Simulation-based optimization of energy harvesting systems for 6G networks using deep reinforcement learning

The increasing demand for ultra-reliable and low-latency communication in 6G networks presents significant challenges in energy management. This paper proposes a simulation-based optimization framework for energy harvesting systems (EHS) in 6G networks using Deep Reinforcement Learning (DRL) and compares it with Deep Deterministic Policy Gradient (DDPG) optimization. As 6G networks integrate a large number of devices and heterogeneous communication technologies, energy harvesting from ambient sources such as solar, wind and radio frequency (RF) becomes crucial for system sustainability. Traditional energy management approaches often fail to adapt to dynamic network conditions, which leads to inefficiencies. In contrast, DRL-based optimization dynamically adjusts energy allocation, storage and communication strategies to maximize energy efficiency, network throughput and device longevity. The results from simulations show that the DRL-based method outperforms conventional optimization techniques, which provides a more scalable and efficient solution for 6G systems. DDPG-based optimization is offering improvements over traditional methods. But it does not achieve the same level of performance as DRL, particularly in large-scale networks with fluctuating energy availability. This study highlights the potential of DRL to address the energy challenges of future wireless communication systems and paves the way for more sustainable 6G network operations.