Tracking A Dynamic Sparse Channel Via Differential Orthogonal Matching Pursuit

TL;DR

This paper introduces the D-OMP algorithm for efficiently tracking dynamic sparse channels in broadband wireless systems by leveraging temporal correlations, resulting in faster and more accurate channel estimation.

Contribution

The paper proposes a novel differential orthogonal matching pursuit (D-OMP) algorithm that exploits temporal correlations for dynamic sparse channel tracking.

Findings

D-OMP tracks channels faster than existing algorithms.

D-OMP achieves higher accuracy in channel estimation.

Simulation results validate the effectiveness of D-OMP.

Abstract

This paper considers the problem of tracking a dynamic sparse channel in a broadband wireless communication system. A probabilistic signal model is firstly proposed to describe the special features of temporal correlations of dynamic sparse channels: path delays change slowly over time, while path gains evolve faster. Based on such temporal correlations, we then propose the differential orthogonal matching pursuit (D-OMP) algorithm to track a dynamic sparse channel in a sequential way by updating the small channel variation over time. Compared with other channel tracking algorithms, simulation results demonstrate that the proposed D-OMP algorithm can track dynamic sparse channels faster with improved accuracy.