Research Article
Channel Estimation Based on Approximated Power Iteration Subspace Tracking for Massive MIMO Systems
@INPROCEEDINGS{10.1007/978-3-319-73564-1_8, author={Liming Zheng and Donglai Zhao and Gang Wang and Yao Xu and Yue Wu}, title={Channel Estimation Based on Approximated Power Iteration Subspace Tracking for Massive MIMO Systems}, proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I}, proceedings_a={MLICOM}, year={2018}, month={2}, keywords={Massive MIMO Channel estimation Semi-blind Subspace tracking}, doi={10.1007/978-3-319-73564-1_8} }- Liming Zheng
Donglai Zhao
Gang Wang
Yao Xu
Yue Wu
Year: 2018
Channel Estimation Based on Approximated Power Iteration Subspace Tracking for Massive MIMO Systems
MLICOM
Springer
DOI: 10.1007/978-3-319-73564-1_8
Abstract
Traditional semi-blind channel estimator is based on eigen value decomposition (EVD) or singular value decomposition (SVD), which effectively reduces the interference through dividing the observed signal into signal subspace and noise subspace. Due to the large computation, Massive MIMO systems could not afford the cost of traditional algorithms in spite of the high performance. In this paper, we propose a channel estimation algorithm based on subspace tracking, in which the signal subspace is obtained by approximating power iteration algorithm. Without sacrificing the estimation performance, the complexity is greatly reduced compared with the traditional semi-blind channel estimation algorithm, which improves the applicability of the estimator.