A Unified Algorithmic Framework for Dynamic Compressive Sensing

TL;DR

This paper introduces a versatile unified framework, PLAY-CS, for dynamic compressive sensing that leverages a new sparsity model to improve signal reconstruction in dynamic scenarios like wireless channels.

Contribution

The paper presents a novel unified framework for dynamic compressive sensing that incorporates a Partial-Laplacian sparsity model, enhancing adaptability and performance.

Findings

Enhanced reconstruction accuracy in dynamic channel tracking

Versatility in encompassing existing DCS algorithms

Improved performance over prior methods in practical scenarios

Abstract

We propose a unified dynamic tracking algorithmic framework (PLAY-CS) to reconstruct signal sequences with their intrinsic structured dynamic sparsity. By capitalizing on specific statistical assumptions concerning the dynamic filter of the signal sequences, the proposed framework exhibits versatility by encompassing various existing dynamic compressive sensing (DCS) algorithms. This is achieved through the incorporation of a newly proposed Partial-Laplacian filtering sparsity model, tailored to capture a more sophisticated dynamic sparsity. In practical scenarios such as dynamic channel tracking in wireless communications, the framework demonstrates enhanced performance compared to existing DCS algorithms.