Sparse Millimeter Wave Channel Estimation From Partially Coherent Measurements

Master thesis (2023)

Authors

W. Yi Electrical Engineering, Mathematics and Computer Science

Contributors

A.J. van der Veen Signal Processing Systems - EEMCS (mentor)
N.J. Myers Team Nitin Myers - Mechanical, Maritime and Materials Engineering (graduation committee member)
G. Joseph Signal Processing Systems - EEMCS (graduation committee member)
P. Mohajerin Esfahani Team Peyman Mohajerin Esfahani - Mechanical, Maritime and Materials Engineering (coach)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Weijia Yi
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Published Date

27-11-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This project develops a channel estimation technique for millimeter wave (mmWave) communication systems. Our method exploits the sparse structure in mmWave channels for low training overhead and accounts for the phase errors in the channel measurements due to phase noise at the oscillator. Specifically, in IEEE 802.11ad/ay-based mmWave systems, the phase errors within a beam refinement protocol packet are almost the same, while the errors across different packets are substantially different.

We show that standard compressed sensing algorithms that treat phase noise as a constant fail when channel measurements are acquired over multiple beam refinement protocol packets. Most of the methods that have addressed this problem treat phase noise as purely random, missing the inherent structure within the measurement packets. We present a novel algorithm called partially coherent matching pursuit for sparse channel estimation under practical phase noise perturbations. The proposed approach leverages this partially coherent structure in the phase errors across multiple packets. Our algorithm iteratively detects the support of sparse signal and employs alternating minimization to jointly estimate the signal and the phase errors.

We numerically show that our algorithm can reconstruct the channel accurately at a lower complexity than the benchmarks, and derive a preliminary support detection bound as a performance guarantee.