On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration
Farhad Rezazadeh, Lanfranco Zanzi, Francesco Devoti, Hatim Chergui, Xavier Costa‐Pérez, Christos V. Verikoukis · IEEE Transactions on Vehicular Technology · 2022
Network slicing enables multiple virtual networks to be instantiated and customized to meet heterogeneous use case requirements over 5G and beyond network deployments. However, most of the solutions available today face scalability issues when considering many slices, due to centralized controllers requiring a holistic view of the resource availability and consumption over different networking domains. In order to tackle this challenge, we design ahierarchical architecture to manage network slices resources in a federated manner. Driven by the rapid evolution of deep reinforcement learning (DRL) schemes and the Open RAN (O-RAN) paradigm, we propose a set of traffic-aware local decision agents (DAs) dynamically placed in the radio access network (RAN). These federated decision entities tailor their resource allocation policy according to the long-term dynamics of the underlying traffic, definingspecializedclusters that enable faster training and communication overhead reduction. Indeed, aided by a traffic-aware agent selection algorithm, our proposedFederated DRLapproach provides higher resource efficiency than benchmark solutions by quickly reacting to end-user mobility patterns and reducing costly interactions with centralized controllers.