Advancing Energy-Efficient Optimization: Trends, Trade-Offs, and Applications in Wireless Networks A Review

The rapid rise of 5G, IoT, and HetNets introduces challenges in optimizing energy efficiency, scalability, and performance. Despite extensive research, a gap remains in integrating diverse optimization techniques across network environments. This review synthesizes insights from traditional methods like Convex Optimization and Genetic Algorithms alongside modern approaches such as Deep Reinforcement Learning (DRL). By systematically analyzing research contributions, it compares these methods, revealing that Hierarchical Frameworks excel in scalability and energy efficiency, while Greedy Algorithms struggle in diverse, latencysensitive applications. DRL effectively balances latency reduction and energy savings, making it particularly useful for urban IoT networks. These findings provide actionable recommendations for optimizing network performance across different scenarios, supporting real-world deployments in urban 5G and rural IoT.