Scalability of Distributed Intelligence Architecture for 6G Network Automation

Distributed automation is expected to play a significant role in the management of 6G networks, as it avoids the drawbacks of a single point of failure and signaling overhead inherent in a centralized paradigm. However, the issue of conflicts is intrinsic to a distributed architecture and when left unaddressed, may severely impair system KPIs. Considering the conflict problem, it is unclear if distributed automation would be scalable to realize the potential of 6G networks. In this paper, we validate the scalability of distributed intelligence, specifically based on Q-Learning, Q-Learning for Cooperation (QLC), consisting of intelligent agents that learn to cooperate on a discrete state space. Results show that the performance of QLC is scalable when compared to the optimal, computed by a centralized solution. Scalability may be limited by the convergence time that increases with the number of agents and the size of the discrete state space. The cooperation overhead is also not critical. These findings indicate that QLC is promising and may be applied to other use cases if the speed of convergence is not a significant detriment in distributing intelligence in 6G.