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IEEE Vehicular Technology Conference

QRADCOM: Quantum Assisted Framework for Joint Detection and Estimation in Radar Communications

Mostafizur Rahaman Laskar, Soumita Naskar, Amit Kumar Dutta

Published Jun 1, 2023• 1 citation
Publisher

Abstract

For next-generation vehicle-to-everything (V2X) communications, there has been a growing emerging technology at the intersection of Radar and Communications, namely RadCom. RadCom provides many benefits such as efficient usage of bandwidth, joint detection and estimation, and improved tracking performance for future intelligent transportation. However, there is a limitation in the computation of parameters for large dimensions (antennas, users, or targets). To provide, a computational complexity advantage for the target detection and parameters, we introduce Quantum-assisted Radar Communication systems, namely QRADCOM. In this work, we have proposed algorithms for the multi-target detection hypothesis based on quantum phase estimation (QPE), and also the quantum inversion method for communication signal detection in presence of observation noise, interference and multi-tap wireless channel. We show numerical results and analysis as well for the QRADCOM framework on both classical and IBM-Quantum computers.

DOI
10.1109/vtc2023-spring57618.2023.10200135

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For next-generation vehicle-to-everything (V2X) communications, there has been a growing emerging technology at the intersection of Radar and Communications, namely RadCom. RadCom provides many benefits such as efficient usage of bandwidth, joint detection and estimation, and improved tracking performance for future intelligent transportation. However, there is a limitation in the computation of parameters for large dimensions (antennas, users, or targets). To provide, a computational complexity advantage for the target detection and parameters, we introduce Quantum-assisted Radar Communication systems, namely QRADCOM. In this work, we have proposed algorithms for the multi-target detection hypothesis based on quantum phase estimation (QPE), and also the quantum inversion method for communication signal detection in presence of observation noise, interference and multi-tap wireless channel. We show numerical results and analysis as well for the QRADCOM framework on both classical and IBM-Quantum computers.
Radar communication system (RadCom) is an emerging area in vehicular technology that provides efficient bandwidth allocation for joint detection of communication symbols and source detection in radar. This finds application in vehicle-to-anything (V2X) system. Towards next-generation communication, such intelligent system requires both accuracy and complexity efficient algorithms for reliable use cases. Classical super-resolution algorithms that provide the high accuracy in parameter estimation are built on matrix sub-space method incurring high computational complexity. By achieving a performance nearly as accurate as that of the classical RadCom algorithm for target detection in radar and mean square error (MSE) performances in communication system, we introduce a quantum-assisted signal processing for radar communication, known as QRadCom. The key contribution in this work is the significant computational complexity reduction with the proposed quantum formalism-based QRadCom, compared to its classical counterpart. Considering a multi-tap communication channel, we demonstrate a quantum-assisted symbol detection and source identification algorithm for a single-input and multiple-output (SIMO) antenna configuration. Further, using random matrix theory for the large-dimensional channel matrix, we derive an approximate bound for the mean square error in the QRadCom system. Numerical results manifest the efficacy of the proposed QRadCom in terms of computational complexity improvement and nearly same performance compared to the classical algorithm.
Integrated sensing and communication (ISAC) is emerging as a key enabler for spectrum-efficient and hardware-converged wireless networks. However, classical radar systems within ISAC architectures face fundamental limitations under low signal power and high-noise conditions. This paper proposes a novel framework that embeds quantum illumination radar into a base station to simultaneously support full-duplex classical communication and quantum-enhanced target detection. The resulting integrated quantum sensing and classical communication (IQSCC) system is optimized via a sum-rate maximization formulation subject to radar sensing constraints. The non-convex joint optimization of transmit power and beamforming vectors is tackled using the successive convex approximation technique. Furthermore, we derive performance bounds for classical and quantum radar protocols under the statistical detection theory, highlighting the quantum advantage in low signal-to-interference-plus-noise ratio regimes. Simulation results demonstrate that the proposed IQSCC system achieves a higher communication throughput than the conventional ISAC baseline while satisfying the sensing requirement.
Quantum radar represents a major shift in sensing by leveraging quantum correlations and entanglement to detect targets beyond the line of sight in complex environments. Unlike classical radar, which suffers severe performance loss in dense urban areas due to multipath effects, clutter, and noise, quantum radar uses biphoton correlations to preserve strong detection sensitivity even under heavy interference. This chapter presents a comprehensive review of quantum radar systems, focusing on NLOS functionality. It describes key concepts such as biphoton generation through spontaneous parametric down-conversion, quantum illumination methods, and optimal detection strategies. Practical implementations using cryogenic superconducting nanowire single-photon detectors and AI-based signal processing are also discussed. The role of quantum radar in future 6G integrated sensing and communication frameworks is explored, highlighting uses in autonomous vehicle safety and smart city monitoring. Simulation studies show detection rates above 95% at a 1 km NLOS distance, with up to a tenfold SNR improvement over classical systems, while experiments demonstrate more than 20 dB of noise reduction. The chapter concludes by addressing ongoing challenges like decoherence, scalability, and standardization, and outlines a pathway toward deployable quantum radar technologies by 2030 for Industry 5.0 applications.
Quantum radar represents a major shift in sensing by leveraging quantum correlations and entanglement to detect targets beyond the line of sight in complex environments. Unlike classical radar, which suffers severe performance loss in dense urban areas due to multipath effects, clutter, and noise, quantum radar uses biphoton correlations to preserve strong detection sensitivity even under heavy interference. This chapter presents a comprehensive review of quantum radar systems, focusing on NLOS functionality. It describes key concepts such as biphoton generation through spontaneous parametric down-conversion, quantum illumination methods, and optimal detection strategies. Practical implementations using cryogenic superconducting nanowire single-photon detectors and AI-based signal processing are also discussed. The role of quantum radar in future 6G integrated sensing and communication frameworks is explored, highlighting uses in autonomous vehicle safety and smart city monitoring. Simulation studies show detection rates above 95% at a 1 km NLOS distance, with up to a tenfold SNR improvement over classical systems, while experiments demonstrate more than 20 dB of noise reduction. The chapter concludes by addressing ongoing challenges like decoherence, scalability, and standardization, and outlines a pathway toward deployable quantum radar technologies by 2030 for Industry 5.0 applications.
This paper presents a quantum-enhanced pulse-Doppler (PD) radar processing framework. It fuses quantum-correlated sensing with classical radar signal processing to improve target detection in complex electromagnetic conditions. We introduce coincidence counting and fidelity estimation, which enable target verification and false-alarm suppression under strong clutter and interference. Simulation experiments show higher detection probability under a low-false-alarm constraint. A two-stage “verification + recheck” mechanism remedies missed detections in the classical chain. The results indicate the potential of quantum radar techniques for practical seeker systems.
The increasing demands for ultra-reliable low-latency communication (URLLC), high-capacity, connectivity, and mobility in next-generation wireless networks have driven the integration of unmanned aerial vehicles (UAVs) as flexible and adaptive communication nodes. UAV-assisted networks offer enhanced coverage, dynamic deployment, and cost-effective infrastructure for the sixth generation (6G) and beyond. However, optimizing UAV trajectory planning, resource allocation, and interference management is particularly challenging, especially in dynamic and large-scale environments. Classical reinforcement learning (RL) techniques, while effective in adaptive optimization, suffer from computational inefficiencies and slow convergence in high-dimensional state-action spaces. To address these constraints, quantum reinforcement learning (QRL) emerges as a novel paradigm that leverages quantum computing principles, such as superposition and entanglement, to enhance decision-making efficiency. This study provides a comprehensive overview of QRL algorithms and investigates their transformative potential in UAV-assisted wireless communication networks. We analyze UAV deployment strategies, network architectures, and topologies, highlighting their advantages as well as inherent challenges. Furthermore, we examine the limitations of classical RL algorithms and assess how QRL overcomes them while improving computational efficiency and exploration capabilities. In addition, we present a detailed review of existing QRL algorithms applicable to next-generation UAV-enhanced wireless networks, identifying key challenges that must be addressed for practical deployment. Finally, we identify open research challenges and offer future directions for integrating QRL into UAV-assisted communication, paving the way for scalable, intelligent, and high-performance wireless systems.