Channel Estimation in MIMO Systems Aided by Microwave Linear Analog Computers (MiLACs)

2026-01-16

AI Summary

This work addresses the lack of efficient channel estimation methods in MiLAC-assisted MIMO systems, where conventional digital-domain least squares (LS) and minimum mean square error (MMSE) algorithms undermine MiLAC’s inherent advantages in hardware and computational cost. To overcome this limitation, the paper presents the first fully analog-domain implementations of LS and MMSE channel estimation, leveraging a training precoder and combiner specifically designed for MiLAC. These analog-domain estimators achieve high accuracy without requiring any digital computation. The proposed approach maintains estimation performance comparable to that of digital counterparts while significantly reducing system complexity, the number of radio-frequency chains, ADC/DAC resolution requirements, and peak-to-average power ratio (PAPR), thereby preserving the low-cost benefits of the MiLAC architecture.

Technology Category

• Machine Learning: Hardware-aware ML
• Multiagent Systems: Multiagent Learning
• Cognitive Modeling & Cognitive Systems: Analogy

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements

Abstract

Microwave linear analog computers (MiLACs) have recently emerged as a promising solution for future gigantic multiple-input multiple-output (MIMO) systems, enabling beamforming with greatly reduced hardware and computational cost. However, channel estimation for MiLAC-aided systems remains an open problem. Conventional least squares (LS) and minimum mean square error (MMSE) estimation rely on intensive digital computation, which undermines the benefits offered by MiLACs. In this letter, we propose efficient LS and MMSE channel estimation schemes for MiLAC-aided MIMO systems. By designing training precoders and combiners implemented by MiLACs, both LS and MMSE estimation are performed fully in the analog domain, achieving identical performance to their digital counterparts while significantly reducing computational complexity, transmit RF chains, analog-to-digital/digital-to-analog converters (ADCs/DACs) resolution requirements, and peak-to-average power ratio (PAPR). Numerical results verify the effectiveness and advantages of the proposed schemes.

Problem

• Channel Estimation
• MIMO Systems
• Microwave Linear Analog Computers
• Analog Signal Processing
• Hardware Efficiency

Innovation

• MiLAC
• analog-domain channel estimation
• MIMO
• LS/MMSE estimation
• hardware-efficient beamforming