Optimized compressive sensing-based direction-of-arrival estimation in massive MIMO
Yujie Gu, Yimin D. Zhang, Nathan A. Goodman
Abstract
As a new emerging technology for wireless communications, massive multiple-input multiple-output (MIMO) faces a significant challenge to deploy a separate receiver chain of front-end circuits in a dense circuit board. In this paper, we apply the compressive sensing technique to reduce the required number of front-end circuits and the overall computational complexity. Unlike the commonly adopted random projections, we utilize the a priori probability distribution of the directions-of-arrival (DOAs) of the signals to optimize compressive sensing kernels for massive MIMO systems, such that the mutual information between the compressed measurement and the DOA is maximized. With the optimized sensing matrix, we present a compressive sensing spatial spectrum estimator under the minimum variance distortionless response criterion. Simulation results demonstrate performance advantages of the proposed optimal sensing kernel over random sensing kernels.
BibTeX
@inproceedings{icassp2017_optimizedcompres,
title = {Optimized compressive sensing-based direction-of-arrival estimation in massive MIMO},
author = {Yujie Gu and Yimin D. Zhang and Nathan A. Goodman},
booktitle = {ICASSP 2017},
year = {2017}
}