Matching pursuit based sparse channel estimation using Pseudorandom Sequences

In this paper, estimation of channels with large delay spread but with few nonzero taps, such as those encountered in hilly broadcast wireless communications, are considered. Exploiting the sparsity, a channel estimate can be obtained by using a matching pursuit (MP) algorithm. To improve the performance of MP algorithm based estimation, the orthogonal matching pursuit (OMP) algorithm for channel estimation is proposed. In OMP, the re-selection problem of MP algorithm is avoided by using the stored dictionary at each iteration, and faster convergence to a sparse solution is obtained. we proposed to use MP algorithm based on Pseudorandom Sequences for training sequences for channel estimation. Using the proposed method, the main taps distorted by the projection of other taps is eliminated by the dictionary with orthogonal property, and more accurate channel estimates can be obtained. The results of channel estimates by using MP, OMP and the proposed method are compared, verifying that the proposed method outperforms the MP and OMP methods, with the same computational complexity as MP algorithm.