HBRO-AlexNet: Honey Badger Remora Optimization Integrated AlexNet for Cooperative Spectrum Sensing in Cognitive Radio Network

Abstract

Cognitive radio (CR) technology enables a secondary user (SU) to make the most use of the licensed spectrum when the primary user (PU) is inactive, hence increasing spectrum efficiency. SUs are required to complete the spectrum sensing process to identify the spectrum utilization. It is required to effectively detect PU signal to SU for using the idle licensed spectrum bands. Even though various spectrum sensing techniques are designed in CR networks, designing test statistics still results in a complex task. To solve spectrum scarcity issues and to increase spectrum utilization, Honey Badger Remora Optimization-based AlexNet (HBRO-based AlexNet) is developed in this research and the test statistics model is modeled with a deep learning approach with signal parameters, like signal energy, and Eigen statistics. However, SU can use AlexNet to boost the spectrum efficiency of PU's licensed spectrum, which will then permit an increase in the chance of detection in the CR network (CRN). Though considering signal-to-noise ratio (SNR) at 5 dB, the proposed spectrum sensing approach yields the probability of detection and probability of false alarm for the Rician channel as 1 and 0.999, respectively.