Genetic algorithm based resource allocation and interference mitigation for OFDMA macrocell-femtocells networks

In this paper, we propose a base station selection and resource allocation model for OFDMA macro-femtocell networks. Dense deployment of femtocells can cause severe interference for femto and macro users alike. Our framework assumes hybrid-access femtocells to grant access to public user in their vicinity and to reduce interference perceived by femto users. Full spectrum sharing is investigated for the purpose of increasing system capacity. The proposed model aims to maximize the network throughput for a given interference threshold. To do so, the model determines the best serving base station based on link conditions. Genetic Algorithm is used to solve the resource optimization problem by finding the appropriate bandwidth and power assignments for each user. Simulations were conducted and a comparison with a modified version of the Weighted Water Filling algorithm is presented.