Remote Compressive Sensing for Noisy Machine-to-Machine Networks
Date Issued
2015
Date
2015
Author(s)
Tsai, Alan Shenghan
Abstract
In recent years, machine-to-machine (M2M) networks are widely con- sidered in wireless communication systems. To avoid the transmission of redundant information to improve the data rate, compressive sensing is a promising tool to be considered. In this paper under the two-tier architec- ture, we propose a remote compressive sensing scheme for the M2M networks with stochastic sources to improve the data rate and formulate a statistical compressive sensing problem. First we propose to use the minimum mean square error estimator at the gateway and the base station to transform the problem as a noisy statistical CS. We derive the form of a optimal decoder by following MMSE estimation for the proposed scheme. Furthermore We find two ways, that can produce the sensing matrices. There are SVD covariance matrix(SCM) and adaptive statistical compressive sensing(ASCS). SCM is using machines covariance matrix to compressed the data by reducing the correlation between each machines. ASCS uses the previous measurements and the sensing matrices obtained in the past states and combines and com- bine the mutual information to become a new method of CS.
Subjects
Machine-to-Machine networks
Compressive sensing
Noisy channel
Mutual information
SVD covariance matrix
Adaptive statistical compressive sensing
Type
thesis
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