An Efficient Energy-Saving Scheme Using Genetic Algorithm for 5G Heterogeneous Networks

Energy-saving (ES) is becoming one of the most challenging tasks that fifth-generation (5G) tends to tackle. The problem of identifying the optimal set cells to be turned off is nondeterministic polynomial time-hard. In this research article, we use heuristic algorithms to save energy in 5G heterogeneous networks (HetNet). Our approach is based on turning off underutilized components of base stations to reduce energy consumption, while satisfying users' requests. Basically, we elaborate a new mechanism providing ES for 5G networks. The proposed mechanism is based on genetic algorithm (GA) and is called ES based on GA in 5G (ESGA-5G). Bio-inspired GA and particle swarm optimization (PSO) algorithms stand for AI solutions that intelligently manage the operation of ES self-organized network mechanisms in 5G HetNet. The performance analysis of the proposed ESGA-5G approach illustrates its efficiency in terms of reducing the energy consumption. In particular, ESGA achieves a higher percentage of ESs compared to PSO algorithm, with a gap to optimality amounting to 28% for GA and 54% for PSO.