An adaptive matching pursuit algorithm for sparse channel estimation

This paper examines the problem of compressed sensing-based sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems. In particular, we present an improved estimation algorithm based on the sparsity adaptive matching pursuit (SAMP), which is referred to as the adaptive step size SAMP (AS-SAMP), and compare it with the existing algorithms. Without requiring a priori knowledge of the sparsity, the proposed algorithm adjusts the step size adaptively to approach the true sparsity, thus improving the estimation accuracy. Simulation results show that the proposed algorithm provides a better trade-off between the mean squared error (MSE) performance and complexity when compared with conventional methods.