Sparse Diffusion Steepest-Descent for One Bit Compressed Sensing in Wireless Sensor Networks
This work addresses distributed compressed sensing for wireless sensor networks, presenting an incremental improvement by adapting existing methods to a distributed setting.
The paper tackles the problem of estimating a common sparse vector from only sign measurements in wireless sensor networks by proposing a sparse diffusion steepest-descent algorithm, with simulation results showing its effectiveness compared to state-of-the-art non-distributive algorithms.
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.