Reinforced Fairness-Aware Multi-Agent Self-Organization for 6G Radio Access Network Orchestration

Abstract

The orchestrators’ deployment problem presents numerous challenges in 6G Network Radio Access Networks due to their large-scale, dynamic conditions, and variable user demands. Most works propose single- or hierarchical-orchestrator solutions, which offer poor resiliency, high signaling overhead, and slow adaptation to variable network dynamics. To tackle these challenges, we propose an online, data-driven, fully decentralized, Multi-Agent Reinforcement Learning (MARL)-based, self-organization orchestrator deployment system for 6G networks, which jointly optimizes the tradeoff between user throughput and fairness, based on time-varying system conditions. In the proposed approach, a flexible variable number of decentralized, cooperative, peer self-organization agents autonomously adapt their associated orchestrator’s deployment location and activity to optimize network operation, without requiring centralized coordination. Simulations show improvements of up to 77% in user throughput compared to Hierarchical and Single Orchestrator baselines in a broad range of realistic scenarios.