Sparse channel estimation for MIMO-OFDM systems using distributed compressed sensing |
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Authors: | LIU Yi MEI Wen-bo DU Hui-qian WANG Hong-yu |
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Affiliation: | 1. School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China;Wuhan Maritime Communication Research Institute, Wuhan 430205, China;2. School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China |
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Abstract: | A sparse channel estimation method is proposed for doubly selective channels in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Based on the basis expansion model (BEM) of the channel, the joint-sparsity of MIMO-OFDM channels is described. The sparse characteristics enable us to cast the channel estimation as a distributed compressed sensing (DCS) problem. Then, a low complexity DCS-based estimation scheme is designed. Compared with the conventional compressed channel estimators based on the compressed sensing (CS) theory, the DCS-based method has an improved efficiency because it reconstructs the MIMO channels jointly rather than addresses them separately. Furthermore, the group-sparse structure of each single channel is also depicted. To effectively use this additional structure of the sparsity pattern, the DCS algorithm is modified. The modified algorithm can further enhance the estimation performance. Simulation results demonstrate the superiority of our method over fast fading channels in MIMO-OFDM systems. |
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Keywords: | multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) distributed compressed sensing doubly selective channel group-sparse basis expansion model |
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