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

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.