Optimizing Configuration Selection in Reconfigurable-Antenna MIMO Systems: Physics-Inspired Heuristic Solvers

Reconfigurable antenna multiple-input multiple-output (MIMO) is a foundational technology for the continuing evolution of cellular systems, including upcoming 6G communication systems. In this paper, we address the problem of flexible/reconfigurable antenna configuration selection for point-to-point...

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Bibliographic Details
Published in:IEEE transactions on communications 2024-12, Vol.72 (12), p.8010-8023
Main Authors: Krikidis, Ioannis, Psomas, Constantinos, Kumar Singh, Abhishek, Jamieson, Kyle
Format: Article
Language:English
Subjects:
and fluid antenna systems
Antennas
Coherent Ising machines
Communications systems
Complexity
Configurations
D-Wave
Europe
Heuristic
Ising model
Mathematical analysis
Maxima
MIMO communication
MIMO systems
Optimization
Parameterization
Polynomials
quantum annealing
quantum computing
Quantum mechanics
Receiving antennas
reconfigurable antennas
Reconfiguration
Signal to noise ratio
Simulated annealing
Solvers
Wireless communication
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Summary:Reconfigurable antenna multiple-input multiple-output (MIMO) is a foundational technology for the continuing evolution of cellular systems, including upcoming 6G communication systems. In this paper, we address the problem of flexible/reconfigurable antenna configuration selection for point-to-point MIMO antenna systems by using physics-inspired heuristics. Firstly, we optimize the antenna configuration to maximize the signal-to-noise ratio (SNR) at the receiver by leveraging two basic heuristic solvers, i.e., coherent Ising machines (CIMs), that mimic quantum mechanical dynamics, and quantum annealing (QA), where a real-world QA architecture is considered (D-Wave). A mathematical framework that converts the configuration selection problem into CIM- and QA- compatible unconstrained quadratic formulations is investigated. Numerical and experimental results show that the proposed designs outperform classical counterparts and achieve near-optimal performance (similar to exhaustive search with exponential complexity) while ensuring polynomial complexity. Moreover, we study the optimal antenna configuration that maximizes the end-to-end Shannon capacity. A simulated annealing (SA) heuristic which achieves near-optimal performance through appropriate parameterization is adopted. A modified version of the basic SA that exploits parallel tempering to avoid local maxima is also studied, which provides additional performance gains. Extended numerical studies show that the SA solutions outperform conventional heuristics (which are also developed for comparison purposes), while the employment of the SNR-based solutions is highly sub-optimal.
ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2024.3420768