To meet the demands of future wireless networks, antenna arrays must scale from massive multiple-input multiple-output (MIMO) to gigantic MIMO, involving even larger numbers of antennas. To address the hardware and computational cost of gigantic MIMO, several strategies are available that shift processing from the digital to the analog domain. Among them, microwave linear analog computers (MiLACs) offer a compelling solution by enabling fully analog beamforming through reconfigurable microwave networks. Prior work has focused on fully-connected MiLACs, whose ports are all interconnected to each other via tunable impedance components. Although such MiLACs are capacity-achieving, their circuit complexity, given by the number of required impedance components, scales quadratically with the number of antennas, limiting their practicality. To solve this issue, in this paper, we propose a graph theoretical model of MiLAC facilitating the systematic design of lower-complexity MiLAC architectures. Leveraging this model, we propose stem-connected MiLACs as a family of MiLAC architectures maintaining capacity-achieving performance while drastically reducing the circuit complexity. Besides, we optimize stem-connected MiLACs with a closed-form capacity-achieving solution. Our theoretical analysis, confirmed by numerical simulations, shows that stem-connected MiLACs are capacity-achieving, but with circuit complexity that scales linearly with the number of antennas, enabling high-performance, scalable, gigantic MIMO.

翻译：为满足未来无线网络的需求，天线阵列必须从大规模多输入多输出（MIMO）扩展至巨型MIMO，涉及数量更多的天线。针对巨型MIMO带来的硬件与计算成本，现有多种策略可将处理任务从数字域转移至模拟域。其中，微波线性模拟计算机（MiLAC）通过可重构微波网络实现全模拟波束赋形，提供了一种极具吸引力的解决方案。先前研究聚焦于全连接MiLAC——其所有端口均通过可调阻抗元件相互连接。尽管此类MiLAC能够达到容量上限，但其所需阻抗元件数目导致的电路复杂度与天线数量的平方成正比，限制了实际应用。为解决这一问题，本文提出基于图论模型的MiLAC，以系统化设计低复杂度MiLAC架构。基于该模型，我们提出枝干连接MiLAC架构族，该架构在保持容量可达性能的同时显著降低电路复杂度。此外，我们通过闭合形式的容量可达解对枝干连接MiLAC进行优化。理论分析及数值仿真结果均表明：枝干连接MiLAC不仅能实现容量可达，其电路复杂度与天线数量呈线性关系，为高性能、可扩展的巨型MIMO提供了实现路径。