Distributed Intelligence for Automated 6G Network Management Using Reinforcement Learning

The deployment of network elements in 6G is expected to be significantly more distributed than the existing 5G deployments. Distributed management paradigms are compatible with such distributed network deployments. Further, owing to their ability to solve complex problems by evaluating the impact of actions on the environment, intelligent solutions based on Reinforcement Learning (RL) for distributed management are promising. However, there are still several unsolved challenges before distributed intelligence could be seamlessly integrated in 6G. This work defines relevant research questions, reports on the progress made in the PhD project and presents the next steps and future directions for the advancement of this topic.