Small-Sample-Support Channel Estimation for Massive Mimo Systems
George Sklivanitis, Konstantinos Tountas, Dimitris A. Pados, Stella N. Batalama
Abstract
We consider the problem of blind channel estimation with minimal pilot signaling in multi-cell multi-user MIMO systems with very large antenna arrays at the base station. We develop a least-squares (LS)-type algorithm that iteratively extracts channel and data estimates in short-data record multicell massive MIMO environments with no prior channel state information. The proposed algorithm utilizes a novel initialization step that is based on auxiliary-vector (AV) subspace decomposition. Simulation studies show that for pilot signaling of about 4%, information data extraction can be achieved with lower probability of error than eigendecomposition-based initialization techniques, while for observation records of sufficient length it nearly attains the error rate performance achieved with complete knowledge of the channels.
BibTeX
@inproceedings{icassp2018_smallsamplesuppo,
title = {Small-Sample-Support Channel Estimation for Massive Mimo Systems},
author = {George Sklivanitis and Konstantinos Tountas and Dimitris A. Pados and Stella N. Batalama},
booktitle = {ICASSP 2018},
year = {2018}
}