Energy-Efficient Resource Allocation and Spectrum Sensing for Heterogeneous Cognitive Radio Network based on Two-Tier Crossover Genetic Algorithm

Abstract

Cognitive Radio (CR) is considered an attractive technology to deal with the spectrum scarcity problem. Multi radio access technology (multi-RAT) can improve network capacity. Thus, multi-RAT embedded in a cognitive Radio Network (CRN) is a promising paradigm for developing spectrum efficiency and network capacity in future wireless networks. In this study, we consider a new CRN model in which the primary user networks consist of heterogeneous Primary Users (PUs). Specifically, we focus on the Energy-Efficient Resource Allocation (EERA) problem for CR. We propose a two-tier cross over genetic algorithm based search scheme to obtain an optimal solution in terms of the power and bandwidth. Spectrum sensing is the basic and essential mechanisms of Cognitive Radio (CR) to find the unused spectrum. Energy detection based spectrum sensing has been proposed. The simulation results show the proposed algorithm is stable and has faster convergence. Our proposal can significantly increase the energy efficiency.