Channel Estimation and Beamforming for Microwave Linear Analog Computers (MiLACs)-Aided Multiuser MISO Systems

2026-07-01

AI Summary

This work addresses the challenge in MiLAC-aided multiuser MISO systems where conventional channel estimation relies heavily on numerous RF chains and high-dimensional digital processing, thereby undermining the benefits of analog computing. To overcome this, the paper proposes a low-complexity joint design that exploits the rank-deficient structure of the channel correlation matrix. By employing a two-stage MiLAC architecture in the analog domain to compress the received signals, the approach confines channel estimation to a low-dimensional digital domain and efficiently implements regularized zero-forcing beamforming (R-ZFBF). This scheme establishes the first end-to-end analog-digital collaborative framework tailored for scenarios with limited RF chains, achieving computational complexity reductions of 1,540× and 16,108× for channel estimation and beamforming, respectively, while maintaining performance close to that of the fully digital benchmark.

Abstract

Microwave linear analog computers (MiLACs) have recently gained attention for future gigantic multiple-input multiple-output (MIMO) systems by enabling beamforming with greatly reduced hardware and computational cost. However, channel estimation for MiLAC-aided multiuser systems remains an open problem. Conventional channel estimation requires many radio-frequency (RF) chains to access full-dimensional received signals, followed by massive digital processing, which undermines the advantages of MiLAC-aided systems in reducing the number of RF chains and computational complexity. In this paper, we propose computationally efficient channel estimation and beamforming schemes for MiLAC-aided multiuser multiple-input single-output (MU-MISO) systems with a limited number of RF chains. We consider the general case where different user groups experience different channel correlation matrices. By exploiting the rank deficiency of these matrices, the proposed schemes use MiLAC to compress the full-dimensional received signals in the analog domain, making them compatible with the available RF chains while preserving the essential channel information. Then, in the digital domain, only low-dimensional channel estimation is performed based on these compressed observations, substantially reducing computational cost. We further show how regularized zero-forcing beamforming (R-ZFBF) can be efficiently realized from the low-dimensional channel estimates through a cascade of two MiLACs, which offers greater computational flexibility than a single MiLAC. Numerical results show that the proposed schemes reduce computational complexity up to $1540\times$ and $16108\times$, for channel estimation and beamforming, respectively, while achieving performance comparable to digital baselines.

Problem

• Channel Estimation
• Microwave Linear Analog Computers
• Multiuser MISO
• RF Chains
• Computational Complexity

Innovation

• MiLAC
• channel estimation
• beamforming
• analog computing
• low-complexity