Machine Learning-Based Traffic Prediction for Frequency-Referenced Optical Multiplexing

Abstract

Frequency-referenced optical multiplexing introduces a distinctive control problem for high-capacity networks because it couples spectral assignment, latency guarantees, and synchronization discipline within a shared physical infrastructure. Machine learning-based traffic prediction has therefore emerged as a critical system-level capability rather than a narrow forecasting exercise. This paper examines how predictive models can support frequency-referenced multiplexing by anticipating demand shifts, reducing provisioning latency, improving spectral utilization, and enabling proactive resource governance. It argues that the value of prediction depends less on raw accuracy than on integration with optical layer constraints, control plane timing, fault management, and policy safeguards. The paper reviews relevant machine learning architectures, including sequence models, attention mechanisms, graph-based learning, and reinforcement learning, while emphasizing that each introduces operational trade-offs in observability, training data quality, interpretability, and deployment cost. It then analyzes architectural choices across centralized, distributed, and hybrid control planes, showing that prediction must be calibrated to the timescales of wavelength assignment, amplifier dynamics, and service-level agreements. Robustness and fairness are treated as first-order design requirements because prediction errors can cascade into unnecessary overprovisioning, service degradation, or inequitable access across tenants and regions. Sustainability concerns are also examined, particularly the energy implications of overprovisioning and the embodied costs of additional optical infrastructure. Through cross-domain comparisons with cloud scheduling, energy forecasting, and transport logistics, the paper identifies governance mechanisms needed to make predictive multiplexing trustworthy and accountable. The conclusion proposes that future systems should adopt uncertainty-aware, auditable, and federated prediction pipelines that preserve operator control while enabling adaptive spectral efficiency.