Sparse channel estimation using orthogonal matching pursuit algorithm

Abstract

Sparse channels are encountered in several communication applications. Exploiting the sparsity, a channel estimate can be obtained by using a matching pursuit (MP) algorithm. Previously, it was demonstrated that the MP based channel estimation outperforms the conventional least squares (LS) estimation algorithm for sparse channels. In this paper, we propose to use the orthogonal matching pursuit (OMP) algorithm for channel estimation. Using OMP, the convergence problem in MP algorithm based on re-selection of the basis vectors is eliminated. It is also verified that by avoiding the re-selection problem more accurate channel estimates can he obtained by using the OMP algorithm. The performance of decision feedback equalizers based on the channel estimates obtained by using the MP and OMP algorithms are compared, verifying that the OMP outperforms the MP, with a comparable computational complexity.