Random sampling consensus (RANSAC) for enhanced joint communication and sensing (JCS)

Abstract

Systems, apparatus, processes, and computer-readable media for wireless communication are disclosed. For example, a network device can perform sensing to obtain a sensing measurement, and perform a random sample consensus (RANSAC) algorithm on the sensing measurement to identify outer and inner points within the sensing measurement.

Description

Technical Field

[0001] The present disclosure generally relates to scheduling and / or processing sensing and communication signals for joint communication and sensing (JCS).For example, aspects of the present disclosure relate to enhancing JCS with random sample consensus (RANSAC). Background Art

[0002] Wireless communication systems are widely deployed to provide various types of communication content, such as voice, video, packet data, messaging, and broadcast. These systems may be able to support communication with multiple users by sharing available system resources (e.g., time, frequency, and power). Examples of such multiple access systems include fourth generation (4G) systems (such as long term evolution (LTE) systems, advanced LTE (LTE-A) systems, or LTE-A Pro systems) and fifth generation (5G) systems (which may be referred to as new radio (NR) systems). These systems may employ techniques such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), or discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM). A wireless multiple access communication system may include one or more base stations or one or more network access nodes, each base station or network access node simultaneously supporting communication for multiple communication devices, which may be further referred to as user equipment (UE). Some wireless communication systems may support communication between UEs, which may involve direct transmission between two or more UEs.

[0003] As larger bandwidths are allocated to wireless cellular communication systems (e.g., including 5G and beyond 5G) and more use cases are introduced into cellular communication systems, multiplexing sensing and communication signals for joint communication and sensing may be an essential feature of existing or future wireless communication systems, such as to enhance the overall spectrum efficiency of the wireless communication network. Summary of the Invention

[0004] The following presents a simplified summary of one or more aspects disclosed herein. Therefore, the following summary should neither be considered an exhaustive overview of all contemplated aspects nor be considered to identify key or critical elements related to all contemplated aspects or to delineate the scope associated with any particular aspect. Therefore, the sole purpose of the following summary is to present certain concepts related to one or more aspects of the mechanisms disclosed herein in a simplified form prior to the detailed description presented below.

[0005] Systems and techniques for wireless communication are described. According to at least one example, a network device for wireless communication is provided. The network device includes at least one memory and at least one processor, the at least one processor coupled to the at least one memory and configured to: perform sensing to obtain sensed measurements; and perform a random sample consensus (RANSAC) algorithm on the sensed measurements to identify outliers and inliers within the sensed measurements.

[0006] In another illustrative example, a method for wireless communication at a network device is provided, comprising: performing sensing by the network device to obtain a sensed measurement; and performing a random sample consensus (RANSAC) algorithm on the sensed measurement by the network device to identify outliers and inliers within the sensed measurement.

[0007] In another illustrative example, a non-transitory computer-readable medium having instructions stored thereon is provided, which instructions, when executed by at least one processor, cause the at least one processor to: perform sensing to obtain a sensed measurement; and perform a random sample consensus (RANSAC) algorithm on the sensed measurement to identify outliers and inliers within the sensed measurement.

[0008] In another illustrative example, an apparatus for wireless communication is provided, comprising: means for performing sensing to obtain a sensed measurement; and means for performing a random sample consensus (RANSAC) algorithm on the sensed measurement to identify outliers and inliers within the sensed measurement.

[0009] In another illustrative example, a network entity for wireless communication is provided. The network entity includes at least one memory and at least one processor coupled to the at least one memory and configured to: receive a sensing measurement from a set of network devices; perform a random sample consensus (RANSAC) algorithm on the sensing measurement to identify outliers and inliers within the sensing measurement; determine, based on the outliers, that one or more network devices in the set of network devices generate ambiguous sensing measurements; and send a command to the one or more network devices in the set of network devices to disable sensing measurement reporting.

[0010] In another illustrative example, a method for wireless communication at a network entity is provided. The method includes: receiving, by the network entity, a sensing measurement from a set of network devices; performing, by the network entity, a random sample consensus (RANSAC) algorithm on the sensing measurement to identify outliers and inliers within the sensing measurement; determining, by the network entity, based on the outliers, that one or more network devices in the set of network devices generate ambiguous sensing measurements; and sending, by the network entity, a command to disable sensing measurement reporting to the one or more network devices in the set of network devices.

[0011] In another illustrative example, a non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one processor, cause the at least one processor to: receive sensing measurements from a set of network devices; perform a random sample consensus (RANSAC) algorithm on the sensing measurements to identify outliers and inliers within the sensing measurements; determine, based on the outliers, that one or more network devices of the set of network devices produced ambiguous sensing measurements; and send a command to the one or more network devices of the set of network devices to disable sensing measurement reporting.

[0012] In another illustrative example, an apparatus for wireless communication is provided. The apparatus includes means for receiving sensing measurements from a set of network devices; means for performing a random sample consensus (RANSAC) algorithm on the sensing measurements to identify outliers and inliers within the sensing measurements; means for determining, based on the outliers, that one or more network devices of the set of network devices produced ambiguous sensing measurements; and means for sending a command to the one or more network devices of the set of network devices to disable sensing measurement reporting.

[0013] In some aspects, one or more of the network devices, apparatuses, or other devices described herein are, are part of, and / or include a user equipment (UE), a base station (e.g., a gNodeB (gNB) or eNodeB (eNB)) or a portion of a base station (e.g., one or more of a central unit (CU), a distributed unit (DU), a radio unit (RU), a near real-time (near-RT) RAN intelligent controller (RIC), or a non-RT RIC of a base station). The UE can be a wearable device, an extended reality (XR) device (e.g., a virtual reality (VR) device, an augmented reality (AR) device, or a mixed reality (MR) device), a head-mounted display (HMD) device, a wireless communication device, a mobile device (e.g., a mobile phone and / or mobile handset and / or a so-called “smart phone” or other mobile device), a camera, a personal computer, a laptop computer, a server computer, a vehicle or a component of a vehicle or a computing device, another device, or a combination thereof. In some aspects, the one or more of the network devices, apparatuses, or other devices can include one or more cameras for capturing one or more images. In some examples, the one or more of the network devices, apparatuses, or other devices can also include a display for displaying one or more images, notifications, and / or other displayable data. In some cases, one or more of the network devices, apparatuses, or other devices can include one or more receivers, transmitters, or transceivers for receiving and / or transmitting wireless communications.

[0014] This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used in determining the scope of the claimed subject matter. The subject matter should be understood from readi ng the entire specification, including the following sections, the claims, and the accompanying drawings.

[0015] The foregoing and other features and aspects will become more apparent upon reading the following specification in conjunction with the accompanying drawings in which: BRIEF DESCRIPTION OF DRAWINGS

[0016] The accompanying drawings are presented to aid in the description of various aspects of the disclosure and are provided solely for illustration of the various aspects and are not intended to limit the scope of the disclosure.

[0017] Figure 1 is a diagram illustrating an example wireless communication system that can be employed by the disclosed systems and techniques for employing RANSAC to enhance JCS in accordance with some aspects of the disclosure.

[0018] Figure 2 is a diagram illustrating an example of a disaggregated base station architecture that can be employed by the disclosed systems and techniques for employing RANSAC to enhance JCS in accordance with some aspects of the disclosure.

[0019] Figure 3 is a diagram illustrating an example of a frame structure that can be employed by the disclosed systems and techniques for employing RANSAC to enhance JCS in accordance with some aspects of the disclosure.

[0020] Figure 4 is a block diagram illustrating an example of a computing system of an electronic device that can be employed by the disclosed systems and techniques for employing RANSAC to enhance JCS in accordance with some aspects of the disclosure.

[0021] Figure 5 is a diagram illustrating an example of a wireless device utilizing radio frequency (RF) monostatic sensing techniques that can be employed by the disclosed systems and techniques described herein for determining one or more characteristics of a target object in accordance with some aspects of the disclosure.

[0022] Figure 6 is a diagram illustrating an example of a receiver utilizing RF bistatic sensing techniques for use with one transmitter that can be employed by the disclosed systems and techniques described herein for determining one or more characteristics of a target object in accordance with some aspects of the disclosure.

[0023] Figure 7 is a diagram illustrating an example of a receiver utilizing RF bistatic sensing techniques for use with multiple transmitters that can be employed by the disclosed systems and techniques described herein for determining one or more characteristics of a target object in accordance with some aspects of the disclosure.

[0024] Figure 8 is a diagram illustrating an example geometry for bi- station (or mono- station) sensing according to some aspects of the present disclosure.

[0025] Figure 9 is a diagram illustrating a bi- station distance for bi- station sensing according to some aspects of the present disclosure.

[0026] Figure 10 is a diagram illustrating an example of devices involved in wireless communication (e.g., sidelink communication) according to some aspects of the present disclosure.

[0027] Figure 11 is a diagram illustrating an example of an existing comb structure for reference signals.

[0028] Figure 12 is a diagram illustrating an example of a system for employing RANSAC to enhance JCS according to some aspects of the present disclosure, where the system is performing bi- station sensing of a target.

[0029] Figure 13 is a plot illustrating an example of identifying inliers and outliers in a data set with RANSAC according to some aspects of the present disclosure.

[0030] Figure 14 is a signaling diagram illustrating an example of signaling for employing RANSAC for a particular application according to some aspects of the present disclosure.

[0031] Figure 15 is a signaling diagram illustrating an example of signaling for employing RANSAC to prune a best set of resources for sensing according to some aspects of the present disclosure.

[0032] Figure 16 is a plot illustrating an example of results due to RANSAC identifying inliers and outliers in a data set according to some aspects of the present disclosure.

[0033] Figure 17 is a signaling diagram illustrating an example of signaling for employing RANSAC to continuously identify inliers and outliers according to some aspects of the present disclosure.

[0034] Figure 18 is a signaling diagram illustrating an example of signaling for employing RANSAC to remove ambiguities in sensing measurements according to some aspects of the present disclosure.

[0035] Figure 19 is a diagram illustrating an example of a time window for obtaining measurements, and an example plot illustrating an example result due to performing RANSAC on the measurements according to some aspects of the present disclosure.

[0036] Figure 20 is a signaling diagram illustrating an example of signaling for a network to employ RANSAC to determine measurement quality of a sensing node in accordance with some aspects of the present disclosure.

[0037] Figure 21 is a diagram illustrating an example of a ghost target according to some aspects of the present disclosure.

[0038] Figure 22A is a flow chart illustrating an example of a process for wireless communication utilizing a method for enhancing JCS with RANSAC in accordance with some aspects of the present disclosure.

[0039] Figure 22B is a flow chart illustrating an example of a process for wireless communication utilizing a method for enhancing JCS with RANSAC in accordance with some aspects of the present disclosure.

[0040] Figure 23 is a block diagram illustrating an example of a computing system that may be employed by the disclosed systems and techniques for enhancing JCS with RANSAC, in accordance with some aspects of the present disclosure. DETAILED DESCRIPTION

[0041] For illustrative purposes, certain aspects of the present disclosure are provided below. Without departing from the scope of the present disclosure, alternative aspects may be designed. In addition, well-known elements of the present disclosure will not be described in detail or will be omitted to avoid making the relevant details of the present disclosure difficult to understand. Some aspects described herein can be applied independently, and some of them can be applied in combination, which will be apparent to those skilled in the art. In the following description, specific details are set forth for explanation purposes to provide a thorough understanding of various aspects of the application. However, it will be apparent that various aspects can be implemented without these specific details. Each drawing and description is not intended to be restrictive.

[0042] The following description provides only exemplary aspects and is not intended to limit the scope, applicability, or configuration of the present disclosure. Instead, the following description of the exemplary aspects will provide those skilled in the art with a description that can be used to implement the exemplary aspects. It should be understood that various changes may be made to the function and arrangement of the elements without departing from the scope of the present application as set forth in the appended claims.

[0043] Radar sensing systems use radio frequency (RF) waveforms to perform RF sensing to determine or estimate one or more characteristics of a target object, such as the distance, angle, and / or velocity of the target object. The target object may include a vehicle, an obstacle, a user, a building, or other objects. A typical radar system includes at least one transmitter, at least one receiver, and at least one processor. When using a single receiver co-located with the transmitter, the radar sensing system can perform single-station sensing. When using a single receiver of a first device positioned away from the transmitter of a second device, the radar system can perform dual-station sensing. Similarly, when using multiple receivers of multiple devices all positioned away from at least one transmitter of at least one device, the radar system can perform multi-station sensing.

[0044] During operation of the radar sensing system, a transmitter transmits an electromagnetic (EM) signal in the RF domain toward a target object. The signal reflects from the target object to produce one or more reflected signals that provide information or properties about the target, such as the target object's position and velocity. At least one receiver receives the one or more reflected signals, and at least one processor, which may be associated with the at least one receiver, utilizes information from the one or more reflected signals to determine information or properties of the target object. The target object may also be referred to herein as a target.

[0045] Generally speaking, RF sensing involves monitoring moving targets with different motions (e.g., moving cars or pedestrians, human body motions such as breathing, and / or other micro-motions associated with the target). Doppler, which measures phase changes in the signal and indicates motion, is an important characteristic for sensing targets.

[0046] In some cases, radar sensing signals, which may be referred to as radar reference signals (RS), such as sensing reference signals (S-RS), may be designed and used for sensing purposes. Radar RSs do not contain any communication information. In contrast, communication RSs, such as demodulation reference signals (DMRSs), are typically designed and used only for communication purposes, such as estimating channel parameters for communication.

[0047] Cellular communication systems are designed to transmit communication signals in designated communication frequency bands (e.g., 23 gigahertz (GHz), 3.5 GHz, etc. for 5G / NR, 2.2 GHz, etc. for LTE). RF sensing systems are designed to transmit RF sensing signals in designated radar RF frequency bands (e.g., 77 GHz for autonomous driving). In future cellular communication systems, it is likely that the spectrum used for communication and sensing will be shared. In this case, communication and sensing should be considered jointly.

[0048] In some cases, as wireless communication systems (e.g., including cellular communication systems (such as 4G / LTE, 5G / NR, and beyond)) are allocated greater bandwidth and more use cases are introduced into wireless communication systems, joint communication and sensing (JCS) may be an essential feature of existing or future wireless communication systems. For example, in the 3rd Generation Partnership Project (3GPP) Release 18, several companies have proposed research on "integrated RF sensing and communication." For 3GPP Release 19, it is expected that more companies will propose research on JCS or RF sensing in NR. Simultaneously performing wireless communication and radar sensing can provide cost-effective deployment for both radar and communication systems.

[0049] In JCS, multiple sensing nodes (e.g., each in the form of a UE or a base station (such as a gNB)) may be included in a sensing session based on sensing management functionality (SMF) scheduling (e.g., which may be scheduled by the network (such as by a network server)). Efficient algorithms for selecting sensing nodes and for filtering sensing measurements by the sensing nodes may be crucial to ensuring accurate sensing performance.

[0050] In some scenarios, multiple coarse measurements (e.g., distance and / or Doppler measurements) may interfere with sensing results. For example, some sensing nodes may have poor channel quality, for example, because these sensing nodes have a low signal-to-noise ratio (SNR). The accuracy of the estimated distance, Doppler, and / or angle measured by these sensing nodes with poor channel quality may be low, and thus the measurements made by these sensing nodes may be considered to interfere with the accurate measurement estimates. Such measurement results (e.g., from sensing nodes with poor channel quality) may degrade the ultimate performance of target detection.

[0051] For another example where coarse measurements may interfere with sensing results, a subset of sensing nodes may provide outlier measurements, for example due to the geometric relationship of the subset of sensing nodes to the target. As another example, in some scenarios, identifying phantom targets hidden within the list of detected targets can be challenging. Phantom targets can be highly correlated with the selection of sensing nodes. Therefore, improved techniques with low complexity can be used to select the optimal sensing nodes to obtain sensing measurements and filter outlier measurements.

[0052] Currently, machine learning (ML) solutions are often used for some sensing scenarios (e.g., for detecting human motion). High quality and efficient datasets of sensing measurements are needed to obtain accurate ML solutions. Inefficient sensing measurements can lead to meaningless computations in ML model training and increased complexity in training. Therefore, the dataset of measurements should be cleaned and filtered to reduce the overhead and resource requirements for ML model training and measurement reporting. If the dataset is not cleaned and filtered, the communication overhead can also be high as inefficient measurements will be sent over the network. Therefore, improved techniques for cleaning the dataset (e.g., for improving data quality) can be beneficial as it can filter out unreliable data and reduce inefficient computations.

[0053] In some aspects of the disclosure, systems, apparatuses, methods (also referred to as processes), and computer-readable media (collectively referred to herein as “systems and techniques”) are described herein that provide solutions to enhance JCS with random sample consensus (RANSAC). The systems and techniques employ RANSAC to address the above challenges for different applications. In one or more examples, the systems and techniques leverage RANSAC to select optimal sensing nodes and filter out outliers with low complexity. In some examples, the systems and techniques use RANSAC to extract valid data such that only valid data (e.g., and not invalid data) is reported, which can save communication resources, overhead, and power.

[0054] In one or more aspects, the systems and techniques provide signaling to enable RANSAC, sensing configuration, and reporting mechanisms for reporting valid data (e.g., inliers) determined by RANSAC. In one or more examples, in a receive (Rx) sensing node, RANSAC can be enabled to prune optimal resources for sensing and prune valid measurements for reporting. In some examples, a buffer window is provided to maintain alignment of sensing measurements. In one or more examples, RANSAC can be enabled at the network side (e.g., in a network server). In some examples, RANSAC can be employed to enhance measurement ambiguity, remove phantom targets, and handle aliasing issues. In one or more examples, data channels are leveraged to enable RANSAC for range, Doppler, and angle estimation.

[0055] Additional aspects of the disclosure are described in more detail below.

[0056] As used herein, the terms “user equipment” (UE) and “network entity” are not intended to be specific to or otherwise limited to any particular radio access technology (RAT), unless otherwise indicated. Generally, a UE can be any wireless communication device (e.g., a mobile phone, router, tablet computer, laptop computer, and / or tracking device, etc.), wearable device (e.g., a smartwatch, smartglasses, wearable ring, and / or an extended reality (XR) device such as a virtual reality (VR) headset, an augmented reality (AR) headset or glasses, or a mixed reality (MR) headset), vehicle (e.g., automobile, motorcycle, bicycle, etc.), and / or an Internet of Things (IoT) device, etc., used by users for communicating over a wireless communication network. A UE can be mobile or can (e.g., at certain times) be stationary, and can communicate with a radio access network (RAN). As used herein, the term “UE” can be referred to interchangeably as an “access terminal” or “AT,” a “client device,” a “wireless device,” a “subscriber device,” a “subscriber terminal,” a “subscriber station,” a “user terminal” or “UT,” a “mobile device,” a “mobile terminal,” a “mobile station,” or variations thereof. Generally, UEs can communicate with a core network via a RAN, and through the core network the UEs can communicate with an external

[0057] The network entity may be implemented in a converged or monolithic base station architecture, or alternatively, in a disaggregated base station architecture, and may include one or more of a central unit (CU), a distributed unit (DU), a radio unit (RU), a near real-time (near-RT) RAN intelligent controller (RIC), or a non-real-time (non-RT) RIC. A base station (e.g., having a converged / monolithic base station architecture or a disaggregated base station architecture) may operate according to one of several RATs for communicating with UEs (depending on the network in which it is deployed) and may be alternatively referred to as an access point (AP), a network node, a NodeB (NB), an evolved NodeB (eNB), a next-generation eNB (ng-eNB), a new radio (NR) NodeB (also referred to as a gNB or gNodeB), etc. A base station may primarily support radio access for UEs, including supporting data, voice, and / or signaling connections for the supported UEs. In some systems, a base station may provide edge node signaling functionality, while in other systems, a base station may provide additional control and / or network management functionality. The communication link by which a UE can transmit signals to a base station is called an uplink (UL) channel (e.g., a reverse traffic channel, a reverse control channel, an access channel, etc.). The communication link by which a base station can transmit signals to a UE is called a downlink (DL) or forward link channel (e.g., a paging channel, a control channel, a broadcast channel, or a forward traffic channel, etc.). As used herein, the term traffic channel (TCH) can refer to an uplink, a reverse or downlink, and / or a forward traffic channel.

[0058] The term “network entity” or “base station” (e.g., with an aggregated / sliced base station architecture or a disaggregated base station architecture) can refer to a single physical transmission-reception point (TRP) or multiple physical transmission-reception points (TRPs) that can or can not be co-located. For example, where the term “network entity” or “base station” refers to a single physical TRP, the physical TRP can be a base station antenna corresponding to a cell (or several cell sectors) of the base station. Where the term “network entity” or “base station” refers to multiple co-located physical TRPs, the physical TRPs can be an array of antennas of the base station (e.g., as in a multiple-input multiple-output (MIMO) system or where the base station employs beamforming). Where the term “base station” refers to multiple non-co-located physical TRPs, the physical TRPs can be a distributed antenna system (DAS) (a network of spatially separated antennas connected to a common source via transmission medium) or a remote radio head (RRH) (a remote base station connected to a serving base station). Alternatively, non-co-located physical TRPs can be the serving base station that receives the measurement report from the UE and a neighbor base station whose reference radio frequency (RF) signals (or simply “reference signals”) the UE is measuring. Because, as used herein, a TRP is the point from or to which a base station transmits and receives wireless signals, a reference to transmitting from or receiving at a base station should be understood to refer to a particular TRP of the base station.

[0059] In some implementations that support positioning of UEs, a network entity or base station can not support wireless access by UEs (e.g., can not support data, voice, and / or signaling connections with UEs), but can instead transmit reference signals to UEs to be measured by the UEs, and / or can receive and measure signals transmitted by UEs. Such a base station can be referred to as a positioning beacon (e.g., where it transmits signals to UEs) and / or as a location measurement unit (e.g., where it receives and measures signals from UEs).

[0060] An RF signal includes an electromagnetic wave of a given frequency that transports information between a transmitter and a receiver. As used herein, a transmitter can transmit a single “RF signal” or multiple “RF signals” to a receiver. However, due to the propagation characteristics of RF signals over multipath channels, the receiver can receive multiple “RF signals” corresponding to each transmitted RF signal. The same transmitted RF signal on different paths between the transmitter and receiver can be referred to as a “multipath” RF signal. As used herein, where it is clear from the context that the term “signal” refers to a wireless signal or an RF signal, an RF signal can also be referred to as a “wireless signal” or simply a “signal.”

[0061] According to various aspects, Figure 1An exemplary wireless communication system 100 is illustrated that can be employed by the disclosed systems and techniques described herein for employing RANSAC to enhance JCS. The wireless communication system 100, which can also be referred to as a wireless wide area network (WWAN), can include various base stations 102 and various UEs 104. In some aspects, the base stations 102 can also be referred to as "network entities" or "network nodes." One or more of the base stations 102 can be implemented in an aggregated or monolithic base station architecture. Additionally or alternatively, one or more of the base stations 102 can be implemented in a disaggregated base station architecture and can include one or more of a central unit (CU), a distributed unit (DU), a radio unit (RU), a near real-time (near-RT) RAN intelligent controller (RIC), or a non-real-time (non-RT) RIC. The base stations 102 can include macrocell base stations (high-power cellular base stations) and / or small cell base stations (low-power cellular base stations). In one aspect, the macro cell base station may include an eNB and / or an ng-eNB (where the wireless communication system 100 corresponds to a long term evolution (LTE) network), or a gNB (where the wireless communication system 100 corresponds to an NR network), or a combination of both, and the small cell base station may include a femto cell, a pico cell, a micro cell, etc.

[0062] The base stations 102 may collectively form a RAN and interface with a core network 170 (e.g., an evolved packet core (EPC) or a 5G core (5GC)) via backhaul links 122, and may interface with one or more location servers 172 (which may be part of the core network 170 or external to the core network 170) via the core network 170. Among other functions, the base stations 102 may perform functions related to one or more of: delivering user data, radio channel encryption and decryption, integrity protection, header compression, mobility control functions (e.g., handover, dual connectivity), inter-cell interference coordination, connection establishment and release, load balancing, distribution of non-access stratum (NAS) messages, NAS node selection, synchronization, RAN sharing, multimedia broadcast multicast service (MBMS), subscriber and equipment tracking, RAN information management (RIM), paging, positioning, and delivery of warning messages. The base stations 102 may communicate with each other directly or indirectly (e.g., via the EPC or 5GC) via backhaul links 134 (which may be wired and / or wireless).

[0063] Base stations 102 can communicate wirelessly with UEs 104. Each of base stations 102 can provide communication coverage for a corresponding geographic coverage area 110. In one aspect, base stations 102 in each coverage area 110 can support one or more cells. A "cell" is a logical communication entity used to communicate with a base station (e.g., on a certain frequency resource, referred to as a carrier frequency, component carrier, carrier, frequency band, etc.) and can be associated with an identifier (e.g., a physical cell identifier (PCI), a virtual cell identifier (VCI), a cell global identifier (CGI)) to distinguish between cells operating on the same or different carrier frequencies. In some cases, different cells can be configured according to different protocol types (e.g., machine type communication (MTC), narrowband IoT (NB-IoT), enhanced mobile broadband (eMBB), or other protocol types) that can provide access to different types of UEs. Because a cell is supported by a specific base station, the term "cell" can refer to either or both of the logical communication entity and the base station supporting the logical communication entity, depending on the context. Furthermore, since a TRP is generally the physical transmission point of a cell, the terms "cell" and "TRP" can be used interchangeably. In some cases, the term "cell" may also refer to a geographic coverage area (eg, a sector) of a base station, so long as a carrier frequency can be detected and used for communications within some portion of the geographic coverage area 110.

[0064] Although the geographic coverage areas 110 of adjacent macrocell base stations 102 may partially overlap (e.g., in a handover area), some areas of the geographic coverage areas 110 may substantially overlap with the larger geographic coverage area 110. For example, a small cell base station 102' may have a coverage area 110' that substantially overlaps with the coverage areas 110 of one or more macrocell base stations 102. A network that includes both small cell base stations and macrocell base stations may be referred to as a heterogeneous network. A heterogeneous network may also include a Home eNB (HeNB), which may provide service to a restricted group known as a Closed Subscriber Group (CSG).

[0065] The communication link 120 between the base station 102 and the UE 104 may include uplink (also referred to as a reverse link) transmissions from the UE 104 to the base station 102 and / or downlink (also referred to as a forward link) transmissions from the base station 102 to the UE 104. The communication link 120 may utilize MIMO antenna technology, including spatial multiplexing, beamforming, and / or transmit diversity. The communication link 120 may be over one or more carrier frequencies. The allocation of carriers may be asymmetric for the downlink and uplink (e.g., more or fewer carriers may be allocated to the downlink than to the uplink).

[0066] The wireless communication system 100 may further include a WLAN AP 150 in communication with a WLAN station (STA) 152 via a communication link 154 in an unlicensed spectrum (e.g., 5 gigahertz (GHz)). When communicating in the unlicensed spectrum, the WLAN STA 152 and / or the WLAN AP 150 may perform a clear channel assessment (CCA) or a listen-before-talk (LBT) procedure before communicating to determine whether the channel is available. In some examples, the wireless communication system 100 may include devices (e.g., UEs, etc.) that communicate with one or more UEs 104, base stations 102, APs 150, etc. using an ultra-wideband (UWB) spectrum. The UWB spectrum may range from 3.1 GHz to 10.5 GHz.

[0067] The small cell base station 102' can operate in licensed and / or unlicensed spectrum. When operating in the unlicensed spectrum, the small cell base station 102' can adopt LTE or NR technology and use the same 5 GHz unlicensed spectrum used by the WLAN AP 150. The small cell base station 102' using LTE and / or 5G in the unlicensed spectrum can boost the coverage of the access network and / or increase the capacity of the access network. NR in the unlicensed spectrum can be referred to as NR-U. LTE in the unlicensed spectrum can be referred to as LTE-U, License Assisted Access (LAA), or MulteFire.

[0068] The wireless communication system 100 may also include a millimeter wave (mmW) base station 180 that can operate at mmW frequencies and / or near-mmW frequencies to communicate with the UE 182. The mmW base station 180 can be implemented in a converged or monolithic base station architecture, or alternatively, in a disaggregated base station architecture (e.g., including one or more of a CU, DU, RU, near-RT RIC, or non-RT RIC). Extremely high frequency (EHF) is a portion of the RF band in the electromagnetic spectrum. EHF has a range of 30 GHz to 300 GHz, with wavelengths between 1 mm and 10 mm. Radio waves in this band may be referred to as millimeter waves. Near-mmW can extend down to frequencies of 3 GHz with a wavelength of 100 mm. Super high frequency (SHF) bands extend between 3 GHz and 30 GHz and are also referred to as centimeter waves. Communications using mmW and / or near-mmW radio frequency bands have high path loss and relatively short range. The mmW base station 180 and the UE 182 can utilize beamforming (transmit and / or receive) on the mmW communication link 184 to compensate for the extremely high path loss and short distance. In addition, it should be understood that in alternative configurations, one or more base stations 102 can also use mmW or near-mmW and beamforming for transmission. Therefore, it should be understood that the foregoing illustrations are merely examples and should not be construed as limiting the various aspects disclosed herein.

[0069] Transmit beamforming is a technique for focusing an RF signal in a specific direction. Traditionally, when a network node or entity (e.g., a base station) broadcasts an RF signal, it broadcasts the signal in all directions (omnidirectionally). With transmit beamforming, the network node determines where a given target device (e.g., a UE) is located (relative to the transmitting network node) and projects a stronger downlink RF signal in that specific direction, thereby providing the receiving device with a faster and stronger RF signal (in terms of data rate). To change the directionality of the RF signal when transmitting, the network node can control the phase and relative amplitude of the RF signal at each of the one or more transmitters broadcasting the RF signal. For example, the network node can use an array of antennas (referred to as a "phased array" or "antenna array") that form RF beams that can be "steered" to point in different directions without actually moving the antennas. Specifically, the RF currents from the transmitters are fed to the individual antennas in the correct phase relationship so that the radio waves from the individual antennas add together in the desired direction to increase radiation, and cancel out in undesired directions to suppress radiation.

[0070] The transmit beams can be quasi-co-located, meaning that they have the same parameters for a receiver (e.g., a UE) regardless of whether the transmit antennas of the network nodes themselves are physically co-located. In NR, there are four types of quasi-co-location (QCL) relationships. Specifically, a given type of QCL relationship means that certain parameters about the second reference RF signal on the second beam can be derived based on information about the source reference RF signal on the source beam. Thus, if the source reference RF signal is QCL type A, the receiver can use the source reference RF signal to estimate the Doppler shift, Doppler spread, average delay, and delay spread of the second reference RF signal sent on the same channel. If the source reference RF signal is QCL type B, the receiver can use the source reference RF signal to estimate the Doppler shift and Doppler spread of the second reference RF signal sent on the same channel. If the source reference RF signal is QCL type C, the receiver can use the source reference RF signal to estimate the Doppler shift and average delay of the second reference RF signal sent on the same channel. If the source reference RF signal is QCL type D, the receiver may use the source reference RF signal to estimate spatial reception parameters of a second reference RF signal transmitted on the same channel.

[0071] In receive beamforming, a receiver uses receive beams to amplify RF signals detected on a given channel. For example, a receiver may increase the gain setting of an antenna array in a particular direction and / or adjust the phase setting of an antenna array in a particular direction to amplify (e.g., increase the gain level of) RF signals received from that direction. Thus, when a receiver is said to be beamforming in a certain direction, it means that the beam gain in that direction is high relative to the beam gain in other directions, or that the beam gain in that direction is the highest compared to the beam gains of other beams available to the receiver. This results in a stronger received signal strength (e.g., reference signal received power (RSRP), reference signal received quality (RSRQ), signal to interference plus noise ratio (SINR), etc.) for RF signals received from that direction.

[0072] The receive beams may be spatially correlated. The spatial relationship means that the parameters for the transmit beam for the second reference signal may be derived based on information about the receive beam for the first reference signal. For example, a UE may receive one or more reference downlink reference signals (e.g., positioning reference signal (PRS), tracking reference signal (TRS), phase tracking reference signal (PTRS), cell-specific reference signal (CRS), channel state information reference signal (CSI-RS), primary synchronization signal (PSS), secondary synchronization signal (SSS), synchronization signal block (SSB), etc.) from a network node or entity (e.g., a base station) using a specific receive beam. The UE may then form a transmit beam based on the parameters of the receive beam for transmitting one or more uplink reference signals (e.g., uplink positioning reference signal (UL-PRS), sounding reference signal (SRS), demodulation reference signal (DMRS), PTRS, etc.) to the network node or entity (e.g., a base station).

[0073] Note that depending on the entity forming the "downlink" beam, the beam can be a transmit beam or a receive beam. For example, if a network node or entity (e.g., a base station) is forming a downlink beam to send a reference signal to a UE, the downlink beam is a transmit beam. However, if the UE is forming a downlink beam, the downlink beam is a receive beam that receives a downlink reference signal. Similarly, depending on the entity forming the "uplink" beam, the beam can be a transmit beam or a receive beam. For example, if a network node or entity (e.g., a base station) is forming an uplink beam, the uplink beam is an uplink receive beam, while if the UE is forming an uplink beam, the uplink beam is an uplink transmit beam.

[0074] In 5G, the spectrum in which wireless network nodes or entities (e.g., base stations 102 / 180, UEs 104 / 182) operate is divided into multiple frequency ranges: FR1 (from 450 megahertz (MHz) to 6000 MHz), FR2 (from 24250 MHz to 52600 MHz), FR3 (above 52600 MHz), and FR4 (between FR1 and FR2). In multi-carrier systems such as 5G, one of the carrier frequencies is referred to as the "primary carrier" or "anchor carrier" or "primary serving cell" or "PCell," and the remaining carrier frequencies are referred to as "secondary carriers" or "secondary serving cells" or "SCells." In carrier aggregation, the anchor carrier is a carrier operating on the primary frequency (e.g., FR1) used by the UE 104 / 182 and the cell in which the UE 104 / 182 performs an initial radio resource control (RRC) connection establishment procedure or initiates an RRC connection reestablishment procedure. The primary carrier carries all common and UE-specific control channels and can be a carrier in a licensed frequency (however, this is not always the case). A secondary carrier is a carrier operating on a second frequency (e.g., FR2) that can be configured and used to provide additional radio resources once an RRC connection is established between the UE 104 and the anchor carrier. In some cases, the secondary carrier can be a carrier in an unlicensed frequency. The secondary carrier can contain only necessary signaling information and signals. For example, since the primary uplink carrier and the primary downlink carrier are typically UE-specific, those UE-specific signaling information and signals may not be present in the secondary carrier. This means that different UEs 104 / 182 in a cell can have different downlink primary carriers. The same is true for the uplink primary carrier. The network can change the primary carrier for any UE 104 / 182 at any time. This is done, for example, to balance the load on different carriers. Since a "serving cell" (whether a PCell or SCell) corresponds to the carrier frequency or component carrier that some base station is using to communicate, the terms "cell," "serving cell," "component carrier," "carrier frequency," etc. may be used interchangeably.

[0075] For example, still referring to Figure 1, one of the frequencies used by the macrocell base station 102 may be an anchor carrier (or "PCell"), and the other frequencies used by the macrocell base station 102 and / or the mmW base station 180 may be secondary carriers ("SCells"). In carrier aggregation, the base station 102 and / or the UE 104 may use spectrum with a bandwidth of up to Y MHz (e.g., 5 MHz, 10 MHz, 15 MHz, 20 MHz, 100 MHz) per carrier, with up to a total of Yx MHz (x component carriers) in each direction for transmission. The component carriers may or may not be spectrally adjacent to each other. The allocation of carriers may be asymmetric with respect to the downlink and uplink (e.g., more or fewer carriers may be allocated to the downlink than to the uplink). Simultaneous transmission and / or reception of multiple carriers enables the UE 104 / 182 to significantly increase its data transmission and / or reception rate. For example, two 20 MHz aggregated carriers in a multi-carrier system would theoretically result in a doubled data rate (i.e., 40 MHz) compared to the data rate achieved with a single 20 MHz carrier.

[0076] In order to operate on multiple carrier frequencies, the base station 102 and / or the UE 104 are equipped with multiple receivers and / or transmitters. For example, the UE 104 may have two receivers, namely "receiver 1" and "receiver 2," where "receiver 1" is a multi-band receiver that can be tuned to either frequency band (i.e., carrier frequency) "X" or frequency band "Y," while "receiver 2" is a single-band receiver that can be tuned to only frequency band "Z." In this example, if the UE 104 is being served in frequency band "X," frequency band "X" will be referred to as the PCell or active carrier frequency, and "receiver 1" will need to tune from frequency band "X" to frequency band "Y" (SCell) to measure frequency band "Y" (and vice versa). In contrast, regardless of whether the UE 104 is being served in frequency band "X" or frequency band "Y," due to the separate "receiver 2," the UE 104 can measure frequency band "Z" without interrupting service on frequency band "X" or frequency band "Y."

[0077] The wireless communication system 100 may further include a UE 164 that may communicate with the macrocell base station 102 over a communication link 120 and / or with the mmW base station 180 over a mmW communication link 184. For example, the macrocell base station 102 may support a PCell and one or more SCells for the UE 164, and the mmW base station 180 may support one or more SCells for the UE 164.

[0078] The wireless communication system 100 may also include one or more UEs, such as UE 190, that are indirectly connected to one or more communication networks via one or more device-to-device (D2D) or peer-to-peer (P2P) links (referred to as “side links”). Figure 1 In the example of FIG1 , UE 190 has a D2D P2P link 192 with one of UEs 104 connected to one of base stations 102 (e.g., UE 190 can indirectly obtain cellular connectivity through the D2D P2P link), and has a D2D P2P link 194 with WLAN STA 152 connected to WLAN AP 150 (UE 190 can indirectly obtain WLAN-based Internet connectivity through the D2D P2P link). In one example, D2D P2P links 192 and 194 can use any well-known D2D RAT (such as LTE Direct (LTE-D), Wi-Fi Direct (Wi-Fi-D), As mentioned above, UE 104 and UE 190 can be configured to communicate using sidelink communications. In some cases, the sidelink transmission may include a request for feedback from the receiving UE (e.g., hybrid automatic repeat request (HARQ)).

[0079] Figure 2 is a diagram illustrating an example of a disaggregated base station architecture that may be employed by the disclosed systems and techniques for enhancing JCS with RANSAC. The deployment of a communication system (such as a 5G NR system) may 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 network equipment (such as a base station (BS)), or one or more units (or one or more components) that perform base station functionality may be implemented in an aggregated or disaggregated architecture. For example, a BS (such as a NodeB (NB), an evolved NB (eNB), an NR BS, a 5G NB, an AP, a transmit receive point (TRP), or a cell, etc.) may be implemented as an aggregated base station (also referred to as a standalone BS or a monolithic BS) or a disaggregated base station.

[0080] A disaggregated base station can be configured to utilize a radio protocol stack that is physically or logically integrated within a single RAN node. A disaggregated base station can be configured to utilize a protocol stack that is distributed, physically or logically, between two or more units, such as one or more central or centralized units (CUs), one or more distributed units (DUs), or one or more radio units (RUs). In some aspects, a CU can be implemented within a RAN node, and one or more DUs can be co-located with the CU, or alternatively, can be geographically or virtually distributed in one or more other RAN nodes. The DUs can be implemented to be in communication with one or more RUs. Each of the CU, DU, and RU can also be implemented as virtual units, i.e., a virtual central unit (VCU), virtual distributed unit (VDU), or virtual radio unit (VRU).

[0081] Base station type operations or network designs can take into account the disaggregated nature of base station functionality. For example, a disaggregated base station can be used in an integrated access backhaul (IAB) network, an open radio access network (O-RAN (such as network configurations initiated by the O-RAN Alliance)), or a virtualized radio access network (vRAN, also referred to as a cloud radio access network (C-RAN)). Disaggregation can include distributing functionality across two or more units at various physical locations, as well as virtually distributing functionality of at least one unit, which can enable flexibility in network design. The various units of a disaggregated base station or disaggregated RAN architecture can be configured for wired or wireless communication with at least one other unit.

[0082] As previously mentioned, Figure 2 An illustration diagram is shown that illustrates an example disaggregated base station 201 architecture. The disaggregated base station 201 architecture can include one or more central units (CUs) 211 that can communicate directly with a core network 223 via a backhaul link, or indirectly through one or more disaggregated base station units, such as a near real-time (near-RT) RAN intelligent controller (RIC) 227 via an E2 link, or a non-real-time (non-RT) RIC 217 associated with a service management and orchestration (SMO) framework 207, or both. The CUs 211 can be in communication with one or more distributed units (DUs) 231 via respective fronthaul links, such as Fl interfaces. The DUs 231 can be in communication with one or more radio units (RUs) 241 via respective front-haul links. The RUs 241 can be in communication with respective UEs 221 via one or more RF access links. In some implementations, a UE 221 can be simultaneously served by multiple RUs 241.

[0083] Each of the units (i.e., CU 211, DU 231, RU 241, and near-RT RIC 227, non-RT RIC 217, and SMO framework 207) can include or be coupled to one or more interfaces configured to receive or transmit signals, data, or information (collectively, signals) via wired or wireless transmission media. Each of the units, or an associated processor or controller providing instructions to the communication interfaces of these units, can be configured to communicate with one or more of the other units via the transmission media. For example, the units can include wired interfaces configured to receive or transmit signals to one or more of the other units over a wired transmission medium. In addition, the units can include wireless interfaces, which can include receivers, transmitters, or transceivers (such as RF transceivers) configured to receive or transmit signals, or both, to one or more of the other units over a wireless transmission medium.

[0084] In some aspects, the CU 211 can host one or more higher layer control functions. Such control functions can include radio resource control (RRC), packet data convergence protocol (PDCP), service data adaptation protocol (SDAP), etc. Each control function can utilize an interface configured to communicate signals with other control functions hosted by the CU 211. The CU 211 can be configured to handle user plane functionality (i.e., central unit-user plane (CU-UP)), control plane functionality (i.e., central unit-control plane (CU-CP)), or a combination thereof. In some implementations, the CU 211 can be logically split into one or more CU-UP units and one or more CU-CP units. When implemented in an O-RAN configuration, the CU-UP units can be in bidirectional communication with the CU-CP units via an interface, such as an El interface. The CU 211 can be implemented to communicate with the DU 231 for network control and signaling as needed.

[0085] The DU 231 may correspond to a logical unit that includes one or more base station functions for controlling the operation of one or more RUs 241. In some aspects, the DU 231 may host one or more of a radio link control (RLC) layer, a medium access control (MAC) layer, and one or more higher physical (PHY) layers (such as modules for forward error correction (FEC) encoding and decoding, scrambling, modulation and demodulation, etc.) based at least in part on a functional split, such as those defined by the Third Generation Partnership Project (3GPP). In some aspects, the DU 231 may also host one or more lower PHY layers. Each layer (or module) may be implemented using an interface configured to communicate signals with other layers (and modules) hosted by the DU 231 or with control functions hosted by the CU 211.

[0086] Lower layer functionality may be implemented by one or more RUs 241. In some deployments, a RU 241 controlled by a DU 231 may correspond to a logical node that hosts RF processing functionality or low PHY layer functionality (such as performing Fast Fourier Transform (FFT), Inverse FFT (iFFT), digital beamforming, Physical Random Access Channel (PRACH) extraction and filtering, etc.), or both, based at least in part on a functional split (such as a lower layer functional split). In such an architecture, the RU 241 may be implemented to handle over-the-air (OTA) communications with one or more UEs 221. In some implementations, real-time and non-real-time aspects of control and user plane communications with the RU 241 may be controlled by the corresponding DU 231. In some scenarios, this configuration may enable the implementation of the DU 231 and CU 211 in a cloud-based RAN architecture (such as a vRAN architecture).

[0087] The SMO framework 207 can be configured to support RAN deployment and orchestration of non-virtualized network elements and virtualized network elements. For non- virtualized network elements, the SMO framework 207 can be configured to support deployment of dedicated physical resources for RAN coverage requirements, which can be managed via an operations and maintenance interface, such as an Ol interface. For virtualized network elements, the SMO framework 207 can be configured to interact with a cloud computing platform, such as an Open Cloud (O-Cloud) 291 to perform network element lifecycle management, such as to instantiate virtualized network elements, via a cloud computing platform interface, such as an 02 interface. Such virtualized network elements can include, but are not limited to, CUs 211, DUs 231, RUs 241, and near-RT RICs 227. In some implementations, the SMO framework 207 can communicate with hardware aspects of a 4G RAN, such as an Open eNB (O-eNB) 213, via an Ol interface. Additionally, in some implementations, the SMO framework 207 can communicate directly with one or more RUs 241 via an Ol interface. The SMO framework 207 can also include a non-RT RIC 217 configured to support functionality of the SMO framework 207.

[0088] The non-RT RIC 217 can be configured to include logical functions that enable near real-time control and optimization of RAN elements and resources, artificial intelligence / machine learning (AI / ML) workflows including model training and update, or policy-based direction of applications / features in the near-RT RIC 227. The non-RT RIC 217 can be coupled to, or in communication with, the near-RT RIC 227, such as via an Al interface. The near-RT RIC 227 can be configured to include logical functions that enable near real-time control and optimization of RAN elements and resources via data collection and actions via an interface, such as via an E2 interface, that connects one or more CUs 211, one or more DUs 231, or both, and the O-eNB 213 with the near-RT RIC 227.

[0089] In some implementations, the non-RT RIC 217 can receive parameters or external enrichment information from an external server in order to generate an AI / ML model to be deployed in the near-RT RIC 227. Such information can be utilized by the near-RT RIC 227 and can be received from non-network data sources or from network functions at the SMO framework 207 or the non-RT RIC 217. In some examples, the non-RT RIC 217 or the near-RT RIC 227 can be configured to tune RAN behavior or performance. For example, the non-RT RIC 217 can monitor long-term trends and patterns in performance and employ AI / ML models to perform corrective actions through the SMO framework 207 (such as via reconfiguration of O1) or via the creation of RAN management policies (such as A1 policies).

[0090] Various radio frame structures may be used to support downlink transmissions, uplink transmissions, and sidelink transmissions between network nodes (eg, a base station and a UE). Figure 3 is a diagram 300 illustrating an example of a frame structure that may be employed by the disclosed systems and techniques for enhancing JCS with RANSAC. Other wireless communication technologies may have different frame structures and / or different channels.

[0091] NR (and LTE) utilizes OFDM on the downlink and single carrier frequency division multiplexing (SC-FDM) on the uplink. However, unlike LTE, NR also has the option of using OFDM on the uplink. OFDM and SC-FDM divide the system bandwidth into multiple (K) orthogonal subcarriers, which are also often called tones, bins, etc. Each subcarrier can be modulated with data. Generally speaking, modulation symbols are transmitted in the frequency domain with OFDM and in the time domain with SC-FDM. The spacing between adjacent subcarriers can be fixed, and the total number of subcarriers (K) can depend on the system bandwidth. For example, the subcarrier spacing can be 15kHz, and the minimum resource allocation (resource block) can be 12 subcarriers (or 180kHz). Thus, for a system bandwidth of 1.25 megahertz (MHz), 2.5 MHz, 5 MHz, 10 MHz, or 20 MHz, the nominal Fast Fourier Transform (FFT) size may be equal to 128, 256, 512, 1024, or 2048, respectively. The system bandwidth may also be divided into subbands. For example, a subband may cover 1.08 MHz (i.e., 6 resource blocks), and there may be 1, 2, 4, 8, or 16 subbands for a system bandwidth of 1.25 MHz, 2.5 MHz, 5 MHz, 10 MHz, or 20 MHz, respectively.

[0092] LTE supports a single numerology (subcarrier spacing, symbol length, etc.). In contrast, NR can support multiple numerologies (μ). For example, subcarrier spacing (SCS) of 15 kHz, 30 kHz, 60 kHz, 120 kHz, and 240 kHz or greater can be available. Table 1, provided below, lists some of the different parameters for different NR numerologies.

[0093]

[0094]

[0095] Table 1

[0096] In one example, a numerology of 15 kHz is used. Thus, in the time domain, a 10 millisecond (ms) frame is divided into 10 equal sized subframes, each of 1 ms, and each subframe includes one slot. In the frequency domain, each slot contains a number of resource blocks (RBs) that are located with the center 20 MHz of the channel bandwidth. Figure 3 In the drawings, time is represented in the horizontal direction (e.g., on the X-axis), where time increases from left to right, and frequency is represented in the vertical direction (e.g., on the Y-axis), where frequency increases (or decreases) from bottom to top.

[0097] A resource grid can be used to represent the time slots, each time slot including one or more time-concurrent resource blocks (RBs) (also referred to as physical RBs (PRBs)) in the frequency domain. Figure 3 An example of a resource block (RB) 302 is illustrated. Data or information for joint communication and sensing can be included in one or more RBs 302. The RB 302 is arranged by placing the time domain on the horizontal (or x) axis and the frequency domain on the vertical (or y) axis. As shown, the RB 302 can be 180 kilohertz (kHz) wide in frequency and one slot long in time (where a slot is 1 millisecond (ms) in time). In some cases, a slot can include fourteen symbols (e.g., in slot configuration 0). The RB 302 includes twelve subcarriers (along the y-axis) and fourteen symbols (along the x-axis).

[0098] An intersection of a symbol and a subcarrier can be referred to as a resource element (RE) 304 or a tone. Figure 3 The RB 302 includes a plurality of REs, including resource elements (REs) 304. An RE 304 is 1 subcarrier x 1 symbol (e.g., OFDM symbol) and is the smallest discrete part of a subframe. An RE 304 includes a single complex value representing data from a physical channel or signal. The number of bits carried by each RE 304 depends on the modulation scheme.

[0099] In some aspects, some REs 304 may be used to transmit downlink reference (pilot) signals (DL-RS). DL-RS may include positioning reference signals (PRS), tracking reference signals (TRS), phase tracking reference signals (PTRS), channel state information reference signals (CSI-RS), demodulation reference signals (DMRS), primary synchronization signals (PSS), secondary synchronization signals (SSS), etc. Figure 3 The resource grid of exemplifies exemplary locations of REs 304 (labeled “R”) for transmitting DL-RS.

[0100] Figure 4 4 is a block diagram illustrating an example of a computing system 470 of an electronic device 407 that may be employed by the disclosed systems and techniques for enhancing JCS with RANSAC. The electronic device 407 is an example of a device that may include hardware and software for connecting to and exchanging data with other devices and systems using a communication network (e.g., a third-generation partner network such as a fifth-generation (5G) / new radio (NR) network, a fourth-generation (4G) / long-term evolution (LTE) network, a WiFi network, or other communication network). For example, the electronic device 407 may include or be a portion of a mobile device (e.g., a mobile phone), a wearable device (e.g., a web-connected or smartwatch), an extended reality device (e.g., a virtual reality (VR) device, an augmented reality (AR) device, or a mixed reality (MR) device), a personal computer, a laptop computer, a tablet computer, an Internet of Things (IoT) device, a wireless access point, a router, a vehicle or a component of a vehicle, a server computer, a robotic device, and / or other device used by a user to communicate over a wireless communication network. In some cases, such as when referring to a device configured to communicate using 5G / NR, 4G / LTE, or other telecommunication standards, the device 407 may be referred to as a user equipment (UE). In some cases, such as when referring to a device configured to communicate using the Wi-Fi standard, the device may be referred to as a station (STA).

[0101] The computing system 470 includes software and hardware components that may be electrically or communicatively coupled via a bus 489 (or may communicate in other ways, as appropriate). For example, the computing system 470 includes one or more processors 484. The one or more processors 484 may include one or more CPUs, ASICs, FPGAs, APs, GPUs, VPUs, NSPs, microcontrollers, dedicated hardware, any combination thereof, and / or other processing devices and / or systems. The bus 489 may be used by the one or more processors 484 to communicate between cores and / or with one or more memory devices 486.

[0102] The computing system 470 can also include one or more memory devices 486, one or more digital signal processors (DSPs) 482, one or more subscriber identity modules (SIMs) 474, one or more modems 476, one or more wireless transceivers 478, one or more antennas 487, one or more input devices 472 (e.g., a camera, a mouse, a keyboard, a touch-sensitive screen, a touchpad, a keypad, a microphone or microphone array, etc.), and one or more output devices 480 (e.g., a display, a speaker, a printer, etc.).

[0103] The one or more wireless transceivers 478 can receive wireless signals (e.g., signals 488) from one or more other devices such as other user devices, network devices (e.g., base stations such as evolved Node Bs (eNBs) and / or gNodeBs (gNBs)), WiFi access points (APs) such as routers, range extenders, etc.), cloud networks, etc. via the antennas 487. In some examples, the computing system 470 can include multiple antennas or antenna arrays that can facilitate simultaneous transmit and receive functionality. The antennas 487 can be omnidirectional, such that RF signals can be received from and sent to in all directions. The wireless signals 488 can be sent via a wireless network. The wireless network can be any wireless network, such as a cellular or telecommunication network (e.g., 3G, 4G, 5G, etc.), a wireless local area network (e.g., a WiFi network), a Bluetooth network, a near-field communication (NFC) network, a local area network (LAN), a metropolitan area network (MAN), a telephone network, a wide area network (WAN), and / or other networks. TM In some examples, the one or more wireless transceivers 478 can include an RF front end that includes one or more components such as amplifiers, mixers (also referred to as signal multipliers) for down-converting frequencies of signals, frequency synthesizers (also referred to as oscillators) that provide frequencies to the mixers, baseband filters, analog-to-digital converters (ADCs), one or more power amplifiers, and other components. The RF front end can generally handle selection of the wireless signals 488 and conversion of that wireless signal to baseband or an intermediate frequency, and can convert RF signals to the digital domain.

[0104] In some cases, the computing system 470 can include a coding-decoding device (or codec) configured to encode and / or decode data transmitted and / or received using the one or more wireless transceivers 478. In some cases, the computing system 470 can include an encryption-decryption device or component configured to encrypt and / or decrypt data transmitted and / or received by the one or more wireless transceivers 478 (e.g., according to Advanced Encryption Standard (AES) and / or Data Encryption Standard (DES) standards).

[0105] The one or more SIMs 474 can each securely store an International Mobile Subscriber Identity (IMSI) number and related key, which are assigned to a user of the electronic device 407. The IMSI and key can be used to identify and authenticate the subscriber when accessing networks provided by network service providers or operators associated with the one or more SIMs 474. The one or more modems 476 can modulate one or more signals to encode information for transmission using the one or more wireless transceivers 478. The one or more modems 476 can also demodulate signals received by the one or more wireless transceivers 478 in order to decode information transmitted. In some examples, the one or more modems 476 can include a WiFi modem, a 4G (or LTE) modem, a 5G (or NR) modem, and / or other types of modems. The one or more modems 476 and the one or more wireless transceivers 478 can be used to communicate data for the one or more SIMs 474.

[0106] The computing system 470 can also include (and / or be in communication with) one or more non-transitory machine-readable storage media (e.g., one or more memory devices 486) 486, which can include, among others, local and / or network accessible storage, a disk drive, a drive array, an optical storage device, a solid-state storage device, such as a RAM and / or ROM, which can be programmable, flash- updateable, and / or the like. Such storage devices can be configured to implement any appropriate data stores, including without limitation, various file systems, database structures, and / or the like.

[0107] In various aspects, functionality can be stored as one or more computer programs (e.g., instructions or code) in the memory device(s) 486 and executed by the one or more processors 484 and / or the one or more DSPs 482. The computing system 470 can further include software elements (e.g., in the one or more memory devices 486), including without limitation an operating system, device drivers, executable libraries, and / or other code, such as one or more application programs, which can include computer programs provided by various aspects, and / or can be designed to implement methods, and / or configure systems, as described herein.

[0108] In some aspects, the electronic device 407 can include means for performing the operations described herein. The means can include one or more components of the computing system 470. For example, the means for performing the operations described herein can include one or more of the input device 472, the SIM 474, the modem 476, the wireless transceiver 478, the output device 480, the DSP 482, the processor 484, the memory device 486, and / or the antenna 487.

[0109] In some aspects, the electronic device 407 may include components for providing joint communication and sensing (JCS), and components for enhancing JCS by employing RANSAC, for example, when multiplexing sensing signals and communication signals for JCS. In some examples, any or all of these components may include one or more wireless transceivers 478, one or more modems 476, one or more processors 484, one or more DSPs 482, one or more memory devices 486, any combination thereof, or other components of the electronic device 407.

[0110] Figure 5 is a diagram illustrating an example of a wireless device 500 utilizing RF monostatic sensing techniques for determining one or more characteristics (eg, position, velocity or speed, heading, etc.) of a target 502 object. Specifically, Figure 5 is a diagram illustrating an example of a wireless device 500 (e.g., a transmit / receive sensing node) that utilizes RF sensing technology (e.g., single-station sensing) to perform one or more functions, such as detecting the presence and location of a target 502 (e.g., an object, user, or vehicle), which is illustrated in the figure as a vehicle.

[0111] In some examples, wireless device 500 may be a mobile phone, a tablet computer, a wearable device, a vehicle, an extended reality (XR) device, a computing device or component of a vehicle, or other device that includes at least one RF interface (e.g., Figure 4 In some examples, the wireless device 500 may be a user device (e.g., Figure 4 An electronic device 407) provides connectivity, such as a base station (e.g., gNB, eNB, etc.), a wireless access point (AP), or other device including at least one RF interface.

[0112] In some aspects, the wireless device 500 may include one or more components for transmitting RF signals. The wireless device 500 may include at least one processor 522 for generating a digital signal or waveform. The wireless device 500 may also include a digital-to-analog converter (DAC) 504 capable of receiving a digital signal or waveform from the processor 522 (e.g., a microprocessor) and converting the digital signal or waveform into an analog waveform. The analog signal as an output of the DAC 504 may be provided to the RF transmitter 506 for transmission. The RF transmitter 506 may be a Wi-Fi transmitter, a 5G / NR transmitter, a Bluetooth transmitter, or a similar transmitter. TM transmitter or any other transmitter capable of transmitting RF signals.

[0113] The RF transmitter 506 can be coupled to one or more transmit antennas, such as a Tx antenna 512. In some examples, the transmit (Tx) antenna 512 can be an omnidirectional antenna capable of transmitting RF signals in all directions. For example, the Tx antenna 512 can be an omnidirectional Wi-Fi antenna capable of radiating Wi-Fi signals (e.g., 2.4 GHz, 5 GHz, 6 GHz, etc.) in a 360-degree radiation pattern. In another example, the Tx antenna 512 can be a directional antenna that transmits RF signals in a specific direction.

[0114] In some examples, the wireless device 500 may also include one or more components for receiving RF signals. For example, the receiver array in the wireless device 500 may include one or more receive antennas, such as a receive (Rx) antenna 514. In some examples, the Rx antenna 514 may be an omnidirectional antenna capable of receiving RF signals from multiple directions. In other examples, the Rx antenna 514 may be a directional antenna configured to receive signals from a specific direction. In another example, the Tx antenna 512 and / or the Rx antenna 514 may include multiple antennas (e.g., elements) configured as an antenna array (e.g., a phased antenna array).

[0115] The wireless device 500 may also include an RF receiver 510 coupled to an Rx antenna 514. The RF receiver 510 may include a signal source for receiving RF waveforms such as Wi-Fi signals, Bluetooth signals, and the like. TM The RF receiver 510 may be configured to receive a received analog RF waveform (e.g., a 5G / NR signal, or any other RF signal) and one or more hardware components thereof. The output of the RF receiver 510 may be coupled to an analog-to-digital converter (ADC) 508. The ADC 508 may be configured to convert the received analog RF waveform into a digital waveform. The digital waveform as an output of the ADC 508 may be provided to a processor 522 for processing. The processor 522 (e.g., a digital signal processor (DSP)) may be configured to process the digital waveform.

[0116] In one example, wireless device 500 can implement an RF sensing technique, e.g., a monostatic sensing technique, by causing Tx waveform 516 to be transmitted from Tx antenna 512. Although Tx waveform 516 is illustrated as a single line, in some cases, Tx waveform 516 can be transmitted in all directions by omni-directional Tx antenna 512. In one example, Tx waveform 516 can be a Wi-Fi waveform transmitted by a Wi-Fi transmitter in wireless device 500. In some cases, Tx waveform 516 can correspond to a Wi-Fi waveform transmitted concurrently or nearly concurrently with a Wi-Fi data communication signal or a Wi-Fi control function signal (e.g., a beacon transmission). In some examples, Tx waveform 516 can be transmitted using the same or similar frequency resources as a Wi-Fi data communication signal or a Wi-Fi control function signal (e.g., a beacon transmission). In some aspects, Tx waveform 516 can correspond to a Wi-Fi waveform transmitted separately from a Wi-Fi data communication signal and / or a Wi-Fi control signal (e.g., Tx waveform 516 can be transmitted at a different time and / or using different frequency resources).

[0117] In some examples, Tx waveform 516 can correspond to a 5G NR waveform transmitted concurrently or nearly concurrently with a 5G NR data communication signal or a 5G NR control function signal. In some examples, Tx waveform 516 can be transmitted using the same or similar frequency resources as a 5G NR data communication signal or a 5G NR control function signal. In some aspects, Tx waveform 516 can correspond to a 5G NR waveform transmitted separately from a 5G NR data communication signal and / or a 5G NR control signal (e.g., Tx waveform 516 can be transmitted at a different time and / or using different frequency resources).

[0118] In some aspects, one or more parameters associated with Tx waveform 516 can be modified, which can be used to increase or decrease RF sensing resolution. These parameters can include a frequency, a bandwidth, a number of spatial streams, a number of antennas configured to transmit Tx waveform 516, a number of antennas configured to receive reflected RF signals corresponding to Tx waveform 516 (e.g., Rx waveform 518), a number of spatial links (e.g., a number of spatial streams multiplied by a number of antennas configured to receive RF signals), a sampling rate, or any combination thereof. The transmitted waveforms (e.g., Tx waveform 516) and the received waveforms (e.g., Rx waveform 518) can include one or more RF sensing signals, which are also referred to as radar reference signals (RSs).

[0119] In another example, the Tx waveform 516 can be implemented as a sequence with perfect or nearly perfect autocorrelation properties. For example, the Tx waveform 516 can include a single-carrier Zadoff sequence or can include symbols similar to orthogonal frequency division multiplexing (OFDM) long training field (LTF) symbols. In some cases, the Tx waveform 516 can include a chirp signal, such as used in frequency modulated continuous wave (FM-CW) radar systems. In some configurations, the chirp signal can include a signal in which the signal frequency increases and / or decreases periodically in a linear and / or exponential manner.

[0120] In some aspects, the wireless device 500 can implement RF sensing technology by performing alternating transmit and receive functions (e.g., performing half-duplex operation). For example, the wireless device 500 can alternately enable its RF transmitter 506 to transmit a Tx waveform 516 when the RF receiver 510 is not enabled to receive (i.e., not receiving), and enable its RF receiver 510 to receive an Rx waveform 518 when the RF transmitter 506 is not enabled to transmit (i.e., not transmitting). When the wireless device 500 performs half-duplex operation, the wireless device 500 can transmit a Tx waveform 516, which can be a radar RS (e.g., a sensing signal).

[0121] In other aspects, the wireless device 500 can implement RF sensing techniques by performing concurrent transmit and receive functions (e.g., performing sub-band or full-band full-duplex operation). For example, the wireless device 500 can enable its RF receiver 510 to receive at or near the same time as it enables its RF transmitter 506 to transmit a Tx waveform 516. When the wireless device 500 performs full-duplex operation (e.g., sub-band full-duplex or full-band full-duplex), the wireless device 500 can transmit a Tx waveform 516, which can be a radar RS (e.g., a sensing signal).

[0122] In some examples, the transmission of a sequence or pattern included in the Tx waveform 516 can be repeated continuously, such that the sequence is transmitted a specific number of times or for a specific duration. In some examples, if the RF receiver 510 is enabled after the RF transmitter 506, the repetition of the pattern in the transmission of the Tx waveform 516 can be used to avoid missing the reception of any reflected signals. In one example implementation, the Tx waveform 516 can include a sequence having a sequence length L that is transmitted two or more times, which can allow the RF receiver 510 to be enabled for a time less than or equal to L to receive reflections corresponding to the entire sequence without losing any information.

[0123] By implementing alternating or simultaneous transmit and receive functionality (e.g., half-duplex or full-duplex operation), the wireless device 500 can receive signals corresponding to the Tx waveform 516. For example, the wireless device 500 can receive signals reflected from objects or people within a range of the Tx waveform 516, such as the Rx waveform 518 reflected from the target 502. The wireless device 500 can also receive a leakage signal (e.g., Tx leakage signal 520) that couples directly from the Tx antenna 512 to the Rx antenna 514 without reflecting from any object. For example, the leakage signal can include a signal that passes from a transmitter antenna (e.g., the Tx antenna 512) on the wireless device to a receiver antenna (e.g., the Rx antenna 514) on the wireless device without reflecting from any object. In some cases, the Rx waveform 518 can include multiple sequences corresponding to multiple copies of the sequence included in the Tx waveform 516. In some examples, the wireless device 500 can combine multiple sequences received by the RF receiver 510 to improve a signal-to-noise ratio (SNR).

[0124] The wireless device 500 can also implement RF sensing techniques by obtaining RF sensing data associated with each of the received signals corresponding to the Tx waveform 516. In some examples, the RF sensing data can include channel state information (CSI) data related to a direct path (e.g., the leakage signal 520) of the Tx waveform 516 and data related to reflected paths (e.g., the Rx waveform 518) corresponding to the Tx waveform 516.

[0125] In some aspects, the RF sensing data (e.g., CSI data) can include information that can be used to determine a manner in which an RF signal (e.g., the Tx waveform 516) propagates from the RF transmitter 506 to the RF receiver 510. The RF sensing data can include data corresponding to an impact on a transmitted RF signal due to scattering, fading, and / or power decay with distance, or any combination thereof. In some examples, the RF sensing data can include complex data (e.g., I / Q components) corresponding to each tone in a frequency domain over a particular bandwidth.

[0126] In some examples, the RF sensing data can be used by the processor 522 to compute a distance and an angle of arrival corresponding to a reflected waveform, such as the Rx waveform 518. In further examples, the RF sensing data can also be used to detect motion, determine a location, detect a change in a location or a motion pattern, or any combination thereof. In some cases, the distance and angle of arrival of a reflected signal can be used to identify a size, a location, a movement, and / or an orientation of a target (e.g., the target 502) in a surrounding environment in order to detect target presence / proximity.

[0127] The processor 522 of the wireless device 500 can calculate the range and angle of arrival corresponding to the reflected waveform (e.g., the range and angle of arrival corresponding to the Rx waveform 518) by utilizing signal processing, machine learning algorithms, any other suitable technique, or any combination thereof. In other examples, the wireless device 500 can transmit or communicate the RF sensing data to at least one processor of another computing device, such as a server or a base station, which can perform the calculations to obtain the range and angle of arrival corresponding to the Rx waveform 518 or other reflected waveforms.

[0128] In one example, the range of the Rx waveform 518 can be calculated by measuring the time difference from receiving the leakage signal to receiving the reflected signal. For example, the wireless device 500 can determine a baseline distance of zero based on the difference between the time at which the Tx waveform 516 is transmitted from the wireless device 500 and the time at which the leakage signal 520 is received by the wireless device 500 (e.g., a propagation delay). The processor 522 of the wireless device 500 can then determine the range associated with the Rx waveform 518 based on the difference between the time at which the Tx waveform 516 is transmitted from the wireless device 500 and the time at which the Rx waveform 518 is received by the wireless device 500 (e.g., a time of flight, which is also referred to as a round trip time (RTT)), and can then adjust the range according to the propagation delay associated with the leakage signal 520. By doing so, the processor 522 of the wireless device 500 can determine the distance traveled by the Rx waveform 518, which can be used to determine the presence and movement of the target (e.g., the target 502) that caused the reflection.

[0129] In further examples, the angle of arrival of the Rx waveform 518 can be calculated by the processor 522 by measuring the time difference of arrival of the Rx waveform 518 between individual elements of a receive antenna array, such as the antennas 514. In some examples, the time difference of arrival can be calculated by measuring the difference in received phase at each element in the receive antenna array.

[0130] In some cases, the range and angle of arrival of the Rx waveform 518 can be used by the processor 522 to determine the distance between the wireless device 500 and the target 502 and the location of the target 502 relative to the wireless device 500. The range and angle of arrival of the Rx waveform 518 can also be used to determine the presence, movement, proximity, identity, or any combination thereof of the target 502. For example, the processor 522 of the wireless device 500 can utilize the calculated range and angle of arrival corresponding to the Rx waveform 518 to determine that the target 502 is moving towards the wireless device 500.

[0131] As mentioned above, the wireless device 500 may include a mobile device (e.g., an IoT device, a smartphone, a laptop, a tablet device, etc.) or other type of device. In some examples, the wireless device 500 may be configured to obtain device location data and device orientation data in addition to the RF sensing data. In some cases, the device location data and device orientation data may be used to determine or adjust the range and angle of arrival of a reflected signal, such as the Rx waveform 518. For example, when a target 502 (e.g., a vehicle) moves toward the wireless device 500 during the RF sensing process, the wireless device may be positioned on the ground facing the sky. In this case, the wireless device 500 may use its location data and orientation data in addition to the RF sensing data to determine the direction in which the target 502 is moving.

[0132] In some examples, the wireless device 500 can collect device location data using techniques including RTT measurements, time of arrival (TOA) measurements, time difference of arrival (TDOA) measurements, passive positioning measurements, angle of arrival (AOA) measurements, angle of departure (AoD) measurements, received signal strength indicator (RSSI) measurements, CSI data, using any other suitable techniques, or any combination thereof. In another example, device orientation data can be obtained from electronic sensors on the wireless device 500, such as a gyroscope, accelerometer, compass, magnetometer, barometer, any other suitable sensor, or any combination thereof.

[0133] Figure 6 is a diagram illustrating an example of a receiver 604 utilizing RF monostatic sensing techniques with one transmitter 600 for determining one or more characteristics (e.g., position, velocity or speed, heading, etc.) of a target 602 object. For example, the receiver 604 may use RF bistatic sensing to detect the presence and position of a target 602 (e.g., an object, user, or vehicle) that is located at a location within a certain range. Figure 6 In one example, the receiver 604 may be in the form of a base station such as a gNB.

[0134] Figure 6 The bistatic radar system includes a transmitter 600 (e.g., a transmitting sensing node), which is depicted in the figure as being in the form of a base station (e.g., a gNB), and a receiver 604 (e.g., a receiving sensing node), which are separated by a distance comparable to the expected target distance. Figure 5 Compared with the single-station system, Figure 6 The transmitter 600 and receiver 604 of a bistatic radar system are located remotely from each other. In contrast, a monostatic radar is one that includes transmitters (e.g., Figure 5 RF transmitter 506 of the wireless device 500) and receiver (e.g., Figure 5The RF receiver 510 of the wireless device 500) of the radar system (e.g., Figure 5 system).

[0135] An advantage of bistatic radar (or more generally, multistatic radar with more than one receiver) over monostatic radar is the ability to collect radar echoes reflected from a scene at angles different from the angle at which the pulse was transmitted. This may be of interest in some applications (e.g., vehicle applications, scenes with multiple objects, military applications, etc.), where targets can reflect transmitted energy in many directions (e.g., where the targets are specifically designed to reflect in many directions), which can minimize the energy reflected back to the transmitter. It should be noted that in one or more examples, a monostatic system can coexist with a multistatic radar system, such as when the transmitter also has a co-located receiver.

[0136] In some examples, Figure 6 The transmitter 600 and / or the receiver 604 may be a mobile phone, a tablet computer, a wearable device, a vehicle, or other device including at least one RF interface (e.g., Figure 4 In some examples, the transmitter 600 and / or the receiver 604 may be a user device (e.g., Figure 4 IoT device 407) provides connectivity to the device, such as a base station (e.g., gNB, eNB, etc.), a wireless access point (AP), or other device including at least one RF interface.

[0137] In some aspects, the transmitter 600 may include one or more components for transmitting RF signals. The transmitter 600 may include at least one processor (e.g., Figure 5 The transmitter 600 may further include an RF transmitter (e.g., Figure 5 The RF transmitter 506 may be a transmitter configured to transmit a cellular signal or a telecommunication signal (e.g., a transmitter configured to transmit a 5G / NR signal, a 4G / LTE signal, or other cellular / telecommunication signal), a Wi-Fi transmitter, a Bluetooth transmitter, or a similar transmitter. TM transmitter, any combination thereof, or any other transmitter capable of transmitting RF signals.

[0138] The RF transmitter may be coupled to one or more transmit antennas, such as Tx antennas (e.g., Figure 5TX antenna 512). In some examples, the Tx antenna can be an omnidirectional antenna capable of transmitting RF signals in all directions, or a directional antenna capable of transmitting RF signals in a specific direction. In some examples, the Tx antenna can include multiple antennas (e.g., elements) configured as an antenna array.

[0139] The receiver 604 may also include one or more components for receiving RF signals. For example, the receiver 604 may include one or more receive antennas, such as Rx antennas (e.g., Figure 5 In some examples, the Rx antenna may be an omnidirectional antenna capable of receiving RF signals from multiple directions, or a directional antenna configured to receive signals from a specific direction. In other examples, the Rx antenna may include multiple antennas (e.g., elements) configured as an antenna array.

[0140] The receiver 604 may also include an RF receiver (eg, Figure 5 The RF receiver may include a RF receiver 510 for receiving RF waveforms (such as Wi-Fi signals, Bluetooth TM signals, 5G / NR signals, or any other RF signals). The output of the RF receiver may be coupled to at least one processor (e.g., Figure 5 The processor may be configured to process the received waveform (eg, Rx waveform 618).

[0141] In one or more examples, the transmitter 600 can implement an RF sensing technique, such as a bistatic sensing technique, by transmitting a Tx waveform 616 from the Tx antenna. It should be noted that although the Tx waveform 616 is illustrated as a single line, in some cases, the Tx waveform 616 can be transmitted in all directions by an omnidirectional Tx antenna.

[0142] In one or more aspects, one or more parameters associated with the Tx waveform 616 can be used to increase or decrease RF sensing resolution. These parameters may include frequency, bandwidth, number of spatial streams, number of antennas configured to transmit the Tx waveform 616, number of antennas configured to receive reflected RF signals corresponding to the Tx waveform 616 (e.g., Rx waveform 618), number of spatial links (e.g., number of spatial streams multiplied by number of antennas configured to receive RF signals), sampling rate, or any combination thereof. The transmitted waveform (e.g., Tx waveform 616) and the received waveform (e.g., Rx waveform 618) may include one or more radar RF sensing signals (also referred to as RF sensing RS).

[0143] During operation, the receiver 604 (e.g., operating as a receiving sensing node) can receive a signal corresponding to the Tx waveform 616, which is transmitted by the transmitter 600 (e.g., operating as a transmitting sensing node). For example, the receiver 604 can receive a signal reflected from an object or person within the range of the Tx waveform 616, such as the Rx waveform 618 reflected from the target 602. In some cases, the Rx waveform 618 may include multiple sequences corresponding to multiple copies of the sequence included in the Tx waveform 616. In some examples, the receiver 604 can combine the received multiple sequences to improve the SNR.

[0144] In some examples, at least one processor within receiver 604 can use the RF sensing data to calculate a distance, angle of arrival, or other characteristics corresponding to a reflected waveform, such as Rx waveform 618. In other examples, the RF sensing data can also be used to detect motion, determine position, detect a change in position or motion pattern, or any combination thereof. In some cases, the distance and angle of arrival of the reflected signal can be used to identify the size, position, movement, and / or orientation of an object (e.g., object 602) in the surrounding environment in order to detect object presence / proximity.

[0145] The processor of the receiver 604 can calculate the distance and arrival angle corresponding to the reflected waveform (e.g., the distance and arrival angle corresponding to the Rx waveform 618) by using signal processing, machine learning algorithms, any other suitable technology, or any combination thereof. In other examples, the receiver 604 can send or transmit the RF sensing data to at least one processor of another computing device, such as a server, which can perform calculations to obtain the distance and arrival angle corresponding to the Rx waveform 618 or other reflected waveforms.

[0146] In one or more examples, the angle of arrival of the Rx waveform 618 can be calculated by the processor of the receiver 604 by measuring the time difference of arrival of the Rx waveform 618 between various elements of the receive antenna array of the receiver 604. In some examples, the time difference of arrival can be calculated by measuring the difference in received phase at each element in the receive antenna array.

[0147] In some cases, the distance and angle of arrival of the Rx waveform 618 can be used by the processor of the receiver 604 to determine the distance between the receiver 604 and the target 602, as well as the position of the target 602 relative to the receiver 604. The distance and angle of arrival of the Rx waveform 618 can also be used to determine the presence, movement, proximity, identity, or any combination thereof of the target 602. For example, the processor of the receiver 604 can use the calculated distance and angle of arrival corresponding to the Rx waveform 618 to determine that the target 602 is moving toward the receiver 604.

[0148] Figure 7 is a diagram illustrating an example of a receiver 704 in the form of a smartphone that utilizes RF bistatic sensing techniques with multiple transmitters (including transmitter 700a, transmitter 700b, and transmitter 700c) that can be used to determine one or more characteristics (e.g., position, velocity or speed, heading, etc.) of a target 702 object. For example, the receiver 704 can use RF bistatic sensing to detect the presence and location of a target 702 (e.g., an object, a user, or a vehicle). The target 702 is depicted in Figure 7 as an object without communication capabilities (which can be referred to as a deviceless object), such as a person, a vehicle (e.g., a vehicle without the capability to transmit and receive messages using a C-V2X or DSRC protocol), or other deviceless object. Figure 7 The bistatic radar system of Figure 6 is similar to the bistatic radar system of Figure 7 , except that, Figure 6 The bistatic radar system of has multiple transmitters 700a, 700b, 700c, whereas

[0149] The bistatic radar system of Figure 7 includes multiple transmitters 700a, 700b, 700c (e.g., transmitting sensing nodes) that are illustrated as being in the form of base stations. Figure 7 The bistatic radar system of Figure 6 also includes a receiver 704 (e.g., a receiving sensing node) that is depicted in the form of a smartphone. Each of the transmitters 700a, 700b, 700c is separated from the receiver 704 by a distance that can be compared to an expected distance from the target 702. Similar to the bistatic system of Figure 7 The transmitters 700a, 700b, 700c and the receiver 704 of the bistatic radar system of are positioned apart from one another.

[0150] In one or more examples, the transmitters 700a, 700b, 700c and / or the receiver 704 can each be a mobile phone, a tablet computer, a wearable device, a vehicle (e.g., a vehicle configured to transmit and receive communications according to a C-V2X, DSRC, or other communication protocol), or other device that includes at least one RF interface (e.g., the device 407 of Figure 4 In some examples, the transmitters 700a, 700b, 700c and / or the receiver 704 can each be a device that provides connectivity for a user device (e.g., an IoT device 407 of Figure 4 ), such as a base station (e.g., a gNB, an eNB, etc.), a wireless access point (AP), or other device that includes at least one RF interface.

[0151] The transmitters 700a, 700b, 700c may include one or more components for transmitting RF signals. Each of the transmitters 700a, 700b, 700c may include at least one processor (e.g., Figure 5 Each of the transmitters 700a, 700b, 700c may also include an RF transmitter (e.g., a RF transmitter) for transmitting Tx signals including Tx waveforms 716a, 716b, 716c, 720a, 720b, 720c. Figure 5 RF transmitter 506). In one or more examples, Tx waveforms 716a, 716b, 716c are RF sensing signals, and Tx waveforms 720a, 720b, 720c are communication signals. In one or more examples, Tx waveforms 720a, 720b, 720c are communication signals that can be used to schedule a transmitter (e.g., transmitters 700a, 700b, 700c) and a receiver (e.g., receiver 704) to perform RF sensing of a target (e.g., target 702) to obtain location information about the target. The RF transmitter can be a transmitter configured to transmit a cellular signal or a telecommunication signal (e.g., a transmitter configured to transmit a 5G / NR signal, a 4G / LTE signal, or other cellular / telecommunication signal, etc.), a Wi-Fi transmitter, a Bluetooth transmitter, or a similar transmitter. TM transmitter, any combination thereof, or any other transmitter capable of transmitting RF signals.

[0152] The RF transmitter may be coupled to one or more transmit antennas, such as Tx antennas (e.g., Figure 5 TX antenna 512). In one or more examples, the Tx antenna can be an omnidirectional antenna capable of transmitting RF signals in all directions, or a directional antenna that transmits RF signals in a specific direction. The Tx antenna may include multiple antennas (e.g., elements) configured as an antenna array.

[0153] Figure 7 The receiver 704 may include one or more components for receiving RF signals. For example, the receiver 704 may include one or more receiving antennas, such as Rx antennas (e.g., Figure 5 RX antenna 514). In one or more examples, the RX antenna can be an omnidirectional antenna capable of receiving RF signals from multiple directions, or a directional antenna configured to receive signals from a specific direction. In some examples, the RX antenna can include multiple antennas (e.g., elements) configured as an antenna array (e.g., a phased antenna array).

[0154] The receiver 704 may also include an RF receiver (eg, Figure 5The RF receiver may include a RF receiver 510 for receiving RF waveforms (such as Wi-Fi signals, Bluetooth TM signals, 5G / NR signals, or any other RF signals). The output of the RF receiver may be coupled to at least one processor (e.g., Figure 5 The processor 522 may be configured to process the received waveform (eg, Rx waveform 718, which is the reflected (echo) RF sensing signal).

[0155] In some examples, transmitters 700a, 700b, 700c can implement RF sensing techniques (e.g., bistatic sensing techniques) by transmitting Tx waveforms 716a, 716b, 716c (e.g., radar sensing signals) from Tx antennas associated with each of the transmitters 700a, 700b, 700c. Although the Tx waveforms 716a, 716b, 716c are illustrated as a single line, in some cases, the Tx waveforms 716a, 716b, 716c can be transmitted in all directions (e.g., via omnidirectional Tx antennas associated with each of the transmitters 700a, 700b, 700c).

[0156] In one or more aspects, one or more parameters associated with the Tx waveforms 716a, 716b, 716c can be used to increase or decrease RF sensing resolution. These parameters may include, but are not limited to, frequency, bandwidth, number of spatial streams, number of antennas configured to transmit the Tx waveforms 716a, 716b, 716c, number of antennas configured to receive reflected (echo) RF signals corresponding to each of the Tx waveforms 716a, 716b, 716c (e.g., Rx waveform 718), number of spatial links (e.g., number of spatial streams multiplied by number of antennas configured to receive RF signals), sampling rate, or any combination thereof. The transmitted waveforms (e.g., Tx waveforms 716a, 716b, 716c) and the received waveforms (e.g., Rx waveform 718) may include one or more radar RF sensing signals (also referred to as RF sensing RS). It should be noted that although Figure 7 Only one reflected sensing signal (eg, Rx waveform 718 ) is shown in , but it should be understood that a separate reflected (echo) sensing signal will be generated by each sensing signal reflected from the target 702 (eg, Tx waveforms 716 a , 716 b , 716 c ).

[0157] exist Figure 7During operation of the system, receiver 704 (e.g., operating as a receiving sensing node) can receive signals corresponding to Tx waveforms 716a, 716b, 716c transmitted by transmitters 700a, 700b, 700c (e.g., each operating as a transmitting sensing node). Receiver 704 can receive signals reflected from objects or people within range of Tx waveforms 716a, 716b, 716c, such as Rx waveform 718 reflected from target 702. In one or more examples, Rx waveform 718 can include multiple sequences corresponding to multiple copies of the sequence included in its corresponding Tx waveform 716a, 716b, 716c. In some examples, receiver 704 can combine the multiple sequences received to improve SNR.

[0158] In some examples, the RF sensing data can be used by at least one processor within receiver 704 to compute a distance, angle of arrival (AOA), TDOA, angle of departure (AoD), or other characteristics corresponding to a reflected waveform (e.g., Rx waveform 718). In further examples, the RF sensing data can also be used to detect motion, determine a location, detect a change in location or motion pattern, or any combination thereof. In one or more examples, the distance and angle of arrival of a reflected signal can be used to identify a size, location, movement, and / or orientation of a target (e.g., target 702) in order to detect target presence / proximity.

[0159] The processor of receiver 704 can compute the distance and angle of arrival corresponding to a reflected waveform (e.g., the distance and angle of arrival corresponding to Rx waveform 718) by using signal processing, machine learning algorithms, any other suitable technique, or any combination thereof. In one or more examples, receiver 704 can transmit or communicate the RF sensing data to at least one processor of another computing device, such as a server, which can perform the computations to obtain the distance and angle of arrival corresponding to Rx waveform 718 or other reflected waveforms (not shown).

[0160] In one or more examples, the processor of receiver 704 can compute the angle of arrival (AOA) of Rx waveform 718 by measuring the TDOA of Rx waveform 718 between individual elements of a receive antenna array of receiver 704. In some examples, the TDOA can be computed by measuring a difference in received phase at each element in the receive antenna array. In one illustrative example, to determine the TDOA, the processor can determine a difference in time of arrival of Rx waveform 718 to the receive antenna array elements using one of the receive antenna array elements as a reference. The difference in time is proportional to a difference in distance.

[0161] In some cases, the processor of receiver 704 can use the ranges, AOAs, TDOAs, other measurement information (e.g., AoD, etc.), any combination thereof, of Rx waveforms 718 to determine a range between receiver 704 and target 702, and to determine a location of target 702 relative to receiver 704. In one example, the processor can use the range, AOA, and / or TDOA information as input to apply a multilateration or other location-based algorithm to determine a location (e.g., 3D location) of target 702. In other examples, the processor can use the ranges, AOAs, and / or TDOAs of Rx waveforms 718 to determine a presence, movement (e.g., velocity or speed, heading or direction, or movement, etc.), proximity, identity, any combination thereof, or other characteristic of target 702. For example, the processor of receiver 704 can use the ranges, AOAs, and / or TDOAs corresponding to Rx waveforms 718 to determine that the target is moving toward receiver 704.

[0162] Figure 8 is a diagram illustrating geometry for bistatic (or monostatic) sensing. Figure 8 A bistatic radar North reference coordinate system in two dimensions is shown. Specifically, Figure 8 A coordinate system and parameters defining bistatic radar operation in a plane containing transmitter 800, receiver 804, and target 802 (referred to as the bistatic plane) are shown. A bistatic triangle lies in the bistatic plane. Transmitter 800, target 802, and receiver 804 are shown relative to one another. Transmitter 800 and receiver 804 are separated by a baseline distance L. An extended baseline is defined as extending the baseline distance L beyond transmitter 800 or receiver 804. Target 802 and transmitter 800 are separated by a range R T , and target 802 and receiver 804 are separated by a range R R .

[0163] Angle θ T and θ R are the transmitter 800 observation angle and receiver 804 observation angle, respectively, which are considered positive when measured clockwise from North (N). Angle θ T and θ R are also referred to as the angle of arrival (AOA) or line of sight (LOS). The bistatic angle (β) is the angle subtended between transmitter 800, target 802, and receiver 804 in radar. Specifically, the bistatic angle is the angle between transmitter 800 and receiver 804 with the vertex at target 802. The bistatic angle is equal to the transmitter 800 observation angle minus the receiver 804 observation angle θ R (e.g., β = θ T - θ R ).

[0164] When the bistatic angle is exactly zero (0°), the radar is considered a monostatic radar; when the bistatic angle is close to zero, the radar is considered a pseudo-monostatic; and when the bistatic angle is close to 180 degrees, the radar is considered a forward scatter radar. Otherwise, the radar is simply considered and referred to as a bistatic radar. The bistatic angle (β) can be used to determine the radar cross section of a target.

[0165] Figure 9 is an illustration of an example of a bistatic range 910 that exemplifies bistatic sensing. In this figure, a transmitter (Tx) 900 of a radar, a target 902, and a receiver (Rx) 904 are shown relative to one another. The transmitter 900 is separated from the receiver 904 by a baseline distance L, the target 902 is separated from the transmitter 900 by a distance Rtx, and the target 902 is separated from the receiver 904 by a distance Rrx.

[0166] The bistatic range 910 (shown as an ellipse) refers to the measured distance made by a radar with a separated transmitter 900 and receiver 904 (e.g., the transmitter 900 and receiver 904 are positioned away from one another). The receiver 904 measures the time of arrival from when the transmitter 900 transmits a signal to when the receiver 904 receives the signal from the transmitter 900 via the target 902. The bistatic range 910 defines an ellipse of constant bistatic range, called an equidistance contour, on which the target 902 lies with foci centered at the transmitter 900 and receiver 904. If the target 902 is at a distance Rrxfrom the receiver 904, at a distance Rtxfrom the transmitter 900, and the receiver 904 and transmitter 900 are separated by a distance L from one another, then the bistatic range is equal to Rrx+ Rtx- L. It should be noted that the motion of the target 902 causes a rate of change of the bistatic range, which results in a bistatic Doppler shift.

[0167] In general, the constant bistatic range points draw an ellipsoid with the transmitter 900 and receiver 904 locations as foci. The bistatic equidistance contour is the location of the ground cut ellipsoid. When the ground is flat, this intersection forms an ellipse (e.g., the bistatic range 910). Note that these ellipses are not centered on mirror points unless the two platforms have equal height.

[0168] Figure 10 An example 1000 of wireless communications between devices based on sidelink communications is exemplified. The communications can be based on a slot structure (e.g., as described in FIG. 3), for example. Figure 3The illustrated time slot structure). For example, a transmitting UE 1002 can transmit a transmission 1014 that is receivable by receiving UEs 1004, 1006, 1008, which includes, for example, a control channel and / or a corresponding data channel. At least one of the UEs can be in the form of an autonomous vehicle or unmanned aerial vehicle. The control channel can include information used to decode the data channel and can also be used by receiving devices to avoid interference by refraining from transmitting during the data transmission on the occupied resources. The time interval of the transmission (TTI) and the number of RBs that the data transmission will occupy can be indicated in a control message from the transmitting device. In addition to operating as a receiving device, UEs 1002, 1004, 1006, 1008 can each also be capable of operating as a transmitting device. Accordingly, UEs 1006, 1008 are illustrated as transmitting transmissions 1016, 1020. Transmissions 1014, 1016, 1020 (and 1018 by network device 1007, such as a road-side unit) can be broadcast or multicast to devices within range. For example, UE 1014 can transmit a communication intended to be received by other UEs within a range 1001 of UE 1014. Additionally / alternatively, network device 1007 can receive and / or transmit communications 1018 to UEs 1002, 1004, 1006, 1008 from these UEs. UEs 1002, 1004, 1006, 1008, or network device 1007 can include a detection component. UEs 1002, 1004, 1006, 1008, or network device 1007 can also include a vehicle-based safety message or mitigation component.

[0169] Figure 11 Examples of comb structures for reference signals (e.g., PRS, SRS, etc.) are shown in FIG. 11. For example, comb structure 1110 is a comb-2 structure with two symbols (denoted as a comb-2 / 2-symbol structure). According to the comb-2 / 2-symbol structure of comb structure 1110, each alternate symbol is assigned to a reference signal resource. Figure 11 The comb patterns in FIG. 11 are for one transmission-reception point (TRP). An overview of comb structures 1110, 1112, 1114, 1116, 1118, 1120, 1122, and 1124 is provided in Table 2 below:

[0170]

[0171]

[0172] Table 2

[0173] As previously noted, systems and techniques are described herein that apply solutions associated with RANSAC to enhance JCS. Figure 12is a diagram illustrating an example of a system 1200 for applying a solution (eg, method or rule) for enhancing JCS using RANSAC. Figure 12 , system 1200 is shown as including a network device 1210 in the form of a UE. Network device 1210 (e.g., a UE) can operate as a radar Rx for sensing purposes. Also shown is a network device 1220 in the form of a base station (e.g., a gNB or a portion of a gNB, such as a CU, DU, RU, near-RT RIC, non-RT RIC, etc.). Network device 1220 (e.g., a gNB) can operate as a radar Tx for sensing purposes. System 1200 also includes multiple network entities 1240 and 1250, where network entity 1240 is in the form of a radar server and network entity 1250 is in the form of a location server.

[0174] System 1200 may include, for example Figure 12 More or fewer network devices and / or more or fewer network entities as shown. Figure 12 Different types of network devices (e.g., vehicles) and / or different types of network entities (e.g., network servers) are shown. In addition, the UE can be used as a radar Tx instead of as Figure 12 12. A base station (e.g., gNB) is shown. Furthermore, in one or more examples, network device 1210 (e.g., UE) can be equipped with heterogeneous capabilities, which can include, but are not limited to, 4G / 5G cellular connectivity, GPS capabilities, camera capabilities, radar capabilities, and / or LIDAR capabilities. Network devices 1210, 1220 and network entities 1240, 1250 can be capable of performing wireless communications with each other via communication signals (e.g., signals 1270a, 1270b, 1270c, 1270d).

[0175] In one or more examples, network devices 1210, 1220 may be capable of sending and receiving some type of sensing signal (e.g., a camera, an RF sensing signal, an optical sensing signal, etc.). In some cases, network devices 1210, 1220 may send and receive sensing signals (e.g., RF sensing signals 1260a, 1260b) for detecting nearby targets (e.g., target 1230 in the form of a vehicle) using one or more sensors. In some cases, network devices 1210, 1220 may detect nearby targets based on one or more images or frames captured using one or more cameras.

[0176] A network device 1220 that can operate as a radar Tx can perform RF sensing (e.g., bistatic sensing or monostatic sensing) of at least one target (e.g., target 1230) to obtain RF sensing measurements (e.g., Doppler measurements, RTT measurements, TOA measurements, and / or TDOA measurements) of the target (e.g., target 1230). The RF sensing measurements of the target (e.g., target 1230) can be used (e.g., by at least one processor of at least one of the network devices 1210, 1220 and / or at least one of the network entities 1240, 1250) to determine one or more characteristics (e.g., velocity, location, distance, movement, heading, size, and / or other characteristics) of the target (e.g., target 1230).

[0177] As previously mentioned, generally, sensing involves monitoring a moving target (e.g., target 1230) with different motion (e.g., a moving car or pedestrian, a body motion of a person such as breathing, and / or other micro-motions related to the target). The Doppler, which measures the phase change in the signal and indicates the motion, is an important characteristic for sensing the target (e.g., target 1230). Thus, to obtain an accurate estimate of the motion of the target, the phase of the signal should be continuous (e.g., the signal should maintain phase continuity).

[0178] During operation of the system 1200, for example, when performing bistatic sensing of a target (e.g., target 1230), a network device 1220 (e.g., a base station) that operates as a radar Tx can transmit an RF sensing signal 1260a toward the target (e.g., target 1230). The RF sensing signal 1260a can be included within a communication signal and a sensing signal that are multiplexed together (e.g., via time division multiplexing and / or frequency division multiplexing) for joint communication and sensing purposes. The sensing signal 1260a can reflect off the target (e.g., target 1230) to produce an RF reflected sensing signal 1260b, which can reflect toward a network device 1210 (e.g., a UE). The network device 1210 (e.g., a UE) that operates as a radar Rx can receive the reflected sensing signal 1260b. After the network device (e.g., a UE) receives the reflected sensing signal 1260b, the network device (e.g., a UE) can obtain measurements (e.g., Doppler measurements, RTT measurements, TOA measurements, and / or TDOA measurements) of the reflected sensing signal 1260b. At least one processor of at least one of the network devices 1210, 1220 and / or at least one of the network entities 1240, 1250 (e.g., Figure 23The processor 2310) can then determine or compute characteristics (e.g., velocity, position, distance, movement, heading, size, etc.) of the target (e.g., target 1230) by using the sensing measurements (e.g., Doppler measurements, RTT measurements, TOA measurements, and / or TDOA measurements) from the received reflected sensing signals 1260b.

[0179] In some examples, the network device 1210 (e.g., UE) can transmit the measurements (e.g., Doppler measurements, RTT measurements, TOA measurements, and / or TDOA measurements) and / or the determined characteristics (e.g., velocity, position, distance, movement, heading, size, etc.) of the target (e.g., target 1230) to the network device 1220 (e.g., base station) and / or the network entity 1240 (e.g., radar server) via the communication signals 1270a, 1270b. The network device 1220 (e.g., base station) and / or the network entity 1240 (e.g., radar server) can then transmit the measurements (e.g., Doppler measurements, RTT measurements, TOA measurements, and / or TDOA measurements) and / or the determined characteristics (e.g., velocity, position, distance, movement, heading, size, etc.) of the target (e.g., target 1230) to the network entity 1240 (e.g., radar server) and / or the network entity 1250 (e.g., location server such as a location management function (LMF)) via the communication signals 1270c, 1270d.

[0180] In JCS, multiple sensing nodes (e.g., each in the form of a UE or a base station such as a gNB) can be included in a sensing session based on a sensing management functionality (SMF) schedule (e.g., which can be scheduled by a network such as by a network server). Efficient algorithms for selecting the sensing nodes and for filtering the sensing measurements by the sensing nodes can be crucial to ensure accurate sensing performance.

[0181] In some scenarios, multiple coarse measurements (e.g., distance and / or Doppler measurements) can interfere with the sensing results. For example, some sensing nodes can have poor channel quality, e.g., because these sensing nodes have low signal-to-noise ratio (SNR). The accuracy of the estimated distance, Doppler, and / or angle measured by these sensing nodes with poor channel quality can be low, and thus the measurements made by these sensing nodes can be considered as interference to accurate measurement estimates. Such measurement results (e.g., from sensing nodes with poor channel quality) can degrade the final performance of target detection.

[0182] For another example where coarse measurements may interfere with sensing results, a subset of sensing nodes may provide outlier measurements, for example due to the geometric relationship of the subset of sensing nodes to the target. As another example, in some scenarios, identifying phantom targets hidden within the list of detected targets can be challenging. Phantom targets can be highly correlated with the selection of sensing nodes. Therefore, improved techniques with low complexity can be used to select the optimal sensing nodes to obtain sensing measurements and filter outlier measurements.

[0183] Currently, machine learning (ML) solutions are commonly used in some sensing scenarios (e.g., for detecting human actions). High-quality and valid datasets of sensory measurements are required to obtain accurate ML solutions. Invalid sensory measurements may lead to meaningless computations in ML model training and increased complexity in training. Therefore, the measured datasets should be cleaned and filtered to reduce the overhead and resource requirements for ML model training and measurement reporting. If the datasets are not cleaned and filtered, the communication overhead may also be high because invalid measurements will be sent over the network. Therefore, improved techniques for cleaning datasets (e.g., for improving data quality) may be beneficial because it can filter out unreliable data and reduce invalid computations.

[0184] In some aspects of the present disclosure, systems, apparatus, methods (also referred to as processes), and computer-readable media (collectively referred to herein as "systems and techniques") are described herein that provide solutions for enhancing JCS using random sampling consensus (RANSAC). The systems and techniques employ RANSAC to address the aforementioned challenges for different applications. In one or more examples, the systems and techniques utilize RANSAC to select optimal sensing nodes and filter outliers with low complexity. In some examples, the systems and techniques use RANSAC to extract valid data so that only valid data (e.g., rather than invalid data) is reported, which can save communication resources, overhead, and power.

[0185] RANSAC is a typical algorithm that is widely used to identify outliers, especially for LIDAR, radar, and other image-based processing. RANSAC can estimate a mathematical model from a data set containing outliers through multiple iterations. The RANSAC algorithm operates by identifying outliers in a data set and estimating the desired model using data from a data set that does not contain outliers (e.g., using inliers).

[0186] Figure 13 An example of the results from RANSAC identifying outliers and inliers of a dataset is shown. Specifically, Figure 131300 is a graph illustrating an example of using RANSAC to identify outliers and inliers in a dataset (e.g., from RF sensing measurements). In one or more examples, in graph 1300, the x-axis can represent distance (e.g., pseudo-normalized distance) and the y-axis can represent Doppler (e.g., pseudo-normalized Doppler). In graph 1300, points within the dataset are plotted. For example, during sensing, for each sensing resource, a measurement can be obtained by an Rx sensing node, and each point in graph 1300 represents one of the measurements (e.g., each point is a data point of the dataset obtained by the Rx sensing node for the sensed measurement).

[0187] RANSAC can estimate the equation of the line (e.g., 1310) that best fits the points in the data set. Line 1310 of graph 1300 represents the linear model of the data set determined by RANSAC. Lines 1320a and 1320b extending parallel to line 1310 are threshold boundary lines for determining whether a point in the data set is an outlier or an inlier. These threshold boundary lines (e.g., lines 1320a, 1320b) can be predetermined (e.g., determined by a network). Points of the data set that are within the threshold boundary lines (e.g., lines 1320a, 1320b) can be determined as inliers of the data set. Points of the data set that are outside the threshold boundary lines (e.g., lines 1320a, 1320b) can be determined as outliers of the data set. In one or more examples, the threshold boundary lines (e.g., lines 1320a, 1320b) can be defined by a threshold parameter (e.g., parameter T) that can indicate a distance that each threshold boundary line (e.g., lines 1320a, 1320b) should be positioned away from line 1310 of the linear model of the data set determined by RANSAC. Figure 13 , each threshold boundary line (eg, lines 1320a, 1320b) is located at the same amount of distance from line 1310. As shown in FIG.

[0188] In one or more examples, the operation steps of RANSAC may include, but are not limited to, randomly selecting a subset of the data set, fitting the model to the selected subset, determining the number of outliers, and repeating the above steps for a specified number of iterations. Compared to other similar algorithms, RANSAC has the benefit of low complexity.

[0189] In one or more aspects, for the systems and techniques, a SMF (sensing management function) (such as a network server (e.g., Figure 12 A network entity 1240 in the form of a radar server, or Figure 12 The network entity 1250 in the form of a location server can configure RANSAC for a specific application. Based on the configuration, the sensing node (e.g. Figure 12 A network device 1210 in the form of a UE, orFigure 12 The network device 1220 in the form of a base station (such as a gNB) can enable RANSAC for one particular application. In one or more examples, RANSAC can be used for various different applications, including but not limited to: filtering out coarse sensing measurements in range, velocity, and / or angle estimates; filtering out ghost targets (e.g., the ghost target 2130 of FIG. 21) in range, velocity, and / or angle estimates; removing ambiguities in range, velocity, and / or angle estimates; pruning the best set of transmission-reception points (TRPs) in Rx sensing nodes; pruning the best set of sensing resources (e.g., resources with different beams) in Rx sensing nodes; and handling aliasing issues, such as aliasing in range and Doppler. Figure 21

[0190] In one or more aspects, the content of the configuration for RANSAC can include, but is not limited to: a parameter K, which is the size of the random set of models used in RANSAC for estimating; a parameter M, which is the number of iterations in RANSAC; and a parameter T, which is the tolerance or threshold used to determine inliers or outliers of a sample of a data set. In one or more examples, these parameters (e.g., K, M, and T) can be defined and indicated separately for each different application. Each particular application can have different requirements on the parameters (e.g., K, M, and T). Some sensing nodes (e.g., UEs or gNBs) can or can not have the capability to meet the requirements of the parameters for some particular applications. In one or more examples, a sensing node knows the particular application being performed and knows the parameter requirements for the particular application.

[0191] Figure 14 is a signaling diagram illustrating an example of signaling 1400 for using RANSAC 1450 for a particular application. In Figure 14 , the SMF 1410 (e.g., a network server, such as a Figure 12 network entity 1240 in the form of a radar server, or Figure 12 network entity 1250 in the form of a location server) and the sensing node 1420 (e.g., Figure 12 network device 1210 in the form of a UE, or Figure 12 ​1420 in the form of a base station (such as a gNB). During operation, the sensing node 1420 may transmit (e.g., report or send) a message 1430 to the SMF 1410, which includes the RANSAC capabilities of the sensing node 1420 (e.g., for a specific application). After the SMF 1410 receives the message 1430, based on the capabilities of the sensing node 1420, the SMF 1410 may determine a configuration for RANSAC 1450. The SMF 1410 may then transmit (e.g., send) a message 1440 to the sensing node 1420, which includes a configuration for RANSAC 1450 for the specific application. After the sensing node 1420 receives the message 1440, based on the configuration for RANSAC 1450 within the message 1440, the sensing node 1420 may configure RANSAC 1450 with the configuration and may then perform RANSAC 1450 for the specific application. After the sensing node 1420 has performed RANSAC 1450 , the sensing node 1420 may transmit (eg, report or send) a measurement report 1460 to the SMF 1410 , the measurement report containing the measurement results from the RANSAC 1450 .

[0192] As mentioned previously, a sensing node (e.g. Figure 14 The sensing node 1420) can send a signal to the network (e.g., Figure 14 In one or more aspects, the capability message (e.g., Figure 14 Various bits within the message 1430 of the UE may be used to indicate the RANSAC capability of the sensing node. In one or more examples, the N_1 bit may be used in the message to indicate which application may be supported using RANSAC in the sensing node (e.g., UE or gNB). Since many different applications may involve RANSAC, the sensing node should report its capabilities to indicate which specific application the sensing node may support using RANSAC. For example, the N_1 bit (e.g., 00101001) may be used to indicate that the sensing node may use RANSAC to filter out phantom targets. In one or more examples, the N_2 bit may be used in the message to indicate the maximum value of a parameter K that the sensing node may support, which is the size of the random set. In some examples, the N_3 bit may be used in the message to indicate the maximum value of a parameter M that the sensing node may support, which is the number of iterations. Based on receiving the reported maximum K parameter and maximum M parameter from the sensing node, the network (e.g., Figure 14 1410) can schedule resources (e.g., sensing resources) accordingly.

[0193] In one or more aspects, a network (e.g., Figure 14The SMF 1410 of the RxSensingNode can configure the delay constraint of RANSAC and does not need to explicitly configure the RANSAC parameters for RANSAC. In one or more examples, the network can indicate the maximum delay of RANSAC or measurement report so that the Rx sensing node (e.g., Figure 14 The sensing node 1420 will need to optimize its RANSAC parameters (e.g., including parameters T, K, and M) through its specific implementation to meet the reporting timeline requirements. This delay is associated with the iteration time of RANSAC. For example, if the delay constraint is high (e.g., meaning a short delay time is required), the threshold T requirement can be relaxed, or the iteration M parameter can be smaller.

[0194] In one or more aspects, when RANSAC is based on the Rx sensing node (e.g., Figure 14 1420) is enabled in the sensing node, and the Rx sensing node sends a signal to the network (eg, Figure 14 When reporting its measurements to the SMF 1410 of the Rx sensing node, the Rx sensing node may also include a RANSAC identification (ID). Each RANSAC ID may correspond to a specific RANSAC scheme (e.g., a specific RANSAC algorithm). For example, RANSAC ID 1 may correspond to a first type of RANSAC algorithm, and RANSAC ID 2 may correspond to a second type of RANSAC algorithm. On the network side (e.g., Figure 14 SMF 1410), network (e.g., Figure 14 The SMF 1410 of the Rx sensing node may not need to know how the Rx sensing node operates RANSAC (e.g., does not need to know the specific RANSAC scheme). However, the network (e.g., Figure 15 The SMF 1410) can use the RANSAC ID to relate a measurement to another specific group of measurements, which other specific group of measurements can share the same bias and / or error within the measurement.

[0195] Generally speaking, in JCS, a large number of resources (e.g., TRP and antenna beam) can be configured for sensing. Currently, the optimal TRP and / or antenna beam for sensing is estimated using signal-to-noise ratio (SNR) and reference signal received power (RSRP). However, this process of determining the optimal sensing resource using SNR and RSRP generally involves a large amount of complexity. For example, a Tx sensing node (e.g., gNB) can configure different reference signals (RS) or antenna ports to different resources. An Rx sensing node (e.g., UE) can estimate the SNR and RSRP separately and report the corresponding SNR and RSRP to identify the optimal resource. However, such metrics (e.g., SNR and RSRP) may not accurately represent the sensing capabilities for distance, angle, and / or Doppler estimation.

[0196] In one or more aspects, for the systems and techniques, a consistency check of distance, rate, and / or angle estimates can be used (e.g., instead of using SNR and RSRP as previously described) to identify optimal resources for sensing. Based on the consistency check of the distance, rate, and angle estimates, RANSAC can prune the resource set to obtain the optimal resource set for sensing.

[0197] In one or more examples, during operations for pruning resource sets to obtain an optimal resource set, a network (e.g., Figure 15 The network 1510 can first enable a large set of resources for sensing. For example, the network can schedule a large number of TRPs or sensing resources. The network can then configure a RANSAC threshold to identify inliers from outliers.

[0198] Also during operation, the Rx sensing node (e.g. Figure 15 The sensing UE 1520 of the receiver can estimate the signal and then buffer the results. In one or more examples, the receiver sensing node can enable RANSAC for distance, velocity, and / or angle estimation to identify inliers. In some examples, the network can trigger the receiver sensing node to enable RANSAC to identify inliers.

[0199] In addition, during operation, in one or more examples, the Rx sensing node may report the measurement index of the inlier to the network. The network may then identify the optimal resources and may also configure the optimal resources for sensing. For example, the network may configure a report of indices for an N number of samples that may be positioned closest to the center of the learning model used for measurement. The network may then configure the corresponding resources. In some examples, the Rx sensing node may directly require specific resources (e.g., after performing RANSAC on the Rx sensing node). For example, the Rx sensing node may request TRP and / or sensing resources on demand.

[0200] Additionally, during operation, new events can trigger the network to enable a large set of resources for sensing and configure RANSAC thresholds to identify inliers. For example, a new event can occur when the current set of TRP and / or sensing resource results has performance that degrades over time. In one or more examples, the new event can trigger the Rx sensing node to estimate the signal using the same set of scheduled resources in a new round of RANSAC and then buffer the results.

[0201] Figure 15 is a signaling diagram illustrating an example of signaling 1500 for employing RANSAC to prune the best resource set for sensing (eg, to obtain the optimal resource set). Figure 12 , a network 1510 (such as an SMF (e.g., a network server such asFigure 12 A network entity 1240 in the form of a radar server, or Figure 12 A network entity 1250 in the form of a location server) and a sensing node 1520 (eg, Figure 12 A network device 1210 in the form of a UE, or Figure 16 15. The network device 1510 in the form of a base station (such as a gNB) may be a network device 1220. During operation, the network 1510 may transmit (e.g., report or send) a message 1530 to the sensing node 1520, the message including a configuration for RANSAC. The network 1510 may also transmit (e.g., report or send) a message 1540 to the sensing node 1520, the message including a configuration for a sensing resource set. Optionally, the network 1510 may transmit (e.g., send) a message 1550 to the sensing node 1520, the message including a command to enable RANSAC.

[0202] Then, at block 1560, the sensing node 1520 may perform sensing, identify inliers in the sensing measurements by performing RANSAC, and estimate and buffer the measurements. The sensing node 1520 may then transmit (e.g., report or send) a message 1570 to the network 1510, the message including the inliers or a sensing resource request. Based on the information in the message 1570 (e.g., the inliers or the sensing resource request), the network 1510 may determine an updated configuration for the sensing resources (e.g., an optimal set of sensing resources). The network 1510 may then transmit (e.g., send) a message 1580 to the sensing node 1520, the message including the updated configuration for the sensing resources (e.g., the optimal set of sensing resources). The sensing node 1520 may then perform sensing 1590 using the updated configuration for the sensing resources (e.g., the optimal set of sensing resources).

[0203] Then, in some examples, a new event may trigger RANSAC 1595. For example, the new event may be that the inliers are stale or out of date. If the new event triggers RANSAC, then operation returns to block 1560 (e.g., to identify new inliers).

[0204] In one or more aspects, RANSAC can continuously identify inliers and report the index of the inliers. In one or more examples, during operation, some sensing resources (e.g., TRPs or beams) can be scheduled. A Tx sensing node (e.g., a network device such as a gNB) and an Rx sensing node (e.g., a network device such as a UE) can perform sensing based on the configured resources. The Rx sensing node can then estimate the signal and buffer the measurement. The Rx sensing node can utilize RANSAC to identify inliers within the buffered samples.

[0205] The Rx sensing node can also report the measurement indices or corresponding resource indices. For example, N number of sample indices can be reported that are positioned closest to the center of the learning model from RANSAC.

[0206] Based on the reporting by the Rx sensing node, the network (e.g., a network entity such as a network server or SMF) can update the sensing resource configuration. For example, within the sensing procedure, RANSAC can be continuously run to identify inliers and outliers. RANSAC can identify outliers and can remove the outliers in the reporting, which can reduce the communication cost in uplink (UL).

[0207] Figure 16 is a plot 1600 illustrating an example of a result due to RANSAC identifying outliers (e.g., outliers 1630) and inliers (e.g., inliers 1620) in a dataset (e.g., from RF sensing measurements). For Figure 19 For the plot 1600, the x-axis represents distance, and the y-axis represents Doppler. In the plot 1600, points within a dataset from sensing measurements are plotted. A threshold boundary (e.g., circle 1610) is shown in the plot 1600. Points of the dataset that are within the boundary (e.g., circle 1610) (e.g., points 1620) can be determined to be inliers of the dataset, and points of the dataset that are outside the boundary (e.g., circle 1610) (e.g., points 1630) can be determined to be outliers of the dataset.

[0208] In one or more examples, the Rx sensing node can only report inliers (e.g., points within the circle 1610) to the network. Based on the reported inliers or outliers, the network can determine whether the configured resources are sufficient for sensing. If the network determines that the configured resources are not sufficient for sensing, the network can update the set of resources with new resources for sensing.

[0209] In one or more aspects, RANSAC can be used to remove ambiguities from measurements for distance, velocity, and angle estimates. Ambiguities in sensing measurements can occur when some sensing measurements are not relevant to the sensing of the target (e.g., some measurements are only related to interference). Thus, RANSAC can be used to remove the interference measurements.

[0210] In one or more examples, the network (e.g., SMF) can explicitly indicate to the sensing node (e.g., UE or gNB) whether RANSAC is to be enabled to remove ambiguities in the sensing measurements. Alternatively, the network can indicate to the sensing node whether to report the raw measurements without any post-processing (e.g., without performing RANSAC). Otherwise, RANSAC can be enabled to perform post-processing on the raw measurements.

[0211] In one or more examples, if RANSAC is enabled in a sensing node, the sensing node may perform sampling within a given window to construct a RANSAC set to identify outliers. In some examples, a high sampling rate may be used to further improve the accuracy of RANSAC in identifying outliers. In one or more examples, given a window (e.g., Figure 18 The window 1910) can be a window in the time domain, frequency domain, or spatial domain (e.g., an antenna beam). In some examples, it can be assumed that the sensing target is stable within the window. In one or more examples, the network can configure a specific window length for sampling.

[0212] In one or more examples, after the sensing node performs RANSAC, the sensing node can report the results to the network. In one or more examples, the sensing node can report only the measurement results identified as inliers to the network. In some examples, the sensing node can report all measurements and corresponding indications of the measured outliers and inliers.

[0213] Figure 18 is a signaling diagram illustrating an example of signaling 1800 for employing RANSAC 1860 to remove ambiguity in sensing measurements. Figure 12 , a network 1810 (such as an SMF (e.g., a network server such as Figure 12 A network entity 1240 in the form of a radar server, or Figure 12 A network entity 1250 in the form of a location server) and a sensing node 1820 (eg, Figure 12 A network device 1210 in the form of a UE, or Figure 19 18. In the example embodiment, the network 1810 may include a network device 1220 in the form of a base station (such as a gNB). During operation, the network 1810 may transmit (e.g., send) a message 1830 to the sensing node 1820, the message including a command for the sensing node 1820 to enable RANSAC 1860 to remove ambiguity in sensing measurements. The network 1810 may also transmit (e.g., send) a message 1840 to the sensing node 1820, the message including a configuration for RANSAC 1860 to remove ambiguity in sensing measurements.

[0214] Then, at block 1850, the sensing node 1820 can perform sensing (e.g., perform sampling of sensing measurements within a window). The sensing node 1820 can configure RANSAC according to the configuration for RANSAC in message 1840, and can perform RANSAC 1860 (e.g., on the sampled sensing measurements obtained within the window). After the sensing node 1820 has performed RANSAC 1860, the sensing node 1820 can transmit (e.g., report or send) a message 1870 to the network 1810, the message including a measurement report containing results of the RANSAC (e.g., measurements with range and / or Doppler estimates).

[0215] Figure 19 is a diagram illustrating an example of a time window 1910 for obtaining measurements, and an example of a plot 1902 illustrating example results due to performing RANSAC on the measurements. In Figure 19 , a timeline 1900 and a plot 1902 are shown. For the timeline 1900, the x-axis represents time (t). The timeline 1900 is shown to include a time window 1910 for a sensing node (e.g., a UE or gNB) to obtain sensing measurement samples (e.g., samples S1, S2, S3, S4, S n-1 , S n ) at sensing measurement occasions 1920 within the window 1910. For example, sample S1 is obtained at sensing measurement occasion 1920 in window 1910.

[0216] In one or more examples, RANSAC can be run on the obtained measurement samples. Results of the RANSAC can be used to determine outliers and inliers. Figure 19 The plot 1902 illustrates an example of results due to RANSAC identifying outliers and inliers in a dataset (e.g., from RF sensing measurements) in order to identify ambiguities in the measurements. For the plot 1902, the x-axis represents range, and the y-axis represents Doppler. In plot 1600, points within a dataset from sensing measurements are plotted. A threshold boundary (e.g., circle 1930) is shown in plot 1902. Points of the dataset that lie within the boundary (e.g., circle 1930) can be determined to be inliers of the dataset, and points of the dataset that lie outside the boundary (e.g., circle 1930) can be determined to be outliers of the dataset. In one or more examples, the inliers can be determined to be interference measurements, and thus can be removed from the set of measurements. The measurement report in message 1870 can include an updated set of measurements with the interference measurements removed. Figure 20

[0217] ​In one or more aspects, a network (e.g., SMF) can collect multiple sensing measurements and can enable RANSAC at the network side (e.g., in the network itself). By performing RANSAC on the measurements, the network can be able to determine that some of the sensing nodes (e.g., in the set of sensing nodes) can have a decrease in their measurement quality or can have redundant measurements.

[0218] In one or more examples, different Rx sensing nodes can report their measurements to the network. Based on the measurement data collected from the different RX sensing nodes, by enabling RANSAC in the network, the network can identify outliers. Such identified outliers can indicate that the measurement quality of one or more Rx sensing nodes is decreasing, or that the measurements from one or more of the Rx sensing nodes are redundant. If the network determines that the measurement quality of one or more Rx sensing nodes is decreasing or that the measurements from one or more of the Rx sensing nodes are redundant, the network can disable reporting from those Rx sensing nodes, can reduce the reporting size of those Rx sensing nodes, and / or enhance the sensing measurements and reporting of other Rx sensing nodes. In some examples, the network can schedule the reporting by the Rx sensing nodes to avoid invalid measurement reporting, thereby saving communication resources.

[0219] Figure 20 is a signaling diagram illustrating an example of signaling 2000 for a network 2010 to employ RANSAC 2040 to determine measurement quality of sensing nodes. In Figure 12 , a network 2010 (such as an SMF (e.g., a network server, such as Figure 12 a network entity 1240 in the form of a radar server, or Figure 12 a network entity 1250 in the form of a location server), a first sensing node 2020a (e.g., Figure 12 a network device 1210 in the form of a UE, or Figure 12 a network device 1220 in the form of a base station (such as a gNB), and a second sensing node 2020b (e.g., Figure 12 a network device 1210 in the form of a UE, or Figure 21 a network device 1220 in the form of a base station (such as a gNB).

[0220] During operation, sensing nodes 2020a, 2020b may each transmit (e.g., report or send) a message 2030a, 2030b to network 2020b, the message including a measurement report containing a sensed measurement. After receiving the sensed measurements from sensing nodes 2020a, 2020b, network 2010 may perform RANSAC 2040 on the sensed measurements. Then, based on the results of RANSAC 2040 (e.g., which may indicate that the measurement quality of the first sensing node 2020a is degrading or that the measurement from the first sensing node 2020a is redundant), network 2010 may transmit (e.g., send) a message 2050 to first sensing node 2020a, the message including a command for configuring to disable reporting of the sensed measurements. First sensing node 2020a may then stop reporting its sensed measurements to network 2010. However, second sensing node 2020b may continue to report its sensed measurements to network 2010. Thus, the second sensing node 2020b may transmit (eg, report or send) a message 2030c including a measurement report including the sensing measurements to the network 2020b.

[0221] In one or more aspects, RANSAC can be employed to identify phantom targets. A phantom target is an object (e.g., an object perceived by a sensor) that is not located at the location indicated by a transmitting network device (e.g., a UE or gNB). A phantom target is an object that has no physical presence, such as a simulated vulnerable road user (VRU) or a simulated vehicle. In some examples, an attacker can create a phantom vehicle that mimics a real vehicle. Phantom targets caused by interreflections are unavoidable in radar measurements, and distinguishing these artifact detections (e.g., phantom targets) from true detections (e.g., real targets) can be challenging.

[0222] Figure 21 21 is a diagram illustrating an example of a ghost target 2130. Specifically, Figure 21 A top view 2100 and a side view 2012 of a road scene are shown, the road scene including a vehicle 2110 (e.g., an autonomous vehicle that can operate as an Rx sensing node), a target vehicle 2120, and a phantom target 2130 (e.g., in the form of a phantom vehicle). Figure 22A As shown by the geometry in FIG, it may be difficult for the vehicle 2110 (eg, an Rx sensing node) to identify the phantom target 2130 from the various different measurements.

[0223] In one or more aspects, RANSAC can be used to remove phantom targets in sensing measurements. In one or more examples, an Rx sensing node can perform sensing to detect targets. A network (e.g., SMF) can configure a corresponding RANSAC threshold to identify outliers. Such outliers can be labeled (e.g., determined to be) phantom targets.

[0224] In one or more aspects, RANSAC can be used to handle aliasing issues, such as aliasing in range and Doppler. In one or more examples, uniformly distributed resources in time and frequency can be employed. If the signal being measured is sparse, there can be aliasing due to sampling the measurements in time and frequency. In some examples, a non-uniform distribution of sensing resources can be performed. Such non-uniform distribution can result in multiple peaks (e.g., but not repeated peaks) in detection. Compressive sensing is typically used in such scenarios. However, RANSAC can be employed to trim the multiple peaks accordingly.

[0225] Figure 4 is a flowchart illustrating an example of a process 2200 for wireless communication utilizing methods for employing RANSAC to enhance JCS. The process 2200 can be performed by a network device such as a UE, a base station (e.g., gNB), a portion of a base station (e.g., one or more of a CU, a DU, a RU, and / or other portions of a base station having a disaggregated architecture), or a component or system (e.g., a chipset) of a UE or base station. The UE can be a mobile device (e.g., a mobile phone), a vehicle, a wearable device (e.g., a watch or other wearable device that connects to a network), an extended reality (XR) device (e.g., a virtual reality (VR) or augmented reality (AR) headset or glasses), or other types of UEs. The operations of process 2200 can be implemented as software components that are executed and run on one or more processors (e.g., processor(s) 484 of FIG. 4, Figure 23 processor(s) 2310 of FIG. 23, or other processors). Further, transmission and reception of signals by the wireless communication devices in process 2200 can be enabled, for example, by one or more antennas, one or more transceivers (e.g., wireless transceivers), and / or one or more communication devices (e.g., communication interface 2340 of FIG. 23), among other examples. Figure 4 Figure 4 Figure 23 Figure 22B

[0226] At block 2210, the network device (or its component) can perform sensing to obtain sensing measurements.

[0227] ​​​​At block 2220, the network device (or a component thereof) may perform a random sample consensus (RANSAC) algorithm on the sensing measurements to identify outliers and inliers within the sensing measurements. In some cases, one or more threshold boundaries are used to identify outliers and inliers. In an illustrative example, as previously described with respect to the threshold boundary (e.g., circle 1930) shown in graph 1902, points of the data set that are within the boundary (e.g., circle 1930) may be determined to be inliers of the data set, and points of the data set that are outside the boundary (e.g., circle 1930) may be determined to be outliers of the data set. Inliers may be determined to be interference measurements and, therefore, may be removed from the sensing measurements. In some aspects, the network device (or a component thereof) may report (e.g., transmit, send, etc.) the inliers of the sensing measurements to a network entity (e.g., a sensing management functionality (SMF), a network server, or other network entity).

[0228] In some aspects, a network device (or a component thereof) may send the RANSAC capabilities of the network device for one or more applications to a network entity (e.g., a sensing management functionality (SMF), a network server, or other network entity). In one illustrative example, the RANSAC capabilities are indicated by one or more bits in a message. For example, the network device (or a component thereof) may send a message comprising one or more bits to the network entity. In some cases, the one or more applications may include filtering outliers from sensing measurements, filtering phantom targets identified from sensing measurements, removing ambiguity in sensing measurements, removing low-quality sensing measurements from sensing measurements, removing redundant sensing measurements from sensing measurements, obtaining an optimal set of sensing resources for sensing, resolving aliasing issues in sensing measurements, any combination thereof, and / or other applications.

[0229] In some examples, the RANSAC capability is related to one or more parameters. For example, the one or more parameters may include a parameter indicating the size of a random set used within the RANSAC algorithm, a parameter indicating the number of iterations used to run the RANSAC algorithm, a parameter indicating the tolerance used to determine outliers and inliers for the sensed measurements, any combination thereof, and / or other parameters.

[0230] Figure 23 2 is a flow chart illustrating an example of a process 2250 for wireless communication using a method for enhancing JCS using RANSAC. The process 2250 may be performed by a network entity such as a sensing management functionality (SMF), a network server, or other device, or by a component or system (e.g., a chipset) of the SMF, network server, or other device. The operations of the process 2250 may be implemented as a processor on one or more processors (e.g., Figure 23In addition, the transmission and reception of signals by the wireless communication device in process 2250 may be performed by, for example, one or more antennas, one or more transceivers (e.g., wireless transceivers), and / or other communication components (e.g., Figure 23 This may be implemented using the communication interface 2340 or other antennas, transceivers and / or components).

[0231] At block 2260 , the network device (or a component thereof) may receive sensing measurements from a set of network devices (eg, one or more user equipment (UEs) or base stations or respective portions of base stations).

[0232] At block 2270, the network device (or a component thereof) may perform a random sample consensus (RANSAC) algorithm on the sensing measurements to identify outliers and inliers within the sensing measurements. In some aspects, one or more threshold boundaries are used to identify outliers and inliers. In an illustrative example, as previously described with respect to the threshold boundary (e.g., circle 1930) shown in graph 1902, points of the data set that are within the boundary (e.g., circle 1930) may be determined as inliers of the data set, and points of the data set that are outside the boundary (e.g., circle 1930) may be determined as outliers of the data set. Inliers may be determined to be interference measurements and, therefore, may be removed from the sensing measurements. In some aspects, the network device (or a component thereof) may report (e.g., transmit, send, etc.) the inliers of the sensing measurements to a network entity (e.g., a sensing management functionality (SMF), a network server, or other network entity).

[0233] At block 2280, the network device (or a component thereof) may determine, based on the outlier, that one or more network devices in the set of network devices produce ambiguous sensing measurements. For example, the ambiguous sensing measurements may be low-quality sensing measurements, redundant sensing measurements, or other ambiguous sensing measurements.

[0234] At block 2290 , the network device (or a component thereof) may send a command to disable sensing measurement reporting to one or more network devices in the set of network devices.

[0235] Figure 23 is a block diagram illustrating an example of a computing system 2300 that may be employed by the disclosed systems and techniques for enhancing JCS using RANSAC. Specifically, ​An example of a computing system 2300 is illustrated, which can be, for example, any computing device making up an internal computing system, a remote computing system, a camera, or any component thereof, where components of the system communicate with each other using connections 2305. Connections 2305 can be physical connections using a bus, or direct connections into processor 2310, such as in a chipset architecture. Connections 2305 can also be virtual, networking, or logical connections.

[0236] In some aspects, computing system 2300 is a distributed system in which the functionality described in this disclosure can be distributed within one data center, multiple data centers, a peer-to-peer network, etc. In some aspects, one or more of the described system components represent a number of such components, each performing some or all of the functions attributed to that component. In some aspects, components can be physical or virtual devices.

[0237] Example system 2300 includes at least one processing unit (CPU or processor) 2310 and connections 2305 that communicatively couple various system components including system memory 2315, such as read-only memory (ROM) 2320 and random access memory (RAM) 2325, to processor 2310. Computing system 2300 can include a cache of high-speed memory directly connected to, in close proximity to, or integrated as part of processor 2310.

[0238] Processor 2310 can include any general purpose processor and a hardware service or software service, such as services 2332, 2334, and 2336 stored in storage device 2330, configured to control processor 2310 as well as a specific-purpose processor in which software instructions are incorporated into the actual processor design. Processor 2310 can be essentially a fully self-contained computing system, containing multiple cores or processors, a bus, memory controller, and cache, etc. Multi-core processors can be symmetric or asymmetric. In one or more examples, processor 2310 can run a RANSAC algorithm.

[0239] To enable user interaction, computing system 2300 includes an input device 2345, which can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and the like. Computing system 2300 can also include output device 2335, which can be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multi modal systems can enable a user to provide multiple types of input / output to communicate with computing system 2300.

[0240] The computing system 2300 may include a communication interface 2340, which may generally govern and manage user input and system output. The communication interface may perform or facilitate receiving and / or sending wired or wireless communications using wired and / or wireless transceivers, including utilizing audio jacks / plugs, microphone jacks / plugs, Universal Serial Bus (USB) ports / plugs, Apple TM Lightning TM Ports / plugs, Ethernet ports / plugs, fiber optic ports / plugs, dedicated wired ports / plugs, 3G, 4G, 5G and / or other cellular data network wireless signal transmission, Bluetooth TM Wireless signal transmission, Bluetooth TM Low energy (BLE) wireless signal transmission, IBEACON TM Wireless signal transmission, radio frequency identification (RFID) wireless signal transmission, near field communication (NFC) wireless signal transmission, dedicated short range communication (DSRC) wireless signal transmission, 802.11 Wi-Fi wireless signal transmission, wireless local area network (WLAN) signal transmission, visible light communication (VLC), Worldwide Interoperability for Microwave Access (WiMAX), infrared (IR) communication wireless signal transmission, public switched telephone network (PSTN) signal transmission, integrated services digital network (ISDN) signal transmission, ad hoc network signal transmission, radio wave signal transmission, microwave signal transmission, infrared signal transmission, visible light signal transmission, ultraviolet light signal transmission, wireless signal transmission along the electromagnetic spectrum, or some combination thereof.

[0241] The communication interface 2340 can also include one or more ranging sensors (e.g., LIDAR sensors, laser rangefinders, RF radar, ultrasonic sensors, and infrared (IR) sensors) configured to collect data and provide measurements to the processor 2310, whereby the processor 2310 can be configured to perform determinations and calculations needed to obtain various measurements for the one or more ranging sensors. In some examples, the measurements can include time of flight, wavelength, azimuth, elevation, distance, linear velocity, and / or angular velocity, or any combination thereof. The communication interface 2340 can also include one or more Global Navigation Satellite System (GNSS) receivers or transceivers for determining a location of the computing system 2300 based on one or more signals received from one or more satellites associated with one or more GNSS systems. GNSS systems include, but are not limited to, GPS in the United States, Global Navigation Satellite System (GLONASS) in Russia, Beidou Navigation Satellite System (BDS) in China, and Galileo GNSS in Europe. There is no limitation as to operation on any particular hardware arrangement, and thus the underlying features herein can be readily substituted for improved hardware or firmware arrangements as they are developed.

[0242] The storage device 2330 can be a nonvolatile and / or non-transitory and / or computer-readable memory device and can be a hard disk or other type of computer readable medium such as a tape cassette, flash memory card, solid-state memory device, digital versatile disc, Blu-ray disc, random access memory (RAM), any other suitable memory device, or a collection of such devices that are capable of storing data that can be accessed by a computer. The storage device 2330 can be used to store data that is accessed by the processor 2310, e.g., firmware instructions, operating system instructions, application program instructions, application data, etc. The storage device 2330 can also be used to store data that is input or output by the computing system 2300, e.g., received data, generated data, etc. a card, a smart card chip, an EMV chip, a subscriber identity module (SIM) card, a mini / micro / nano / pico SIM card, another integrated circuit (IC) chip / card, a random access memory (RAM), a static RAM (SRAM), a dynamic RAM (DRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash EEPROM (flash EPROM), a cache memory (e.g., a level 1 (LI) cache, a level 2 (L2) cache, a level 3 (L3) cache, a level 4 (L4) cache, a level 5 (L5) cache, or other (L#) cache), a resistive random access memory (RRAM / ReRAM), a phase change memory (PCM), a spin-transfer torque RAM (STT-RAM), another memory chip or cartridge, and / or combinations thereof.

[0243] The storage device 2330 can include software services, servers, services, and the like that, when code defining such software is executed by the processor 2310, cause the system to perform a function. In some aspects, a hardware service that performs a particular function can include the software components stored in a computer-readable medium that are necessary to perform the function coupled with the necessary hardware components, such as a processor 2310, a connection 2305, an output device 2335, and the like, to perform the function. The term “computer-readable medium” includes, but is not limited to, portable or non-portable storage devices, optical storage devices, and various other mediums capable of storing, containing, or carrying instruction and / or data. A computer-readable medium can include a non-transitory medium in which data can be stored and from which data can be

[0244] Specific details are provided in the description above to provide a thorough understanding of the various aspects and examples provided herein, but those skilled in the art will recognize that the present application is not limited thereto. Thus, although the illustrative aspects of the present application have been described in detail herein, it is to be understood that each inventive concept can be implemented and adopted in various other ways, and the appended claims are not intended to be interpreted as including these variations, unless limited by the prior art. The various features and aspects of the above-mentioned applications can be used individually or in combination. In addition, without departing from the broader scope of this specification, each aspect can be used in any number of environments and applications beyond the environment and application described herein. Therefore, the description and the accompanying drawings should be considered as illustrative rather than restrictive. For illustrative purposes, each method is described in a specific order. It should be understood that, in alternative aspects, each method can be performed in a different order than described.

[0245] For clarity of explanation, in some instances, the present technology can be presented as including separate functional blocks, which include devices, device components, steps or routines in the method embodied in software or a combination of hardware and software. Additional components other than those components shown in the drawings and / or described herein can be used. For example, circuits, systems, networks, processes and other components can be shown as components in block diagram form to avoid confusing these aspects in unnecessary details. In other cases, well-known circuits, processes, algorithms, structures and techniques can be shown without unnecessary details to avoid confusing various aspects.

[0246] In addition, it will be appreciated by those skilled in the art that the various exemplary logic blocks, modules, circuits, and algorithmic steps described in conjunction with the various aspects disclosed herein can be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate this interchangeability of hardware and software, various exemplary components, blocks, modules, circuits, and steps have been generally described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints proposed for the entire system. Those skilled in the art can implement the described functions in different ways for each specific application, but such specific implementation decisions should not be interpreted as resulting in departure from the scope of this disclosure.

[0247] Various aspects may be described above as processes or methods, which may be depicted as flow charts, flowcharts, data flow diagrams, structure diagrams, or block diagrams. Although a flow chart may describe operations as a sequential process, many of the operations may be performed in parallel or concurrently. Furthermore, the order of the operations may be rearranged. A process is terminated when its operations are completed, but a process may have additional steps not included in the figures. A process may correspond to a method, function, procedure, subroutine, subprogram, etc. When a process corresponds to a function, termination of the process may correspond to the function returning to the calling function or the main function.

[0248] The processes and methods described above according to the examples can be implemented using stored computer-executable instructions or computer-executable instructions acquired (e.g., downloaded) in some other manner from computer-readable media. Such instructions can include, for instance, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or a processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible via a network. The computer-executable instructions can be, for example, binaries, intermediate format instructions such as assembly language, firmware, source code, etc. Examples of computer-readable media that can be used to store instructions, information used, and / or information created during methods according to described examples include magnetic or optical disks, flash memory, USB devices provided with non-volatile memory, network storage, and the like.

[0249] In some aspects, computer-readable storage devices, media and memory can include cables or wireless signals containing bitstreams and the like. However, where mentioned, non-transitory computer-readable storage media expressly excludes media such as power supply, carrier waves, electromagnetic waves, and signals per se.

[0250] Those skilled in the art will understand that information and signals can be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips that can be referenced throughout the above description can be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof consistent with the state of the art.

[0251] The various illustrative logical blocks, modules, and circuits described in connection with the aspects disclosed herein can be implemented or performed with a hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof and can be embodied in any of a number of various forms. When implemented in software, firmware, middleware, or microcode, the program code or code segments (e.g., computer program products) can be stored in a computer-readable or machine-readable medium. A processor(s) can perform the necessary tasks. Examples of various shapes include: a laptop computer, a smart phone, a mobile phone, a tablet device, or other small form factor personal computer, a personal digital assistant, a rackmount device, a stand alone device, etc. The functionality described herein can also be embodied in peripheral devices or in interposers. By way of further example, such functionality can also be implemented on circuit boards among different chips or different processes executing on a single device.

[0252] Instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are example means for providing the functionality described in this disclosure.

[0253] The techniques described herein can also be implemented in electronic hardware, computer software, firmware, or any combination thereof. Such techniques can be implemented in any of various devices such as a general purpose computer, a wireless communication device handset, or an integrated circuit device having a variety of purposes such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other integrated circuit. Any features described as modules or components can be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, the techniques can be realized at least in part by a computer-readable data storage medium comprising program code including instructions that, when executed, performs one or more of the methods, algorithms and / or operations described above. The computer-readable data storage medium can form part of a computer program product, which can include packaging materials. The computer-readable medium can comprise memory or data storage media, such as random access memory (RAM) such as synchronous dynamic random access memory (SDRAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), FLASH memory, magnetic or optical data storage media, and the like. The techniques additionally, or alternatively, can be realized at least in part by a computer-readable communication medium that carries or communicates program code in the form of instructions or data structures and that can be accessed, read, and / or executed by a computer, such as a propagated signal or wave. The program code can be executed by a processor, which can include one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Such a processor can be configured to perform any of the techniques described in this disclosure. A general purpose processor can be a microprocessor; but in the alternative, the processor can be any conventional processor, controller, microcontroller, or state machine. A processor can also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Accordingly, the term "processor," as used herein can refer to any of the foregoing structure, any combination of the foregoing structure, or any other structure or apparatus suitable for implementation of the techniques described herein.

[0254] The program code can be executed by a processor, which can include one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Such a processor can be configured to perform any of the techniques described in this disclosure. A general purpose processor can be a microprocessor; but in the alternative, the processor can be any conventional processor, controller, microcontroller, or state machine. A processor can also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Accordingly, the term "processor," as used herein can refer to any of the foregoing structure, any combination of the foregoing structure, or any other structure or apparatus suitable for implementation of the techniques described herein.

[0255] Those of ordinary skill in the art will appreciate that the less than (“<”) and greater than (“>”) symbols or terminology used herein can be replaced with less than or equal to (“≤”) and greater than or equal to (“≥”) symbols, respectively, without departing from the scope of this description.

[0256] Where components are described as being “configured to” perform certain operations, such configuration can be accomplished, for example, by designing electronic circuitry or other hardware to perform the operation, by programming programmable electronic circuitry (e.g., microprocessors or other suitable electronic

[0257] The phrase “coupled to” or “communicatively coupled to” means that any component is either directly or indirectly physically connected to another component, and / or any component is either directly or indirectly in communication with another component (e.g., connected to that other component through a wired or wireless connection and / or other suitable communication interface).

[0258] Claim language reciting “at least one of” a set of items or other collectivity, and / or a claim language reciting “one or more of” a set of items, indicates an intention that one member of the set or multiple members of the set, in any combination, satisfy the claim. For example, claim language reciting “at least one of A and B” or “at least one of A or B” means A, B, or A and B. In another example, claim language reciting “at least one of A, B, and C” or “at least one of A, B, or C” means A, B, C, or A and B, or A and C, or B and C, or A and B and C. Language reciting “at least one of” a set of items or other collectivity does not limit the set to items listed. For example, claim language reciting “at least one of A and B” or “at least one of A or B” can mean A, B, or A and B, and can additionally include items not listed in the set of A and B.

[0259] Exemplary aspects of the present disclosure include:

[0260] Aspect 1. A network device for wireless communication, the network device comprising: at least one memory; and at least one processor coupled to the at least one memory and configured to: perform sensing to obtain sensing measurements; and perform a random sample consensus (RANSAC) algorithm on the sensing measurements to identify inliers and outliers within the sensing measurements.

[0261] Aspect 2. The network device of aspect 1, wherein the inliers and the outliers are identified using one or more threshold boundaries.

[0262] Aspect 3. The network device of any one of aspects 1 or 2, wherein the network device is one of a user equipment or a base station.

[0263] Aspect 4. The network device according to any one of aspects 1 to 3, wherein the network device is configured to send the RANSAC capability of the network device for one or more applications to a network entity.

[0264] Aspect 5. The network device according to aspect 4, wherein the network entity is one of a sensing management functionality (SMF) or a network server.

[0265] Aspect 6. A network device according to any one of Aspects 4 or 5, wherein the one or more applications include at least one of the following: filtering out the outliers from the sensing measurements, filtering out phantom targets identified from the sensing measurements, removing ambiguity in the sensing measurements, removing low-quality sensing measurements from the sensing measurements, removing redundant sensing measurements in the sensing measurements, obtaining an optimal set of sensing resources for sensing, or resolving aliasing problems in the sensing measurements.

[0266] Aspect 7. The network device according to any one of aspects 4 to 6, wherein the RANSAC capability is related to one or more parameters.

[0267] Aspect 8. A network device according to Aspect 7, wherein the one or more parameters include at least one of the following: a parameter for indicating the size of the random set used within the RANSAC algorithm, a parameter for indicating the number of iterations used to run the RANSAC algorithm, or a parameter for indicating the tolerance for determining the outliers and the inliers of the sensing measurement.

[0268] Aspect 9. The network device according to any one of aspects 4 to 8, wherein the RANSAC capability is indicated by one or more bits in a message.

[0269] Aspect 10. The network device according to any one of aspects 1 to 9, wherein the network device is configured to report the inward point of the sensing measurement to a network entity.

[0270] Aspect 11. A method for wireless communication at a network device, the method comprising: performing sensing by the network device to obtain sensing measurements; and performing a random sample consensus (RANSAC) algorithm by the network device on the sensing measurements to identify outliers and inliers within the sensing measurements.

[0271] Aspect 12. The method of aspect 11, wherein the outliers and the inliers are identified using one or more threshold boundaries.

[0272] Aspect 13. The method of any of aspects 11 or 12, wherein the network device is one of a user equipment or a base station.

[0273] Aspect 14. The method of any of aspects 11 to 13, further comprising transmitting, by the network device, a RANSAC capability of the network device for one or more applications to a network entity.

[0274] Aspect 15. The method of aspect 14, wherein the network entity is one of a sensing management functionality (SMF) or a network server.

[0275] Aspect 16. The method of any of aspects 14 or 15, wherein the one or more applications comprise at least one of: filtering out the outliers from the sensing measurements, filtering out ghost targets identified from the sensing measurements, removing ambiguity in the sensing measurements, removing low quality sensing measurements from the sensing measurements, removing redundant sensing measurements in the sensing measurements, obtaining an optimal set of sensing resources for sensing, or resolving an aliasing problem in the sensing measurements.

[0276] Aspect 17. The method of any of aspects 14 to 16, wherein the RANSAC capability is related to one or more parameters.

[0277] Aspect 18. The method of aspect 17, wherein the one or more parameters comprise at least one of: a parameter indicating a size of a random set used within the RANSAC algorithm, a parameter indicating a number of iterations for running the RANSAC algorithm, or a parameter indicating a tolerance for determining the outliers and the inliers of the sensing measurements.

[0278] Aspect 19. The method of any of aspects 14 to 18, wherein the RANSAC capability is indicated by one or more bits in a message.

[0279] Aspect 20. The method of any of aspects 11 to 19, further comprising reporting, by the network device, the inliers of the sensing measurements to a network entity.

[0280] Aspect 21. A network device for wireless communication, comprising: at least one memory; and at least one processor coupled to the at least one memory and configured to: receive, from a set of network devices, sensing measurements; perform a random sample consensus (RANSAC) algorithm on the sensing measurements to identify inliers and outliers within the sensing measurements; determine, based on the outliers, that one or more network devices of the set of network devices produced ambiguous sensing measurements; and send, to the one or more network devices of the set of network devices, a command to disable sensing measurement reporting.

[0281] Aspect 22. The network device of aspect 21, wherein the network entity is one of a sensing management functionality (SMF) or a network server.

[0282] Aspect 23. The network device of any one of aspects 21 or 22, wherein the network device is one of a user equipment or a base station.

[0283] Aspect 24. The network device of any one of aspects 21-23, wherein the ambiguous sensing measurements are at least one of low quality sensing measurements or redundant sensing measurements.

[0284] Aspect 25. The network device of any one of aspects 21-24, wherein the outliers and the inliers are identified using one or more threshold boundaries.

[0285] Aspect 26. A method for wireless communication at a network entity, comprising: receiving, by the network entity, sensing measurements from a set of network devices; performing, by the network entity, a random sample consensus (RANSAC) algorithm on the sensing measurements to identify outliers and inliers within the sensing measurements; determining, by the network entity, based on the outliers, that one or more network devices of the set of network devices produced ambiguous sensing measurements; and sending, by the network entity, to the one or more network devices of the set of network devices, a command to disable sensing measurement reporting.

[0286] Aspect 27. The method of aspect 26, wherein the network entity is one of a sensing management functionality (SMF) or a network server.

[0287] Aspect 28. The method of any one of aspects 26 or 27, wherein the network device is one of a user equipment or a base station.

[0288] Aspect 29. The method of any one of aspects 26-28, wherein the ambiguous sensing measurements are at least one of low quality sensing measurements or redundant sensing measurements.

[0289] Aspect 30. The method of any one of aspects 26-29, wherein the outer points and the inner points are identified using one or more threshold boundaries.

[0290] Aspect 31. A non-transitory computer-readable medium having instructions stored thereon, the instructions, when executed by at least one processor, cause the at least one processor to perform operations in accordance with any one of aspects 11-20.

[0291] Aspect 32. An apparatus for wireless communication, the apparatus comprising one or more means for performing operations in accordance with any one of aspects 11-20.

[0292] Aspect 33. A non-transitory computer-readable medium having instructions stored thereon, the instructions, when executed by at least one processor, cause the at least one processor to perform operations in accordance with any one of aspects 26-30.

[0293] Aspect 34. An apparatus for wireless communication, the apparatus comprising one or more means for performing operations in accordance with any one of aspects 26-30.

[0294] The preceding description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other aspects. Thus, the claims are not intended to be limited to the aspects shown herein, but is to be accorded the full scope consistent with the language claims, wherein reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more.” Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application cover modifications and variations of this application provided they come within the scope of the appended claims and their equivalents.

Claims

1. A network device for wireless communication, the network device comprising: at least one memory; and at least one processor coupled to the at least one memory and configured to: performing sensing to obtain sensed measurements; as well as A random sample consensus (RANSAC) algorithm is performed on the sensed measurements to identify outliers and inliers within the sensed measurements. 2 . The network device of claim 1 , wherein the outliers and the inliers are identified using one or more threshold boundaries. The network device of claim 1 , wherein the network device is one of a user equipment or a base station. 4 . The network device of claim 1 , wherein the network device is configured to send the RANSAC capability of the network device for one or more applications to a network entity. 5 . The network device of claim 4 , wherein the network entity is one of a sensing management functionality (SMF) or a network server.

6. The network device of claim 4, wherein the one or more applications comprise at least one of filtering outliers from the sensing measurements, filtering out phantom targets identified from the sensing measurements, removing ambiguity in the sensing measurements, removing low-quality sensing measurements from the sensing measurements, removing redundant sensing measurements from the sensing measurements, obtaining an optimal set of sensing resources for sensing, or resolving aliasing issues in the sensing measurements. The network device according to claim 4 , wherein the RANSAC capability is related to one or more parameters.

8. The network device of claim 7 , wherein the one or more parameters include at least one of: a parameter indicating a size of a random set used within the RANSAC algorithm, a parameter indicating a number of iterations for running the RANSAC algorithm, or a parameter indicating a tolerance for determining the outliers and the inliers of the sensing measurements.

9. The network device of claim 4, wherein the RANSAC capability is indicated by one or more bits in a message.

10. The network device of claim 1, wherein the network device is configured to report the inliers of the sensing measurements to a network entity.

11. A method for wireless communication at a network device, the method comprising: performing sensing by the network device to obtain sensing measurements; as well as A random sample consensus (RANSAC) algorithm is performed by the network device on the sensing measurements to identify outliers and inliers within the sensing measurements.

12. The method of claim 11, wherein the outliers and the inliers are identified using one or more threshold boundaries.

13. The method of claim 11, wherein the network device is one of a user equipment or a base station. 14 . The method according to claim 11 , further comprising sending, by the network device, the RANSAC capability of the network device for one or more applications to a network entity.

15. The method of claim 14, wherein the network entity is one of a sensing management functionality (SMF) or a network server.

16. The method of claim 14, wherein the one or more applications comprise at least one of filtering outliers from the sensing measurements, filtering out phantom targets identified from the sensing measurements, removing ambiguity in the sensing measurements, removing low-quality sensing measurements from the sensing measurements, removing redundant sensing measurements from the sensing measurements, obtaining an optimal set of sensing resources for sensing, or resolving aliasing issues in the sensing measurements. The method of claim 14 , wherein the RANSAC capability is related to one or more parameters.

18. The method of claim 17, wherein the one or more parameters include at least one of: a parameter indicating a size of a random set used within the RANSAC algorithm, a parameter indicating a number of iterations for running the RANSAC algorithm, or a parameter indicating a tolerance for determining the outliers and the inliers of the sensed measurements.

19. The method of claim 14, wherein the RANSAC capability is indicated by one or more bits in a message.

20. The method of claim 11, further comprising reporting, by the network device, the inlier point of the sensing measurement to a network entity.

21. A network device for wireless communication, the network device comprising: at least one memory; and at least one processor coupled to the at least one memory and configured to: receiving sensing measurements from a set of network devices; performing a random sample consensus (RANSAC) algorithm on the sensed measurements to identify outliers and inliers within the sensed measurements; determining, based on the external point, that one or more network devices in the set of network devices produce a fuzzy sensing measurement; as well as A command for disabling sensing measurement reporting is sent to the one or more network devices in the set of network devices.

22. The network device of claim 21, wherein the network entity is one of a sensing management functionality (SMF) or a network server.

23. The network device of claim 21, wherein the network device is one of a user equipment or a base station.

24. The network device of claim 21, wherein the ambiguous sensing measurement is at least one of a low-quality sensing measurement or a redundant sensing measurement.

25. The network device of claim 21, wherein the outliers and the inliers are identified using one or more threshold boundaries.

26. A method for wireless communication at a network entity, the method comprising: receiving, by the network entity, sensing measurements from a set of network devices; performing, by the network entity, a random sample consensus (RANSAC) algorithm on the sensing measurements to identify outliers and inliers within the sensing measurements; determining, by the network entity based on the external point, that one or more network devices in the set of network devices generate fuzzy sensing measurements; as well as A command for disabling sensing measurement reporting is sent by the network entity to the one or more network devices in the set of network devices.

27. The method of claim 26, wherein the network entity is one of a sensing management functionality (SMF) or a network server.

28. The method of claim 26, wherein the network device is one of user equipment or a base station.

29. The method of claim 26, wherein the ambiguous sensing measurement is at least one of a low-quality sensing measurement or a redundant sensing measurement.

30. The method of claim 26, wherein the outliers and the inliers are identified using one or more threshold boundaries.

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