Wireless cognitive radio networks based on software-defined network approach

Cognitive radio network technology is a current approach that allows secondary (unlicensed) users to access licensed spectrum without interfering with the communication of primary (licensed) users. This approach suggests solutions to current problems such as spectrum inefficiency and ineffective use of spectrum. The software-defined network approach is a new network paradigm and it suggests that the integrated data and control planes be abstracted from each other to remove the limitations of the traditional network infrastructure. This approach offers many possibilities, such as simplification of network and management functions, policy-based, applicable to existing network infrastructure, and network to achieve autonomous structure. Many different software-defined network studies are still in progress in terms of architecture, such as data centres, cloud computing, wireless sensor network technologies. In this study, a new architecture based on a software-defined network approach was proposed to solve the existing network infrastructure problems of cognitive radio network technologies, a form of wireless communication. Some management functions of base stations in cognitive radio networks have been transferred to the controller to develop a semi-distributed control mechanism. While resource virtualization is carried out on cognitive radio networks, dynamic, infrastructure-independent, and efficient resource allocation are provided for secondary users. So, while network performance is improved by reducing the overhead of base stations, the dependence of secondary users on base stations and physical network resources is also reduced. The performance analysis of the proposed network environment was carried out through the Riverbed Modeler simulation software. In this study, end-to-end delays for the primary network, primary network delays for five secondary users, packet loss rates and bit error rates have been investigated by selecting different workloads for the secondary users. Also, for the same and different packet sizes, primary network throughput and proposed network architecture throughput were examined and network performance was improved by using channel bonding technique. When compared to another study in the literature with similar media and scenarios, it was observed that the throughput increased by about 45%. Consequently, the software-defined network approach has been found to be a suitable solution for cognitive radio networks.