Cognitive radio is a technology developed for the effective use of radio spectrum sources. The spectrum sensing function plays a key role in the performance of cognitive radio networks. In this study, a new threshold determination method based on online learning algorithm is proposed to increase the spectrum sensing performance of spectrum sensing methods and to minimize the total error probability. The online learning algorithm looks for the optimum decision threshold, which is the most important parameter to decide the presence or absence of the primary user, using historical detection data. Energy detection- and matched filter-based spectrum sensing methods are discussed in detail. The performance of the proposed algorithm was tested over non-fading and different fading channels for low signal-to-noise ratio regime with noise uncertainty. In the conclusion of the simulation studies, improvement in spectrum sensing performance according to optimal threshold selection was observed.
Cognitive radio (CR) is a technology developed for the effective use of radio spectrum sources. The spectrum sensing function plays a key role in the performance of cognitive radio networks. In this study, a new threshold determination method based on online learning algorithm is proposed to increase the spectrum sensing performance of spectrum sensing methods and to minimize the total error probability. The online learning algorithm looks for the optimum decision threshold, which is the most important parameter to decide the presence or absence of the primary user, using historical detection data. Energy detection and matched filter based spectrum sensing methods are discussed in detail. The performance of the proposed algorithm was tested over non-fading and different fading channels for low signal-to-noise ratio (SNR) regime with noise uncertainty. In the conclusion of the simulation studies, improvement in spectrum sensing performance according to optimal threshold selection was observed.
Cognitive radio is a technology developed for the effective use of radio spectrum sources. The spectrum sensing function plays a key role in the performance of cognitive radio networks. In this study, we propose an online learning algorithm for the energy detection scheme, which aims to maximizing spectrum detection performance. Optimal threshold value, which is critical for the determination of the absence or the presence of a licensed user, was mathematically expressed in accordance with the balance between probability of detection and probability of false alarm. Performance of the proposed algorithm was tested on non-fading and different fading channels for low signal-to-noise ratio (SNR) regime with noise uncertainty. In conclusion of the simulation studies, improvement in spectrum detection performance according to optimal threshold value selection was observed.
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