Adaptive Modulation and Wavelength Assignment for High-Capacity Optical Networks Using Deep Reinforcement Learning

Abstract

High-capacity optical networks are confronting a structural inflection point in which static provisioning, conservative margins, and human-designed control loops are increasingly misaligned with dynamic traffic, heterogeneous service classes, and stringent sustainability constraints. Adaptive modulation and wavelength assignment have emerged as central mechanisms for matching transmission formats and spectral resources to time-varying channel conditions, yet their operational value depends on whether decisions can be made rapidly, robustly, and equitably across multi-vendor, multi-domain infrastructures. This paper examines deep reinforcement learning as a system-level control paradigm for adaptive modulation and wavelength assignment in high-capacity optical networks. Rather than treating learning as a narrow optimization patch, the analysis frames it as an architectural layer that interacts with physical-layer uncertainty, network governance, deployment economics, energy intensity, and fairness among tenants and regions. The discussion emphasizes structural trade-offs among spectral efficiency, latency, resilience, operational complexity, and policy accountability. It also considers how simulation-to-reality gaps, reward misspecification, and fragmented telemetry can undermine learning agents, and why observability, safety envelopes, and human oversight remain essential. Through conceptual analysis, case illustrations, and cross-domain comparisons with data-center interconnects and cloud resource management, the paper argues that deep reinforcement learning can improve wavelength utilization and modulation adaptivity only when embedded within transparent, sustainable, and equitable network governance. The conclusion outlines a research agenda for trustworthy learning-based optical control.