🤖 AI Summary
Traditional reconfigurable antenna arrays are constrained by the half-wavelength minimum element spacing and the requirement for a large number of RF ports, leading to a limited solution space and high hardware complexity. To address this, this paper proposes a novel paradigm of arbitrarily spaced array design with controllable inter-element coupling: tunable analog loads are employed to actively regulate mutual coupling, thereby relaxing the spacing constraint; two new array architectures—passively loaded (leveraging coupling) and fully active decoupled (suppressing coupling)—are introduced; and greedy/heuristic port selection combined with low-complexity load optimization is applied to maximize the sum rate in MISO broadcast channels. Simulation results demonstrate that, under transmit power constraints, the proposed scheme significantly outperforms benchmark methods, exhibits robustness against quantized load errors, and substantially enhances array gain and configuration flexibility.
📝 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. 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 extit{exploit} mutual coupling and increase array gain, and (2) employing tunable loads on each RA in an all-active design to extit{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, and 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. Furthermore, we analyze performance degradation from quantized loads and propose corresponding robust designs. Numerical simulations reveal significant performance gains over benchmarks and provide valuable insights.