NDLI: Unilateral left-tail Anderson Darling test-based spectrum sensing with Laplacian noise

Content Provider	IET Digital Library
Author	Jiang, Lijuan Li, Yongzhao Ye, Yinghui Chen, Yunfei Jin, Ming Zhang, Hailin
Abstract	This study focuses on spectrum sensing under Laplacian noise. To mitigate the negative effects caused by the heavy-tailed behaviour of Laplacian noise, the fractional lower order moments (FLOM) technology is employed to pre-process the received samples before spectrum sensing. Through exploiting the asymmetrical difference between the distribution for the FLOM of received samples in the absence and presence of primary users, the authors formulate the spectrum sensing problem under Laplacian noise as a unilateral goodness-of-fit (GoF) test problem. Based on this test problem, they propose a new GoF-based detector, which is called a unilateral left-tail Anderson Darling (ULAD) detector. The analytical expressions for the theoretical performance, in terms of false-alarm and detection probabilities, of the ULAD are derived. Moreover, a closed-form expression for the optimal detection threshold is also derived to minimise the total error rate. Simulation results are provided to validate the theoretical analyses and to demonstrate the superior performance of the proposed detector than others.
Starting Page	696
Ending Page	705
Page Count	10
ISSN	17518628
Volume Number	13
e-ISSN	17518636
Issue Number	Issue 6, Apr (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-com/13/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-com.2018.5598
Journal	IET Communications
Publisher Date	2019-01-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Cognitive Radio FLOM Fractional Lower Order Moments Technology GoF-based Detector Goodness-of-fit Test Problem Heavy-tailed Behaviour Laplacian Noise Probability Radio Link And Equipment Radio Spectrum Management Received Sample Signal Detection Spectrum Sensing Problem Statistical Distribution Statistics Unilateral Left-tail Anderson Darling Test-based Spectrum Sensing
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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