A compressive channel estimation technique robust to synchronization impairments

TL;DR

This paper presents a novel compressive channel estimation method for mmWave systems that remains effective despite hardware impairments like carrier frequency offset and phase noise, outperforming existing algorithms.

Contribution

It introduces a tensor-based estimation algorithm that jointly models the mmWave channel and CFO, enhancing robustness to synchronization impairments.

Findings

Outperforms existing algorithms under phase errors

Maintains high estimation accuracy with hardware non-idealities

Exploits channel sparsity for improved performance

Abstract

Initial access at millimeter wave frequencies is a challenging problem due to hardware non-idealities and low SNR measurements prior to beamforming. Prior work has exploited the observation that mmWave MIMO channels are sparse in the spatial angle domain and has used compressed sensing based algorithms for channel estimation. Most of them, however, ignore hardware impairments like carrier frequency offset and phase noise, and fail to perform well when such impairments are considered. In this paper, we develop a compressive channel estimation algorithm for narrowband mmWave systems, which is robust to such non idealities. We address this problem by constructing a tensor that models both the mmWave channel and CFO, and estimate the tensor while still exploiting the sparsity of the mmWave channel. Simulation results show that under the same settings, our method performs better than comparable algorithms that are robust to phase errors.