Dynamic sparse channel estimation over doubly selective channels: Differential simultaneous orthogonal matching pursuit

Channel estimation for an orthogonal frequency-division multiplexing (OFDM) broadband system over a dynamic sparse channel has become a challenging problem. Due to the special feature of dynamic sparse channels: path delays change over time, the channel sparsity changes dynamically. We propose a differential simultaneous orthogonal matching pursuit (DSOMP) algorithm based joint multi-symbol channel estimation to estimate dynamic channel parameters accurately. Taking advantage of the complex exponential basis expansion model (CE-BEM) in the time domain and exploiting the channel sparsity in the delay domain, we can transform the original goal into estimating a series of jointly sparse CE-BEM coefficient vectors. We employ DSOMP algorithm on the jointly sparse receivers on pilot subcarriers to estimate the dynamic sparsity, and then employ the actual sparsity on channel estimation. Simulation results demonstrate that the proposed DSOMP algorithm achieves better performance than the conventional SOMP algorithm.