Aritrik Ghosh

About Me

Hi, I am Aritrik Ghosh, I am a 4th year Ph.D in Computer Science at the University of Maryland College Park. I am working at iCoSMoS Lab with Prof. Nirupam Roy.

Research interests: Wireless Localization, Quantum Sensing , E.M. Sensing.

Lab : IRB 3245

Office : IRB 2104

Publications

[Mobisys 2026] SpecSentry: Micro-power Wideband Spectrum Surveillance for Ephemeral Transmissions

Abstract— Covert radio-frequency exfiltration using short, low-duty-cycle transmissions highlights the need for continuous, low-latency spectrum monitoring capable of capturing fleeting emissions. Modern RF environments increasingly feature bursty, opportunistic signals that appear only for milliseconds, making them difficult for traditional receivers to detect. Conventional wideband sensing architectures depend on high-speed ADCs, local oscillators (LO), and mixer chains that draw significant power, while swept-LO and sequential scanning approaches suffer from limited real-time visibility. Parallel filter-bank designs improve instantaneous coverage but remain bulky and energy-intensive. We present SpecSentry, a low-power spectrum-monitoring architecture that maintains wideband visibility while reducing analog complexity by more than an order of magnitude. SpecSentry implements a "mark-fold-capture-detect" workflow in which the RF front end passively imprints frequency-dependent codes onto any active signal and folds the result into a compact representation sampled at rates more than 20× below the Nyquist rate. A two-stage neural regression pipeline analyzes this compressed snapshot to infer key signal characteristics, achieving median detection errors of 0.3 MHz for the bandwidth and 7.89 MHz for the center-frequency of the transmission over a 400 MHz band. This hybrid analog-ML design enables persistent, wideband, ultra-low-power monitoring suited for mobile, unattended, and maintenance-free deployments where detecting ephemeral transmissions is essential.

SpecSentry_ghosh.pdf

[GetMobile 2025] Sparse is Fast: Accelerating Synthetic Aperture Sonar via Reduced Sampling

Abstract— High-resolution underwater imaging is central to deep-sea exploration, marine science, and subsea infrastructure inspection. As interest in deep-sea missions grows - from ecological monitoring and resource management to defense and offshore energy - autonomous underwater vehicles (AUVs) have become key enablers. These mobile robots must traverse large, complex terrains where visibility is minimal, and GPS is unavailable. Acoustic imaging, particularly synthetic aperture sonar (SAS), is a leading technique for underwater scene reconstruction, offering high spatial resolution with compact form factors. However, its widespread deployment remains limited by one critical constraint: the speed at which imaging can be performed.

3774505.3774511.pdf

[arXiv 2025] DeepSync: A Learning Framework for Pervasive Localization using Code Synchronization on Compressed Cellular Spectrum

Abstract— Pervasive localization is essential for continuous tracking applications, yet existing solutions face challenges in balancing power consumption and accuracy. GPS, while precise, is impractical for continuous tracking of micro-assets due to high power requirements. Recent advances in non-linear compressed spectrum sensing offer low-power alternatives, but existing implementations achieve only coarse positioning through Received Signal Strength Indicator (RSSI) measurements. We present DeepSync, a deep learning framework that enables precise localization using compressed cellular spectrum. Our key technical insight lies in formulating sub-sample timing estimation as a template matching problem, solved through a novel architecture combining temporal CNN encoders for multi-frame processing with cross-attention mechanisms. The system processes non-linear inter-modulated spectrum through hierarchical feature extraction, achieving robust performance at SNR levels below -10dB -- a regime where conventional timing estimation fails. By integrating real cellular infrastructure data with physics-based ray-tracing simulations, DeepSync achieves 2.128-meter median accuracy while consuming significantly less power than conventional systems. Real-world evaluations demonstrate 10x improvement over existing compressed spectrum approaches, establishing a new paradigm for ultra-low-power localization.

2505.05783v1.pdf

[Sensys 2024] Litefoot: Ultra-low-power localization using ambient cellular signals

Abstract— In this paper, we introduce a low-power wide-area cellular localization system, called LiTEfoot. The core architecture of the radio carefully applies non-linear transform of the entire cellular spectrum to obtain a systematic superimposition of the synchronization signals at the baseband. The system develops methods to simultaneously identify all the base stations that are active at any cellular band from the transformed signal. The radio front end uses a simple envelop detector to realize the non-linear transformation. We build on this low-power radio to implement a self-localization system leveraging ambient 4G-LTE signals. We show that the core system can also be extended to other cellular technologies like 5G-NR and NB-IoT. The prototype achieves a median localization error of 22 meters in urban areas and 50 meters in rural areas. It can sense a 3GHz wideband LTE spectrum in 10ms using non-linear intermodulation while consuming 0.9 mJ of energy for a PCB-based implementation and 40 μJ for CMOS simulation. In other words, LiTEfoot tags can last for 11 years on a coin cell while continuously estimating location every 5 seconds. We believe that LiTEfoot will have widespread implications in city-scale asset tracking and other location-based services. The radio architecture can be useful beyond low-power self-localization and can find application in synchronization and communication on battery-less platforms.

3666025.3699356.pdf

[DYSPAN 2021] A Gaussian Process Based Technique of Efficient Sensor Selection for Transmitter Localization

Abstract :- Spectrum monitoring via crowdsourcing is a technique that promises to enable opportunistic spectrum access. Crowdsourcing aims to provide incentives to users to deploy a large number of cheap but potentially noisy sensors. The sensors all send their data to a fusion center, where typically some algorithms are used to remove the noise from the data. Such crowdsourced monitoring of spectrum has been shown to be feasible in practice in multiple studies. One of the key goals of such monitoring is to identify any users that are violating the protocols of accessing spectrum. While a number of crowdsourcing techniques to identify such violations have been proposed, a key challenge that remains is to minimize the cost of data consumption and energy of running the sensors. In this work, we propose sequential probing of sensors to accurately localize/identify such transmitters. We formulate this as a Gaussian Process multi-armed bandit problem, and use a widely known solution technique called Upper Confidence Bound to solve it. We next observe that such sequential probing incurs additional latency, and use batched selection of sensors in few rounds to reduce latency. We show that instead of naively selecting sensors in parallel batches, an intelligent technique of selecting sensors called Gaussian Process Adaptive Upper Confidence Bound (GP-AUCB) can lead to selection of sensors that can lead to more accurate localization. Finally, we show the tradeoff between accuracy of localization, latency incurred and number of selected sensors via simulations.

Dyspan21.pdf

[InCAP 2021] SIW-fed Metallic Cap Loaded Cylindrical DRA Array Antenna for 5g mm-wave Application

Abstract— In this article, a cylindrical dielectric resonator array antenna fed with a SIW (substrate integrated waveguide) based Wilkinson power divider network is proposed for 5G mm wave application. The design consists of four cylindrical dielectric resonator antennas (DRAs) that are mounted on the top of the four slots, two on each arm of a 1:2 Wilkinson power divider. Ansys HFSS simulation results confirm that the proposed array operates at 28 GHz with a bandwidth of 330 MHz with good monopole type radiation and a peak gain of 9.9 dBi.

APS_2894.pdf

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