Fast Compressive Channel Estimation for MmWave MIMO Hybrid Beamforming Systems

TL;DR

This paper introduces two low-complexity compressive sensing-based channel estimation methods for mmWave MIMO hybrid beamforming, improving efficiency while maintaining accuracy compared to traditional techniques.

Contribution

It proposes two novel low-complexity channel estimation strategies, two-stage CS and 2-D CS, tailored for hybrid beamforming architectures in mmWave MIMO systems.

Findings

Two-stage CS reduces computational complexity with slightly lower performance.

2-D CS achieves similar performance to 1-D CS but with lower complexity.

Both methods outperform traditional 1-D CS in efficiency.

Abstract

Given the high degree of computational complexity of the channel estimation technique based on the conventional one-dimensional (1-D) compressive sensing (CS) framework employed in the hybrid beamforming architecture, this study proposes two low-complexity channel estimation strategies. One is two-stage CS, which exploits row-group sparsity to estimate angle-of-arrival (AoA) first and uses the conventional 1-D CS method to obtain angle-of-departure (AoD). The other is two-dimensional (2-D) CS, which utilizes a 2-D dictionary to reconstruct the 2-D sparse signal. To conduct a meaningful comparison of the three CS frameworks, i.e., 1-D, two-stage and 2-D CS, the orthogonal match pursuit (OMP) algorithm is employed as the basic algorithm and is expanded to two variants for the proposed frameworks. Analysis and simulations demonstrate that when the 1-D CS method is compared, two-stage CS has somewhat lower performance but significantly lower computational complexity, while 2-D CS is not only the same as 1-D CS in terms of performance but also slightly lower in computational complexity than two-stage CS.