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.
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.
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.
As wireless networks advance toward the Sixth Generation (6G), which will support highly heterogeneous scenarios and massive data traffic, conventional computing methods may struggle to meet the immense processing demands in a resource-efficient manner. This paper explores the potential of quantum computing (QC) to address these challenges, specifically by enhancing the efficiency of Maximum-Likelihood detection in Multiple-Input Multiple-Output (MIMO) Non-Orthogonal Multiple Access (NOMA) communication systems, an essential technology anticipated for 6G. The study proposes the use of the Quantum Approximate Optimization Algorithm (QAOA), a variational quantum algorithm known for providing quantum advantages in certain combinatorial optimization problems. While current quantum systems are not yet capable of managing millions of physical qubits or performing high-fidelity, long gate sequences, the results indicate that QAOA is a promising QC approach for radio signal processing tasks. This research provides valuable insights into the potential transformative impact of QC on future wireless networks. This sets the stage for discussions on practical implementation challenges, such as constrained problem sizes and sensitivity to noise, and opens pathways for future research aimed at fully harnessing the potential of QC for 6G and beyond.
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.
Inspired by recent developments in various areas of science relevant to quantum computing, we introduce quantum manifold optimization (QMO) as a promising framework for solving constrained optimization problems in next-generation wireless communication systems. We begin by showing how classical wireless design problems - such as pilot design in cell-free (CF)-massive MIMO (mMIMO), beamformer optimization in gigantic multiple input multiple output (MIMO), and reconfigurable intelligent surface (RIS) phase tuning - naturally reside on structured manifolds like the Stiefel, Grassmannian, and oblique manifolds, with the latter novelly formulated in this work. Then, we demonstrate how these problems can be reformulated as trace-based quantum expectation values over variationally-encoded quantum states. While theoretical in scope, the work lays a foundation for a new class of quantum optimization algorithms with broad application to the design of future beyond-sixth-generation (B6G) systems.
Rydberg atomic quantum receivers (RAQRs) are capable of receiving multi-band radio-frequency (RF) signals simultaneously, which are expected to break Chu's limit for classical electronic antennas. However, signals from different users will interfere with each other in the optical intermediate frequency (IF) domain of the multi-band quantum receiver, which is termed the IF interference (IFI) problem. To address this problem, in this paper, we propose a multi-input multi-output (MIMO) architecture for Rydberg atomic quantum receiver (RAQ-MIMO) by exploiting the additional spatial diversity of MIMO receivers. Specifically, by applying the dynamic signal model of RAQRs, we clarify the physical relationship between the quantum local oscillator (LO) configurations and the multi-band gains with the concept of quantum transconductance. Then, with the quantum transconductance-based signal model, we formulate the spectral efficiency (SE) maximization problem and further propose the quantum weighted minimum mean square error (qWMMSE) algorithm, which jointly optimizes the quantum LO configurations and the classical precoder/combiner matrices. Furthermore, we test the qWMMSE algorithm within the standard space division multiple access (SDMA) scheme and the frequency division multiple access (FDMA) scheme. Simulation results demonstrate that the qWMMSE optimization framework can significantly improve the SE of RAQ-MIMO systems for both multiple access schemes, and that RAQ-MIMO systems can outperform classical electronic receiver-based multi-user MIMO systems by eliminating the mutual coupling effect between classical antennas.
Massive multiple-input multiple-output (MIMO) systems are a key enabling technology for 5G-Advanced and emerging 6G networks, yet their performance is fundamentally constrained by high-dimensional, non-convex optimization problems arising from beamforming, power allocation, and user scheduling under imperfect channel state information (CSI).This paper proposes a hybrid quantum-classical optimization framework that formulates discrete and continuous massive MIMO resource allocation problems using Quadratic Unconstrained Binary Optimization (QUBO) and variational quantum algorithms.Discrete beam selection is addressed using quantum annealing, while continuous power control and hybrid beamforming are refined using variational quantum circuits within a classical feedback loop.Simulation results demonstrate improvements in aggregate spectral efficiency, runtime scalability, and robustness under imperfect CSI compared to conventional ZF and WMMSE baselines.The benefits of using the proposed solution include scalable runtime performance with respect to larger numbers of antennas; thus, it highlights the potential of optimizing quantum-assisted technologies for the future of wireless networks based on realistic assumptions regarding near-term NISQ-era performance.