Maximizing Network Capacity of Cognitive Radio Networks by Capacity-Aware Spectrum Allocation

In this paper, we present a novel capacity-aware spectrum allocation model for cognitive radio networks. First, we model interference constraints based on the interference temperature model, and let the secondary users (SUs) increase their transmission power until the interference temperature on one of their neighbors exceeds its interference temperature threshold. Then, knowing the SINR and bandwidth of potential links, we calculate the link capacity based on the Shannon formula, and model the co-channel interference between potential links on each channel by using an interference graph. Next, we formulate the spectrum assignment problem in the form of a binary integer linear programming (BILP) to find the optimal feasible set of simultaneously active links among all the potential links in the sense of maximizing the overall network capacity. We also propose a new radix tree based algorithm that, by removing the sparse areas in the search space, leads to a considerable decrease in time complexity of solving the spectrum allocation problem as compared to the BILP algorithm. The simulation results have shown that this proposed model leads to a considerable improvement in overall network capacity as compared to genetic algorithm, and leads to a considerable decrease in time duration needed to find the optimal solution as compared to the BILP algorithm.