Data-driven Energy Optimization in Mobile Networks with User Experience Guarantees

In this paper, we model carrier shutdown in multi-carrier mobile networks as a deep reinforcement learning problem. Our model takes energy-saving actions by turning off carriers and reallocating their users while in addition to maintaining connectivity guarantees a novel user experience metric. Leveraging real and recent datasets, we train and evaluate our model over realistic network scenarios. Our results show more than 15% energy saving with the fulfillment of the user experience constraints, outperforming currently deployed solutions and researched approaches in the literature by almost 50%. More interestingly our approach exhibits generalization properties, a very promising characteristic for its adoption in real mobile networks deployment.