Federated deep reinforcement learning improves 6G resource allocation with explainable AI
Explanation-Guided Federated Deep Reinforcement Learning for Joint Resource Allocation and Scheduling in 6G in-X Subnetworks
Networking and Internet Architecture
Summary
Managing radio resources in future 6G networks is difficult because many devices need to communicate reliably without interfering with each other. The authors propose a way for different subnetworks to work together using AI that learns how to share channels and schedule transmissions. Their method keeps each subnetwork’s data private and also explains the AI’s decisions to make the process more transparent. Simulations show that this new approach can improve communication performance and fairness compared to older methods.
What this means in practice
- •For network schedulers: Optimize channel and transmission scheduling across multiple subnetworks in 6G networks while preserving privacy and improving fairness.
- •For industrial wireless teams: Improve reliable communication for dense deployments of industrial robots or vehicles by dynamically managing interference with explainable AI.
Authors
Ramoni Adeogun
Abstract
Sixth-generation (6G) wireless systems are envisioned as networks of networks, integrating diverse in-X subnetworks that provide localized, high-performance connectivity. Ensuring reliable communication in dense deployments, such as industrial robots and vehicles, is challenging due to dynamic interference and strict performance requirements. Traditional radio resource management (RRM) methods have limitations, prompting the need for AI-based solutions. In this paper, we address the challenges of dynamic resource allocation and scheduling in 6G in-X subnetworks supporting applications with heterogeneous characteristics by proposing a novel framework that combines Multi-Agent Reinforcement Learning (MARL), Federated Learning (FL), and Explainable AI (XAI). Our solution is designed to improve the reliability, robustness, and transparency of resource management and intra-subnetwork scheduling while ensuring data privacy and fairness across multiple co-existing subnetworks. Unlike existing works, our approach considers a realistic scenario with multiple devices per subnetwork, thereby offering a more comprehensive and scalable solution. The proposed explainable RL framework enables agents to collaboratively optimize channel allocation and scheduling without the need to share raw data, preserving the privacy of each participating subnetwork. Extensive simulations based on 3GPP scenarios demonstrate the effectiveness of our approach, showing significant improvements in performance, and transparency over existing solutions.