Energy-Aware 5G Device-to-Device Optimization Using Hybrid Grey Wolf and Evolutionary Schemes

The device-to-device (D2D) communication that underlays cellular networks is a key enabler in the process of improving the utilization spectrum and energy efficiency (EE) of 5G systems. Most EE optimization studies have focused solely on a single-band configuration or single cell, while practical deployments inherently involve multi-cell and multi-band interference coupling that significantly affects the power allocation and system-level EE performance. In this study, we investigated EE maximization for multi-band, multi-cell D2D underlaying networks and propose two hybrid metaheuristic optimization algorithms: the evolutionary algorithm enhanced-particle grey wolf optimizer (EA-PGWO) and the memetic particle-guided grey wolf optimizer with derivative local learning (MPGWO-DLL). For fairness and a more comprehensive evaluation, three baseline algorithms—the derivative algorithm (DA), particle swarm optimization (PSO), and the genetic algorithm (GA)—were benchmarked and compared against our proposed algorithms. The proposed hybrid algorithms use population-based global exploration with local refinement to increase and stabilize the optimization under non-convex and interference-limited conditions. From the obtained simulation results, we obtained a clear outperformance from both the EA-PGWO and MPGWO-DLL in terms of EE against all three baseline algorithms and across varying D2D and cellular user densities. Among all the evaluated methods, MPGWO-DLL achieved the highest EE gains due to its memetic local learning stage combined with its derivative-guided refinement.