Microwave Linear Analog Computers (MiLACs) for Communications: Opportunities and Challenges

2026-07-24

AI Summary

This work addresses the hardware complexity and power consumption limitations of conventional digital MIMO systems under massive antenna arrays and high traffic loads. It proposes a radio-frequency analog computing architecture based on Microwave Linear Analog Computers (MiLAC), demonstrating for the first time that tunable linear microwave networks can generate nonlinear output responses, thereby enabling efficient computation of operations beyond linear transformations—such as matrix inversion—with only O(N²) complexity. By integrating reconfigurable microwave networks, zero-forcing beamforming, and low-resolution ADCs/DACs, the proposed approach substantially reduces the number of RF chains, relaxes the requirements on analog-to-digital conversion precision, and dramatically lowers beamforming computational overhead, offering a scalable and energy-efficient hardware solution for future wireless systems.

Abstract

Future wireless systems will require ever larger antenna arrays and heavier signal processing, making conventional digital multiple-input multiple-output (MIMO) architectures difficult to scale. In this paper, we show that a possible solution is to offload part of the processing from the digital to the analog domain. This can be done through linear microwave networks designed to compute directly using the communication signals at radio frequency (RF). These networks, denoted as microwave linear analog computers (MiLACs), can perform useful matrix operations instantly through wave propagation. Remarkably, although MiLACs are linear, the output signals can depend nonlinearly on the tunable parameters of the network, enabling the computation of operations beyond simple linear transforms. In particular, MiLACs can realize matrix inversion and pseudo-inversion with complexity scaling quadratically with matrix size, rather than cubically, which is essential in zero-forcing beamforming. We then review how MiLAC-aided MIMO architectures can reduce the number of RF chains, relax the resolution requirements on digital-to-analog converters (DACs) and analog-to-digital converters (ADCs), and decrease the beamforming complexity. We finally discuss the main challenges related to MiLAC and promising directions for future research.

Problem

• MIMO
• scalability
• RF complexity
• beamforming
• analog computing

Innovation

• Microwave Linear Analog Computers
• analog computing
• MIMO
• matrix inversion
• beamforming