activity
20242026
most citedSynergizing Hyper-accelerated Power Optimization and Wavelength-Dependent QoT-Aware Cross-Layer Design in Next-Generation Multi-Band EONs

13 citations · 20 across the 15 of their papers we have counts for

collaborators

16 papers

cs.LG2026

TRACE-CRC: Trajectory-Adaptive Conformal Risk Control for Multi-Step Channel State Information Prediction

Kiarash Rezaei, Mehdi Sattari, Javad Aliakbari +3

Reliable prediction of time-varying channel state information (CSI) is essential for efficient wireless communication. Each CSI frame is a matrix-valued representation of the wirel…

cs.NI2026

Radio-Optical Confluence in Intelligent Edge Networks

Akshita Gupta, Devika Dass, Agastya Raj +4

Challenges associated with densification of radio access networks are motivating exploration of more efficient and scalable architectures. We examine recent progress in one directi…

eess.SP2026

Retrieval-Based Cross-Domain Generalization in Optical Networks via Global Features

Ali Al Housseini, Carlos Natalino, Paolo Monti +1

We propose a retrieval-based framework for crossdomain quality-of-transmission (QoT) estimation that leverages transferable feature representations while avoiding reliance on sourc…

cs.LG2026

Explanation-Based Runtime Verification for Trustworthy ML-driven Optical Networks

Omran Ayoub, Carlos Natalino, Ali Al Housseini +5

Machine learning (ML) models are increasingly integrated into optical network automation frameworks to support tasks such as failure management, performance monitoring and resource…

cs.NI2026

Cross-Domain Generalization in Optical Networks via Joint Contrastive and Classification Learning

Ali Al Housseini, Carlos Natalino, Paolo Monti +1

The robustness of machine learning techniques across heterogeneous network domains remains an open challenge in optical networks. Models trained on data from a specific topology or…

cs.NI2026

Human Grounded Evaluation of Large Language Models for Optical Network Automation

Kiarash Rezaei, Omran Ayoub, Paolo Monti +1

Large language models (LLMs) are increasingly adopted for network automation, yet their output quality and inference cost can vary substantially across LLM families. We present HuG…

cs.NI2026

User-Mobility-Aware Optimization of Fiber Placement in Hybrid Fiber-IAB Networks

Piotr Lechowicz, Charitha Madapatha, Carlos Natalino +2

Metaheuristic optimization of hybrid fiber-IAB networks demonstrates that integrating user dynamics into topology design enables more adaptive and cost-efficient backhaul architect…

cs.NI2026

Energy-Efficient FSO Reconfiguration under User Mobility in Hybrid Fiber-IAB Backhaul

Piotr Lechowicz, Charitha Madapatha, Carlos Natalino +2

User mobility creates stochastic, time-varying backhaul demand that static capacity provisioning cannot match. We propose a closed-loop, load-aware hysteresis controller for hybrid…

cs.NI2026

A T-API-Compliant ReAct Agentic Loop for Optical Networks: Generic vs. Domain-Specific Tool Abstractions

Seyed Morteza Ahmadian, Paolo Monti, Carlos Natalino

Optical networks need intent-driven, closed-loop agentic management, a key enabler for higher autonomy levels. We present the first T-API-compliant reasoning and act (ReAct) loop.…

cs.LG2026★ 2 cited

Policy-driven Conformal Prediction for Trustworthy QoT Estimation

Kiarash Rezaei, Omran Ayoub, Paolo Monti +1

We propose Conformal QoT, a policy-driven framework that combines statistically guaranteed QoT estimation with operational decision policies, enabling reliable lightpath-feasibilit…

cs.NI2026★ 1 cited

Generative Explainability for Next-Generation Networks: LLM-Augmented XAI with Mutual Feature Interactions

Kiarash Rezaei, Omran Ayoub, Sebastian Troia +3

As artificial intelligence and machine learning (AI/ML) models become integral to network operations, their lack of transparency poses a significant barrier to operator trust. Exis…

cs.NI2026★ 1 cited

A Unified Siamese Learning Framework for Zero-Day Anomaly Detection and Classification in Optical Networks

Carlos Natalino, Flávia P. Monteiro, Paolo Monti

A multi-similarity Siamese neural network unifies zero-day anomaly detection and one-shot classification in optical networks, achieving over 99% accuracy and instant adaptability a…

cs.LG2026

Variational Autoencoder Domain Adaptation for Cross-System Generalization in ML-Based SOP Monitoring

Leyla Sadighi, Stefan Karlsson, Carlos Natalino +7

Machine learning (ML) models trained to detect physical-layer threats on one optical fiber system often fail catastrophically when applied to a different system, due to variations…

cs.NI2025★ 2 cited

Joint Fiber and Free Space Optical Infrastructure Planning for Hybrid Integrated Access and Backhaul Networks

Charitha Madapatha, Piotr Lechowicz, Carlos Natalino +2

Integrated access and backhaul (IAB) is one of the promising techniques for 5G networks and beyond (6G), in which the same node/hardware is used to provide both backhaul and cellul…

eess.SY2024★ 13 cited

Synergizing Hyper-accelerated Power Optimization and Wavelength-Dependent QoT-Aware Cross-Layer Design in Next-Generation Multi-Band EONs

Farhad Arpanaei, Mahdi Ranjbar Zefreh, Yanchao Jiang +12

The extension of elastic optical networks (EON) to multi-band transmission (MB-EON) shows promise in enhancing spectral efficiency, throughput, and long-term cost-effectiveness for…

cs.NI2024★ 1 cited

DRL-Assisted Dynamic QoT-Aware Service Provisioning in Multi-Band Elastic Optical Networks

Yiran Teng, Carlos Natalino, Farhad Arpanaei +4

We propose a DRL-assisted approach for service provisioning in multi-band elastic optical networks. Our simulation environment uses an accurate QoT estimator based on the GN/EGN mo…