A novel OFDM channel estimation method based on Kalman filtering and distributed compressed sensing

Channel estimation is important for coherent detection in orthogonal frequency-division multiplexing (OFDM) systems. Current frequency-domain Kalman filtering (FDKF) channel tracking method requires a large number of pilots, which reduces the spectral efficiency of the system and increases the complexity. In this paper, in order to solve this problem, a new channel estimation method based on the recent methodology of distributed compressed sensing (DCS) and FDKF is proposed. By exploiting the sparse attribute of OFDM channels and introducing DCS, the number of pilots could be reduced greatly, which means more resources are saved for data transmission. Moreover, simulations indicate the proposed method achieves a better performance than conventional FDKF and least square (LS) method.