Mitigating eavesdropping by using fuzzy based MDPOP-Q learning approach and multilevel Stackelberg game theoretic approach in wireless CRN

The secrecy rate of physical layer security in the presence of eavesdropper of a Cognitive Radio Network (CRN) via multilevel Stackelberg Game is enhanced. Primary propagator rents its own licensed spectrum to the secondary propagator for remuneration. The secondary dispatcher is considered as trusted relays to send the messages in decipher and promote fashion. The secondary propagator uses its partial power to transmit the jamming signal along with the information signal and also charges will be claimed by the jammer for their service. A Stackelberg game is formulated for this topic; the leader and followers are jammer and dispatcher. An advanced encryption scheme is proposed to increase the effective security level while accessing the primary spectrum under eavesdropper scenario. The proposed methodology results in the secrecy rate maximization and the power consumption is minimized. Besides the power strategy, the eavesdropping is a major concern, where the outcomes of the transmission make the best effort strategy in CRN and this optimization is highlighted with a mathematical approach of fuzzy based Markov Decision Process outcome prediction (MDPOP)-Q learning algorithm to eradicate the eavesdropping occurrence in CRN with an optimal solution.