Sparse Diffusion Steepest-Descent for One Bit Compressed Sensing in Wireless Sensor Networks

TL;DR

This paper introduces a distributed steepest-descent algorithm leveraging diffusion strategies for efficient one bit compressed sensing in wireless sensor networks, improving cooperative sparse vector estimation from sign measurements.

Contribution

It presents a novel sparse diffusion steepest-descent method that integrates diffusion strategies into one bit compressed sensing for wireless sensor networks, enhancing distributed learning capabilities.

Findings

Outperforms non-distributive algorithms in simulations

Effectively estimates sparse vectors from sign measurements

Demonstrates improved convergence and accuracy

Abstract

This letter proposes a sparse diffusion steepest-descent algorithm for one bit compressed sensing in wireless sensor networks. The approach exploits the diffusion strategy from distributed learning in the one bit compressed sensing framework. To estimate a common sparse vector cooperatively from only the sign of measurements, steepest-descent is used to minimize the suitable global and local convex cost functions. A diffusion strategy is suggested for distributive learning of the sparse vector. Simulation results show the effectiveness of the proposed distributed algorithm compared to the state-of-the-art non distributive algorithms in the one bit compressed sensing framework.