Quantum Optimization of Reconfigurable Intelligent Surfaces for Mitigating Multipath Fading in Wireless Networks

• Published in: IEEE journal on multiscale and multiphysics computational techniques Vol. 9; pp. 403 - 414
• Main Authors: Colella, Emanuel, Bastianelli, Luca, Primiani, Valter Mariani, Peng, Zhen, Moglie, Franco, Gradoni, Gabriele
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 6G mobile communication; Algorithms; Antennas; Computational modeling; Cost function; Eigenvalues and eigenfunctions; Electromagnetic radiation; Fading; Fading channels; fast fading; ising model; Mathematical models; Metamaterials; Network latency; Noise levels; Optimization; Performance enhancement; Quantum computing; Receiving antennas; reconfigurable intelligent surface; Reconfigurable intelligent surfaces; Spin glasses; Training; Transmitting antennas; Wireless communication; Wireless communications; Wireless networks
• ISSN: 2379-8815; 2379-8815
• DOI: 10.1109/JMMCT.2024.3494037

Wireless communication technology has become important in modern life. Real-world radio environments present significant challenges, particularly concerning latency and multipath fading. A promising solution is represented by reconfigurable intelligent surfaces (RIS), which can manipulate electromagnetic waves to enhance transmission quality. In this study, we introduce a novel approach that employs the quantum approximate optimization algorithm (QAOA) to efficiently configure RIS in multipath environments. Applying the spin glass (SG) theoretical framework to describe chaotic systems, along with a variable noise model, we propose a quantum-based minimization algorithm to optimize RIS in various electromagnetic scenarios affected by multipath fading. The method involves training a parameterized quantum circuit using a mathematical model that scales with the size of the RIS. When applied to different EM scenarios, it directly identifies the optimal RIS configuration. This approach eliminates the need for large datasets for training, validation, and testing, streamlines, and accelerates the training process. Furthermore, the algorithm will not need to be rerun for each individual scenario. In particular, our analysis considers a system with one transmitting antenna, multiple receiving antennas, and varying noise levels. The results show that QAOA enhances the performance of RIS in both noise-free and noisy environments, highlighting the potential of quantum computing to address the complexities of RIS optimization and improve the performance of the wireless network.