Lossy Microwave Linear Analog Computer (MiLAC) for Future MIMO: Learning-based Architecture Designs for Spectral and Energy Efficiency Maximization

2026-06-01

AI Summary

This work addresses the performance–overhead trade-off in microwave linear analog computers (MiLACs) for MIMO systems, where hardware losses induce mutual coupling interference and degrade energy efficiency. To overcome these limitations, the paper introduces the first learning-driven joint architecture and performance optimization framework (LJAPOF), which co-optimizes the lossy MiLAC hardware topology and analog beamforming configuration. By integrating analog-domain signal processing, tunable admittance modeling, and system-level performance evaluation, LJAPOF intelligently balances interference suppression, hardware loss, and power consumption. The proposed approach significantly enhances both spectral and energy efficiency, outperforming conventional star and fully connected baseline architectures.

Abstract

Microwave linear analog computers (MiLACs) offer a transformative paradigm for future multiple-input multiple-output (MIMO) systems by shifting complex signal processing into the analog domain, thereby significantly reducing computational complexity, radio-frequency chains, and analog-digital converters, while speeding up computation. However, the practical deployment of MiLACs is severely constrained by the inherent hardware losses of the tunable admittance components (TACs) interconnecting MiLAC ports, which introduce severe inter-stream interference and fundamentally limit the spectral efficiency (SE) of the system. In addition, while denser architectures offer greater spatial degrees of freedom to mitigate inter-stream interference, the cumulative hardware losses and power consumption of massive TACs severely degrade the system's energy efficiency (EE). Consequently, designing architectures for lossy MiLACs emerges as a critical yet unresolved challenge, as it necessitates striking a delicate tradeoff between interference suppression and cumulative hardware losses/power consumption. To address this challenge, this paper investigates the joint MiLAC architecture design and performance (SE/EE) maximization in lossy MiLAC-aided MIMO systems. We propose a novel learning-based joint architecture and performance optimization framework (LJAPOF) that unifies the design of MiLAC architectures and analog beamforming configurations for lossy MiLACs under both SE- and EE-oriented objectives. Numerical results demonstrate that by intelligently navigating the fundamental tradeoff between interference suppression and hardware/power consumption, the proposed LJAPOF can design optimal MiLAC architectures that consistently outperform stem-connected and fully-connected MiLACs in maximizing the system's SE and EE.

Problem

• MiLAC
• MIMO
• spectral efficiency
• energy efficiency
• hardware loss

Innovation

• Microwave Linear Analog Computer
• Lossy Hardware
• Learning-based Architecture Design
• Spectral Efficiency
• Energy Efficiency