Reinforcement Learning for Dynamic Spectrum Allocation in Next-Generation Optical Networks

Abstract

Next-generation optical networks are expected to support heterogeneous traffic with stringent latency, capacity, reliability, and energy constraints. Fixed-grid wavelength division multiplexing has historically provided a stable operational model, but it struggles to accommodate bursty, asymmetric, and service-differentiated demand. Elastic optical networking, multi-band transmission, spatial division multiplexing, and advanced coherent interfaces create a richer but more complex spectrum allocation problem. This paper examines reinforcement learning as a control-layer approach for dynamic spectrum allocation in next-generation optical networks. Rather than treating spectrum assignment as a narrow optimization task, the analysis frames it as a socio-technical infrastructure problem involving architecture, deployment, governance, robustness, fairness, sustainability, and policy. The paper reviews the structural evolution of optical spectrum management, explains how reinforcement learning can operate across centralized, distributed, and hybrid control planes, and identifies trade-offs between adaptability and stability, autonomy and accountability, and efficiency and equity. It then discusses how learning-based allocation affects resilience under failures and adversarial conditions, how fairness can be defined across tenants and regions, and how energy and embodied carbon concerns complicate the pursuit of spectral efficiency. The paper argues that reinforcement learning can improve dynamic allocation only if it is embedded in auditable, interoperable, and policy-aware network architectures. The conclusion emphasizes that future deployments require multidisciplinary evaluation, standardized telemetry, safe exploration mechanisms, and governance frameworks that align algorithmic performance with public-interest objectives in critical communication infrastructure.