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

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.

Figures (12)

• Figure 1: The considered point-to-point reconfigurable MIMO system with $N_T$ and $N_R$ antennas at the transmitter and the receiver, respectively; $N$ states at each antenna. The symbol $\times$ represents an antenna state, while the set of bold symbols $\pmb{\times}$ corresponds to the selected configuration.
• Figure 2: (left) CDF of the SNR objective function; (right) CDF of the capacity objective function, for both joint and configuration-based ES schemes. Setup with $N_T=N_R=2$, $N=2$ and $P=10$ (dB).
• Figure 3: [CIM] Expected value of the SNR-maximization objective function with CIM-based antenna configuration selection for different values of the penalty parameter $\lambda$.
• Figure 4: [CIM] (left) Probability of constraint satisfaction by CIM-based antenna configuration selection solutions for different values of the penalty parameter $\lambda$. (right) Probability of constraint satisfaction by CIM-based antenna configuration selection solutions as CIM dynamics evolve with time.
• Figure 5: [CIM] Occurrence probability of different solutions ranked in descending order of $Q_0$ for $N_T = 3, N_R = 3, N = 3$ over $10^4$ Rayleigh fading channel instances and $100$ anneals per instance.