Diffusion Adaptation Framework for Compressive Sensing Reconstruction

TL;DR

This paper introduces a diffusion adaptation framework for compressive sensing that enables distributed sparse signal reconstruction across networks, reducing storage and computational costs while maintaining high accuracy.

Contribution

It proposes a novel distributed diffusion algorithm for CS reconstruction, including a mini-batch variant, with proven convergence and improved speed.

Findings

Achieves high reconstruction accuracy

Demonstrates fast convergence in simulations

Reduces storage and computational requirements

Abstract

Compressive sensing(CS) has drawn much attention in recent years due to its low sampling rate as well as high recovery accuracy. As an important procedure, reconstructing a sparse signal from few measurement data has been intensively studied. Many reconstruction algorithms have been proposed and shown good reconstruction performance. However, when dealing with large-scale sparse signal reconstruction problem, the storage requirement will be high, and many algorithms also suffer from high computational cost. In this paper, we propose a novel diffusion adaptation framework for CS reconstruction, where the reconstruction is performed in a distributed network. The data of measurement matrix are partitioned into small parts and are stored in each node, which assigns the storage load in a decentralized manner. The local information interaction provides the reconstruction ability. Then, a simple and efficient gradient-descend based diffusion algorithm has been proposed to collaboratively recover the sparse signal over network. The convergence of the proposed algorithm is analyzed. To further increase the convergence speed, a mini-batch based diffusion algorithm is also proposed. Simulation results show that the proposed algorithms can achieve good reconstruction accuracy as well as fast convergence speed.