Cooperrative Spectrum Sensing in Cognitive Radio Network Using Flying Fox Optimization Based AlexNet
T Devi., Mohammed Al‐Farouni, S Pradeep Kumar, B. R. Sathishkumar, Y. Nagendar · 2024
Nowadays, availability of spectrum becomes an essential task among the users for preventing the channel interference and effective utilization of the available spectrum in the scenario of Cognitive Radio (CR) sensing. The need and increasing of radio spectrum are due to the growth of wireless communication and its uses. To identify distribute free spectrum channels technology, CR is designed, where it also manages the available spectrum difficulties. Result of test statistics designing is still a complex task in CR networks even, if there are different spectrum sensing methods. To increase the utilization of spectrum and solve the problem scarcity of spectrum, Alex net based on the Flying Foxes Optimization (FFO-based AlexNet) is developed. In this paper, parameters like signal energy and Eigen signal statistics with approaches of deep learning are used to model test statistics. The FFO-based AlexNet results like false alarm probability for Rician channel using developed spectrum sensing method compared to Honey Badger Remora Optimization-based AlexNet (HBRO-based AlexNet) have gained 0.899 at 5dB by considering Signal-To-Noise Ratio (SNR) respectively.