Iterative sparse channel estimation and decoding for underwater MIMO-OFDM

Authors:
Jie Huang;Jianzhong Huang;Christian R. Berger;Shengli Zhou;Peter Willett
Affiliations:
Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT;Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT;Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA;Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT;Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT
Venue:
EURASIP Journal on Advances in Signal Processing - Special issue on advanced equalization techniques for wireless communications
Year:
2010

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Abstract

We propose a block-by-block iterative receiver for underwater MIMO-OFDM that couples channel estimation with multiple-input multiple-output (MIMO) detection and low-density parity-check (LDPC) channel decoding. In particular, the channel estimator is based on a compressive sensing technique to exploit the channel sparsity, the MIMO detector consists of a hybrid use of successive interference cancellation and soft minimum mean-square error (MMSE) equalization, and channel coding uses nonbinary LDPC codes. Various feedback strategies from the channel decoder to the channel estimator are studied, including full feedback of hard or soft symbol decisions, as well as their threshold-controlled versions. We study the receiver performance using numerical simulation and experimental data collected from the RACE08 and SPACE08 experiments. We find that iterative receiver processing including sparse channel estimation leads to impressive performance gains. These gains are more pronounced when the number of available pilots to estimate the channel is decreased, for example, when a fixed number of pilots is split between an increasing number of parallel data streams in MIMO transmission. For the various feedback strategies for iterative channel estimation, we observe that soft decision feedback slightly outperforms hard decision feedback.