Complex-valued sparse channel estimation method basedon smoothed ℓ0 norm algorithm

Channel estimation provides channel state information (CSI) for equalizing channel distortion and demodulating received signals. As a consequence, this technique takes an important part in modern wireless communications systems. Comparing with conventional pilot-aided channel estimators, compressive sensing based channel estimation methods can exploit the sparse property of the wireless channel and achieve the same estimation performance using much fewer pilots, hence with high spectral efficiency. However, most compressive sensing based estimators are feasible only in real-valued multipath channels, but invalid in general complex-valued systems. In this paper, we propose a compressive sensing based sparse channel estimation method, which probes channel with sparsely inserted pilots efficiently and extract CSI rapidly from received pilots utilizing smoothed ℓ₀ norm algorithm (SL0). Simulation results show that, comparing with conventional pilot-aided channel estimation methods, the proposed method can exploit the sparse property of complex-valued channel with high spectral efficiency, and recover CSI with high accuracy and acceptable computational complexity.