We address the problem of distributed recovery of sparse signals in a resource constrained network. We assume that the network nodes sense a common sparse signal and therefore share an approximately common support. We propose a Bayesian algorithm that performs distributed recovery of the sparse signals and is agnostic to the sparse signal distribution. The algorithm requires nodes in the network to communicate only with their neighbors to estimate the sparse signals and is designed to reduce the communication load between nodes. Simulations have been performed to show that the algorithm requires significantly less communication among the nodes as compared to other algorithms.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Distributed Bayesian Sparse Signal Recovery Algorithm with Minimal Communication Load in Networks


    Contributors:


    Publication date :

    2024-06-24


    Size :

    393048 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A Pseudo-Inverse-Based Hard Thresholding Algorithm for Sparse Signal Recovery

    Wen, Jinming / He, Hongyu / He, Zihao et al. | IEEE | 2023


    Unambiguous Sparse Recovery of Migrating Targets With a Robustified Bayesian Model

    Bidon, Stephanie / Lasserre, Marie / Le Chevalier, Francois | IEEE | 2019


    Sparse Signal Recovery via Residual Minimization Pursuit

    Song, Heping / Wang, Guoli | British Library Online Contents | 2014


    Stopping Condition for Greedy Block Sparse Signal Recovery

    Luo, Yu / Xie, Ronggui / Yin, Huarui et al. | IEEE | 2017