Energy-Efficient Bandwidth Allocation for 5G Heterogeneous Networks Using Differential Evolution

This study presents a novel energy-efficient bandwidth allocation scheme for 5G heterogeneous networks using a Differential Evolution (DE) algorithm. A simulated macro–small cell environment was used to evaluate multivariate performance metrics under Quality of Service (QoS) constraints. The DE algorithm was implemented using Python’s SciPy optimization library and executed across 202 generations. The optimized model achieved a throughput of 573.6 Mbps and an energy efficiency of 5.76 Mbits/Joule, with zero QoS violations and a 124.77% improvement over benchmark methods. This work demonstrates DE’s suitability for bandwidth optimization in energy-constrained, high-density 5G environments.