A channel estimation method based on distributed compressed sensing and time-domain Kalman filtering in OFDM systems

• Published in: 2011 4th IEEE International Conference on Broadband Network and Multimedia Technology pp. 157 - 161
• Main Authors: Wenbo Xu, Donghao Wang, Kai Niu, Zhiqiang He, Jiaru Lin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2011
• Subjects: Channel estimation; Complexity theory; Compressed sensing; Delay; distributed compressed sensing; kalman filtering; Kalman filters; OFDM
• ISBN: 9781612841588; 1612841589
• DOI: 10.1109/ICBNMT.2011.6155916

Channel estimation is important for coherent detection in orthogonal frequency-division multiplexing (OFDM) systems. Current time-domain Kalman filtering (TDKF) method has a good performance in estimating the channel responses, but is impractical since it requires the knowledge of multipath delays. In this paper, we propose a new scheme to relax such requirement by combining the recent methodology of distributed compressed sensing (DCS) and TDKF. By exploiting the sparse attribute of OFDM channels, the number of pilots could be reduced greatly. Furthermore, to reduce the complexity, a threshold on the change of channel responses is designed to avoid unnecessary DCS execution. Simulations indicate the proposed method achieves better performance than conventional least square method.