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Nicola Di Cicco
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2020 – today
- 2024
- [j9]Nicola Di Cicco, Memedhe Ibrahimi, Sebastian Troia, Massimo Tornatore:
DeepLS: Local Search for Network Optimization Based on Lightweight Deep Reinforcement Learning. IEEE Trans. Netw. Serv. Manag. 21(1): 108-119 (2024) - [j8]Nicola Di Cicco, Memedhe Ibrahimi, Omran Ayoub, Federica Bruschetta, Michele Milano, Claudio Passera, Francesco Musumeci:
ASAP Hardware Failure-Cause Identification in Microwave Networks Using Venn-Abers Predictors. IEEE Trans. Netw. Serv. Manag. 21(5): 5400-5409 (2024) - [j7]Nicola Di Cicco, Memedhe Ibrahimi, Francesco Musumeci, Federica Bruschetta, Michele Milano, Claudio Passera, Massimo Tornatore:
Machine Learning for Failure Management in Microwave Networks: A Data-Centric Approach. IEEE Trans. Netw. Serv. Manag. 21(5): 5420-5431 (2024) - [c8]Aryanaz Attarpour, Memedhe Ibrahimi, Nicola Di Cicco, Francesco Musumeci, Andrea Castoldi, Mario Ragni, Massimo Tornatore:
Joint QoT-Aware Optimization of OTN and WDM Layers for Low-Cost Optical Metro Networks. ICC 2024: 4979-4984 - [c7]Gaetano Francesco Pittalà, Cristian Zilli, Nicola Di Cicco, Gianluca Davoli, Alessio Sacco:
Recovering Missing Monitoring Data to Enhance Service Provisioning in the Edge-to-Cloud Continuum. NetSoft 2024: 25-30 - [c6]José Santos, Mattia Zaccarini, Filippo Poltronieri, Mauro Tortonesi, Cesare Sleianelli, Nicola Di Cicco, Filip De Turck:
Efficient Microservice Deployment in Kubernetes Multi-Clusters through Reinforcement Learning. NOMS 2024: 1-9 - [c5]Giovanni Sticca, Memedhe Ibrahimi, Nicola Di Cicco, Francesco Musumeci, Massimo Tornatore:
Hollow-Core-Fiber Placement in Latency-Constrained Metro Networks with edgeDCs. OFC 2024: 1-3 - 2023
- [j6]Faruk Pasic, Nicola Di Cicco, Marco Skocaj, Massimo Tornatore, Stefan Schwarz, Christoph F. Mecklenbräuker, Vittorio Oegli-Esposti:
Multi-Band Measurements for Deep Learning-Based Dynamic Channel Prediction and Simulation. IEEE Commun. Mag. 61(9): 98-104 (2023) - [j5]Marco Skocaj, Nicola Di Cicco, Tommaso Zugno, Mate Boban, Jiri Blumenstein, Ales Prokes, Tomas Mikulasek, Josef Vychodil, Konstantin Mikhaylov, Massimo Tornatore, Vittorio Degli-Esposti:
Vehicle-to-Everything (V2X) Datasets for Machine Learning-Based Predictive Quality of Service. IEEE Commun. Mag. 61(9): 106-112 (2023) - [j4]Aryanaz Attarpour, Memedhe Ibrahimi, Nicola Di Cicco, Francesco Musumeci, Andrea Castoldi, Mario Ragni, Massimo Tornatore:
Minimizing the cost of hierarchical optical transport network traffic grooming boards in metro networks. J. Opt. Commun. Netw. 15(10): E18-E28 (2023) - [c4]Nicola Di Cicco, Jacopo Talpini, Memedhe Ibrahimi, Marco Savi, Massimo Tornatore:
Uncertainty-Aware QoT Forecasting in Optical Networks with Bayesian Recurrent Neural Networks. ICC 2023: 441-446 - [c3]Nicola Di Cicco, Gaetano Francesco Pittalà, Gianluca Davoli, Davide Borsatti, Walter Cerroni, Carla Raffaelli, Massimo Tornatore:
DRL-FORCH: A Scalable Deep Reinforcement Learning-based Fog Computing Orchestrator. NetSoft 2023: 125-133 - [c2]Nicola Di Cicco, Amir Al Sadi, Chiara Grasselli, Andrea Melis, Gianni Antichi, Massimo Tornatore:
Poster: Continual Network Learning. SIGCOMM 2023: 1096-1098 - 2022
- [j3]Nicola Di Cicco, Federico Tonini, Valentina Cacchiani, Carla Raffaelli:
Optimization over time of reliable 5G-RAN with network function migrations. Comput. Networks 215: 109216 (2022) - [j2]Omran Ayoub, Nicola Di Cicco, Fatima Ezzeddine, Federica Bruschetta, Roberto Rubino, Massimo Nardecchia, Michele Milano, Francesco Musumeci, Claudio Passera, Massimo Tornatore:
Explainable Artificial Intelligence in communication networks: A use case for failure identification in microwave networks. Comput. Networks 219: 109466 (2022) - [j1]Giovanni Sticca, Memedhe Ibrahimi, Francesco Musumeci, Nicola Di Cicco, Andrea Castoldi, Rosanna Pastorelli, Massimo Tornatore:
Selective hybrid EDFA/Raman amplifier placement to mitigate lightpath degradation in (C + L) networks. JOCN 15(8): C232-C241 (2022) - 2021
- [c1]Nicola Di Cicco, Valentina Cacchiani, Carla Raffaelli:
Scalable Multi-objective Optimization of Reliable Latency-constrained Optical Transport Networks. DRCN 2021: 1-6
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