spectrumsensing is a key enabling technique for implementing cognitive radio (CR) networks. Based on the detection of primary users' signals, a CR can fully exploit wireless radio resources, thus to increase spec...
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spectrumsensing is a key enabling technique for implementing cognitive radio (CR) networks. Based on the detection of primary users' signals, a CR can fully exploit wireless radio resources, thus to increase spectrum efficiency and allow opportunistic transmissions for secondary users. This work presents a spectrumsensing approach for its applications in a wireless regional area network (WRAN) based on features detection of advanced television systems committee (ATSC) digital TV and WRAN signals over a Rayleigh fading channel. The scheme aims at detecting and identifying both ATSC and WRAN signals. To improve spectrumsensing performance in low-signal-to-noise ratio (SNR) regions, the characteristics of both ATSC and WRAN signals are exploited in spectrum sensing algorithms based on a correlation-based feature identification approach. In this study, real working scenarios of a WRAN CR network are considered. The effectiveness of the proposed detector has been verified by simulations.
In this study, a cognitive radio network is considered in which multiple secondary users intend to detect a primary user frequency band in order to specify whether it is occupied or not. To this end, a blind Bayesian ...
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In this study, a cognitive radio network is considered in which multiple secondary users intend to detect a primary user frequency band in order to specify whether it is occupied or not. To this end, a blind Bayesian framework is proposed by which secondary users cooperatively perform spectrumsensing. In practice, it is impossible to estimate the noise variance accurately (noise uncertainty problem) and this can degrade the performance of some previous spectrum sensing algorithms like energy detection (ER). To overcome this issue, unlike the conventional ER, the proposed algorithm utilises marginalisation to eliminate the effect of uncertainty in noise variance estimation. By computer simulations using MATLAB, it can be seen that the authors' algorithm reaches the ideal case for a by improving the level of cooperation (increasing the number of secondary users) and yet its Pmd is also improved compared to ER in practical situations (presence of noise uncertainty).
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