D
Darko Zibar
Researcher at Technical University of Denmark
Publications - 343
Citations - 5735
Darko Zibar is an academic researcher from Technical University of Denmark. The author has contributed to research in topics: Transmission (telecommunications) & Demodulation. The author has an hindex of 32, co-authored 320 publications receiving 4497 citations. Previous affiliations of Darko Zibar include University of Erlangen-Nuremberg & University of California, Santa Barbara.
Papers
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Journal ArticleDOI
An Overview on Application of Machine Learning Techniques in Optical Networks
Francesco Musumeci,Cristina Rottondi,Avishek Nag,Irene Macaluso,Darko Zibar,Marco Ruffini,Massimo Tornatore +6 more
TL;DR: An overview of the application of ML to optical communications and networking is provided, relevant literature is classified and surveyed, and an introductory tutorial on ML is provided for researchers and practitioners interested in this field.
Journal ArticleDOI
100 Gbit/s hybrid optical fiber-wireless link in the W-band (75-110 GHz).
Xiaodan Pang,Antonio Caballero,Anton Dogadaev,Valeria Arlunno,Robert Borkowski,Jesper S. Pedersen,Lei Deng,Fotini Karinou,Fabien Roubeau,Darko Zibar,Xianbin Yu,Idelfonso Tafur Monroy +11 more
TL;DR: An 100 Gbit/s hybrid optical fiber-wireless link is demonstrated by employing photonic heterodyning up-conversion of optical 12.5 Gbaud polarization multiplexed 16-QAM baseband signal with two free running lasers with bit-error-rate performance below the FEC limit.
Journal ArticleDOI
Machine Learning Techniques in Optical Communication
TL;DR: In this paper, a nonlinear state-space model for nonlinearity mitigation, carrier recovery, and nanoscale device characterization is proposed, which allows for tracking and compensation of the XPM induced impairments by employing approximate stochastic filtering methods such as extended Kalman or particle filtering.
Journal ArticleDOI
Single-source chip-based frequency comb enabling extreme parallel data transmission
Hao Hu,Francesco Da Ros,Minhao Pu,Feihong Ye,Kasper Ingerslev,Edson Porto da Silva,Nooruzzaman,Yoshimichi Amma,Yusuke Sasaki,Takayuki Mizuno,Yutaka Miyamoto,Luisa Ottaviano,Elizaveta Semenova,Pengyu Guan,Darko Zibar,Michael Galili,Kresten Yvind,Toshio Morioka,Leif Katsuo Oxenløwe +18 more
TL;DR: A frequency comb realized by a non-resonant aluminium-gallium-arsenide-on-insulator (AlGaAsOI) nanowaveguide with 66% pump-to-comb conversion efficiency is presented, which is significantly higher than state-of-the-art resonant comb sources.
Journal ArticleDOI
Machine Learning Techniques for Optical Performance Monitoring From Directly Detected PDM-QAM Signals
TL;DR: In this article, a brief overview of the various machine learning methods and their application in optical communication is presented and discussed, and supervised machine learning algorithms, such as neural networks and support vector machine, are experimentally demonstrated for in-band optical signal to noise ratio estimation and modulation format classification, respectively.