Enhanced capsule generative adversarial network for spectrum and energy efficiency of cooperative spectrum prediction framework in cognitive radio network

Abstract

Cognitive radio network (CRN) is an intellective technology that frequently monitors the unused licensed spectrum in a particular frequency band. When more count of secondary users enters inside the channel may results in collisions. So, the secondary user may not sense the state of inactive and busy state of the primary user resulting reduces the spectrum efficiency (SE) and energy efficiency (EE) of CRN. To overcome these issues, an enhanced capsule generative adversarial network (ECGAN) for spectrum and energy efficiency of cooperative spectrum prediction (CSP) framework in CRN is proposed to improve the channel occupancy. Initially, CRN system is designed to improve the spectrum efficiency and energy efficiency. Then, channel state of primary user is predicted by using the coupled hidden Markov model (CHMM) and ECGAN predictors. Hence, the correlation among the secondary user predict the next channel status, like AND, OR, majority rule fusion methods. The prediction error, spectrum efficiency, energy efficiency based these methods are compared with CHMM and ECGAN predictors as the process of channel occupancy based on. Also, the effect of busy and idle state prediction errors on SE is detected.The proposed method is simulated in MATLAB. Then, the performance of the proposed method shows higher energy efficiency 13.86%, 24.84%, 34.75%, compared with existing methods, such as spectrum and energy efficiency of CSP in CRN (SEE-CSP-CRN-HMM), (SEE-CSP-CRN-MLP), differential evolution based machine learning scheme for secure cooperative spectrum sensing system (SEE-CSP-CRN-EML), cooperative spectrum sensing for primary user detection in CRN utilizing support vector machine (SEE-CSP-CRN-SVM), respectively.