Energy Efficiency Optimization in HetNets for Smart Grid Wide Area Network Using Deep Reinforcement Learning

The rapid expansion of smart grid infrastructure necessitates advanced communication networks capable of supporting extensive data exchange with high reliability and energy efficiency. This paper investigates energy efficiency in cellular networks using Heterogeneous Networks (HetNets) for Smart Grid Wide Area Networks (SG-WANs). HetNets, which include both macro and small cells, could enhance coverage and capacity of cellular network but are challenging to manage due to their complexity and dynamic traffic demands. This work adopts a deep reinforcement learning (DRL)-based framework to optimize network resources, adapting to environment changes and minimizing energy consumption while maintaining Quality of Service (QoS). The DRL agent learns to make real-time decisions on cell activation, user association, and power control, improving the network's overall energy efficiency.