Beam Alignment in Multipath Environments for Integrated Sensing and Communication using Bandit Learning

Prior works have explored multi-armed bandit (MAB) algorithms for the selection of optimal beams for millimeter-wave (mmW) communications between base station and mobile users. However, when the number of beams is large, the existing MAB algorithms are characterized by long exploration times, resulting in poor overall communication throughput. In this work, we propose augmenting the upper confidence bound (UCB) based MAB with integrated sensing and communication (ISAC) to address this limitation. The premise of the work is that the radar and communication functionalities share the same field-of-view and that communication mobile users are detected by the radar as mobile targets. The radar information is used for significantly reducing the number of candidate beams for the UCB, resulting in an overall reduction in the exploration time. Further, the radar information is used to estimate the realignment time in quasi-stationary scenarios. We have realized the MAB and radar signal processing algorithms on the system on chip (SoC) via hardware-software co-design (HSCD) and fixed-point analysis. We demonstrate the significant gain in execution time using accelerators. The simulations consider complex propagation channels involving direct and multipath, with simple and extended radar targets in the presence of significant static clutter. The resulting experiments show that the proposed ISAC-based MAB achieves a 35% reduction in the overall exploration time and 1.4 factor higher throughput as compared to the conventional MAB that is based only on communications.

IEEE 802.11ad-Aided 5-D Sensing with a UAV Swarm in Urban Environment

Aerial base stations mounted on unmanned aerial vehicles (UAVs) support next-generation wireless networks in challenging environments such as urban areas, disaster zones, and remote locations. Further, UAV swarms overcome the challenges of limited battery life and other operational constraints of a single UAV. However, tracking mobile users on the ground by each UAV and the corresponding synchronization between the UAVs is a significant issue that must be addressed before this framework can be deployed in reality. Incorporating additional sensing capabilities to facilitate this additional requirement would introduce significant overhead in terms of hardware, cost, and power to each UAV. Instead, we propose an integrated sensing and communications-enabled swarm UAV system, based on the millimeter-wave IEEE 802.11ad protocol. Further, we show that our proposed system is capable of five-dimensional (5-D) ground target sensing (range, Doppler velocity, azimuth, elevation, and polarization) in an urban environment. Numerical experiments using realistic models demonstrate and validate the performance of 5-D sensing using our proposed 802-11ad-aided UAV system.

Radar Operating Metrics and Network Throughput for Integrated Sensing and Communications in Millimeter-wave Urban Environments

Millimeter wave integrated sensing and communication (ISAC) systems are being researched for next-generation intelligent transportation systems. Here, radar and communication functionalities share a common spectrum and hardware resources in a time-multiplexed manner. The objective of the radar is to first scan the angular search space and detect and localize mobile users/targets in the presence of discrete clutter scatterers. Subsequently, this information is used to direct highly directional beams toward these mobile users for communication service. The choice of radar parameters such as the radar duty cycle and the corresponding beamwidth are critical for realizing high communication throughput. In this work, we use the stochastic geometry-based mathematical framework to analyze the radar operating metrics as a function of diverse radar, target, and clutter parameters and subsequently use these results to study the network throughput of the ISAC system. The results are validated through Monte Carlo simulations.

RFSoC-Based Integrated Navigation and Sensing Using NavIC

Prior art has proposed a secondary application for Global Navigation Satellite System (GNSS) infrastructure for remote sensing of ground-based and maritime targets. Here, a passive radar receiver is deployed to detect uncooperative targets on Earth's surface by capturing ground-reflected satellite signals. This work demonstrates a hardware prototype of an L-band Navigation with Indian Constellation (NavIC) satellite-based remote sensing receiver system mounted on an AMD Zynq radio frequency system-on-chip (RFSoC) platform. Two synchronized receiver channels are introduced for capturing the direct signal (DS) from the satellite and ground-reflected signal (GRS) returns from targets. These signals are processed on the ARM processor and field programmable gate array (FPGA) of the RFSoC to generate delay-Doppler maps of the ground-based targets. The performance is first validated in a loop-back configuration of the RFSoC. Next, the DS and GRS signals are emulated by the output from two ports of the Keysight Arbitrary Waveform Generator (AWG) and interfaced with the RFSoC where the signals are subsequently processed to obtain the delay-Doppler maps. The performance is validated for different signal-to-noise ratios (SNR).

Reconfigurable Low-Complexity Architecture for High Resolution Doppler Velocity Estimation in Integrated Sensing and Communication System

In millimeter wave integrated sensing and communication (ISAC) systems for intelligent transportation, radar and communication share spectrum and hardware in a time division manner. Radar rapidly detects and localizes mobile users (MUs), after which communication proceeds through narrow beams identified by radar. Achieving fine Doppler resolution for MU clutter discrimination requires long coherent processing intervals, reducing communication time and throughput. To address this, we propose a reconfigurable architecture for Doppler estimation realized on a system on chip using hardware software codesign. The architecture supports algorithm level reconfiguration, dynamically switching between low-complexity, high-speed FFT-based coarse estimation and high complexity ESPRIT based fine estimation. We introduce modifications to ESPRIT that achieve 6.7 times faster execution while reducing memory and multiplier usage by 79% and 63%, respectively, compared to state of the art approaches, without compromising accuracy. Additionally, the reconfigurable architecture can switch to lower slow time packets under high SNR conditions, improving latency further by 2 times with no loss in performance.

Fine Grained Analysis and Optimization of Large Scale Automotive Radar Networks

Advanced driver assistance systems (ADAS) enabled by automotive radars have significantly enhanced vehicle safety and driver experience. However, the extensive use of radars in dense road conditions introduces mutual interference, which degrades detection accuracy and reliability. Traditional interference models are limited to simple highway scenarios and cannot characterize the performance of automotive radars in dense urban environments. In our prior work, we employed stochastic geometry (SG) to develop two automotive radar network models: the Poisson line Cox process (PLCP) for dense city centers and smaller urban zones and the binomial line Cox process (BLCP) to encompass both urban cores and suburban areas. In this work, we introduce the meta-distribution (MD) framework upon these two models to distinguish the sources of variability in radar detection metrics. Additionally, we optimize the radar beamwidth and transmission probability to maximize the number of successful detections of a radar node in the network. Further, we employ a computationally efficient Chebyshev-Markov (CM) bound method for reconstructing MDs, achieving higher accuracy than the conventional Gil-Pelaez theorem. Using the framework, we analyze the specific impacts of beamwidth, detection range, and interference on radar detection performance and offer practical insights for developing adaptive radar systems tailored to diverse traffic and environmental conditions.

Impact of Urban Street Geometry on the Detection Probability of Automotive Radars

Prior works have analyzed the performance of millimeter wave automotive radars in the presence of diverse clutter and interference scenarios using stochastic geometry tools instead of more time-consuming measurement studies or system-level simulations. In these works, the distributions of radars or discrete clutter scatterers were modeled as Poisson point processes in the Euclidean space. However, since most automotive radars are likely to be mounted on vehicles and road infrastructure, road geometries are an important factor that must be considered. Instead of considering each road geometry as an individual case for study, in this work, we model each case as a specific instance of an underlying Poisson line process and further model the distribution of vehicles on the road as a Poisson point process - forming a Poisson line Cox process. Then, through the use of stochastic geometry tools, we estimate the average number of interfering radars for specific road and vehicular densities and the effect of radar parameters such as noise and beamwidth on the radar detection metrics. The numerical results are validated with Monte Carlo simulations.

Radar Enhanced Multi-Armed Bandit for Rapid Beam Selection in Millimeter Wave Communications

Multi-arm bandit (MAB) algorithms have been used to learn optimal beams for millimeter wave communication systems. Here, the complexity of learning the optimal beam linearly scales with the number of beams, leading to high latency when there are a large number of beams. In this work, we propose to integrate radar with communication to enhance the MAB learning performance by searching only those beams where the radar detects a scatterer. Further, we use radar to distinguish the beams that show mobile targets from those which indicate the presence of static clutter, thereby reducing the number of beams to scan. Simulations show that our proposed radar-enhanced MAB reduces the exploration time by searching only the beams with distinct radar mobile targets resulting in improved throughput.

Hardware Software Co-Design Based Reconfigurable Radar Signal Processing Accelerator for Joint Radar-Communication System

Millimeter wave (mmW) codesigned 802.11ad-based joint radar communication (JRC) systems have been identified as a potential solution for realizing high bandwidth connected vehicles for next-generation intelligent transportation systems. The radar functionality within the JRC enables accurate detection and localization of mobile targets, which can significantly speed up the selection of the optimal high-directional narrow beam required for mmW communications between the base station and mobile target. To bring JRC to reality, a radar signal processing (RSP) accelerator, co-located with the wireless communication physical layer (PHY), on edge platforms is desired. In this work, we discuss the three-dimensional digital hardware RSP framework for 802.11ad-based JRC to detect the range, azimuth, and Doppler velocity of multiple targets. We present a novel efficient reconfigurable architecture for RSP on multi-processor system-on-chip (MPSoC) via hardware-software co-design, word-length optimization, and serial-parallel configurations. We demonstrate the functional correctness of the proposed fixed-point architecture and significant savings in resource utilization (~40-70), execution time (1.5x improvement), and power consumption (50%) over floating-point architecture. The acceleration on hardware offers a 120-factor improvement in execution time over the benchmark Quad-core processor. The proposed architecture enables on-the-fly reconfigurability to support different azimuth precision and Doppler velocity resolution, offering a real-time trade-off between functional accuracy and detection time. We demonstrate end-to-end RSP on MPSoC with a user-friendly graphical user interface (GUI).

Estimation of Electrical Characteristics of Inhomogeneous Walls Using Generative Adversarial Networks

Through-wall radars are researched and developed for the detection, localization, and tracking of human activities in indoor environments. Electromagnetic wave propagation through walls introduces refraction, attenuation, multipath, and ghost targets in the radar signatures. Estimation of wall characteristics (dielectric profile and thickness) can enable wall effects to be deconvolved from through-wall radar signatures. We propose to use generative adversarial networks (GAN) to estimate wall characteristics from narrowband scattered electric fields on the same side of the wall as the transmitter. We demonstrate that the GANs, consisting of two neural networks configured in an adversarial manner, are capable of solving the highly nonlinear regression problem with limited training data to estimate the dielectric profile and thickness of actual walls up to 95\% accuracy based on training with simulated data generated from full-wave solvers.