Integer-Based Multi-Objective Algorithm for Small Cell Allocation Optimization

Abstract

Densification has been acknowledged as a main technological pillar for the Fifth Generation (5G) of networking, to address the foreseen huge number of connected devices owing to the wide adoption of Internet of Things (IoT), along with the unprecedented high amounts of data and requested data rates. Small cells (SCs) are at the center of such approach; however, the problem of small cell allocation (SCA) in heterogeneous networks (HetNets) yields a highly nonlinear, and integer programming formulation. In this letter, we address such challenging problem through the proposal of an innovative Integer-based Non-Dominated Sorting Genetic Algorithm (I-NSGA) to deal with the integer-based multi-objective optimization modeling in SCA. The proposed I-NSGA offers lower computation costs compared to conventional integer programming, while maintaining high optimality searching for the Pareto Front. Energy consumption and the total achieved data rates are optimized in SC deployment. The simulation results, not only prove the applicability of the I-NSGA in integer-based multi-objective optimization, but also confirm its superiority compared to existing solutions.