A Sparsity Enhanced Channel Estimation Algorithm Based on Compressed Sensing in MIMO-OFDM Systems

Channel estimation which based on Compressed Sensing (CS) can achieve the purpose of reducing pilots, but in the transformation of channel matrix from frequency-time domain to delay-Doppler sparse domain exists spectral leakage phenomenon which affects the sparsity of the channel and the Mean Squared Error (MSE) performance of estimation. For this, this paper studies the sparsity of the channel and a compressed channel estimation algorithm which optimized the sparsity by time domain windowing is proposed. With time domain windowing, the proposed algorithm restrains the leakage of Doppler domain which is caused by discretization and truncation, then the measurement matrix is designed. By this method, the sparsity of the delay-Doppler domain channel is enhanced and the more accurate sparse channel matrix is reconstructed. The channel estimation performance is improved. Simulation results show that with the signal-to-noise ratio increasing, windowed CS algorithm improves effectively the performance of channel estimation compared with no windows CS algorithm.