Open Access

ARTICLE

Performance Analysis of Three Spectrum Sensing Detection Techniques with Ambient Backscatter Communication in Cognitive Radio Networks

by Shayla Islam¹, Anil Kumar Budati^1,*, Mohammad Kamrul Hasan², Saoucene Mahfoudh³, Syed Bilal Hussian Shah³

1 Department of Computer Science & Digital Innovation, ICSDI, UCSI University, Kuala Lumpur, 56000, Malaysia
2 Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Selangor, 43600, Malaysia
3 School of Engineering, Computing and Informatics, Dar Al-Hekma University, Jeddah, 22246, Saudi Arabia

* Corresponding Author: Anil Kumar Budati Email:

(This article belongs to the Special Issue: Recent Advances in Backscatter and Intelligent Reflecting Surface Communications for 6G-enabled Internet of Things Networks)

Computer Modeling in Engineering & Sciences 2023, 137(1), 813-825. https://doi.org/10.32604/cmes.2023.027595

Received 05 November 2022; Accepted 29 December 2022; Issue published 23 April 2023

Abstract

In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promising approach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio (SNR), there is no dedicated power for the users. Instead, they can transmit information by reflecting the ambient Radio Frequency (RF) signals in the spectrum. Therefore, it is essential to detect user presence in the spectrum for the transmission of data without loss or without collision at a specific time. In this paper, the authors proposed a novel Spectrum Sensing (SS) detection technique in the Cognitive Radio (CR) spectrum, by developing the AmBC. Novel Matched Filter Detection with Inverse covariance (MFDI), Cyclostationary Feature Detection with Inverse covariance (CFDI) and Hybrid Filter Detection with Inverse covariance (HFDI) approaches are used with AmBC to detect the presence of users at low power levels. The performance of the three detection techniques is measured using the parameters of Probability of Detection (PD), Probability of False Alarms (Pfa), Probability of Missed Detection (P_md), sensing time and throughput at low power or low SNR. The results show that there is a significant improvement via the HFDI technique for all the parameters.

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Keywords

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