Scalable framework for green large cognitive radio networks

Abstract

Cognitive radio networks (CRNs) have the capacity to be aware of the conditions of their operating environment, and dynamically reconfigure their own characteristics in order to reach the best available performances. These performances may be seriously impacted when the number of users in CRNs grows significantly. This study deals with efficient energy consumption and interference avoidance in large CRNs. To enhance the network lifetime, a new framework combining cognitive hierarchical clustering and the coalitional game is introduced. In this study, a new CRLEACH protocol is proposed and the well-known LEACH protocol is used in CRNs. The authors prove theoretically that their coalition model with a new strategic learning algorithm leads to Nash equilibrium. Finally, the network performances of their framework are illustrated by numerical results.

Keywords

• game theory
• cognitive radio
• radio networks
• telecommunication network reliability
• interference suppression
• pattern clustering
• routing protocols
• interference avoidance
• network lifetime
• cognitive hierarchical clustering
• crn
• energy consumption
• green large cognitive radio networks
• coalitional game
• crleach protocol
• strategic learning algorithm
• nash equilibrium