Machine Learning-Based Nonlinear Impairment Compensation for High-Speed Hollow-Core Fiber Transmission

Abstract

High-speed hollow-core fiber transmission promises lower latency, reduced nonlinear propagation, and new architectural possibilities for data centers, metropolitan networks, and long-haul systems. However, practical hollow-core fiber links still exhibit complex impairment behavior arising from residual mode coupling, structural irregularities, interfaces, splices, environmental sensitivity, and interaction with high-order modulation formats. Conventional digital signal processing pipelines are effective but often struggle when impairment statistics shift across wavelengths, routes, temperatures, and fiber vintages. This paper examines machine learning-based nonlinear impairment compensation for high-speed hollow-core fiber transmission from a system-level perspective. It argues that compensation should not be treated only as a receiver-side equalization problem but as an infrastructure capability spanning optical telemetry, control-plane governance, hardware acceleration, model lifecycle management, and multi-tenant fairness. The analysis considers supervised, unsupervised, and physics-informed learning architectures, then evaluates deployment trade-offs involving latency, energy, explainability, robustness, and vendor interoperability. It also discusses sustainability implications, including whether computational overhead is justified by reduced regeneration, lower latency, and extended equipment life. Cross-domain comparisons with wireless, submarine, and power-grid systems clarify governance and reliability lessons. The paper concludes that machine learning can improve nonlinear compensation in hollow-core fiber systems, but only when models are embedded in resilient, auditable, and sustainable network architectures rather than deployed as isolated algorithmic add-ons.