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.
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.
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.
The proposal outlines the design of a radar and relay-assisted communication system with multiple input multiple output (MIMO) configuration, utilizing quantum algorithm in coexistence scenario. We name it MIMO QRadRCom framework. Quantum computation offers significant speed-ups and holds promise for future technologies like sixth generation (6G) cellular networks. By integrating quantum simulation, we aim to design parameters, like an equalizer at the communication receiver and others, and then classically optimize the rest of the design parameters. The equalizer comes from the linear minimum mean square error (LMMSE) criterion, which involves matrix inversion and is classically expensive. The mathematical formulations address classical and quantum inaccuracies, supported by numerical simulations on the IBM quantum machine.
This paper introduces a pioneering method in the realm of signal processing for Massive Multiple-Input Multiple-Output (MIMO) systems, leveraging the synergistic potential of hybrid quantum-classical computing paradigms. The escalating demand for high data rate communications necessitates the advancement of MIMO technologies. However, the computational complexity associated with signal processing in such systems, especially in scenarios involving large array sizes, presents a significant challenge. This study proposes an innovative framework that integrates quantum computing algorithms with traditional classical methods to address this challenge. The quantum algorithms are specifically designed to expedite matrix operations and optimization tasks that are central to MIMO signal processing, such as channel estimation and precoding. A comparative analysis with purely classical approaches demonstrates a substantial improvement in computational efficiency and latency reduction, while maintaining, and in certain aspects enhancing, the accuracy of signal detection and system throughput. Furthermore, this hybrid approach offers scalability and adaptability to varying channel conditions and system configurations, a crucial feature for next-generation wireless communication systems. The theoretical foundation is complemented by simulation results, showcasing the feasibility and effectiveness of this approach in practical scenarios. This research not only paves the way for advanced signal processing techniques in Massive MIMO systems but also opens new horizons in the application of quantum computing in telecommunications.
In this research, the goal is to assess the performance and computational efficiency of the Quantum Approximate Optimization Algorithm for signal detection in MIMO-NOMA systems compared with classical detection methods. The deep learning detection framework supported by the Quantum Approximate Optimization Algorithm (QAOA). The Quantum Approximate Optimization Algorithm (QAOA) assistance in the deep learning-based detection system. For this group, the sample size of signal examples considered is 5,000, and the simulations are carried out based on various signal to noise ratios, antenna array, modulations, and channel estimation errors. The bit error rate, accuracy of detection, channel estimation error, complexity, and throughput of the system are evaluated. The output of the simulation showed that, for a given detection performance, the QAOA-assisted deep learning detection framework outperforms other detection methods in terms of 18% lower bit error rate, 14% higher detection accuracy, 12% lower channel estimation error, 10% lower computational complexity, and 16% higher throughput. The result of the experiment suggests that the proposed framework for signal detection using the assistance of the QAOA-based deep learning algorithm is capable of carrying out the detection of the multiple users' signal compared to other signal detection methods in terms of the mentioned criteria.
Quantum machine learning (QML) and quantum communication (QC) are emerging areas of research for complexity-efficient data processing, information retrieval, system, and simulations. Towards next-generation vehicular technology, quantum circuits and algorithms are critical in providing faster and more reliable solutions. Here, we show a modified quantum K-nearest neighbour (QKNN) algorithm for symbol detection in the communication system. In the proposed algorithm, we embed a quantum swap-test circuit for the distance computation between unknown symbols with known training symbols with a memory-efficient strategy, which eliminated quantum search operations for the small-search space such as 16-QAM symbols. The proposed quantum circuit is simulated on an IBM quantum machine and the numerical results are shown to demonstrate the efficacy of the proposed framework. The classification accuracy as a performance metric is shown and the improvement of quantum resources in terms of gate complexity is discussed.
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.