Distributed Compressed Sensing for Sensor Networks Using p-Thresholding

Distributed compressed sensing is the extension of compressed sampling (CS) to sensor networks. The idea is to design a CS joint decoding scheme at the base station which exploits the inter-sensor correlations, in order to recover the whole observations from very few number of random measurements per node. Here, the questions are about modeling the correlations, design of the joint recovery algorithms, analysis of those algorithms, the comparison between the performance of the joint and separate decoder and finally determining how optimal they are.