Abstract
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 xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k $ </tex-math></inline-formula> -out-of- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <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.
Highlights
T HE current high demand for new telecommunications systems and services has become the main driver for the development of new technologies, as can be noticed, for example, from the recent advances involving the Internet of things (IoT) and the fifth generation (5G) of communication networks, as well as the discussions already started on the sixth generation (6G) of these networks
This paper presented an analysis of the signal-to-noise ratio wall of non-cooperative spectrum sensing and cooperative spectrum sensing based on energy detection
The analysis considered a novel realistic noise uncertainty model in which it is assumed that an estimated noise variance is used to determine the decision threshold, instead of assuming that the unknown noise variance is the one that corrupts the received signal
Summary
T HE current high demand for new telecommunications systems and services has become the main driver for the development of new technologies, as can be noticed, for example, from the recent advances involving the Internet of things (IoT) and the fifth generation (5G) of communication networks, as well as the discussions already started on the sixth generation (6G) of these networks. Compared to the traditional assumptions in the literature, it adopts a different perspective on the source of NU at the receivers and, according to this new assumption, it derives the SNRw for the ED in nCSS and CSS with SD and HD for the k-out-of-M rule, considering a new NU model in terms of NU range and distribution. 1) the adoption of a novel approach on the NU source at receivers; 2) the use of the NU range proposed in [14]; 3) the assumption of a truncated Gaussian distribution for mimicking the estimated noise power at receivers; 4) the derivation of novel expressions for the SNRw of ED in nCSS and CSS in SD and HD k-out-of-M rule according to the novel NU model; 5) the proposal of two algorithms for empirically finding the SNRw of ED or other detectors in nCSS and CSS.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
