Title: Modelling and performance analysis of energy detector-based spectrum sensing with maximum ratio combining over Nakagami-m/log-normal fading channels

Authors: Pappu Kumar Verma; Rahul Kumar

Addresses: Department of Electronics and Communication Engineering, Delhi Technological University (formerly Delhi College of Engineering), Delhi – 110042, India; Department of Electronics Engineering, Rajkiya Engineering College, Churk, Sonbhadra, Uttar Pradesh – 231206, India ' Electronics and Communication Engineering Department, National Institute of Technology (NIT), Hamirpur, HP – 177005, India

Abstract: Spectrum sensing (SS) is one of the crucial functions of cognitive radio networks (CRNs). It decides whether the band or sub-band of the spectrum is available or not for secondary users (SUs). Energy detection (ED) is one of the very fundamental approaches of SS to detect whether primary users (PUs) are present or absent. It is mathematically intractable to derive closed-form expressions of composite multipath/shadowing for average probability of detection and average area under the receiver operating characteristic curve. In this paper, we have considered Nakagami-m/log-normal as composite fading with maximum ratio combining (MRC) diversity, and it is approximated by Gaussian-Hermite integration (G-HI). In addition, adaptive threshold or optimised threshold has been incorporated to overcome the problem of spectrum sensing at low signal-to-noise ratio (SNR). To verify the correctness of exact results and obtained analytical expression is collaborated with Monte Carlo simulations.

Keywords: cognitive radio; probability density function; average probability of detection; complementary receiver operating characteristics; average AUC; diversity reception; composite fading; energy detector; optimised threshold.

DOI: 10.1504/IJCNDS.2019.101914

International Journal of Communication Networks and Distributed Systems, 2019 Vol.23 No.3, pp.290 - 306

Received: 13 Apr 2017
Accepted: 31 Mar 2018
Published online: 30 Aug 2019 *

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