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Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Low-Complexity LMMSE Channel Estimation Method for OFDM-Based Cooperative Diversity Systems with Multiple Amplify-and-Forward Relays | Hindawi Publishing Corporation EURASIP Journal on Wireless Communications and Networking Volume 2008 Article ID 149803 9 pages doi 10.1155 2008 149803 Research Article A Low-Complexity LMMSE Channel Estimation Method for OFDM-Based Cooperative Diversity Systems with Multiple Amplify-and-Forward Relays Kai Yan Sheng Ding Yunzhou Qiu Yingguan Wang and Haitao Liu Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Road ChangNing 865 Shanghai 200050 China Correspondence should be addressed to Kai Yan yankai@mail.sim.ac.cn Received 20 January 2008 Accepted 18 May 2008 Recommended by George Karagiannidis Orthogonal frequency division multiplexing- OFDM- based amplify-and-forward AF cooperative communication is an effective way for single-antenna systems to exploit the spatial diversity gains in frequency-selective fading channels but the receiver usually requires the knowledge of the channel state information to recover the transmitted signals. In this paper a training-sequences-aided linear minimum mean square error LMMSE channel estimation method is proposed for OFDMbased cooperative diversity systems with multiple AF relays over frequency-selective fading channels. The mean square error MSE bound on the proposed method is derived and the optimal training scheme with respect to this bound is also given. By exploiting the optimal training scheme an optimal low-rank LMMSE channel estimator is introduced to reduce the computational complexity of the proposed method via singular value decomposition. Furthermore the Chu sequence is employed as the training sequence to implement the optimal training scheme with easy realization at the source terminal and reduced computational complexity at the relay terminals. The performance of the proposed low-complexity channel estimation method and the superiority of the derived optimal training scheme are verified through simulation results. Copyright 2008 Kai Yan et al. This is an open access .
