Traffic-load aware spectrum allocation in cloud assisted cognitive radio networks

With diverse technological advancements, the necessity of opportunistic spectrum usage is increasing rapidly to address the rising dearth of available spectrum. Cloud assisted Cognitive Radio Network (CCRN) offers huge computation and storage resources for handling heterogeneous spectrum usage decisions. In this paper, we develop a traffic-load aware channel allocation mechanism for secondary users with respect to their application Quality-of-Service (QoS) requirements. A historical analysis based channel ranking is also formulated recognizing both availability prediction and transmission quality. The simulation results demonstrate the effectiveness of our allocation scheme compared to the state-of-the-art works.