@Article{cmes.2023.027595,
AUTHOR = {Shayla Islam, Anil Kumar Budati, Mohammad Kamrul Hasan, Saoucene Mahfoudh, Syed Bilal Hussian Shah},
TITLE = {Performance Analysis of Three Spectrum Sensing Detection Techniques with Ambient Backscatter Communication in Cognitive Radio Networks},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {1},
PAGES = {813--825},
URL = {http://www.techscience.com/CMES/v137n1/52325},
ISSN = {1526-1506},
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<sub>md</sub>), 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.},
DOI = {10.32604/cmes.2023.027595}
}