Geolocation-aware resource management in cloud computing-based cognitive radio networks

Abstract

With the rapid development of cognitive radios, spectrum efficiency in cognitive radio networks (CRN) has increased by secondary users (SU) accessing the licensed spectrum dynamically and opportunistically without creating harmful interference to primary users. However, the performance and security of CRN is considerably constrained by its limited power, memory and computational capacity. Fortunately, the advent of cloud computing has the potential to mitigate these constraints due its vast storage and computational capacity. In this paper, we propose geolocation-aware radio resource management algorithm for CRN where distributed storage and computing resource in cloud computing platform and geolocation of secondary users are leveraged to store spectrum occupancy information of heterogeneous wireless networks and facilitates the access of spectrum opportunities for secondary users (SU). The proposed algorithm leverages the geolocation of secondary users and idle licensed bands to facilitate efficient allocation of radio resources to SU. Furthermore, the secondary users who provide high benefit are admitted while satisfying the quality of service (QoS) requirement of secondary users in terms of data rate and service time. We also propose a scalable mapping method using storm, a real-time distributed processing model in cloud computing platform to dynamically partition the geographical area according to the SU density. Simulation results are presented to demonstrate the performance of the proposed geolocation-aware radio resource management algorithm.