Hybrid Approach for WDM Network Restoration: Deep Reinforcement Learning and Graph Neural Networks

Ensuring robust and efficient service restoration in Wavelength Division Multiplexing (WDM) networks is crucial for maintaining network reliability amidst failures caused by disasters, equipment malfunctions, or power outages. This paper presents a hybrid framework that integrates Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN) to optimize WDM network restoration. The proposed method leverages the decision-making capabilities of DRL and the graph-structured learning potential of GNN to dynamically adapt to network disruptions. By modeling network topology as a graph, the GNN extracts structural features, while the DRL agent intelligently selects restoration paths, balancing network load and minimizing restoration time. Experimental evaluations across multiple network topologies and failure scenarios reveal that the hybrid DRL+GNN approach outperforms conventional restoration techniques in terms of restoration success rate, resource utilization, and scalability. The framework's ability to generalize across diverse network configurations highlights its adaptability and potential for deployment in real-world optical communication systems. This study underscores the transformative impact of combining AI techniques on advancing WDM network resilience and recovery capabilities.