A compressive channel estimation technique robust to synchronization impairments

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.

5 pages, 3 figures, To appear in the proceedings of the 18th IEEE International Workshop on Signal Processing Advances in Wireless Communications

References in corpus (2)

• Tensor Decomposition for Signal Processing and Machine Learning
• Structured Compressed Sensing: From Theory to Applications

Cited by in corpus (6)

• Fast Beam Alignment for Millimeter Wave Communications: A Sparse Encoding and Phaseless Decoding Approach
• Compressive Initial Access and Beamforming Training for Millimeter-Wave Cellular Systems
• Swift-Link: A compressive beam alignment algorithm for practical mmWave radios
• Message passing-based joint CFO and channel estimation in millimeter wave systems with one-bit ADCs
• Joint Synchronization, Phase Noise and Compressive Channel Estimation in Hybrid Frequency-Selective mmWave MIMO Systems
• Fast Beam Training and Alignment for IRS-Assisted Millimeter Wave/Terahertz Systems