Intent-Based Networking (IBN) has emerged as a promising paradigm in network management, enabling the translation of high-level user intents into automated network configurations. To fully realize the potential of IBN, Artificial Intelligence (Al) plays an important role in enhancing the efficiency, adaptability, and scalability of its core building blocks. This paper explores the role and impact of AI across the main functionalities of IBN, including intent recognition and translation, intent validation and optimization, real-time assurance, and closed-loop feedback systems. We detail how techniques such as Natural Language Processing (NLP), Machine Learning (ML), and Reinforcement Learning (RL) are leveraged to automate and refine each phase of the IBN process. Additionally, we examine the benefits of AI integration with IBN, such as improved network performance, reduced operational complexity, and predictive capabilities, while addressing challenges like interpretability, computational overhead, and data privacy. This study highlights the critical contributions of AI to advancing IBN frameworks and provides a roadmap for future innovations in this field.
Programmable photonic integrated circuits have the potential to increase the speed at which photonic applications are developed. However, in order to use these circuits effectively, there is a lack of efficient algorithms that compute an appropriate configuration for a given use case. We discuss a place -and -route algorithm that quickly calculates a solution to this problem. A specialized multi -stage simulated annealing process is introduced that gives a fast and effective way to configure the port -to -port response of programmable photonic integrated circuits.
An optical line controller has been developed for a partially disaggregated network, enabling the creation of a telemetry plane that collects data from optical amplifiers. These data are structured according to the OpenConfig standard, simplifying post -processing tasks linked to the Digital Twin operations.
Practical implementations of Continuous Variable (CV) Quantum Key Distribution (QKD) systems can be performed over the existing optical fiber telecommunication infrastructure. However, optical fibers suffer from random variations in the state of polarization affecting the performance of the system. In this work, we study the impact of the polarization drift on the performance of a CV-QKD setup with a polarization diversity receiver, in which the polarization drift is pre -compensated on Alice's side. We show that pre -compensating for the polarization drift can successfully realign Alice's and Bob's raw signal, without affecting the excess noise, up to a polarization drift variance of 10-1, compatible with most practical implementations.
The paper explores advanced sensing technologies for improving the security and resilience of optical networks. It introduces systems like Distributed Acoustic Sensing (DAS) and polarization analysis, enabling accurate anomaly detection and real-time monitoring, which assist in identifying and preventing security breaches. The paper also examines how machine learning analyzes complex patterns to optimize detection processes. Practical experiments and simulations demonstrate the effectiveness of these technologies in safeguarding critical infrastructures, including fiber manipulation detection and environmental diagnostics.
