@inproceedings{8beeb2e74ddf4d3ab4bea7a73cd63c4b,
title = "A hybrid approach for multi-user massive MIMO sparse channel estimation based on Bayesian recovery and hard thresholding",
abstract = "We propose an efficient sparse channel estimation algorithm based on the compressed sensing (CS) approach for large scale multi-user (MU) MIMO systems. The proposed scheme is a hybrid one comprising Bayesian and greedy methods. It can improve the estimation performance by incorporating the spatial channel knowledge that the neighboring antennas in an array share the same support. The pilot overhead can be reduced by utilizing the data symbols using a reliability measure for channel estimation. Moreover, the effect of interfering and non-interfering pilots on the estimation performance will be investigated. It will be shown that the proposed hybrid technique performs similar or better than the Bayesian method with substantially reduced complexity.",
keywords = "Compressed sensing, Distributed channel estimation, Multi-user massive MIMO, OFDM, Sparse recovery",
author = "Javier Garcia and Jawad Munir and Amine Mezghani and Nossek, {Josef A.}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Globecom Workshops, GC Wkshps 2015 ; Conference date: 06-12-2015 Through 10-12-2015",
year = "2015",
doi = "10.1109/GLOCOMW.2015.7414028",
language = "English",
series = "2015 IEEE Globecom Workshops, GC Wkshps 2015 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2015 IEEE Globecom Workshops, GC Wkshps 2015 - Proceedings",
}