Complex Network Evolving Model with Preference and Anti-Preference for Cognitive Radio Ad Hoc Networks

In order to describe the network evolving features for cognitive radio ad hoc networks (CRAHNs) and improve the network performance, we propose a network evolving model with preference and anti-preference based on complex network theory, aiming at optimizing network structures considering the residual node energy, the time and location varying spectrum availability and user's behaviors in CRAHNs. Analysed by the mean field theory, the network produced by the proposed model is accorded with the scale-free network. This evolving model can improve the scale-free property in degree correlation, clustering coefficient and average path length with node energy and spectrum heterogeneity. Comparing with Topology Control (TC) protocols in conventional ad hoc networks, i.e., the K-Neigh protocol, the proposed network evolving model requires only minimal local information and is especially suitable to perform under non-centralized node arrangements. Numerical simulation results indicate that the evolving network model has superior performance in enhancing the network connectivity, extending the network lifetime and achieving efficient spectrum management for CRAHNs, which are affected by the node failure due to node energy depletion and the link interruption due to the activity of primary users on licensed spectrum bands. From the analysis and numerical simulation results, the proposed network evolving model is proved an available approach for establishing and analyzing the real CRAHNs and improving network performance.