A Distributed Intelligence Architecture for B5G Network Automation
This addresses network automation conflicts for B5G systems, but is incremental as it builds on existing distributed intelligence concepts.
The paper tackles the problem of performance degradation from conflicting closed loops in B5G network automation by proposing a Q-Learning-based distributed architecture that encourages cooperation among agents, validated via simulations.
The management of networks is automated by closed loops. Concurrent closed loops aiming for individual optimization cause conflicts which, left unresolved, leads to significant degradation in performance indicators, resulting in sub-optimal network performance. Centralized optimization avoids conflicts, but impractical in large-scale networks for time-critical applications. Distributed, pervasive intelligence is therefore envisaged in the evolution to B5G networks. In this letter, we propose a Q-Learning-based distributed architecture (QLC), addressing the conflict issue by encouraging cooperation among intelligent agents. We design a realistic B5G network slice auto-scaling model and validate the performance of QLC via simulations, justifying further research in this direction.