Gaurav Singh

Gaurav Singh

PhD Student in Computer Science
Department of Computer Science and Engineering
The University of Texas at Arlington

Publications

You can also find my articles on my Google Scholar profile.

PROTEUS: Proactive Latency-Constrained Enhanced Ubiquitous Surveillance

In this paper we introduce a system for robotic swarms for autonomous surveillance to cover an entire area and meet tight communication-latency budgets using a computationally efficient approach.

Recommended citation: G. Singh, A. Ghosh, and D. Roy, "PROTEUS: Proactive Latency-Constrained Enhanced Ubiquitous Surveillance," in Proc. 2026 24th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), Columbus, OH, USA, 2026, pp. 1–8, doi: 10.23919/WiOpt71098.2026.11568238. https://doi.org/10.23919/WiOpt71098.2026.11568238

Secure and Efficient Transmission in Hybrid Sparse RIS-Enabled Internet of Robotic Things

In this paper we propose a Hybrid RIS (HRIS)-assisted secure transmission framework, where a Base Station (BS) communicates with IoRT devices under eavesdropping by jointly exploiting active and passive RIS elements.

Recommended citation: G. Singh, S. Kurma, D. Roy, and V. Chaudhary, "Secure and Efficient Transmission in Hybrid Sparse RIS-Enabled Internet of Robotic Things," in Proc. 2026 24th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), Columbus, OH, USA, 2026, pp. 1–8, doi: 10.23919/WiOpt71098.2026.11568220. https://doi.org/10.23919/WiOpt71098.2026.11568220

Realizing the 6G-Enabled Industrial Internet of Robotic Things: Targets, Enablers, and a Case Study

This paper explores the evolution towards 6th Generation (6G)-enabled IIoRT, addressing critical 5G limitations regarding latency determinism, ultra-reliability, and massive connection density.

Recommended citation: G. Singh, S. Amatare, E. Natalizio, and D. Roy, "Realizing the 6G-Enabled Industrial Internet of Robotic Things: Targets, Enablers, and a Case Study," IEEE Internet of Things Magazine, early access, 2026, doi: 10.1109/MIOT.2026.3678202. http://arxiv.org/abs/2008.11398

RagNAR: Ray-tracing based Navigation for Autonomous Robot in Unstructured Environment

This paper introduces a novel concept of Radio Frequency (RF) map creation derived from ray-tracing within a digital twin of an unstructured environment.

Recommended citation: S. Amatare, G. Singh, M. Samson, and D. Roy, "RagNAR: Ray-tracing based Navigation for Autonomous Robot in Unstructured Environment," in GLOBECOM 2024 - 2024 IEEE Global Communications Conference, Cape Town, South Africa, 2024, pp. 3631–3636, doi: 10.1109/GLOBECOM52923.2024.10901133. https://doi.org/10.1109/GLOBECOM52923.2024.10901133

Dt-radar: Digital twin assisted robot navigation using differential ray-tracing

In this paper, we propose DT-RaDaR, a robust privacy-preserving, deep reinforcement learning-based framework for robot navigation that leverages RF ray-tracing in both static and dynamic indoor scenarios as well as in smart cities.

Recommended citation: S. Amatare, G. Singh, R. Shakya, A. Kharel, A. Alkhateeb, D. Roy, "DT-RaDaR: Digital Twin Assisted Robot Navigation using Differential Ray-Tracing," arXiv preprint arXiv:2411.12284, 2024. [Online]. Available: https://arxiv.org/abs/2411.12284 https://arxiv.org/abs/2411.12284

POSTER: Analysis of Latency for Wireless Connectivity in Networked Robots

This study presents a comparative analysis of various types of communication latency in networked robotic setup.

Recommended citation: A. Kharel, R. Shakya, E. Barrientos, G. Singh, X. Zhang and D. Roy, "POSTER: Analysis of Latency for Wireless Connectivity in Networked Robots," 2025 IEEE 26th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), Fort Worth, TX, USA, 2025, pp. 154-156, doi: 10.1109/WoWMoM65615.2025.00036. https://ieeexplore.ieee.org/document/11027042

SauRON: Smart Surveillance using Robotic Swarms with Optimized Networks

This paper discusses the strategic placement of swarm of robots within the dynamic environments to ensure full coverage implemented using Reinforcement Learning.

Recommended citation: Singh, Gaurav and Amatare, Sunday and Roy, Debashri, SauRON: Smart Surveillance using Robotic Swarms with Optimized Networks (January 01, 2025). Available at SSRN: https://ssrn.com/abstract=5143487 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5143487

Spec-SCAN: Spectrum Learning in Shared Channel using Neural Networks

This paper presents a novel supervised deep learning framework for radar detection in CBRS Band.

Recommended citation: R. Hazari et al., "Spec-SCAN: Spectrum Learning in Shared Channel using Neural Networks," 2025 IEEE 22nd Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 2025, pp. 1-6, doi: 10.1109/CCNC54725.2025.10976013. keywords: {YOLO;Training;Time-frequency analysis;Wireless networks;Radar detection;Radar;Interference;Spread spectrum communication;Spectrogram;Signal to noise ratio;CBRS;object detection;radar detection;spectrum learning;YOLO}, https://ieeexplore.ieee.org/document/10976013

Real-Time Localization of Objects using Radio Frequency Propagation in Digital Twin

This paper introduces an innovative real-time object localization system within a digital twin, designed to detect and locate objects within an environment.

Recommended citation: S. Amatare, G. Singh, A. Kharel and D. Roy, "Real-Time Localization of Objects using Radio Frequency Propagation in Digital Twin," MILCOM 2024 - 2024 IEEE Military Communications Conference (MILCOM), Washington, DC, USA, 2024, pp. 653-654, doi: 10.1109/MILCOM61039.2024.10774060. keywords: {Location awareness;Radio frequency;Military communication;Shape;Training data;Real-time systems;Digital twins;Sensors;Reliability;Object recognition;Digital Twin;RF propagation;Object localization}, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5143487

Speclearn: Spectrum Learning in Shared Band Under Extreme Noise Conditions

This paper focuses on the detection of radar signals within the shared spectrum such as the Citizen Broadband Radio Service band employing YOLO algorithms under the influence of various noisy conditions.

Recommended citation: M. H. Rahman, G. Singh and D. Roy, "Speclearn: Spectrum Learning in Shared Band under Extreme Noise Conditions," 2024 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Washington, DC, USA, 2024, pp. 1-2, doi: 10.1109/DySPAN60163.2024.10632806. keywords: {Noise;Pipelines;Radar detection;Radar;Object detection;Machine learning;Robustness}, https://ieeexplore.ieee.org/document/10632806