Distributing Intelligence for 6G Network Automation: Performance and Architectural Impact

In future 6G networks, distributed management of network elements is expected to be a promising paradigm. Recent research progress in Artificial Intelligence (AI) is rapidly driving the adoption of distributed management. However, distributed management using intelligence or distributed AI inherently suffers from a number of issues - potential conflicts, signaling required to ensure cooperation and the convergence time of the algorithm. To this end, an early understanding and analysis of the overall effort to implement distributed AI in 6G, is still unexplored. This work, therefore, examines the impact of distributed AI, by analyzing its performance and how the existing 5G architecture could be enhanced to support it in 6G. We aim to understand the impact of distributed AI in 6G by selecting a relevant beyond 5G use case - auto-scaling virtual resources in a network slice. We present the performance and architecture analysis for two distributed algorithms from the domain of Reinforcement Learning - Q-Learning and Deep Q-Networks. We argue that despite its aforementioned issues, distributed AI brings benefits such as dynamic and adaptive decision-making, making it highly applicable for certain use cases in 6G.