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

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