Connectivity-Aware Routing Algorithms for Cognitive Radio Networks

Abstract

The increased demand on wireless applications, coupled with the current inefficiency in spectrum usage, mandate a new communication paradigm shift from fixed spectrum assignment to dynamic spectrum sharing which can be achieved using the cognitive radio technology. Cognitive radio allows unlicensed secondary nodes to form communication links over licensed spectrum bands on an opportunistic basis which increases the spectrum management efficiency. Cognitive radio networks (CRN), however, impose unique challenges due to the fluctuation in the available spectrum as well as the diverse quality of service requirements. One of the main challenges is the establishment and maintenance of routes in multi-hop CRNs. In this thesis, we critically investigate the problem of routing in multi-hop CRNs. The main objective of this research is to maximize network connectivity while limiting routing delay. We developed a general connectivity metric for single-band and multi-band CRNs based on the properties of the Laplacian matrix eigenvalues spectrum. We show through analytical and simulation results that the developed metric is more robust and has lower computational complexity than the previously proposed metrics. Furthermore, we propose a new position-based routing algorithm for large scale CRNs which significantly reduces the routing computational complexity with negligible performance degradation compared to the traditional full node search algorithm. In addition, the connectivity metric developed in this thesis is used to develop a connectivity-aware distributed routing protocol for CRNs. Finally, we use a commodity cognitive radio testbed to demonstrate the concept of CR Wi-Fi networks.