Low-Complexity Blind Spectrum Sensing in Alpha-Stable Distributed Noise Based on a Gaussian Function

Jinjun LUO  Shilian WANG  Eryang ZHANG  

IEICE TRANSACTIONS on Communications   Vol.E102-B   No.7   pp.1334-1344
Publication Date: 2019/07/01
Publicized: 2019/01/09
Online ISSN: 1745-1345
DOI: 10.1587/transcom.2018EBP3250
Type of Manuscript: PAPER
Category: Antennas and Propagation
spectrum sensing,  impulsive noise,  alpha stable distribution,  Gaussian function,  computational complexity,  

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Spectrum sensing is a fundamental requirement for cognitive radio, and it is a challenging problem in impulsive noise modeled by symmetric alpha-stable (SαS) distributions. The Gaussian kernelized energy detector (GKED) performs better than the conventional detectors in SαS distributed noise. However, it fails to detect the DC signal and has high computational complexity. To solve these problems, this paper proposes a more efficient and robust detector based on a Gaussian function (GF). The analytical expressions of the detection and false alarm probabilities are derived and the best parameter for the statistic is calculated. Theoretical analysis and simulation results show that the proposed GF detector has much lower computational complexity than the GKED method, and it can successfully detect the DC signal. In addition, the GF detector performs better than the conventional counterparts including the GKED detector in SαS distributed noise with different characteristic exponents. Finally, we discuss the reason why the GF detector outperforms the conventional counterparts.