Toward end-to-end deep learning for autonomous management in next-generation networks

Abstract

The evolution towards next-generation of mobile networks demands for autonomous network management, emphasizing data-driven solutions based on Artificial Intelligence (AI), in particular machine learning. To attain such goals, this paper proposes a hybrid end-to-end learning approach that integrates imitation learning, deep reinforcement learning, simulation, domain adaptation, multi-agent cooperation, explainable AI, and generative AI. The work outlines a comprehensive vision for online agent learning about optimum network management policies while ensuring safety, interpretability, and adaptability in highly complex and dynamic use cases at the network periphery.