On the trade-off between energy efficiency and estimation error in compressive sensing

被引：4

作者：

Hu, Donglin ^{[1
]}

Mao, Shiwen ^{[1
]}

Billor, Nedret ^{[2
]}

Agrawal, Prathima ^{[1
]}

机构：

[1] Auburn Univ, Dept Elect & Comp Engn, Auburn, AL 36849 USA

[2] Auburn Univ, Dept Math & Stat, Auburn, AL 36849 USA

来源：

AD HOC NETWORKS | 2013年 / 11卷 / 06期

基金：

美国国家科学基金会;

关键词：

Bayesian estimation; Cognitive radio; Compressive sensing; Isotonic regression; Polynomial regression; Spectrum sensing;

D O I：

10.1016/j.adhoc.2013.04.008

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Compressive 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 the trade-off between 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. (C) 2013 Elsevier B.V. All rights. reserved.

引用

页码：1848 / 1857

页数：10

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