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

TL;DR

This paper introduces physics-inspired heuristic algorithms, including quantum annealing and simulated annealing, to optimize reconfigurable antenna MIMO systems, achieving near-optimal performance with polynomial complexity.

Contribution

It develops and applies physics-inspired heuristic solvers for antenna configuration selection, outperforming classical methods and providing a scalable approach for MIMO system optimization.

Findings

Heuristic solvers outperform classical methods in antenna configuration optimization.

Quantum-inspired algorithms achieve near-optimal performance with polynomial complexity.

Parallel tempering enhances simulated annealing results by avoiding local maxima.

Abstract

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.