Wireless sensing and communication systems with matching capabilities to improve interoperability

Abstract

The present invention relates to a sensing and communication system for sensing targets (objects or people) in an environment, in which first actual sensing data of the target collected by a first device is used to generate synthetic data that emulates second actual sensing data that would be generated by a second device of the same target. The synthetic data is then used to determine whether the second actual sensing data collected by the second device matches the first actual sensing data collected by the first device, thereby confirming whether both devices are sensing or have sensed the same target, or to identify objects within the field of view of the device (e.g., AR glasses).

Description

[Technical Field]

[0001] The present invention relates to the field of integrated sensing and communication (ISAC) in wireless networks such as, but not limited to, fifth generation (5G) cellular communication systems. [Background technology]

[0002] As wavelengths in communication systems become shorter, it becomes easier to use the same wavelength bands for increasingly sophisticated sensing applications.

[0003] So-called "mmWave" radar is a non-contact sensing technology that detects objects and provides their range, speed, and angle. It operates in the 30 GHz to 300 GHz spectrum. Because this technology uses short wavelengths, it can achieve submillimeter accuracy, penetrate certain materials such as plastic, drywall, and clothing, and is relatively immune to environmental conditions such as rain, fog, dust, and snow. The ability to sense surface position and movement at the submillimeter scale could enable such systems to perform vital signs monitoring.

[0004] As an example, the signal wavelength bands of a 5G communication system or other suitable wireless communication system can be used as mmWave radar to measure, for example, the location and movement of vehicles and people, as well as vital sign signals such as heart rate and breathing rate, although this requires knowledge of the transmission environment, knowledge of approximate target locations, and appropriate modifications to the signal system.

[0005] Recently, mmWave frequency Doppler radar systems for human pose estimation have been studied (see Sizhe An et al.: “Fast and Scalable Human Pose Estimation using mmWave Point Cloud,” Design Automation Conference (DAC) 2022, arXiv:2205.00097[eess.IV]). Such systems typically utilize frequency-modulated continuous wave (FMCW) chirps. The chirps are emitted from a transmitter, deflected by objects in the scene, and received by one or more antennas. Both the distance and Doppler shift (velocity) of a person moving through the scene can be recovered, which can be plotted in two dimensions (2D) and used as a signature of a particular pose. Human pose estimation has also been used to identify individuals in a crowd, primarily in security applications.

[0006] Synthetic data is artificial data generated from original data and a model trained to reproduce the characteristics and structure of the original data. Recently, generating mmWave synthetic data representing 3D human poses from original video datasets has been demonstrated (see Karan Ahuja et al.: “Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition,” CHI'21, May 8-13, 2021, Yokohama, Japan). This method provides synthetic mmWave Doppler radar measurements that closely match real Doppler radar measurements over time in both range and Doppler shift. This can be decomposed into two main steps: (1) generating a 3D mesh model of a human in a given pose from video data; and (2) generating synthetic Doppler radar measurements based on the 3D mesh model and a given synthetic viewpoint. Equivalents to step (1) have been demonstrated using other initial data sets, such as generating 3D mesh data from LIDAR, sonar, and body-worn accelerometers (see Amin Ahmadi et al.: “3D Human Gait Reconstruction and Monitoring Using Body-Worn Inertial Sensors and Kinematic Modeling,” DOI 10.1109 / JSEN.2016.2593011, Journal of IEEE Sensors). Step (2) should be compatible with 3D meshes generated from these other data types, demonstrating that synthetic mmWave Doppler radar data can be generated from many initial base data sets.

[0007] Beyond human pose identification, research has shown that the radar cross sections of objects such as drones or different vehicle types can be sufficiently different to perform discrimination functions. Generating synthetic radar data of cars and other objects is commonly applied to virtual sensor training, such as for autonomous vehicles.

[0008] ISAC is considered a new service for next-generation communication networks, where network resources are intelligently shared between communication and sensing tasks, potentially enabling the network infrastructure to also operate as a sensor network. Such "perceptive" networks could be utilized to improve the core communication functions on the network, but could also enable "sensing as a service," where third parties can access the output of the sensing functions.

[0009] Recently, the term "metaverse" has been used to refer to a set of shared, interactable spaces where users can interact with mutually perceptible virtual features (augmented reality (AR)) or where the entire space is composed of virtual features (virtual reality (VR)). This definition has recently been adopted in several 3GPP® work items. Throughout this disclosure, VR and AR will be generically referred to as "mixed reality" (MR).

[0010] Interoperability of metaverse and mixed reality (MR) services is predicted to be a significant challenge to address. More specifically, some services, such as digital storefronts and the digital goods and items sold therein, are expected to be non-interoperable by default. That is, digital assets acquired by a user interacting in one metaverse environment will not be "visible" to another user interacting in another metaverse environment.

[0011] These interoperability challenges lead to user experience problems (because users are more likely to purchase digital assets if they are visible to other users even outside their own metaverse).Therefore, there is a need to find a solution that limits interoperability issues (and especially the sharing of digital assets) between metaverses without compromising the de facto monopoly that each metaverse service provider has over its own digital storefront. Summary of the Invention

[0012] An object of the present invention is to improve object identification in sensing-related services.

[0013] This object is achieved by an apparatus according to claim 1, a terminal device according to claim 12, an access device according to claim 13, a system according to claim 14, a method according to claim 15 and a computer program product according to claim 16.

[0014] According to a first aspect relating to a wireless communication device or other network device (e.g., an access device or a terminal device), the device comprises at least one first sensor characterized by at least one sensing parameter, in particular at least one of a sensor type and a viewpoint, an orientation, or a field of view, and the device comprises: obtaining a first measurement of the target object; receiving second measurement data relating to the target object from a second sensor; obtaining composite data of the second measurement data and / or the first measurement data based on at least one sensing parameter of the first sensor and / or at least one sensing parameter related to the second measurement data, in particular at least one of the type of sensor and the viewpoint, orientation, or field of view; Based on the acquired combined data, it is determined whether the first measurement data and the second measurement data match.

[0015] According to a second aspect relating to procedures performed in a wireless communication device or other network device (e.g., an access device or a terminal device), a method for matching a target object in a wireless network includes: obtaining a first measurement of a target object; receiving second measurement data about the target object from a second sensor; obtaining synthetic data from the second measurement data and / or the first measurement data based on at least one sensing parameter of the first sensor and / or at least one sensing parameter related to the second measurement data, in particular at least one of the type of sensor and the viewpoint or line of sight; and determining whether the first measurement data and the second measurement data match based on the obtained combined data.

[0016] According to a third aspect, there is provided a terminal device (eg, UE) comprising the apparatus of the first aspect.

[0017] According to a fourth aspect, there is provided an access device (eg, a base station, an access point, etc.) comprising the apparatus of the first aspect.

[0018] According to a fifth aspect, there is provided a wireless communication system including at least one of the terminal device according to the third aspect and the access device according to the fourth aspect.

[0019] Finally, according to a sixth aspect there is provided a computer program product comprising code means for generating the steps of the method of the second aspect when executed on a processor of a network device.

[0020] The proposed solution therefore enables a matching service that can confirm or deny that two different devices have seen or otherwise sensed the same object by generating synthetic data based on the data of a first device that closely resembles the data that would be generated if a second device were to see the same object.

[0021] In this way, a "perceptual" network with a matching service is provided, which may consist of a large number of terminal devices (e.g., UEs) and access devices (e.g., gNBs) with various capabilities and modalities deployed to sense target objects (e.g., people and objects) in an environment. First (actual) sensing data of the target object collected by a first device is used to generate synthetic data adapted to emulate as accurately as possible second (actual) sensing data generated by a second device of the same target object. The synthetic data is then used to determine whether the second sensing data collected by the second device matches the first sensing data collected by the first device, thereby confirming whether both devices are sensing or have sensed the same target.

[0022] Therefore, by specifically generating synthetic data based on sensing data from a first sensor that matches sensing data from a second sensor, it becomes easier to determine the same target object sensed by multiple different sensors. The synthetic data generation process may take into account factors such as the position, viewing angle, or viewpoint of the multiple different sensors.

[0023] The proposed matching concept can be applied to various use-case-specific embodiments (as described below) to provide methods and systems that enable generating synthetic data by or for one device to match with real-world data of a second device, where the synthetic data is generated based on knowledge of at least one sensing parameter (e.g., sensor type, viewpoint, gaze direction, etc.) used by the second device.

[0024] Depending on the specific use case, the following benefits can be achieved:

[0025] If the identity of the second device is unknown to the first device, the network may be able to arbitrate the transmission of information between the first and second devices. More specifically, in MR / Metaverse use cases, the network may enable the first device to determine the identity of the user that it is specifically viewing, thereby enabling it to obtain any additional information needed (e.g., the correct digital assets to be loaded for the particular user being viewed).

[0026] Furthermore, through the transmission of data representing their radar cross section, it will be possible to track individual devices between cells, ensuring service continuity for sensing tasks on the network. More specifically, it may be possible to track non-communicating objects as they move from cell to cell, which could play an important role in areas such as law enforcement (e.g., vehicle tracking).

[0027] Additionally, users may be able to use easily accessible sensors (e.g., smartphone cameras) to provide data that can be used as part of the authentication services provided by the network.

[0028] According to a first option, which may be combined with any of the first to sixth aspects above, the at least one sensing parameter relating to the second measurement data may be at least one sensing parameter of a second sensor, wherein the at least one sensing parameter of the second sensor is obtained from another device.

[0029] According to a second option, which may be combined with the first option or any of the first to sixth aspects above, the device may determine whether the obtained composite data of the first measurement data matches the obtained composite data of the second measurement data, or determine whether the first measurement data matches the obtained composite data of the second measurement data, or determine whether the second measurement data matches the obtained composite data of the first measurement data.

[0030] According to the first or second option, or a third option which may be combined with any of the first to sixth aspects above, determining whether there is a match (e.g. by the device) may be achieved by using at least one of: a first algorithm for segmenting the first and second measurement data into first and second object-related data; a second algorithm for generating synthetic data from the first or second object-related data or one of the previous inputs; a third algorithm for converting the first or second object-related data or one of the previous inputs into a first or second intermediate form, in particular a multidimensional representation, representing the object described by the first or second object-related data; a fourth algorithm for converting the first or second intermediate form or one of the previous inputs into synthetic data; and a fifth algorithm for aligning the first and second intermediate forms.

[0031] According to a fourth option, which may be combined with any of the first to third options or any of the first to sixth aspects above, an identity of the target object may be determined (e.g., by the device) based on the acquired composite data and the first measurement data.

[0032] According to a fifth option, which may be combined with any of the first to fourth options or any of the first to sixth aspects above, the first measurement data may be obtained (e.g., by the device) from a database.

[0033] According to a sixth option, which may be combined with any of the first to fifth options or any of the first to sixth aspects above, additional data about the target object may be obtained (e.g., by the device) from a metaverse or mixed reality service provider, and the additional data may be provided to a second sensor.

[0034] According to a seventh option, which may be combined with any of the first to fifth options or any of the first to sixth aspects above, the device may perform asset tracking of the target object. This allows tagged assets to be checked intermittently to ensure that tracking tags are still attached to the appropriate assets.

[0035] According to an eighth option, which may be combined with any of the first to fifth options or any of the first to sixth aspects above, the device may request the target object to allow network-assisted identification.

[0036] According to a ninth option, which may be combined with any of the first to fifth options or any of the first to sixth aspects above, the device may acknowledge the target object if a match is identified.

[0037] According to a tenth option, which may be combined with any of the first to fifth options or any of the first to sixth aspects above, the apparatus may support service continuity, in particular when the target object moves outside the coverage area of ​​the remotely located device.

[0038] According to a seventh aspect, the above object is achieved by an apparatus comprising a receiver and a sensing sensor characterized by certain sensing parameters (e.g., sensor type, viewpoint), the apparatus comprising: - sensing or acquiring first sensing data of a target object; - receiving second sensing data from the first device regarding the target object; - (optionally) receiving sensing parameters (e.g., sensor type, viewpoint) from the first device; - generating synthetic data from the second sensing data (or the first sensing data) based on the parameters of the apparatus and the parameters of the first device; - Determine whether the first sensing data (and second sensing data) from the real world matches the generated synthetic data. According to the first option, the device of the seventh aspect comprises: - generating first sensed data and synthetic data from the first sensed data based on the parameters of the apparatus and the parameters of the first device; There is a possibility of determining whether the synthesized data generated from the first sensing data matches the synthesized data generated from the second sensing data.

[0039] According to a second option, which may be combined with the first option, the apparatus of the seventh aspect may further comprise at least one of a segmentation algorithm, a synthetic data algorithm, an X-to-Mesh algorithm, a Mesh-to-Y algorithm, a synthetic transformation algorithm, and a mesh alignment algorithm.

[0040] According to a third option, which may be combined with the first or second option, the device of the seventh aspect may further determine an identity of the target object based on the generated composite data and the first sensing data.

[0041] According to a fourth option, which may be combined with any of the first to third options, the first sensing data may be stored in a database.

[0042] According to a fifth option, which may be combined with any of the first to fourth options, the apparatus of the seventh aspect may obtain additional data about the target object from a metaverse / MR service provider and provide the additional data to the first device.

[0043] According to a sixth option, which may be combined with any of the first to fifth options, the device of the seventh aspect may perform asset tracking of the target object.

[0044] According to a seventh option, which may be combined with any of the first to sixth options, the device of the seventh aspect may require the target object to opt in to network-assisted identification.

[0045] According to an eighth option, which may be combined with the seventh option, the device of the seventh aspect may approve the target object if there is a match.

[0046] According to a ninth option, which may be combined with any of the first through eighth options, the apparatus of the seventh aspect may support service continuity when the target object moves outside the coverage area of ​​the first device.

[0047] According to an eighth aspect, the above object is solved by a UE comprising the apparatus of the seventh aspect and the first to fourth, sixth and seventh options.

[0048] According to a ninth aspect, the above object is achieved by a base station comprising the device of the seventh aspect and the first to ninth options.

[0049] According to a tenth aspect, the above object is achieved by: - sensing first sensing data of a target object; - receiving second sensing data related to the target object from the first device; - (optionally) receiving sensing parameters (e.g., sensor type, viewpoint) from the first device; - generating synthetic data from the second sensing data (or the first sensing data) based on the parameters of the apparatus and the parameters of the first device; - determining whether the first sensing data (and the second sensing data) and the generated synthetic data match.

[0050] According to an eleventh aspect, the above object is solved by a computer program product comprising code means for generating the steps of the method of the tenth aspect when the computer program product is executed on a computing device.

[0051] According to a twelfth aspect, the above object is solved by a system in which synthetic data is generated by one device to enable matching with real-world data of a second device, the synthetic data being generated using knowledge of parameters (e.g., sensor type or viewpoint) used for sensing by the second device.

[0052] It should be noted that the above apparatus may be implemented based on a separate hardware circuit having discrete hardware components, integrated chips, or chip module configurations, or based on a signal processing device or chip controlled by software routines or programs stored in a memory, written on a computer-readable medium, or downloaded from a network such as the Internet.

[0053] It is to be understood that the apparatus according to claim 1, the terminal device according to claim 12, the access device according to claim 13, the system according to claim 14, the method according to claim 15 and the computer program product according to claim 16 may have similar and / or identical preferred embodiments, in particular as set out in the dependent claims.

[0054] It is to be understood that a preferred embodiment of the invention can also be any combination of the dependent claims or the above embodiments with the independent claims.

[0055] These and other aspects of the invention will be elucidated with reference to the embodiment(s) described hereinafter. [Brief explanation of the drawings]

[0056] [Figure 1] Figure 1 shows a schematic representation of the distributed sensing capabilities created over a 5G communication system link. [Figure 2] FIG. 2 illustrates schematically an embodiment of a transmitter and receiver architecture that may be used in embodiments of the present invention. [Figure 3] FIG. 3 shows a schematic representation of a sensing and communication system with data processing and matching algorithms according to a first embodiment. [Figure 4] FIG. 4 shows a schematic process flow diagram according to the first embodiment. [Figure 5] FIG. 5 shows a schematic flow diagram of a matching procedure according to the second embodiment. [Figure 6] FIG. 6 shows a schematic diagram of a modified system structure according to a third embodiment. [Figure 7] FIG. 7 shows a schematic flow diagram of a matching procedure according to the fourth embodiment. [Figure 8] FIG. 8 illustrates a schematic of a target approval procedure according to various embodiments of the present invention. [Figure 9] FIG. 9 illustrates a schematic embodiment of a flow diagram of a sensing operation according to an embodiment of the present invention. [Figure 10] FIG. 10 illustrates generally an embodiment of a flow diagram of a position and motion detection process that may be used in embodiments of the present invention. [Figure 11] FIG. 11 illustrates generally an embodiment of a flow diagram of a heart rate and respiration rate detection process that may be used in embodiments of the present invention. [Figure 12] FIG. 12 shows a schematic diagram of a sensing system according to one embodiment of the present invention. [Figure 13] FIG. 13 shows a schematic diagram of the architecture of a wireless network with overlapping sensing structures in which the present invention can be implemented. [Figure 14] FIG. 14 shows a schematic block diagram illustrating how a handover is performed according to a first embodiment of the present invention. [Figure 15] FIG. 15 shows a schematic block diagram illustrating how a handover is performed according to a second embodiment of the present invention. [Figure 16] FIG. 16 illustrates a schematic diagram of a cooperative sensing procedure according to another embodiment of the present invention. [Figure 17] FIG. 17 shows a schematic block diagram and signal exchange of a cooperative sensing procedure according to another embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0057] Although embodiments of the present invention are described in the context of a cellular sensing and communication network environment (e.g., 5G), the present invention can also be used in combination with other wireless technologies that provide or can implement target sensing (e.g., IEEE 802.11 / Wi-Fi or IEEE 802.15.4 / Ultra-Wideband (UWB)).

[0058] Throughout this disclosure, the abbreviations "gNB" (5G terminology) or "BS" (base station) shall mean a radio access device such as a cellular base station, a WiFi access point, or a Universal Serial Bus (USB) personal area network (PAN) coordinator. A gNB may consist of a centralized control plane unit (gNB-CU-CP), multiple centralized user plane units (gNB-CU-UPs), and / or multiple distributed units (gNB-DUs). A gNB is part of a radio access network (RAN) that provides an interface with the functions of a core network (CN). The RAN is part of a wireless communication network. The RAN implements a radio access technology (RAT). Conceptually, the RAN resides between communication devices such as mobile phones, computers, and remote control machines and provides connectivity to the CN. The CN is the core part of the communication network, providing various services to customers interconnected through the RAN. More specifically, it transmits communication streams through the communication network and possibly other networks.

[0059] Furthermore, in this disclosure, the terms "base station" and "network" may be used synonymously. That is, for example, when a "network" is described as performing a particular operation, the operation may be performed by a CN function of the wireless communication network or by at least one base station that is part of the wireless communication network, or vice versa. Also, some of the functions may be performed by a CN function of the wireless communication network and some of the functions may be performed by a base station.

[0060] Furthermore, the terms “radar sensing” and “wireless sensing” encompass not only technologies in which a single device both transmits and receives radar signals, but also distributed, RF-based sensing technologies, e.g., technologies in which sensing signals are received in a distributed manner by multiple devices, or technologies based on sensing of channel state information (CSI) in CSI-based distributed sensing solutions, and / or technologies based on other types of measurement information related to RF signals (e.g., multiple-input / multiple-output (MIMO) sounding signal feedback, Doppler phase shift measurements). Note that the terms “radar sensing” and “wireless sensing” above may be used interchangeably throughout this disclosure, and embodiments described using radar sensing as an example may be extended to other types of wireless sensing, e.g., using reference signals to enable UEs to report related measurements of the reference signals (e.g., channel state information (CSI)-based sensing).

[0061] The term "sensing" refers not only to "radar sensing" or "wireless sensing" but also to sensing using any sensor modality, such as cameras, pressure sensors, motion sensors, thermal sensors, etc.

[0062] The term "data type" encompasses different types of data output by different types of sensors. For example, a video camera may output video data, and a LIDAR sensor may output point cloud data. It also encompasses intermediate data types that may not be directly output by a sensor but may be generated by processing other types of datasets (e.g., three-dimensional (3D) mesh data (see below)). Furthermore, the terms "target" and "target object" refer to any entity that may be the subject of wireless sensing. This may include people, animals, inanimate objects, and structures composed of several smaller entities (e.g., a cloud composed of small water droplets). In this disclosure, the terms "target" and "target object" may be used synonymously.

[0063] Unless otherwise specified, the term "object" encompasses the three-dimensional form of any object of interest in the environment about which information may be collected by a sensor. Generally, this includes people (and their movements), animals, vehicles, drones, stationary objects, structures made up of several smaller entities (e.g., a cloud made up of small water droplets), etc.

[0064] It should be noted that throughout this disclosure, the accompanying drawings only show blocks, components, and / or devices related to the proposed sensing and / or matching functions. Other blocks have been omitted for the sake of brevity. Blocks with the same reference numbers have the same or at least similar functions, and the functions of such blocks will not be repeated hereinafter.

[0065] Sensing Signal The sensing functionality of the following embodiments may be implemented, for example, by radar functionality in wireless communications involving one or more access devices (e.g., base stations (BSs)) and / or one or more terminal devices (e.g., UEs).

[0066] As an example, a frequency-modulated continuous wave (FMCW) mmWave radar system can measure the range, velocity, and angle of arrival (if two receivers are present) of objects reflecting radio waves in a scene. Such a radar system transmits a chirp signal (e.g., a sine wave) whose frequency increases over time. The chirp signal (e.g., a continuous wave pulse) has a bandwidth and a frequency increase rate. Typically, multiple chirps are transmitted in succession. The transmitted and received analog chirp signals are mixed to generate an intermediate frequency (IF) signal that corresponds to the frequency difference between the two signals (transmit and receive). The output phase of the IF signal corresponds to the phase difference between the two signals.

[0067] Thus, each surface in the scene or environment generates a constant-frequency IF signal with a frequency related to the distance to the surface (i.e., the first distance from the chirp signal transmitter to the surface, and the second distance from the surface to the chirp signal receiver). To separate two surfaces at different distances, the two IF signals can be frequency resolved. A longer time window for the IF signal improves resolution. Because the chirp duration is related to the chirp bandwidth (the change in chirp frequency is constant), the radar resolution is related to the chirp bandwidth. The IF signal is then bandpass filtered (to remove signals below a minimum distance and frequencies above the maximum frequency of the subsequent analog-to-digital converter (ADC)) and digitized before further processing. The bandpass filter and the upper limit of the ADC's frequency sensing range set the maximum detectable distance (i.e., the IF frequency increases with distance).

[0068] Phase is important in detecting vibration because the phase of the IF signal (i.e., the phase difference between the transmitted and received chirp signals) is a measure that is sensitive to small changes in surface distance. Small changes in distance can be detected by the phase signal but may not be discernible by the frequency signal. Also, measurements of the phase difference between two consecutive chirp signals can be used to determine the velocity of the surface.

[0069] For example, performing a Fast Fourier Transform (FFT) on multiple chirp signals can separate multiple objects at the same distance but with different velocities. The Fourier transform converts a signal from the spatial or time domain into the frequency domain. In the frequency domain, a signal is represented by a weighted sum of sine and cosine waves. A discrete digital signal with N samples can be accurately represented by the sum of N waves. The FFT provides a faster way to compute the discrete Fourier transform by exploiting the symmetry and repetition of waves to combine samples and reuse partial results. This method can save significant processing time, especially for real-world signals that may have thousands or even millions of samples.

[0070] As another example, angle estimation can be performed by using the phase difference between chirp signals received at two separate receivers.

[0071] Another option is to use channel state information (CSI). CSI is a measure of the phase and amplitude of multiple frequencies detected at the receiver, thus forming a complex "map" of the radio environment, including the effects of objects in that environment. CSI characterizes how a radio signal at a particular carrier frequency propagates from the transmitter to the receiver. The amplitude and phase of CSI are affected by multipath effects, such as amplitude attenuation and phase shift (e.g., due to the displacement and movement of the transmitter, receiver, and surrounding objects and people). In other words, CSI captures the radio characteristics of the nearby environment. These characteristics, aided by mathematical modeling or machine learning algorithms, can be used for various sensing applications.

[0072] A wireless channel may be divided into multiple subcarriers (e.g., using Orthogonal Frequency Division Multiplexing (OFDM)), as is done in 5G communication systems. To measure the CSI, the transmitter may send long training symbols (LTFs) containing predefined symbols for each subcarrier (e.g., in the preamble of a packet). Once these LTFs are received, the receiver can estimate the CSI matrix using the received signal and the original LTFs. For each subcarrier, the channel can be modeled as follows:

number

[0073] Processing such CSI matrices can be used for vital signs monitoring, presence detection, and human movement recognition. For example, these types of recognition can be performed by processing the CSI matrices using recognition techniques such as neural networks.

[0074] It should be noted that systems using channel state information (CSI) have some relevance to systems with FMCW mmWave radar. In a CSI-based system, an input signal X may be defined, and the receiver may use the received signal Y to obtain H (i.e., H = (YN) / X). ​​In an FMCW mmWave radar, the transmitted signal chirp X may also be predefined, and the receiver may use the received signal Y to obtain the transfer function H = Y / X. This last step actually has some relevance to multiplying the locally calculated chirp signal with the received chirp signal and applying a bandpass filter. According to various embodiments described below, the above wireless sensing technology is implemented in a mobile communication system (e.g., 5G, other cellular communication systems, or WiFi communication systems), and functional coexistence between radar and communication operating in the same frequency band is configured to avoid interference bandwidths. This enables wireless sensing to be integrated into large-scale mobile networks, building perceptual mobile networks.

[0075] As another example, a sensing signal may consist of multiple pulses transmitted by a sensing transmitter at a specific frequency and timing (sensing signal parameter information). The sensing receiver may include multiple bandpass filters that enable it to identify the sensing signal parameter information (e.g., the timing and frequency of the received pulses). In particular, when a transmitter determines a pseudorandom sequence of predetermined frequency / timing pulses and emits them in a specific direction, for example by beamforming, if the transmitter communicates the timing / frequency of the transmitted sensing signal, generally the sensing signal parameter information, to a receiver, the receiver can use its own bandpass filters to identify reception of the same transmitted pulses, i.e., the sensing signal, based on the received sensing signal parameter information.

[0076] (Distributed) Sensing Configuration and Operation FIG. 1 illustrates schematically a distributed radar function created over a wireless communication system link that may be used in embodiments of the present invention.

[0077] However, it should be noted that the present invention may equally be applied to non-distributed sensing services / functions where a first sensing device performs sensing centrally and then (after handover) a second sensing device performs sensing centrally. Thus, the described embodiments may be implemented as a single device that includes both a transmitter and a receiver.

[0078] Unlike purely centralized radar solutions (i.e., where the transmitter and receiver of the radar signal are part of or operated by the same device), a distributed radar function involves establishing a distributed radar system between, for example, a base station (BS) 100 or UE (as a transmitter) and at least one UE 120 or base station (as a receiver) where a portion of the 5G (or other cellular or WiFi) network spectrum is configured or detected to be in a quiet / communication-free state (e.g., set to radar mode) for a period of time to enable remote vital signs and other measurements. Meanwhile, the lack of analog signal exchange and additional path lengths due to the distance from the transmitter to the receiver and the distance from the receiver (e.g., UE 120) to the target (e.g., human) may be compensated for. To achieve this, the base station 100 (or UE) acting as a transmitter may set up a communication link with the UE 120 (or base station) acting as a receiver (or vice versa) to exchange some control information and / or sensing measurements and / or (partial) sensing results. The control information may include a set of configuration parameters related to the distributed sensing operation. The parameters may include, for example, the distance and angle from the transmitter to the receiver, the pulse occurrence time, the pulse phase, the frequency, and possibly the chirp timing (CT), the chirp profile (CP), the target location (TL), the phase offset (PO), the time between subsequent sensing signals, the number of repetitions, the sensing signal waveform information, the amplitude, the MIMO / beamforming parameters, the number of transmit antennas used, the transmit power, potential interference patterns, the identifier / address (e.g., Internet Protocol (IP) address / Uniform Resource Locator (URL) address) of the destination server and / or network function / device to which the sensing results are sent (e.g., for storage or further processing), session or application-related information (e.g., a session identifier or application identifier), the desired accuracy of the sensing measurements, etc.The parameters may be conveyed in a response to a request (RR) from the receiver (e.g., UE 120) to the radar (e.g., an initial Attach Request message from the UE to the base station extended with a sensing request field, or a radio resource control (RRC) message or System Information from the base station to the UE as specified in 3GPP TS 38.331) (e.g., an RRC Connection Request message including a measurement configuration as specified in 3GPP TS 38.331 extended with a sensing configuration parameter). Reconfiguration message), or sent by the transmitter to the receiver before the transmitter starts transmitting sensing signals (e.g., as part of a configuration / assistance information message / signal), or (partially) pre-configured in the receiver (e.g., stored in a Universal Subscriber Identity Module (USIM) or in non-volatile memory at the time of manufacture), or configured in the receiver by a local application, or provided by the network as part of policy / system information / RRC configuration / session configuration (e.g., upon initial registration or connection setup of the receiver with the network, or at an earlier initial registration / connection setup) (possibly via the transmitter itself or another transmitter, or by, for example, the access and mobility management function (AMF), policy control function (PCF), network exposure function (NEF), location management function (LMF), gateway mobile location center (GMLC), or other core network function (e.g., as described in 3GPP TS 23.501). The configuration of these parameters can vary from application to application (e.g., based on the sensing target or based on the sensing algorithm).A set of parameters may be combined as an identifiable sensing profile (e.g., by a profile identifier, application identifier, or device identifier). After the sensing profile is transmitted / configured / preconfigured in the receiver, activation of the sensing profile may be triggered by transmitting a signal / message to the receiver indicating the sensing profile identifier. The sensing profile and / or configuration parameters may also include an algorithm identifier, filter identifier, or machine learning model identifier that triggers the application of a particular sensing algorithm, filter, or machine learning model to be used to analyze / process the received sensing signal. These algorithms, filters, or models may be preconfigured / stored in the receiver, or may be transmitted (e.g., in a separate message) from the transmitter to the receiver (e.g., as virtual machine code, filter parameters / code, or model data), or may be downloaded by the receiver based on, for example, a download URL or server IP address (e.g., as virtual machine code, filter parameters / code, or model data) and configured for the required application. For example, if precise distance measurement is required, a complete parameter set may be communicated, while if phase-based velocity is required, only chirp parameters may be needed. In some applications, the chirp parameters may be predefined, and only an identifier indicating the set of chirp parameters may be exchanged. The parameters may also include a set of time / frequency resources (e.g., a semi-persistent schedule defined in 3GPP TS38.321) and / or a time / frequency offset during which the sensing signal is scheduled to be transmitted and / or during which the sensing signal is expected to arrive at the receiver. This information may also be provided as the time interval during which the receiver is expected to listen for the reflected sensing signal (e.g., as an offset relative to the start time or system frame number / subframe / symbol of the signal transmission by the transmitter).The start time, offset, or time interval for the receiver's sensing execution may be specified to begin at the start or end time at which the receiver receives the first instance of the sensing signal (i.e., received via a direct, non-reflected path), i.e., the reception of the first instance of the sensing signal may be used by the receiver to trigger / enable active sensing of reflected sensing signals. The parameters may also include information about quiet periods or guard intervals that the receiver device may consider. The parameters may also include information about encoded identification information, special symbols / preambles, or unique signal characteristics that allow the receiver to uniquely identify the sensing signal from other sensing or communication signals. To enable the receiver to determine which portion of the sensing signal contains encoded information (e.g., signal identification information, timestamp of when the transmitter transmitted the signal), additional timing or frequency information may be provided to identify the start / end times or portions of the time interval for receiving a complete sensing signal, indicating where the receiver can find the encoded information in the sensing signal.As with the sensing receiver, the sensing transmitter may also include a network (e.g., access and mobility management function (AMF), policy control function (PCF), network exposure function (NEF), location management function (LMF), gateway mobile location center (GMLC), or other core network functions (e.g., 3GPP® As part of the policy / system information / RRC configuration / session configuration (e.g., at the time of the initial registration or connection setup of the sensing transmitter with the network or at an earlier initial registration / connection setup), parameters regarding how sensing is to be performed (e.g., pulse occurrence time, pulse phase, frequency, possibly chirp timing (CT), chirp profile (CP), target location (TL), phase offset (PO), time between subsequent sensing signals, sensing signal waveform information, amplitude, MIMO / beamforming parameters, number of transmitter antennas to use, transmit power, quiet periods or guard intervals that may be taken into account, etc.), and / or algorithms, filters, sensing profile to be used, and / or destination server and / or network function / device to which the sensing results should be sent (e.g., for storage or further processing), and / or session or application related information (e.g., session identifier / application identifier), etc. may be configured by the RRC configuration (e.g., as described in TS23.501). Additionally, the above parameters for sensing may be pre-configured on the transmitter (e.g., stored in the USIM or in non-volatile memory at the time of manufacture), or may be configured on the transmitter by a local application, or may be provided by the receiver.

[0079] It should be noted that an LMF may include or be connected to a set of services / functions (e.g., a Gateway Mobile Location Centre (GMLC)) that together may be responsible / capable of identifying, verifying, providing, and / or storing a set of location, distance, angle, coordinates, and other related information for location and / or ranging services. Additionally or alternatively, the set of services / functions may together be responsible / capable of managing / configuring / operating a set of location and / or ranging services and / or combining distance, angle, location information with distance, angle, location information obtained from other sources or various location / ranging mechanisms. The term "LMF" refers to such services / functions or combinations thereof.

[0080] To facilitate the configuration of the above sensing parameters, the sensing receiver or sensing transmitter device may provide its sensing-related capabilities to the network (e.g., a core network function, or a service (operated / provided by the network) responsible for managing and / or performing the sensing (i.e., sensing service), or an application function for managing and / or using the results of the sensing operation (i.e., sensing application)), one or more base stations, or other devices involved in distributed sensing (e.g., the sensing transmitter device in the case of a sensing receiver device) via a capability exchange message (e.g., as part of a radar message request extended with some field indicating the sensing-related capabilities, or as part of an RRC UECapabilityInformation message described in 3GPP TS38.331). The sensing-related capability information may include, for example, device information (e.g., the number of antennas or supported frequency ranges), wireless sensing signal processing capabilities (e.g., supported algorithms and / or whether a particular sensing result / target can be determined (e.g., whether the position, movement, or shape of a target object can be determined), one or more supported sensing profiles, etc.), wireless sensing signal transmission capabilities (whether this is supported, and if supported, the frequencies, etc.). The sensing receiver may have different configurations based on the receiving capabilities of the sensing receiver and / or sensing transmitter. The sensing transmitter may have different configurations and / or adjust the sensing signal based on the receiving capabilities of the sensing receiver and / or sensing transmitter.

[0081] The parameters used to configure the sensing transmitter and sensing receiver may depend on and be adjusted based on sensing requirements, which may be provided, for example, through an application function, a network publishing function, or other core network function / service or application (e.g., a sensing service or sensing application). Such sensing requirements may identify, for example, the type of sensing result expected to be calculated (e.g., motion, position, shape, material, biometrics), information about one or more target objects (e.g., rough location, last known location, identifiable features, or information about known features such as size, material, or shape), and / or quality of service (e.g., required accuracy, sampling rate), and / or information about the algorithms / filters to be used, and / or session / application-related information (e.g., application identifier or session identifier).

[0082] 1, a base station (BS) 100 and / or a UE 120 identify the (approximate) location, area, or volume of a target 150 by emitting a series of signals, e.g., chirp signals, that may be beamformed in the direction of the target 150. The (approximate) location may be in the form of a relative location, e.g., a set of distances and / or angles relative to some reference point (e.g., a transmitter or receiver).

[0083] Optionally, for example, before the actual radar sensing procedure between the transmitter and receiver begins, the target angle and distance, and / or target shape, and / or target material / reflectivity characteristics, unless already known, may be determined using a localization radar operation and / or a target shape determination operation and / or a target material / reflectivity characteristics determination operation at the transmitter (e.g., base station (BS) 100). This information may be stored at the transmitter and / or provided to the receiver and / or provided to a network function responsible for collecting sensing measurements and / or (partial) sensing results and potentially performing further processing on the sensing measurements / results to determine further sensing characteristics of a particular target.

[0084] Depending on the target sensing application, the precise timing of the phase and frequency (and optionally amplitude) of each individual (chirp) signal may be communicated to the receiver (i.e., UE 120) (e.g., using a protected standard communication signal) before transmitting the (chirp) signal. At the same time, the location or relative location of the transmitter (i.e., BS 100) and optionally the approximate location of the target 150 may be communicated. The idea of ​​the protected communication (encryption and / or integrity protection) is to ensure that only the target receiver can use this information. Based on this, the receiver can optionally determine the path length and angle from the transmitter to the receiver and internally synthesize an analog (chirp) signal that matches the transmitted (chirp) signal. The received signal and the combined signal can be used for sensing purposes.

[0085] If the relative position and precise time are known, the path length of the reflected sensing signal through the target 150 can be determined and / or the target surface can be accurately reconstructed (e.g., by detecting the correct intermediate frequency (IF) signal at the mixer output if the signal is a chirp signal). By knowing the approximate position of the target object and / or by detecting the angle of arrival of the incident reflected sensing signal, the distance or angle between the receiver and the target object and / or the distance or angle between the transmitter and the target object can be calculated. By knowing the phase, and thus the phase difference, the velocity of the target 150 can be determined based on the frequency.

[0086] If only the velocity of the target 150 is needed (rather than its position and velocity), the transmitter can optionally avoid providing its relative position and communicate only the phase, timing, and frequency of the emitted sensing signal. In some cases, only the sensing signal itself is transmitted to the receiver, which can use it, subject to a certain time delay, to calculate an IF signal from which to derive the target's velocity. This can be particularly important when measuring vital signs such as respiration or heart rate. For example, this could allow measuring breast velocity during breathing and deriving breathing rhythm therefrom.

[0087] If a sufficiently good estimation of the location of the reflecting surface is possible, the receiver may enable the collection of further data such as skin conductivity.

[0088] For example, the radar sensing function may be realized by the following steps: The parameters of the sensing signal to be used in the transmitter sensing generation process, i.e., the rough or relative position of the target and the position offset / angle from the transmitter to the receiver (e.g., UE 120), or the absolute position / geographical location of the transmitter and the future time or set of time / frequency resources for the initial (and subsequent) sensing signal, are determined and communicated from the transmitter to the receiver, e.g., using a protected communication signal.

[0089] Alternatively, some of the parameters may be pre-configured in the receiver, configured in the receiver by a local application, or transmitted earlier by the transmitter or network (e.g., during a previous session). The transmitted parameter information is then (optionally) decoded and / or verified by the receiver. The transmitter then generates a sensing signal by transmitting the sensing signal at the specified time using, for example, a discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM) signal generation process. The receiver may listen for the sensing signal at the time / resource indicated in the parameter information. The receiver may use its own DFT-s-OFDM signal generation process to generate an internal synthesized sensing signal (e.g., a chirp) that matches the provided parameters. An optional delay corresponding to the direct distance from the transmitter to the receiver may be added to minimize the IF frequency generated at the receiver. The receiver may use the provided sensing parameter information and / or internal representation of the sensing signal to configure its radio frequency (RF) receiving front-end or signal detection unit to identify / detect the sensing signal from among signals received by the RF receiving front-end. Upon detection and / or further processing of the received sensing signal, the receiver may determine and record / store the sensing signal's start / end time, phase shift, frequency, amplitude, signal deformation, signal strength, interference pattern, detected special symbol / preamble, encoded identification information, and / or timing of quiet periods between sensing signals. The receiver may use this information to further identify whether the received sensing signal was actually reflected by the target object or received via a direct, non-reflected path between the transmitter and receiver, thereby filtering out only relevant sensing signals to extract sensing information about the target object.To this end, the receiver may calculate the expected path loss and / or timing between the transmitter and receiver for a direct path, as well as the expected path loss and / or timing through an indirect reflected path through the target, and use this to determine whether the received sensing signal was actually reflected by the target object or whether it was received via a direct non-reflected path between the transmitter and receiver.

[0090] Alternatively, the transmitter may calculate the expected path loss and / or timing between the transmitter and receiver for the direct path, as well as the expected path loss and / or timing through the indirect reflected path via the target, and transmit this information to the receiver, which uses it to make decisions. The receiver may form an IF signal using (e.g., mix) the internally synthesized “outgoing” sensing signal and the received sensing signal reflected by the target 150, perform band-pass (or optionally, only high-pass filtering at the maximum frequency of the ADC) filtering and ADC, and digitize the IF data and / or the raw / filtered received reflected sensing signal data. To this end, the receiver may create a compressed or uncompressed digital sampled representation of the IF signal or the received raw / filtered reflected sensing signal using a sampling frequency preset in the receiver device or provided by the transmitter (e.g., as part of the sensing signal parameters). Information regarding the compression method / format to be applied may also be provided by the transmitter (e.g., as part of the sensing signal parameters) or preset in the receiver. The digital IF signal may then be processed to generate application-specific data (e.g., the output of one or more (pre-configured) algorithms or machine learning models), in this case sensing results related to the target (e.g., target characteristics such as target position, velocity, shape, size, material composition, etc. that can be determined after performing signal processing / analysis on the received (reflected) sensing signals, respectively). Alternatively, the digitized data may be sent from the receiver to a transmitter or network function / device or cloud for further application-specific processing.

[0091] In addition to the processed or digitized data, the receiver may include signal, sensing profile, algorithm / model, and / or device identification information, and may also include timing and / or measurement information (e.g., arrival / end times of the sensing signal, phase shift, frequency, amplitude, or signal deformation), and / or antenna information / antenna sensitivity / MIMO configuration / beamforming configuration used by the receiver for sensing, and / or position / distance / angle related information or absolute coordinates of the receiver relative to the transmitter and / or target, and / or information regarding the sensing application or sensing session (e.g., application identifier or session identifier).

[0092] The complete separation of transmitter and receiver in digital wireless systems such as 5G means that the receiver does not have access to the analog version of the directly emitted signal (phase, frequency), but only the reflected sensing signal, and therefore cannot form the IF signal in the analog domain, i.e. all processing is performed on the received analog signal (a very fast ADC is required to digitize the received "raw" sensing signal).

[0093] Furthermore, in the proposed distributed radar sensing system, the distance to the reflective surface of the target 150 depends on both the distance from the transmitter to the target 150 and the distance from the receiver to the target 150 (rather than just twice the distance from the transmitter to the target, as in non-distributed radar systems).

[0094] The "minimum distance" of a measurement corresponds to the straight-line distance from the transmitter to the receiver. Of course, objects closer to the receiver can also be measured, but the distance indicated for those objects will always be greater than the straight-line distance from the transmitter to the receiver. The isotime returns lie on a spatial position ellipse (return ellipse) whose two foci are the transmitter position and the receiver position. The minimum (degenerate) ellipse (the straight line between the transmitter and receiver) whose minor axis has a length of zero has the smallest delay time, which is the time it takes for a radio wave to travel directly from the transmitter to the receiver.

[0095] The receiver may receive a signal corresponding to a signal sent directly from the transmitter to the receiver (a pseudo-surface at "zero" distance, i.e., a point on the straight line between the transmitter and the receiver).

[0096] Therefore, the proposed integrated distributed radar system may require a kind of clock-level synchronization between the transmitter and receiver to eliminate ambiguity in sensing parameter estimation.

[0097] Additionally, aiding information signals (e.g., radar request and response parameters) may be communicated from the transmitter to the receiver via alternative communication routes (e.g., a different band, a sub-beam beamformed toward the receiver, or a signal interspersed in time among the sensing signals) to allow the receiver to obtain a representation of the transmit signal details (e.g., the precise timing and phase of a continuous (chirp) signal) necessary to simulate the mixing of the transmit and receive signals to obtain an IF signal without directly analyzing the analog transmitted signal. This may be performed, for example, by internally generating an analog version of the same transmitted sensing signal using parameters provided via the aiding information signal. Therefore, to ensure that the correct timing is provided in the mixing of the simulated transmitter sensing signal and the actual received sensing signal, the transmitter must communicate the precise timing, phase, frequency, etc. of the transmitted signal to the receiver in advance.

[0098] Furthermore, the receiver can use auxiliary / (pre)set information / parameters about the sensing signal to distinguish between sensing signals received via the direct non-reflective path and reflected sensing signals. The receiver may ignore sensing signals received via the direct non-reflective path (e.g., by ignoring the first received instance of the sensing signal (e.g., by checking the arrival time of the sensing signal or by checking the corresponding phase shift, frequency change, signal deformation, amplitude change, or interference pattern to identify which sensing signals are reflected or not)), or may use these signals to more accurately determine its own relative position / distance / angle to the transmitter. The receiver may also use signals received via the direct non-reflective path as further inputs to signal analysis algorithms / models, e.g., as additional reference signals for IF calculations, (relative) position calculations, or additional phase shift / signal deformation / frequency / amplitude calculations.

[0099] Additionally, the distance and angle from the transmitter to the receiver or the absolute / geographical position of the transmitter may also be transmitted in order to calculate the correct position of the detected surface.

[0100] Finally, if the receiver (or transmitter, or both) is a handheld device, in some sensing applications, the device's movements and vibrations may be measured by corresponding sensors and subtracted from the detected surface movements and vibrations.

[0101] The proposed distributed radar system (e.g., a system between a base station (BS) 100 (as a transmitter) and a UE 120 (as a receiver and analyzer)) offers the advantage that the receiver can be preferentially positioned to acquire the reflected sensing signal at a higher signal strength than the transmitter (i.e., using the receiver portion of the transmitter device to monitor the reflected sensing signal, as is the case in non-distributed sensing, for example), e.g., the receiver may be closer to or more in the path of the reflected sensing signal, potentially avoiding some of the clutter from the transmitted signal.

[0102] Furthermore, a single antenna may not operate in full continuous duplex mode, while the proposed distributed radar system separates the transmit antenna from the receive antenna.

[0103] As a further advantage, multiple receivers can be used with a single transmitter, each potentially associated with collecting vital signs from a different target (e.g., an individual human).

[0104] However, as described above, sensing may be performed centrally by a single first device, and after sensing handover, sensing may be performed centrally again by a single second device.

[0105] Sensing Transmitter and Receiver Architecture FIG. 2 illustrates an embodiment of a summary transmitter and receiver architecture (including optional elements and features) for a communication system with distributed sensing capabilities.

[0106] Although this architecture shows the transmitter device and receiver device as separate devices in a distributed sensing system, the sensing transmitter and sensing receiver functions may be co-located within the same device (e.g., in the case of a centralized sensing architecture), in which case similar functions and elements (subsets of) are expected to be present.

[0107] The proposed distributed radio wave sensing radar / communication system includes a transmitter device (TX) 10 and a receiver device (RX) 20 configured to operate in an appropriate radio frequency range (e.g., the mmWave range mentioned earlier) and includes RF hardware and signal processing algorithms that enable both standard communications such as 5G and radar sensing for vital signs, object detection, and / or motion recognition. In 5G systems, two options are offered for the uplink (UL) waveform: one is cyclic prefix OFDM (CP-OFDM, identical to the downlink (DL) waveform) and the other is discrete Fourier transform spread OFDM (DFT-s-OFDM), which corresponds to the UL waveform of long term evolution (LTE) systems (i.e., fourth generation (4G)). The first step in creating a DFT-s-OFDM waveform is transform precoding, followed by subcarrier mapping, inverse FFT, and cyclic prefix (CP) insertion. Whether a UE should use CP-OFDM or DFT-s-OFDM can be determined by a radio resource control (RRC) parameter.

[0108] A 5G transmitter or receiver with integrated radar sensing capabilities may have slightly modified DFT-s-OFDM and frequency-domain spectral shaping (FDSS) filters to generate suitable chirps. Through appropriately designed FDSS filters, linear and other chirp signals can be generated using DFT-s-OFDM signals, allowing radar-friendly signals to be generated with only minor modifications to standard communications hardware. These frameworks provide an efficient way to synthesize chirps that can be used in dual-function radar and communications (DFRC) or wireless sensing applications using existing DFT-s-OFDM transceivers.

[0109] Cong Li, "Radar Communication Integrated Waveform Design Based on OFDM and Circular Shift Sequence," Mathematical Problems in Engineering, July 2017, describes other options for generating signals suitable for simultaneous data transmission and radar sensing. These methods are based on the peak-to-mean envelope power ratio (PMERP) and peak-to-sidelobe ratio (PSLR) of the OFDM waveform. Specifically, Gray code technology can be used to reduce PMERP while simultaneously improving the PSLR of the OFDM waveform by selecting an optimal cyclic sequence. The optimal cyclic sequence is dynamically generated to continuously provide an optimal waveform in response to changes in communication data. Furthermore, two simple methods can be used to adjust the bandwidth of the OFDM waveform to meet the requirements of various radar detection tasks. One method is to design different subcarrier complex weights, and the other is to use phase coding technology.

[0110] The transmitter device (TX) 10 may be an access device (e.g., a base station) or a terminal device (e.g., a UE or an Internet of Things (IoT) device) and includes a standard transmitter communication unit or system (S-TX-COM) 101 that enables standard communication capabilities, such as 5G, using, for example, a data communication signal generated with DFT-s-OFDM. By operating in a "radar mode," the transmitter communication system 101 can form a radar mode signal generator (RM-SIG-GEN) 102 that can generate, for example, a linear chirp signal (chirp) using minimally modified communication components. This can be achieved by using a (slightly) modified DFT-s-OFDM with an appropriate FDSS filter to transform the single-carrier nature of the DFT-s-OFDM signal into a linear combination of circularly transformed chirp signals in the time domain, as described, for example, in Alphan Sahin et al.: "DFT-spread-OFDM Based Chirp Transmission," IEEE Communications Letters, Volume 25, Issue 3, March 2021. By utilizing the properties of Fourier series and Bessel functions of the first kind, an FDSS filter for any chirp can be obtained.

[0111] The transmitter device 10 also includes a transmit front end (TX / ANT) 103 (eg, operable at mmWave frequencies) that includes a transmitter coupled to an antenna with beamforming capabilities.

[0112] Optionally, a receive front end (RX / ANT) 104 (e.g., operable at mmWave frequencies) may be provided (e.g., as a separate component or integrated with the transmit front end 103 in a joint transceiver front end), the receive front end 104 including a receiver coupled to an antenna with beamforming receive capability (e.g., if non-distributed transmitter-only radar operations are additionally performed to determine target location, shape / size, or material / reflectivity properties).

[0113] Additionally, the transmitter device 10 includes a transmitter clock generator (TX-CLK) 105 that generates an accurate system clock for the transmitter device 10 .

[0114] Optionally, a transmitter time delay measurement function (not shown) may be provided (e.g., implemented by a processor / controller of the transmitter device 10) that uses the standard transmitter communication unit 101 to perform two-way time delay measurements with a cooperative receiver device (e.g., receiver device 20).

[0115] Optionally, an encryption and decryption function (ENCR / DECR) 106 may be provided to implement a suitable data encryption / decryption scheme (e.g., a scheme based on the Advanced Encryption Standard (AES) algorithm or the Rivest, Shamir and Adleman (RSA) algorithm) and data integrity verification (e.g., a data verification scheme using a message authentication code or a digital signature). For example, the data may be delivered in protected radio resource control (RRC) messages.

[0116] As a further option, the transmitter device 10 may include a non-distributed (low-resolution) transmitter radar analysis system (L-RES RAS) 107, i.e., a radar analysis system that may include a receiver device and / or a (low-resolution) transmitter radar analysis system. This radar analysis system provides a non-distributed position radar scanning function, which may include an intermediate frequency (IF) generation mixer (IF-MIX) 107-1. The IF-MIX 107-1 is supplied with a copy of the transmitted sensing signal and an externally received reflected sensing signal, and mixes them to generate a mixed signal including the IF signal. Additionally, the transmitter radar analysis system 107 may include electronic signal processing components (including a transmitter bandpass filter (BPF) 107-2 and an analog-to-digital converter (ADC) 107-3) that can perform IF filtering and analog-to-digital conversion of the generated IF signal. The transmitter radar analysis system 107 may also include a digital signal processing component and algorithm system (DSP, e.g., implemented by a digital signal processor) 107-4 that provides DSP functionality (e.g., for location detection, pre-processing with clutter removal, etc.).

[0117] As yet another option, the transmitter device 10 may include a sensor component including a transmitter motion sensor (TX-MOV-SEN) 108 such as an accelerometer that measures movement and vibration of the transmitter device 10 .

[0118] Furthermore, the receiver device 20 may be an access device (e.g., a base station) or a terminal device (e.g., a UE or an Internet of Things (IoT) device) and may include a standard receiver communication unit or system (S-RX-COM) 201 that provides standard communication functions, such as 5G, using, for example, a data communication signal generated with DFT-s-OFDM. By operating in a “radar mode,” the receiver communication system 201 may be able to form a radar mode signal generator (RM-SIG-GEN) 202 that generates, for example, a linear sensing signal, for example, by using a (slightly) modified DFT-s-OFDM signal. The generated sensing signal may be used internally and not coupled to a transmitter and an antenna. The waveform of the sensing signal may be generated from specific input parameters, including at least one of a specific start time, phase, amplitude, fundamental frequency, bandwidth, frequency slope, repetition frequency of the sensing signal, gap between sensing signals, and total number of sensing signals.

[0119] Additionally, receiver device 20 includes a receive front end (RX / ANT) 204 including a receiver coupled to an antenna with beamforming receiving capabilities, where RX / ANT 204 may be capable of operating at mmWave frequencies.

[0120] Additionally, receiver device 20 includes a receiver clock generator (RX-CLK) 205 that generates an accurate system clock for receiver device 20 .

[0121] Optionally, a receiver time delay measurement function (not shown) may be provided (e.g., implemented by a processor / controller of the receiver device 20) that uses the standard receiver communication unit 201 to perform two-way time delay measurements with a cooperative transmitter device (e.g., transmitter device 10).

[0122] Optionally, an encryption and decryption function (ENCR / DECR) 206 may be provided to implement a suitable data encryption / decryption scheme (e.g., a scheme based on the Advanced Encryption Standard (AES) algorithm or the Rivest, Shamir, and Adleman (RSA) algorithm) and data integrity verification (e.g., a data verification scheme using a message authentication code or a digital signature) tailored to the scheme used on the transmitter side. For example, data may be delivered in protected radio resource control (RRC) messages.

[0123] As a further option, receiver device 20 may include a (low-resolution) non-distributed radar analysis system (L-RES RAS) 207 that provides non-distributed position radar scanning capabilities, i.e., a radar analysis system that may include a receiver device and / or include a (low-resolution) transmitter radar analysis system. The radar analysis system may include a transmitter front-end (TX / ANT) 203 that includes a transmitter coupled to an antenna with beamforming capabilities of receiver device 20.

[0124] Additionally, the (low-resolution) radar analysis system 207 may include components shared with an additional high-resolution distributed radar analysis system (H-RES RAS) 209. These components may include an IF generation mixer (IF-MIX) 207-1 that receives as inputs an (internally generated) copy of the emitted sensing signal and an externally received reflected sensing signal and generates a mixed signal including an IF signal, electronic signal processing components including a receive bandpass filter (BPF) 207-2 and an ADC 207-3 that can perform IF filtering and analog-to-digital conversion of the generated IF signal, and an electronic digital component and algorithm system (DSP, e.g., digital signal processor) 207-4 that provides DSP functionality, e.g., for location detection, pre-processing with clutter rejection, etc.

[0125] The high-resolution distributed radar analysis system 209 may be configured to share electronic components: an IF generation mixer 207-1 configured to mix inputs (an internally generated sensing signal based on timing / phase parameters generated by a radar mode signal generator 202 and an externally received sensing signal provided by a receiver front-end 204); a receiver bandpass filter 207-2 and an ADC 207-3 that receive the analog IF signal, filter it with appropriate bandpass filters, and perform analog-to-digital conversion; and an electronic digital component and algorithm system 207-4 that provides DSP functionality for the desired application, including pre-processing such as clutter rejection. To prevent leakage / tampering of sensing information that may require privacy protection regarding targets, radar analysis should be performed in a secure, tamper-resistant subsystem, and the resulting sensing information should be stored in secure storage and / or encrypted with non-tamper-resistant credentials (e.g., Subscriber Identity Module (e.g., USIM) credentials).

[0126] Alternatively, the final digital processing may be offloaded from the receiver device 20 to the transmitter device 10, a network function / device, or a cloud computing resource, with that entity returning the results obtained.

[0127] Optionally, the receiver device 20 may include a user interface (UI / MEM) 210 with data storage and display capabilities that can input information from a user, store data on the receiver device 20, and output a display to the user. The specific elements of the user interface 210 may depend on the type of receiver device (e.g., UE) and its capabilities. For example, a handheld smartphone device may have a sophisticated user interface 210 and display, while an IoT monitoring device may simply have a visual or audible alarm.

[0128] As a further option, the receiver device 20 may include a receiver motion sensor (RX-MOV-SEN) 208, such as an accelerometer, camera, and structured light sensor, that measures the movements and vibrations of the receiver device 20 and the positions of nearby objects. The receiver device 20 may also communicate its movements and vibrations to the transmitter device 10 via its transmitter standard communication unit 201 using a series of receiver movement data during a sensing time interval acquired by the receiver movement sensor 208. The series of receiver movement data may be transmitted to the transmitter using a separate communication channel between the receiver and the transmitter (e.g., as a series of RRC or media access control (MAC) control element (CE) messages).

[0129] Matching objects sensed from multiple viewpoints The following embodiments enable various use cases for identifying objects within the field of view of a device (e.g., AR glasses) and for identifying that two different devices / sensors have sensed or are sensing the same person or object. The underlying “perceptual” wireless network may consist of a multitude of terminal devices (e.g., UEs) and base stations (e.g., gNBs) with various capabilities and modalities deployed to sense objects in the environment. Identification / matching functionality can be achieved when one or more devices / sensors generate data from one or more viewpoints, the data is matched against a set of criteria, and / or the data is matched against a simulated / calculated / received (possibly synthetic) set of data from the same or different viewpoints, and / or data from two or more devices / sensors generates data of different data types, and the semantics of the data of one data type matches the semantics of the data of another data type.

[0130] A viewpoint (or field of view) is the location in a reference coordinate system from which an object or target area / volume is sensed. The viewpoint, along with the field of view of the sensor used to sense the target object or target area / volume and the sensor's heading (e.g., the direction / orientation / angle of the sensor's field of view, or field of view angle), determines how the object or area is sensed (e.g., what portion of the object's surface the sensor can capture / sense, and at what angle).

[0131] Field of View (FoV) is typically the extent to which a sensor (e.g., a camera) can image a particular scene, i.e., the extent of the observable world that can be sensed. It may be expressed as a set of angles in 3D space, and / or an angle together with a direction / orientation, and / or an angular area, and / or a focal length (e.g., of a lens) and sensor size.

[0132] The sensor may be omnidirectional, i.e., capable of sensing all around, which is indicated by a 360 degree FoV or omnidirectional flag / attribute.

[0133] The proposed solution can be implemented in several use cases, such as:

[0134] 1. In a metaverse or mixed reality scenario in which multiple users use UEs or other terminal devices to interact with their respective metaverses at a single shared physical location, interoperability issues can arise when digital assets associated with a user must be rendered by the UE or other terminal device of the user currently viewing those digital assets. The viewing user's UE may not locally have the information necessary to identify key details of the viewed user and / or the metaverse in which the viewed user participates (e.g., the currently viewed user's ID), and therefore the digital assets (e.g., digital outfits) to be rendered in association with them. Thus, the viewing user's UE must obtain this information. In one example, the viewed user's information (e.g., ID) may be derived from data locally available to the viewing user's UE (e.g., the viewed user's posture data) and / or from the capabilities (including sensing and communication) of the wireless network to which all participating UEs are connected.

[0135] 2. In asset tracking, assets are typically tracked by attaching asset tags connected to a network to the assets. However, such asset tags may be removed from the asset or attached to other assets without the knowledge of the network or the asset owner. Therefore, the proposed matching function can be applied to such asset tags.

[0136] 3. Perceptual networks that can collect posture, gait, or morphological information of an individual via Doppler radar could potentially contribute to authenticating that individual.

[0137] 4. When a UE or other terminal device passes between cells, service continuity is required for sensing functions (as is the case for call and data functions). This can be achieved by using the proposed matching function to "anticipate" (predict) the arrival of a UE or other terminal device with a specific physical shape and motion at a cell. In sensing scenarios, sensing the same target object from multiple different viewpoints and / or different sensing modalities can improve the accuracy of the sensing measurements / results and / or improve target object identification / matching (e.g., in crowded areas) and / or improve the robustness of target sensing (if one of the sensing modalities is impaired (e.g., the camera's view is obstructed by something)). For this reason, it is important to be able to identify if two devices / sensors are sensing the same target object and / or be able to convert one type of sensing data into another type of sensing data and / or calculate, match, or interpret sensing data from different viewpoints or matching criteria.

[0138] FIG. 3 shows a schematic representation of a sensing and communication system with data processing and matching algorithms according to a first embodiment.

[0139] The first device (A) 10 includes, for example, a mobile station (MS) 100 such as a UE, a radio access network (RAN) 200 including at least one access device (e.g., gNB) 220, and an antenna, communication interface, and other components (e.g., a central processing unit (CPU) 14 for processing data) necessary to communicate over a communication network such as a 5G system including a core network (CN) 300.

[0140] Furthermore, the first device 10 includes at least one sensor (S) for generating at least one type of data (measurement data of the first device 10) based on the sensed information. A) 12 (optionally having a transmit (Tx) unit and a receive (Rx) unit for wireless-based sensing). The at least one sensor 12 may include a video camera, LIDAR, sonar, accelerometer, radar, etc.

[0141] The second device (B) 20 may have a similar structure to the first device 10 (e.g., a CPU 24 and an optional transmitter and receiver for wireless-based sensing). The second device 20 may have at least one sensor (S) that generates a different data type (measurement data of the second device 20) based on the sensed information. B ) 22, where the sensors 22 have different sensing parameters (e.g., different positions, different fields of view, different viewing angles, etc.).

[0142] The sensors 12 and 22 may have a set of properties that may include the sensor's field of view (FoV) and / or the sensor's orientation and / or the sensor's 3D position. Furthermore, the sensors 12 and 22 may be operated by and / or contained in and / or attached to the same device. That is, the first device 10 and the second device 20 may be the same device. In such cases, communication of measurement data and / or segmented data and / or functional information, etc., may be implemented using communication mechanisms internal to the device.

[0143] Depending on the specific use cases of the embodiments (described above and in more detail below), the first and second devices 10, 20 may be terminal devices (e.g., UEs), base stations (e.g., gNBs) or other types of access devices, or any other (network) devices with sensing capabilities and network access.

[0144] Further, the system includes a set of algorithms, which may be executed locally on the hardware of the first device 10 and / or the second device 20, and / or may be executed as part of a network function on the core network (CN) 300 of the wireless network (e.g., a Location Management Function (LMF) 320) using hardware accessible to the core network 300, and / or may be executed on third party hardware as part of an application function (AF), and / or may be executed on any other hardware accessible by the first device 10 or the second device 20.

[0145] The algorithm may include (a set of) segmentation algorithms (SA) 40 that can segment the measurement data of the first device 10 and the measurement data of the second device 20 into object-related data about individual objects of interest (segmented measurement data of the first device 10 and segmented measurement data of the second device 20) and, optionally, into additional (meta)data related to the individual objects (e.g., location). The nature of the segmentation algorithm depends on the data type and use case-specific implementation. For example, in the case of a Doppler radar data type, the segmentation algorithm may be configured to cluster reflections and assign temporal labels, while in the case of a video data type, the segmentation algorithm could be equivalent to a pre-trained object recognition algorithm that classifies objects in the video.

[0146] Further, the algorithms may include (a set of) synthetic data algorithms (SDA) 30 that may be configured to generate a set of synthetic data based on the segmented measurement data of the first device 10. The synthetic data algorithms 30 may be divided into a first algorithm and a second algorithm. The first algorithm is an X-to-Mesh algorithm (XTM) 32 that is configured to, for a particular segmented measurement data set of a particular data type of the first device 10, generate a 3D mesh data set representing the object described by the segmented measurement data of the first device 10. The second algorithm is a Mesh-to-Y algorithm (MTY) 34 that generates synthetic data using the 3D mesh data. The Mesh-to-Y algorithm may have information about the second device 20 to generate synthetic data that meets certain criteria, including at least one of the same data type as the segmented measurement data of the second device and the same viewpoint as the sensor 22 of the second device 20 (i.e., to match the appearance of the same object in the segmented measurement data of the second device 20), or vice versa (i.e., to match the appearance of the same object in the segmented measurement data of the first device 10 based on the object detected based on the measurement data of the second device 20).

[0147] Additionally, the algorithm may include a matching algorithm (MA) 50 that compares composite data derived from the measurement data of the first device 10 with the segmented measurement data of the second device 20 to identify whether any segments of the segmented measurement data of the second device 20 match the composite data of the first device 10, and outputs a binary result (matching result) for each segment. Optionally, the matching algorithm may be configured to generate a confidence metric (matching confidence) for each matching result.

[0148] It should be noted that the above algorithms 30, 32, 34, 40, and 50 are not necessarily required, or may be combined into more complex algorithms (e.g., all algorithms combined into one algorithm) or split into simpler algorithms (e.g., a matching algorithm may include a match algorithm and a confidence estimation algorithm).

[0149] FIG. 4 shows a schematic process flow diagram according to the first embodiment.

[0150] A process is described for matching an object described by data of one data type collected by one device with an object described by data of a different specific data type collected by a different device, the process being designed to allow two different devices, potentially having different data collection capabilities, to distinguish between the same object when sensed.

[0151] The sensor (S) of the first device (A) 10 A ) 12 includes measurement data D which may include data about one or more objects (O) 60. MA The device (A) 10 may transmit this data to a second device (B) 20 or to a wireless network (e.g., an object matching service). In addition to the data, the device (A) 10 may transmit information regarding the field of view (FoV) of the sensor 12, the capabilities of the sensor 12 (e.g., sensing modality, resolution), the orientation of the sensor 12, and / or the 3D position of the sensor 12 or the first device 10 to the second device (B) 20 or to the wireless network.

[0152] The sensor (SB) 22 of the second device (B) 20 receives measurement data D, which may include data about one or more objects (including, for example, the one or more objects 60). MBThe device (B) 20 may transmit this data to the first device (A) 10 or to a wireless network (e.g., an object matching service). In addition to the data, the device (B) 20 may transmit information regarding the field of view (FoV) of the sensor 22, the capabilities of the sensor 22 (e.g., sensing modality, resolution), the orientation of the sensor 22, and / or the 3D position of the sensor 22 or the second device 20 to the first device (A) 10 or to the wireless network.

[0153] A segmentation algorithm (SA) 40 processes the measurement data D of the first device 10. MA and / or measurement data D of the second device 20 MB and the segmented measurement data D of the first device 10 relating to one or more objects 60 in the data. MAS and / or the segmented measurement data D of the second device 20 MBS may be generated respectively.

[0154] A synthetic data algorithm (SDA) 30 processes the segmented measurement data D MAS is performed on the synthetic data D S For example, first, an X-to-Mesh algorithm (XTM) 32 generates mesh data for each segment-related data or object-related data, and then a Mesh-to-Y algorithm (MTY) 34 uses the mesh data to generate synthetic data D for each segment-related data or object-related data. S Generate.

[0155] Finally, a matching algorithm (MA) 50 is applied to the synthetic data D S Each segment of the segmented measurement data D MBS and outputs a binary matching result for each segment (i.e., match or no match) and, optionally, a matching confidence.

[0156] The segmentation step and synthetic data generation step are carried out by a synthetic data algorithm that generates the measurement data D of the first device 10. MA 1 is an example of a method for generating synthetic data that includes data about at least one object.

[0157] In another embodiment, the measurement data D of the second device 20 MB The segmentation and synthetic data generation steps are performed on the first device 10, and the resulting synthetic data is combined with the segmented measurement data D MAS may be compared with

[0158] FIG. 5 shows a schematic flow diagram of a matching procedure according to the second embodiment.

[0159] In step S310, measurement data D of a first data type collected by a first device about one or more objects is MA is received or obtained (eg, by a network device such as a terminal device, an access device, or a network function).

[0160] In step S320, measurement data D of a second data type collected by a second device located remotely from the first device is MB is received or acquired (eg, by a network device), and this measurement data may include data regarding one or more objects (eg, including one or more objects of step S310).

[0161] In the next step S330, the measurement data D of the first device is MA and / or measurement data D of the second device MB is segmented (e.g., at a network device) and the segmented measurement data D of the first device is associated with one or more objects in the data. MAS and / or segmented measurement data D of the second device MBS are generated respectively.

[0162] Then, in step S340, the segmented measurement data D of the first device is MAS is processed (e.g., in a network device) to generate the composite data D S This is done by first generating mesh data for each segment-related data or object-related data, and then using the mesh data to generate synthetic data D for each segment-related data or object-related data. S This may be achieved by generating:

[0163] Finally, in step S350, the composite data D S Each segment of the segmented measurement data D MBS is compared (CMP) with each segment of the , and the output can be a binary matching result (i.e., match or not) for each segment (MO), and optionally a matching confidence.

[0164] In an additional embodiment, which can be combined with other embodiments, the (segmented) measurement data of the second device may be converted into the same intermediate format (e.g., mesh data) as the measurement data of the first device, and matching may be performed in the intermediate mesh domain, as described in the third embodiment below.

[0165] FIG. 6 shows a schematic diagram of a modified system architecture according to a third embodiment, in which a different set of algorithms is used to determine a match.

[0166] This other set of algorithms is implemented to analyze the segmented measurement data D of both the first and second devices 10, 20. MAS , D MBSThe system includes a synthetic transformation algorithm (STA) 70 (set of) that can map the set of images, data, and images to a common domain (e.g., the 3D mesh data described above) intended to facilitate matching. Other examples of common domains include machine vision transforms such as Fourier transforms, or intermediate forms used for subsequent comparison. Such intermediate forms may be, for example, 3D mesh data or the Hough transform used in computer vision.

[0167] The composite transform algorithm 70 may be divided to encompass two classes of transforms that can be implemented by first and second algorithms.

[0168] The first algorithm is the X-to-Mesh algorithm (XTM) 32 (described above), which generates specific segmented measurement data D of a specific data type of the first device 10. MAS For the set, the segmented measurement data D of the first device 10 MAS 3D mesh data D representing an object described by MAM It is configured to generate a set.

[0169] The second algorithm is a Y-to-Mesh algorithm (YTM) 74, which generates a specific segmented measurement data set D of a specific data type of the second device 20. MBS , whereas the segmented measurement data D of the second device 20 MBS 3D mesh data D representing an object described by MBM It is configured to generate a set.

[0170] Furthermore, this further set of algorithms includes an additional mesh alignment algorithm (MAL) 76, which optionally aligns the 3D mesh data sets D of the first and second devices 10, 20. MAM and D MBMare aligned using, for example, the positions, viewpoint data, FoV, and / or sensing capabilities of the first and second devices 10, 20, thereby providing an aligned mesh data set D MAML and D MBML is obtained.

[0171] Alternatively, one of the devices may have information about the other device (eg, location information), and transformed data (eg, mesh data) may be generated from the other device's perspective.

[0172] Alternatively, new viewpoint coordinates may be provided (e.g., by a network) to each device that is required to acquire and / or generate transformed data, thereby enabling matching between distant devices.

[0173] Furthermore, another set of algorithms mentioned above can be applied to two aligned mesh datasets D MAML and D MBML and compare the aligned mesh D of the second device 20. MBML If any of the segments of data is included in the aligned mesh data D of the first device 10, MAML and optionally outputting a binary result (matching result (MO)) for each segment. Optionally, the matching algorithm may be configured to generate a confidence metric (matching confidence) for each matching result.

[0174] It should be noted that the above algorithms 70, 32, 74, 76, and 50 are not necessarily required, or may be combined into more complex algorithms (e.g., all algorithms combined into one algorithm) or split into simpler algorithms (e.g., a matching algorithm may include a match algorithm and a confidence estimation algorithm).

[0175] FIG. 7 shows a schematic flow diagram of a matching procedure according to the fourth embodiment.

[0176] Steps S510 to S530 in FIG. 7 correspond to steps S310 to S330 in FIG. 5, and therefore will not be described again here.

[0177] In step S540, the segmented measurement data D of the first and second devices are MAS and D MBS is processed (e.g., in a network device) and mesh data D MAM and D MBM is generated.

[0178] Then, in step S550, the mesh data D of the first and second devices are MAM and D MBM are aligned (e.g., at the network device), e.g., using the respective positions, viewpoint data, and / or sensing capabilities of the first and second devices, thereby generating an aligned mesh data set D MAML and D MBML is obtained.

[0179] Finally, in step S560, the two aligned mesh data sets D MAML and D MBML are compared, and the aligned mesh data D of the second device MBML If any of the segments of MAML The output may be a binary result (Matching Result (MO)) for each segment. Optionally, the matching algorithm may be configured to generate a confidence metric (Matching Confidence) for each matching result.

[0180] In an additional embodiment, which can be combined with other embodiments, measurement data D of the first device and / or the second device MA and / or D MB may be stored in a database, for example.

[0181] In an additional embodiment, which can be combined with other embodiments, the segmented measurement data D of the first and / or second device MAS and / or D MBS may be stored in a database, for example.

[0182] In additional embodiments that can be combined with other embodiments, mesh data or synthetic data D S may be stored in a database, for example.

[0183] In additional embodiments that can be combined with other embodiments, data stored in, for example, a database, may be stored with metadata that identifies the type of device or algorithm used to sense or calculate the data.

[0184] In an additional embodiment, which can be combined with other embodiments, when matching is required, the device performing the matching can retrieve the most appropriate data (e.g., mesh data) from a storage service (e.g., a database), thereby reducing the computational complexity of the device (since only a single additional transformation is required).

[0185] In an additional embodiment, which can be combined with other embodiments, measurement data D of the first device and / or the second device MA and D MBmay correspond to measurements of an object over a period of time. This may correspond, for example, to video measurements of a user walking, from which mesh data representing the user's shape or posture may be extracted, as well as frequency data related to the user's gait or the user's heart rate. In creating synthetic data (which may correspond to mmWave data), the synthetic data algorithm may not only transform a particular aspect (e.g., shape or posture) of the sensed object's measurement data (in this example, video), but may also transform multiple aspects of the measurement data (in this example, shape / posture plus gait type and / or heart rate).

[0186] Additionally or alternatively, an AI model aimed at / trained to identify specific objects or movement patterns (e.g., walking, breathing, or heart rate) may be used to determine whether a set of object measurements over a period of time matches a specific object or movement pattern based on a set of measurement data and / or mesh data or other segmented or synthetic data.

[0187] In additional embodiments, which can be combined with other embodiments, the matching algorithm can not only match a single aspect of the measurement data, but can also match multiple aspects, making it a multi-dimensional matching algorithm. This matching can be performed in multiple ways, for example, by ensuring that all n aspects match when viewed as an n-dimensional vector, by ensuring that all n aspects match when viewed as an n-dimensional weighted vector, or by deriving a combined confidence value as a norm (e.g., norm 0, norm 1, norm 2, infinity norm) of an n-dimensional (weighted / normalized) vector of confidence values.

[0188] In an additional embodiment, which may be combined with other embodiments or implemented independently, if a match is found by the first device 10, the second device 20, or a service in the network (e.g., an object matching service) based on data received from the first device 10 and / or the second device 20 (e.g., if the same object can be detected by both sensors or a target object is found that matches a stored dataset for an object model or a set of viewpoints, sensor orientations, and / or FoVs), the first device 10, the second device 20, or the service in the network may send a message to the first device 10 or the second device 20 to adjust the FoV, sensor orientation, viewpoint, or sensing resolution (e.g., to improve sensing of a particular target or to begin sensing of another target). Such a message may include FoV information, angle information, location information, and resolution information. Upon receiving such a message, the first device 10 or the second device 20 may adjust its sensors according to the information in the message. Additionally or alternatively, such a message may include information about the focal point, target object information (e.g., mesh data, a semantic model of the target, a set of target identification information, a set of reference coordinates), or target area / volume information. The first device 10 or the second device 20 can use this information to determine an FoV, a sensor orientation, or a viewpoint that enables the first device 10 or the second device 20 to sense the focal point, target object, or target area. The first device 10 or the second device 20 can then adjust its sensor according to the determined FoV, sensor orientation, or viewpoint. The first device 10 or the second device 20 may have several hardware components to achieve this (e.g., a variable lens to change the focal point, a shutter to block part of the sensor, a rotation mechanism to change the sensor orientation, a different radio to perform wireless sensing at a higher frequency).Similarly, if a match is not found by the first device 10, the second device 20, or a service in the network based on data received from the first device 10 and / or the second device 20, the first device 10, the second device 20, or a service in the network may send a message to the first device 10 or the second device 20 to adjust the FoV, sensor orientation, point of view, or sensing resolution (e.g., to improve sensing of a particular target or to begin sensing of a different target).

[0189] Additionally or alternatively, the first device 10 may transmit information regarding the field of view (FoV) of the sensor 22, the orientation of the sensor 22, and / or the 3D position of the sensor 22 to the second device 20 or a service within the network. This information may indicate which viewpoint, angle, and / or FoV the data transmitted by the first device was adapted for (e.g., in DAML and DMBML) and / or indicate a desired viewpoint, sensor orientation, and / or FoV that the sensor 22 of the device 20 should use to perform sensing and / or matching. Based on this information, the device 20 may be configured or instructed to adapt the viewpoint, sensor orientation, or FoV according to the information. Similarly, the second device 20 may transmit information regarding the field of view (FoV) of the sensor 12, the orientation of the sensor 12, and / or the 3D position of the sensor 12 to the second device 10 or a service within the network. This information may indicate which viewpoint, angle, and / or FoV the data transmitted by the sensor device is adapted for (e.g., in DMAML and DMBML) and / or indicate a desired viewpoint, sensor orientation, and / or FoV that sensors 12 of device 10 should use to perform sensing and / or matching. Based on this information, device 10 may be configured or instructed to adapt the viewpoint, sensor orientation, or FoV accordingly.

[0190] The above-described embodiment explained with reference to FIGS. 1 to 7 will now be described in more detail based on a specific use case.

[0191] Asset Tracking In asset tracking use cases, assets are typically tracked by a physically attached tag, which can be detached and attached to another object or can become lost.

[0192] To solve this problem, a user can use a UE (e.g., a smartphone with a camera, LIDAR) or other terminal device to image the object at the time the tag is attached and use it to generate synthetic radar data that can later be compared with actual radar measurements acquired by the wireless network to verify that the tag is still attached to the same physical asset.

[0193] It should be noted that if the UE has radar capabilities, the user may use the UE to generate or acquire radar data directly, in which case the radar data can be combined with the synthetic data and the combined data can be used for subsequent comparison.

[0194] Note that the radar data acquired by the UE and the radar data acquired by the wireless network may not directly match due to possible differences in sensor pose / orientation. Therefore, it may still be necessary to transform the data contained in one of the datasets (e.g., radar data acquired by the UE) into a composite dataset of radar data, taking into account the sensors, viewpoints, and / or line of sight of the UE and the wireless network, etc.

[0195] In such an asset tracking use case, the first device (A) 10 in FIG. 3 may be a UE such as a smartphone, and the second device (B) 20 may be a line-of-sight sensor (e.g., a camera or LIDAR sensor). MA may be a series of photographs or videos from multiple angles of the asset (if sensor 12 is a camera), and / or may be a series of LIDAR measurements taken from multiple angles of the asset (if sensor 12 is a LIDAR), and / or may be other measurements from other sensors.

[0196] 3 may be a UE or a base station in proximity to the asset to be tracked. The sensor 22 may be a radar sensor that may include at least one of a dedicated radar sensor located at the base station (e.g., gNB), a communication component used in a dedicated sensing mode (e.g., ISAC), and a passive sensing option derived from normal communication signals.

[0197] In this use case, the X-to-Mesh algorithm 32 may be an image-to-mesh algorithm (such as an algorithm configured to receive multiple photographs of an object (e.g., from different angles) as input and generate a representative mesh) or a LIDAR-to-Mesh algorithm (such as an algorithm configured to receive LIDAR measurements from one or more different angles of an object and generate a representative mesh (the more angles covered, the more accurate the mesh)).

[0198] Additionally, the Mesh-to-Y algorithm 34 generates synthetic data D that emulates one or more cross-sectional areas of the tracked asset. S may be generated, and multiple cross-sections based on multiple different viewpoints may be synthesized, which may correspond to predicted views of the imaged asset acquired by the sensor 22 of the second device 20.

[0199] An additional device (asset tag) may be designed to be attached to a specific object (asset) or to be easily geographically trackable. The asset tag may include all antennas and other components necessary to communicate over a communications network, such as a 5G system. As part of normal operation, the asset tag may periodically report its location to the 5G system. The asset tag may have an associated identifier (asset tag ID) known to the 5G system.

[0200] Additionally, an additional database (asset database) may be provided that stores a set of asset tag IDs and mesh data and / or (other) matching criteria that describe the associated assets.

[0201] Additionally, an additional algorithm (tag check algorithm) may be provided that initiates the process of verifying that the asset tag is still attached to the correct asset by requesting measurements and verification from the second device 20. This may be triggered periodically, opportunistically triggered based on the proximity of the asset tag to an appropriate second device 20, or invoked by another process on the network when a check is needed. If triggered periodically or by an external process, the tag check algorithm may also be responsible for identifying appropriate second devices 20, i.e., devices that have, for example, radar or LIDAR sensing capabilities and are within range of the asset tag location.

[0202] Below we describe example steps associated with this use case.

[0203] A user attaches an asset tag to an asset to be tracked. Upon attachment, the user uses the sensor 12 of the first device 10 to capture one or more images of the asset as measurement data D. MAThis data is sent to an X-to-Mesh algorithm 32 which generates mesh data for the asset (with asset tag). The mesh data and associated asset tag IDs are stored in an asset database. Additionally or alternatively, the original measurement data D MA may also be preserved.

[0204] The tag check algorithm may then be triggered periodically or on-demand. This may be requested, for example, by the asset owner. In one example, it may be triggered when the location of the asset tag is determined to be within range of a suitable second device 20 (i.e., a UE or gNB with radar capabilities). A suitable second device 20 in the vicinity of the asset tag may then be triggered by the tag check algorithm to obtain the measurement data D of the second device 20. MB The known asset tag locations may be used to guide the measurement parameters.

[0205] Next, the Mesh-to-Y algorithm 34 is triggered, for example by a tag check algorithm, to generate synthetic data D of the asset from the perspective of the sensor 22 of the second device 20. S The algorithm first synthesizes the correct virtual viewpoint using the known asset tag locations and the known locations of the appropriate second devices 20, and then retrieves the appropriate mesh data from the asset database to generate the synthetic data D S Generate.

[0206] Finally, a matching algorithm 50 is applied to the measurement data D MB The generated synthetic data D Sand outputting a positive or negative matching result. A positive matching result may be output if the similarity between the virtual Doppler radar data set and the actual Doppler radar data set exceeds a predetermined numerical threshold. Furthermore, a matching confidence may be calculated based on the average size of the differences between the data sets. If a negative matching result is returned, an alarm may be triggered by the tag checking algorithm and / or an action may be requested.

[0207] User Authentication and Authorization In a user authentication (identification) use case, an object (e.g., a user) may gain access to a network-assisted (network-supported) authentication (identification) system, where the user first uses UE sensors to image themselves and / or objects (e.g., vehicles) surrounding / carrying the UE and uses this data to generate synthetic radar data representing the user / object. The UE then requests an authentication function, where a local base station (e.g., gNB) or other UE images the user / object via radar and compares the image data with the synthetic data to authenticate the user of the UE or the objects surrounding / carrying the UE.

[0208] In this use case, the first device (A) 10 in FIG. 3 may be a UE such as a smartphone or a wearable device, and the sensor 12 of the first device 10 may be an eye-gaze sensor (e.g., a camera or a LIDAR sensor). Here, the measurement data D MA may be a series of photographs or videos taken from multiple angles of the user's body, face, or other biometric information and / or objects carrying / surrounding the UE (if the sensor 12 is a camera), or may be a set of LIDAR measurements taken from multiple angles of the user's body, face, or other biometric information and / or objects carrying / surrounding the UE (if the sensor 12 is a LIDAR).

[0209] The second device (B) 20 may be a base station, and the sensor 22 of the second device 20 may be a radar sensor that may include at least one of a dedicated radar sensor located at the base station (e.g., gNB), a communication component used in a dedicated sensing mode (e.g., ISAC), and a passive sensing option derived from normal communication signals.

[0210] In this use case, the synthetic data algorithm 30 may include:

[0211] Measurement data D of the second device 20 MB The X-to-Mesh algorithm 32 may consist of a pre-trained adversarial network that generates 3D mesh data of the user's body, face, or other biometric information and / or objects carrying / surrounding the UE based on the

[0212] Synthetic data D that emulates the radar cross section of the user / object being imaged S In this case, cross sections from multiple different viewpoints may be synthesized that correspond to predicted views of the user / object as imaged by the sensor 22 of the second device 20 based on the positions, viewpoint data, field of view angles, and / or sensing capabilities of the second device 20 and the first device 10.

[0213] An additional database (user mesh database) may be provided to store the generated mesh data.

[0214] Furthermore, an additional algorithm (Authentication Request Algorithm) may be provided which, when triggered by the first device 10, initiates authentication assisted by synthetic data.

[0215] Below we describe an example procedure for this use case.

[0216] A user opts in to a network-assisted authentication (identification) system and can use sensors 12 of first device 10 to image or otherwise sense their body at multiple different angles and output this data as measurement data D. The output is sent to an X-to-Mesh algorithm 32 that generates mesh data of the user's body. The mesh data is stored in a user mesh database.

[0217] When the user subsequently uses the first device 10, the authentication request algorithm triggers an authentication session. A local second device 20 (e.g., a base station (e.g., a gNB) served by the first device 10) receives the user's body measurement data D MB The device location of the first device 10 may be used to assist in configuring the sensors 22 of the second device 20 to collect this data.

[0218] A Mesh-to-Y algorithm 32 is run on the mesh data of the user to generate synthetic data D of the user's body from the perspective of the sensor 22 of the second device 20. S is generated.

[0219] Finally, a matching algorithm 50 is applied to the measurement data D MB The generated synthetic data D S and outputs a positive or negative matching result. A positive matching result may be output if the similarity between the virtual Doppler radar data set and the actual Doppler radar data set exceeds a predetermined numerical threshold. Furthermore, a matching confidence may be calculated based on the average size of the differences between the data sets. If the matching result is negative or the matching confidence is too low, authentication fails. In that case, re-authentication may be requested or a warning may be sent to the operator of the first device 10. Otherwise, authentication is successful and the user is allowed to access the first device 10.

[0220] In one embodiment, which can be used with other embodiments, the second device 20 may rely on the first device 10 to perform certain measurements. For example, the second device 20 may use a particular sensing technology, such as mmWave radar, as a transmitter, and the first device 10 may act as a receiver and be instructed to provide measurements to the second device 20.

[0221] In one embodiment, which can be used with other embodiments, this use case may also be applicable to UEs or other terminal devices (including standalone devices), for example, to verify or enhance user verification, since the increased use of UEs with sensing capabilities (e.g., mmWave sensing capabilities) may also enable such UEs to be used to sense parameters of the user or the user's environment.

[0222] In one embodiment, user / object authentication may be performed when a user attempts to access a UE. User / object authentication may also be performed when a user / object's UE initiates access to a wireless network or service (e.g., a sensing service) in the core network, as detailed in other embodiments of the present disclosure.

[0223] In one embodiment, user / object authentication may be performed based on data sensed over a period of time, for example, as the user walks towards the UE to access it, since this allows some unique user characteristics (e.g., the user's gait) to be identified.

[0224] One common problem with radar systems and wireless sensing systems in general, including distributed and non-distributed / centralized radar systems as well as other systems using other sensing modalities, is that there may be multiple objects that can be sensed / detected within the sensing area / volume. Not all of these objects may receive wireless sensing services, may subscribe to such wireless sensing services, may not be targets for wireless sensing, and / or may not even want to be sensed or participate in the sensing service or procedure. This problem is similar for systems using other sensing modalities. This problem does not only apply to wireless sensing services, but also to other services that utilize wireless sensing or other sensing modalities, such as surveillance services, metaverse or augmented reality services, and / or matching or viewed object identification services. Hereinafter, the term sensing service will encompass these types of services.

[0225] This is problematic because wireless sensing, or sensing using other sensing modalities, can reveal personally identifiable information (e.g., biometric information) and / or other information that may require privacy protection (e.g., a person's location or behavior). In many countries, user consent is required to participate in services that may obtain privacy-sensitive information, especially in private domains such as the home. Similarly, as described in Appendix V of TS33.501, user consent may also be required for 3GPP® functions depending on local regulations, and, for example, the collection, processing, and use of privacy-sensitive data, such as through sensing, may also be restricted to certain uses. Additionally, devices involved in sensing, especially those that may obtain / receive privacy-sensitive information in the process, must be properly authorized.

[0226] In general, if the sensing service, sensing transmitter, or sensing receiver determines that the sensing information (e.g., sensing measurements or sensing results related to a detected object) or the input / output data of the sensing signal processing does not correspond to given information about how to identify the target (e.g., the identified location is too far from the UE carried by the target, the target is of a different shape / size (e.g., small or large), the biometric information does not apply to the target, one or more sensing measurements / results are above or below a certain threshold, or the mesh data does not match (as described in other embodiments)), the sensing service, sensing transmitter, or sensing receiver may discard the received sensing signal and / or discard the sensing information (e.g., sensing measurements or sensing results) and / or discard the input / output data of the sensing signal processing, and / or may not perform further processing on and / or send these measurements, results, and / or input / output data to a further processing unit. The sensing service, sensing transmitter, or sensing receiver may also generate a notification message and send it to an application, application server, or core network function (possibly via the NEF) for further processing or storage, for example, and / or, if the target is no longer detected, may store information about such an occurrence in non-volatile storage (e.g., a database).

[0227] In the embodiments described below, authentication can be obtained / provided to initiate sensing of specific targets and to mitigate / prevent sensing of unauthorized / unintended targets (especially detailed sensing).

[0228] To achieve this, the sensing service, sensing transmitter, or sensing receiver may provide information about the target location / area / volume (e.g., a delimited geographic area indicated by a set of coordinates, length, size, diameter) where the target is to be or is expected to be sensed, and / or how to identify the target (e.g., physical characteristics of the target (e.g., size, shape, mass, material composition), biometric information associated with the target (e.g., heart rate signal characteristics, body type, body absorbance / reflectance characteristics, posture / movement, size / mass, disease / disorder resulting in identifiable characteristics (e.g., sleep apnea resulting in cessation of breathing during sleep)). sleep apnea, asthma resulting in fast, irregular breathing rate or shortness of breath, shuffling resulting in abnormal body movements, tremors (e.g., Parkinson's disease), expected body temperature patterns, heart rate variability / patterns), identity and / or location of wireless communication devices or other devices that the target may own, surround, contain, or carry (e.g., from / by an application, network publishing function, policy control function, subscription database (e.g., home subscriber service, integrated data management service), identity database, authentication / authorization control function, public safety answering point, e.g., as part of sensing configuration / parameters). This target identification information may include thresholds or other criteria related to sensing measurements / results, such as minimum / maximum deviation from a particular location, a set of different shapes or a scatter plot of allowed shapes, multiple shape definitions showing minimum and maximum shape contours, deviations in size (e.g., maximum / minimum tolerance in height, width, length, or kilograms), minimum / maximum speed, a set of possible movement patterns and / or deviations therefrom, minimum / maximum values ​​of sensed biometric information (e.g., minimum / maximum heart rate or respiration rate), etc.Note that target identification information may be defined with different levels of precision, granularity, and matching criteria (e.g., thresholds) depending on the resolution / accuracy of the radar-based sensing (e.g., number of transmitters and receivers, frequencies used, non-distributed vs. distributed sensing).

[0229] Alternatively, or in addition, a target may be designated by an exclusion, i.e., a person, animal, structure, or another type of object that does not match a set of criteria (e.g., not a person, animal, object, or structure that matches a particular known biometric or size) and / or a person, animal, structure, or another type of object that should not be detected in a particular location / area or volume (e.g., not matching the biometric information of a person registered to reside in a particular home, whose biometric information may be pre-stored) (e.g., to detect a home intruder / burglary). Information provided to the sensing service, sensing transmitter, or sensing receiver (possibly indirectly via the sensing service) may further include a set of phone numbers to contact in the event of a particular alert situation (e.g., an emergency number), a set of times of day (e.g., nighttime only), or a set of triggers for when the service should be active (e.g., when a person is known to be at home, such as by receiving a signal from a device the person may be carrying).

[0230] The sensing service, sensing transmitter, or sensing receiver may also obtain information about devices (e.g., IDs and estimated locations) in the vicinity of the target or the target's address / location / home (e.g., a set of nearby base stations or nearby UEs (e.g., UEs located in a person's home or UEs carried by the person or their family, friends, neighbors) that can participate in the (distributed) sensing of the target and that may (or have been) authorized by the network, the device user, and / or the target object). Authentication information (which may also include user consent information) may be stored as part of the user's subscription and / or as part of the sensing service (e.g., in a Unified Data Management (UDM) function of the core network) and / or may be received from a service, application function, external application, or AAA (authentication, authorization, and accounting) server (e.g., via the NEF).

[0231] Based on the target location / area / volume information and / or target identification information, the sensing service may select and / or configure one or more sensing transmitter devices and / or sensing receiver devices having a sensing configuration that enables sensing to be performed with the accuracy required to match with the target identification information. This is performed, for example, by configuring a set of frequencies for the sensing signal, measurements that need to be performed, the number of times the sensing signal is transmitted, or the number of times measurements are performed. The sensing service may also provide information about the target area / location / volume to be sensed and / or a (sub)set of target identification information to the sensing transmitter devices and / or sensing receiver devices.

[0232] Based on the target identification information, the sensing service, the sensing transmitter, and / or the sensing receiver may be configured to perform a target matching procedure based on the set target identification information. The target matching procedure includes determining (e.g., through algorithmic processing of sensing data (i.e., sensing measurements and / or (partial) sensing results) generated by and / or obtained from the sensing receiver) whether a set of sensing measurements performed on the received sensing signals or results of processing performed on the set of sensing measurements and / or (partial) sensing results meet one or more set criteria (e.g., thresholds) for identifying a target object. This may include determining whether the sensing measurements and / or sensing results satisfy thresholds and / or boundary conditions, such as whether the object is close enough to the target location, whether the identified shape of the object falls within certain boundaries, whether the size of the object is close to the expected size, whether the object's speed is below the minimum expected speed or above the maximum expected speed of the target, or below the minimum or above the maximum value for adequately sensing the target object, whether the movement pattern is the expected movement pattern for the target, whether the measured biometric information (e.g., heart rate or respiration rate) is below / above the expected value for the target, etc.

[0233] In one embodiment, which may be combined with other embodiments or implemented independently, the sensing service, the sensing transmitter, the sensing receiver, the first device 10 (which may be a sensing transmitter and / or receiver), and / or the second device 20 (which may be a sensing transmitter and / or receiver) may receive (e.g., from / by an application) or acquire pre-configured or determined information, which could be information about a model (e.g., a semantic model, a digital twin) of the object (e.g., a set of metadata describing the object, or a virtual graphical representation of the object), a set of viewpoints, sensor orientations, and / or sensor-related data about the FoV (e.g., radar cross section) of the object (e.g., measurement data, (pre-)processed sensor output, or segmentation data or mesh data that may be acquired, stored, and / or (pre-)processed by a sensor and / or synthesized by a sensing device, sensing service, or application), a set of video images of the object, and / or a set of mesh data of the object stored in an object database. A set of viewpoint, sensor orientation, and / or FoV data may be generated or stored according to a set of predefined positions (e.g., known positions of base stations) or angles (e.g., one or more angles relative to a reference line pointing to magnetic north) and / or FoVs (e.g., omnidirectional). This information may be used as target identification information and may also be used to match sensing data from the sensing transmitter, sensing receiver, first device 10, and / or second device 20 with received information using procedures described in other embodiments.Examples of procedures described in other embodiments include generating data for a particular angle and / or viewpoint of the FoV (e.g., radar cross section) from received sensing measurements / data, sensing capabilities, and / or information regarding viewpoint, orientation, and FoV from the sensing transmitter / receiver, first device 10, or second device 20, and matching this with viewpoint, sensor orientation, and / or FoV (e.g., radar cross section) data included in a received, pre-set, or determined set of viewpoints, sensor orientations, and / or FoVs. Depending on whether a match is found (e.g., with a certain confidence level), the sensing session may continue or be aborted, the sensing data may be accepted or discarded for further processing / storage, additional sensing operations may be initiated, a (different) target, sensing receiver, sensing transmitter, first device 10, or second device 20 may be selected for further sensing, an event may be generated or a signal may be sent indicating that a matching target has or has not been detected, or authentication of the target of sensing may be initiated / continued (e.g., verifying whether the detected object is an authenticated target of the sensing service). In the case of a metaverse or augmented reality service, the target object may be another participant in the metaverse or augmented reality service. Identification of the target object may be performed to prevent “innocent” bystanders from being recognized or sensed without permission.

[0234] A target matching procedure may receive as input an (ideal) representation of a signal, which may need to be matched with the results of signal processing (which may include passing the signal through one or more filters) by finding similarities between the processed signal and such (ideal) representation of the signal. This may include identifying overlaps between the processed signal and the (ideal) representation of the signal, possibly while altering the signal's amplitude or timing. This may also include identifying signal shapes and peaks and verifying whether these occur within the (ideal) representation of the signal. Similarly, in the case of sensing modalities such as video or image feeds, various image processing algorithms, such as denoising, sharpening, resizing, etc., may first need to be run on the video or image output before performing further actions, such as creating segmented data, mesh data, or radar representations of the video or image.

[0235] Additionally or alternatively, input to such a target matching procedure may include a description of at least one or more signal characteristics (e.g., a particular signal peak or shape) that must be present for the signals to match. Input to such a target matching procedure may also include a set of signal characteristics (e.g., a particular signal shape, peak) that must not be present for the signals to match. Because some sensing measurements and / or sensing results may not be stable (e.g., measurements that fluctuate between particular values, motion that causes Doppler shifts, small movements, or RF signal interference that causes noise in a particular signal), the signal may need to be passed through a particular filter (e.g., a low-pass or high-pass filter) to remove noise, undesirable spikes, outliers or trends, or Doppler shifts from the signal. It should be noted that sensing measurements and sensing results may be measured or calculated over a period of time, any outliers over that period may be removed, and / or any average value over that period may be used in the signal processing and matching algorithm. The output result after these processing procedures may be a signal that matches a given (ideal) representation and / or a given set of signal characteristics, although 100% matching accuracy is highly unlikely. This is exacerbated if the original or processed sensing signals, sensing measurements, or (partial) sensing results do not have sufficient accuracy (e.g., due to the use of low-frequency signals or low sampling rates, or less accurate signal representations). Therefore, the matching results may be provided with a confidence level, a match rate, or a standard deviation value. The target matching procedure may be given a minimum confidence level, a confidence interval, a minimum match rate, or a maximum standard deviation value for one or more of the configured criteria or other target identities. If the minimum confidence level, minimum match rate, or maximum deviation is not met, the target matching procedure may not include it in the set of matched criteria or other target identities.

[0236] Additionally or alternatively, the target matching procedure may use an artificial intelligence (AI) model to match the sensing measurements and / or (partial) sensing results. The AI ​​model may be trained to identify a particular target or set of targets, for example, by using sensing measurements and / or (partial) sensing results based on sensing targets in controlled environments (possibly in various settings). Such an AI model may be provided as input to the target matching procedure, or the target matching procedure may be made available via a communication interface (e.g., executed on an edge server). By applying such an AI model to a set of sensing measurements and / or (partial) sensing results of actual targets, the AI ​​model can determine whether a given sensing measurement and / or (partial) sensing result (e.g., a particular processed signal) matches what the model has learned to identify a particular target. Additionally or alternatively, the AI ​​model may determine a machine-learned confidence score that a given sensing measurement and / or (partial) sensing result actually identifies a target and / or that it matches one or more target identities of the target. The AI ​​model may provide a matching learning confidence score and / or other matching results for further processing in the target matching procedure.

[0237] The target matching procedure may result in a set of objects (i.e., matching targets) that match a subset of the target identities in the case of a partial match, or the entire target identities in the case of a full match. As described above, matching may be enhanced or conditioned by a confidence level or match rate. In summary, a matching target is an object that matches (with a particular confidence level or match rate) with at least a subset of the target identities, for example, by meeting one or more set criteria (e.g., thresholds) for identifying the target object. The sensing service, sensing transmitter, sensing receiver, first device 10, or second device 20 performing the target sensing or target matching procedure may assign a new identifier when a new and / or different object is detected, an identifier associated with a set of target identities (e.g., matching criteria for the target), an identifier based on a target identifier provided by an application, an identifier based on a subscription identifier associated with the sensing service and / or target sensing, or an identifier based on the identification of the sensing session. The entity responsible for the target matching procedure may replace the assigned identifier by an identifier associated with the set of target identities in case of an exact match and / or if the confidence level or percentage of match exceeds a pre-set threshold.

[0238] To identify targets within the target location / area / volume, the sensing service, sensing transmitter, sensing receiver, first device 10, or second device 20 may initiate an initial scan of the target area and / or take sensing measurements / results obtained from the initial scan of the target area as input for the target matching procedure. Such an initial scan may be a low-resolution scan, or, if permitted / enabled / approved in a particular country or facility (e.g., for closed networks) or for a particular use case (e.g., lawful interception or emergency situations), a higher-precision scan using higher frequencies, such as distributed radar sensing or mmWave. Processing the sensing signals / measurements / results as described in other embodiments results in a set of sensing results indicating either no objects or one or more objects (e.g., people, animals, houses, vehicles) are detected within the target area. Similarly, in the FoV of sensor 12 of first device 10 or sensor 22 of second device 20 of FIG. 3 , the initial sensing (i.e., initial scan) performed by sensor 12 or sensor 22 may be at low resolution (e.g., low-resolution video, or only contour detection, which may obscure a person's face or other features), although sensing may be performed at high resolution (e.g., high-resolution video) if permitted / enabled / authorized, for example, in a particular country or facility (e.g., for closed networks) or for a particular use case (e.g., in the case of lawful interception or emergency situations). Processing these sensing signals / measurements / results as described in other embodiments results in a set of sensing results indicating that no objects or one or more objects have been detected within the FoV of sensor 12 or sensor 22.

[0239] If only one object that may (or may not) match the set of target identities is detected (according to the target matching procedure), the sensing service, sensing transmitter, sensing receiver, first device 10 or second device 20 may generate an event and / or send a signal (which may include a message, for example to an application server or core network function or via the NEF) indicating that a matching or intended target is present within the scan area / volume / FoV (possibly augmented by location information and / or other sensing results regarding the detected target), and / or may store information regarding the detection of the target (possibly augmented by location information and / or other sensing results regarding the detected target) in non-volatile storage (e.g., a database) if a target is detected according to the target identification, and / or may initiate additional scans, and / or may perform further sensing measurements for verification or to determine additional matches to the target identification, and / or may initiate / trigger the start of a (detailed) sensing session if the intended target is detected. To this end, the sensing service, sensing transmitter, sensing receiver, first device 10, or second device 20 may notify one or more other sensing services, sensing transmitters, sensing receivers, or other devices (e.g., first device 10 or second device 20) that are capable of performing sensing using one or more sensing modalities that may be involved in (detailed) sensing of the target, for example by sending a signal to initiate additional sensing operations.

[0240] If multiple objects are detected that may (or may not) match the target identification set (according to the target matching procedure), the sensing service, the sensing transmitter, the sensing receiver, the first device 10, or the second device 20 may generate an event and / or send a signal (which may include a message, for example, to an application server or core network function or via the NEF) indicating that multiple intended targets have been detected and / or that multiple objects (e.g., matching targets) that match (or may not match) the identification set (but that the intended target is present within the scan area / volume / field of view and / or which of the detected objects cannot be determined with sufficient confidence to be the intended target), and / or may delay / cancel the start of the (detailed) sensing session and / or initiate another scan, and / or may perform further sensing measurements for verification or to determine additional matches to the target identification (e.g., to reduce the number of potential targets and / or to find a better match to the set of target identification for the intended target).

[0241] In another embodiment, the sensing service, the first device 10, or the second device 20 may initiate an initial scan of a target location / area / volume / FoV and obtain sensing measurements / results obtained from the initial scan of the target area or FoV. This may include raw measurement results (e.g., signal timing or strength) or may include (partial) sensing results derived from the sensing measurements (e.g., number of detected objects, object location, object velocity, object size, object motion pattern). The sensing service, the sensing transmitter, the sensing receiver, the first device 10, or the second device 20 (as a requesting entity) may provide these sensing measurements / results, or a subset thereof, to another sensing service, a sensing application, a core network function (e.g., an authentication server function (AUSF) or UDM), or an external server that performs a target matching procedure. For security and privacy reasons, the target matching procedure may need to be performed within the security / privacy domain for a particular target (e.g., within the home network (e.g., home public land mobile network (H-PLMN)) that owns the target's subscription to the sensing service, or by a server operated by a trusted identification authority (e.g., a government-provided entity), or by a server operated by an authorized / trusted application provider). Thus, target identification information may not be shared with the sensing service, sensing transmitter, or sensing receiver, especially if they operate within a visited network (e.g., visited public land mobile network (V-PLMN)).After performing a target matching procedure on the provided sensing measurements / results, the sensing service, sensing application, or core network can send a response to the requesting entity indicating whether a match was found or not and / or a (temporary) ID associated with the identified target. This ID can be used for further authentication and authorization, as described in later embodiments. The response can also include information about whether the target is authorized to be a target for sensing and / or credentials that can be used to encrypt / decrypt subsequent messages (e.g., if the (temporary) ID is provided in a subsequent message protected by a credential-based key). Based on the received response, the sensing service, sensing transmitter, sensing receiver, first device 10, or second device 20 may stop or continue the sensing operation, initiate additional sensing operations, generate or signal an event indicating that a matching target was or was not detected, or initiate / continue authentication of the target for sensing (e.g., verify whether the detected object is an authorized target for the sensing service).

[0242] In another embodiment, a sensing service, a sensing transmitter, a sensing receiver, the first device 10, or the second device 20 may initiate an initial scan of a target location / area / volume / FoV and obtain sensing measurements / results obtained from the initial scan of the target area. This may include raw measurement results (e.g., signal timing or strength) or may include (partial) sensing results derived from the sensing measurements (e.g., number of detected objects, object location, object velocity, object size, object movement pattern). The sensing service, the sensing transmitter, the sensing receiver, the first device 10, or the second device 20 (as a requesting entity) may request one or more (other) sensing services, sensing applications, core network functions (e.g., AUSF or UDM), or ID databases (e.g., those provided by a government) that maintain / store target identities to provide one or more target identities (e.g., target identities that match one or more sensing measurements / results). Such a request may include information about the scanned target location / area / volume / FoV, one or more sensing parameters / measurements / results (e.g., object size, object location, object velocity, object motion pattern, object shape, object material), and / or information about one or more targets that are subscribed to the sensing service (which may be a service that configures / controls the sensing transmitter or sensing receiver) and / or are expected to be located within / near the target location / area / volume / FoV. Based on this request, one or more (other) sensing services, sensing applications, core network functions, or ID databases can provide a (sub)set of target identities for targets whose target location / area / volume and / or target identification information (partially) match the provided information (e.g., information about the scanned target location / area / volume and / or one or more sensing parameters / measurements / results).Similarly, matching may be performed by calculating multiple different viewpoints, sensor orientations, and / or FoVs (e.g., radar cross sections) based on sensing data (e.g., measurement data or segmented data) from the sensor 12 of the first device 10, sensing data from the sensor 22 of the second device, a model (e.g., semantic model) of the object, or a dataset of multiple different viewpoints, sensor orientations, and / or FoVs of the object (e.g., stored in a matching database), as described in other embodiments of the present disclosure. Additionally or alternatively, a set of target identification information may be provided by a network (e.g., the sensing service), an application (e.g., via a NEF), or an external entity (e.g., a Public Safety Answering Point (PSAP)) to the sensing service, the sensing transmitter and / or the sensing receiver, the first device 10, or the second device 20 as part of configuration information for sensing targets based on the set of target identification information, or as part of a request to initiate sensing of the target or target location / area / volume / FoV. The (sub)set of target identifications received by the sensing service, the sensing transmitter, the sensing receiver, the first device 10, or the second device 20 may then be used by the sensing service, the sensing transmitter, the sensing receiver, the first device 10, or the second device 20 to perform further and / or more detailed sensing of targets, which may be fine-tuned to the (sub)set of target identifications provided. This may achieve better matching results or may, for example, enable one or more objects detected in the initial (low-resolution) scan to be further determined / ruled out as intended targets, which may or may not match the set of target identifications (e.g., be included or not included within a set of thresholds for position, velocity, size, or other parameters).

[0243] In some situations, detailed / long-term / continuous sensing of a target area, a set of target objects, or other objects detected within the target area or FoV (which may include, for example, sensing using higher frequencies, sensing using distributed radar, sensing for a longer period of time, or sensing using more advanced / more accurate algorithms of targets) may be initiated anyway. Examples of such situations include, for example, an emergency call or lawful intercept that initiated a request to initiate radar-based sensing of one or more targets and / or a specific area. Another example is when the target area / volume covers a home or facility (e.g., a factory) to which the owner / occupant gave permission (implicitly or explicitly) (e.g., provided user consent) when subscribing to the sensing service, or subscribing to an operator's network communication service, or, for example, through a healthcare provider / service; or when the sensing service / equipment operates within and / or covers the intended facility because, for example, the sensing service is operated by a private network (which may include privately owned infrastructure equipment and core network components that may include a sensing service, a sensing receiver, or a sensing transmitter). A further example is when the target, sensing receiver, sensing transmitter, first device 10, and / or second device 20 subscribe to the same service / application or belong to the same group (e.g., share a group ID or share group credentials).

[0244] In an exemplary embodiment, a UE (which may be a first device 10 having sensor 12 or a second device having sensor 22) initiates an emergency call to an emergency call reception center over the (cellular) network to which it is connected, and based on location information provided / obtained in accordance with local regulations (e.g., Enhanced 911) during the emergency call, a sensing transmitter near the emergency call's origination location (e.g., a nearby base station, a nearby mobile phone, or the mobile phone making the emergency call) may be instructed (e.g., via a configuration message) to perform a sensing session (e.g., a session to sense a specified target victim, a target area / volume around the victim, an emergency area / volume, or a specific FoV based on the mobile phone's location). The instructions may include authentication information, information about the target (e.g., target location / area / volume, or characteristics of the target victim (e.g., whether the target victim is moving, lying on the ground, in cardiac arrest, etc.)), context (e.g., how many people are gathered around the victim, the distance between the people and their device relative to the victim, the number of injured people, nearby debris), or an identifier or address (e.g., IP address, URL) of a destination server, network function / device, or emergency call reception center, credentials (e.g., public key) used to encrypt the results, or requested sensor output / results (e.g., target location, movement, or vital signs). The information about the target may be a set of target identification information (as described elsewhere in this disclosure).The set of target identification information may be provided by the UE to the core network (e.g., the emergency call session control function (E-CSCF) described in 3GPP TS 23.167) and / or the PSAP, for example, by including this information in an emergency connection setup request (e.g., the establishment of an emergency PDU session (e.g., the emergency PDU session defined in 3GPP TS 23.501 and TS 23.167, extended accordingly)) or via the emergency connection (e.g., the emergency PDU session), and the E-CSCF may forward the received information to the PSAP. If the UE is capable of wireless sensing, this information may be determined by the UE performing an initial scan (e.g., a radar sweep) of the victim, another target, or the emergency area. Similarly, the UE may be a first device 10 having a sensor 12 and may be capable of sensing one or more objects within the FoV of the sensor 12. Such a UE may include measurement or segmented data from sensors 12, result information regarding detected objects, or FoV information (and possibly other information related to sensors 12 (e.g., point of view, sensing orientation, and / or sensing capabilities)) in the emergency connection setup request or over the emergency connection. The UE may be configured to sense target victims (e.g., matching one or more default characteristics, such as the shape or size of a person lying on the ground, breathing heavily, or bleeding) based on, for example, a pre-configured set of target identification information (e.g., sensing criteria).The information provided by the UE and received by the core network and / or PSAP (e.g., a set of target identification information) may then be provided (together with one or more of the above instructions) to a sensing service (or another network function, e.g., a location retrieval function (LRF) or LMF responsible for initiating sensing of the target based on the provided information) and / or directly to a set of sensing transmitters and / or receivers.

[0245] Additionally or alternatively, the UE may provide the set of wireless sensing measurements or sensing results to the core network (e.g., E-CSCF) and / or PSAP, for example, by including this information in an emergency connection setup request (e.g., establishment of an emergency PDU session) or via an emergency connection (e.g., an emergency PDU session). The core network or PSAP receiving this can determine a set of target identification information based on the provided set of wireless sensing measurements or sensing results, which may then be provided (together with one or more of the above instructions) to a sensing service (or another network function, e.g., an LRF or LMF responsible for initiating sensing of targets based on the provided information) and / or directly to a set of sensing transmitters and / or receivers. Similarly, the UE may be a first device 10 having a sensor 12 and may be capable of sensing one or more objects within the FoV of the sensor 12. Such a UE may include measurement or segmented data from the sensor 12, result information regarding detected objects, or FoV information (and possibly other information related to the sensor 12, such as point of view, sensing orientation, and / or sensing capabilities) in the emergency connection setup request or via the emergency connection.

[0246] Additionally or alternatively, based on a UE ID received from the UE that placed the emergency call, a set of target identities may be obtained (e.g., by the E-CSCF) from a core network function or database (e.g., UDM / UDR) that maps UE IDs to a set of associated target identities. To this end, the UE may indicate (e.g., in a message field during emergency connection setup) whether the target is carrying or surrounding the UE that initiated the emergency call (e.g., because it is the victim). The obtained set of target identities may then be provided (together with one or more of the above instructions) to a sensing service (or another network function, such as an LRF or LMF, responsible for initiating sensing of the target based on the provided information) and / or directly to a set of sensing transmitters and / or receivers or other devices (e.g., the first device 10 or the second device 20) capable of performing sensing using one or more sensing modalities.

[0247] In the case of an emergency call, authorization (including user consent) to perform sensing of the target (e.g., initial scan or detailed / long-term sensing) and / or to share the target sensing results with the PSAP may be implicitly provided, for example, because the request to initiate sensing was made by a specific core network function (e.g., E-CSCF or LRF) or PSAP used in the case of an emergency call, or because the request to initiate sensing includes a flag indicating that it is for an emergency call. Similarly, one or more receivers (including when the receivers belong to the same device as the transmitter) or other devices capable of performing sensing using one or more sensing modalities (e.g., the first device 10 or the second device 20) are activated to participate in the sensing session, and this activation enables the transmitter and receiver or other devices capable of performing sensing using one or more sensing modalities (e.g., the first device 10 or the second device 20) to perform sensing in accordance with other embodiments herein. The sensing results (which may be filtered to include only results pertaining to specified targets, i.e., targets that match a set of target identification criteria provided, for example, by the E-CSCF or PSAP, to the sensing service, sensing transmitter, or sensing receiver) may be forwarded by the core network (e.g., E-CSCF) to the PSAP. For this purpose, the sensing results may be provided to the E-CSCF via the AMF if the UE includes them during PDU session establishment. Alternatively, the E-CSCF may obtain the sensing results via the LRF / GMLC, which are extended for this purpose as specified in 3GPP TS 23.167 / 23.273.This can be achieved, for example, by providing functionality similar to a sensing service, or by involving a sensing service as described in other embodiments of the present disclosure, or by collecting sensing results directly from a sensing receiver and / or a sensing transmitter or other device capable of performing sensing using one or more sensing modalities (e.g., the first device 10 or the second device 20).

[0248] FIG. 8 illustrates a schematic example of a target authentication procedure according to various embodiments of the present invention.

[0249] In one embodiment, which may be combined with other embodiments or implemented independently, the first device 10 or the second device 20 may be required to initiate an authentication procedure before starting or continuing a sensing session (e.g., before starting another scan or before initiating detailed sensing and / or distributed sensing). Such an authorization procedure may include verifying the authorization information and / or credentials of a network operator, application, user, device, or third party that issued a request to sense a target and / or provided information on how to identify the intended target, and / or a user subscribed to a sensing service, to assess whether the applicable entity is authorized to receive sensing measurements, sensing results, or other sensing data, and / or to initiate a sensing request for the target, and / or to provide configuration information for sensing the target (e.g., to provide a set of target identification information). Authentication may be provided by, stored in, and / or retrieved from a subscription database (e.g., UDM) of the requesting user, device, or intended target. Alternatively, authentication may be provided and / or obtained by an application server or core network function, or through a NEF, or by a lawful intercept service or PSAP.In the case of lawful intercept or emergency calls (i.e., calls with a PSAP), authentication (including user consent) to perform sensing of the target (e.g., initial scan or detailed / long-term sensing) and / or to share the target sensing results with the PSAP or lawful intercept may be implicitly provided, for example, because the request to initiate sensing is made by a specific core network function (e.g., E-CSCF or LRF) used for emergency calls or by the PSAP, or because the request to initiate sensing includes a flag indicating that it is for an emergency call. If authentication fails or cannot be verified, an event and / or error message may be generated and / or sensing of the target may be aborted. Because rules for sensing in public spaces and those for sensing in private spaces may differ, authentication and whether it is required to be performed may also vary depending on the location, area, or volume where the target is being sensed or where sensing is being performed. Such an authentication procedure or verification may also need to be performed when only one object (i.e., a matching target) that may match the target identity set is detected (partially if it matches a subset of the target identity set, or fully if it matches the entire target identity set). In one example, the set of target identities (and / or a set of sensing measurements and / or results within a certain threshold) may be linked to a mobile subscription identifier or user identifier (e.g., a subscription permanent identifier (SUPI)) or to an identifier that can be used to derive a mobile subscription identifier or user identifier (e.g., after authentication by an authentication server function (AUSF)) (e.g., an identifier of a wireless communication device carried or surrounding by the target, or carried or possessed by a person who has subscribed to the sensing service).The link between the set of target identities and the mobile subscription identifier or user identifier may be stored, for example, as part of a unified data repository (UDR) or UDM function in the core network, or may be stored in a separate database or AAA server from which a core network function (e.g., AUSF) can retrieve this linking information during or after authentication. Information for linking the set of target identities to the mobile subscription identifier or user identifier may be provided by or retrieved from the application in advance, during or after authentication. For this purpose, the application may communicate with a core network function such as the UDM / UDR or AUSF via the NEF. The information linking the set of target identities to the mobile subscription identifier or user identifier may further include an association with one or more identifiers (preferably temporary or intermediate identifiers that may change or be updated for privacy reasons (e.g., based on a set of rules or matching criteria)) and may also include information regarding whether the user or device is subscribed to the sensing service and / or whether the device associated with the mobile subscription can be authenticated and / or operate as a sensing transmitter, sensing receiver, first device 10, or second device 20. These (temporary or intermediate) identifiers may be provided to a target matching entity that can perform the target matching procedure (e.g., a sensing service, a sensing transmitter, a sensing receiver (possibly indirectly via a sensing service), a sensing application, or a core network function).If a potential target is discovered by the target matching entity (e.g., through an initial radar scan), the target matching entity (or the sensing transmitter, sensing receiver, first device 10, second device 20, or sensing service that has received the results of the target matching procedure (which may include a (temporary) target identifier)) may initiate the authentication and / or authorization procedure using such (temporary / intermediate) identifier as input to an authentication and / or authorization request, and / or may use the set of target identities (and / or the set of sensing measurements and / or results) as input to the authentication and / or authorization request, and / or may first obtain a mobile subscription identifier or user identifier based on the set of target identities and / or the set of sensing measurements and / or results, and then use the obtained identifier as input to the authentication and / or authorization request. The authentication and / or authorization request may be directed to a core network function, such as the AUSF and / or UDM, which may verify that the intended target is authorized for the sensing service and / or that user consent has been provided. For this purpose, the core network functions (e.g., AUSF and / or UDM) may use the (temporary / intermediate) identifier and / or target identification information (and / or the set of sensing measurements / results) to obtain a mobile subscription identifier or user identifier (e.g., from a UDR or a database).Because the mobile subscription identifier or user identifier may be associated with one or more UEs, the network may also send a notification to one or more UEs (e.g., to inform the user that a sensing service has been activated), request confirmation from the user of one or more UEs to initiate and / or allow sensing of the target, request the location of one or more UEs and use it to verify whether the one or more UEs are near the target (e.g., to further verify that the target is correct or to request that one or more UEs participate in sensing of the target), and / or perform primary authentication with one or more UEs.

[0250] 8 , the sensing service 30, the first device (e.g., sensing transmitter) 10, and / or the second device (e.g., sensing receiver) 20 perform an initial scan 801 (i.e., initial sensing operation 801) of a target location, area, or volume. As shown, the sensing service 30 (or the first device 10 or the second device 20) may transmit sensing measurements and / or results (e.g., a set of sensing information) from the initial scan 801 to the target matching entity 40 (i.e., may transmit the output of the initial sensing operation 801) via a message exchange 802. The target matching entity 40 then performs a target matching procedure 803 based on the sensing measurements and / or results from the initial scan 801 and the set of target identification information. If a target is found that partially or completely matches the set of target identities (i.e., a target is identified that matches (with a certain confidence or match rate) a subset of the set of target identities in the case of a partial match, or the entire set of target identities in the case of a complete match), as shown, the target matching entity 40 (or the sensing service 30, first device 10, or second device 20 that received the results of the target matching) may request authentication of the target by sending a (temporary) identifier associated with the target or set of target identities to an authentication and / or authorization entity 50 (e.g., an AUSF, a UDM, an AAA server, a sensing application, or a combination thereof) via message exchange 804. The authentication and / or authorization entity 50 may perform an authentication and / or authorization procedure 805.In this procedure, the authentication and / or authorization entity 50 may further authenticate the target (e.g., by deriving the identity of the device or user subscribed to the sensing service based on the provided (temporary) identifier and, e.g., by performing a primary authentication with the device) and / or verify whether the target is authorized to be a target of sensing (e.g., based on subscription information directly or indirectly linked to the provided (temporary) identifier associated with the target or a set of target identities) and / or verify whether the user or target subscribed to or for whom the sensing service is intended has provided user consent to be / be sensed. The authentication and / or authorization entity 50 may also (possibly in cooperation with other core network and RAN entities) send a notification to the device 60 linked to the same subscription indicating that the target has subscribed as a target of the sensing service in message exchange 806. In the same message exchange 806, the device 60 may send a message back to the authentication and / or authorization entity 50 to confirm that it is okay to perform sensing of the target. Upon completion of the authentication and / or authorization procedure 805 performed by the authentication and / or authorization entity 50, the entities involved in the sensing operation of the target (e.g., 10, 20, 30, and 40) are notified by the authentication and / or authorization entity 50 through an authorization information procedure 807 that the target is authorized to be a target for sensing, and that based on this, the entities may proceed with sensing the target.

[0251] In one embodiment, which may be combined with other embodiments or implemented independently, to further improve the privacy of sensing operations, a device carried or surrounded by a target, particularly a wireless communication device (e.g., a UE device), may be used to trigger or initiate a sensing operation of a target or target object carrying or surrounding the device. The wireless communication device may establish a connection to a network that may operate a sensing service, or to an application server or core network function that may communicate with the sensing service, or to a sensing transmitter or sensing receiver, and may trigger or initiate the sensing operation by sending, directly or indirectly, a signal (which may carry a message) indicating such a trigger to the sensing service, sensing transmitter, or sensing receiver. Such a message may include the device's potential sensing transmitter or sensing receiver capabilities, may include location, area, or volume information related to the device itself or the intended target, may include authorization information and / or credentials and / or user consent information, may include an identifier associated with the target's identity or a set of target identities, and / or may include a set of sensing measurements or results. Upon receiving such a trigger to initiate a sensing operation, the sensing service may obtain and / or verify authorization of the device (e.g., by obtaining the device's ID and verifying, based on information in a subscription database (e.g., UDM), that the device's user has subscribed to the sensing service and / or that the device is authorized to participate in the sensing operation and / or that the device's user has consented to being the target of the sensing operation).If the device user or device is indeed authorized, the sensing service and / or the first device 10 and / or the second device 20 may initiate and / or perform sensing operations as described in this disclosure, where initiating the sensing operations may include receiving and / or obtaining a set of target identities or identifiers associated with the set of target identities (e.g., if the set of target identities and their associated identifiers have already been provided previously or during pre-configuration of the associated first device 10 and / or second device 20 and / or sensing service 30).

[0252] Optionally, the sensing service may send a signal to a device carried by or surrounding the target indicating that a sensing operation is initiating or will begin. The device may display a notification to the user or request that the user provide confirmation that they agree to begin the sensing operation (or automatically provide confirmation based on the device's configuration). In response, a signal is sent back to the sensing service indicating whether confirmation was obtained, and then, if confirmation is obtained, the sensing operation is initiated or further performed. In one example, a wireless communication device (e.g., a mobile phone, IoT device, sensor device, wireless tag, or other UE) carried by or surrounding the target has a subscription to a network. A user of the wireless communication device may also subscribe to the sensing service. A core network function (e.g., a UDR, UDM, another database, or AAA server) may store an association between a mobile subscription identifier, a user identifier (e.g., the SUPI of a wireless communication device carried or surrounded by a target (object), or the SUPI of a wireless communication device carried or owned by a person subscribed to a sensing service), or a (temporary or intermediate) identifier that can be used to derive a mobile subscription identifier or user identifier, and a set of target identities (and / or a set of sensing measurements and / or results within a certain threshold). The information for linking these identifiers to a set of target identities may also include information about whether the device user or device subscribes to the sensing service and / or whether the device associated with the mobile subscription is authorized and / or can function as a sensing transmitter or sensing receiver and / or the first device 10 or the second device 20.When a target (object) or a wireless communication device carried by or surrounding a user subscribed to a sensing service registers with a network and / or sensing service, or when a target or a wireless communication device carried by or surrounding a user subscribed to a sensing service connects to a sensing transmitter, a sensing receiver, a first device 10, or a second device 20, an identifier of the wireless communication device may be provided by the wireless communication device to a core network function (e.g., an AUSF or UDM) responsible for device authentication and / or authorization. After authentication, or as part of the authentication procedure, the core network function responsible for device authentication and / or authorization may use information stored in the UDR, UDM, another database, or AAA server to verify whether the device user or device has subscribed to the sensing service, and / or whether user consent has been provided, and / or whether the device associated with the mobile subscription can be authorized and / or can operate as a sensing transmitter or sensing receiver for the first device 10 or the second device 20, based on the given identifier, and / or may obtain a mobile subscription identifier or a user identifier based on the given identifier. The identifier used by the wireless communication device carried by or surrounding the target may be the subscription concealed identifier (SUCI) or 5G globally unique temporary identifier (GUTI) that the wireless communication device sends to the core network as part of the connection setup and / or primary authentication procedures specified in TS33.501. However, it may be advantageous if the wireless communication device carried by or surrounding the target can use a different or additional identifier, preferably a temporary identifier, to indicate to the core network that the wireless communication device is the subject of wireless sensing or sensing by other sensing modalities.This identifier may be pre-configured in the device by the core network (e.g., by a Policy Control Function (PCF)), configured by the network (e.g., by an AMF) when the device connects to the network, or provided as part of a sensing request (e.g., a mobile-terminated or network-initiated sensing request by the RSMF). Core network functions responsible for device authentication and / or authorization (e.g., the AUSF or UDM) can use the identifier to verify whether the device is authorized to use sensing services, to obtain user consent information, or to obtain a set of target identities associated with the device.

[0253] Alternatively, or additionally, authorization may be provided or obtained from an application server or a core network function, or through the NEF, or from a lawful intercept service or a PSAP. A core network function responsible for device authentication and / or authorization may obtain a set of target identities associated with a given identifier, or a mobile subscription identifier or user identifier obtained based on the given identifier, or may obtain or create an (intermediate or temporary) identifier associated with the set of target identities. If the authentication and / or authorization is successful and / or if the set of target identities is successfully obtained, the set of target identities or the (intermediate or temporary) identifier associated therewith may be provided to the sensing service 30, the sensing transmitter, the sensing receiver, the first device 10, or the second device 20.

[0254] Additionally, the sensing service, or other devices, services, and / or applications involved in the sensing session and / or operation, may be provided with credentials that the participating devices (e.g., sensing transmitters and / or sensing receivers) must use to securely (e.g., integrity- and / or confidentiality-protected) transmit sensing measurements and / or results, or other sensing information regarding one or more targets / objects, and / or sensing configuration information.

[0255] Additionally or alternatively, it is verified whether the target identification information (and / or set of sensing measurements and / or results) provided during registration and / or connection setup matches the obtained set of target identification information.

[0256] Additionally or alternatively, a mobile subscription identifier or user identifier associated with one or more UEs may be used by the network to send notifications to one or more UEs (e.g., to inform the user that a sensing service has been activated), to request confirmation from the user of the UE to initiate and / or allow sensing of a target, and / or to request the location of the UE and use it to verify whether the UE is near a target (e.g., for additional verification that the target is correct or to trigger a sensing session and / or action).

[0257] Additionally or alternatively, wireless communication devices carried by or surrounding a potential target may be instructed, for example, while performing an initial radar scan or at a particular time, to indicate to the target or target object an instruction to perform a particular movement (e.g., a wave, a wiggle, or take a few steps in a particular direction) that can be detected by the sensing service. If movement can indeed be detected, it can be determined that the potential target is indeed the intended target or target object.

[0258] In one embodiment, which may be combined with other embodiments or implemented independently, a wireless communication device, such as first device 10 or second device 20 (possibly in cooperation with a set of sensors and / or a sensing transmitter and / or a sensing receiver connected to the wireless communication device) may determine a set of target identification information by performing wireless medium measurements or sensor readings or performing an initial radar scan of the target to identify a set of unique characteristics that make the target identifiable. For example, as described in other embodiments, signal / data processing / analysis, such as analyzing segmented or mesh data, may be used to detect specific patterns (e.g., unique movement patterns or biometric information) in the wireless medium measurements, sensor readings, or radar scan measurements / results. To this end, the wireless communication device, or a network function / server to which the wireless communication device is connected, may execute an AI model to learn how to identify a target (e.g., how to identify a specific pattern) by inputting the wireless medium measurements, sensor readings, and / or radar scan measurements / results into the AI ​​model. The model may also be input with information related to other objects that should not be identified as targets (e.g., wireless medium measurements, sensor readings, radar scan measurements / results). The AI ​​model may first be configured with a coarse classification of objects and people based on some high-level characteristics (e.g., male, medium height, heavy weight) and may use such a set of characteristics for a particular target object as input to determine a set of specific target objects with those characteristics that are present in a particular area and / or select a specific AI model trained for objects with those characteristics. A user / subscriber of the sensing service may be requested (e.g., based on a request / message received from the network) to bring their mobile phone very close to the target object once.This allows the AI ​​model to learn about a specific target object at this early stage, and then the AI ​​model can be used to sense the target without the phone even needing to be near the target.

[0259] Additionally or alternatively, a wireless communication device carried / surrounded by a potential target may be instructed, for example, while performing an initial radar scan or at a specific time, to indicate instructions to the target to perform a specific movement (e.g., waving, wiggling, taking a few steps in a specific direction) that can be detected by the AI ​​model. The resulting set of target identification information or the AI ​​model itself (or a portion thereof) may be transmitted to a core network function or application server and / or a sensing service, a sensing transmitter, a sensing receiver, the first device 10, or the second device 20. Once received, these entities can use the set of target identification information or the AI ​​model to identify the target using mechanisms described in other embodiments. The set of target identifiers may be stored along with the identifier of the wireless communication device or the identifier of the AI ​​model. The sensing service, the sensing transmitter, the sensing receiver, the first device 10, or the second device 20 can use such identifiers to identify the wireless communication device or AI model and send a request to verify whether the set of target identification information and / or the set of sensing measurements / results matches the target.

[0260] In embodiments that may be combined with other embodiments or implemented independently, the sensing service, sensing transmitter, sensing receiver, first device 10, or second device 20 may receive information about the target of sensing and / or other potential objects to be excluded from sensing or excluded from sensing measurements / results from a thermal / motion sensor, camera, or surveillance system (e.g., one that can generate heat maps or process video footage) or via an external application interface (e.g., network publishing functionality). The sensing service, sensing transmitter, sensing receiver, first device 10, or second device 20 may use this information to determine the target location, target area, or target direction on which to transmit sensing signals and / or on which the receiver will focus its antenna / receiving unit (e.g., by changing the FoV). The sensing service, sensing transmitter, sensing receiver, first device 10, or second device 20 may also use this information to correlate sensing measurements / results with this information to determine whether the sensing measurements / results correspond to the sensing target (e.g., by comparing the measured / calculated characteristics of the sensed object / target with the measured / calculated characteristics of the object / target based on this information). The sensing service, sensing transmitter, sensing receiver, first device 10, or second device 20 may also use this information to trigger the start of a (detailed / distributed) sensing session if (and only if) the presence of the intended target is detected within this information.

[0261] According to further embodiments, the sensing service, sensing transmitter, sensing receiver, first device 10, or second device 20, based on an initial (low-resolution) scan of the target or (e.g., if authorized / enabled / authorized) a detailed scan of the target, determines that the sensing signal does not or cannot properly identify the target object based on the set of target identification information, for reasons such as: resolution / accuracy is too low (e.g., due to a low frequency being used), the sensing signal is obstructed, the distance is too long, the target does not sufficiently reflect the sensing signal or absorbs the sensing signal too much, the target is moving, or the sensing transmitter or sensing receiver is moving. Based on this determination, the sensing service, the sensing transmitter, the sensing receiver, the first device 10, or the second device 20 may decide to change its position, delay transmitting the sensing signal (e.g., wait until the target, receiver, or transmitter moves to a new location), adapt the transmission characteristics / waveform of the sensing signal, send information / instructions, e.g., via the NEF, to the sensing transmitter, the sensing receiver, the first device 10, the second device 20, the sensing service, or the sensing application (e.g., a warning signal, requesting the receiver to move closer to or away from the target's location or change its angle relative to the target, reconfigure the antenna, adapt sensing signal parameters, or sensing measurements / results that the transmitter can use, e.g., to adapt the transmission of the sensing signal), select another receiver, the first device 10, or the second device 20 to sense the target, or send a signal to another transmitter, receiver, the first device 10, or the second device 20 to begin sensing the target. If the first device 10 or the second device 20 has a display (e.g., in the case of a mobile phone) or is connected to a display, the first device 10 or the second device 20 may display the notification.The notification may indicate a request and / or instruction to the user to move the first device 10, the second device 20, or the target to another position or to change its FoV or sensor orientation.

[0262] FIG. 9 illustrates generally one embodiment of a flow diagram for a sensing operation (eg, a radar-based sensing operation) including identification and approval of a sensing target.

[0263] In optional initial session request (RS-REQ) step S401, a receiver device (e.g., terminal device), a transmitter device (e.g., access device), the first device 10, the second device 20, a sensing service, a sensing application (e.g., via an NEF), a mobile device carried / surrounded by a target, or a mobile device subscribed to the sensing service may send a sensing session request (e.g., using an RRC or NAS message) or a request for a service that may involve sensing (e.g., a metaverse or augmented reality service), possibly accompanied by information about the location of the receiver device, transmitter device, first device 10, the second device 20, or the mobile device, to the wireless network operating or providing access to that service. The request and / or information may be sent as part of a PDU session request.

[0264] Optionally, information about the target location, area, volume, or FoV, and / or a set of target identities or an identifier associated with the set of target identities, and / or (part of) an AI model capable of identifying the target, and / or an identifier for authorizing a device to use the sensing service or to engage in a sensing operation, and / or the required scan time may also be provided by the receiver device, the transmitter device, the first device 10, the second device 20, or the mobile device.

[0265] Optionally, the receiver device, transmitter device, first device 10, second device 20, or mobile device is authenticated by the network and authorized to use the sensing service or to participate in sensing operations (e.g., by verifying that subscription information associated with the device's unique identifier includes information on whether the device has subscribed to the sensing service or is authorized by a user (e.g., a target of the sensing service) to participate in the target's sensing operations).

[0266] In an optional response request confirmation (REQ-CONF) / rejection (REQ-DEN) step S402, the sensing service, the transmitter device, the receiver device, the first device 10, and / or the second device 20 determine whether they can respond to the request (e.g., whether they have sufficient bandwidth available for the signal given current communication demands) and send an acknowledgement or rejection response to the requesting device or service. The response (e.g., using an RRC or NAS message) may include a set of target identities, or identifiers associated with the set of target identities, and / or other sensing configuration information. The response may also include credentials used to securely (e.g., integrity / confidentiality protected) transmit sensing measurements / results or other sensing information about one or more targets / objects and / or sensing configuration information to the sensing service or other devices, services, or applications involved in the sensing session / operation. The response may further include a message informing the user that the sensing service has been activated and / or a message requesting confirmation from the user of the UE to initiate / authorize sensing of the target. It may also include a request to request the location of the UE (if not provided in the request), for example to see if the UE is near the target.

[0267] Optionally, or instead, the sensing service, transmitter device, first device 10, or second device 20 may search for potential transmitter or receiver devices, or potential first device 10 or second device 20, located near the intended target (e.g., by requesting the last known location from a location database / service, or by requesting the potential device to send known location information, or by obtaining the location from the potential device through trilateration / triangulation / round-trip time calculation based on signals received from the potential device), and then proactively request (e.g., using an RRC message or an NAS message) the transmitter or receiver device, or first device 10 or second device 20, to use for sensing. Based on the set of target identification information, the sensing service, transmitter device, first device 10, or second device 20 may transmit sensing configuration information to the transmitter or receiver devices, or first device 10 or second device 20, involved in sensing the target. Similar to the acknowledgement or denial responses described above, the request may include a set of target identities or an identifier associated with the set of target identities and / or other sensing configuration information, and may also include credentials used to securely (e.g., integrity / confidentiality protected) transmit sensing measurements / results or other sensing information about one or more targets / objects and / or sensing configuration information to the sensing service or other devices, services, or applications involved in the sensing session / operation. The request may also include a message informing the user that the sensing service has been activated and / or a message requesting confirmation from the user of the UE to initiate / authorize sensing of the target. It may also include a request for the location of the UE (if not provided in the request), for example, to confirm whether the UE is near the target.

[0268] Next, an optional time synchronization (T-SYNC) and delay compensation (D-COMP) step S403 is initiated to synchronize the receiver clock to the transmitter clock by sending a timing signal to the receiver device. Similarly, the clocks of other devices involved in sensing, such as the first device 10 or the second device 20, may be synchronized.

[0269] In an optional subsequent target position acquisition (TP-ACQ) step S404, the transmitter device, receiver device, first device 10, second device 20, or sensing service determines the target's location / area / volume information, for example, by performing a rough radar object location scan in the direction or FoV indicated by the receiver device, first device 10, or second device 20 as the target initial position estimate.

[0270] Alternatively or additionally, information about the target location / area / volume / FoV may be provided as part of the sensing configuration or may be provided (indirectly) from the receiver device, the transmitter device, the first device 10, the second device 20 or the mobile device (as described in step RS-REQ), and the transmitter device may use that information as target location information.

[0271] Alternatively or additionally, the set of target identities, or an identifier associated with the set of target identities, may be provided to the receiver device, the transmitter device, the first device 10, the second device 20, or the sensing service.

[0272] Next, in a transmitter beamforming direction selection (BFD-SEL) step S405, the transmitter device selects an appropriate direction (for beamforming) as the transmitter target direction for performing radar-based sensing (distributed or non-distributed) based on the target location information / area / volume. Similarly, the first device 10 or the second device 20 can select an appropriate direction or FoV for performing target sensing based on the target location information / area / volume, or can select an appropriate viewpoint for calculating different viewpoints, sensor orientations, and / or FoV (e.g., radar cross section) data to be used for matching (e.g., corresponding to the viewpoints, sensor orientations, and / or FoVs of other devices involved in matching (e.g., the second device 20 in the case of the first device 10, or the first device 10 in the case of the second device 20), or corresponding to the positions, angles, and / or FoVs of data stored or combined for a (predefined) set of viewpoints, sensor orientations, and / or FoVs).

[0273] In a subsequent signal parameter generation, e.g., chirp parameter generation (CP-GEN) step S406, the transmitter device selects appropriate parameters for the signal and generates matching parameters for signal generation (e.g., by DFT-s-OFDM processing).

[0274] Then, in an optional subsequent (radar-based) sensing session parameters transmission (RSP-TX) step S407, the generation parameters and / or the selected start time and / or the location of the transmitter device, first device 10 or second device 20 and / or the target location information / area / volume / FoV are protected, e.g. encrypted using an encryption algorithm, transmitted to the receiver device, first device 10 or second device 20 and decrypted using a corresponding decryption algorithm.

[0275] In a subsequent optional receiver-transmitter relative position and delay estimation (RX-TX-P / D-EST) step S408, the receiver device can use its own known receiver position or round-trip delay measurements in addition to the transmitter position to calculate the relative offset (or equivalent optical transmission time) between the transmitter and receiver devices. Similarly, the first device 10 and the second device 20 can use each other's positions or round-trip delay measurements to calculate the relative offset (or equivalent optical transmission time) between the first device 10 and the second device 20.

[0276] At the signal (e.g., chirp) start time, the transmitter device can initiate a transmitter generation (TX-C-GEN) step S409 to generate and transmit a signal or signal sequence based on the generation parameters via an antenna beam formed in the transmitter target direction. Similarly, the first device 10 and the second device 20 can start sensing the target at the specified start time.

[0277] In an optional transmitter motion sequence transmission (TX-MOV-TX) step S410, the transmitter device, the first device 10, or the second device 20 communicates the motion and / or vibration detected during the sequence to a receiver device, another device (e.g., to the second device 20 in the case of the first device 10, or to the first device 10 in the case of the second device 20), or a sensing service as transmitter motion sequence data. This information may be used to compensate for motion during matching (e.g., between the viewpoint of the first device 10 and the viewpoint of the second device 20).

[0278] In a receiver reflected signal acquisition (RX-R-SIG-ACQ) step S411, the receiver acquires the received signal by collecting the reflected radio signal. The receiver may perform the collection using beamforming reception directed toward the target.

[0279] The above steps S401 to S411 can also be applied to a CSI-based distributed sensing system.

[0280] In an optional receiver IF signal generation (RX-IF-GEN) step S412, the receiver device uses the obtained sensing signal start time, transmitter-receiver delay, and sensing signal generation parameters to generate a synthetic internal analog sensing signal that matches the emitted sensing signal, and mixes this signal with the received signal to generate an IF signal.

[0281] Additionally or alternatively, the receiver may perform measurements of the received sensing signal (e.g., determining the time of arrival of the received sensing signal, determining the angle of arrival of the sensing signal, determining the amplitude or frequency of the signal), or digital signal processing (e.g., performing signal filtering such as bandpass filtering, or determining signal transformations).

[0282] In a subsequent optional receiver signal processing (RX-SIG-PROC) step S413, the resulting IF digital signal data, or the output obtained from measurements and / or digital signal processing performed on the received sensing signals, is processed to generate sensing information (e.g., sensing measurements / results, or application-specific data (e.g., position, movement, vibration, etc. of detected objects that may be potential / intended targets)).

[0283] Additionally or alternatively, the resulting IF digital signal data or output resulting from measurements and / or digital signal processing performed on the received sensing signals and / or generated sensing information is transmitted to a sensing service or transmitter device for further processing.

[0284] In an optional target identification (T-ID) step, the receiver device, transmitter device, or sensing service uses the IF digital signal data, or output obtained from measurements and / or digital signal processing performed on the received sensing signals, or generated sensing information from the previous step, to detect a set of objects and / or determine a set of sensing information for one or more detected objects and / or determine whether the set of sensing information for the one or more detected objects meets one or more configured criteria for identifying targets based on the set of target identification information. Similarly, the first device 10, the second device 20, or the sensing service can use measurement data or segmented data obtained from sensing targets (within the FoV of the device's sensor) for object matching and / or identification, as described in other embodiments. The set of target identifiers may, for example, be pre-configured, or may be sent as part of a request for a sensing operation / session (e.g., from a core network function or application (e.g., via the NEF) or from a device connected to the network), or may be sent to the receiver device, the transmitter device, the first device 10, the second device 20, or the sensing service upon authentication or authorization of the sensing service for a device connected to the network (which may be carried or surrounded by the sensing target) (e.g., from the AUSF or UDM), or may be obtained from a core network function or database based on an identifier received as part of the request for a sensing operation / session or as part of the authentication / authorization step, where the identifier is associated with the set of target identities. Based on the above determination, the receiver device, the transmitter device, the first device 10, the second device 20, and / or the sensing service may: Stop or continue the sensing operation, Initiating additional sensing operations, Generate an event or send a signal indicating whether a matching target was detected, or Initiate / continue target acceptance for sensing (e.g., check whether the detected object is an accepted target for the sensing service).

[0285] Further, in an optional Receiver Target Position Update Transmission (RX-TP-UD-TX) step S414, the receiver device may transmit updated and improved target position information to the transmitter device based on the results of step S413, allowing the transmitter device to continue accurate beamforming towards the target.

[0286] Additionally, an optional transmitter and receiver motion compensation (TX / RX-MOV-COMP) step S415 may be incorporated, in which measured motion and / or vibration of the transmitter and receiver devices is subtracted from the motion detected by the radar.

[0287] Finally, in User Interface, Data Storage and Display (UI / DS / DISP) step S416, the obtained data (e.g., sensing information about the target object or information about whether a matching target was detected) may be stored and / or transmitted (e.g., via the NEF) to a network service or application and / or displayed by a receiver device, transmitter device, first device 10, second device 20, or other device (e.g., a mobile device carried / surrounding the target) that may receive the obtained data from the core network service or application. User interface and input information is collected (if necessary) using the user interface.

[0288] Additional embodiments below provide details of the application-specific processing applied to the digitized IF signal (eg, step S413 of FIG. 4).

[0289] FIG. 10 illustrates generally one embodiment of a flow diagram for a position and motion detection process.

[0290] This embodiment may be relevant for use cases such as object counting, object motion detection and measurement, infrastructure monitoring, etc. In such cases, the receiver or transmitter device may require setting up a process such as periodic radar operation, e.g., repeating radar sensing every 15 minutes.

[0291] In the initial background clutter subtraction (BG-C-SUB) step S501, background subtraction of clutter (e.g., unwanted multipath signals) is performed from the digital IF signal (i.e., IF frequency data). Background subtraction can be achieved by distinguishing foreground information from background information based on variations in the data received at different times. This can be achieved by applying recursive moving averaging (RMA) or Gaussian mixture model (GMM) to learn the mean value of the path distribution.

[0292] Next, in a surface identification (SF-ID) step S502, individual surfaces are identified from the constant lines detected in the IF frequency data.

[0293] For objects with measurable velocities, the velocity of each isolated surface is identified in a surface velocity identification (SF-V-ID) step S503 by the average phase change of data extracted from the surface over multiple consecutive sensing signals after phase extraction and phase unwrapping (e.g., by applying a Doppler FFT). Sensing signals reflected from a moving surface experience a Doppler frequency shift proportional to the velocity of the surface. This frequency shift results in a phase shift in the detected sensing signals.

[0294] For objects that move slowly over a long period of time, their movement can be found in the slow motion detection (SL-MOV-DET) step S504 by periodically determining their position (distance, direction) and calculating the change in position over time.

[0295] FIG. 11 illustrates generally one embodiment of a flow diagram for a heart rate and respiration rate detection process.

[0296] To reiterate, in the initial background clutter subtraction (BG-C-SUB) step S601, background subtraction of clutter is performed from the digital IF signal (ie, IF frequency data).

[0297] Then, in a target surface selection (T-SF-SEL) step S602, the correct surface is selected (at the correct distance) for the target user from a constant line in the IF frequency data.

[0298] In a subsequent phase data separation (PD-ISO) step S603, the phase data from the selected surface is separated and phase unwrapped (eg, by applying a Doppler FFT).

[0299] Alternatively, the phase can be represented by the complex sine and cosine components of the signal (which eliminates the need for phase unwrapping).

[0300] Next, in a phase filtering (PS-FIL) step S604, the phase signal is bandpass filtered for the heart rate frequency range (e.g., 0.6-4 Hz) and / or respiration rate (e.g., 0.1-0.6 Hz) to derive heart rate data and / or respiration rate data.

[0301] Finally, in a vital signal extraction (VS-EXTR) step S605, the resulting data is processed to extract vital sign signals from the noise, using algorithms such as deep neural networks trained on datasets collected with "gold standards" such as electrocardiograms (ECGs) and / or stretchable respiration sensors to correct for noise and background motion and extract desired signals (heart rate, respiration rate), signal variability (e.g., heart rate variability), and confidence values ​​regarding the accuracy of the data values.

[0302] Another embodiment is shown in Figure 12. This figure schematically illustrates an example of a sensing system 700 in which a network (e.g., an RF sensing management function (RSMF) deployed by a wireless network as shown in Figure 7) configures or controls configuration parameters and / or sensing requirements, and / or collects or combines sensing results of a sensing transmitter (sTX), a sensing receiver (sRX), a first device 10, and / or a second device 20, e.g., to perform matching and / or identify target objects. Such an RSMF may be deployed as a separate function or service within the core network, as part of an existing function within the core network (e.g., as part of or an extension to the Location Management Function (LMF) as specified in 3GPP TS 23.273), as part of a wireless access device (e.g., a base station), or as part of, for example, an application function, edge application, or cloud server (which may indirectly provide configuration information or sensing requirements and / or receive sensing results via a Network Published Function (NEF)), and may generally be considered a sensing service and / or support the sensing functions described with respect to the sensing services of this disclosure.

[0303] The RSMF may include a (network) communication unit capable of exchanging messages with a sensing transmitter (sTX), a sensing receiver (sRX), the first device 10, the second device 20, and / or other core network functions and / or services (e.g., functions and / or services specified in 3GPP TS23.501, in particular the UDM, UDR, AUSF, and AMF functions and / or services as shown in FIG. 12), and may also include non-volatile storage for storing sensing functions received from the sensing transmitter (sTX) and / or the sensing receiver (sRX). The RSMF may also include a processing unit that executes sensing applications or operations, determines parameters to set in the sensing transmitter (sTX) and / or sensing receiver (sRX) (e.g., based on sensing capabilities received from the sensing receiver (sRX) and / or sensing transmitter (sTX), and / or based on sensing requirements (e.g., received / determined from / by an application or other service), and / or based on information about the target object (TO) (e.g., a set of target identification information), as described for other embodiments of the present disclosure), collects sensing results from the sensing transmitter (sTX) and / or sensing receiver (sRX), and / or performs matching or object identification (e.g., using algorithms such as a synthetic data algorithm (SDA) 30, a segmentation algorithm (SA) 40, a matching algorithm (MA) 50, etc.), and / or further processes the collected sensing results.

[0304] The RSMF may be deployed as part of a system 700 that includes a set of sensing transmitter devices (sTX) (e.g., base stations, access points, or UEs (e.g., mobile phones)) and a set of sensing receiver devices (sRX) (e.g., base stations, access points, or UEs (e.g., mobile phones)). The sensing transmitter devices and receiver devices (sTX, sRX) may be co-located and therefore part of the same device (and therefore may be controlled and operated as a single entity). The system 700 may also include a set of first devices 10 and a set of second devices 20, and the RSMF may be directly or indirectly (securely) connected to these sensing transmitter devices, receiver devices (sTX, sRX), first devices 10, and / or second devices 20 via a set of wireless and / or wired connections. The RSMF and the involved sensing transmitter device, receiver device (sTX, sRX), first device 10, and / or second device 20 may communicate with each other via a messaging protocol (e.g., a messaging protocol based on or extending the NAS protocol defined in 3GPP® TS 24.501, the RRC protocol defined in 3GPP® TS 38.331, the Long-Term Evolution (LTE) Positioning Protocol (LPP) defined in 3GPP® TS 37.355, or the New Radio (NR) Positioning Protocol (NRPP) defined in TS 38.455).

[0305] The RSMF, the sensing transmitter device (sTX), the sensing receiver device (sRX), the first device 10, and / or the second device 20 may support a method or service flow that includes the following steps, which may be performed in any order:

[0306] When a sensing receiver device (sRX) (e.g., a UE) or the first device 10 or the second device 20 registers with the network, the sensing receiver device (sRX) may provide its wireless sensing capabilities (e.g., device information (e.g., number of antennas or supported frequency ranges), wireless sensing signal processing capabilities, ability to operate as a sensing receiver, a sensing transmitter, or both, wireless sensing signal transmission capabilities (e.g., frequency, timing, phase, types of signals it can generate), etc.) and other sensing capabilities (e.g., video or other sensing modalities) to the RSMF directly using a signal or message transmitted from the sensing receiver device (sRX) to the RSMF via 709, or may provide them indirectly to the RSMF using a signal or message transmitted from the sensing receiver device (sRX) to the sensing transmitter (sTX) via 712 and then transmitted from the sensing transmitter (sTX) to the RSMF via 710. This may also include capability information such as supported sensing data types, algorithms (e.g., for data segmentation), etc. The sensing receiver device (sRX) may also include its own location information if known.

[0307] Alternatively or additionally, the location of the sensing receiver device (sRX) may be obtained from an LMF or a location server, or from a wireless access device (e.g., a base station) to which the sensing receiver device (sRX) is connected or co-located.

[0308] Similarly, when a sensing transmitter device (sTX) (e.g., a wireless access device such as a base station (e.g., a mobile base station relay device)), a first device 10, or a second device 20 is added to the network, the device can provide its wireless sensing capabilities to the RSMF using a signal or message via 710.

[0309] In another embodiment, the (wireless) access device may be under OAM (Operation, Administration and Maintenance) management, in which case the RSMF may be deployed as part of the OAM or may be connected to the RSMF for the exchange of signals or messages (e.g., sensing capabilities of the wireless access device, configuration messages for sensing, sensing measurements or sensing results).

[0310] The radio access device or core network function (e.g., AMF) to which the sensing receiver device (sRX), the sensing transmitter device (sTX), the first device 10, or the second device 20 is registered may forward or redirect signals, messages, or capability information received from the sensing receiver device (sRX), the sensing transmitter device (sTX), the first device 10, or the second device 20 to the RSMF (e.g., based on a device ID, session ID, or RSMF ID that may be provided in the registration message). The sensing receiver device (sRX), the sensing transmitter device (sTX), the first device 10, or the second device 20 can also transmit its capabilities after initial registration, for example, using the RRC UECapabilityInformation message specified in 3GPP® TS38.331, or through the LPP PowerCapabilities message specified in 3GPP® TS37.355, or as part of a sensing session setup request message (e.g., a separate / new NAS message by extending a message specified in 3GPP® TS24.501, a separate or new RRC message by extending a message specified in 3GPP® TS38.331, or a separate or new LPP or NRPP message by extending a message specified in 3GPP® TS37.355 and TS38.455). Note that the capabilities of each device involved in sensing may be different. For example, the RF signal processing capabilities of the UE may differ from those of the base station; for example, the UE may be able to determine the position or motion of a target object (TO) but not the shape of the target object (TO); for example, the UE may be able to receive sensing signals and perform measurements but not be able to generate and transmit wireless sensing signals; and the supported sensing modalities or data types may differ.Thus, the configuration of each of these sensing transmitter and sensing receiver devices (sTX, sRX), the first device 10, and the second device 20 may differ or change depending on the function or role they play (e.g., functioning as a sensing transmitter or a sensing receiver). When a device can operate as both a sensing transmitter (sTX) and a sensing receiver (sRX), the roles of the sensing transmitter and receiver may be independently configured and / or activated, and may also be dynamically changed (e.g., may operate intermittently as a sensing transmitter and a sensing receiver, or may operate simultaneously as both a sensing transmitter and a sensing receiver according to a given schedule, or based on received messages (e.g., messages received by the RSMF or another network function, or a local application).

[0311] Based on the sensing needs and / or received capabilities of a (5G) core network service (e.g., a service provided by or via GMLC, LMF, or AMF), an external application (e.g., one provided via the NEF), or a UE (e.g., one provided upon registration with the core network), the RSMF determines a set of radio access devices (e.g., base stations), UEs, and / or other devices to be used for sensing, and can configure one or more of these devices as transmitters of wireless sensing signals or to participate in sensing of targets (e.g., sensing using sensor 12 or sensor 22) using signals or messages sent directly from the RSMF to the sensing transmitter device (sTX) via 706. For this purpose, the core network service can provide relevant information for sensing (e.g., sensing needs / requirements) by issuing a sensing request to the RSMF using a network-initiated location request (NI-LR) (e.g., as defined in TS 23.273). The NL-LR may be extended to include information about the target to be sensed, and / or information about the sensing requirements (e.g., sensing results to be calculated (e.g., speed) and / or accuracy requirements), and / or information about sensing configuration information (e.g., target location / area / volume information based on the location of the UE that initiated the request, or an identifier of the UE that initiated the request if the UE's location is unknown and needs to be determined), and / or information about capability information of one or more sensing receivers, sensing transmitters, first device 10, or second device 20.This may also include information about a model of the object (e.g., a semantic model), a dataset for multiple different viewpoints, sensor orientations, and / or FoVs (e.g., radar cross section) of the object, and / or a set of mesh data for the object stored in the object database; information about the FoV of the devices involved in sensing the target (and possibly other information related to the sensors used, such as viewpoints, sensing orientations, and / or sensing capabilities); or information about the viewpoints / angles of the stored data for multiple different viewpoints, sensor orientations, and / or FoVs (e.g., radar cross section) of the target; information about sensing modalities or data types supported by the devices involved in sensing the target; and information about supported or required segmentation algorithms. The RSMF can receive and interpret such information and, based thereon, initiate the selection and configuration of a sensing transmitter device. Similarly, the UE may issue a Mobile Originated Location Request (MO-LR) containing the above information to the RMSF, or another client (e.g., an application function) may issue a Mobile Terminated Location Request (MT-LR) containing the above information to the RMSF. The location of the UE that initiated the request (or the ID of the UE included in a request to the RSMF, e.g. triggered by a core network function to start sensing via the RSMF) is used as the target location, but if the location of that UE is not yet known, the RSMF may first request the LMF to determine the location of the UE and then use the resulting location as the target location. In some cases, other information may also be used, such as the (pre-configured or estimated) distance between the UE and the target, or information about the (pre-configured or estimated) emergency / disaster area.Furthermore, one or more of the set of wireless access devices (e.g., base stations), UEs, and / or other devices used for sensing may be configured as a second device 20 for matching or as a receiver of wireless sensing signals, using signals or messages transmitted directly from the RSMF to the sensing receiver device (sRX) via 707, or signals or messages transmitted indirectly from the RSMF, i.e., from the RSMF to the sensing transmitter device (sTX) via 706 and then from the sensing transmitter device (sTX) to the sensing receiver device (sRX) via 711. The sensing transmitter and receiver devices (sTX, sRX) may be co-located.

[0312] The device configuration information may include information about the wireless sensing signal used (e.g., timing, frequency, phase offset, identity of the wireless sensing signal), identity of the algorithm or filter used for processing, a wireless sensing application or session identifier, destination of the signal processing results, information about the FoV of the device involved in sensing the target or multiple different viewpoints of the target, sensor orientation, and / or viewpoint / angle of stored data about the FoV (e.g., radar cross section), information about sensing modalities or data types supported by the device involved in sensing the target, information about segmentation algorithms supported or to be used, etc. (e.g., those described in this disclosure). Some of these parameters may be determined by the device itself; for example, the sensing transmitter device (sTX) may determine the timing of the sensing signal (i.e., the resources used for the sensing signal). Such parameters may be exchanged directly with the sensing receiver device (e.g., via DCI, Sidelink Control Information (SCI) signals or messages specified in 3GPP TS38.212 (e.g., having a specific (new) format identifiable to indicate reception or transmission of sensing signals and / or sensing signal parameters such as frequency), or via a semi-persistent schedule (SPS) indicating a recurring set of resources used for sensing signals), or indirectly via the core network.

[0313] Based on the sensing needs of (5G) core network services or external applications (e.g., as part of a target authentication and / or authorization procedure for the sensing service), the RSMF may obtain information about a set of target objects. This information may include (rough) location information (or, e.g., last known location) or area information (e.g., factory, hospital, or home address, or a designated (geographical) area or volume) where the target objects are expected to be or are frequently present, information about how to identify a particular target object (e.g., physical characteristics, material, shape, etc.) (i.e., a set of target identification information as described in other embodiments of this disclosure), or IDs of devices that a person may own or carry (e.g., as described in other embodiments of this disclosure). The RSMF may use this information about the set of target objects to select and configure a set of sensing transmitters and / or receiver devices (sTX, sRX) and / or the first device 10 and / or the second device 20 to participate in sensing of the indicated target or target area / volume, or sensing of a particular FoV (which may include information about the wireless sensing signal used, as described in the previous bullet point), and / or forward and / or configure some of this information to the set of sensing transmitters and / or sensing receiver devices (sTX, sRX) and / or the first device 10 and / or the second device 20. For example, the RSMF may provide a set of target identities, or an identifier associated with a set of target identities, to the set of sensing transmitters and / or receiver devices (sTX, sRX) and / or the first device 10 and / or the second device 20.

[0314] Additionally or alternatively, a UE device carried or surrounded by a target object (TO) may trigger or initiate a sensing operation by establishing a connection to the network (e.g., to the AMF) via 701 and sending a signal (which may include a message) indicating the trigger or initiation to the RSMF directly (e.g., through a tunnel connection via 701 and 715) or indirectly (e.g., via the AMF via 715 or via the AUSF via 705 (where the UE may use a different set of messages towards the AMF or AUSF than those used between the AMF or AUSF and the RSMF)). Upon receiving such a trigger or initiation to initiate sensing, the AMF, AUSF, or RSMF may initiate or request authorization of the sensing service for the UE. For example, based on an ID provided by the UE to the AMF via 701 and then sent from the AMF to the AUSF via a message via 702, or based on an ID provided by the UE to the AMF via 701 and then sent from the AMF to the RSMF via a message via 715, and then sent from the RSMF to the AUSF via a message via 714, the AUSF may obtain a SUPI for the UE, and based on the obtained SUPI, the AUSF may check or confirm information in a subscription database (e.g., a UDM) by sending a message to the UDM via 703 to determine whether the user of the UE device has subscribed to the sensing service and / or whether the UE device is authorized to participate in sensing operations and / or whether the user of the UE device has consented to being a target of sensing operations. If the user of the UE device is indeed authorized, the UDM, the AUSF, or the AMF may notify the RSMF of this via a message sent via 704, 705, or 715, respectively. This message may include a set of target identities or an identifier associated with the set of target identities (e.g., an identifier provided by a UDM / UDR that may store this information as part of the sensing service subscription information).Thereafter, the RSMF, the sensing transmitter device (sTX), the sensing receiver device (sRX), the first device 10, and / or the second device 20 may initiate or perform sensing as described in this disclosure. Note that initiating the sensing operation may include receiving or obtaining a set of target identities or identifiers associated with the set of target identities (e.g., if the set of target identities and their associated identifiers have already been provided previously or during pre-configuration of the associated sensing transmitter device and sensing receiver device (sTX, sRX)).

[0315] Optionally, the RSMF may send a signal or message (e.g., through the tunnel connection via 715 and 701) to a UE device carried or surrounded by the target object (TO) indicating that a sensing operation is initiating or about to begin. The UE device may display a notification to the user or request that the user provide confirmation that they agree to begin the sensing operation (or automatically provide confirmation based on the UE device configuration). In response, a signal may be sent back to the sensing service (e.g., through the tunnel connection via 715 and 701) indicating whether confirmation has been obtained. If confirmation has been obtained, the sensing operation is initiated or further performed. The AUSF may optionally provide credentials to the RSMF via 705 and / or the RSMF may determine a set of credentials and provide them to the AUSF via 714. After successful authentication and / or authorization, the AUSF or RSMF may provide the credentials to the involved devices (e.g., the sensing transmitter device (sTX), the sensing receiver device (sRX), the first device 10, the second device 20), which may use the credentials to securely (e.g., with integrity and / or confidentiality protected) transmit sensing measurements and / or results (i.e., a set of sensing information), or other sensing information about one or more target objects, and / or sensing configuration information, to the RSMF or other devices, services, or applications involved in the sensing session or sensing operation.

[0316] A UE device carried by or surrounding a target object (TO) may, when or after being registered with the network, provide information about its location and / or a set of target identities or an identifier associated with the set of target identities, and / or other sensing capabilities / configuration information to the RSMF (e.g., via the AMF or LMF to which the RSMF may be connected to obtain the location of the UE). The UE device may also be requested by the RSMF or LMF to provide its location or to participate in a location procedure to enable the RSMF to obtain the location of the UE device, and / or to provide a set of target identities or an identifier associated with the set of target identities, and / or other sensing capabilities / configuration information.

[0317] The location information may be used as an (initial) target location and / or may be used to configure the sensing receiver, sensing transmitter, first device 10, and / or second device 20 for sensing of the TO. That is, the RSMF may use the location information of the UE device, together with location information of a set of sensing receiver devices, sensing transmitter devices, first device 10, and / or second device 20 that the RSMF may have stored or obtained, to select and configure a set of sensing transmitters, sensing receiver devices (sTX, sRX), first device 10, and / or second device 20 in the vicinity of the UE device (and thus in the vicinity of the TO) so that these devices (sTX, sRX) can participate in the sensing operation of the TO. Similarly, the RSMF may use the set of target identities, or identifiers and / or other sensing capability / configuration information associated with the set of target identities, to select and configure a set of sensing transmitters, sensing receiver devices (sTX, sRX), first devices 10, and / or second devices 20 that are capable of or best suited to participate in sensing of the TO based on the target identities (e.g., by determining the required / desired accuracy, the RSMF may select sensing transmitters, sensing receiver devices (sTX, sRX), first devices 10, and / or second devices 20 that are likely to be able to achieve the required / desired accuracy (e.g., to support sensing using high frequency bands)).

[0318] It should be noted that the RSMF may provide location information of the UE device, and / or location information of the sensing transmitter device (sTX), the sensing receiver device (sRX), the first device 10, and / or the second device 20 to one or more of the UE device, and / or the (selected) sensing transmitter device (sTX), the (selected) sensing receiver device (sRX), the first device 10, and / or the second device 20. Furthermore, the sensing transmitter device (sTX), the sensing receiver device (sRX), the first device 10, or the second device 20 may each provide its own location information, or location information of other sensing transmitter devices (sTX), other sensing receiver devices (sRX), other first devices 10, or second devices 20, to (further) other sensing transmitter devices, sensing receiver devices (sTX, sRX), the first device 10, and the second device 20. This information may be used during processing of sensing measurements and / or sensing results to determine the location of a target object (TO) (as described in other embodiments of this disclosure).

[0319] Alternatively, if target location, area, or volume information, or FoV information is not available, the RSMF may trigger a (broadcast) search function that requests the originating transmitter device (sTX), the first device 10, or the second device 20 to sense the environment and identify rough location information of a set of target objects (TO).

[0320] Alternatively, or in addition, one or more of the devices involved in sensing (e.g., a base station that includes both sensing transmitter and receiver functionality) may determine the approximate position or location of the target objects (TO / TOs) (e.g., based on non-distributed radar-based sensing) and provide this information regarding the approximate position or location of the target objects (TO / TOs) to the RSMF.

[0321] Alternatively, or in addition, the rough location (or last known location) of the target object (TO), or more generally the target location, area, or volume, or FoV, may be provided by an external application (e.g., via the NEF), or may be obtained, for example, from the LMF as specified in 3GPP® TS 23.273, or from a Network Data Analysis Function (NWDAF) as specified in 3GPP® TS 23.288, for example, based on the identity of devices or UE devices that are expected or known to be attached to or carried by the target object (TO). The RSMF, sensing transmitter device (sTX), sensing receiver device (sRX), first device 10, or second device 20 may provide the associated sensing receiver device (sRX / sRXs), sensing transmitter device (sTX / sTXs), first device 10, and second device 20 with coarse position or location information of the target object (TO / TOs), or more generally, the target position, area, volume, or FoV.

[0322] Alternatively or additionally, the sensing function or service (RSMF) may obtain information (e.g., IDs and estimated locations) about devices in the vicinity of a target object (TO) (e.g., a set of nearby base stations or nearby UEs that can participate in (distributed) sensing of the target and that can be (or have been) authorized by the network, the device user, and / or the owner of the target person or target object). The authorization information (which may also include user consent information) may be stored as part of the user's subscription and / or as part of the sensing service (e.g., in the UDM function of the core network) and / or as part of the RSMF and / or received from a service, application function, or external application (e.g., via the NEF). The RSMF may use information about the sensing transmitters, receiver devices (sTX, sRX), first device 10, and / or second device 20 in the vicinity of the target object (TO) in selecting and configuring the sensing transmitters, sensing receiver devices (sTX, sRX), first device 10, and / or second device 20 to use. Devices involved in sensing may be invited and / or configured to participate in the sensing session by sending messages that may include a session identifier, a sensing signal identifier, and / or a set of target identities or an identifier associated with a set of target identities.

[0323] Alternatively, or in addition, an initial radar scan or sensing operation performed by one sensing device, which may support both transmitter and receiver roles or support sensor 12, such as first device 10, or sensor 22, such as second device 20, may indicate that the accuracy obtained for the sensing measurements may be insufficient to meet a desired accuracy (the desired accuracy being indicated, received, or configured in the RSMF by, for example, an external application). The involved sensing devices may determine this themselves and report it to the RSMF, or the RSMF may determine this based on sensing results it may receive from the respective sensing devices.

[0324] Alternatively, or in addition, the RSMF may determine, based on the capabilities of the sensing transmitter device, sensing receiver device (sTX, sRX), first device 10, and / or second device 20, and / or based on the bands or spectrum available within a particular area, and / or using previous measurements (e.g., obtained by or from network analysis functions such as NWDAF), that the accuracy provided by the involved sensing transmitter device, sensing receiver device (sTX, sRX), first device 10, and / or second device 20, and / or the accuracy obtained within a particular sensing area, may not meet the requirements of the application. The RSMF may use this information (possibly together with received capability and location information of sensing transmitter devices, receiver devices (sTX, sRX), first device 10, and / or second device 20 in the area) as a trigger to select other or additional sensing transmitter devices, sensing receiver devices, first device 10, and / or second device 20 in the vicinity of the target object (TO) and / or may improve sensing measurements and sensing accuracy (e.g., by changing to a higher frequency and / or wider bandwidth, by increasing the number of signals and / or the number of signal measurements, or by selecting a different algorithm).

[0325] The RSMF may activate sensing by activating one or more of the selected sensing transmitter device (sTX), sensing receiver device (sRX), first device 10, and / or second device 20 to initiate a sensing session, e.g., directly via 706 and / or 707, respectively, or indirectly via the sensing transmitter device (sTX), first via 706 and then via 711. The activation of the sensing transmitter device (sTX), sensing receiver device (sRX), first device 10, and / or second device 20 may be automatically triggered by receiving the sensing configuration (e.g., given a start time or a set of time intervals at which sensing signals are transmitted), or may be triggered via a separate message (e.g., an additional LPP message including a sensing session identifier) ​​or another signal (e.g., detection of an identifiable sensing signal that matches one or more given signal characteristics or signal identifiers provided during the configuration).

[0326] Based on the received configuration information and / or the rough location of the target object (TO), more generally the target location, area, volume, or FoV, one or more of the sensing transmitter device (sTX), the first device 10, and / or the second device 20 transmits a wireless sensing signal 708 in the direction of the target location, area, volume, or FoV, or starts sensing in that direction using the sensor 12 or the sensor 22. The time at which such wireless sensing signal 708 is transmitted is based on the time at which sensing starts using the sensor 12 or the sensor 22. This time is, for example, configured timing information (e.g., a sensing start time, a set of time intervals for sensing, or a (preconfigured) set of time or frequency resources), which is, for example, shared with the sensing receiver device (sRX), the other sensing transmitter device (sTX), the first device 10, and / or the second device 20. In the case of wireless sensing (e.g., radar-based sensing or distributed sensing), the sensing receiver device (sRX / sRXs) receives the reflected wireless sensing signal 708R and can recognize and process the received reflected wireless sensing signal 708R based on the provided wireless sensing configuration information (e.g., as described in this disclosure). In one example, based on the received reflected wireless sensing signal 708R, the timing information at which the signal was transmitted (e.g., as configured or as part of the timestamp information in the signal), its own known position, and the position of the sensing transmitter device (sTX) (e.g., a base station), the position or location of a (potential) target object (TO) may be estimated using, for example, triangulation. When sensing using the sensor 10 of the first device 10 or the sensor 12 of the second device 12, the first device 10 or the second device 20 transmits its measurements or segmented data to the RSMF or another device (e.g., the second device 20 in the case of the first device 10).The first device 10, the second device 20, or the RSMF may calculate mesh data and / or match the data to the viewpoint, sensor orientation, and / or FoV of the first device 10 or the second device 20, or against data stored for a different (predefined) viewpoint, sensor orientation, and / or FoV (e.g., radar cross section), or against data that matches a synthetic model of the target.

[0327] Additionally or alternatively, each sensing receiver device, sensing transmitter device (sRX, sTX), first device 10, or second device 20 transmits its wireless sensing signal measurements and / or processing results to a configured destination (e.g., RSMF). The destination can collect the results and perform further processing on these results. This may include matching or object identification (e.g., using algorithms such as a synthetic data algorithm (SDA) 30, a segmentation algorithm (SA) 40, or a matching algorithm (MA) 50) as described in other embodiments of the present disclosure. The RSMF may use all received / collected measurements and / or (partial) sensing results to determine a set of sensing results. This may be based on sensing requirements received in a location information / sensing request (e.g., received from an application). The sensing results (e.g., target location) may be provided to the entity (e.g., GMLC / AMF / NEF / UE) that issued or forwarded the "enhanced" location information request to the RSMF. Alternatively, or in addition, the sensing results may be stored in a shared storage, such as a unified data repository (UDR), from which other entities may retrieve the sensing results based on an identifier provided to each entity by the RSMF.

[0328] Part of this further processing in the sensing receiver device (sRX), the sensing transmitter device (sTX), the first device 10, or the second device 20, or in the configured destination (e.g., RSMF), may include detecting a set of objects and / or identifying a set of sensing information (i.e., measurements and / or results) of one or more detected objects using an initial scan or sensing operation of a target area or volume performed by the sensing service (i.e., RSMF) and / or the sensing transmitter device (sTX) and / or the sensing receiver device (sRX), for example, as described in other embodiments, and / or determining whether the set of sensing information (i.e., measurements and / or results obtained as output of the sensing operation) of the one or more detected objects meets one or more configured criteria (e.g., thresholds) for identifying a target based on a set of target identification information. Based on the latter determination, the sensing receiver device (sRX), the sensing transmitter device (sTX), the first device 10, the second device 20, and / or the configured destination (e.g., RSMF) may perform the following: Stop or continue the sensing operation. This may be done, for example, by sending a signal or message directly to the sensing transmitter device (sTX), the first device 10, or the second device 20 via 706, and / or directly to the sensing receiver device (sRX), the first device 10, or the second device 20 via 707, or indirectly to the sensing receiver device (sRX), the first device 10, or the second device 20 (e.g., via the sensing transmitter device (sTX) via 706 and then from the sensing transmitter device (sTX) to the sensing receiver device (sRX) via 711). Initiating additional sensing operations. This may be done, for example, by sending a signal or message directly to the sensing transmitter device (sTX), the first device 10, or the second device 20 via 706 and / or directly to the sensing receiver device (sRX), the first device 10, or the second device 20 via 707, or indirectly to the sensing receiver device (sRX), the first device 10, or the second device 20 (e.g., via the sensing transmitter device (sTX) via 706 and then from the sensing transmitter device (sTX) to the sensing receiver device (sRX) via 711). Generate an event or send a signal indicating whether a matching target has been detected (a matching target is a partial match, where the matching target matches a subset of the target identification set (with a particular confidence or match rate) or an exact match, where the matching target matches the entire target identification set). This may be done, for example, by sending a signal or message directly to the RSMF via the sensing transmitter device (sTX), the first device 10, or the second device 20 via 710 and / or via the sensing receiver device (sRX), the first device 10, or the second device 20 via 709. Alternatively, the signal or message may be sent indirectly from the sensing receiver device (sRX), the first device 10, or the second device 20 to the RSMF (e.g., from the sensing receiver device (sRX) to the sensing transmitter device (sTX) via 712, and then from the sensing transmitter device (sTX) to the RSMF via 710). Alternatively, it may be performed by sending a signal or message from the RSMF to the UDM / UDR via 713, or to the AUSF via 714 (e.g., as part of an authentication and / or authorization procedure), or to an application function or AAA server (e.g., via the NEF, not shown), or to a non-volatile storage unit. Initiate or continue authorization of a target or target object (TO) for sensing (e.g., verify whether a detected object is an authorized target for sensing services). This may be performed, for example, by sending a signal or message from the RSMF to the UDM / UDR via 713, or to the AUSF via 714, or to an application function or AAA server (e.g., via the NEF, not shown).

[0329] Thus, the RSMF obtains wireless sensing capabilities of the sensing receiver device (sRX), the sensing transmitter device (sTX), the first device 10, and / or the second device 20, configures one or more of the sensing transmitter device (sTX), the sensing receiver device (sRX), the first device 10, and / or the second device 20 to participate in wireless sensing of the target object (TO), and transmits the wireless sensing results to the wireless sensing-enabled receiver device (sRX), the wireless sensing-enabled transmitter device (sTX), the first device 10, and / or the second device 20. The wireless sensing device may configure the wireless sensing enabled transmitter device (sTX), the wireless sensing enabled receiver device (sRX), the first device 10, and / or the second device 20 with information regarding when and how to transmit, receive, and / or process the wireless sensing signals, and collect wireless sensing measurements and / or results (i.e., sets of sensing information) from the sensing transmitter device (sTX), the sensing receiver device (sRX), the first device 10, and / or the second device 20 for further processing.

[0330] Object Identification (for metaverse or mixed reality services) A metaverse or MR service may be a service operated by a third party that has access to certain network functions of a wireless network through a service application programming interface (API) (e.g., similar to a network publishing strategy). A metaverse or MR service provider may be a third party that operates the metaverse or MR service, and a user may be an individual who actively or passively participates in the metaverse or MR service. A user ID may be an identifier specific to a particular user. A user ID may be a unique identifier of the user's UE, an ID provided by the metaverse or MR service provider, a local ID assigned by a (serving) base station (e.g., gNB) or a local network, etc. A user ID may be fixed or temporary. A watching user may be a user who is currently watching another user. Sensors in a user's UE are also considered to be monitoring other users. A watched user is a user who is watched by a watching user, whose ID is initially unknown to the watching user and / or his / her UE. A watched user location should be understood as the location of the watched user. If the viewed user has a UE, this may be functionally identical to the location of the UE provided, for example, by the LMF 320, or GNSS location data provided by the UE to the wireless network. If the viewed user does not have a UE, this may be determined, for example, from radar data collected by sensor 22 or other sensors on a device (shown as second device 20 in FIG. 3 or FIG. 4) carried by the viewing user.

[0331] In a metaverse or mixed reality (MR) scenario (as described earlier), multiple users may use user devices (e.g., UEs) to interact with both real objects and virtual assets. The virtual assets are rendered by the UEs. Such virtual assets may be associated with a particular user and may be, for example, virtual costumes or virtual pets. When one user comes into view of another user, the virtual assets associated with the viewed user must be correctly rendered by the viewing user's UE.

[0332] This requires the viewing user's UE to identify the viewed user, i.e., to retrieve the correct virtual assets from the metaverse provider and render them by the UE. However, the viewing user's UE may initially have no information about (or access to) the viewed user other than what may be available from local sensors (e.g., video pose data of the viewed user acquired by a camera in the viewing user's UE). The situation can become even more complicated if the viewing user and viewed user participate in different metaverses (and thus the viewing user's metaverse service may also initially have no information about the viewed user), or if the viewed user does not have their UE with them when being viewed (and therefore cannot provide identifying information).

[0333] To solve this problem, the wireless network to which the UE is connected may utilize its sensing capabilities (e.g., a gNB in ​​sensing mode collecting radar data of the user's attitude) to provide a "seen user identification service." Here, data about the seen user from the UE sensors (e.g., cameras) is used to generate synthetic data, which may then be matched with data collected about the same seen user by wireless network sensors according to the above embodiments. If a match is confirmed, the wireless network provides the seen user's identifier (e.g., a unique user ID) to the seeing user's UE. This identifier may then be used to request the digital asset for rendering from a metaverse service provider. Alternatively, the wireless network may directly request the digital asset, for example, from an ID service or subscription service operated by the network itself and / or a user / object database, or from a third-party application function (e.g., via a network publishing function (NEF)), and provide the digital asset to the seeing user's UE. Similarly, other objects (not just users) may be detected. This alternative approach protects user privacy because it does not require the disclosure of unique IDs. As an example, the actual digital assets provided may depend on the privacy policy linked to the matching service and the permissions of the viewing user's UE.

[0334] This final step requires that the wireless network has prior access to a list of identifiers (eg, unique user IDs) associated with the data collected by the wireless network sensors.

[0335] If the object being viewed (e.g., a user) has a UE, this association may be achieved by providing a unique ID to the UE, or by locating the UE (e.g., by the LMF320 of the 5GS), or by locating the object being viewed via a wireless network sensor, or by assigning the UE's unique ID, a related ID (e.g., an ID derived from the UE's unique ID), or a location- or time-based pseudonym to the object co-located with the UE. Further data collected by the wireless network sensor may then be associated with that ID.

[0336] If the viewed object does not have a UE, the network may assign a temporary ID to all users sensed by the wireless network sensors. If necessary, additional steps may be taken to identify these objects. For example, if the viewed object is known to some viewing users but not to others, a viewing user who knows the viewed object may be able to provide an identifier for the viewed object, which may replace the temporary ID and be sent to the other viewing users. Alternatively, if authentication capabilities are available (e.g., biometric data is obtained using network sensors), the network may be able to directly authenticate the viewed object and assign a unique ID to the viewed object.

[0337] 3, the wireless system (5GS) may consist of a core network (5GC) 300, an access network (5G-AN) 200, and mobile stations or terminal devices 100 (e.g., UEs). An LMF 320 in the core network 300 may provide the geographic locations of some or all of the mobile stations 100.

[0338] For example, the sensing function performed by the second device (B) 20 may be realized via or supported by one or more of the base stations (e.g., gNBs) of the access network 200. Also, such sensing function may be based on raw base station measurement data for identification and may be performed by a network function of the core network 300 or an external application.

[0339] The implementation of some or all of the algorithms described in the above embodiments may be performed by hardware in the base station 220, the mobile station (e.g., UE) 100, or the network functions of the core network 300, or by an external application, or by a combination of some or all of the above entities.

[0340] The first device (A) 10 in Figures 3 and 4 may be a UE used for MR or AR reality applications, such as AR glasses or a smartphone. In this use case, we assume that the first device 10 is a UE of a viewing user, which may have established a communication link with a wireless network. Thus, the position of the first device 10 may be provided to the system (e.g., by the LMF 320 or by the first device 10 itself). The sensor 12 of the first device 10 may be a gaze sensor, such as a camera or a LIDAR sensor. Measurement data D MA may be a short section of data (e.g., a few hundred frames of video footage) recorded by the sensor 12 of the first device 10 that captures (e.g., shows, presents) an object with motion in its pose.

[0341] The second apparatus (B) 20 of FIGS. 3 and 4 may be one or more devices that are part of a wireless network, which has access to the sensors 22 of the second device 20 .

[0342] In some embodiments, the second device 20 may be a gNB or base station that is part of the access network 200. The sensor 22 of the second device 20 may be a radar sensor that may include at least one of a dedicated radar sensor located at the gNB, a communication component used in a dedicated sensing mode (e.g., ISAC), and a passive sensing function derived from normal communication signals.

[0343] In some embodiments, the sensor 22 of the second device 20 may be fully virtualized and provided as part of the network functionality on the core network 300, with raw measurement data provided by a number of sensors accessible by the network. In such cases, there may not even be a single second device 20. The sensor 22 of the second device 20 may have a set of properties including at least one of a data type, a field of view (FoV) of the sensor, a sensor orientation, and a 3D position of the sensor 22 or the second device 20. The sensor 22 may provide measurement data D of the second device 20. MB (i.e., pose data of the object being viewed), but may also be able to determine the position (i.e., distance and angle measurements) of the object being viewed.

[0344] In another embodiment, which may be combined with other embodiments or implemented independently, instead of matching measured or segmented data from the sensor 12 of the first device 10 with data (e.g., measured or virtualized data) from the sensor 22 of the second device 20, one or more radar cross sections may be generated based on the measured or segmented data from the sensor 12 of the first device 10. The one or more radar cross sections may be generated for a plurality of different field of view angles, which may correspond to a set of predefined angles (e.g., angles relative to a magnetic north-oriented reference line), FoVs, and / or locations. These generated radar cross sections may be matched against a set of cross sections generated (for a plurality of different viewpoints, sensor orientations, and / or fields of view) from a model (e.g., a digital twin) of the object, and / or a set of data (e.g., radar cross sections) for a plurality of different viewpoints, sensor orientations, and / or FoVs (e.g., radar cross sections), and / or a set of mesh data for the object stored in an object database (e.g., by a “matching service” or “seen object identification service” running in the core network). A set of data about viewpoints, sensor orientations, and / or FoVs may be generated or stored according to a predefined position (e.g., a known location of a base station) or angle (e.g., an angle relative to a magnetic north reference line), and / or a set of FoVs (e.g., multiple different focal lengths). Such a model of an object, or a set of stored data about multiple different viewpoints, sensor orientations, and / or FoVs of an object, may be associated with an ID (e.g., a UE ID such as a Subscription Permanent Identifier (SUPI)). If a match is found between the data from the sensor 12 of the first device 10 and the model of the object or the set of viewpoints, the resulting ID may be provided to an entity that uses these entities for further action (e.g., another core network service such as AUSF or UDM that uses the ID for authentication and / or authorization, or an application function that uses the ID, for example, to retrieve a metaverse asset).

[0345] In another embodiment, which may be combined with other embodiments or implemented independently, measurement data or segmented data from the sensor 12 of the first device 10 (e.g., by a network service (e.g., a sensing service or "seen object identification service") that receives this data or the second device 20) is used to determine the distance and / or angle between the first device 10 and an object in the field of view of the sensor 12. In conjunction with location information of the first device 10 (e.g., GNSS location data provided by the first device itself to the network (e.g., a location management function that allows other services such as a sensing service or a matching service to obtain location information) or the second device 20, or a location obtained by triangulation / trilateration of (sensing or position-based) signal measurements by the first device and / or one or more base stations or other devices), the location of the object in the field of view of the sensor 12 may be determined. To this end, the network service or second device may receive the location information of the first device 10 and the point of view, FoV, and / or orientation of the sensors 12, or may obtain location information of one or more objects detected in the area through other means (e.g., based on sensing by the second device 20 or sensing by a UE whose location has been received or which carries / surrounds the locatable object), and may use this obtained location information together with the received location information of the first device 10 and its FoV and / or orientation to calculate the distance / angle between the location of the first device 10 and the one or more objects. Based on these calculations, the network service or second device may determine whether these objects are within a given FoV and / or orientation of the first device 10 (i.e., based on the FoV and / or orientation information received from the first device 10, and possibly other information related to the sensors 12 of the first device 10, such as point of view and / or sensing capabilities).Additionally, the network service or second device may use size information, material information, and / or other target identification information that may be acquired or retrieved (e.g., by running a sensing service or from an external application) to determine (e.g., by performing ray tracing) whether an object may obscure another object and / or whether an object is only partially observable based on the viewpoint, orientation, and / or FoV of the first device 10. The location of the identified object is then matched with the locations of objects and / or UEs (e.g., UEs registered with the same gNB or within a (pre-configured) maximum distance) in the area (e.g., near the first device 10), and based on the location matching results, the respective object and / or UE is identified, and the associated ID is provided to an entity that uses these entities for further action. Examples of additional actions include providing the first device 10 with information about these objects (e.g., position and / or angle / distance from the first device or a reference point, ID information, whether the target object carries / surrounds the UE, metadata describing the object, the size of the object, whether it is obscured by another object or is only partially observable). The first device 10 may use this to retrieve data about these objects from a metaverse service (e.g., to overlay / render data about these objects on a rendering device), set up connections to one or more targets (if these targets carry or surround the UE), or begin / continue sensing these objects. The locations of the objects and / or UEs in the area may be obtained in a similar manner to the first device's location information (e.g., based on GNSS position data, using triangulation / trilateration).

[0346] In this use case (Metaverse or MR), the algorithm deployed in the above embodiment of FIGS. 1-7 may be configured as follows:

[0347] The X-to-Mesh algorithm 32 may be implemented, for example, by using measurement data D MB It may consist of a pre-trained neural network that maps a 3D mesh to a human pose in real time and outputs a set of mesh data that changes over time.

[0348] The Mesh-to-Y algorithm 34 generates synthetic data D that emulates the radar cross section of the user being viewed. S More specifically, a viewpoint corresponding to a predicted view of the viewed user may be synthesized by the sensors 22 of the second device 20 based on corresponding sensor parameters (properties) and the position of the viewed user.

[0349] Additionally or alternatively, a viewpoint (e.g., radar cross section) may be synthesized based on measurements of the object by the sensor 22 of the second device 20, or from a model of the object and / or a set of viewpoints of the object and / or a set of mesh points of the object, to match the predicted view of the user by the sensor 12 of the first device 10. These may be stored in and retrieved from an object database (e.g., a user database that associates a UE with a model, mesh data, or set of viewpoints representing the UE device, the user of the UE device, or objects carrying / surrounding such device).

[0350] Optionally, an additional UE device (e.g., UE) associated with the viewed object may be provided. This may be relevant if the viewed user has a UE and participates in a metaverse or MR service. The additional device may be functionally identical to the first device 10. The location of the additional device may be determined by the LMF 320 or ISAC service (e.g., based on sensing data from one or more base stations). This location is assumed to be substantially identical to the location of the viewed user.

[0351] Furthermore, for all seen users and their associated user IDs, the segmented measurement data D of the second device 20 is MBS An additional database may be provided that stores:

[0352] Also, segmented measurement data D in the user database MBS An additional algorithm (e.g., an ID assignment algorithm) may be provided to assign a user ID to a viewed object carrying a UE. This may utilize the location of the additional device and the user location determined by the sensing sensor 22 of the second device 20 to generate a given segmented measurement data D MBS may be assigned a user ID. For viewed objects that do not have a UE, a temporary user ID may be assigned.

[0353] The user database may be updated as multiple users interact with their respective metaverse or MR services, for example, within a single physical area.

[0354] The sensor 22 of the second device 20 receives measurement data D MB is collected (continuously) and measurement data D MB By executing a segmentation algorithm 40 using MBS is output. Each segment represents the body movement of an individual object. This data may be stored in a user database.

[0355] Segmented measurement data D MBS For each object identified in, a distance and angle measurement to the object is made using the sensor 22 of the second device 20, and thus the object position of each object is also output, which is also stored in the user database.

[0356] Next, the ID assignment algorithm assigns all the segmented measurement data D MBS Then, the steps of providing the locations of all the first and additional devices in the area by the LMF 320, comparing the user locations with the device locations by an ID assignment algorithm, requesting device locations that are similar within a tolerance range to provide a user ID, and assigning this user ID to the segmented measurement data D MBS and assigning it to a user location and storing it in a user database. For user locations where there is no corresponding device location (i.e., the user does not have a UE), a temporary user ID is generated and associated segmented measurement data D MBS and stored in a user database.

[0357] An object (eg, a user) may be identified as follows:

[0358] When a (new) object enters the field of view of another user, initially the object being viewed is unknown to the first device 10 of the viewing user. The sensor 12 of the first device 10 receives measurement data D of the viewed object. MA and transmits it to the synthetic data algorithm 30. The synthetic data algorithm 30 may be located, for example, locally on the first device 10, or on an edge server, the second device 20, or the core network 300.

[0359] The synthetic data algorithm 30 may deploy the following procedures.

[0360] The X-to-Mesh algorithm 32 analyzes the measured data D MA Based on this, time-varying mesh data of the object being viewed is generated.

[0361] A Mesh-to-Y algorithm 34 uses the mesh data to generate synthetic data D of the same data type as the sensor 22 of the second device 20, emulating measurements of the seen object from the viewpoint of the sensor 22 of the second device 20. S If the sensor 22 of the second device 20 is of radar data type, it generates synthetic data D that emulates a measurement of the radar cross section of the object being seen. S The steps of this algorithm 34 method may include generating a synthetic viewpoint at a position, angle, and FoV relative to the mesh data in the virtual scene such that the synthetic viewpoint replicates the position, orientation, and FoV of the sensor 22 of the second device 20 relative to the object position of the object being viewed in the real world.

[0362] As described above, a synthetic dataset (e.g., a synthetically generated radar cross section) for a viewpoint, sensor orientation, and / or FoV may be generated from data of the sensor 12 of the first device 10 for multiple different field of view angles, positions, and / or FoVs, which may correspond to a predefined set of angles, positions, and / or FoVs. The synthetic dataset for viewpoint, sensor orientation, and / or FoV may be such that one or more of these datasets match a synthetic dataset for viewpoint, sensor orientation, and / or FoV generated from a model of the object, and / or a dataset for viewpoint, sensor orientation, and / or FoV of an object stored in an object database and / or a mesh dataset of the object. The data for the set of viewpoint, sensor orientation, and / or FoV may be generated or stored according to a predefined set of positions (e.g., positions of one or more gNBs), angles (e.g., one or more angles relative to a magnetic north-oriented reference line), or FoV (e.g., from a narrow field of view to a wide-angle view).

[0363] Based on the synthetic viewpoint and mesh data of the object being viewed, the radar cross section of each vertex of the mesh data can be calculated for a virtual radar sensor located at the virtual viewpoint, which may have a (pre-determined) sensor orientation and / or FoV. This may be performed by calculating the radial velocity of each vertex of the mesh data relative to the sensor located at the synthetic viewpoint. Mesh data vertices that are obscured by other objects (e.g., body parts) with respect to the synthetic viewpoint, or that are outside the FoV (based on the (pre-defined) sensor orientation and / or FoV for the virtual radar sensor), are included in the final radar synthetic data D. S The radial velocities of the remaining vertices are then filtered out to avoid contributing to . A rough initial synthetic radial velocity profile may then be generated from the radial velocities of the remaining vertices. A pre-trained encoder-decoder model is then run with the initial radial velocity profile to generate the final radar synthetic data D S may be generated. The encoder-decoder model may be pre-trained using corresponding pairs of real and synthetic radar data.

[0364] The matching algorithm 50 of FIG. 4 is MBS By processing the most recent instance of S to identify a match. The actual Doppler radar measurements may be measured in real time or may be stored in a database (e.g., a database containing user-related information used for user identification / authentication). If the similarity between the virtual Doppler radar data set and the actual Doppler radar data set exceeds a predetermined numerical threshold, a positive match result may be output. A match confidence may be calculated, for example, based on the average size of the difference between the data sets.

[0365] If the match is positive, the user ID associated with the matched segment may be returned to the first device 10. This allows the first device 10 to use the user ID to identify the user and to request additional data (e.g., instructions for rendering the digital asset) from the metaverse or MR service provider.

[0366] Alternatively, the second device 20 (or another entity in the wireless network) may obtain additional data from the metaverse or MR service provider on behalf of the first device 10 and provide the additional data to the first device 10.

[0367] Optionally, an ID may be assigned to a user who does not have the device. This may be achieved by observing the user or by pre-registered identification information. In the first case, if an unknown user has been assigned a temporary user ID, the unknown user's corresponding user location may be sent to a nearby device along with a request for the actual user ID. The unknown user's actual user ID may be provided to the ID assignment algorithm by direct user input or by an automated service provided by the metaverse or MR service provider (e.g., a facial recognition algorithm), which may update the ID assignment algorithm's user database with the actual user ID. In the second case of pre-registered identification information, the wireless network may have access to a database of existing data that can serve as a biometric identifier for a particular user (e.g., a given unknown user's mmWave radar cross section data, gait, or other matching criteria associated with the actual user ID). In such a case, the segmented measurement data D of the second device 20 may be used to identify the unknown user. MBS By comparing the data with that database, unknown users can be matched with their biometric data and assigned a real user ID.

[0368] In another embodiment, which may be combined with other embodiments or implemented independently, the first device 10 may be able to determine a FoV and / or orientation in which one or more objects are known / identified / expected / assumed to be present (e.g., based on user input, or with a gyroscope or other orientation sensor, or by using ranging / sidelink positioning (which may include calculating or receiving information regarding distance / angle to nearby objects carrying / surrounding the UE)). The first device 10 may transmit information regarding that FoV and / or orientation to the second device 20, a “seen object identification service,” or other sensing service. Based on the location information of the first device 10 (e.g., GNSS location data provided by the first device itself to the network or the second device 20, or a location obtained by triangulation / trilateration of (sensing or position-based) signal measurements by the first device and / or one or more base stations or other devices) and location information about one or more objects detected in the area (e.g., within a (pre-set) maximum distance from the first device 10) using other means (e.g., based on sensing by the second device 20, or based on sensing by a UE carried / surrounded by objects whose locations are received or identifiable), distances / angles between the location of the first device 10 and a set of objects can be calculated. Based on these calculations, it can be determined (e.g., by a "seen object identification service") whether these objects are within a given FoV and / or orientation (i.e., based on FoV and / or orientation information received from the first device 10 (and possibly other information related to sensors 12 that the device may optionally have, such as point of view and / or sensing capabilities)).Additionally, size information, material information, and / or other target identification information that may be obtained or retrieved (e.g., by executing a sensing service or from an external application) may be used to determine whether an object may obscure another object and / or whether an object is only partially observable based on the first device's viewpoint, orientation, and / or FoV (e.g., by performing ray tracing). Based on the results of these calculations, objects and / or UEs within a given FoV may be identified, and associated IDs may be provided to an entity that uses these entities for additional actions. Examples of additional actions include sending information about these objects to the first device 10 (e.g., position and / or angle / distance from the first device or a reference point, ID information, whether the target object carries / surrounds the UE, metadata describing the object, the size of the object, whether it is obscured by another object or is only partially observable). The first device 10 may use this to retrieve data about these objects from a metaverse service (e.g., to overlay / render data about these objects on a rendering device), set up a connection to one or more targets (if these targets carry or surround the UE), or start / continue sensing these objects. The locations of objects and / or UEs in the area may be obtained in a similar manner to the location information of the first device (e.g., based on GNSS position data, using triangulation / trilateration).

[0369] In other words, the first device may be adapted to communicate over a wireless network, may be adapted to determine information about its location and FoV and / or orientation, and may be adapted to transmit information about its location and FoV and / or orientation to a service or a second device in the wireless network, which is adapted to receive this information from the first device and is further adapted to obtain location information and / or target identification information about one or more objects detected near and / or within a maximum distance from the first device, and calculate whether the objects are within the FoV and / or orientation of the first device based on the information received from the first device and the obtained location information and / or target identification information about the one or more objects. The first device may be adapted to receive information about a set of objects within a given FoV and / or orientation (i.e., transmitted by the first device to a service or a second device in the wireless network), and may be further adapted to obtain additional information about these objects or set up connections to these objects based on the received information.

[0370] This may be useful when the first device has limited or no sensing capability for sensing nearby objects, or when the first device is deployed in a system / configuration where sensing capability for sensing nearby objects is not used.

[0371] Service Continuity In the following, a use case related to service continuity is described: When a given UE or other terminal device (or an object (e.g., a human) associated with a given UE or other terminal device) crosses or moves between cells of two different base stations (e.g., gNBs) of a cellular network, service continuity of sensing functions between cells can be ensured by making it easy to recognize that the object leaving the first cell is the same (matches) the object currently entering the second cell.

[0372] This can also generally be used to track a particular object or UE across multiple cells, for example, to track a particular drone or car of interest as it passes through an area.

[0373] 3 may both be base stations (e.g., gNBs). The sensor 12 of the first device 10 and the sensor 22 of the second device 20 may be radar sensors that may include at least one of a dedicated radar sensor located at the base station, a communication component used in a dedicated sensing mode (e.g., ISAC), and a passive sensing option derived from normal communication signals.

[0374] Additionally, an additional device may be provided, which may be any terminal device (e.g., UE), such as a smartphone, a wearable device, a vehicle, a drone, etc. The additional device may be assigned a unique identifier (device ID), which may be an ID associated with the UE or a unique identifier assigned by a wireless system (e.g., access network 200). The additional device is always associated with a location (device location) and a perceptible radar cross section (device cross section). If the additional device is a handheld or wearable device (e.g., smartphone), the cross section may be the radar cross section of the device itself and / or of a user carrying the device.

[0375] Measurement data D collected by the first device 10 AM and measurement data D collected by the second device 20 MB may both be measurements of the device cross-sectional area of ​​the additional device and / or its user.

[0376] In this use case, the input data and output data of the synthetic data algorithm 30 may be of the same data type, for example Doppler radar measurements. Here, the synthetic data algorithm processes the measurement data D of the first device 10. MA and performing a view synthesis operation using the S may be configured to generate

[0377] Furthermore, an additional database (device database) may be provided for storing device IDs and device locations of additional devices that pass through the sensing range of the first device 10.

[0378] Furthermore, the measurement data D of the first device 10 MA Collection of synthetic data D S An additional algorithm (handover algorithm) may be provided that may be configured to trigger the generation of

[0379] Below we describe an example procedure for this use case.

[0380] An additional device is detected within the cell of the first device 10, but is about to leave the cell. The handover algorithm triggers the sensor 12 of the first device 10 to complete a Doppler radar measurement of the additional device, which measures the device cross section.

[0381] Next, the synthetic data algorithm 30 first generates the measured data D MA Synthetic data D can be generated by generating point clouds or other 3D data representing additional devices using SA technique for achieving this may rely on a deep learning network that has been pre-trained to construct point cloud data. The generated point cloud data is then used to generate a synthetic device cross section from the viewpoint of the sensor 22 of the second device 20. This can be achieved by using the 3D data to reconstruct the radar cross section of the object from a particular viewpoint, sensor orientation, and / or FoV. This constructed cross section is then used to generate the synthetic data D. S and may be sent from the first device 10 to the second device 20, together with the associated device ID, before the additional device leaves the cell of the first device 10, or from an entity that executes the combined data algorithm (e.g., an entity in the radio access network or core network).

[0382] The second device 20 receives the composite data D S Upon receiving, or before, or independently of, receiving measurement data D via a sensor 22 that images the device cross section of the additional device. MB This measurement data D MB and synthetic data D S may be passed to a matching algorithm 50, which uses the measurement data D MB The generated synthetic data D S and output a positive or negative match result. A positive match result may be output if the similarity between the virtual Doppler radar data set and the actual Doppler radar data set exceeds a numerical threshold. Additionally, a match confidence may be calculated based on the average size of the differences between the data sets. If the match result is positive, the second device 20 may (immediately) associate the additional device with its device ID and inherit any settings or other data associated with the additional device from the first device 10.

[0383] A similar use case for inter-cell tracking may become possible, where non-communicating objects (e.g., vehicles, people without devices, or drones that are not connected to the network) may be tracked as they move between cells of a cellular network.

[0384] Such non-communicating objects may be sensed while in the initial cell, and their device IDs may be generated and stored in a database. The object's composite data D S may be generated for surrounding base stations (e.g., gNBs). This process may be repeated each time the silent object moves from one cell to the next. In this way, it is possible to track a silent object moving within a cellular network cell without ever communicating with the cellular network.

[0385] The procedure of the above use case is based on the measurement data D of both the first device 10 and the second device 20. MA , D MB 4 and 5, in which the data sets are converted into synthetic data sets and compared by the matching algorithm 50.

[0386] The following figures and embodiments explain in more detail how these mechanisms are used to achieve service continuity.

[0387] FIG. 13 shows a schematic diagram of the architecture of a wireless network with overlapping sensing structures in which the present invention can be implemented.

[0388] As described above, wireless sensing may require a single wireless device to sense a target when the target is within the device's sensing range, or may require a means to enable multiple wireless access devices to work cooperatively to sense targets within a region of interest (ROI), such as a city or building. This is illustrated in FIG. 5, which shows a hexagonal ROI 024. The ROI may be defined as the location, area, or volume within which the target is sensed / detected (i.e., the target location, area, volume, or FoV), or the location, area, or volume within which sensing is performed may be defined as the region of interest (ROI) (i.e., the sensing location, area, volume, or FoV). The target 021 may move freely within the ROI. Multiple wireless sensing devices with overlapping sensing areas may be used to sense / track / monitor the target, with each wireless sensing device's sensing area being smaller than the ROI. For example, wireless sensing device 022 has a sensing area 023. Similarly, device 10 with sensor 12 or device 20 with sensor 22 may also have a particular FoV that is smaller than the ROI.

[0389] 14 illustrates a schematic representation of a sensing mobility procedure, which may be necessary when the sensing infrastructure is distributed, for example, when it is implemented by multiple sensing devices (e.g., 0401 and 0402 in FIG. 14), each with a given sensing range or area. The sensing devices 0401 and 0402 may correspond to the first device 10 with the sensor 12 and the second device 20 with the sensor 22, each with a different FoV, as described in the above embodiment.

[0390] The first sensing device 0401 may include a first sensing receiver S_Rx1 and a first sensing transmitter S_Tx1, or may include a different sensing modality, such as the sensor 12 in the case of the first device 10 described in the above embodiment. The second sensing device 0402 may include a second sensing receiver S_Rx2 and a second sensing transmitter S_Tx2, or may include a different sensing modality, such as the sensor 12 in the case of the first device 10 described in the above embodiment. As shown in FIG. 13 , the sensing infrastructure covers an entire area (ROI), which is typically larger than the sensing area of ​​a single sensing device. The sensing devices 0401 and 0402 need to cooperate to sense targets moving throughout the sensing area. For example, such a sensing infrastructure may be based on base stations responsible for tracking and sensing vehicles, UAVs, humans, etc.

[0391] 14 , a first sensing device 0401 and a second sensing device 0402 cooperate to track a target 0400 moving from a first target location 0403 toward a second target location 0404. In this mobility procedure, the first sensing device 0401 and the second sensing device 0402 have limited sensing ranges or FoVs, and it is desirable to track a moving target 0400, such as a vehicle, a UAV, or a human. The first sensing device 0401 and the second sensing device 0402 can use multiple of the above sensing technologies. For example, the first sensing device 0401 may transmit a sensing signal 0405 toward the target 0400 at the first target location 0403 and receive a message / signal 0406, which may be, for example, (i) a message including a set of CSI measurements collected by the target 0400 at the first location 0403 and / or (ii) a reflected component of the (radar) sensing signal 0405. In either case, the message / signal 0406 allows the first sensing device 0401 to determine certain aspects (e.g., position, velocity, acceleration, beam alignment, heart rate, etc.) of the target 0400 at the first location 0403. The same is true if the first sensing device 0401 uses other sensing modalities. By monitoring certain aspects (e.g., signal strength, frequency shift, or measurements performed by the target and that may be included in the received signal 0406), the first sensing device 0401 can determine that the target 0400 is moving away from its sensing area. If this occurs, the first sensing device 0401 notifies the second sensing device 0402 about the approaching target 0400. In practice, the first sensing device can look up which device in its list of neighboring sensing devices is likely to be the closest sensing device to the target (device) 0400 and initiate a handover based on this.

[0392] 15, or based on a predetermined map of the sensing devices, or based on other embodiments of the present application. The first sensing device 0401 can then know which sensing devices to notify, for example, based on the second procedure described below in connection with FIG. 15, or based on a predetermined map of the sensing devices, or based on other embodiments of the present application. The first sensing device 0401 then notifies the second sensing device 0402, for example a base station, of the ID of the target 0400, and / or its current location (e.g., first location 0403), and / or other parameters related to the target (e.g., the monitored state of the target), and / or measurement data or segmented data from the sensors 12, or a calculated dataset for a certain viewpoint, sensor orientation, and / or FoV (e.g., radar cross section), or result information on detected objects, or FoV information (and possibly other information related to the sensors 12, such as the viewpoint and / or sensing orientation and / or sensing capabilities), by sending a message 0407, for example via a communication interface (e.g., Xn interface) to the second sensing device 0402, for example a base station. The second sensing device 0402 may also be a UE, in which case the messages may be, for example, RRC messages exchanged via a Uu interface. The first sensing device 0402 and the second sensing device 0402 may both be UEs, in which case the messages may be exchanged via a PC5 interface. The first sensing device 0401 may know which sensing devices to notify if it knows, for example, the locations, sensing areas, or FoVs of surrounding sensing devices. The first sensing device 0401 may notify other sensing devices, for example, proactively or based on a policy. The second sensing device 0402 may start sensing the target upon receiving the message 0407. For example, the second sensing device 0402 may transmit one or more sensing signals 0409.The second sensing device 0402 may also monitor for detection of a sensing signal 0408 emitted from the first sensing device 0401 and reflected by the target 0400 at location 0403. The second sensing device 0402 can identify whether this signal 0408 came from the target 0400 if the first sensing device 0401 uses a particular sensing signal (e.g., one that includes an identifier that identifies the sensing signal, or at least any signal that can be identified within the area (e.g., a very specific radar signal, e.g., very specific chirp timing / frequency)). The ID of this sensing signal from the first sensing device 0401 (and / or the signal characteristics of the sensing signal and / or other information related to the sensing, e.g., sensing measurements received so far, sensing results, predicted / estimated trajectory / speed / direction of the intended target, target application ID / URL, ID of the involved core network function (e.g., sensing service), ID or location of the first sensing device, information about surrounding sensor / receiver devices that may be involved in the measurement (e.g., their locations), synchronization information, a set of target identification information (e.g., target matching criteria), or a sensing session identifier) ​​may be communicated to the second sensing device 0402 in message 0407. The second sensing device 0402 may also use measurement data or segmented data from the sensor 12, or a dataset calculated for a certain viewpoint, sensor orientation, and / or FoV (e.g., radar cross section), or result information about detected objects, or FoV information (and possibly other information about the sensor 12, such as viewpoint and / or sensing orientation and / or sensing capabilities) received from the first sensing device 0401, to match the sensing output of its own sensor 22 with received information (which may be corrected for a different FoV), as described in other embodiments of the present disclosure.It should be noted that the target's location and the first sensing device's location may be indicated as absolute locations (e.g., geographic coordinates), relative locations (e.g., distance / angle from a receiver, other reference device, or reference coordinates), or areas / volumes (e.g., the area / volume where the target is expected to be, or the area / volume where the target is expected to be, with slight variations due to measurement error, signal fluctuations, etc.), and may in some cases be formatted as a set of GAD (Universal Geographical Area Description) shapes (as specified in 3GPP TS 23.032). Upon receiving the signal 0408, the second sensing device 0402 may begin sensing the target. For example, it may begin transmitting its own sensing signal 0409, which is identifiable by means such as embedding an identifier. The second sensing device 0402 may then receive a signal 0410 (e.g., a reflected component of the sensing signal 0409, or other information) that enables the second sensing device 0402 to sense / monitor / track the target 0400. The second sensing device 0402 may use the information (i.e., parameters) about the target received (from the first sensing device 0401) (e.g., the latest value of the monitored state of the target 0400, or a set of target identification information) to identify the target 0400 in its own sensing data and / or verify whether the detected object matches the target 0400 (e.g., by verifying whether the sensing results match the received target information within a (pre)configured error range).The second sensing device 0402 may also use measurement data or segmented data from the sensor 12, or a data set calculated for a certain viewpoint, sensor orientation, and / or FoV (e.g., radar cross section), or result information about detected objects, or FoV information (and possibly other information about the sensor 12, such as viewpoint and / or sensing orientation and / or sensing capabilities) received from the first sensing device 0401 to match the sensing output of its own sensor 22 with received information (potentially corrected for a different FoV), as described in other embodiments of the present disclosure. Because the sensing measurement of the target 0400 by the second sensing device 0402 may begin after some delay, the first sensing device 0401 may include a timestamp in the message or in the information about the target 0400 in the message (e.g., an absolute or relative time at which the value of the monitored condition was determined before transmission to the second sensing device 0402). The second sensing device 0402 may calculate a delay between such timestamp (or the time it received the message if no timestamp is included) and the time it began sensing the target 0400 or calculated (or calculated) the first sensing result for the target 0400. This delay may be used to correct for any transitions / differences that may have occurred during that time in sensing the target 0400. For example, if the target was moving in a particular direction at a particular speed, the position of the target 0400 identified by the second sensing device 0402 would be expected to be off. The second sensing device 0402 may use the time delay to estimate / predict (e.g., by extrapolation) the target's new position or the value of the target's monitored state, and may use the expected difference in position or state in identifying, matching, or verifying the target 0400 with information about the target 0400 received from the first sensing device 0401.Alternatively, or in addition, the time delay may estimate / predict a new position of the target 0400 and use this new position for beamforming to or sensing the target 0400 (e.g., changing the FoV or other parameters of sensor 12 or sensor 22). At this point, the second sensing device 0402 may communicate to the first sensing device 0401, for example, by message 0412, that it has sensed the target 0400 at the second target position 0404. This message may include the ID of the sensing signal 0409 used by the second sensing device 0402 and information about the sensing quality. The sensing quality may refer, for example, to position accuracy, velocity accuracy, target frequency (e.g., breathing, heart rate in the case of a human), signal strength of the received signal, etc. At this point, the first sensing device 0401 may measure a sensing signal 0411 from the second sensing device (e.g., a component of the sensing signal 0409 reflected by the target 0400 at the second location 0404, or a set of measurements by the target 0400 on the signal 0409 transmitted to 0401). The first sensing device 0401 may release tracking of the target 0400 and notify the second sensing device 0402 via message 0413 that it is stopping tracking. Alternatively, the first sensing device 0401 may stop tracking after transmitting message 0407. In particular, the first sensing device 0401 may stop tracking upon sensing / receiving the signal / message 0411. Alternatively, the first sensing device 0401 may include in advance in the message 0407 a condition for ceasing sensing (e.g., when the second sensing device 0402 achieves a certain sensing quality for the target, or the time at which the first sensing device 0401 will stop sensing the target).When the second sensing device 0402 achieves this target sensing quality, or when other conditions are met (e.g., a timer expires), or when the second sensing device 0402 starts transmitting a sensing signal, or when the second sensing device 0402 successfully receives the signal 0410, the signal 0408, or the message 0407, the second sensing device 0402 may notify the first sensing device 0401 of this in message 0412. In this case, the first sensing device 0401 may not need to send message 0413.

[0393] In another variation, the first sensing device 0401 may stop tracking the target (device) 0400 for a predetermined time after the start of handover. In this case, no additional notification from the second sensing device 0402 is required. Optionally, this predetermined time may be based on an estimate of the target 0400's speed or the rate of change of signal quality. Monitoring may also continue as long as the first sensing device 0401 can detect the target (device) 0400, reducing the likelihood of losing sight of the target. However, since the quality of sensing is low at the end of handover (when the target 0400 is moving away from the first sensing device 0401), measurements may be discarded or at least given a lower weight to maintain estimation accuracy.

[0394] 15 illustrates a cooperative sensing procedure according to another embodiment. In this procedure, at least two sensing devices 0501 and 0502 (e.g., two base stations, two UEs, or a base station and a UE) are involved in wireless sensing of a target 0500 (e.g., a car, an on-board relay, a UAV, or a human) moving from a first location 0503 to a second location 0504 and then to a third location 0505. The sensing devices 0501 and 0502 may correspond to the first device 10 with the sensor 12 and the second device 20 with the sensor 22, respectively, described in the above embodiments, each having a different FoV. The cooperative sensing procedure may be important because a single sensing device may not always be able to ensure line of sight (LoS) to the target 0500 (e.g., an obstacle such as a building may block the LoS). For example, in Figure 15, a first obstacle 0507 blocks the LoS between a second sensing device 0502 and a target 0500 at a first location 0503. For example, in Figure 15, a second obstacle 0506 blocks the LoS between a first sensing device 0501 and a target 0500 at a second location 0504. To handle this situation, the sensing system has several procedures.

[0395] The first procedure, which can be combined with other embodiments, refers to a procedure that enables a sensing device to create and maintain a map of its sensing area or sensing volume. This procedure may be triggered by the sensing device itself, by a sensing function (SF) 0510 in the core network 0509, or by an external AF via the NEF. A sensing area is defined as the area around the sensing device that the sensing device can sense. A sensing volume is defined as the volume around the sensing device that the sensing device can sense. The sensing area or volume is determined by the sensing range of the sensing device (the distance the sensing device can sense, assuming free space) and obstacles in the environment (e.g., fixed obstacles) that may limit the sensing of the sensing device. Creation of this map may be performed, for example, by setting a map on the sensing device or SF based on a known environment (e.g., buildings in a city) and the location of the sensing device (e.g., gNB). This map may be obtained, for example, from an external application via the NEF. This map may be created by actively sensing the environment, for example by emitting sensing signals around the sensing device, storing the locations of obstacles around the sensing device in a database co-located with the sensing device or in a SF 0510 in the core network 0509, and determining the sensing area or sensing volume of the sensing device. By repeating this procedure periodically, the map can be adapted according to seasonal changes (e.g., vegetation). The map should also change with weather (e.g., rain).

[0396] The second procedure, which can be combined with other embodiments, refers to a procedure for configuring cooperative sensing devices in a sensing device, or a procedure for configuring relationships between sensing devices in SF0510. In particular, SF0510 may acquire and store in a database maps acquired by the sensing devices. SF0510 may determine white spots that trigger the deployment of additional sensing devices. SF0510 may determine neighboring sensing devices for each sensing device. The first sensing device 0501 may be notified by the SF 0510 about a second sensing device 0502 that can cooperate to ensure wider / better sensing coverage. In particular, the SF 0510 may notify the first sensing device 0501 of a sensing device (e.g., the second sensing device 0502) that can perform sensing at the boundary of the sensing area or sensing volume of the first sensing device 0501. For example, the first sensing device 0501 may be notified that the second sensing device 0502 can sense a target located at a second location 0504. To avoid interference during sensing, the sensing devices may (1) be configured with appropriate timing / frequency resources (e.g., sensing signals using different timing, frequencies, or codes) via the first and second interfaces ...

Claims

1. 1. An apparatus for processing data from at least one first sensor characterized by at least one sensing parameter, in particular at least one of a sensor type and a viewpoint, an orientation, or a field of view, said apparatus comprising: obtaining a first measurement of the target object; receiving second measurement data about the target object from a second sensor; obtaining a composite of the second measurement data and / or the first measurement data based on the at least one sensing parameter of the first sensor and / or at least one sensing parameter related to the second measurement data, in particular based on at least one of a sensor type, a viewpoint, an orientation, or a field of view; The apparatus determines whether the first measurement data and the second measurement data match based on the acquired composite data.

2. The apparatus of claim 1 , wherein the at least one sensing parameter related to the second measurement data is at least one sensing parameter of the second sensor, and the apparatus acquires the at least one sensing parameter of the second sensor from another device.

3. The apparatus further comprises: determining whether the derived composite data of the first measurement data and the derived composite data of the second measurement data match; or determining whether the first measurement data and the combined data obtained from the second measurement data match; or 3. The apparatus of claim 1, further comprising: a determining unit configured to determine whether the second measurement data and the obtained composite data of the first measurement data match.

4. The device comprises: a first algorithm for segmenting the first and second measurement data into first and second object-related data; a second algorithm that generates the synthetic data from one of the first or second object-related data or a previous input; a third algorithm for converting the first or second object-related data, or one of the previous inputs, into a first or second intermediate form, in particular a multidimensional representation, representative of the object described by the first or second object-related data; a fourth algorithm that converts one of the first or second intermediate forms or the previous input into the composite data; and Fifth Algorithm for Aligning the First and Second Intermediate Forms 4. The apparatus of claim 1, wherein the apparatus determines whether there is a match using at least one of:

5. The apparatus of claim 1 , wherein the apparatus determines an identity of the target object based on the acquired composite data and the first measurement data.

6. The device according to claim 1 , wherein the device obtains the first and / or second measurement data from a database.

7. The device of claim 1 , wherein the device obtains additional data about the target object from a metaverse or a mixed reality service provider and provides the additional data to the second sensor.

8. The device of claim 1 , wherein the device performs asset tracking of the target object.

9. The device of claim 1 , wherein the device requests the target object to allow network-assisted identification.

10. The device of claim 1 , wherein the device accepts the target object if a match is determined.

11. The device according to claim 1 , wherein the device supports service continuity, in particular when the target object moves out of the coverage area of ​​the second sensor.

12. A terminal device comprising an apparatus according to any one of claims 1 to 6, 8 and 9.

13. An access device comprising an apparatus according to any one of claims 1 to 11.

14. A wireless communication system comprising at least one of a terminal device according to claim 12 and an access device according to claim 13.

15. 1. A method for matching target objects in a wireless network, the method comprising: obtaining a first measurement of a target object; receiving second measurement data about the target object from a second sensor; - obtaining synthetic data from the second measurement data and / or the first measurement data based on at least one sensing parameter of the first sensor and / or at least one sensing parameter related to the second measurement data, in particular based on at least one of a sensor type, a viewpoint, an orientation or a field of view; determining whether the first measurement data and the second measurement data match based on the obtained composite data.

16. 16. A computer program comprising code means for generating the steps of the method of claim 15 when executed on a processor of a network device.

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