A Modified Adaptive Sparse Channel Estimator for OFDM Systems Based On Singular Value Decomposition

In this paper, a modified adaptive sparse channel estimator based on singular value decomposition (SVD) for OFDM systems is proposed. The conventional adaptive sparsity matching pursuit (ASMP) based compressive channel estimation has bad anti-noise performance, although not needing the information of sparsity. Because using the SVD to modify the measurement matrix of CS can improve the robustness to noise. So we use the SVD to modify the measurement matrix of ASMP based compressive channel estimation. The proposed channel estimation has better robustness to noise and low error. The simulation results show that comparing with ASMP based compressive channel estimation, the proposed algorithm has 1 dB gain at MSE and 1.3 dB gain at BER.