On the Signal-to-Noise Ratio Wall of Energy Detection in Spectrum Sensing

• Published in: IEEE access Vol. 10; pp. 16499 - 16511
• Main Authors: Costa, Lucas Dos Santos, Guimaraes, Dayan Adionel, Uchoa-Filho, Bartolomeu F.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cognitive radio; Detectors; Empirical analysis; energy detection; False alarms; noise uncertainty; Normal distribution; Radio frequency; Receivers; Sensors; Signal to noise ratio; signal-to-noise ratio wall; spectrum sensing; Telecommunications; Uncertainty
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3149476

In this paper, a comprehensive analysis of the signal-to-noise ratio wall (SNRw) of cognitive radio (CR)-based non-cooperative spectrum sensing (nCSS) and cooperative spectrum sensing (CSS) using energy detection (ED) is presented. The analysis considers a novel realistic noise uncertainty (NU) model in which it is assumed that the estimated noise variance used to determine the decision threshold is unbiased and follows a truncated-Gaussian random distribution with configurable limits. Expressions are derived for the individual detection performances at CRs and global detection performances at the fusion center in terms of probability of false alarm and probability of detection and the SNRw of ED in nCSS and CSS in hard-decision fusion under the <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula>-out-of- <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula> rule, and soft-decision fusion, considering the proposed NU model, respectively. Empirical SNRw algorithms are also proposed, allowing for the SNRw computation of any detector, including the ED, in nCSS and CSS. All theoretical findings are verified through computer simulations or empirical results.