arXiv subjects

Shobha Sundar Ram

Publications and source records attributed to Shobha Sundar Ram.

At least 19 recordsLinked to original sources

arXiv2026

Adaptive High-Speed Radar Signal Processing Architecture for 3D Localization of Multiple Targets on System on Chip

Integrated Sensing and Communication (ISAC) is a key enabler of high speed, ultra low latency vehicular communication in 6G. ISAC leverages radar signal processing (RSP) to localize multiple unknown targets amid static clutter by jointly estimating range, azimuth, and Doppler velocity (3D), thereby enabling highly directional beamforming toward intended mobile users. However, the speed and accuracy of RSP significantly impact communication throughput. This work proposes a novel 3D reconfigurable RSP accelerator, implemented on a Zynq Multi processor System on Chip (MPSoC) using a hardware software codesign approach and fixed point optimization. We propose two RSP frameworks: (1) high accuracy and high complexity, and (2) low complexity and low accuracy, along with their respective architectures. Then, we develop an adaptive architecture that dynamically switches between these two frameworks based on the signal to clutter plus noise ratio. This adaptive reconfiguration achieves up to 5.6 times faster RSP compared to state of the art designs. At the system level, the proposed RSP based ISAC delivers a 24% improvement in communication throughput without increasing hardware complexity.

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arXiv2026

Radar Cross Section Characterization of Quantized Reconfigurable Intelligent Surfaces

We present a radar sensing framework based on a low-complexity, quantized reconfigurable intelligent surface (RIS) that enables programmable manipulation of electromagnetic wavefronts for enhanced detection in non-specular and shadowed regions. We develop closed-form expressions for the scattered field and radar cross section (RCS) of phase-quantized RIS apertures based on aperture field theory, accurately capturing the effects of quantized phase, periodicity, and grating lobes on radar detection performance. The theory enables us to analyze the RIS's RCS along both the forward and backward paths from the radar to the target. The theory is benchmarked against full-wave electromagnetic simulations incorporating realistic unit-cell amplitude and phase responses. To validate practical feasibility, a $[16\times10]$ 1-bit RIS operating at 5.5 GHz is fabricated and experimentally characterized inside an anechoic chamber. Measurements of steering angles, beam-squint errors, and peak-to-specular ratios of the RCS patterns exhibit strong agreement with analytical and simulated results. Further experiments demonstrate that the RIS can redirect the beam in a non-specular direction and recover micro-Doppler signatures that remain undetectable with a conventional radar deployment.

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arXiv2026

Demo: An RFSoC-Based Testbed for Over-the-Air Wireless Transceiver at Millimeter Wave Frequency

The millimeter wave (mmW) frequency spectrum has been explored recently for large bandwidth communication. At these frequencies, narrow directional beams are required for communication since the signal attenuation is high due to atmospheric absorption. This work presents an AMD RFSoC and Sivers Semiconductors analog front-end based hardware testbed capable of directional communication via analog beamforming at mmW. The proposed testbed comprises orthogonal frequency division multiplexing (OFDM) based baseband physical layer and digital front-end on an ARM processor and field programmable gate array (FPGA), respectively, integrated with high-speed data converters of the RFSoC. The RFSoC output at sub-6GHz is integrated with a mmW multi-antenna analog-front end for over- the-air communication at 29.8 GHz. We demonstrate end-to-end communication over the air and present bit error rate (BER) analysis in the presence of radio frequency impairments and beam misalignments in real radio channels.

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arXiv2026

Millimeter Wave Frontend for Integrated Sensing and Communication System Transceiver on Edge

IEEE 802.11ad standard uses analog beamforming for high-speed directional communication with mobile user (MU) in the millimeter wave (mmWave) spectrum. However, the lengthy beam alignment procedures involving large data packets between the base station (BS) and the MU introduce considerable overhead, deteriorating the overall throughput. Prior works have proposed 802.11ad-based integrated sensing and communication (ISAC) BS transceivers to eliminate time-consuming beam alignment. Instead, the radar and communication functionalities use the same waveform, spectrum, and millimeter wave front end (MFE) with a common spatial field of view. The radar detects and localizes the MU, enabling the subsequent directional communication with the MU. This work proposes an end-to-end IEEE 802.11ad-based ISAC BS transceiver prototype, wherein the digital baseband hardware frontend on edge is integrated with a Simulink-based MFE. The proposed prototype facilitates a systematic link budget and detailed performance analysis for different wireless channels, target motions, signal-to-noise ratios, hardware configurations, and impairments. We also investigate how these impairments affect radar performance and, in turn, the communication metrics since the performances of both systems are uniquely interrelated in an ISAC system. Our results show that even with hardware impairments, the 802.11ad-based ISAC offers 34% higher throughput than the standard with an ideal MFE.

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arXiv2026

Radar Enabled Adaptive Modulation for Millimeter Wave Integrated Sensing and Communication

An integrated sensing and communication (ISAC) framework comprises radar sensing to enable reliable direction beam-based communication between a base station (BS) and mobile user (MU). The ISAC will be an integral part of 6G with potential applications for high-speed vehicular communications. Existing works have explored azimuth and Doppler velocity estimated via radar sensing for beam identification and identification in dynamic environments. In this work, we propose radar-enabled modulation scheme selection for ISAC, thereby eliminating conventional time-consuming downlink-uplink feedback-based modulation scheme selection. We have analyzed the performance of the proposed approach for four different trajectories and shown an improvement in throughput between 54-209% over state-of-the-art ISAC.

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arXiv2026

Around-the-corner Radar Sensing Using Reconfigurable Intelligent Surface

Around-the-corner radar (ACR) sensing of targets in non-line-of-sight (NLOS) conditions has been explored for security and surveillance applications and look-ahead warning systems in automotive scenarios. Here, the targets are detected around corners without direct line-of-sight (LOS) propagation by exploiting multipath bounces from the walls. However, the overall detection metrics are weak due to the low strength of the multipath signals. Our study presents the application of reconfigurable intelligent surface (RIS) to improve radar sensing in ACR scenarios by directing incident beams on the RIS into NLOS regions. Experimental results at 5.5 GHz demonstrate that micro-Doppler signatures of the walking motion of humans can now be captured in NLOS conditions through the strategic deployment of RIS.

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arXiv2026

Estimation of Electrical Characteristics of Complex Walls Using Deep Neural Networks

Electromagnetic wave propagation through complex inhomogeneous walls introduces significant distortions to through-wall radar signatures. Estimation of wall thickness, dielectric, and conductivity profiles may enable wall effects to be deconvolved from target scattering. We propose to use deep neural networks (DNNs) to estimate wall characteristics from broadband scattered electric fields on the same side of the wall as the transmitter. We demonstrate that both single deep artificial and convolutional neural networks and dual networks involving generative adversarial networks are capable of performing the highly nonlinear regression operation of electromagnetic inverse scattering for wall characterization. These networks are trained with simulation data generated from full wave solvers and validated on both simulated and real wall data with approximately 95% accuracy.

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arXiv2026

Radar Sensing using Dual-Beam Reconfigurable Intelligent Surface

Around-the-corner radar sensing offers an opportunity for the radar to exploit multipath scattering along walls to detect targets beyond blockages. However, the radar detection performance is limited to spotting uncooperative targets at specular angles. Recently, reconfigurable intelligent surfaces (RIS) involving metasurfaces with tunable unit cells have been researched for enhancing radar coverage around corners by directing beams towards non-specular angles. This article examines how practical considerations regarding the phase tuning of unit cells impact the RIS performance. Specifically, we examine the radar cross-section (RCS) obtained from two RIS configurations: In the first, each atom of the RIS is tuned based on a theoretical analog phase shift to realize idealized one-beam patterns at the desired angles. In the second configuration, each atom of the RIS is tuned based on a low-complexity, one-bit quantized element phase shift, which results in dual symmetric beams. The RIS configurations are then benchmarked with a metal plate of similar dimensions in both simulations and measurements.

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arXiv2026

Orthogonal Circular Polarized Transmitter and Receiver Antennas for Mitigation of Mutual Coupling in Monostatic Radars

Through-wall radar systems require compact, wideband and high gain antennas for detecting targets. Building walls introduce considerable attenuation on the radar signals. When the transmitted power is raised to compensate the through-wall attenuation, the direct coupling between the transmitter and receiver can saturate the receiver because of which weaker reflections off the target may remain undetected. In this paper, we propose using transmitter and receiver antennas of orthogonal circular polarization to reduce the direct coupling between the transmitter and receiver while retaining the first bounce off the target. In our paper, we demonstrate that the quadrafilar helical antenna (QHA) is a good candidate for this operation since it is characterized by a small size, wide frequency band of operation, high gain and low axial ratio over a wide field of view. We compare the reduced mutual coupling between the transmitter and receiver elements for the oppositely polarized QHA antennas with other commonly used through-wall radar antennas such as the Vivaldi and horn antennas. The system is tested in through-wall conditions.

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arXiv2026

Performance Analysis of Millimeter Wave Radar Waveforms for Integrated Sensing and Communication

Next-generation intelligent transportation systems require both sensing and communication between road users. However, deploying separate radars and communication devices involves the allocation of individual frequency bands and hardware platforms. Integrated sensing and communication (ISAC) offers a robust solution to the challenges of spectral congestion by utilizing a shared waveform, hardware, and spectrum for both localization of mobile users and communication. Various waveforms, including phase-modulated continuous waves (PMCW) and frequency-modulated continuous waves (FMCW), have been explored for target localization using traditional radar. On the other hand, new protocols such as the IEEE 802.11ad have been proposed to support wideband communication between vehicles. This paper compares both traditional radar and communication candidate waveforms for ISAC to detect single-point and extended targets. We show that the response of FMCW to mobile targets is poorer than that of PMCW. However, the IEEE 802.11ad radar outperforms PMCW radar and FMCW radar. Additionally, the radar signal processing algorithms are implemented on Zynq system-on-chip through hardware-software co-design and fixed-point analysis to evaluate their computational complexity in real-world implementations.

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arXiv2026

Motion Compensation for Multiple-Input-Multiple-Output Inverse Synthetic Aperture Imaging of Automotive Targets

Inverse synthetic aperture radar (ISAR) images generated from single-channel automotive radar data provide critical information about the shape and size of automotive targets. However, the quality of ISAR images degrades due to road clutter and when translational and higher order rotational motions of the targets are not suitably compensated. One method to enhance the signal-to-clutter-and-noise ratio (SCNR) of the systems is to leverage the advantages of the multiple-input-multiple-output (MIMO) framework available in commercial automotive radars to generate MIMO-ISAR images. While substantial research has been devoted to motion compensation of single-channel ISAR images, the effectiveness of these methods for MIMO-ISAR has not been studied extensively. This paper analyzes the performance of three popular motion compensation techniques - entropy minimization, cross-correlation, and phase gradient autofocus - on MIMO-ISAR. The algorithms are evaluated on the measurement data collected using Texas Instruments millimeter-wave MIMO radar. The results indicate that the cross-correlation MOCOMP performs better than the other two MOCOMP algorithms in the MIMO configuration, with an overall improvement of 36%.

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arXiv2026

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.

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arXiv2026

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.

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arXiv2026

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.

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arXiv2026

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.

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arXiv2026

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).

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arXiv2026

Modeling and Statistical Characterization of Large-Scale Automotive Radar Networks

The impact of discrete clutter and co-channel interference on the performance of automotive radar networks has been studied using stochastic geometry, in particular, by leveraging two-dimensional Poisson point processes (PPPs). However, such characterization does not take into account the impact of street geometry and the fact that the location of the automotive radars are restricted to the streets as their domain rather than the entire Euclidean plane. In addition, the structure of the streets may change drastically as a vehicle moves out of a city center towards the outskirts. Consequently, not only the radar performance change but also the radar parameters and protocols must be adapted for optimum performance. In this paper, we propose and characterize line and Cox process-based street and point models to analyze large-scale automotive radar networks. We consider the classical Poisson line process (PLP) and the newly introduced Binomial line process (BLP) model to emulate the streets and the corresponding PPP-based Cox process to emulate the vehicular nodes. In particular, the BLP model effectively considers the spatial variation of street geometry across different parts of the city. We derive the effective interference set experienced by an automotive radar, the statistics of distance to interferers, and characterize the detection probability of the ego radar as a function of street and vehicle density. Finally, leveraging the real-world data on urban streets and vehicle density across different cities of the world, we present how the radar performance varies in different parts of the city as well as across different times of the day. Thus, our study equips network operators and automotive manufacturers with essential system design insights to plan and optimize automotive radar networks.

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arXiv2024

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.

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