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International Journal of Antennas and Propagation
Volume 2013 (2013), Article ID 914734, 6 pages
http://dx.doi.org/10.1155/2013/914734
Research Article

Sparse Channel Estimation for MIMO-OFDM Two-Way Relay Network with Compressed Sensing

1School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China
2School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou 450007, China
3Department of Communication Engineering, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan

Received 30 September 2012; Revised 7 February 2013; Accepted 12 February 2013

Academic Editor: Antonio Faraone

Copyright © 2013 Aihua Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Accurate channel impulse response (CIR) is required for equalization and can help improve communication service quality in next-generation wireless communication systems. An example of an advanced system is amplify-and-forward multiple-input multiple-output two-way relay network, which is modulated by orthogonal frequency-division multiplexing. Linear channel estimation methods, for example, least squares and expectation conditional maximization, have been proposed previously for the system. However, these methods do not take advantage of channel sparsity, and they decrease estimation performance. We propose a sparse channel estimation scheme, which is different from linear methods, at end users under the relay channel to enable us to exploit sparsity. First, we formulate the sparse channel estimation problem as a compressed sensing problem by using sparse decomposition theory. Second, the CIR is reconstructed by CoSaMP and OMP algorithms. Finally, computer simulations are conducted to confirm the superiority of the proposed methods over traditional linear channel estimation methods.