DefinePK

Abstract

The advent of sixth generation (6G) wireless networks brings unprecedented challenges in managing ultra-dense, dynamic, and heterogeneous environments. Classical optimization methods lack the scalability, adaptability, and selflessness required to address the challenge of resource management. This paper proposes an AI-enabled resource allocation framework specifically designed for 6G networks through the integration of state-of-the-art machine learning (ML), deep reinforcement learning (DRL), federated learning (FL), and graph neural networks (GNNs). The envisioned multi-layered architecture allows edge devices, UAVs, and base stations to perceive nearby environments, forecast traffic trends, make in-real-time decisions, and jointly train models with privacy preserved. An end-to-end global controller from GNN provides orchestration over the network topology. We review state-of-the-art AI methods and discuss their adequacy in accommodating resource allocation complexity with trade-offs between convergence, latency, and scalability. We conclude by describing current challenges—heterogeneous data, stable convergence, and limited computations—and sketch future directions of research towards reliable, explainable, and energy-efficient AI deployment in 6G systems.