AI-Driven Multi-Agent Routing for 6G Networks

Abstract

The sixth generation (6G) of communication networks is envisioned to deliver ubiquitous intelligence, extremely low latency, and seamless connectivity across terrestrial, aerial, and satellite domains. These goals demand a fundamental transformation in how routing decisions are made. Traditional deterministic algorithms are unable to adapt to the rapidly changing, complex, and multi-layered 6G environment. Artificial Intelligence (AI), particularly Reinforcement Learning (RL), offers a pathway for routing mechanisms that can self-learn, self-correct, and self-optimize without human intervention. This paper presents a conceptual and theoretical discussion on AI-driven multi-agent routing for 6G networks, emphasizing the principles of distributed learning, federated intelligence, and knowledge-defined networking (KDN). Simplified theoretical models and possible architectural directions are provided, along with open challenges for future research.