  In this paper, by exploiting the special features of temporal correlations of
dynamic sparse channels that path delays change slowly over time but path gains
evolve faster, we propose the structured matching pursuit (SMP) algorithm to
realize the reconstruction of dynamic sparse channels. Specifically, the SMP
algorithm divides the path delays of dynamic sparse channels into two different
parts to be considered separately, i.e., the common channel taps and the
dynamic channel taps. Based on this separation, the proposed SMP algorithm
simultaneously detects the common channel taps of dynamic sparse channels in
all time slots at first, and then tracks the dynamic channel taps in each
single time slot individually. Theoretical analysis of the proposed SMP
algorithm provides a guarantee that the common channel taps can be successfully
detected with a high probability, and the reconstruction distortion of dynamic
sparse channels is linearly upper bounded by the noise power. Simulation
results demonstrate that the proposed SMP algorithm has excellent
reconstruction performance with competitive computational complexity compared
with conventional reconstruction algorithms.
