Structured Matching Pursuit for Reconstruction of Dynamic Sparse Channels

TL;DR

This paper introduces the structured matching pursuit (SMP) algorithm that leverages temporal correlations in dynamic sparse channels, effectively detecting common channel taps and tracking dynamic ones for improved reconstruction.

Contribution

The paper proposes a novel SMP algorithm that separates common and dynamic channel taps, providing theoretical guarantees and demonstrating superior performance over existing methods.

Findings

High probability of successful common tap detection

Linear upper bound on reconstruction distortion

Competitive computational complexity and improved accuracy

Abstract

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.