Reconfigurable Antenna Arrays With Tunable Loads: Expanding Solution Space via Coupling Control

TL;DR

This paper introduces techniques to expand the solution space of reconfigurable antenna arrays by controlling mutual coupling with tunable loads, enabling more flexible array configurations and improving sum-rate performance.

Contribution

It proposes methods to exploit or eliminate mutual coupling using tunable loads, along with algorithms for efficient port selection and load optimization in large array configurations.

Findings

Achieved 20-56% sum-rate gains over benchmarks.

Demonstrated around 60% performance recovery with load quantization.

Developed algorithms handling over 10^{20} configurations.

Abstract

The emerging reconfigurable antenna (RA) array technology promises capacity enhancement through dynamic antenna positioning. Traditional approaches enforce half-wavelength or greater spacing among RA elements to avoid mutual coupling, limiting the solution space. Additionally, achieving sufficient spatial channel sampling requires numerous discrete RA positions (ports), while high-frequency scenarios with hybrid processing demand many physical RAs to maintain array gains. This leads to exponential growth in the solution space. In this work, we propose two techniques to address the former challenge: (1) surrounding a limited number of active RAs with passive ones terminated to tunable analog loads to \textit{exploit} mutual coupling and increase array gain, and (2) employing tunable loads on each RA in an all-active design to \textit{eliminate} mutual coupling in the analog domain. Both methods enable arbitrary RA spacing, unlocking the full solution space. Regarding the latter challenge, we develop greedy and meta-heuristic port selection algorithms, alongside low-complexity heuristic variants, that efficiently handle over $10^{20}$ array configurations. Furthermore, we optimize the loading values to maximize the sum-rate in a multiple-input single-output broadcast channel under transmission power constraints, assuming a heuristic linear precoder. In addition, we analyze performance degradation from quantized loads and propose corresponding robust designs. Numerical simulations reveal 20-56\% sum-rate gains over benchmarks and around 60\% performance recovery under quantization errors.