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Mathematical Problems in Engineering
Volume 2015, Article ID 259890, 16 pages
http://dx.doi.org/10.1155/2015/259890
Research Article

Energy-Based Spectrum Sensing under Nonreconstruction Framework

1Department of Communication Engineering, Harbin Institute of Technology, Harbin 150080, China
2School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, China

Received 10 April 2015; Revised 28 July 2015; Accepted 3 August 2015

Academic Editor: Chaudry Masood Khalique

Copyright © 2015 Yulong Gao and Yanping Chen. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

To reduce the computational complexity and rest on less prior knowledge, energy-based spectrum sensing under nonreconstruction framework is studied. Compressed measurements are adopted directly to eliminate the effect of reconstruction error and high computational complexity caused by reconstruction algorithm of compressive sensing. Firstly, we summarize the conventional energy-based spectrum sensing methods. Next, the major effort is placed on obtaining the statistical characteristics of compressed measurements and its corresponding squared form, such as mean, variance, and the probability density function. And then, energy-based spectrum sensing under nonreconstruction framework is addressed and its performance is evaluated theoretically and experimentally. Simulations for the different parameters are performed to verify the performance of the presented algorithm. The theoretical analysis and simulation results reveal that the performance drops slightly less than that of conventional energy-normalization method and reconstruction-based spectrum sensing algorithm, but its computational complexity decreases remarkably, which is the first thing one should think about for practical applications. Accordingly, the presented method is reasonable and effective for fast detection in most cognitive scenarios.