Radix-tree based spectrum allocation model for cognitive radio networks: Maximizing network capacity

Cognitive radio (CR) technology is a promising technology that provides opportunistic access to free channels for secondary users (SUs), and enhances the spectrum efficiency [1]. In this paper, we present a novel capacity-aware spectrum allocation model for cognitive radio networks. We first modeled interference constraints based on the interference temperature concept and let the SUs to increase their transmission power until the interference temperature on one of their neighbors exceeds its interference temperature threshold. The, knowing the potential links SINR and bandwidth, we calculated links capacity based on Shannon formula and modeled the co-channel interference between potential links on each channel using an interference graph. Finally, we formulated a spectrum assignment problem in the form of a binary integer linear problem (BILP) to find an optimal feasible set of simultaneously active links among all the potential links in an interference graph in a way that overall network capacity would be maximized. To reduce complexity, we also formulated this problem using genetic algorithm (GA) to find a sub optimal solution in less time. We also proposed a new radix tree based algorithm that, by removing the sparse areas in search space, leads to a considerable decrease in time complexity of spectrum allocation problem as compared to BILP algorithm. Simulation results have shown that this proposed model leads to a considerable improvement in overall network capacity as compared to genetic algorithm. We also showed that maximizing the number of active links between SUs as an objective function does not necessarily maximize the network capacity.