A Novel Channel Estimation Method Based on Kalman Filter Compressed Sensing for Time-Varying OFDM System

In this paper, we propose a novel pilot-aided channel estimation method for orthogonal frequency-division multiplexing (OFDM) systems where the wireless channel is assumed to be both sparse and time-varying. In the proposed method, we firstly model the time-varying sparse channel as an autoregressive (AR) process. Then, utilizing the time-domain convergence property of Kalman filter, we formulate the channel estimation as an iteration problem. During the iteration, the path delays are estimated through a simple reconstruction algorithm. After the path delays are estimated, the Kalman filter is perfiirmed on the path delays to obtain the minimum mean squared error (MMSE) estimation of the channel impulse response (CIR). Simulation results demonstrate the effectiveness of the proposed channel estimation method. Compared with the conventional compressed sensing (CS) based channel estimators which perform CS at each time separately, the method proposed here enjoys superior performance in terms of bit error rate (BER) and mean square error (MSE).