Poster: Sparsity adaptive matching pursuit algorithm for channel estimation in non-sample-spaced multipath channels

For non-sample-spaced multipath channels, multi-path energy leakage leads to an increase in the channel sparsity and detection difficulties. In this paper, we propose the sparsity adaptive matching pursuit (SAMP) algorithm for the estimation of non-sample-spaced multipath channels. Compared with other greedy algorithms, the most innovative feature of the SAMP algorithm is its capability of signal reconstruction without the prior information of sparsity. To further improve the reconstruction quality, a regularized backtracking step which can flexibly remove the inappropriate atoms is adapted to SAMP algorithm. Simulation results show that channel estimation based on the proposed SAMP algorithm outperforms other greedy algorithms in non-sample-spaced multipath channels.