MIMO Systems Aided by Microwave Linear Analog Computers: Capacity-Achieving Architectures with Reduced Circuit Complexity

2025-06-18

AI Summary

In massive MIMO systems, the quadratic circuit complexity—O(N²)—of fully connected microwave linear analog computers (MiLACs) severely limits scalability. To address this, we propose a novel stem-connected MiLAC architecture, formulated via graph-theoretic modeling of analog beamforming as a structured optimization problem. Crucially, our design preserves Shannon capacity optimality while reducing the number of required impedance elements to O(N), achieving linear hardware scaling. We derive a closed-form analytical solution for the optimal component configuration. Comprehensive microwave circuit synthesis, information-theoretic capacity analysis, and numerical simulations validate that the proposed architecture significantly enhances hardware scalability and energy efficiency. This work establishes a new paradigm for real-time, low-power analog beamforming in ultra-massive antenna systems.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Analogy

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

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.

Problem

• Reducing circuit complexity in gigantic MIMO systems
• Designing capacity-achieving microwave linear analog computers
• Scaling antenna arrays with linear complexity growth

Innovation

• Microwave linear analog computers enable analog beamforming
• Graph theory models reduce MiLAC circuit complexity
• Stem-connected MiLACs achieve linear complexity scaling