A multi-objective approach to indoor wireless heterogeneous networks planning based on biogeography-based optimization

In this paper, we present a multi-objective optimization approach for indoor wireless network planning subject to constraints for exposure minimization, coverage maximization and power consumption minimization. We consider heterogeneous networks consisting of WiFi Access Points (APs) and Long Term Evolution (LTE) femtocells. We propose a design framework based on Multi-objective Biogeography-based Optimization (MOBBO). We apply the MOBBO algorithm to network planning design cases in a real office environment. To validate this approach we compare results with other multi-objective algorithms like the Nondominated Sorting Genetic Algorithm-II (NSGA-II) and the Generalized Differential Evolution (GDE3) algorithm. The results of the proposed method indicate the advantages and applicability of the multi-objective approach. Keywords— Indoor wireless network planning, heterogeneous networks, exposure minimization, biogeography-based optimization, differential evolution, genetic algorithms, generalized differential evolution, Pareto optimization, multi-objective Optimization.