Remote Compressive Sensing for Noisy M2M Networks

In recent years, machine-to-machine (M2M) networks are widely considered in wireless communication system. Machines typically have constrained power, and their processing and communication capabilities are limited. To avoid the transmission of redundant information to improve the data rate, compressive sensing is a promising tool to be considered. Compressive sensing (CS) is especially useful for avoiding the redundant information to be transmitted such that the amount of transmitted data can be reduced. A framework for two-tier architecture of a remote compressive sensing scheme for M2M networks is developed where a statistical model replaces the standard sparsity model of classical compressive sensing. We consider this framework with noisy channels and derive an minimum mean square error (MMSE) decoder. Furthermore, we provide a way to produce sensing matrices and compare the proposed sensing matrices with random ones.