Machine learning techniques for optical communication, laser metrology and optical fibre sensing
Abstract
Recently, there has been an increasing amount of research focused on the application of machine learning techniques to optical communication and photonics. These applications have varied from component characterization, ultra-sensitive optical phase detection, performance prediction and system optimization, and more recently, within the field of quantum communication and optical fibre sensing. In this talk, a brief overview of the application of machine learning in optical communication will be given. Then, techniques from Bayesian machine learning and digital coherent detection will be presented on how to perform ultra-sensitive, and optimum in a statistical sense, detection of optical amplitude and phase, which later can be used to perform relative intensity and frequency laser noise characterization. Within the field of optical fibre sensing, it will also be demonstrated how techniques from machine learning can be used to learn non-trivial mappings and provide monitoring of temperature in terms of mean value as well as confidence interval. Finally, a novel concept on information transmission over the optical fibre, by employing modulation of eigenvalues, will be presented.
Biography
Darko Zibar received the M.Sc. degree in telecommunication and the Ph.D. degree in optical communications from the Technical University of Denmark, in 2004 and 2007, respectively. He was a Visiting Researcher with the Optoelectronic Research Group (Prof. John E. Bowers), University of California, Santa Barbara, CA, USA, in 2006 and 2008, where he worked on coherent receivers for analog optical links. In 2009, he was a Visiting Researcher with Nokia-Siemens Networks, where he worked on clock recovery techniques for polarization multiplexed systems. He is currently Associate Professor at DTU Fotonik, Technical University of Denmark. His research efforts are currently focused on the application of machine learning methods to optical communication, ultra-sensitive amplitude and phase detection and optical fibre sensing systems. He is a recipient of Young Researcher Award by University of Erlangen-Nurnberg, in 2016, for his contributions to applications of machine learning techniques to optical technologies. He was a part of the team that won the HORIZON 2020 prize for breaking the optical transmission barriers. In 2017, he was granted European Research Council (ERC) Consolidator Grant where the focus is on the demonstration of nonlinear-distortion free optical communication systems by employing modulation of eigenvalues.