Dynamic sparse channel estimation over doubly selective channels for large-scale MIMO systems

Doubly selective channel estimation for a dynamic sparse channel in the large-scale multiple-input multiple-output (MIMO) systems has become a challenging problem. Considering the two parts: the large number of channel coefficients for MIMO systems requiring unaffordable pilot overheads, and the special feature of dynamic sparse channels: path delays change over time, we propose a differential block simultaneous orthogonal matching pursuit (DBSOMP) algorithm based on joint multi-symbol channel estimation to estimate dynamic channel parameters accurately. Taking advantage of the complex basis expansion model (CE-BEM), we can transform the original goal into estimating a series of jointly sparse CE-BEM coefficient vectors. Considering the common sparsity of the coefficients in different BEM orders among different antennas, we firstly arrange the coefficients in the way of block structure. The channel information corresponding to continuous OFDM symbols has temporal time correlation. Then based on superimposed pilot design, we apply DBSOMP algorithm to estimate the dynamic sparsity of each time slot and apply the actual sparsity on channel estimation. Simulation results demonstrate that the proposed DBSOMP algorithm achieves better performance than the conventional BSOMP algorithm.