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

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 onl...

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Bibliographic Details
Published in:arXiv.org 2016-01
Main Authors: Zayyani, Hadi, Korki, Mehdi, Marvasti, Farrokh
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Descent
Detection
Diffusion
Manufacturing
Remote sensors
Wireless sensor networks
Online Access:Get full text
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Summary: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.
ISSN:2331-8422