Title :
On balancing energy efficiency and estimation error in compressed sensing
Author :
Donglin Hu ; Shiwen Mao ; Billor, N.
Author_Institution :
Dept. of Electr. & Comput. Eng., Auburn Univ., Auburn, AL, USA
Abstract :
Compressed sensing (CS) refers to the process of reconstructing a signal that is supposed to be sparse or compressible. CS has wide applications, such as in cognitive radio networks. In this paper, we investigate effective CS schemes for balancing energy efficiency and estimation error. We propose an enhancement to a Bayesian estimation approach and an enhancement to the isotonic regression approach that is based on nearly isotonic regression. We also show how to compute the routing matrix for selecting active sensor nodes. The proposed enhancements are evaluated with trace-driven simulations. Considerable gaps are observed between the original approaches and the proposed enhancements in the simulation results. The near isotonic regression method achieves the best performance among all the CS schemes examined in this paper.
Keywords :
Bayes methods; matrix algebra; signal reconstruction; Bayesian estimation approach; active sensor nodes; compressed sensing; energy efficiency; estimation error; near isotonic regression method; routing matrix; signal reconstruction;
Conference_Titel :
Global Communications Conference (GLOBECOM), 2012 IEEE
Conference_Location :
Anaheim, CA
Print_ISBN :
978-1-4673-0920-2
Electronic_ISBN :
1930-529X
DOI :
10.1109/GLOCOM.2012.6503790