Structured Sensing Matrix Design for In-sector Compressed mmWave Channel Estimation

Fast millimeter wave (mmWave) channel estimation techniques based on compressed sensing (CS) suffer from low signal-to-noise ratio (SNR) in the channel measurements, due to the use of wide beams. To address this problem, we develop an in-sector CS-based mmWave channel estimation technique that focuses energy on a sector in the angle domain. Specifically, we construct a new class of structured CS matrices to estimate the channel within the sector of interest. To this end, we first determine an optimal sampling pattern when the number of measurements is equal to the sector dimension and then use its subsampled version in the sub-Nyquist regime. Our approach results in low aliasing artifacts in the sector of interest and better channel estimates than benchmark algorithms.

Automated summary

In this paper, an in-sector compressed sensing-based mmWave channel estimation technique is proposed to deal with the low SNR problem caused by wide beams. By focusing the energy on the sector of interest and using a new class of structured CS matrices, the proposed approach achieves better channel estimates with reduced aliasing artifacts in the sector of interest compared to benchmark algorithms.