellular baseband processor 1224 / application processor 1206 when executing the software.In some examples, the cellular baseband processor 1224 / application processor 1206 may be a component of the UE 350 and may include the memory 360 and / or at least one of the TX processor 368, the RX processor 356, and the controller / processor 359. In other examples, the cellular baseband processor 1224 / application processor 1206 may be a component of a base station and may include the TX processor 316, the RX processor 370, and the controller / processor 375. In one configuration, the device 1204 may be a processor chip (modem and / or application) and include only the cellular baseband processor 1224 and / or the application processor 1206, while in another configuration, the device 1204 may be the entire UE (e.g., see. Figure 3 350) and includes additional modules of device 1204.

[0148] As discussed above, the sensory data processing component 198 is configured to obtain first information including at least one characteristic associated with a plurality of devices, and select a first subset of the plurality of devices for measurement based on the at least one characteristic associated with the plurality of devices. The sensory data processing component 198 may be located within the cellular baseband processor 1224, the application processor 1206, or both the cellular baseband processor 1224 and the application processor 1206. The sensory data processing component 198 may be one or more hardware components that are specifically configured to perform the stated process / algorithm, implemented by one or more processors configured to perform the stated process / algorithm, stored in a computer-readable medium to be implemented by one or more processors, or some combination thereof. As shown, the device 1204 may include a variety of components configured for various functions. In one configuration, the apparatus 1204, and in particular the cellular baseband processor 1224 and / or the application processor 1206, includes: means for obtaining first information including at least one characteristic associated with a plurality of devices; means for performing classification of the plurality of devices based on the first information; means for selecting a first subset of the plurality of devices for measurement based on at least one characteristic associated with the plurality of devices; means for identifying that the first subset of the plurality of devices is associated with the first information as redundant information or overlapping information; means for classifying the first subset of the plurality of devices as the same category based on the first subset of the plurality of devices being associated with the first information as redundant information or overlapping information; means for directing the first subset of the plurality of devices to perform at least one measurement with one or more other devices to verify the accuracy of the first information; means for augmenting the first information with data received from at least one other platform, wherein the classification of the plurality of devices is further based on the data; means for verifying or confirming the accuracy of the classification of the plurality of devices based on RF sensing; and / or means for excluding the first subset of the plurality of devices from application or measurement based on the classification of the plurality of devices. The means may be the sensing data processing component 198 of the apparatus 1204 or configured to perform the functions recited by the means. As described above, the apparatus 1204 may include the TX processor 368, the RX processor 356, and the controller / processor 359. Thus, in one configuration, the means may be the TX processor 368, the RX processor 356, and / or the controller / processor 359 configured to perform the functions recited by the means.

[0149] Fig.131300 is a diagram illustrating an example of a hardware implementation of a network entity 1302. The network entity 1302 may be a BS, a component of a BS, or may implement BS functionality. The network entity 1302 may include at least one of a CU 1310, a DU 1330, or a RU 1340. For example, depending on the layer functionality handled by the component 199, the network entity 1302 may include a CU 1310; both the CU 1310 and the DU 1330; each of the CU 1310, the DU 1330, and the RU 1340; the DU 1330; both the DU 1330 and the RU 1340; or the RU 1340. The CU 1310 may include a CU processor 1312. The CU processor 1312 may include an on-chip memory 1312'. In some aspects, the CU 1310 may also include an additional memory module 1314 and a communication interface 1318. CU 1310 communicates with DU 1330 via a midhaul link, such as an F1 interface. DU 1330 may include a DU processor 1332. DU processor 1332 may include on-chip memory 1332'. In some aspects, DU 1330 may also include an additional memory module 1334 and a communication interface 1338. DU 1330 communicates with RU 1340 via a fronthaul link. RU 1340 may include a RU processor 1342. RU processor 1342 may include on-chip memory 1342'. In some aspects, RU 1340 may also include an additional memory module 1344, one or more transceivers 1346, an antenna 1380, and a communication interface 1348. RU 1340 communicates with UE 104. On-chip memory 1312', 1332', 1342' and additional memory modules 1314, 1334, 1344 can each be considered as a computer-readable medium / memory. Each computer-readable medium / memory can be non-transitory. Each processor in processor 1312, 1332, 1342 is responsible for general processing, including executing software stored on computer-readable medium / memory. The software, when executed by the corresponding processor, enables the processor to perform the various functions described above. The computer-readable medium / memory can also be used to store data manipulated by the processor when executing the software.

[0150] As discussed above, component 199 is configured to obtain first information including at least one characteristic associated with a plurality of devices, and select a first subset of the plurality of devices for measurement based on the at least one characteristic associated with the plurality of devices. Component 199 may be within one or more processors of one or more of CU 1310, DU 1330, and RU 1340. Component 199 may be one or more hardware components specifically configured to perform the stated process / algorithm, implemented by one or more processors configured to perform the stated process / algorithm, stored in a computer-readable medium to be implemented by one or more processors, or some combination thereof. Network entity 1302 may include a variety of components configured for various functions. In one configuration, the network entity 1302 includes: means for obtaining first information including at least one characteristic associated with a plurality of devices; means for performing classification of the plurality of devices based on the first information; means for selecting a first subset of the plurality of devices for measurement based on at least one characteristic associated with the plurality of devices; means for identifying that the first subset of the plurality of devices is associated with the first information as redundant information or overlapping information; means for classifying the first subset of the plurality of devices into the same category based on the first subset of the plurality of devices being associated with the first information as redundant information or overlapping information; means for directing the first subset of the plurality of devices to perform at least one measurement with one or more other devices to verify the accuracy of the first information; means for augmenting the first information with data received from at least one other platform, wherein the classification of the plurality of devices is further based on the data; means for verifying or confirming the accuracy of the classification of the plurality of devices based on RF sensing; and / or means for excluding the first subset of the plurality of devices from application or measurement based on the classification of the plurality of devices. The means may be a component 199 of the network entity 1302 configured to perform the functions recited by the means. As described above, the network entity 1302 may include the TX processor 316, the RX processor 370, and the controller / processor 375. Thus, in one configuration, a component may be the TX processor 316, the RX processor 370, and / or the controller / processor 375 configured to perform the functions recited by the component.

[0151] Fig.141400 is an example of a hardware implementation for a network entity 1460. In one example, the network entity 1460 may be within the core network 120. The network entity 1460 may include a network processor 1412. The network processor 1412 may include an on-chip memory 1412'. In some aspects, the network entity 1460 may also include an additional memory module 1414. The network entity 1460 communicates with the CU 1402 directly (e.g., backhaul link) or indirectly (e.g., through the RIC) via a network interface 1480. The on-chip memory 1412' and the additional memory module 1414 may each be considered as a computer-readable medium / memory. Each computer-readable medium / memory may be non-transitory. The processor 1412 is responsible for general processing, including executing software stored on a computer-readable medium / memory. The software, when executed by the corresponding processor, enables the processor to perform the various functions described above. The computer-readable medium / memory may also be used to store data manipulated by the processor when executing the software.

[0152] As discussed above, component 1404 is configured to obtain first information including at least one characteristic associated with a plurality of devices, and select a first subset of the plurality of devices for measurement based on the at least one characteristic associated with the plurality of devices. Component 1404 may be within processor 1412. Component 1404 may be one or more hardware components specifically configured to perform the stated process / algorithm, implemented by one or more processors configured to perform the stated process / algorithm, stored in a computer readable medium to be implemented by one or more processors, or some combination thereof. Network entity 1460 may include a variety of components configured for various functions. In one configuration, the network entity 1460 includes: a component for obtaining first information including at least one characteristic associated with a plurality of devices; a component for performing classification of the plurality of devices based on the first information; a component for selecting a first subset of the plurality of devices for measurement based on at least one characteristic associated with the plurality of devices; a component for identifying that the first subset of the plurality of devices is associated with the first information as redundant information or overlapping information; a component for classifying the first subset of the plurality of devices into the same category based on the first subset of the plurality of devices being associated with the first information as redundant information or overlapping information; a component for directing the first subset of the plurality of devices to perform at least one measurement with one or more other devices to verify the accuracy of the first information; a component for augmenting the first information with data received from at least one other platform, wherein the classification of the plurality of devices is further based on the data; a component for verifying or confirming the accuracy of the classification of the plurality of devices based on RF sensing; and / or a component for excluding the first subset of the plurality of devices from application or measurement based on the classification of the plurality of devices. The component can be a component 1404 of the network entity 1460 configured to perform the function recited by the component.

[0153] It should be understood that the specific order or hierarchy of the blocks in the disclosed process / flowchart is merely an illustration of the exemplary method. It should be understood that the specific order or hierarchy of the blocks in the process / flowchart may be rearranged based on design preferences. Further, some blocks may be combined or omitted. The attached method claims provide the elements of each block in a sample order, but are not limited to the specific order or hierarchy provided.

[0154] The foregoing description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects. Therefore, the claims are not limited to the various aspects described herein, but should be given the full scope consistent with the language claims. Unless otherwise specified, reference to an element in the singular form does not mean "one and only one", but "one or more". Terms such as "if", "when ......" and "while" do not mean a direct temporal relationship or reaction. That is, these phrases (such as "when ......") do not mean an immediate action in response to the occurrence of an action or during the occurrence of an action, but simply imply that if the conditions are met, then the action will occur, but no specific or immediate time limit is required for the occurrence of the action. The word "exemplary" is used herein to mean "used as an example, instance, or illustration". Any aspect described as "exemplary" herein is not necessarily interpreted as being preferred or having advantages over other aspects. Unless otherwise specified, the term "some" refers to one or more. Combinations such as "at least one of A, B, or C", "one or more of A, B, or C", "at least one of A, B and C", "one or more of A, B, and C", and "A, B, C, or any combination thereof" include any combination of A, B, and / or C, which may include multiple A, multiple B, or multiple C. Specifically, combinations such as "at least one of A, B, or C", "one or more of A, B, or C", "at least one of A, B, and C", "one or more of A, B, and C", and "A, B, C, or any combination thereof" may be only A, only B, only C, A and B, A and C, B and C, or A and B and C, where any such combination may contain one or more members of A, B, or C. A set should be interpreted as a set of elements, where the number of elements is one or more. Thus, for a set of X, X will include one or more elements. If the first device receives data from the second device or sends data to the second device, data can be directly received / sent between the first device and the second device, or indirectly received / sent between the first device and the second device through a collection of devices. All structural and functional equivalents of the elements throughout the various aspects described in this disclosure that are known or will be known later to those of ordinary skill in the art are expressly incorporated herein by reference and are covered by the claims. In addition, nothing disclosed herein is intended to be dedicated to the public, regardless of whether such disclosure is explicitly stated in the claims. Words such as "module", "mechanism", "element", "device" cannot replace the word "component". Therefore, no claim element will be understood as a component plus function unless the element is explicitly recorded using the phrase "component for..."

[0155] As used herein, the phrase "based on" should not be interpreted as referring to a closed set of information, one or more conditions, one or more factors, etc. In other words, the phrase "based on A" (where "A" can be information, a condition, a factor, etc.) should be interpreted as "based at least on A" unless specifically stated differently.

[0156] The following aspects are merely illustrative and may be combined with other aspects or teachings described herein without limitation.

[0157] Aspect 1 is an apparatus for wireless communication at a network node, comprising: a memory; and at least one processor, the at least one processor being coupled to the memory and, based at least in part on information stored in the memory, the at least one processor being configured to: obtain first information comprising at least one characteristic associated with a plurality of devices; and select a first subset of the plurality of devices for measurement based on the at least one characteristic associated with the plurality of devices.

[0158] Aspect 2 is the apparatus according to aspect 1, wherein the network node is a UE, a component of the UE, a base station, a component of the base station, a network entity, or a location server.

[0159] Aspect 3 is an apparatus according to any one of aspects 1 and 2, wherein the first information is obtained from the multiple devices, at least one base station or a component of the at least one base station, at least one RSU, or a combination thereof.

[0160] Aspect 4 is an apparatus according to any one of aspects 1 to 3, wherein the measurement is associated with at least one of the following: a navigation application, a position location application, an advertising application, a messaging application, a location server, a crowdsourcing server, or a venue statistics calculation.

[0161] Aspect 5 is an apparatus according to any one of Aspects 1 to 4, wherein the at least one feature corresponds to the approximate positions of the multiple devices, the relative positions of the multiple devices, the positions of the multiple devices on the corresponding travel paths, the number of the multiple devices, Doppler information associated with the multiple devices, the orientations of the multiple devices, the speeds of the multiple devices, or a combination thereof.

[0162] Aspect 6 is an apparatus according to any one of Aspects 1 to 5, wherein the at least one feature corresponds to the positioning of the multiple devices, and wherein the positioning of the multiple devices is based on V2X positioning, UWB positioning, Wi-Fi positioning, control plane positioning, or a combination thereof.

[0163] Aspect 7 is an apparatus according to any one of aspects 1 to 6, wherein the at least one characteristic corresponds to an acceleration of each device of the plurality of devices.

[0164] Aspect 8 is an apparatus according to any one of aspects 1 to 7, wherein the at least one feature corresponds to sound captured by the plurality of devices.

[0165] Aspect 9 is an apparatus according to any one of aspects 1 to 8, wherein the at least one processor is further configured to: perform classification of the plurality of devices based on the first information.

[0166] Aspect 10 is an apparatus according to Aspect 9, wherein in order to perform the classification of the multiple devices based on the first information, the at least one processor is further configured to: identify or determine that the first subset of the multiple devices is associated with the first information as redundant information or overlapping information; and classify the first subset of the multiple devices into the same category based on the association of the first subset of the multiple devices with the first information as the redundant information or the overlapping information.

[0167] Aspect 11 is an apparatus according to aspect 10, wherein the at least one processor is further configured to: direct the first subset of the plurality of devices to perform at least one measurement with one or more other devices to verify accuracy of the first information.

[0168] Aspect 12 is the apparatus of aspect 9, wherein the at least one processor is further configured to augment the first information with data received from at least one other platform, wherein the classification of the plurality of devices is further based on the data.

[0169] Aspect 13 is an apparatus according to aspect 9, wherein the at least one processor is further configured to: verify or confirm the accuracy of the classification of the plurality of devices based on radio frequency (RF) sensing.

[0170] Aspect 14 is an apparatus according to aspect 9, wherein to select the first subset of the plurality of devices for the measurement, the at least one processor is configured to: exclude a second subset of the plurality of devices from the measurement based on the classification of the plurality of devices.

[0171] Aspect 15 is an apparatus according to any one of aspects 1 to 14, wherein the at least one feature corresponds to an identification of the plurality of devices or one or more objects associated with the plurality of devices.

[0172] Aspect 16 is an apparatus according to aspect 15, wherein the identification of the plurality of devices or the one or more objects associated with the plurality of devices is based on an advanced driver assistance system.

[0173] Aspect 17 is an apparatus according to any one of aspects 1 to 16, further comprising at least one of a transceiver or an antenna coupled to the at least one processor.

[0174] Aspect 18 is a method of performing wireless communication to implement any one of aspects 1 to 17.

[0175] Aspect 19 is an apparatus for wireless communication, comprising means for implementing any one of aspects 1 to 17.

[0176] Aspect 20 is a computer-readable medium storing computer-executable code, wherein the code, when executed by a processor, causes the processor to implement any one of aspects 1 to 17.

Claims

1. An apparatus for wireless communication at a network node, the apparatus comprising: Memory; and at least one processor coupled to the memory and based at least in part on the information stored in the memory, the at least one processor configured to: obtaining first information including at least one characteristic associated with a plurality of devices; as well as A first subset of the plurality of devices is selected for measurement based on the at least one characteristic associated with the plurality of devices.

2. The apparatus of claim 1, wherein the network node is a user equipment (UE), a component of the UE, a base station, a component of the base station, a network entity, or a location server.

3. The apparatus according to claim 1, wherein the first information is obtained from the plurality of devices, at least one base station or a component of the at least one base station, at least one road side unit (RSU), or a combination thereof.

4. The device of claim 1, wherein the measurements are associated with at least one of: a navigation application, a position location application, an advertising application, a messaging application, a location server, a crowdsourcing server, or venue statistics calculations.

5. An apparatus according to claim 1, wherein the at least one feature corresponds to the approximate positions of the multiple devices, the relative positions of the multiple devices, the positions of the multiple devices on corresponding travel paths, the number of the multiple devices, Doppler information associated with the multiple devices, the orientations of the multiple devices, the speeds of the multiple devices, or a combination thereof.

6. The apparatus of claim 1 , wherein the at least one feature corresponds to positioning of the plurality of devices, and wherein the positioning of the plurality of devices is based on vehicle-to-everything (V2X) positioning, ultra-wideband (UWB) positioning, Wi-Fi positioning, control plane positioning, or a combination thereof.

7. The apparatus of claim 1, wherein the at least one characteristic corresponds to an acceleration of each of the plurality of devices.

8. The apparatus of claim 1, wherein the at least one feature corresponds to sound captured by the plurality of devices.

9. The apparatus of claim 1 , wherein the at least one processor is further configured to: Classification of the plurality of devices is performed based on the first information.

10. The apparatus of claim 9, wherein to perform the classification of the plurality of devices based on the first information, the at least one processor is further configured to: identifying that the first subset of the plurality of devices is associated with the first information as redundant information or overlapping information; and The first subset of the plurality of devices is classified into the same category based on the first subset of the plurality of devices being associated with the first information being the redundant information or the overlapping information.

11. The apparatus of claim 10, wherein the at least one processor is further configured to: The first subset of the plurality of devices is directed to perform at least one measurement with one or more other devices to verify accuracy of the first information.

12. The apparatus of claim 9, wherein the at least one processor is further configured to: The first information is augmented with data received from at least one other platform, wherein the categorization of the plurality of devices is further based on the data.

13. The apparatus of claim 9, wherein the at least one processor is further configured to: The accuracy of the classification of the plurality of devices is verified or confirmed based on radio frequency (RF) sensing.

14. The apparatus of claim 9, wherein to select the first subset of the plurality of devices for the measurement, the at least one processor is configured to: A second subset of the plurality of devices is excluded from the measurement based on the classification of the plurality of devices.

15. The apparatus of claim 1, wherein the at least one characteristic corresponds to an identification of the plurality of devices or one or more objects associated with the plurality of devices. 16 . The apparatus of claim 15 , wherein the identification of the plurality of devices or the one or more objects associated with the plurality of devices is based on an advanced driver assistance system (ADAS).

17. A method for wireless communication at a network node, the method comprising: obtaining first information including at least one characteristic associated with a plurality of devices; as well as A first subset of the plurality of devices is selected for measurement based on the at least one characteristic associated with the plurality of devices.

18. The method of claim 17, wherein the first information is obtained from the plurality of devices, at least one base station or a component of the at least one base station, at least one road side unit (RSU), or a combination thereof.

19. The method of claim 17, wherein the at least one feature corresponds to the approximate positions of the multiple devices, the relative positions of the multiple devices, the positions of the multiple devices on corresponding travel paths, the number of the multiple devices, Doppler information associated with the multiple devices, the orientations of the multiple devices, the speeds of the multiple devices, or a combination thereof.

20. The method of claim 17, wherein the at least one feature corresponds to positioning of the plurality of devices, and wherein the positioning of the plurality of devices is based on vehicle-to-everything (V2X) positioning, ultra-wideband (UWB) positioning, Wi-Fi positioning, control plane positioning, or a combination thereof.

21. The method of claim 17, wherein the at least one characteristic corresponds to an acceleration of each of the plurality of devices or a sound captured by the plurality of devices.

22. The method of claim 17, further comprising: Classification of the plurality of devices is performed based on the first information.

23. The method of claim 22, wherein performing the classification of the plurality of devices based on the first information comprises: identifying that the first subset of the plurality of devices is associated with the first information as redundant information or overlapping information; as well as The first subset of the plurality of devices is classified into the same category based on the first subset of the plurality of devices being associated with the first information being the redundant information or the overlapping information.

24. The method according to claim 23, further comprising: The first subset of the plurality of devices is directed to perform at least one measurement with one or more other devices to verify accuracy of the first information.

25. The method of claim 22, further comprising: The first information is augmented with data received from at least one other platform, wherein the categorization of the plurality of devices is further based on the data.

26. The method of claim 22, further comprising: The accuracy of the classification of the plurality of devices is verified or confirmed based on radio frequency (RF) sensing.

27. The method of claim 22, wherein selecting the first subset of the plurality of devices for the measurement comprises: A second subset of the plurality of devices is excluded from the measurement based on the classification of the plurality of devices.

28. The method of claim 17, wherein the at least one characteristic corresponds to an identification of the plurality of devices or one or more objects associated with the plurality of devices.

29. An apparatus for wireless communication at a network node, the apparatus comprising: means for obtaining first information comprising at least one characteristic associated with a plurality of devices; as well as Means for selecting a first subset of the plurality of devices for measurement based on the at least one characteristic associated with the plurality of devices.

30. A computer readable medium storing computer executable code at a network node, the code, when executed by a processor, causing the processor to: obtaining first information including at least one characteristic associated with a plurality of devices; and A first subset of the plurality of devices is selected for measurement based on the at least one characteristic associated with the plurality of devices.

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